![2 V. Charles et al.
1 Introduction
Operations management is a fundamental organisational function involved in the
management of activities to produce and deliver products and services [3]. Services
have long been the dominant sector of industrialised nations; nevertheless, the field
of operations management has been traditionally associated with manufacturing and
supply chains rather than with services [17]. And while it is true that service oper-
ations management (SOM) has struggled to find its fit as a distinct discipline in the
literature, it is fair to say that the number of studies on the topic have increased
substantially over the years. For example, a recent review study by Roth and Rosen-
zweig [31] concluded that, in their sample, service-oriented papers represented 58%
of the publications, with the healthcare and retail sectors at the forefront. Today, big
data have been accelerating this shift in research. All in all, the presence of big data
has been ‘pushing’ organisations to review their practices and identify opportunities
that would allow them to embrace data-driven decision-making processes to a greater
extent.
Although there is no unique definition of big data [7, 8], it is commonly accepted
that big data are “datasets that are too large for traditional data-processing systems
and that therefore require new technologies” [30]. In the literature, it is common to
refer to the four dimensions of big data defined by Laney [21]: volume, velocity,
variety, and veracity, which are indicative of the computational complexities and
technical requirements associated with big data. Coupled with ethical challenges
[10], analysing big data confronts the researchers with many difficulties [4]. Charles
and Gherman [9] underlined that in order to create value and competitive advan-
tage, big data should be further considered in view of the dimensions of context,
connectedness, and complexity. The growth of big data has created opportunities
for pursuing new avenues of research in SOM. In particular, big data analytics may
supportimprovedpolicy-anddecision-makinganddriveorganisationalperformance.
It is vital to both comprehensively and quantitatively evaluate the development
trend in research on big data for SOM, which can help not only practitioners and
managers, but also academics interested in making informed decisions in their future
research endeavours. Bibliometric analysis, which has been widely used across
different fields, is a feasible means that can quantitatively and qualitatively assess
trends in research fields over time. It can be used to systematically identify, organise,
and analyse the main elements of a research topic [28, 38], as well as to clearly
determine the development trends of a particular research field [2].
After decades of research developments in SOM, there is currently a growing
interest in exploring the potential that big data pose for this field. This is supported not
only by the increasing availability of data, but also by the methodological advances
in a number of fields. Therefore, this research investigates the characteristics and
trends of studies integrating big data and SOM through a bibliometric analysis so as
to facilitate a comprehensive understanding of the state-of-the-art of current research
directions and progress in the field.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-18-320.jpg)
![Characteristics and Trends in Big Data for Service Operations … 3
2 Methods and Materials
Bibliometric analysis is a valuable research technique that can help in discovering the
global research trends on a topic or field from multiple angles, providing an overview
of future lines of research [24]. By bibliometric analysis, this paper reveals the char-
acteristics and trends in studies integrating big data with SOM in view of publi-
cation outputs and major journals, subject categories, geographic and institutional
distribution of publications, and keywords analysis.
The bibliometric analysis was conducted based on the Scopus database, which
is the abstract and citation database of Elsevier. Although there are other databases
available, Scopus was deemed to be one of the best choices in view of the fact that
it is characterised by consistent citation metrics and precision in locating authors
and institutions. Relevant data on big data for SOM research were downloaded on
13 March 2021 via the concurrent search for the keywords “big data”, “operations
management”, and “service”. These keywords were searched in the title, abstract, and
keyword lists of each publication. Finally, only publications in English were selected
for further analysis. The search criteria along with the Boolean expression was as
follows: TITLE-ABS-KEY ( “big data” “operations management” “service”) AND (
LIMIT-TO ( PUBYEAR, 2020) OR LIMIT-TO ( PUBYEAR, 2019) OR LIMIT-TO (
PUBYEAR, 2018) OR LIMIT-TO ( PUBYEAR, 2017) OR LIMIT-TO ( PUBYEAR,
2016) OR LIMIT-TO ( PUBYEAR, 2015) OR LIMIT-TO ( PUBYEAR, 2014) OR
LIMIT-TO ( PUBYEAR, 2013)) AND ( LIMIT-TO ( LANGUAGE, “English”)).
3 Results
The concurrent search for the keywords “big data”, “operations management”, and
“service” yielded 57 document results on 13 March 2021, all published between
2013–2020. In Sect. 3.1, we proceed to analyse these document results by means
of summary statistics. Subsequently, in Sect. 3.2, we continue with a bibliometric
analysis of the same material, while in Sect. 3.3, we focus on exploring the themes
of the journal research articles only.
3.1 Descriptive Summary Statistics of Published Material
In this section, we provide an overall analysis of the 57 document results by means of
various visualisations. Figure 1 shows the evolution of the number of publications in
big data for SOM. Several observations are worth mentioning. First, all 57 documents
were published after the year 2013, which means that at least in the Scopus database,
there were no publications that integrated the three keywords together before this
year. Second, we can observe an increased interest in the topic in recent years, with
a peak in the year 2018 (16 publications).](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-19-320.jpg)
![4 V. Charles et al.
Fig. 1 Annual scientific production. (Source Scopus [33])
Fig. 2 Documents per year by source. (Source Scopus [33])
Figure 2 shows the number of documents per year by source, with a compar-
ison of the document counts for the top four sources. The journals that have
published most of the material on big data for SOM are Production and Opera-
tions Management (4 publications), Annals of Operations Research (2 publications),
International Journal of Operations and Production Management (2 publications),
and International Journal of Systems Assurance Engineering and Management (2
documents).
Figure 3 presents the documents by affiliation, comparing the document counts for
the first 15 affiliations. Affiliation-wise, there are five institutions that lead the ranking
with most document counts (2 publications each), namely Hong Kong Polytechnic
University,UniversityofLeeds,PohangUniversityofScienceandTechnology,Ulsan
National Institute of Science and Technology, and Leeds University Business School.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-20-320.jpg)
![Characteristics and Trends in Big Data for Service Operations … 5
Fig. 3 Documents by affiliation. (Source Scopus [33])
Fig. 4 Documents by country or territory. (Source Scopus [33])
These institutions represent a mixture of countries (China, United Kingdom, and
South Korea). All remaining institutions each have one publication.
Figure 4 visually depicts the countries with the highest number of publications,
comparing the document counts for 15 countries/territories. The countries of origin
for the 57 documents were determined by considering the country of the corre-
sponding author. It can be easily observed that China and the United States share the
first place, with 16 publications each. As a matter of fact, a notable observation is
that China and the United States together account for more than half (i.e., 56.14%)
of the total number of publications. The United Kingdom occupies the third place
with 4 publications, followed by India, Japan, South Korea, and Taiwan, each with
3 publications. All remaining countries each have one publication, while there are
also 8 documents for which the corresponding author information is not available.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-21-320.jpg)
![6 V. Charles et al.
Table 1 Documents by
publication type (Source
Scopus [33])
Document type No. of Documents
Article 22
Conference Paper 21
Conference Review 8
Book Chapter 3
Review 2
Editorial 1
Total 57
Table 1 depicts the number of documents by publication type, while Fig. 5 shows
the same visually. In this sense, we can note that the literature is dominated by arti-
cles (22 documents, which constitute 38.6% of the publications), followed closely by
conference papers (21 documents, representing 36.8% of the publications). There-
fore, journal articles and conference papers are the most frequent publication types
in the literature. The third place is occupied by conference reviews with 8 docu-
ments, which account for 14% of the publications. Lastly, there are 3 book chapters,
2 reviews, and 1 editorial, which represent 5.3%, 3.5%, and 1.8% of the publications,
respectively.
Finally, an analysis of the published documents by subject area (Table 2 and Fig. 6)
indicates that the area of “engineering” has received the most interest, with 29 docu-
ments or 23.2% of the publications. This is followed closely by the areas of “business,
management, and accounting”, with 25 documents or 20.0% of the publications. We
Fig. 5 Documents by publication type. (Source Scopus [33])](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-22-320.jpg)
![Characteristics and Trends in Big Data for Service Operations … 7
Table 2 Documents by
subject area (Source Scopus
[33])
Subject area No. of Documents
Engineering 29
Business, Management, and Accounting 25
Computer Science 21
Decision Sciences 19
Social Sciences 8
Mathematics 6
Energy 5
Economics, Econometrics, and Finance 3
Earth and Planetary Sciences 2
Environmental Science 2
Physics and Astronomy 2
Biochemistry, Genetics, and Molecular
Biology
1
Chemistry 1
Medicine 1
Note A publication can be classified under more than one subject
area
then have “computer science” (with 21 documents or 16.8% of the publications) and
“decision sciences” (with 19 documents or 14.2% of the publications).
Fig. 6 Documents by subject area. (Source Scopus [33])](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-23-320.jpg)
![Characteristics and Trends in Big Data for Service Operations … 9
all the keywords as the unit of analysis, with minimum number of occurrences as
three, and with full counting as the counting method.
Figure 7 reveals the network map of international cooperation among major coun-
tries (with the greatest total link strength) participating in research on big data for
SOM. The colours indicate the clusters to which the countries are attributed according
to the strength of their relationships, while the size of the circles is indicative of the
number of publications held by each country. We can observe that there are 3 clusters
in the figure. The first cluster (red colour) is dominated by the United States, and
includes a mix of Eastern and Western countries, namely Taiwan and South Korea
(in Asia), and France (in Europe). The second cluster (blue colour) is headed equally
by Hong Kong and Japan. Lastly, the third cluster (green colour) is led by China, but
also includes Australia.
Figure 8 shows that a total of 20 most common keywords have been identified.
Keywords are labelled with coloured frames, whose size is positively correlated
with the occurrence of the keyword in the document. Moreover, these keywords are
grouped into five clusters that seem to assume a prominent role vis-à-vis “compu-
tational paradigms” (three items, yellow cluster), “big data for quality control and
electric utilities” (four items, blue cluster), “information services and operations
management” (five items, red cluster), “data analytics for SOM” (four items, purple
cluster), and “big data analytics, internet of things, and smart city” (four items, green
cluster).
3.3 Bibliometric Analysis of Journal Articles
In this section, we have proceeded to analyse only the journal articles on big data
for SOM. Such decision was guided by both literature and practical considerations.
First, conference papers generally do not provide enough information on the research
conducted, as we encounter in full papers, being normally written with the aim of
presenting preliminary results [29]. Book chapters, conference reviews, and reviews,
also, do different work than journal articles, as do editorials; hence, these were also
excluded from further analysis. This screening led to the consideration of 22 research
articles for further processing, constituting 38.6% of the publications (Fig. 5).
Figure 9 shows the increasing number of journal articles in the field of big data
for SOM in recent years, Fig. 10 positions the United States as the country with most
of the journal articles publications, and Fig. 11 illustrates that the area of “business,
management, and accounting” accounts for most of the journal article publications
(27.7%), followed by “engineering” (21.3%), and “decision sciences” (19.1%).
A brief bibliometric analysis of the 22 journal articles composing the final sample
of studies integrating big data with SOM identified a variety of keywords as the
most common keywords (whose co-occurrence is at least two times) (Fig. 12). These
keywords were further classified by the software into four clusters that seem to
assume a prominent role vis-à-vis “big data analytics in supply chain management”
(four items, red cluster), “information systems and SOM” (three items, blue cluster),](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-25-320.jpg)
![10 V. Charles et al.
Fig. 9 Annual scientific production of studies on big data for SOM (Source Scopus [33])
Fig. 10 Studies on big data for SOM by country or territory. (Source Scopus [33])
“internet of things and smart city” (four items, green cluster), and “computational
paradigms” (two items, yellow cluster). Below, we present briefly the pool of 22
journal articles identified, which are arranged in chronological order, starting from
the most recent one.
Ruan et al. [32] presented an IoT-based e-business model of intelligent vegetable
greenhouses with details of the basic process and key nodes of the e-business model.
The authors recognised key operation issues including big-data-driven pricing,
planting structure and time optimisation, water and fertilizer integrated control, plant
light supplement, and order-driven picking and packing. Kumar et al. [19] proposed
a reliable, more accurate and efficient model based on the statistical analysis of the](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-26-320.jpg)
![Characteristics and Trends in Big Data for Service Operations … 11
Fig. 11 Studies on big data for SOM by subject area. (Source Scopus [33])
Fig. 12 Network map showing the relations between various topics in the journal article literature
on big data for SOM (22 documents)
sensor-based data for occupancy detection. The paper also proposed one online and
adaptive model-based online sequential extreme learning machine to perform occu-
pancy detection on real-time data when complete data are not available, and learning
is done with recent data points coming in the form of streams. Wang [37] focused on](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-27-320.jpg)
![12 V. Charles et al.
deployment and optimisation of wireless network node deployment and optimisa-
tion in smart cities. In this sense, aiming at problems such as poor network security
connectivity, weak node attack resistance, and large storage overhead in the existing
key management schemes, the author designed a three-phase key pre-distribution
mechanism and direct sharing of the key management scheme based on node group
deployment, via an adaptive particle swarm optimisation algorithm. Bag et al. [3]
used the dynamic capability theory as a foundation for evaluating the role of big data
analytics capability as an operational excellence approach in improving sustainable
supply chain performance. The authors surveyed mining executives in South Africa
and analysed the data using Partial Least Squares Structural Equation Modelling
(PLS-SEM). The paper contributes to identifying two pathways that managers can
use to improve sustainable supply chain outcomes in the mining industry, based on
big data analytics capabilities. Roth and Rosenzweig [31] provided an examination
of the rise of empirical operations management research in Manufacturing & Service
Operations Management. The authors advocated for a tighter integration of analytical
and empirical operations management knowledge in order to address the challenges
and opportunities of the twenty-first century. Tamás and Koltai [36] aimed to review
the relevant literature related to the development, improvement and application of
learning curves in the age of big data, and to demonstrate the possible insight which
its application can provide in manufacturing and service operations decision-making.
Datta and Goyak [12] presented an efficient method for reliability evaluation of
stochastic flow networks that can pass various demands simultaneously from multiple
source nodes to multiple destination nodes. March and Scudder [25] viewed the IoT
through the lens of predictive maintenance and analysed optimal preventive mainte-
nance policies in an environment where equipment is subject to a deterioration, which
shifts it from its initial, fully-productive state, having a specified, age-dependent
failure rate to a less-productive or deteriorated state, having a different, presumably
higher, age-dependent failure rate. Lim et al. [23] developed an original, specific
framework for a company’s use of customer-related data to advance its services and
create customer value. Building upon four action research projects, the proposed
customer process management framework suggests steps a service provider can take
when providing information to its customers to improve their processes and create
more value-in-use by using data related to their processes. Albergaria and Chuaopetta
Jabbour [1] aimed to provide an original exploration of the challenges of informa-
tion and operations management in the sharing economy, by focusing on the classic
example of a shared service represented by library operations. The paper addresses
the organisational use of big data analytics capabilities, with the main goal of helping
organisations make better business decisions, in terms of information and operations
management issues. Carnerud and Bäckström [6] aimed to identify and depict the
key areas around which research on quality has centred during the past 37 years
and to explore longitudinal patterns in the identified key areas. The study identi-
fied seven central topics around which research on quality has centred during the
time period analysed: Service Quality & Customer Satisfaction; Process design &
Control; ISO Certification & Standards; TQM—Implementation, Performance &](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-28-320.jpg)
![Characteristics and Trends in Big Data for Service Operations … 13
Culture; QM—Practices & Performance; Reliability, Costs, Failure & Problems and
Excellence—BEMs, Quality Awards & Excellence in Higher Education.
Focusing on the area of after-sales service, Boone et al. [5] developed a framework
that seeks to define service parts performance goals for the purpose of outlining where
scholars and practitioners can further examine where, how, and why big data applica-
tions can be employed to enhance service parts management performance. To objec-
tively evaluate emergency physicians across facilities, Foster et al. [13] leveraged big
data from an emergency physician management network and proposed data-driven
metrics using a large-scale database. The proposed indices benchmark physicians
from the perspectives of revenue potential, patient volume, patient complexity, and
patient experience by controlling for exogenous factors at the facility level. As the
authors acknowledge, the proposed framework can also be adapted to non-medical
professional settings such as value chains, where employees often provide services
in various profit- and cost-centres. Silva et al. [34] proposed a big data analytics-
embedded experimental architecture for smart cities. The mentioned architecture
facilitates the exploitation of urban big data in planning, designing, and maintaining
smart cities, as well as it shows how big data analytics can be used to manage and
process voluminous urban big data to enhance the quality of urban services. Cohen
[11] discussed how the tremendous volume of available data collected by firms has
been transforming the service industry, with particular focus on services in the sectors
of finance/banking, transportation and hospitality, and online platforms (subscrip-
tion services, online advertising, and online dating). Kuo et al. [20] applied big data
mining and machine learning analysis techniques and used the Waikato Environ-
ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores
energy consumption performance in Taiwan. Shmueli and Yahv [35] introduced
the use of Classification and Regression Trees for automated detection of potential
Simpson’s paradoxes in data with few or many potential confounding variables, and
even with large samples (big data). The authors illustrate the approach via several real
applications in e-governance and healthcare. Kim et al. [18] proposed an approach
to analysing and utilizing vehicle operations management (VOM)-related data for
designing VOM services. The feasibility and effectiveness of the proposed approach
is demonstrated by means of a case study on the design of an eco-driving service.
By adopting an exploratory approach to the secondary research which examines
vendors’ offerings, Matthias et al. [26] focused on the application and exploitation
of big data to create competitive advantage. To this aim, the authors presented a
framework of application areas, and how these help the understanding of targeting
and scoping specific areas for sustainable improvement. Mehmood et al. [27] aimed
to advance knowledge of the transformative potential of big data on city-based trans-
port models. In this sense, the authors developed a Markov-based model with several
scenarios to explore a theoretical framework focused on matching the transport
demands (of people and freight mobility) with city transport service provision using
big data. Li et al. [22] developed and applied a framework to case examples that
demonstrate how smart cities are redefining the characteristics of operations models
around their scalability, analytical output, and connectivity. The paper contributes to
our understanding of how smart cities can potentially transform operational models](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-29-320.jpg)
![14 V. Charles et al.
and sets out a research agenda for operations management in smart cities in the digital
economy. Lastly, Huang and Rust [16] discussed the characteristics of information
technology associated with consumer centricity.
4 Conclusions
In this chapter, we have aimed to review the literature on big data for SOM via an
exploration of the Scopus database and by means of using the VOSviewer visualisa-
tion software for bibliometric analysis in order to highlight the research trends in the
field. From a practical perspective, our analysis reveals patterns in scientific outputs
and serves as a guide for global research trends in big data for SOM.
Overall, the findings reveal an increased interest in studies in the fields of
urban planning and smart city decision management empowered by real-time data
processing using IoT, big data analytics, and cloud computing technologies. Other
research strands include big data for quality control, electric utilities, informa-
tion services and information systems. Additionally, there is continued interest in
exploring big data analytics to predict and mitigate the effect of supply chain risks
and disruptions, which have been shown that can severely disrupt operations and
supply chains. As Hazen et al. [15] stated, it is important for organisations to improve
the quality of the analytical outputs of their decision-making processes by means of
paying attention to the quality of the big data on which they base their decisions.
