CSCMP 2014: Big Data Use in Retail Supply Chains
- 1. Big Data Use in Retail Supply Chains Drs. Mark Barratt, Anníbal Sodero and Yao Jin
- 2. Acknowledgements • The researchers are grateful for the financial and collaborative support of CSCMP for this research project. • We appreciate the opportunity to partner with CSCMP and the CSCMP Research Strategies Committee on this research endeavor. • Additionally, we appreciate the support of the Supply Chain Alumni Group at Miami University and the Supply Chain Management Research Center at the University of Arkansas in helping us collect the research data. • Finally, we offer our sincere thanks to the individuals and firms that participated in the research process, who were promised anonymity in exchange for their participation.
- 3. Big Data search pattern
- 4. Big Data vs. Supply Chain Management search pattern
- 5. Big Data vs. Supply Chain Management
- 6. Big Data vs. Supply Chain Management
- 7. Research purpose How Managers see Big Data in retail supply chains • What it is and its perceived level of use? • Characteristics of firms implementing it. • What it is doing for them? • How well it is working? • What are the barriers and benefits achieved?
- 8. What is Big Data? “The nearest to real-time as possible gathering, storage, analysis of, and decision-making based on large sets of both quantitative and qualitative data in structured (tabular) and unstructured formats” Implies four dimensions of Big Data: 1. Volume: large amounts in terms of bytes, 2. Variety: many forms of structured and unstructured data 3. Velocity: real-time creation and use of data, and 4. Veracity: trustworthy, relevant, and useful data.
- 9. What is (and is not) Big Data? What Big Data is Not • Simply demand forecasting • A lot of data in the ERP system (Small and Medium data) What Big Data is ….. • Comes from multiple traditional and non-traditional sources • Beyond B.I.- enables real-time decision making • New software platforms and technology (e.g. Hadoop, NoSQL)
- 10. Overall Finding Big Data use in Retail SCs still elusive! Three States: Initiation Adoption Routinization Initial and some significant cases of use, but mostly using traditional, transactional data • Point of Sale (POS) and on-hand inventory data • Social media data but for marketing purposes only - better understanding of consumer preferences
- 11. Big Data: Good News As reported by firms in more advanced state (i.e. routinization) • More positive view of Big Data • Success in recognizing and overcoming challenges in implementation • Success in recognizing and overcoming integrating Big Data into planning and replenishment
- 13. Shifting Retail Landscape and Role of BD • Being efficient and becoming more effective • Goal: right consumer, place, time, quality, condition and price • Task is much more difficult and complex • Consumer behavior: new level of whenever and wherever. • Demanding more of an Omni-channel experience • Enabling the SC to become more demand driven
- 14. Research Methodology Phase 1 – Survey Questionnaires • 174 managers in retail supply chain firms • Identify factors that significantly contribute to, inhibit, and result from Big Data use • Derive insight regarding the state of Big Data use in firms positioned across retail supply chains Phase 2 – In-Depth Interviews • 18 senior supply chain managers • Obtain greater details regarding their Big Data use efforts
- 15. Factors that influence BD adoption Knowing… Being able to… • Analyze data • Merge BD with traditional data • Establish data-sharing protocols • External integration with customers • Invest necessary resources • All sources of data • Questions to ask of data • What data to share • Possible benefits versus cost • Data trustworthiness • Supply-driven versus demand-driven supply chain
- 16. BD: Benefits and Success Factors Direct Benefits – Critical Success Factors • Improved quality of data • Increased demand and supply visibility both internally and across the SC • Re-designed shared inter-organizational processes • Significantly enhanced data analytic capabilities Strategic Benefits – Omni-Channel and Demand-Driven Supply Chains • Predictive analyses of consumer demand patterns • Advanced insights into procurement and distribution operations • Strategic questions to shape supply chains
- 17. GAP: Definition - Practice Veracity Velocity Variety Volume Managerial Definition Significant Data Quality Issues Little Evidence POS & On-hand Inventory Practice
- 18. Demographics: Job title & Revenue Other, 10% Director, 47% Planner/ Analyst, 25% Presiden t/VP, 17% Less than $250 million, 28% $251- $500 million, 5% Greater than $10 billion, 24% $1 billion - $10 billion, 32% $500 million - $1 billion, 11%
- 20. Big Data: States of Adoption Initiation 34% Adoption 11% Routinization 55% Initiation Adoption Routinization
- 21. Functional Use of Big Data - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Security Finances HRM SC Planning Procurement After Sales Marketing Adoption State Routinization State
- 22. Extent of Big Data Use Dimensions • Routinization: Volume, Velocity, and Variety • Initiation: Veracity Types of Data • Use of transactional and environmental data significantly higher than consumer data • Firms are likely to be constrained and restricted to particular sources of data • Incorporating new sources of data remains an opportunity
- 23. Big Data: Perceived Usefulness - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Can increase job effectiveness Can increase job efficiency Necessary to get the job done Initiation State Adoption State Routinization State
- 24. BD: Perceived Ease of Use - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Allows me to do what I want to do with it Requires litle mental effort Clear and understandable Initiation Adoption Routinization
- 26. Current Use of Technology - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 WMS TMS EDI APO ERP Initiation Adoption Routinization
- 27. Current Data Capabilities - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Close work with technology service providers Use of current data to the maximum effectiveness Enough data storage capacity to use Big Data effectively People with extensive data analysis skills Initiation Adoption Routinization
- 28. Organizational Environment and Design
- 29. Big Data: Market Uncertainty - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Core production and delivery technology often change Marketing promotions of competitors are unpredictable Performance of major suppliers is unreliable Customer demand patterns change on a weekly basis Initiation Adoption Routinization
- 30. BD: Supply Chain Integration - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Actively involved in activities to streamline the supply chain Interlocking programs and activities with supply chain partners Information sharing externally across supply chain partners Information sharing internally across departments Management of cross-functional processes Extensive use of cross-functional teams Initiation Adoption Routinization
- 31. BD: Supply Chain Agility Short-term capacity increases as needed Quick addressing of environmental opportunities Resolute decision-making to deal with environmental changes Quick detection of changes in the environment 2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 Initiation State Adoption State Routinization State
- 32. Operational and Financial Performance
- 33. Performance Outcomes vs. Major Competitors More efficient than competitors Short order fulfillment lead-time Consistent on-time delivery to major customers 3.00 3.20 3.40 3.60 3.80 4.00 4.20 Initiation Adoption Routinization
- 34. Financial Performance vs. Major Competitors Profit Growth Return on Investment Sales Growth 2.80 2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 3.70 3.80 Initiation Adoption Routinization
- 35. Conclusions
- 36. Conclusions I Current Concept Ill-defined and under-explored by retail supply chain member firms Current Use Limited scope in terms of sources, formats, and applications Concurrent Use Collaboration, visibility, and integration
- 37. Conclusions II Caution Big data use is a double-edge sword Success is Not Easy New mindset and a business process design based around Big Data Substantial Rewards Firms at more advanced states of use are significantly outperforming their competitors Virtuous Innovation BD use is an innovation that may act as both a catalyst and a byproduct of success
- 38. Speakers • Anníbal Sodero – Assistant Professor, Department of Supply Chain Management – Sam M. Walton College of Business, University of Arkansas – Email: asodero@walton.uark.edu • Mark Barratt – Associate Professor, Department of Management – College of Business, Marquette University – Email: mark.barratt@marquette.edu • Yao “Henry” Jin – Neil R. Anderson Assistant Professor of Supply Chain Management – Farmer School of Business, Miami University – Email: jiny3@miamioh.edu
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