The review of the studies included in this research has further shown that there is
an increased interest towards studying computational paradigms in the field of big
data for SOM. Although great progress has been made so far, nonetheless, much
more research is needed; in particular, more research is necessary to build big data-
driven analytical models which are not only explainable and interpretable [14], but
also deployable in the Cloud. Furthermore, there is a need for more interdisciplinary
research that integrates traditional modes of enquiry in (service) operations manage-
ment with, for example, behavioural approaches, with a blend of analytical and
empirical methods.
Acknowledgements The authors are thankful to the reviewers for their valuable comments on the
previous version of this work.
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1 Introduction
This manuscript is concerned with the evolution of service operations management
(SOM) in the age of big data and how big data can help to redefine the concept of
service operations strategy to build an enhanced competitive advantage for organi-
sations. Overall, SOM has had a rather unclear path in the literature, being mostly
perceived as a branch of operations management (OM). Despite this, as Grönroos
(1994) also acknowledged almost two decades ago, “the business logic is different in
service” (p. 13); therefore, there is a constant need to refine our understanding of the
principles and assumptions underpinning SOM, something that has been changing
over the years, even more so during the big data age.
The relatively recent phenomenon posed by the exponential growth of big data
has brought with it new challenges, one of the most intriguing of which deals with
knowledge discovery and large-scale data-mining [26]. The presence of big data has
been ‘pushing’ organisations to review their practices and identify opportunities that
would allow them to base a substantial portion of their operational decisions on data,
otherwise known as data-driven decision-making [9]. Big data are transforming
the service industry, in areas ranging from finance/banking, to transportation and
hospitality, education and government, among others. But at the same time, this
transformation is only in its infancy and undoubtedly will require more cross- and
inter-disciplinary approaches and collaborations, bringing together a variety of stake-
holders, from researchers to data scientists, marketers, psychologists and behavioural
analysts, regulators and policymakers, just to name a few. The potential held by big
data to transform strategic decision-making in SOM is, indeed, substantial, but so
seem to be the challenges. Today, big data analytics and high-speed computing are no
longer the main concerns, but rather how “to carefully exploit and unlock the power
of big data while preserving fairness, trust, and consumers’ happiness” (Cohen [19],
p. 1722). In this manuscript, we investigate a rich range of bibliographic material
and identify strategic aspects that need to be considered in SOM in the context of
big data, with a particular focus on technology, people, and ethics. We also postulate
that if we want to understand the promise of big data in redefining SOM strategy,
we must first understand how service management hypotheses and assumptions have
changed as a result of innovations brought about by the big data age.
The remainder of the manuscript is organised as follows: Sect. 2 introduces and
discusses the concept of SOM, while Sect. 3 explores traditional misconceptions
in SOM and Sect. 4 details the current state-of-the-art in SOM. Subsequently, we
introduce the concept of big data in Sect. 5, discuss the various types of analytics
in Sect. 6, and explore the big data pipeline in Sect. 7. An extensive narrative of
strategic considerations in SOM in the big data age is proffered in Sect. 8. Here, we
further introduce the BD-SOM strategy. Lastly, Sect. 9 concludes with final thoughts
and a discussion of future directions.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-36-320.jpg)
![Strategy Formulation and Service Operations in the Big Data Age … 21
2 The Concept of Service Operations Management
Operations management has traditionally been associated with manufacturing
production [5], having had little contact with customers and not always under-
standing their needs and desires. Activity scheduling charts and assembly lines, linear
programming, queuing theory, and PERT are just some of the traditional areas that
OM researchers have been generally concerned with, with Chopra et al. [18] noting
that most of the early research “focused on tactical issues such as line balancing,
scheduling, production planning, inventory control, and lot sizing” (p. 9).
In recent decades, however, the focus has shifted more strongly towards the
services sector, which has experienced an accelerated growth. As Peinado et al.
[71, p. 374] assessed, “operations management is a discipline that originated to
solve management problems in a factory environment, but since the mid-twentieth
century researchers, lecturers and practitioners have begun to adapt the knowledge
of the field to also support service operations.” So, in time, the concept of OM has
come to encompass the area of services; in the words of Manikas et al. [56], “in
the 1990s, business process reengineering, six-sigma, enterprise resource planning
systems (ERP), and the internet became enablers of operations management. As
the techniques described by operations have also been applied to service indus-
tries and other non-manufacturing areas of organisations, the scope of operations
management increased to include service” (p. 1442).
The globalisation of markets and the technological progress in information and
communications technology have altered the management landscape not only for
manufacturing enterprises, but also for the productive management of services.
Services are increasingly important particularly in today’s industrialised economies,
where technological changes have translated into a greater number of people being
employed in services, a sector that accounts for most of the countries’ gross domestic
product [30, 66]. As a matter of fact, Levitt’s [53] observation about the importance
and pervasiveness of services for business is just as true today as it was the day it
was first uttered, almost half a century ago. The increasing importance of studying
service management was echoed by researchers at different points in time: Miller
et al. [61], Amoako-Gyampah and Meredith [2], Pannirselvam et al. [67], Nie and
Kellogg [64], and Johnson [45]; these researchers concluded that there is still not
enough research being done in the SOM field and called for more research on the
topic, especially empirical research.
The history of SOM as a distinct topic in the academic literature has had its own
trajectory. Generally, studies can be traced back to the 1970s [46], although there are
some studies of service economy even prior to that time (e.g., Pearce [70], Penrose
[72]). But for some reason, the evolution of SOM as a standalone discipline has
remained unclear and the research community has continued to hold SOM as a part
of OM. For example, in [60], Meredith et al. defined OM as consisting of 17 focus
areas: (1) Aggregating planning, (2) Capacity Planning, (3) Distribution, (4) Facility
Layout, (5) Facility location, (6) Forecasting, (7) Inventory control, (8) Mainte-
nance, (9) Process design/technology, (10) Project management, (11) Purchasing,](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-37-320.jpg)
![22 V. Charles et al.
(12) Quality, (13) Quality of working life, (14) Scheduling, (15) Services, (16)
Strategy, and (17) Work measurement. As it can be noted, this study acknowledged
services as a branch of OM. All in all, this perspective over SOM as a part of OM
has meant that research on SOM has traditionally been scarce. At least, such was the
situation in 2007, when Machuca et al. [55] performed the first in-depth study on the
state of the then current SOM research.
But one may wonder how things would have changed since then. In a recent study,
Manikas et al. [56] performed a review of the literature on OM using a data-driven
approach; in this sense, the authors performed a historical analysis of major research
topics and trends between 1961 and 2017 and found 18 topic areas that received
most of the attention. One among these topics is the topic of service operations,
which contains articles mostly focused on buyer–seller relationship, queuing theory,
and service quality, with sub-themes such as multi-criteria evaluation of strategic
issues in service quality, design of service, information technology in service, and
globalisation of services. Furthermore, the authors concluded that service operations
are a growing area of publications, with particular interest in lean management (waste
reduction, improvement in resource usage, and so on). An interesting observation
that the authors further made is that an evaluation of only a recent subset of the entire
data that they used would show that emerging topics include healthcare efficiency and
effectiveness, environmental and sustainability, security and infrastructure, reverse
logistics, and big data methods. Despite the granular and useful analysis, we can see,
however, that once more, the SOM’s ‘curse’ of being perceived as a part of the OM
literature has not changed till date.
In time, SOM has struggled to find its fit not only as a distinct discipline in the
literature but also within the broader field of OM itself. This has been fueled by the
constant concern in the minds of researchers regarding how to identify the distinctive
contribution that SOM research makes compared to OM research. This SOM opacity
is, of course, surprising, especially considering that service operations represent, as
previously mentioned, the clear majority of economic activity (at least in developed
countries) [80].
3 Traditional Misconceptions in Service Operations
Management
A predominant view in the literature is that services are defined by four attributes:
intangibility, heterogeneity, inseparability, and perishability (IHIP); these have
commonly been accepted as the paradigm for services. There are studies, however,
that have refuted such view and concluded that it is without merit. For example, Vargo
and Lusch [82] argued that the IHIP characteristics “(a) do not distinguish services
from goods, (b) only have meaning from a manufacturing perspective, and (c) imply
inappropriate normative strategies” (p. 324). In a similar fashion, Edvardsson et al.
[23] highlighted that “the IHIP characteristics should not be used in the future as](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-38-320.jpg)
![Strategy Formulation and Service Operations in the Big Data Age … 23
generic service characteristics” (p. 115). Like these, there are many other myths
and misconceptions about services that continue to exist. What this means for SOM
is that not only has SOM research been characterised by opacity, but traditional
misconceptions have also accompanied the field. In his work, Sampson [78, p. 183])
identified five main stereotypes around SOM. These are:
1. “the operations function of firms is simply (or primarily) about managing
physical products and product inventories”;
2. “service operations are an unscientific type of operations, which is sometimes
based on the observation that traditional manufacturing operations models do
not often fit well in service contexts”;
3. “there is a traditional assumption that there is some dichotomy between goods
and services” (e.g., Greenfield [34]),
4. “some have asserted that service is inherently customer oriented and solution
focused” (e.g., Grönroos [36, p. 46], Vargo & Lusch [83, p. 138]),
5. “service is adequately defined by what it is not” [e.g., “nonmanufacturing”,
which is a residual definition (Morey [63]); or “nonownership” (Judd [47]).
All of the above have eventually led to confusion as to what a service is, as well
as with regards to the very principles that lay as a foundation for SOM. This has also
meant that SOM has had a rich history of key but not widely recognised contributions
to both research and practice. Another interesting aspect that has contributed to the
perpetuation of SOM stereotypes has been the fact that quite a lot of the research
involving service management has come from the marketing discipline [54] (Rust,
2004). In this sense, marketing scholars have focused on addressing traditional OM
topics (e.g., quality management, facility layout, process focus) in service contexts.
Particular attention has been paid to quality management, which is by excellence an
OM topic; nevertheless, the most widely used model of service quality and associated
SERVQUAL instrument belong to the field of service marketing (Parasuraman et al.
[68, 69]).
Sampson [78] proposed that the stereotypes surrounding SOM could be rectified
by showing how service operations can be conceptualised, visualised, and analysed.
To this aim, the author introduced a Process-Chain-Network (PCN) visual framework
to clarify the fundamental concepts of SOM. As stated by the author, “the frame-
work is built upon PCN Diagrams that depict processes and interactions involving
networks of entities. PCN Analysis includes identifying the value proposition of a
given process network, assessing performance characteristics and value propositions
of a process configuration, and identifying opportunities for process improvement
and innovation” (Sampson [78, p. 182]). Among others, his findings showed that
“the basic structural elements of PCN Diagrams can reveal commonality among
seemingly disparate lines of business”, that “every business has a mix of interactive
processes and independent processing”, and that “service interaction requires some
degree of integration of processes across multiple entities and can, therefore, be more
difficult to design and execute than independent processes” (p. 194).](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-39-320.jpg)
![24 V. Charles et al.
4 Current State-Of-The-Art in Service Operations
Management
Previous review articles have documented the history and evolution of research on
service operations. For example, Chase and Apte [16], Heineke and Davis [40]
described the history of service operations, Machuca et al. [55], Smith et al. [80]
provided a taxonomy of research in view of content topic, research methodology,
journal, and author affiliation of service operations research, and Bretthauer [7],
Chase [15], Hill et al. [42], Johnston [45], Roth and Menor [77] performed an analysis
of the research gaps, limitations, and avenues for future research.
In their review paper of published work in the Journal of Service Manage-
ment during 2010–2016, Victorino et al. [84] used the Delphi method to identify
research themes in service operations that have great potential for exciting and inno-
vative conceptual and empirical work. Their efforts resulted in the identification
of the following key themes: service supply networks, evaluating and measuring
service operations, performance, understanding customer and employee behaviour
in service operations, managing servitisation, managing knowledge-based service
contexts, managing participation roles and responsibilities in service operations,
addressingsociety’schallengesthroughserviceoperations,andtheoperationalimpli-
cations of the sharing economy. The authors reviewed nearly 700 articles, which
were classified based on the discipline focus into one of the following categories:
“Operations,” “Marketing,” “Organisational Behavior,” Management of Information
Systems (MIS),” or “Interdisciplinary.” Table 1 depicts the characteristics of the
articles in each category.
Although limited to analysing only one journal, the findings reveal two important
trends. First, the authors noted that over the years, the percentage of articles in each
category has declined, with the exception of interdisciplinary research, which has
seen a continuous upward trend. Second, another interesting observation that the
authors make is that what most of the research articles in the literature seem to share
isacallformoreresearchthatinvolvesaninterdisciplinaryapproachtounderstanding
service operations, by drawing from fields such as marketing, human resources, and
information systems, among others. This perspective has been further expanded in the
approach termed as “service science,” which focuses on combining knowledge from
scientific management and engineering disciplines for service innovation (Spohrer &
Maglio [81], as cited by Victorino et al. [84]).
Victorino et al.’s [84] study is accompanied by a follow-up paper by Field et al.
[29], in which the authors take a broader view by means of including a wider litera-
ture to offer a comprehensive review of each research theme and posit future research
questions for advancing the field of service operations. One of their main conclu-
sions is that the field of service operations has many interesting research topics and
questions that remain largely unexplored till date.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-40-320.jpg)
![Strategy Formulation and Service Operations in the Big Data Age … 25
Table
1
Characteristics
of
SOM
articles
by
discipline
[84]
Operations
Marketing
Organisational
Behaviour
Management
of
Information
Systems
(MIS)
Interdisciplinary
Focus
Process
or
production-oriented
Customer-oriented
People-oriented
Information-technology
oriented
Majority
of
articles
found
at
the
interface
of
operations
and
marketing
Topics
Productivity
Capacity
management
Quality
management
Customer
behaviour
Customer
loyalty
Customer
segmentation
Service
climate
Leadership
Employee
satisfaction
Service
quality
Service
design
and
innovation
%
of
Articles
20%
43%
11%
1%
26%](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-41-320.jpg)
![26 V. Charles et al.
5 The Concept of Big Data
Before proceeding with an assessment of SOM in the context of big data, let us first
briefly review the concept of ‘big data’. Just like with the concept of SOM, ambiguity
is also surrounding the concept of ‘big data’. Initially coined in 1997, the term ‘big
data’ (Cox & Ellsworth [21]) has evolved to become today the new normal. However,
big data in themselves are not a new ‘thing’.
In 1908, on the island of Crete, archaeologists discovered a disc of fired clay, which
was dated to the middle or late Minoan Bronze Age, hence from around 2000 B.C.
The disc, called the Phaistos Disc (Fig. 1) was dubbed the ‘first Minoan CD-ROM’.
The disk is round in shape and covered on both sides with a spiral of inscriptions,
whose meaning remains a mystery till today. Nevertheless, the point is that this is
an example of what data used to look like thousands of years before the advent of
CDs. This is how society used to store and transmit data at some point in time in the
distant past. A rudimentary attempt, by all accounts, considering the limited ‘storage’
available and the impossibility of rewriting it or making any operations with it.
So, (big) data have always been with us and, as a society, we have constantly aimed
at finding ways to store them and passing them on. What has changed, however, is
the fact that the increase in IT-related infrastructure over the past years, coupled with
the emergence of complex analytics, has enabled us to store virtually any amount of
data at a significantly reduced cost and analyse and interpret such data in ways that
could not have been done before [24].
Today, there is no universally accepted definition of big data. Big data is a multi-
dimensional concept. As cited by Charles and Emrouznejad [10] and Emrouznejad
and Charles [25], Hammond [38] associated big data with evidence-based decision-
making, Beyer and Laney [6] defined it as high volume, high velocity, and/or high
Fig. 1 The Phaistos Disk (or the ‘first Minoan CD-ROM’). Note. Source https://araenil.files.wor
dpress.com/2011/06/phaistosdisklarge.jpg](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-42-320.jpg)
![Strategy Formulation and Service Operations in the Big Data Age … 27
variety information assets, and Ohlhorst [65] described big data as vast data sets
which are difficult to analyse or visualise with conventional information technolo-
gies. Most commonly today, big data are defined in terms of data characteristics or
dimensions, often with names starting with the letter ‘V’. By some accounts, there
are today as many as 10 Vs (Markus [58]). Laney [51] defined the initial four Vs,
which have become central to understanding big data; these are as follows:
Volume: Big data are characterised by their extremely large volume. It is estimated
that by the end of 2020, the volume of data would be around 40 zettabytes, or 300
times bigger than the volume of data in 2005 (Herschel & Mori [41]). To be noted
that the focus is not only on human-generated data (which are mostly structured and
represent a small fraction of the entire data being generated), but also data produced
by devices, such as sensors and connected devices (which are mostly unstructured
and account for most of the data out there).
Velocity: Big data arrive at un unprecedented speed. Velocity refers to a real-time
or near-real time stream of data, which poses an issue for real-time processing. Real-
time processing is essential for businesses looking to obtain a competitive advantage
overtheircompetitors;forexample,IBM[44]statedthat“fortime-sensitiveprocesses
such as catching fraud, big data must be used as it streams into your enterprise in
order to maximize its value”. This means that velocity is a double-edged sword. On
the one hand, the possibility to collect mountains of data at such high speed opens
up new possibilities to derive more insights, but on the other hand, the time needed
to translate the data into intelligent decisions remains a challenge [51].
Variety: The characteristic of ‘variety’ of big data refers to different types of data,
such as structured, semi-structured, and unstructured, which arrive from a variety
of sources [51] (IBM, 2016). As expected, the biggest challenge is posed by the
semi-structured and unstructured data, which are hard to analyse due to not having
an easily identifiable internal structure. Examples include photos, audio and video,
web pages, wikis, and blogs, streaming data, emails, social media data, and so on.
Veracity: It refers to the trustworthiness of the data and the reason behind ques-
tioning the existence of inherent discrepancies in the data is rooted precisely in the
unstructured feature of big data. Another reason is the presence of inaccuracies.
Inaccuracies can be due to the data being intrinsically inaccurate or from the data
becoming inaccurate through processing errors [51].
In their paper, Charles and Gherman [11] argued that the term big data is a
misnomer, stating that while the term in itself refers to the large volume of data,
Big Data is essentially about the phenomenon that we are trying to record and the
hidden patterns and complexities of the data that we attempt to unpack. The authors
advanced an expanded model of Big Data, wherein they included three additional
dimensions, namely the 3 Cs: Context, Connectedness, and Complexity. The authors
statedthatunderstandingtheContext isessentialwhendealingwithBigData,because
“raw data could mean anything without a thorough understanding of the context that
explains it” (p. 1072); Connectedness was defined as the ability to understand Big
Data in its wider Context and in view of its ethical implications; and Complexity was
defined from the perspective of having the skills to survive and thrive in the face of](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-43-320.jpg)
![28 V. Charles et al.
Fig. 2 The evolution of big data. Note. Source Epstein and Hagen [27]
complex data, by means of being able to identify the key data and differentiate the
information that truly has an impact on the organisation.
Figure 2 depicts the evolution of big data from their initial primitive and structured
forms housed locally, to the unstructured, highly complex forms housed in the Cloud.
Perhaps one more observation to make in relation to big data, at this point, has
to do with the ‘risk’ accompanying them. We define risk in terms of its dual nature,
materialisedbyunderstandingtheconceptbothasadanger andasanopportunity.Big
data can indeed translate into a big opportunity for organisations, helping decision-
making processes become more effective. But one must not forget the other side of
the coin, the challenges that come with big data and how big data’s usefulness can
be constrained by the ability of the researchers to ask the right questions and apply
the right tools, all within an ethical framework.
For example, take the case of the Google Flu Trends Project, which has shown
just how vulnerable the exploitation of big data can turn out to be, when not done
properly. Google Flu Trends was a web service operated by Google, launched in 2008
and abandoned in 2015 (although problems started being apparent in 2009 itself). The
project’sgoalwastodevelopameansofidentifyingtheemergenceofflusothathealth
resources could be mobilised to treat the illness immediately and prevent a possible
outbreak. The web service used data readily available from Google’s search engine,
mainly data on the frequency of “flu” searches, it aggregated them, and attempted to
predict future rates of flu across more than 25 countries. After apparent early success,
the project had to be abandoned as the predictions were not accurate enough. It was
found that the model was consistently over-inflating future occurrences and was less
accurate than existing ‘small data’ strategies that utilised data on confirmed cases
of flu. The Google Flu Trends’ epic failure showed just how vital the elements of
rigour, criticality, and the correct consideration of the wider context, among others,](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-44-320.jpg)
![Strategy Formulation and Service Operations in the Big Data Age … 29
actually are. In the words of Lazer et al. [52, p. 1203], “the quantity of data does not
mean that one can ignore foundational issues of measurement and construct validity
and reliability and dependencies among data”. In the absence of a specific criticality
in the analysis performed and in the interpretation of the results obtained, big data
cannot add value to any organisation.
The above considerations led Fung [31] to conclude that McKinsey’s widely
circulated definition of big data as “datasets whose size is beyond the ability of typical
database software tools to capture, store, manage, and analyze” is not helpful in
providing any answers to prevent future failures. Instead, he proposed a new approach
to defining big data, called the OCCAM framework, which has the advantage of
bringing to one’s attention the assumptions underlying the concept. In view of the
OCCAM framework, big data are [31]:
• “Observational: much of the new data come from sensors or tracking devices
that monitor continuously and indiscriminately without design, as opposed to
questionnaires, interviews, or experiments with purposeful design.”
• “Lacking Controls: controls are typically unavailable, making valid comparisons
and analysis more difficult.”
• “Seemingly Complete: the availability of data for most measurable units and the
sheer volume of data generated is unprecedented, but more data creates more
false leads and blind alleys, complicating the search for meaningful, predictable
structure.”
• “Adapted: third parties collect the data, often for purposes unrelated to the data
scientists’, presenting challenges of interpretation.”
• “Merged: different datasets are combined, exacerbating the problems relating to
lack of definition and misaligned objectives.”
6 Types of Analytics
Data analytics is a multidimensional term, often interchangeably used with data
science, data mining, and so on. The distinction between these terms is vague, but in
essence, they all refer to the extraction of useful information from a preprocessed set
of data. The techniques that can be used to this aim are varied and stem from across the
disciplines, such as statistics (e.g., inferential statistics, various types of regression);
machine learning (e.g., ensemble decision trees like random forest), in particular
kernel methods (e.g., support vector machines); and biology (e.g., neural networks,
genetic algorithms, and nature-inspired algorithms like ant colony algorithm and
swan particle algorithm, among others).
There is a plethora of analytics that organisations can perform. Below, we discuss a
more comprehensive framework (Fig. 3), comprising five types of analytics: descrip-
tive, diagnostic, predictive, prescriptive, and cognitive. It should be noted at the
outset, however, that there is no consensus over this taxonomy, with most frame-
works considering only three types (descriptive, predictive, and prescriptive). Others
consider four, namely descriptive, diagnostic, predictive, and prescriptive [Gartner](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-45-320.jpg)
![30 V. Charles et al.
Fig. 3 Extended data analytics framework
Analytics Ascendancy Model (GAAM), Maoz [57]]; while others do not differentiate
between descriptive and diagnostic analytics. Most recently, also, in the context of
marketing strategy formulation, Eriksson et al. [28] proposed a new form of analytics,
namely creative analytics, which is seen as a capability to address the potential of
artistic creativity. Independent of these views, however, there are few observations
worth making:
(a) each type of analytics provides distinct value propositions (hindsight, insight,
foresight, context, or inference), serving distinct purposes (information,
optimisation, or intelligence);
(b) the types of analytics are interconnected, but should not be viewed sequentially
within a growth model framework; they can work in parallel and measure value
differently.
(c) the more advanced the analytics get, the more complexity and difficulty are
added, requiring greater resources, both computational and human resources;
and normally, this should be accompanied by an increase in the value propo-
sition made. But this does not always hold true and it depends on application.
Sometimes there just is not enough information in the data to make higher
levels of analytics (such as predictive, prescriptive, or cognitive) valuable.
The data analytics framework (Fig. 3) is an extension to the existing frameworks,
by means of considering all five levels of analytics. We position this framework in
a three-dimensional view along three axes: complexity, difficulty, and value. It is
relevant to note that most literature has failed to differentiate between complexity](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-46-320.jpg)
![32 V. Charles et al.
analytics can help improve decision-making in ways that previous analytics cannot
do, creating a real competitive advantage for the organisation; but, the effort, organ-
isational commitment, and resources needed to carry it out are substantial and not
all organisations can afford them.
Cognitive analytics: This is the most advanced form of analytics, aiming to mirror
human thinking, translating into pure intelligence. It is also known as “intelligent
analytics”. It mimics the human brain by drawing inferences from existing data and
then inserting these inferences back into the knowledge base for future inferences—
a self-learning feedback loop. Common approaches to perform cognitive analytics
include a blend of artificial intelligence (AI), machine learning (ML) algorithms
(more precisely, reinforcement learning), deep learning models, semantics, and game
theory. Cognitive analytics blends traditional analytics techniques with AI and ML
features for advanced analytics outcomes.
Perhaps one additional observation worth making at this point is that, in building
analytical models, several requirements should be considered, depending on the
application area [3]: business relevance, statistical performance (statistical signifi-
cance and predictive power in terms of related performance metrics), interpretability,
justifiability, operational efficiency, economic cost, and compliance with interna-
tional regulation and legislation. To be noted that interpretability often needs to be
balanced against statistical performance and this is an important trade-off to keep in
mind. For example, neural networks are high performing (i.e., have higher accuracy),
but offer no insight into the underlying patterns in the data, lacking clarity around
inner workings. On the other hand, a linear regression model has limited modelling
power, but is highly comprehensible and interpretable. Now, as Roda et al. [76]
argued, sophisticated models, also known as black-box models, might not be more
reliable than simpler models, also known as white-box models, this is especially
the case when we deal with a phenomenon we know almost nothing about or do
not yet fully understand (e.g., the recent COVID-19 pandemic), in which case it is
more beneficial to have models that we can clearly explain how they behave, how
they produce predictions, what the influencing variables are, and so on. In a nutshell,
transforming data into meaningful knowledge is an art and not a blind application
of analytical models. A comparison between white-box and black-box models is
offered in Table 2.
7 The Big Data Pipeline
The value of data is unlocked only after they are transformed from their raw form
into actionable knowledge, and when that value proposition is promptly delivered to
relevant stakeholders. In the context of big data, an organisation may wish to rely
on a data pipeline, which is a series of data processing steps, to successfully achieve
this aim. A data pipeline is especially useful when the organisation deals with large
amounts of data from multiple sources which are moreover generally stored in the
Cloud, and when it requires real-time or near real-time complex data analysis. So, in](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-48-320.jpg)
![Strategy Formulation and Service Operations in the Big Data Age … 33
Table 2 A comparison between black-box models and white-box models
Black-box models White-box models
Address highly non-linear structures Address linear or stepwise linear or curve
linear structures
Logically well-defined and mathematically
complex
Logically and mathematically well defined,
and simple
Large number of parameters; hence, models
are high-dimensional
Small number of parameters
Large number of features Relatively small number of features
Statistical hypothesis testing is irrelevant Statistical hypothesis testing is relevant
High computational complexity Low computational complexity
Lack clarity around inner workings The input–output relationship is visible, and
the process through which the output is
produced is also visible
Large data set Relatively small data set
Do not warrant any statistical distributional
assumptions
Warrant statistical distributional assumptions
Modelling is usually a trial and error and
iterative process
Modelling is less of a trial and error process
and more of a systematic approach
Guided by rules of thumb Guided by established criteria
Results depend on the hyperparameter tuning
strategy
Results depend on the statistical estimation
properties
Lower explainability or interpretability Higher explainability or interpretability
Lower transparency and accountability Higher transparency and accountability
Higher accuracy Relatively lower accuracy
Source Hansun et al. [39]
considering a big data pipeline, big data principles need to be applied to the pipeline;
and here, we are specifically referring to the consideration of the 4 Vs of big data:
volume, variety, velocity, and veracity, all of which will impact the organisation’s
big data journey.
The full data pipeline for big data traditionally passes through several stages, as
can be appreciated in Fig. 4. It consists of three main phases, namely data engineering,
analytics, and delivery. Furthermore, the data engineering phase is split into three
steps: data collection, ingestion, and preparation.
The data source layer is composed of the raw data arriving in the organisation. It
includes all types of data: structured, semi-structured, and unstructured, which can
come from static sources, as well as from real-time sources, such as IoT devices and
sensors. The ingestion stage is about getting all the data needed, in a raw format,
in a single repository called a data lake. The data storage layer is where the data
are kept after being collected and ingested from the various sources. In view of the
explosion of data volume available, sophisticated and accessible systems have been
developed to help with this task. It should be mentioned that for simple, small data](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-49-320.jpg)
![Seventeenth Iowa.
My report is already so long, that I must forbear
mentioning acts of individual merit. These will be
recorded in the reports of division commanders, which
I will cheerfully indorse; but I must say that it is but
justice that colonels of regiments, who have so long
and so well commanded brigades, as in the following
cases, should be commissioned to the grade which
they have filled with so much usefulness and credit to
the public service, viz.: Colonel J. R. Cockerell,
Seventieth, Ohio; Colonel J. M. Loomis, Twenty-sixth
Illinois; Colonel C. C. Walcutt, Forty-sixth Ohio; Colonel
J. A. Williamson, Fourth Iowa; Colonel G. B. Raum,
Fifty-sixth Illinois; Colonel J. I. Alexander, Fifty-ninth
Indiana.
My personal staff, as usual, have served their country
with fidelity, and credit to themselves, throughout
these events, and have received my personal thanks.
Inclosed you will please find a map of that part of the
battle-field of Chattanooga fought over by the troops
under my command, surveyed and drawn by Captain
Jenney, engineer on my staff. I have the honor to be,
your obedient servant,
W. T. SHERMAN, Major-General commanding.
[General Order No. 68.]
WAR DEPARTMENT ADJUTANT-GENERAL'S OFFICE
WASHINGTON, February 21, 1884
Joint resolution tendering the thanks of Congress to](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-56-320.jpg)
![till just in time to assist General George H. Thomas to defeat General
Hood before Nashville, on the 15th and 16th of December, 1864.
General Grant's letter of instructions, which was brought me by
General Butterfield, who had followed me to New Orleans, enjoined
on me, after concluding with General Banks the details for his Red
River expedition, to make all necessary arrangements for furloughing
the men entitled to that privilege, and to hurry back to the army at
Huntsville, Alabama. I accordingly gave the necessary orders to
General McPherson, at Vicksburg, and continued up the river toward
Memphis. On our way we met Captain Badeau, of General Grant's
staff, bearing the following letter, of March 4th, which I answered on
the 10th, and sent the answer by General Butterfield, who had
accompanied me up from New Orleans. Copies of both were also
sent to General McPherson, at Vicksburg:
[Private.]
NASHVILLE, TENNESSEE, March 4, 1864
DEAR SHERMAN: The bill reviving the grade of
lieutenant-general in the army has become a law, and
my name has been sent to the Senate for the place.
I now receive orders to report at Washington
immediately, in person, which indicates either a
confirmation or a likelihood of confirmation. I start in
the morning to comply with the order, but I shall say
very distinctly on my arrival there that I shall accept no
appointment which will require me to make that city
my headquarters. This, however, is not what I started
out to write about.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-73-320.jpg)
![While I have been eminently successful in this war, in
at least gaining the confidence of the public, no one
feels more than I how much of this success is due to
the energy, skill, and the harmonious putting forth of
that energy and skill, of those whom it has been my
good fortune to have occupying subordinate positions
under me.
There are many officers to whom these remarks are
applicable to a greater or less degree, proportionate to
their ability as soldiers; but what I want is to express
my thanks to you and McPherson, as the men to
whom, above all others, I feel indebted for whatever I
have had of success. How far your advice and
suggestions have been of assistance, you know. How
far your execution of whatever has been given you to
do entitles you to the reward I am receiving, you
cannot know as well as I do. I feel all the gratitude this
letter would express, giving it the most flattering
construction.
The word you I use in the plural, intending it for
McPherson also. I should write to him, and will some
day, but, starting in the morning, I do not know that I
will find time just now. Your friend,
U. S. GRANT, Major-General.
[PRIVATE AND CONFIDENTIAL]
NEAR MEMPHIS, March 10, 1864
General GRANT.](https://image.slidesharecdn.com/40611-250517095331-14de76d7/85/Big-Data-and-Blockchain-for-Service-Operations-Management-Ali-Emrouznejad-74-320.jpg)
Big Data and Blockchain for Service Operations Management Ali Emrouznejad
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- 5. Studies in Big Data 98 Ali Emrouznejad Vincent Charles Editors Big Data and Blockchain for Service Operations Management
- 6. Studies in Big Data Volume 98 Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: kacprzyk@ibspan.waw.pl
- 7. The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are reviewed in a single blind peer review process. Indexed by SCOPUS, EI Compendex, SCIMAGO and zbMATH. All books published in the series are submitted for consideration in Web of Science. More information about this series at https://link.springer.com/bookseries/11970
- 8. Ali Emrouznejad · Vincent Charles Editors Big Data and Blockchain for Service Operations Management
- 9. Editors Ali Emrouznejad Surrey Business School The University of Surrey Guildford, UK Vincent Charles Center for Value Chain Innovation CENTRUM Católica Graduate Business School, Pontifical Catholic University of Peru Lima, Peru ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-87303-5 ISBN 978-3-030-87304-2 (eBook) https://doi.org/10.1007/978-3-030-87304-2 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
- 10. Preface Big Data is a major source of change in today’s world. It is without doubt a source of immense economic and social value with the potential to impact individuals, organisations and society alike in ways that are yet to be fully explored. On the other hand, Blockchain is poised to play the role of foundation technology to store Big Data, ensuring that the data remain trustworthy, immutable and traceable. In this sense, then, Blockchain will make Big Data even more valuable. Altogether, Big Data and Blockchain are two complementary technologies that are expected to radically transform the way organisations are run in the upcoming years. Organisations are constantly collecting a variety of data, such as standard tables, text, pictures and videos, of unprecedented sizes (millions or billions of records / variables) and from various sources, with the aim to use such data to improve their operations/services and create competitive advantage. There is a collective assump- tion that if organisations can learn to harness Big Data and Blockchain technolo- gies, then their operational capabilities would be transformed. In this context, both academics and practitioners interested in Service Operations could benefit from Big Data and Blockchain technology to enhance operational performance. The present book titled “Big Data and Blockchain for Service Operations Manage- ment” aims to provide the state-of-the-art on the use of Big Data and Blockchain to improve Service Operations in a variety of domains. Along theory and applications, the book compiles the authors’ experiences so that these may be aggregated for a better understanding. The book is well organised in fourteen chapters, contributed by authors from all around the globe: Argentina, Austria, Canada, Chile, China, Czech Republic, India, Iran, Malaysia, Peru, Portugal, Turkey, United Arab Emirates, United Kingdom and the United States. The first eight chapters address the topic of big data and service operations management from various angles. The chapter “Characteristics and Trends in Big Data for Service Operations Management Research: A Blend of Descriptive Statistics and Bibliometric Anal- ysis” provides an introduction to big data for service operations management by v
- 11. vi Preface reviewing relevant literature and highlighting developments in research and appli- cation. The analysis reveals patterns in scientific outputs and serves as a guide for global research trends in big data for service operations management. Among others, the chapter emphasises the need for research on building big data-driven analytical models which are not only explainable and interpretable, but also deployable in the Cloud. The chapter “Strategy Formulation and Service Operations in the Big Data Age: The Essentialness of Technology, People, and Ethics” complements the chapter “Characteristics and Trends in Big Data for Service Operations Management Research: A Blend of Descriptive Statistics and Bibliometric Analysis” and explores the promise of big data in redefining strategy in service operations management by means of investigating a rich range of bibliographic material. As the authors indicate, service operations management research in the big data age implies a shift in atten- tion from being increasingly integrative across themes to being integrative across multiple disciplines, requiring the expertise of and tuning between different actors and expertise domains. The chapter “Modeling Big Data Enablers for Service Operations Management” identifies big data enablers in service operations management and analyses the inter- actions between them. The findings allow decision-makers to select the desired enablers and drop the undesired ones in the implementation of big data initia- tives to improve service operations management performance. The chapter makes contributions by proposing the use of MCDM-based hierarchical models and causal diagram. The chapter “Data Architecture for Big Data Service Operations Management (The New Vision of Data Architecture for the Future Human Society)” proposes a platform construction for data management and control called Data Architec- ture, which can be used in big data service operations management and provide complicated data applications with data protection in the open Internet environment. The chapter “Big Data for Educational Service Management” presents a survey of the state-of-the-art applications of big data analytics in the field of educational services.Theauthorsexplainhowbigdatacanhelpinimprovingtheoveralleducation services, describe the challenges that institutions face while implementing big data- based solutions and suggest future research avenues on the topic. The chapter “A Novel Big Data Approach for Text Supported Service Operations Management” presents the latest advances in artificial intelligence for text data anal- ysis and operations management. It provides the state-of-the-art of the text processing approaches, discusses selected use-cases from the field of operations management and how the latest methods can help to solve those problems, and outlines some ideas for further improvement of the current approaches in terms of how to effectively analyse data in a multilingual environment and decrease memory demands. The chapter “Toward a Comprehensive Framework of Social Media Analytics” proposes a practical analytics framework for gaining more actionable insights from social media content. The framework is developed based on a series of machine learning and data analysis algorithms along with the required ETL modules. Having
- 12. Preface vii the ability to get embedded in big data clusters, the proposed analytics engine can be utilised in analysing large social media datasets through big data analytics solutions. The chapter “Data Mining Approach in Repair and Service Systems of Elec- tronic Products Under Warranty” provides the only study in the literature aimed at contributing to operational processes by evaluating the only type of product in the electronic repair service sector using data mining methods relative to the type of repair. The results of the research are expected to contribute to the evaluation of the processes of firms operating in a similar field. The role of blockchain has been expanding in recent years due to its increasing application in various domains. The second part of the book comprises six chapters that deal with the topic of blockchain in the context of big data for service operations management. The chapter “Integrative Applications of Blockchain and Contemporary Tech- nologies from a Big Data Perspective” introduces the concept of blockchain and high- lights the integrated applications of blockchain, Internet-of-Things, fog computing and artificial intelligence. It focuses on 3D Printing based on blockchain, introduces the integrative applications of blockchain and swarm robotic systems, as well as it features the composite application geotagging and blockchain. Several managerial and policy implications of managing big data from a service operations perspective are also proffered. The chapter “Blockchain for Disaster Management” explores the application of blockchain in disaster management to address issues related to poor coordination among responding agencies, late disaster response and inadequate distribution of resources. The authors offer a comprehensive blockchain framework for disaster management which includes governments, residents, telecommunication providers, shelter providers, food service providers, medical service providers and suppliers, transportation providers and non-governmental relief organisations. The chapter “Blockchain Production Planning in Mass Personalized Envi- ronments” presents the tools to generate a business strategy for mass customised/personalised production in Industry 4.0 environments. To this aim, the authors propose an autonomous and decentralised blockchain-based system managed fundamentally by cyber-physical systems (CPS), which allows associ- ating the decision-making processes concerning production planning to the CPS. The proposal is readily applicable to service operations management, given that personalised goods embody many features shared with services. The chapter “Frontiers of Blockchain for Railways” explores blockchain-based applications in the railway ecosystem from a service operations perspective. More specifically,theauthorsadvanceaconceptualmodel(i.e.aspecificprovenanceframe- work) to understand the contribution of blockchain technology in the Indian Railway system. The chapter “Blockchain Interoperability Issues in Supply Chain: Exploration of Mass Adoption Procedures” provides an insight into blockchain interoperability in supply chains for mass adoption. To this end, a three-step approach is applied through conducting a literature review of blockchain technology and commonly
- 13. viii Preface used methodologies for blockchain interoperability, analysing four blockchain real- life use case applications in supply chains that address interoperability concerns and mass adoption, and discussing the results of the analysis based on the comments of interviewees. Finally, the chapter “Blockchain Technology Enablers in Physical Distribution and Logistics Management” identifies the critical enablers for the adoption of blockchain technology in the logistics and physical delivery sector, which it then validates by experts for highlighting their prioritisation using an analytic hierarchy process approach. Findings show that traceability and transparency were the factors given the utmost priority; they also underline how the adoption of blockchain tech- nology will enhance the operational efficiency in the case of service operations management. The chapters contributed to this book should be of considerable interest and provide our readers with informative reading. Guildford, UK Lima, Peru February 2022 Ali Emrouznejad Vincent Charles
- 14. Acknowledgments First among these are the contributing authors—without them, it was not possible to put together such a valuable book, and we are deeply grateful to them for bearing with our repeated requests for materials and revisions while providing the high-quality contributions. We are also grateful to the many reviewers for their critical review of the chapters and the insightful comments and suggestions provided. Thanks are also due to Professor Janusz Kacprzyk, the Editor of this Series, for supporting and encouraging us to complete this project. The editors would like to thank Dr. Thomas Ditzinger (Springer Senior Editor, Interdisciplinary and Applied Sciences & Engi- neering), Ms. Sylvia Schneider (Springer Project Coordinator, Production Heidel- berg), Ms. Divya Meiyazhagan (Springer Production Editor, Project Manager), Mr. Viju Falgon (in the Production team) for their excellent editorial and production assistance in producing this volume. We hope the readers will share our excitements with this important scientific contribution to the body of knowledge in Big Data and Blockchain. The Editors Ali Emrouznejad Vincent Charles ix
- 15. Contents Characteristics and Trends in Big Data for Service Operations Management Research: A Blend of Descriptive Statistics and Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Vincent Charles, Tatiana Gherman, and Ali Emrouznejad Strategy Formulation and Service Operations in the Big Data Age: The Essentialness of Technology, People, and Ethics . . . . . . . . . . . . . . . . . . 19 Vincent Charles, Ali Emrouznejad, and Tatiana Gherman Modeling Big Data Enablers for Service Operations Management . . . . . . 49 Mahdi Nasrollahi and Mohammad Reza Fathi Data Architecture for Big Data Service Operations Management (The New Vision of Data Architecture for the Future Human Society) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Fang Miao, Wenhui Yang, Yan Xie, and Wenjie Fan Big Data for Educational Service Management . . . . . . . . . . . . . . . . . . . . . . . 139 Santosh Kumar Ray, Mohammed M. Alani, and Amir Ahmad A Novel Big Data Approach for Text Supported Service Operations Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Lukas Povoda, Radim Burget, Martin Rajnoha, and Peter Brezany Toward a Comprehensive Framework of Social Media Analytics . . . . . . . 191 Vala Ali Rohani and Shahid Shayaa Data Mining Approach in Repair and Service Systems of Electronic Products Under Warranty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Filiz Ersöz and Deniz Merdin Integrative Applications of Blockchain and Contemporary Technologies from a Big Data Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Amit Karamchandani, Samir K. Srivastava, and Abha xi
- 16. xii Contents Blockchain for Disaster Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Kyle Hunt and Jun Zhuang Blockchain Production Planning in Mass Personalized Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Fernando Tohmé, Daniel Alejandro Rossit, Mariano Frutos, Óscar Vásquez, and Andrea Teresa Espinoza Pérez Frontiers of Blockchain for Railways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Mohita G. Sharma and Sachinder Mohan Sharma Blockchain Interoperability Issues in Supply Chain: Exploration of Mass Adoption Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Yaşanur Kayıkcı and Nachiappan Subramanian Blockchain Technology Enablers in Physical Distribution and Logistics Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Rohit Sharma and Anjali Shishodia
- 17. Characteristics and Trends in Big Data for Service Operations Management Research: A Blend of Descriptive Statistics and Bibliometric Analysis Vincent Charles, Tatiana Gherman, and Ali Emrouznejad Abstract The field of service operations management has a plethora of research opportunities to capitalise on, which are nowadays heightened by the presence of big data. In this research, we review and analyse the current state-of-the-art of the literature on big data for service operations management. To this aim, we use the Scopus database and the VOSviewer visualisation software for bibliometric analysis to highlight developments in research and application. Our analysis reveals patterns in scientific outputs and serves as a guide for global research trends in big data for service operations management. Some exciting directions for the future include research on building big data-driven analytical models which are deployable in the Cloud, as well as more interdisciplinary research that integrates traditional modes of enquiry with for example, behavioural approaches, with a blend of analytical and empirical methods. Keywords Analytics · Big data · Operations management · Services · Bibliometrics V. Charles (B) CENTRUM Católica Graduate Business School, Lima, Peru e-mail: vcharles@pucp.pe Pontifical Catholic University of Peru, Lima, Peru T. Gherman Faculty of Business and Law, University of Northampton, Northampton, UK e-mail: tatiana.gherman@northampton.ac.uk A. Emrouznejad Surrey Business School, The University of Surrey, Guildford, UK e-mail: a.emrouznejad@surrey.ac.uk © Springer Nature Switzerland AG 2022 A. Emrouznejad and V. Charles (eds.), Big Data and Blockchain for Service Operations Management, Studies in Big Data 98, https://doi.org/10.1007/978-3-030-87304-2_1 1
- 18. 2 V. Charles et al. 1 Introduction Operations management is a fundamental organisational function involved in the management of activities to produce and deliver products and services [3]. Services have long been the dominant sector of industrialised nations; nevertheless, the field of operations management has been traditionally associated with manufacturing and supply chains rather than with services [17]. And while it is true that service oper- ations management (SOM) has struggled to find its fit as a distinct discipline in the literature, it is fair to say that the number of studies on the topic have increased substantially over the years. For example, a recent review study by Roth and Rosen- zweig [31] concluded that, in their sample, service-oriented papers represented 58% of the publications, with the healthcare and retail sectors at the forefront. Today, big data have been accelerating this shift in research. All in all, the presence of big data has been ‘pushing’ organisations to review their practices and identify opportunities that would allow them to embrace data-driven decision-making processes to a greater extent. Although there is no unique definition of big data [7, 8], it is commonly accepted that big data are “datasets that are too large for traditional data-processing systems and that therefore require new technologies” [30]. In the literature, it is common to refer to the four dimensions of big data defined by Laney [21]: volume, velocity, variety, and veracity, which are indicative of the computational complexities and technical requirements associated with big data. Coupled with ethical challenges [10], analysing big data confronts the researchers with many difficulties [4]. Charles and Gherman [9] underlined that in order to create value and competitive advan- tage, big data should be further considered in view of the dimensions of context, connectedness, and complexity. The growth of big data has created opportunities for pursuing new avenues of research in SOM. In particular, big data analytics may supportimprovedpolicy-anddecision-makinganddriveorganisationalperformance. It is vital to both comprehensively and quantitatively evaluate the development trend in research on big data for SOM, which can help not only practitioners and managers, but also academics interested in making informed decisions in their future research endeavours. Bibliometric analysis, which has been widely used across different fields, is a feasible means that can quantitatively and qualitatively assess trends in research fields over time. It can be used to systematically identify, organise, and analyse the main elements of a research topic [28, 38], as well as to clearly determine the development trends of a particular research field [2]. After decades of research developments in SOM, there is currently a growing interest in exploring the potential that big data pose for this field. This is supported not only by the increasing availability of data, but also by the methodological advances in a number of fields. Therefore, this research investigates the characteristics and trends of studies integrating big data and SOM through a bibliometric analysis so as to facilitate a comprehensive understanding of the state-of-the-art of current research directions and progress in the field.
- 19. Characteristics and Trends in Big Data for Service Operations … 3 2 Methods and Materials Bibliometric analysis is a valuable research technique that can help in discovering the global research trends on a topic or field from multiple angles, providing an overview of future lines of research [24]. By bibliometric analysis, this paper reveals the char- acteristics and trends in studies integrating big data with SOM in view of publi- cation outputs and major journals, subject categories, geographic and institutional distribution of publications, and keywords analysis. The bibliometric analysis was conducted based on the Scopus database, which is the abstract and citation database of Elsevier. Although there are other databases available, Scopus was deemed to be one of the best choices in view of the fact that it is characterised by consistent citation metrics and precision in locating authors and institutions. Relevant data on big data for SOM research were downloaded on 13 March 2021 via the concurrent search for the keywords “big data”, “operations management”, and “service”. These keywords were searched in the title, abstract, and keyword lists of each publication. Finally, only publications in English were selected for further analysis. The search criteria along with the Boolean expression was as follows: TITLE-ABS-KEY ( “big data” “operations management” “service”) AND ( LIMIT-TO ( PUBYEAR, 2020) OR LIMIT-TO ( PUBYEAR, 2019) OR LIMIT-TO ( PUBYEAR, 2018) OR LIMIT-TO ( PUBYEAR, 2017) OR LIMIT-TO ( PUBYEAR, 2016) OR LIMIT-TO ( PUBYEAR, 2015) OR LIMIT-TO ( PUBYEAR, 2014) OR LIMIT-TO ( PUBYEAR, 2013)) AND ( LIMIT-TO ( LANGUAGE, “English”)). 3 Results The concurrent search for the keywords “big data”, “operations management”, and “service” yielded 57 document results on 13 March 2021, all published between 2013–2020. In Sect. 3.1, we proceed to analyse these document results by means of summary statistics. Subsequently, in Sect. 3.2, we continue with a bibliometric analysis of the same material, while in Sect. 3.3, we focus on exploring the themes of the journal research articles only. 3.1 Descriptive Summary Statistics of Published Material In this section, we provide an overall analysis of the 57 document results by means of various visualisations. Figure 1 shows the evolution of the number of publications in big data for SOM. Several observations are worth mentioning. First, all 57 documents were published after the year 2013, which means that at least in the Scopus database, there were no publications that integrated the three keywords together before this year. Second, we can observe an increased interest in the topic in recent years, with a peak in the year 2018 (16 publications).
- 20. 4 V. Charles et al. Fig. 1 Annual scientific production. (Source Scopus [33]) Fig. 2 Documents per year by source. (Source Scopus [33]) Figure 2 shows the number of documents per year by source, with a compar- ison of the document counts for the top four sources. The journals that have published most of the material on big data for SOM are Production and Opera- tions Management (4 publications), Annals of Operations Research (2 publications), International Journal of Operations and Production Management (2 publications), and International Journal of Systems Assurance Engineering and Management (2 documents). Figure 3 presents the documents by affiliation, comparing the document counts for the first 15 affiliations. Affiliation-wise, there are five institutions that lead the ranking with most document counts (2 publications each), namely Hong Kong Polytechnic University,UniversityofLeeds,PohangUniversityofScienceandTechnology,Ulsan National Institute of Science and Technology, and Leeds University Business School.
- 21. Characteristics and Trends in Big Data for Service Operations … 5 Fig. 3 Documents by affiliation. (Source Scopus [33]) Fig. 4 Documents by country or territory. (Source Scopus [33]) These institutions represent a mixture of countries (China, United Kingdom, and South Korea). All remaining institutions each have one publication. Figure 4 visually depicts the countries with the highest number of publications, comparing the document counts for 15 countries/territories. The countries of origin for the 57 documents were determined by considering the country of the corre- sponding author. It can be easily observed that China and the United States share the first place, with 16 publications each. As a matter of fact, a notable observation is that China and the United States together account for more than half (i.e., 56.14%) of the total number of publications. The United Kingdom occupies the third place with 4 publications, followed by India, Japan, South Korea, and Taiwan, each with 3 publications. All remaining countries each have one publication, while there are also 8 documents for which the corresponding author information is not available.
- 22. 6 V. Charles et al. Table 1 Documents by publication type (Source Scopus [33]) Document type No. of Documents Article 22 Conference Paper 21 Conference Review 8 Book Chapter 3 Review 2 Editorial 1 Total 57 Table 1 depicts the number of documents by publication type, while Fig. 5 shows the same visually. In this sense, we can note that the literature is dominated by arti- cles (22 documents, which constitute 38.6% of the publications), followed closely by conference papers (21 documents, representing 36.8% of the publications). There- fore, journal articles and conference papers are the most frequent publication types in the literature. The third place is occupied by conference reviews with 8 docu- ments, which account for 14% of the publications. Lastly, there are 3 book chapters, 2 reviews, and 1 editorial, which represent 5.3%, 3.5%, and 1.8% of the publications, respectively. Finally, an analysis of the published documents by subject area (Table 2 and Fig. 6) indicates that the area of “engineering” has received the most interest, with 29 docu- ments or 23.2% of the publications. This is followed closely by the areas of “business, management, and accounting”, with 25 documents or 20.0% of the publications. We Fig. 5 Documents by publication type. (Source Scopus [33])
- 23. Characteristics and Trends in Big Data for Service Operations … 7 Table 2 Documents by subject area (Source Scopus [33]) Subject area No. of Documents Engineering 29 Business, Management, and Accounting 25 Computer Science 21 Decision Sciences 19 Social Sciences 8 Mathematics 6 Energy 5 Economics, Econometrics, and Finance 3 Earth and Planetary Sciences 2 Environmental Science 2 Physics and Astronomy 2 Biochemistry, Genetics, and Molecular Biology 1 Chemistry 1 Medicine 1 Note A publication can be classified under more than one subject area then have “computer science” (with 21 documents or 16.8% of the publications) and “decision sciences” (with 19 documents or 14.2% of the publications). Fig. 6 Documents by subject area. (Source Scopus [33])
- 24. 8 V. Charles et al. Fig. 7 Co-authorship network of countries Fig. 8 Network map showing the relations between various topics in the literature on big data for SOM (57 documents) 3.2 Bibliometric Analysis of Published Material Using the VOSviewer software, we have created co-authorship and keyword co- occurrence maps based on bibliographic data. The co-authorship analysis (Fig. 7) consideredtwoastheminimumnumberofdocumentsofacountry;ofthe25countries identified, 11 met the threshold, although the largest set of connected items consisted of 8 countries. The keyword co-occurrence analysis (Fig. 8) has been performed using
- 25. Characteristics and Trends in Big Data for Service Operations … 9 all the keywords as the unit of analysis, with minimum number of occurrences as three, and with full counting as the counting method. Figure 7 reveals the network map of international cooperation among major coun- tries (with the greatest total link strength) participating in research on big data for SOM. The colours indicate the clusters to which the countries are attributed according to the strength of their relationships, while the size of the circles is indicative of the number of publications held by each country. We can observe that there are 3 clusters in the figure. The first cluster (red colour) is dominated by the United States, and includes a mix of Eastern and Western countries, namely Taiwan and South Korea (in Asia), and France (in Europe). The second cluster (blue colour) is headed equally by Hong Kong and Japan. Lastly, the third cluster (green colour) is led by China, but also includes Australia. Figure 8 shows that a total of 20 most common keywords have been identified. Keywords are labelled with coloured frames, whose size is positively correlated with the occurrence of the keyword in the document. Moreover, these keywords are grouped into five clusters that seem to assume a prominent role vis-à-vis “compu- tational paradigms” (three items, yellow cluster), “big data for quality control and electric utilities” (four items, blue cluster), “information services and operations management” (five items, red cluster), “data analytics for SOM” (four items, purple cluster), and “big data analytics, internet of things, and smart city” (four items, green cluster). 3.3 Bibliometric Analysis of Journal Articles In this section, we have proceeded to analyse only the journal articles on big data for SOM. Such decision was guided by both literature and practical considerations. First, conference papers generally do not provide enough information on the research conducted, as we encounter in full papers, being normally written with the aim of presenting preliminary results [29]. Book chapters, conference reviews, and reviews, also, do different work than journal articles, as do editorials; hence, these were also excluded from further analysis. This screening led to the consideration of 22 research articles for further processing, constituting 38.6% of the publications (Fig. 5). Figure 9 shows the increasing number of journal articles in the field of big data for SOM in recent years, Fig. 10 positions the United States as the country with most of the journal articles publications, and Fig. 11 illustrates that the area of “business, management, and accounting” accounts for most of the journal article publications (27.7%), followed by “engineering” (21.3%), and “decision sciences” (19.1%). A brief bibliometric analysis of the 22 journal articles composing the final sample of studies integrating big data with SOM identified a variety of keywords as the most common keywords (whose co-occurrence is at least two times) (Fig. 12). These keywords were further classified by the software into four clusters that seem to assume a prominent role vis-à-vis “big data analytics in supply chain management” (four items, red cluster), “information systems and SOM” (three items, blue cluster),
- 26. 10 V. Charles et al. Fig. 9 Annual scientific production of studies on big data for SOM (Source Scopus [33]) Fig. 10 Studies on big data for SOM by country or territory. (Source Scopus [33]) “internet of things and smart city” (four items, green cluster), and “computational paradigms” (two items, yellow cluster). Below, we present briefly the pool of 22 journal articles identified, which are arranged in chronological order, starting from the most recent one. Ruan et al. [32] presented an IoT-based e-business model of intelligent vegetable greenhouses with details of the basic process and key nodes of the e-business model. The authors recognised key operation issues including big-data-driven pricing, planting structure and time optimisation, water and fertilizer integrated control, plant light supplement, and order-driven picking and packing. Kumar et al. [19] proposed a reliable, more accurate and efficient model based on the statistical analysis of the
- 27. Characteristics and Trends in Big Data for Service Operations … 11 Fig. 11 Studies on big data for SOM by subject area. (Source Scopus [33]) Fig. 12 Network map showing the relations between various topics in the journal article literature on big data for SOM (22 documents) sensor-based data for occupancy detection. The paper also proposed one online and adaptive model-based online sequential extreme learning machine to perform occu- pancy detection on real-time data when complete data are not available, and learning is done with recent data points coming in the form of streams. Wang [37] focused on
- 28. 12 V. Charles et al. deployment and optimisation of wireless network node deployment and optimisa- tion in smart cities. In this sense, aiming at problems such as poor network security connectivity, weak node attack resistance, and large storage overhead in the existing key management schemes, the author designed a three-phase key pre-distribution mechanism and direct sharing of the key management scheme based on node group deployment, via an adaptive particle swarm optimisation algorithm. Bag et al. [3] used the dynamic capability theory as a foundation for evaluating the role of big data analytics capability as an operational excellence approach in improving sustainable supply chain performance. The authors surveyed mining executives in South Africa and analysed the data using Partial Least Squares Structural Equation Modelling (PLS-SEM). The paper contributes to identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities. Roth and Rosenzweig [31] provided an examination of the rise of empirical operations management research in Manufacturing & Service Operations Management. The authors advocated for a tighter integration of analytical and empirical operations management knowledge in order to address the challenges and opportunities of the twenty-first century. Tamás and Koltai [36] aimed to review the relevant literature related to the development, improvement and application of learning curves in the age of big data, and to demonstrate the possible insight which its application can provide in manufacturing and service operations decision-making. Datta and Goyak [12] presented an efficient method for reliability evaluation of stochastic flow networks that can pass various demands simultaneously from multiple source nodes to multiple destination nodes. March and Scudder [25] viewed the IoT through the lens of predictive maintenance and analysed optimal preventive mainte- nance policies in an environment where equipment is subject to a deterioration, which shifts it from its initial, fully-productive state, having a specified, age-dependent failure rate to a less-productive or deteriorated state, having a different, presumably higher, age-dependent failure rate. Lim et al. [23] developed an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Building upon four action research projects, the proposed customer process management framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. Albergaria and Chuaopetta Jabbour [1] aimed to provide an original exploration of the challenges of informa- tion and operations management in the sharing economy, by focusing on the classic example of a shared service represented by library operations. The paper addresses the organisational use of big data analytics capabilities, with the main goal of helping organisations make better business decisions, in terms of information and operations management issues. Carnerud and Bäckström [6] aimed to identify and depict the key areas around which research on quality has centred during the past 37 years and to explore longitudinal patterns in the identified key areas. The study identi- fied seven central topics around which research on quality has centred during the time period analysed: Service Quality & Customer Satisfaction; Process design & Control; ISO Certification & Standards; TQM—Implementation, Performance &
- 29. Characteristics and Trends in Big Data for Service Operations … 13 Culture; QM—Practices & Performance; Reliability, Costs, Failure & Problems and Excellence—BEMs, Quality Awards & Excellence in Higher Education. Focusing on the area of after-sales service, Boone et al. [5] developed a framework that seeks to define service parts performance goals for the purpose of outlining where scholars and practitioners can further examine where, how, and why big data applica- tions can be employed to enhance service parts management performance. To objec- tively evaluate emergency physicians across facilities, Foster et al. [13] leveraged big data from an emergency physician management network and proposed data-driven metrics using a large-scale database. The proposed indices benchmark physicians from the perspectives of revenue potential, patient volume, patient complexity, and patient experience by controlling for exogenous factors at the facility level. As the authors acknowledge, the proposed framework can also be adapted to non-medical professional settings such as value chains, where employees often provide services in various profit- and cost-centres. Silva et al. [34] proposed a big data analytics- embedded experimental architecture for smart cities. The mentioned architecture facilitates the exploitation of urban big data in planning, designing, and maintaining smart cities, as well as it shows how big data analytics can be used to manage and process voluminous urban big data to enhance the quality of urban services. Cohen [11] discussed how the tremendous volume of available data collected by firms has been transforming the service industry, with particular focus on services in the sectors of finance/banking, transportation and hospitality, and online platforms (subscrip- tion services, online advertising, and online dating). Kuo et al. [20] applied big data mining and machine learning analysis techniques and used the Waikato Environ- ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption performance in Taiwan. Shmueli and Yahv [35] introduced the use of Classification and Regression Trees for automated detection of potential Simpson’s paradoxes in data with few or many potential confounding variables, and even with large samples (big data). The authors illustrate the approach via several real applications in e-governance and healthcare. Kim et al. [18] proposed an approach to analysing and utilizing vehicle operations management (VOM)-related data for designing VOM services. The feasibility and effectiveness of the proposed approach is demonstrated by means of a case study on the design of an eco-driving service. By adopting an exploratory approach to the secondary research which examines vendors’ offerings, Matthias et al. [26] focused on the application and exploitation of big data to create competitive advantage. To this aim, the authors presented a framework of application areas, and how these help the understanding of targeting and scoping specific areas for sustainable improvement. Mehmood et al. [27] aimed to advance knowledge of the transformative potential of big data on city-based trans- port models. In this sense, the authors developed a Markov-based model with several scenarios to explore a theoretical framework focused on matching the transport demands (of people and freight mobility) with city transport service provision using big data. Li et al. [22] developed and applied a framework to case examples that demonstrate how smart cities are redefining the characteristics of operations models around their scalability, analytical output, and connectivity. The paper contributes to our understanding of how smart cities can potentially transform operational models
- 30. 14 V. Charles et al. and sets out a research agenda for operations management in smart cities in the digital economy. Lastly, Huang and Rust [16] discussed the characteristics of information technology associated with consumer centricity. 4 Conclusions In this chapter, we have aimed to review the literature on big data for SOM via an exploration of the Scopus database and by means of using the VOSviewer visualisa- tion software for bibliometric analysis in order to highlight the research trends in the field. From a practical perspective, our analysis reveals patterns in scientific outputs and serves as a guide for global research trends in big data for SOM. Overall, the findings reveal an increased interest in studies in the fields of urban planning and smart city decision management empowered by real-time data processing using IoT, big data analytics, and cloud computing technologies. Other research strands include big data for quality control, electric utilities, informa- tion services and information systems. Additionally, there is continued interest in exploring big data analytics to predict and mitigate the effect of supply chain risks and disruptions, which have been shown that can severely disrupt operations and supply chains. As Hazen et al. [15] stated, it is important for organisations to improve the quality of the analytical outputs of their decision-making processes by means of paying attention to the quality of the big data on which they base their decisions. The review of the studies included in this research has further shown that there is an increased interest towards studying computational paradigms in the field of big data for SOM. Although great progress has been made so far, nonetheless, much more research is needed; in particular, more research is necessary to build big data- driven analytical models which are not only explainable and interpretable [14], but also deployable in the Cloud. Furthermore, there is a need for more interdisciplinary research that integrates traditional modes of enquiry in (service) operations manage- ment with, for example, behavioural approaches, with a blend of analytical and empirical methods. Acknowledgements The authors are thankful to the reviewers for their valuable comments on the previous version of this work. References 1. Albergaria, M., & Chuaopetta Jabbour, C. J. (2019). The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries. International Journal of Information Management, 51, 102023. 2. Albort-Morant, G., Henseler, J., Leal- Millán, A., & Cepeda-Carrión, G. (2017). Mapping the field: A bibliometric analysis of green innovation. Sustainability, 9(6), 1011.
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- 33. Characteristics and Trends in Big Data for Service Operations … 17 Vincent Charles PhD, PDRF, FRSS, FPPBA, MIScT, is a Full Professor and the Director of the Center for Value Chain Innova- tion at CENTRUM Católica Graduate Business School, PUCP, Lima, Peru. Additionally, he holds multiple visiting professor- ship positions across the Globe. He is an experienced researcher in the fields of artificial intelligence, data science, and OR/MS. He has more than two decades of teaching, research, and consul- tancy experience in various countries, in the fields of applied analytics/optimisation and big data science, having been a Full Professor and Director of Research for more than a decade in triple-crown business schools. He holds Executive Certificates from the MIT, HBS, and IE Business School. He has published over 150 research outputs. He is a recipient of many interna- tional academic honours and awards. An AWS Certified Cloud Practitioner, AWS Accredited Educator, a certified Six Sigma Black Belt, and an Advance HE Certified External Examiner, UK. He has developed the 4ˆ3 Architecture for Service Inno- vation. His area of focus includes productivity, quality, effi- ciency, effectiveness, competitiveness, innovation, and design thinking. He is a Co-Founder and the Chief Analytics Officer at Anthrolytics Ltd., UK. Tatiana Gherman MBA, PhD, is a Senior Lecturer and has more than a decade of teaching and research experience. She is currently researching how Machine Learning and Artificial Intelligence tools can support various business functions to help make business management more effective; with partic- ular interest in how to design Artificial Intelligence techniques grounded in and informed by patterns of social interaction and communication. Working towards the creation of a new field of research grounded in ethno-data science. Her area of research interests further includes multi-attribute decision-making tech- niques and other advanced quantitative analytics at different levels. She has research publications in reputed journals and is one of the recipients of the 2019 GDN Informs/Springer Researcher Award. Her field of expertise includes Artificial Intelligence, Data Science, Big Data, Group Decision Support, Workplace Studies, Behavioural Studies, Quantitative Analytics (at different levels), Conversation Analysis, and Ethnomethod- ology.
- 34. 18 V. Charles et al. Ali Emrouznejad PhD, is a Professor and Chair in Busi- ness Analytics at The University of Surrey, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as big data and data mining. Dr. Emrouznejad is editor of Annals of Operations Research, associate/guest editor or member of editorial board in number of other journals including Euro- pean journal of Operational Research, Journal of Operational Research Society, Socio-Economic Planning Sciences, IMA journal of Management Mathematics, OR Spectrum, RAIRO - Operations Research. He has published over 200 articles in top ranked journals; he is author of the book on “Applied Oper- ational Research with SAS”, editor of the books on “Perfor- mance Measurement with Fuzzy Data Envelopment Analysis” (Springer), “Managing Service Productivity” (Springer), “Big Data Optimization” (Springer), “Big Data for Greater Goods” (Springer) and “Handbook of Research on Strategic Perfor- mance Management and Measurement” (IGI Global). He is also co-founder of Performance Improvement Management Software (PIM-DEA), see http://www.Emrouznejad.com.
- 35. Strategy Formulation and Service Operations in the Big Data Age: The Essentialness of Technology, People, and Ethics Vincent Charles, Ali Emrouznejad, and Tatiana Gherman Abstract Studies have shown that the sensible operation of big data may yield powerful insights that can improve the organisations’ strategic decision-making process and contribute to achieving an enhanced competitive advantage. In this manuscript, we explore the promise of big data in redefining strategy in service oper- ations management (SOM) by means of investigating a rich range of bibliographic material. The SOM field has a plethora of research opportunities to capitalise on, which are enhanced by the presence of big data. SOM research in the big data age implies a shift in attention from being increasingly integrative across themes to being integrative across multiple disciplines, requiring the expertise of and tuning between different actors and expertise domains. Our aim is to stimulate debate in the field and set out a renewed research agenda by means of calling for additional considerations of strategic aspects, namely technology, people, and ethics, that can help guide and move the field forward. Keywords Analytics · Big data · Service operations management · BD-SOM strategy · Competitive advantage V. Charles (B) CENTRUM Católica Graduate Business School, Lima, Peru e-mail: vcharles@pucp.pe Pontifical Catholic University of Peru, Lima, Peru A. Emrouznejad Surrey Business School, The University of Surrey, Guildford, UK e-mail: a.emrouznejad@surrey.ac.uk T. Gherman Faculty of Business and Law, University of Northampton, Northampton, UK e-mail: tatiana.gherman@northampton.ac.uk © Springer Nature Switzerland AG 2022 A. Emrouznejad and V. Charles (eds.), Big Data and Blockchain for Service Operations Management, Studies in Big Data 98, https://doi.org/10.1007/978-3-030-87304-2_2 19
- 36. 20 V. Charles et al. 1 Introduction This manuscript is concerned with the evolution of service operations management (SOM) in the age of big data and how big data can help to redefine the concept of service operations strategy to build an enhanced competitive advantage for organi- sations. Overall, SOM has had a rather unclear path in the literature, being mostly perceived as a branch of operations management (OM). Despite this, as Grönroos (1994) also acknowledged almost two decades ago, “the business logic is different in service” (p. 13); therefore, there is a constant need to refine our understanding of the principles and assumptions underpinning SOM, something that has been changing over the years, even more so during the big data age. The relatively recent phenomenon posed by the exponential growth of big data has brought with it new challenges, one of the most intriguing of which deals with knowledge discovery and large-scale data-mining [26]. The presence of big data has been ‘pushing’ organisations to review their practices and identify opportunities that would allow them to base a substantial portion of their operational decisions on data, otherwise known as data-driven decision-making [9]. Big data are transforming the service industry, in areas ranging from finance/banking, to transportation and hospitality, education and government, among others. But at the same time, this transformation is only in its infancy and undoubtedly will require more cross- and inter-disciplinary approaches and collaborations, bringing together a variety of stake- holders, from researchers to data scientists, marketers, psychologists and behavioural analysts, regulators and policymakers, just to name a few. The potential held by big data to transform strategic decision-making in SOM is, indeed, substantial, but so seem to be the challenges. Today, big data analytics and high-speed computing are no longer the main concerns, but rather how “to carefully exploit and unlock the power of big data while preserving fairness, trust, and consumers’ happiness” (Cohen [19], p. 1722). In this manuscript, we investigate a rich range of bibliographic material and identify strategic aspects that need to be considered in SOM in the context of big data, with a particular focus on technology, people, and ethics. We also postulate that if we want to understand the promise of big data in redefining SOM strategy, we must first understand how service management hypotheses and assumptions have changed as a result of innovations brought about by the big data age. The remainder of the manuscript is organised as follows: Sect. 2 introduces and discusses the concept of SOM, while Sect. 3 explores traditional misconceptions in SOM and Sect. 4 details the current state-of-the-art in SOM. Subsequently, we introduce the concept of big data in Sect. 5, discuss the various types of analytics in Sect. 6, and explore the big data pipeline in Sect. 7. An extensive narrative of strategic considerations in SOM in the big data age is proffered in Sect. 8. Here, we further introduce the BD-SOM strategy. Lastly, Sect. 9 concludes with final thoughts and a discussion of future directions.
- 37. Strategy Formulation and Service Operations in the Big Data Age … 21 2 The Concept of Service Operations Management Operations management has traditionally been associated with manufacturing production [5], having had little contact with customers and not always under- standing their needs and desires. Activity scheduling charts and assembly lines, linear programming, queuing theory, and PERT are just some of the traditional areas that OM researchers have been generally concerned with, with Chopra et al. [18] noting that most of the early research “focused on tactical issues such as line balancing, scheduling, production planning, inventory control, and lot sizing” (p. 9). In recent decades, however, the focus has shifted more strongly towards the services sector, which has experienced an accelerated growth. As Peinado et al. [71, p. 374] assessed, “operations management is a discipline that originated to solve management problems in a factory environment, but since the mid-twentieth century researchers, lecturers and practitioners have begun to adapt the knowledge of the field to also support service operations.” So, in time, the concept of OM has come to encompass the area of services; in the words of Manikas et al. [56], “in the 1990s, business process reengineering, six-sigma, enterprise resource planning systems (ERP), and the internet became enablers of operations management. As the techniques described by operations have also been applied to service indus- tries and other non-manufacturing areas of organisations, the scope of operations management increased to include service” (p. 1442). The globalisation of markets and the technological progress in information and communications technology have altered the management landscape not only for manufacturing enterprises, but also for the productive management of services. Services are increasingly important particularly in today’s industrialised economies, where technological changes have translated into a greater number of people being employed in services, a sector that accounts for most of the countries’ gross domestic product [30, 66]. As a matter of fact, Levitt’s [53] observation about the importance and pervasiveness of services for business is just as true today as it was the day it was first uttered, almost half a century ago. The increasing importance of studying service management was echoed by researchers at different points in time: Miller et al. [61], Amoako-Gyampah and Meredith [2], Pannirselvam et al. [67], Nie and Kellogg [64], and Johnson [45]; these researchers concluded that there is still not enough research being done in the SOM field and called for more research on the topic, especially empirical research. The history of SOM as a distinct topic in the academic literature has had its own trajectory. Generally, studies can be traced back to the 1970s [46], although there are some studies of service economy even prior to that time (e.g., Pearce [70], Penrose [72]). But for some reason, the evolution of SOM as a standalone discipline has remained unclear and the research community has continued to hold SOM as a part of OM. For example, in [60], Meredith et al. defined OM as consisting of 17 focus areas: (1) Aggregating planning, (2) Capacity Planning, (3) Distribution, (4) Facility Layout, (5) Facility location, (6) Forecasting, (7) Inventory control, (8) Mainte- nance, (9) Process design/technology, (10) Project management, (11) Purchasing,
- 38. 22 V. Charles et al. (12) Quality, (13) Quality of working life, (14) Scheduling, (15) Services, (16) Strategy, and (17) Work measurement. As it can be noted, this study acknowledged services as a branch of OM. All in all, this perspective over SOM as a part of OM has meant that research on SOM has traditionally been scarce. At least, such was the situation in 2007, when Machuca et al. [55] performed the first in-depth study on the state of the then current SOM research. But one may wonder how things would have changed since then. In a recent study, Manikas et al. [56] performed a review of the literature on OM using a data-driven approach; in this sense, the authors performed a historical analysis of major research topics and trends between 1961 and 2017 and found 18 topic areas that received most of the attention. One among these topics is the topic of service operations, which contains articles mostly focused on buyer–seller relationship, queuing theory, and service quality, with sub-themes such as multi-criteria evaluation of strategic issues in service quality, design of service, information technology in service, and globalisation of services. Furthermore, the authors concluded that service operations are a growing area of publications, with particular interest in lean management (waste reduction, improvement in resource usage, and so on). An interesting observation that the authors further made is that an evaluation of only a recent subset of the entire data that they used would show that emerging topics include healthcare efficiency and effectiveness, environmental and sustainability, security and infrastructure, reverse logistics, and big data methods. Despite the granular and useful analysis, we can see, however, that once more, the SOM’s ‘curse’ of being perceived as a part of the OM literature has not changed till date. In time, SOM has struggled to find its fit not only as a distinct discipline in the literature but also within the broader field of OM itself. This has been fueled by the constant concern in the minds of researchers regarding how to identify the distinctive contribution that SOM research makes compared to OM research. This SOM opacity is, of course, surprising, especially considering that service operations represent, as previously mentioned, the clear majority of economic activity (at least in developed countries) [80]. 3 Traditional Misconceptions in Service Operations Management A predominant view in the literature is that services are defined by four attributes: intangibility, heterogeneity, inseparability, and perishability (IHIP); these have commonly been accepted as the paradigm for services. There are studies, however, that have refuted such view and concluded that it is without merit. For example, Vargo and Lusch [82] argued that the IHIP characteristics “(a) do not distinguish services from goods, (b) only have meaning from a manufacturing perspective, and (c) imply inappropriate normative strategies” (p. 324). In a similar fashion, Edvardsson et al. [23] highlighted that “the IHIP characteristics should not be used in the future as
- 39. Strategy Formulation and Service Operations in the Big Data Age … 23 generic service characteristics” (p. 115). Like these, there are many other myths and misconceptions about services that continue to exist. What this means for SOM is that not only has SOM research been characterised by opacity, but traditional misconceptions have also accompanied the field. In his work, Sampson [78, p. 183]) identified five main stereotypes around SOM. These are: 1. “the operations function of firms is simply (or primarily) about managing physical products and product inventories”; 2. “service operations are an unscientific type of operations, which is sometimes based on the observation that traditional manufacturing operations models do not often fit well in service contexts”; 3. “there is a traditional assumption that there is some dichotomy between goods and services” (e.g., Greenfield [34]), 4. “some have asserted that service is inherently customer oriented and solution focused” (e.g., Grönroos [36, p. 46], Vargo & Lusch [83, p. 138]), 5. “service is adequately defined by what it is not” [e.g., “nonmanufacturing”, which is a residual definition (Morey [63]); or “nonownership” (Judd [47]). All of the above have eventually led to confusion as to what a service is, as well as with regards to the very principles that lay as a foundation for SOM. This has also meant that SOM has had a rich history of key but not widely recognised contributions to both research and practice. Another interesting aspect that has contributed to the perpetuation of SOM stereotypes has been the fact that quite a lot of the research involving service management has come from the marketing discipline [54] (Rust, 2004). In this sense, marketing scholars have focused on addressing traditional OM topics (e.g., quality management, facility layout, process focus) in service contexts. Particular attention has been paid to quality management, which is by excellence an OM topic; nevertheless, the most widely used model of service quality and associated SERVQUAL instrument belong to the field of service marketing (Parasuraman et al. [68, 69]). Sampson [78] proposed that the stereotypes surrounding SOM could be rectified by showing how service operations can be conceptualised, visualised, and analysed. To this aim, the author introduced a Process-Chain-Network (PCN) visual framework to clarify the fundamental concepts of SOM. As stated by the author, “the frame- work is built upon PCN Diagrams that depict processes and interactions involving networks of entities. PCN Analysis includes identifying the value proposition of a given process network, assessing performance characteristics and value propositions of a process configuration, and identifying opportunities for process improvement and innovation” (Sampson [78, p. 182]). Among others, his findings showed that “the basic structural elements of PCN Diagrams can reveal commonality among seemingly disparate lines of business”, that “every business has a mix of interactive processes and independent processing”, and that “service interaction requires some degree of integration of processes across multiple entities and can, therefore, be more difficult to design and execute than independent processes” (p. 194).
- 40. 24 V. Charles et al. 4 Current State-Of-The-Art in Service Operations Management Previous review articles have documented the history and evolution of research on service operations. For example, Chase and Apte [16], Heineke and Davis [40] described the history of service operations, Machuca et al. [55], Smith et al. [80] provided a taxonomy of research in view of content topic, research methodology, journal, and author affiliation of service operations research, and Bretthauer [7], Chase [15], Hill et al. [42], Johnston [45], Roth and Menor [77] performed an analysis of the research gaps, limitations, and avenues for future research. In their review paper of published work in the Journal of Service Manage- ment during 2010–2016, Victorino et al. [84] used the Delphi method to identify research themes in service operations that have great potential for exciting and inno- vative conceptual and empirical work. Their efforts resulted in the identification of the following key themes: service supply networks, evaluating and measuring service operations, performance, understanding customer and employee behaviour in service operations, managing servitisation, managing knowledge-based service contexts, managing participation roles and responsibilities in service operations, addressingsociety’schallengesthroughserviceoperations,andtheoperationalimpli- cations of the sharing economy. The authors reviewed nearly 700 articles, which were classified based on the discipline focus into one of the following categories: “Operations,” “Marketing,” “Organisational Behavior,” Management of Information Systems (MIS),” or “Interdisciplinary.” Table 1 depicts the characteristics of the articles in each category. Although limited to analysing only one journal, the findings reveal two important trends. First, the authors noted that over the years, the percentage of articles in each category has declined, with the exception of interdisciplinary research, which has seen a continuous upward trend. Second, another interesting observation that the authors make is that what most of the research articles in the literature seem to share isacallformoreresearchthatinvolvesaninterdisciplinaryapproachtounderstanding service operations, by drawing from fields such as marketing, human resources, and information systems, among others. This perspective has been further expanded in the approach termed as “service science,” which focuses on combining knowledge from scientific management and engineering disciplines for service innovation (Spohrer & Maglio [81], as cited by Victorino et al. [84]). Victorino et al.’s [84] study is accompanied by a follow-up paper by Field et al. [29], in which the authors take a broader view by means of including a wider litera- ture to offer a comprehensive review of each research theme and posit future research questions for advancing the field of service operations. One of their main conclu- sions is that the field of service operations has many interesting research topics and questions that remain largely unexplored till date.
- 41. Strategy Formulation and Service Operations in the Big Data Age … 25 Table 1 Characteristics of SOM articles by discipline [84] Operations Marketing Organisational Behaviour Management of Information Systems (MIS) Interdisciplinary Focus Process or production-oriented Customer-oriented People-oriented Information-technology oriented Majority of articles found at the interface of operations and marketing Topics Productivity Capacity management Quality management Customer behaviour Customer loyalty Customer segmentation Service climate Leadership Employee satisfaction Service quality Service design and innovation % of Articles 20% 43% 11% 1% 26%
- 42. 26 V. Charles et al. 5 The Concept of Big Data Before proceeding with an assessment of SOM in the context of big data, let us first briefly review the concept of ‘big data’. Just like with the concept of SOM, ambiguity is also surrounding the concept of ‘big data’. Initially coined in 1997, the term ‘big data’ (Cox & Ellsworth [21]) has evolved to become today the new normal. However, big data in themselves are not a new ‘thing’. In 1908, on the island of Crete, archaeologists discovered a disc of fired clay, which was dated to the middle or late Minoan Bronze Age, hence from around 2000 B.C. The disc, called the Phaistos Disc (Fig. 1) was dubbed the ‘first Minoan CD-ROM’. The disk is round in shape and covered on both sides with a spiral of inscriptions, whose meaning remains a mystery till today. Nevertheless, the point is that this is an example of what data used to look like thousands of years before the advent of CDs. This is how society used to store and transmit data at some point in time in the distant past. A rudimentary attempt, by all accounts, considering the limited ‘storage’ available and the impossibility of rewriting it or making any operations with it. So, (big) data have always been with us and, as a society, we have constantly aimed at finding ways to store them and passing them on. What has changed, however, is the fact that the increase in IT-related infrastructure over the past years, coupled with the emergence of complex analytics, has enabled us to store virtually any amount of data at a significantly reduced cost and analyse and interpret such data in ways that could not have been done before [24]. Today, there is no universally accepted definition of big data. Big data is a multi- dimensional concept. As cited by Charles and Emrouznejad [10] and Emrouznejad and Charles [25], Hammond [38] associated big data with evidence-based decision- making, Beyer and Laney [6] defined it as high volume, high velocity, and/or high Fig. 1 The Phaistos Disk (or the ‘first Minoan CD-ROM’). Note. Source https://araenil.files.wor dpress.com/2011/06/phaistosdisklarge.jpg
- 43. Strategy Formulation and Service Operations in the Big Data Age … 27 variety information assets, and Ohlhorst [65] described big data as vast data sets which are difficult to analyse or visualise with conventional information technolo- gies. Most commonly today, big data are defined in terms of data characteristics or dimensions, often with names starting with the letter ‘V’. By some accounts, there are today as many as 10 Vs (Markus [58]). Laney [51] defined the initial four Vs, which have become central to understanding big data; these are as follows: Volume: Big data are characterised by their extremely large volume. It is estimated that by the end of 2020, the volume of data would be around 40 zettabytes, or 300 times bigger than the volume of data in 2005 (Herschel & Mori [41]). To be noted that the focus is not only on human-generated data (which are mostly structured and represent a small fraction of the entire data being generated), but also data produced by devices, such as sensors and connected devices (which are mostly unstructured and account for most of the data out there). Velocity: Big data arrive at un unprecedented speed. Velocity refers to a real-time or near-real time stream of data, which poses an issue for real-time processing. Real- time processing is essential for businesses looking to obtain a competitive advantage overtheircompetitors;forexample,IBM[44]statedthat“fortime-sensitiveprocesses such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value”. This means that velocity is a double-edged sword. On the one hand, the possibility to collect mountains of data at such high speed opens up new possibilities to derive more insights, but on the other hand, the time needed to translate the data into intelligent decisions remains a challenge [51]. Variety: The characteristic of ‘variety’ of big data refers to different types of data, such as structured, semi-structured, and unstructured, which arrive from a variety of sources [51] (IBM, 2016). As expected, the biggest challenge is posed by the semi-structured and unstructured data, which are hard to analyse due to not having an easily identifiable internal structure. Examples include photos, audio and video, web pages, wikis, and blogs, streaming data, emails, social media data, and so on. Veracity: It refers to the trustworthiness of the data and the reason behind ques- tioning the existence of inherent discrepancies in the data is rooted precisely in the unstructured feature of big data. Another reason is the presence of inaccuracies. Inaccuracies can be due to the data being intrinsically inaccurate or from the data becoming inaccurate through processing errors [51]. In their paper, Charles and Gherman [11] argued that the term big data is a misnomer, stating that while the term in itself refers to the large volume of data, Big Data is essentially about the phenomenon that we are trying to record and the hidden patterns and complexities of the data that we attempt to unpack. The authors advanced an expanded model of Big Data, wherein they included three additional dimensions, namely the 3 Cs: Context, Connectedness, and Complexity. The authors statedthatunderstandingtheContext isessentialwhendealingwithBigData,because “raw data could mean anything without a thorough understanding of the context that explains it” (p. 1072); Connectedness was defined as the ability to understand Big Data in its wider Context and in view of its ethical implications; and Complexity was defined from the perspective of having the skills to survive and thrive in the face of
- 44. 28 V. Charles et al. Fig. 2 The evolution of big data. Note. Source Epstein and Hagen [27] complex data, by means of being able to identify the key data and differentiate the information that truly has an impact on the organisation. Figure 2 depicts the evolution of big data from their initial primitive and structured forms housed locally, to the unstructured, highly complex forms housed in the Cloud. Perhaps one more observation to make in relation to big data, at this point, has to do with the ‘risk’ accompanying them. We define risk in terms of its dual nature, materialisedbyunderstandingtheconceptbothasadanger andasanopportunity.Big data can indeed translate into a big opportunity for organisations, helping decision- making processes become more effective. But one must not forget the other side of the coin, the challenges that come with big data and how big data’s usefulness can be constrained by the ability of the researchers to ask the right questions and apply the right tools, all within an ethical framework. For example, take the case of the Google Flu Trends Project, which has shown just how vulnerable the exploitation of big data can turn out to be, when not done properly. Google Flu Trends was a web service operated by Google, launched in 2008 and abandoned in 2015 (although problems started being apparent in 2009 itself). The project’sgoalwastodevelopameansofidentifyingtheemergenceofflusothathealth resources could be mobilised to treat the illness immediately and prevent a possible outbreak. The web service used data readily available from Google’s search engine, mainly data on the frequency of “flu” searches, it aggregated them, and attempted to predict future rates of flu across more than 25 countries. After apparent early success, the project had to be abandoned as the predictions were not accurate enough. It was found that the model was consistently over-inflating future occurrences and was less accurate than existing ‘small data’ strategies that utilised data on confirmed cases of flu. The Google Flu Trends’ epic failure showed just how vital the elements of rigour, criticality, and the correct consideration of the wider context, among others,
- 45. Strategy Formulation and Service Operations in the Big Data Age … 29 actually are. In the words of Lazer et al. [52, p. 1203], “the quantity of data does not mean that one can ignore foundational issues of measurement and construct validity and reliability and dependencies among data”. In the absence of a specific criticality in the analysis performed and in the interpretation of the results obtained, big data cannot add value to any organisation. The above considerations led Fung [31] to conclude that McKinsey’s widely circulated definition of big data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” is not helpful in providing any answers to prevent future failures. Instead, he proposed a new approach to defining big data, called the OCCAM framework, which has the advantage of bringing to one’s attention the assumptions underlying the concept. In view of the OCCAM framework, big data are [31]: • “Observational: much of the new data come from sensors or tracking devices that monitor continuously and indiscriminately without design, as opposed to questionnaires, interviews, or experiments with purposeful design.” • “Lacking Controls: controls are typically unavailable, making valid comparisons and analysis more difficult.” • “Seemingly Complete: the availability of data for most measurable units and the sheer volume of data generated is unprecedented, but more data creates more false leads and blind alleys, complicating the search for meaningful, predictable structure.” • “Adapted: third parties collect the data, often for purposes unrelated to the data scientists’, presenting challenges of interpretation.” • “Merged: different datasets are combined, exacerbating the problems relating to lack of definition and misaligned objectives.” 6 Types of Analytics Data analytics is a multidimensional term, often interchangeably used with data science, data mining, and so on. The distinction between these terms is vague, but in essence, they all refer to the extraction of useful information from a preprocessed set of data. The techniques that can be used to this aim are varied and stem from across the disciplines, such as statistics (e.g., inferential statistics, various types of regression); machine learning (e.g., ensemble decision trees like random forest), in particular kernel methods (e.g., support vector machines); and biology (e.g., neural networks, genetic algorithms, and nature-inspired algorithms like ant colony algorithm and swan particle algorithm, among others). There is a plethora of analytics that organisations can perform. Below, we discuss a more comprehensive framework (Fig. 3), comprising five types of analytics: descrip- tive, diagnostic, predictive, prescriptive, and cognitive. It should be noted at the outset, however, that there is no consensus over this taxonomy, with most frame- works considering only three types (descriptive, predictive, and prescriptive). Others consider four, namely descriptive, diagnostic, predictive, and prescriptive [Gartner
- 46. 30 V. Charles et al. Fig. 3 Extended data analytics framework Analytics Ascendancy Model (GAAM), Maoz [57]]; while others do not differentiate between descriptive and diagnostic analytics. Most recently, also, in the context of marketing strategy formulation, Eriksson et al. [28] proposed a new form of analytics, namely creative analytics, which is seen as a capability to address the potential of artistic creativity. Independent of these views, however, there are few observations worth making: (a) each type of analytics provides distinct value propositions (hindsight, insight, foresight, context, or inference), serving distinct purposes (information, optimisation, or intelligence); (b) the types of analytics are interconnected, but should not be viewed sequentially within a growth model framework; they can work in parallel and measure value differently. (c) the more advanced the analytics get, the more complexity and difficulty are added, requiring greater resources, both computational and human resources; and normally, this should be accompanied by an increase in the value propo- sition made. But this does not always hold true and it depends on application. Sometimes there just is not enough information in the data to make higher levels of analytics (such as predictive, prescriptive, or cognitive) valuable. The data analytics framework (Fig. 3) is an extension to the existing frameworks, by means of considering all five levels of analytics. We position this framework in a three-dimensional view along three axes: complexity, difficulty, and value. It is relevant to note that most literature has failed to differentiate between complexity
- 47. Strategy Formulation and Service Operations in the Big Data Age … 31 and difficulty; here, we contribute by making a clear distinction between the two. We define difficulty as computational difficulty or the amount of resources needed to run computational algorithms (with particular focus on time and memory requirements); while complexity is seen as stemming from the challenges associated with extracting valuable knowledge in a meaningful manner. These two dimensions hold different ontological and epistemological stances; and, therefore, have different implications for a big data strategy. Next, let us explore the five types of analytics exhibited in Fig. 3. Descriptive analytics: It is the most common and simple type of analytics. It seeks to understand what phenomenon took place over a certain period of time, answering the question “What happened?”. Hence, the aim is to gain a view into historical data, an understanding of the significance and nature of past events—in one word, hindsight. It focuses on summarising past data, usually in the form of dashboards. Common approaches to perform descriptive analytics include visual analytics. Diagnostic analytics: It seeks to understand why or what caused the phenomenon to happen in the first place; hence, the aim is to find the causes of past outcomes, answering the question “Why did it happen?”. More specifically, it helps identify anomalies or outliers in the data; it drills into analytics, assisting in the identification of the data sources that help explain the anomalies; and it attempts to determine hidden causal relationships. Although relatively easy to be performed, this is a very important type of analytics, as it helps identify patterns in the data; so that, should the phenomenon happen again, the organisation can be prepared to act on time for a regrettable outcome to be avoided. Simply put, it provides insight. Common approaches to perform diagnostic analytics include clustering techniques, outlier detection, Naïve Bayes, and time-series data analytics. Predictive analytics: It attempts to answer the question “What will happen”? or “What is likely to happen?”. Hence, it seeks to understand what can happen in the future by utilising past data to make logical predictions about future outcomes. The goal is to achieve a state of self-awareness; namely, foresight. This type of analytics is also relatively easy to perform, but in practice, it can pose some serious challenges for organisations. This is because it requires added technology and a meaningful set of skills. Prediction and forecasting are, ultimately, only an estimate; and the accuracy of predictions and forecasts depends on both the quality of the data and the analyst’s skills. Common approaches to perform predictive analytics include supervised machine learning techniques, such ensemble decision trees— random forest, support vector machines, and artificial neural networks, among others. Prescriptive analytics: One of the most sought after type of analytics, prescriptive analytics aims to answer the question “How can we make it happen?”. It combines the knowledge obtained from all the previous analyses to predict the likely outcome of various corrective measures. Otherwise stated, it advises organisations on all possible outcomes and the actions that are likely to optimise the desired outputs; it considers and builds upon the wider context. Common approaches to perform prescriptive analytics include simulation techniques, nature-inspired algorithms, and optimisation; it uses mathematical programming like linear, integer, and stochastic programming, Monte Carlo simulation, and game theory, among others. Prescriptive
- 48. 32 V. Charles et al. analytics can help improve decision-making in ways that previous analytics cannot do, creating a real competitive advantage for the organisation; but, the effort, organ- isational commitment, and resources needed to carry it out are substantial and not all organisations can afford them. Cognitive analytics: This is the most advanced form of analytics, aiming to mirror human thinking, translating into pure intelligence. It is also known as “intelligent analytics”. It mimics the human brain by drawing inferences from existing data and then inserting these inferences back into the knowledge base for future inferences— a self-learning feedback loop. Common approaches to perform cognitive analytics include a blend of artificial intelligence (AI), machine learning (ML) algorithms (more precisely, reinforcement learning), deep learning models, semantics, and game theory. Cognitive analytics blends traditional analytics techniques with AI and ML features for advanced analytics outcomes. Perhaps one additional observation worth making at this point is that, in building analytical models, several requirements should be considered, depending on the application area [3]: business relevance, statistical performance (statistical signifi- cance and predictive power in terms of related performance metrics), interpretability, justifiability, operational efficiency, economic cost, and compliance with interna- tional regulation and legislation. To be noted that interpretability often needs to be balanced against statistical performance and this is an important trade-off to keep in mind. For example, neural networks are high performing (i.e., have higher accuracy), but offer no insight into the underlying patterns in the data, lacking clarity around inner workings. On the other hand, a linear regression model has limited modelling power, but is highly comprehensible and interpretable. Now, as Roda et al. [76] argued, sophisticated models, also known as black-box models, might not be more reliable than simpler models, also known as white-box models, this is especially the case when we deal with a phenomenon we know almost nothing about or do not yet fully understand (e.g., the recent COVID-19 pandemic), in which case it is more beneficial to have models that we can clearly explain how they behave, how they produce predictions, what the influencing variables are, and so on. In a nutshell, transforming data into meaningful knowledge is an art and not a blind application of analytical models. A comparison between white-box and black-box models is offered in Table 2. 7 The Big Data Pipeline The value of data is unlocked only after they are transformed from their raw form into actionable knowledge, and when that value proposition is promptly delivered to relevant stakeholders. In the context of big data, an organisation may wish to rely on a data pipeline, which is a series of data processing steps, to successfully achieve this aim. A data pipeline is especially useful when the organisation deals with large amounts of data from multiple sources which are moreover generally stored in the Cloud, and when it requires real-time or near real-time complex data analysis. So, in
- 49. Strategy Formulation and Service Operations in the Big Data Age … 33 Table 2 A comparison between black-box models and white-box models Black-box models White-box models Address highly non-linear structures Address linear or stepwise linear or curve linear structures Logically well-defined and mathematically complex Logically and mathematically well defined, and simple Large number of parameters; hence, models are high-dimensional Small number of parameters Large number of features Relatively small number of features Statistical hypothesis testing is irrelevant Statistical hypothesis testing is relevant High computational complexity Low computational complexity Lack clarity around inner workings The input–output relationship is visible, and the process through which the output is produced is also visible Large data set Relatively small data set Do not warrant any statistical distributional assumptions Warrant statistical distributional assumptions Modelling is usually a trial and error and iterative process Modelling is less of a trial and error process and more of a systematic approach Guided by rules of thumb Guided by established criteria Results depend on the hyperparameter tuning strategy Results depend on the statistical estimation properties Lower explainability or interpretability Higher explainability or interpretability Lower transparency and accountability Higher transparency and accountability Higher accuracy Relatively lower accuracy Source Hansun et al. [39] considering a big data pipeline, big data principles need to be applied to the pipeline; and here, we are specifically referring to the consideration of the 4 Vs of big data: volume, variety, velocity, and veracity, all of which will impact the organisation’s big data journey. The full data pipeline for big data traditionally passes through several stages, as can be appreciated in Fig. 4. It consists of three main phases, namely data engineering, analytics, and delivery. Furthermore, the data engineering phase is split into three steps: data collection, ingestion, and preparation. The data source layer is composed of the raw data arriving in the organisation. It includes all types of data: structured, semi-structured, and unstructured, which can come from static sources, as well as from real-time sources, such as IoT devices and sensors. The ingestion stage is about getting all the data needed, in a raw format, in a single repository called a data lake. The data storage layer is where the data are kept after being collected and ingested from the various sources. In view of the explosion of data volume available, sophisticated and accessible systems have been developed to help with this task. It should be mentioned that for simple, small data
- 50. Other documents randomly have different content
- 51. advantage of our absence to resume the offensive. I asked him to reduce this to writing, which he did, and I here introduce it as part of my report: HEADQUARTERS OF THE OHIO KNOXVILLE, December 7, 1863 Major-General W. T. SHERMAN, commanding, etc. GENERAL: I desire to express to you and your command my most hearty thanks and gratitude for your promptness in coming to our relief during the siege of Knoxville, and I am satisfied your approach served to raise the siege. The emergency having passed, I do not deem, for the present, any other portion of your command but the corps of General Granger necessary for operations in this section; and, inasmuch as General Grant has weakened the forces immediately with him in order to relieve us (thereby rendering the position of General Thomas less secure), I deem it advisable that all the troops now here, save those commanded by General Granger, should return at once to within supporting distance of the forces in front of Bragg's army. In behalf of my command, I desire again to thank you and your command for the kindness you have done us. I am, general, very respectfully, your obedient servant, A. E. BURNSIDE, Major-General commanding.
- 52. Accordingly, having seen General Burnside's forces move out of Knoxville in pursuit of Longstreet, and General Granger's move in, I put in motion my own command to return. General Howard was ordered to move, via Davis's Ford and Sweetwater, to Athena, with a guard forward at Charleston, to hold and repair the bridge which the enemy had retaken after our passage up. General Jeff. C. Davis moved to Columbus, on the Hiawaesee, via Madisonville, and the two divisions of the Fifteenth Corps moved to Tellico Plains, to cover movement of cavalry across the mountains into Georgia, to overtake a wagon-train which had dodged us on our way up, and had escaped by way of Murphy. Subsequently, on a report from General Howard that the enemy held Charleston, I diverted General Ewing's division to Athena, and went in person to Tellico with General Morgan L. Smith's division. By the 9th all our troops were in position, and we held the rich country between the Little Tennessee and the Hiawasaee. The cavalry, under Colonel Long, passed the mountain at Tellico, and proceeded about seventeen miles beyond Murphy, when Colonel Long, deeming his farther pursuit of the wagon-train useless, returned on the 12th to Tellico. I then ordered him and the division of General Morgan L. Smith to move to Charleston, to which point I had previously ordered the corps of General Howard. On the 14th of December all of my command in the field lay along the Hiawassee. Having communicated to General Grant the actual state of affairs, I received orders to leave, on the line of the Hiawassee, all the cavalry, and come to Chattanooga with the rest of my command. I left the brigade of cavalry commanded by Colonel Long, reenforced by the Fifth Ohio Cavalry (Lieutenant-Colonel Heath)—the only cavalry properly
- 53. belonging to the Fifteenth Army Corps—at Charleston, and with the remainder moved by easy marches, by Cleveland and Tyner's Depot, into Chattanooga, where I received in person from General Grant orders to transfer back to their appropriate commands the corps of General Howard and the division commanded by General Jeff. C. Davis, and to conduct the Fifteenth Army Corps to its new field of operations. It will thus appear that we have been constantly in motion since our departure from the Big Black, in Mississippi, until the present moment. I have been unable to receive from subordinate commanders the usual full, detailed reports of events, and have therefore been compelled to make up this report from my own personal memory; but, as soon as possible, subordinate reports will be received and duly forwarded. In reviewing the facts, I must do justice to the men of my command for the patience, cheerfulness, and courage which officers and men have displayed throughout, in battle, on the march, and in camp. For long periods, without regular rations or supplies of any kind, they have marched through mud and over rocks, sometimes barefooted, without a murmur. Without a moment's rest after a march of over four hundred miles, without sleep for three successive nights, we crossed the Tennessee, fought our part of the battle of Chattanooga, pursued the enemy out of Tennessee, and then turned more than a hundred and twenty miles north and compelled Longstreet to raise the siege of Knoxville, which gave so much anxiety to the whole country. It is hard to realize the importance of these events without recalling the memory of the general feeling which pervaded all minds at
- 54. Chattanooga prior to our arrival. I cannot speak of the Fifteenth Army Corps without a seeming vanity; but as I am no longer its commander, I assert that there is no better body of soldiers in America than it. I wish all to feel a just pride in its real honors. To General Howard and his command, to General Jeff. C. Davis and his, I am more than usually indebted for the intelligence of commanders and fidelity of commands. The brigade of Colonel Bushbeck, belonging to the Eleventh Corps, which was the first to come out of Chattanooga to my flank, fought at the Tunnel Hill, in connection with General Ewing's division, and displayed a courage almost amounting to rashness. Following the enemy almost to the tunnel- gorge, it lost many valuable lives, prominent among them Lieutenant-Colonel Taft, spoken of as a most gallant soldier. In General Howard throughout I found a polished and Christian gentleman, exhibiting the highest and most chivalric traits of the soldier. General Davis handled his division with artistic skill, more especially at the moment we encountered the enemy's rear-guard, near Graysville, at nightfall. I must award to this division the credit of the best order during our movement through East Tennessee, when long marches and the necessity of foraging to the right and left gave some reason for disordered ranks: Inasmuch as exception may be taken to my explanation of the temporary confusion, during the battle of Chattanooga, of the two brigades of General Matthias and Colonel Raum, I will here state that I saw the whole; and attach no blame to any one. Accidents will happen in battle, as elsewhere; and at the point
- 55. where they so manfully went to relieve the pressure on other parts of our assaulting line, they exposed themselves unconsciously to an enemy vastly superior in force, and favored by the shape of the ground. Had that enemy come out on equal terms, those brigades would have shown their mettle, which has been tried more than once before and stood the test of fire. They reformed their ranks, and were ready to support General Ewing's division in a very few minutes; and the circumstance would have hardly called for notice on my part, had not others reported what was seen from Chattanooga, a distance of nearly five miles, from where could only be seen the troops in the open field in which this affair occurred. I now subjoin the best report of casualties I am able to compile from the records thus far received: Killed; Wounded; and Missing............... 1949 No report from General Davis's division, but loss is small. Among the killed were some of our most valuable officers: Colonels Putnam, Ninety-third Illinois; O'Meara, Ninetieth Illinois; and Torrence, Thirtieth Iowa; Lieutenant-Colonel-Taft, of the Eleventh Corps; and Major Bushnell, Thirteenth Illinois. Among the wounded are Brigadier-Generals Giles A. Smith, Corse, and Matthias; Colonel Raum; Colonel Waugelin, Twelfth Missouri; Lieutenant-Colonel Partridge, Thirteenth Illinois; Major P. I. Welsh, Fifty- sixth Illinois; and Major Nathan McAlla, Tenth Iowa. Among the missing is Lieutenant-Colonel Archer,
- 56. Seventeenth Iowa. My report is already so long, that I must forbear mentioning acts of individual merit. These will be recorded in the reports of division commanders, which I will cheerfully indorse; but I must say that it is but justice that colonels of regiments, who have so long and so well commanded brigades, as in the following cases, should be commissioned to the grade which they have filled with so much usefulness and credit to the public service, viz.: Colonel J. R. Cockerell, Seventieth, Ohio; Colonel J. M. Loomis, Twenty-sixth Illinois; Colonel C. C. Walcutt, Forty-sixth Ohio; Colonel J. A. Williamson, Fourth Iowa; Colonel G. B. Raum, Fifty-sixth Illinois; Colonel J. I. Alexander, Fifty-ninth Indiana. My personal staff, as usual, have served their country with fidelity, and credit to themselves, throughout these events, and have received my personal thanks. Inclosed you will please find a map of that part of the battle-field of Chattanooga fought over by the troops under my command, surveyed and drawn by Captain Jenney, engineer on my staff. I have the honor to be, your obedient servant, W. T. SHERMAN, Major-General commanding. [General Order No. 68.] WAR DEPARTMENT ADJUTANT-GENERAL'S OFFICE WASHINGTON, February 21, 1884 Joint resolution tendering the thanks of Congress to
- 57. Major-General W. T. Sherman and others. Be it resolved by the Senate and House of Representatives of the United States of America in Congress assembled, That the thanks of Congress and of the people of the United States are due, and that the same are hereby tendered, to Major-General W. T. Sherman, commander of the Department and Army of the Tennessee, and the officers and soldiers who served under him, for their gallant and arduous services in marching to the relief of the Army of the Cumberland, and for their gallantry and heroism in the battle of Chattanooga, which contributed in a great degree to the success of our arms in that glorious victory. Approved February 19, 1864. By order of the Secretary of War: E. D. TOWNSEND, Assistant Adjutant-General. On the 19th of December I was at Bridgeport, and gave all the orders necessary for the distribution of the four divisions of the Fifteenth Corps along the railroad from Stevenson to Decatur, and the part of the Sixteenth Corps; commanded by General Dodge, along the railroad from Decatur to Nashville, to make the needed repairs, and to be in readiness for the campaign of the succeeding year; and on the 21st I went up to Nashville, to confer with General Grant and conclude the arrangements for the winter. At that time General Grant was under the impression that the next campaign would be up the valley of East Tennessee, in the direction of Virginia; and as it was likely to be the last and most important campaign of the war, it became necessary to set free as many of the
- 58. old troops serving along the Mississippi River as possible. This was the real object and purpose of the Meridian campaign, and of Banks's expedition up Red River to Shreveport during that winter.
- 59. CHAPTER XV. MERIDIAN CAMPAIGN. JANUARY AND FEBRUARY, 1864.
- 60. Full Size The winter of 1863-'64 opened very cold and severe; and it was manifest after the battle of Chattanooga, November 25, 1863, and the raising of the siege of Knoxville, December 5th, that military operations in that quarter must in a measure cease, or be limited to Burnside's force beyond Knoxville. On the 21st of December General
- 61. Grant had removed his headquarters to Nashville, Tennessee, leaving General George H. Thomas at Chattanooga, in command of the Department of the Cumberland, and of the army round about that place; and I was at Bridgeport, with orders to distribute my troops along the railroad from Stevenson to Decatur, Alabama, and from Decatur up toward Nashville. General G. M. Dodge, who was in command of the detachment of the Sixteenth Corps, numbering about eight thousand men, had not participated with us in the battle of Chattanooga, but had remained at and near Pulaski, Tennessee, engaged in repairing that railroad, as auxiliary to the main line which led from Nashville to Stevenson, and Chattanooga. General John A. Logan had succeeded to the command of the Fifteenth Corps, by regular appointment of the President of the United States, and had relieved General Frank P. Blair, who had been temporarily in command of that corps during the Chattanooga and Knoxville movement. At that time I was in command of the Department of the Tennessee, which embraced substantially the territory on the east bank of the Mississippi River, from Natchez up to the Ohio River, and thence along the Tennessee River as high as Decatur and Bellefonte, Alabama. General McPherson was at Vicksburg and General Hurlbut at Memphis, and from them I had the regular reports of affairs in that quarter of my command. The rebels still maintained a considerable force of infantry and cavalry in the State of Mississippi, threatening the river, whose navigation had become to us so delicate and important a matter. Satisfied that I could check this by one or two quick moves inland, and thereby set free a considerable body of men held as local garrisons, I went up to Nashville and represented the case to General Grant, who consented that I might go down the Mississippi River, where the bulk of my command lay, and strike a blow on the east of the river, while General Banks from New Orleans should in like manner strike another to the west; thus preventing any further molestation of the boats navigating the main river, and thereby widening the gap in the Southern Confederacy.
- 62. After having given all the necessary orders for the distribution, during the winter months, of that part of my command which was in Southern and Middle Tennessee, I went to Cincinnati and Lancaster, Ohio, to spend Christmas with my family; and on my return I took Minnie with me down to a convent at Reading, near Cincinnati, where I left her, and took the cars for Cairo, Illinois, which I reached January 3d, a very cold and bitter day. The ice was forming fast, and there was great danger that the Mississippi River, would become closed to navigation. Admiral Porter, who was at Cairo, gave me a small gunboat (the Juliet), with which I went up to Paducah, to inspect that place, garrisoned by a small force; commanded by Colonel S. G. Hicks, Fortieth Illinois, who had been with me and was severely wounded at Shiloh. Returning to Cairo, we started down the Mississippi River, which was full of floating ice. With the utmost difficulty we made our way through it, for hours floating in the midst of immense cakes, that chafed and ground our boat so that at times we were in danger of sinking. But about the 10th of January we reached Memphis, where I found General Hurlbut, and explained to him my purpose to collect from his garrisons and those of McPherson about twenty thousand men, with which in February to march out from Vicksburg as far as Meridian, break up the Mobile & Ohio Railroad, and also the one leading from Vicksburg to Selma, Alabama. I instructed him to select two good divisions, and to be ready with them to go along. At Memphis I found Brigadier-General W. Sooy Smith, with a force of about twenty-five hundred cavalry, which he had by General Grant's orders brought across from Middle Tennessee, to assist in our general purpose, as well as to punish the rebel General Forrest, who had been most active in harassing our garrisons in West Tennessee and Mississippi. After staying a couple of days at Memphis, we continued on in the gunboat Silver Cloud to Vicksburg, where I found General McPherson, and, giving him similar orders, instructed him to send out spies to ascertain and bring back timely information of the strength and location of the enemy. The winter continued so severe that the river at Vicksburg was full of floating ice, but in the Silver Cloud we breasted it manfully, and got back to Memphis by the 20th. A chief part of the enterprise was to
- 63. destroy the rebel cavalry commanded by General Forrest, who were a constant threat to our railway communications in Middle Tennessee, and I committed this task to Brigadier-General W. Sooy Smith. General Hurlbut had in his command about seven thousand five hundred cavalry, scattered from Columbus, Kentucky, to Corinth, Mississippi, and we proposed to make up an aggregate cavalry force of about seven thousand "effective," out of these and the twenty- five hundred which General Smith had brought with him from Middle Tennessee. With this force General Smith was ordered to move from Memphis straight for Meridian, Mississippi, and to start by February 1st. I explained to him personally the nature of Forrest as a man, and of his peculiar force; told him that in his route he was sure to encounter Forrest, who always attacked with a vehemence for which he must be prepared, and that, after he had repelled the first attack, he must in turn assume the most determined offensive, overwhelm him and utterly destroy his whole force. I knew that Forrest could not have more than four thousand cavalry, and my own movement would give employment to every other man of the rebel army not immediately present with him, so that he (General Smith) might safely act on the hypothesis I have stated. Having completed all these preparations in Memphis, being satisfied that the cavalry force would be ready to start by the 1st of February, and having seen General Hurlbut with his two divisions embark in steamers for Vicksburg, I also reembarked for the same destination on the 27th of January. On the 1st of February we rendezvoused in Vicksburg, where I found a spy who had been sent out two weeks before, had been to Meridian, and brought back correct information of the state of facts in the interior of Mississippi. Lieutenant-General (Bishop) Polk was in chief command, with headquarters at Meridian, and had two divisions of infantry, one of which (General Loring's) was posted at Canton, Mississippi, the other (General French's) at Brandon. He had also two divisions of cavalry—Armstrong's, composed of the three brigades of Ross, Stark, and Wirt Adams, which were scattered from the neighborhood of Yazoo City to Jackson and below; and Forrest's,
- 64. which was united, toward Memphis, with headquarters at Como. General Polk seemed to have no suspicion of our intentions to disturb his serenity. Accordingly, on the morning of February 3d, we started in two columns, each of two divisions, preceded by a light force of cavalry, commanded by Colonel E. F. Winslow. General McPherson commanded the right column, and General Hurlbut the left. The former crossed the Big Black at the railroad-bridge, and the latter seven miles above, at Messinger's. We were lightly equipped as to wagons, and marched without deployment straight for Meridian, distant one hundred and fifty miles. We struck the rebel cavalry beyond the Big Black, and pushed them pell-mell into and beyond Jackson during the 6th. The next day we reached Brandon, and on the 9th Morton, where we perceived signs of an infantry concentration, but the enemy did not give us battle, and retreated before us. The rebel cavalry were all around us, so we kept our columns compact and offered few or no chances for their dashes. As far as Morton we had occupied two roads, but there we were forced into one. Toward evening of the 12th, Hurlbut's column passed through Decatur, with orders to go into camp four miles beyond at a creek. McPherson's head of column was some four miles behind, and I personally detached one of Hurlbut's regiments to guard the cross- roads at Decatur till the head of McPherson's column should come in sight. Intending to spend the night in Decatur, I went to a double log-house, and arranged with the lady for some supper. We unsaddled our horses, tied them to the fence inside the yard, and, being tired, I lay down on a bed and fell asleep. Presently I heard shouts and hallooing, and then heard pistol-shots close to the house. My aide, Major Audenried, called me and said we were attacked by rebel cavalry, who were all around us. I jumped up and inquired where was the regiment of infantry I had myself posted at the cross- roads. He said a few moments before it had marched past the house, following the road by which General Hurlbut had gone, and I told him to run, overtake it, and bring it back. Meantime, I went out into the back-yard, saw wagons passing at a run down the road, and
- 65. horsemen dashing about in a cloud of dust, firing their pistols, their shots reaching the house in which we were. Gathering the few orderlies and clerks that were about, I was preparing to get into a corn-crib at the back side of the lot, wherein to defend ourselves, when I saw Audenried coming back with the regiment, on a run, deploying forward as they came. This regiment soon cleared the place and drove the rebel cavalry back toward the south, whence they had come. It transpired that the colonel of this infantry regiment, whose name I do not recall, had seen some officers of McPherson's staff (among them Inspector-General Strong) coming up the road at a gallop, raising a cloud of duet; supposing them to be the head of McPherson's column, and being anxious to get into camp before dark, he had called in his pickets and started down the road, leaving me perfectly exposed. Some straggling wagons, escorted by a New Jersey regiment, were passing at the time, and composed the rear of Hurlbut's train. The rebel cavalry, seeing the road clear of troops, and these wagons passing, struck them in flank, shot down the mules of three or four wagons, broke the column, and began a general skirmish. The escort defended their wagons as well as they could, and thus diverted their attention; otherwise I would surely have been captured. In a short time the head of McPherson's column came up, went into camp, and we spent the night in Decatur. The next day we pushed on, and on the 14th entered Meridian, the enemy retreating before us toward Demopolis, Alabama. We at once set to work to destroy an arsenal, immense storehouses, and the railroad in every direction. We staid in Meridian five days, expecting every hour to hear of General Sooy Smith, but could get no tidings of him whatever. A large force of infantry was kept at work all the time in breaking up the Mobile & Ohio Railroad south and north; also the Jackson & Selma Railroad, east and west. I was determined to damage these roads so that they could not be used again for hostile purposes during the rest of the war. I never had the remotest idea of going to Mobile, but had purposely given out that
- 66. idea to the people of the country, so as to deceive the enemy and to divert their attention. Many persons still insist that, because we did not go to Mobile on this occasion, I had failed; but in the following letter to General Banks, of January 31st, written from Vicksburg before starting for Meridian, it will be seen clearly that I indicated my intention to keep up the delusion of an attack on Mobile by land, whereas I promised him to be back to Vicksburg by the 1st of March, so as to cooperate with him in his contemplated attack on Shreveport: HEADQUARTERS DEPARTMENT OF THE TENNESSEE VICKSBURG, January 31, 1864 Major-General N. P. BANKS, commanding Department of the Gulf, New Orleans. GENERAL: I received yesterday, at the hands of Captain Durham, aide-de-camp, your letter of the 25th inst., and hasten to reply. Captain Durham has gone to the mouth of White River, en route for Little Rock, and the other officers who accompanied him have gone up to Cairo, as I understand, to charter twenty-five steamboats for the Red River trip. The Mississippi River, though low for the season, is free of ice and in good boating order; but I understand that Red River is still low. I had a man in from Alexandria yesterday, who reported the falls or rapids at that place impassable save by the smallest boats. My inland expedition is now moving, and I will be off for Jackson and Meridian to-morrow. The only fear I have is in the weather. All the other combinations are good. I want to keep up the delusion of an attack on Mobile and the Alabama River, and therefore would be obliged if you
- 67. would keep up an irritating foraging or other expedition in that direction. My orders from General Grant will not, as yet, justify me in embarking for Red River, though I am very anxious to move in that direction. The moment I learned that you were preparing for it, I sent a communication to Admiral Porter, and dispatched to General Grant at Chattanooga, asking if he wanted me and Steele to cooperate with you against Shreveport; and I will have his answer in time, for you cannot do any thing till Red River has twelve feet of water on the rapids at Alexandria. That will be from March to June. I have lived on Red River, and know somewhat of the phases of that stream. The expedition on Shreveport should be made rapidly, with simultaneous movements from Little Rock on Shreveport, from Opelousas on Alexandria, and a combined force of gunboats and transports directly up Red River. Admiral Porter will be able to have a splendid fleet by March 1st. I think Steele could move with ten thousand infantry and five thousand cavalry. I could take about ten thousand, and you could, I suppose, have the same. Your movement from Opelousas, simultaneous with mine up the river, would compel Dick Taylor to leave Fort De Russy (near Marksville), and the whole combined force could appear at Shreveport about a day appointed beforehand. I doubt if the enemy will risk a siege at Shreveport, although I am informed they are fortifying the place, and placing many heavy guns in position. It would be better for us that they should stand there, as we might make large and important captures. But I do not believe the enemy will fight a force of thirty thousand men, acting in concert with gunboats.
- 68. I will be most happy to take part in the proposed expedition, and hope, before you have made your final dispositions, that I will have the necessary permission. Half the Army of the Tennessee is near the Tennessee River, beyond Huntsville, Alabama, awaiting the completion of the railroad, and, by present orders, I will be compelled to hasten there to command it in person, unless meantime General Grant modifies the plan. I have now in this department only the force left to hold the river and the posts, and I am seriously embarrassed by the promises made the veteran volunteers for furlough. I think, by March 1st, I can put afloat for Shreveport ten thousand men, provided I succeed in my present movement in cleaning out the State of Mississippi, and in breaking up the railroads about Meridian. I am, with great respect, your obedient servant, W. T. SHERMAN, Major-General, commanding. The object of the Meridian expedition was to strike the roads inland, so to paralyze the rebel forces that we could take from the defense of the Mississippi River the equivalent of a corps of twenty thousand men, to be used in the next Georgia campaign; and this was actually done. At the same time, I wanted to destroy General Forrest, who, with an irregular force of cavalry, was constantly threatening Memphis and the river above, as well as our routes of supply in Middle Tennessee. In this we failed utterly, because General W. Sooy Smith did not fulfill his orders, which were clear and specific, as contained in my letter of instructions to him of January 27th, at Memphis, and my personal explanations to him at the same
- 69. time. Instead of starting at the date ordered, February 1st, he did not leave Memphis till the 11th, waiting for Warings brigade that was ice-bound near Columbus, Kentucky; and then, when he did start, he allowed General Forrest to head him off and to defeat him with an inferior force, near West Point, below Okalona, on the Mobile & Ohio Railroad. We waited at Meridian till the 20th to hear from General Smith, but hearing nothing whatever, and having utterly destroyed the railroads in and around that junction, I ordered General McPherson to move back slowly toward Canton. With Winslow's cavalry, and Hurlbut's infantry, I turned north to Marion, and thence to a place called "Union," whence I dispatched the cavalry farther north to Philadelphia and Louisville, to feel as it were for General Smith, and then turned all the infantry columns toward Canton, Mississippi. On the 26th we all reached Canton, but we had not heard a word of General Smith, nor was it until some time after (at Vicksburg) that I learned the whole truth of General Smith's movement and of his failure. Of course I did not and could not approve of his conduct, and I know that he yet chafes under the censure. I had set so much store on his part of the project that I was disappointed, and so reported officially to General Grant. General Smith never regained my confidence as a soldier, though I still regard him as a most accomplished gentleman and a skillful engineer. Since the close of the war he has appealed to me to relieve him of that censure, but I could not do it, because it would falsify history. Having assembled all my troops in and about Canton, on the 27th of February I left them under the command of the senior major- general, Hurlbut, with orders to remain till about the 3d of March, and then to come into Vicksburg leisurely; and, escorted by Winslow's cavalry, I rode into Vicksburg on the last day of February. There I found letters from General Grant, at Nashville, and General Banks, at New Orleans, concerning his (General Banks's) projected movement up Red River. I was authorized by the former to contribute aid to General Banks for a limited time; but General Grant insisted on my returning in person to my own command about
- 70. Huntsville, Alabama, as soon as possible, to prepare for the spring campaign. About this time we were much embarrassed by a general order of the War Department, promising a thirty-days furlough to all soldiers who would "veteranize"—viz., reenlist for the rest of the war. This was a judicious and wise measure, because it doubtless secured the services of a very large portion of the men who had almost completed a three-years enlistment, and were therefore veteran soldiers in feeling and in habit. But to furlough so many of our men at that instant of time was like disbanding an army in the very midst of battle. In order to come to a perfect understanding with General Banks, I took the steamer Diana and ran down to New Orleans to see him. Among the many letters which I found in Vicksburg on my return from Meridian was one from Captain D. F. Boyd, of Louisiana, written from the jail in Natchez, telling me that he was a prisoner of war in our hands; had been captured in Louisiana by some of our scouts; and he bespoke my friendly assistance. Boyd was Professor of Ancient Languages at the Louisiana Seminary of Learning during my administration, in 1859-'60; was an accomplished scholar, of moderate views in politics, but, being a Virginian, was drawn, like all others of his kind, into the vortex of the rebellion by the events of 1861, which broke up colleges and every thing at the South. Natchez, at this time, was in my command, and was held by a strong division, commanded by Brigadier-General J. W. Davidson. In the Diana we stopped at Natchez, and I made a hasty inspection of the place. I sent for Boyd, who was in good health, but quite dirty, and begged me to take him out of prison, and to effect his exchange. I receipted for him; took him along with me to New Orleans; offered him money, which he declined; allowed him to go free in the city; and obtained from General Banks a promise to effect his exchange, which was afterward done. Boyd is now my legitimate successor in Louisiana, viz., President of the Louisiana University, which is the present title of what had been the Seminary of Learning. After the war was over, Boyd went back to Alexandria,
- 71. reorganized the old institution, which I visited in 1866 but the building was burnt down by an accident or by an incendiary about 1868, and the institution was then removed to Baton Rouge, where it now is, under its new title of the University of Louisiana. We reached New Orleans on the 2d of March. I found General Banks, with his wife and daughter, living in a good house, and he explained to me fully the position and strength of his troops, and his plans of action for the approaching campaign. I dined with him, and, rough as I was—just out of the woods—attended, that night, a very pleasant party at the house of a lady, whose name I cannot recall, but who is now the wife of Captain Arnold, Fifth United States Artillery. At this party were also Mr. and Mrs. Frank Howe. I found New Orleans much changed since I had been familiar with it in 1853 and in 1860-'61. It was full of officers and soldiers. Among the former were General T. W. Sherman, who had lost a leg at Port Hudson, and General Charles P: Stone, whom I knew so well in California, and who is now in the Egyptian service as chief of staff. The bulk of General Banks's army was about Opelousas, under command of General Franklin, ready to move on Alexandria. General Banks seemed to be all ready, but intended to delay his departure a few days to assist in the inauguration of a civil government for Louisiana, under Governor Hahn. In Lafayette Square I saw the arrangements of scaffolding for the fireworks and benches for the audience. General Banks urged me to remain over the 4th of March, to participate in the ceremonies, which he explained would include the performance of the "Anvil Chorus" by all the bands of his army, and during the performance the church-bells were to be rung, and cannons were to be fired by electricity. I regarded all such ceremonies as out of place at a time when it seemed to me every hour and every minute were due to the war. General Banks's movement, however, contemplated my sending a force of ten thousand men in boats up Red River from Vicksburg, and that a junction should occur at Alexandria by March 17th. I therefore had no time to wait for the grand pageant of the 4th of March, but took
- 72. my departure from New Orleans in the Diana the evening of March 3d. On the next day, March 4th, I wrote to General Banks a letter, which was extremely minute in conveying to him how far I felt authorized to go under my orders from General Grant. At that time General Grant commanded the Military Division of the Mississippi, embracing my own Department of the Tennessee and that of General Steele in Arkansas, but not that of General Banks in Louisiana. General Banks was acting on his own powers, or under the instructions of General Halleck in Washington, and our assistance to him was designed as a loan of ten thousand men for a period of thirty days. The instructions of March 6th to General A. J. Smith, who commanded this detachment, were full and explicit on this point. The Diana reached Vicksburg on the 6th, where I found that the expeditionary army had come in from Canton. One division of five thousand men was made up out of Hurlbut's command, and placed under Brigadier-General T. Kilby Smith; and a similar division was made out of McPherson's and Hurlbut's troops, and placed under Brigadier-General Joseph A. Mower; the whole commanded by Brigadier-General A. J. Smith. General Hurlbut, with the rest of his command, returned to Memphis, and General McPherson remained at Vicksburg. General A. J. Smith's command was in due season embarked, and proceeded to Red River, which it ascended, convoyed by Admiral Porter's fleet. General Mower's division was landed near the outlet of the Atchafalaya, marched up by land and captured the fort below Alexandria known as Fort De Russy, and the whole fleet then proceeded up to Alexandria, reaching it on the day appointed, viz., March 17th, where it waited for the arrival of General Banks, who, however, did not come till some days after. These two divisions participated in the whole of General Banks's unfortunate Red River expedition, and were delayed so long up Red River, and subsequently on the Mississippi, that they did not share with their comrades the successes and glories of the Atlanta campaign, for which I had designed them; and, indeed, they, did not join our army
- 73. till just in time to assist General George H. Thomas to defeat General Hood before Nashville, on the 15th and 16th of December, 1864. General Grant's letter of instructions, which was brought me by General Butterfield, who had followed me to New Orleans, enjoined on me, after concluding with General Banks the details for his Red River expedition, to make all necessary arrangements for furloughing the men entitled to that privilege, and to hurry back to the army at Huntsville, Alabama. I accordingly gave the necessary orders to General McPherson, at Vicksburg, and continued up the river toward Memphis. On our way we met Captain Badeau, of General Grant's staff, bearing the following letter, of March 4th, which I answered on the 10th, and sent the answer by General Butterfield, who had accompanied me up from New Orleans. Copies of both were also sent to General McPherson, at Vicksburg: [Private.] NASHVILLE, TENNESSEE, March 4, 1864 DEAR SHERMAN: The bill reviving the grade of lieutenant-general in the army has become a law, and my name has been sent to the Senate for the place. I now receive orders to report at Washington immediately, in person, which indicates either a confirmation or a likelihood of confirmation. I start in the morning to comply with the order, but I shall say very distinctly on my arrival there that I shall accept no appointment which will require me to make that city my headquarters. This, however, is not what I started out to write about.
- 74. While I have been eminently successful in this war, in at least gaining the confidence of the public, no one feels more than I how much of this success is due to the energy, skill, and the harmonious putting forth of that energy and skill, of those whom it has been my good fortune to have occupying subordinate positions under me. There are many officers to whom these remarks are applicable to a greater or less degree, proportionate to their ability as soldiers; but what I want is to express my thanks to you and McPherson, as the men to whom, above all others, I feel indebted for whatever I have had of success. How far your advice and suggestions have been of assistance, you know. How far your execution of whatever has been given you to do entitles you to the reward I am receiving, you cannot know as well as I do. I feel all the gratitude this letter would express, giving it the most flattering construction. The word you I use in the plural, intending it for McPherson also. I should write to him, and will some day, but, starting in the morning, I do not know that I will find time just now. Your friend, U. S. GRANT, Major-General. [PRIVATE AND CONFIDENTIAL] NEAR MEMPHIS, March 10, 1864 General GRANT.
- 75. DEAR GENERAL: I have your more than kind and characteristic letter of the 4th, and will send a copy of it to General McPherson at once. You do yourself injustice and us too much honor in assigning to us so large a share of the merits which have led to your high advancement. I know you approve the friendship I have ever professed to you, and will permit me to continue as heretofore to manifest it on all proper occasions. You are now Washington's legitimate successor, and occupy a position of almost dangerous elevation; but if you can continue as heretofore to be yourself, simple, honest, and unpretending, you will enjoy through life the respect and love of friends, and the homage of millions of human beings who will award to you a large share for securing to them and their descendants a government of law and stability. I repeat, you do General McPherson and myself too much honor. At Belmont you manifested your traits, neither of us being near; at Donelson also you illustrated your whole character. I was not near, and General McPherson in too subordinate a capacity to influence you. Until you had won Donelson, I confess I was almost cowed by the terrible array of anarchical elements that presented themselves at every point; but that victory admitted the ray of light which I have followed ever since. I believe you are as brave, patriotic, and just, as the great prototype Washington; as unselfish, kind- hearted, and honest, as a man should be; but the chief
- 76. characteristic in your nature is the simple faith in success you have always manifested, which I can liken to nothing else than the faith a Christian has in his Saviour. This faith gave you victory at Shiloh and Vicksburg. Also, when you have completed your best preparations, you go into battle without hesitation, as at Chattanooga—no doubts, no reserve; and I tell you that it was this that made us act with confidence. I knew wherever I was that you thought of me, and if I got in a tight place you would come—if alive. My only points of doubt were as to your knowledge of grand strategy, and of books of science and history; but I confess your common-sense seems to have supplied all this. Now as to the future. Do not stay in Washington. Halleck is better qualified than you are to stand the buffets of intrigue and policy. Come out West; take to yourself the whole Mississippi Valley; let us make it dead-sure, and I tell you the Atlantic slope and Pacific shores will follow its destiny as sure as the limbs of a tree live or die with the main trunk! We have done much; still much remains to be done. Time and time's influences are all with us; we could almost afford to sit still and let these influences work. Even in the seceded States your word now would go further than a President's proclamation, or an act of Congress. For God's sake and for your country's sake, come out of Washington! I foretold to General Halleck, before he left Corinth, the inevitable result to him, and I now exhort you to come out West. Here lies the seat of the coming empire; and from the West, when our task is
- 77. done, we will make short work of Charleston and Richmond, and the impoverished coast of the Atlantic. Your sincere friend, W. T. SHERMAN We reached Memphis on the 13th, where I remained some days, but on the 14th of March received from General Grant a dispatch to hurry to Nashville in person by the 17th, if possible. Disposing of all matters then pending, I took a steamboat to Cairo, the cars thence to Louisville and Nashville, reaching that place on the 17th of March, 1864. I found General Grant there. He had been to Washington and back, and was ordered to return East to command all the armies of the United States, and personally the Army of the Potomac. I was to succeed him in command of the Military Division of the Mississippi, embracing the Departments of the Ohio, Cumberland, Tennessee, and Arkansas. General Grant was of course very busy in winding up all matters of business, in transferring his command to me, and in preparing for what was manifest would be the great and closing campaign of our civil war. Mrs. Grant and some of their children were with him, and occupied a large house in Nashville, which was used as an office, dwelling, and every thing combined. On the 18th of March I had issued orders assuming command of the Military Division of the Mississippi, and was seated in the office, when the general came in and said they were about to present him a sword, inviting me to come and see the ceremony. I went back into what was the dining-room of the house; on the table lay a rose- wood box, containing a sword, sash, spurs, etc., and round about the table were grouped Mrs. Grant, Nelly, and one or two of the boys. I was introduced to a large, corpulent gentleman, as the mayor, and another citizen, who had come down from Galena to make this presentation of a sword to their fellow-townsman. I think