Optimizing New Product Development and Introduction (NPD&I) Through Analytics

Leveraging Analytics to Manage Supply Chain
Complexity in Highly Innovative Companies:
A Consumer Goods Industry Perspective
White Paper
Consumer Packaged Goods

Will Ruiz
Will Ruiz is a Consulting Partner with the Consumer Packaged Goods (CPG) and Retail
business units at Tata Consultancy Services (TCS). With over 25 years of experience in the
areas of new product development, manufacturing operations and strategy, IT strategy,
and business process innovation, he currently heads the unit's North America Consulting
practice. Before joining TCS, Ruiz held strategic roles at various companies – the leader of
HP's US Consumer and Retail Consulting practices, a principal with IBM's Business
Innovation Services group, a manager with Ernst & Young's Management Consulting
Performance Improvement practice, and a senior manufacturing engineer with Analog
Devices, Inc. He holds an MBA degree (high honors) and an MS degree in Manufacturing
Engineering from Boston University, United States, and has completed the Strategy
Value Creation Programme at the London Business School, United Kingdom
About the Author

Consumer goods companies are constantly on the lookout for smart business strategies that
help improve the effectiveness and efficiency of the product development process, which
helps sharpen an organization's competitive advantage. The rate of successful product
introduction in consumer goods companies has not improved over the years.¹ Additionally,
the need to manage the ever-increasing number of stock keeping units (SKUs) has made it
difficult to maintain operational expenses while increasing process complexity. Annual SKU
rationalization and the optimization of New Product Development and Introduction (NPD&I)
processes can help streamline supply chain management and allow companies to reinvest
savings in brand-building activities or game-changing innovation. This paper discusses how
an analytics-based approach can transform organizational performance and recharge
innovation processes.
Abstract
[1] IRI, 2014 New Product Pacesetters, April 2015

Contents
The Need to Address Supply Chain Complexity 5
Recommended Approaches for Effective Supply Chain Management 5
SKU rationalization 5
Optimization of NPD&I Processes 7
The Importance of Data 10
The Road Ahead: Reimagining Business Performance with an
Analytics-driven Approach 12

Collect, Aggregate, and
Analyze SKU Data
Leverage
Pre-Determined
Criteria to Identify
Candidates
-
Develop
Rationalization
Plan
Execute, Plan,
and Manage
Value
Realization
Establish
Annual SKU
Rationalization
Process
Leverage Analytics
The Need to Address Supply Chain Complexity
Nothing drives true top-line growth and competitive advantage more than innovation. It’s therefore not a surprise
to see this topic rank high in the strategic priorities of senior executives of consumer goods companies. However,
with the continued drive to introduce new products into the market, many companies are realizing that managing
a growing and significantly larger set of stock keeping units (SKUs) is increasing business complexity and operating
costs. These costs typically include investments related to the design, development, manufacture, customization,
warehousing, distribution, and marketing and promotion of products. This is especially true in‘highly creative
companies’where, on a yearly basis, new products with a relatively short lifecycle constitute a high percentage
(>50%) of the total saleable (active) products portfolio.
While some level of product complexity (that which serves to meet consumer needs) is welcome, not all scenarios
are recommended. For instance, unwanted product variations with limited appeal tend to consume resources that
could otherwise be directed to brand-building activities in support of products that are succeeding in the
marketplace, or invested in game-changing innovation.
Recommended Approaches for Effective Supply Chain
Management
Here are some strategies innovative companies can employ to manage supply chain complexity.
SKU rationalization
SKU rationalization is a process that leverages product information such as revenue, margin, and sold-to data, as
well as the strategic relevance of a product in a given geography, to decide which SKUs should be retired from a
product portfolio. This being a one-time event, a company should have a defined annual process to decide which
active SKUs need to be eliminated. An overview of a typical rationalization process is depicted in Figure 1.
5
Figure 1: An Overview of the SKU Rationalization Process (Source: TCS Internal)
n Identify required
SKU data
n Identify source
systems for the data
n Aggregate data to
provide end-to-end
and top-to-bottom
view of SKU
performance
n Analyze SKU data
n Leverage criteria for
sales, variable
margin, and
strategic
importance / value
of SKU (e.g. product
portfolio, retailer,
and geographic
region strategies) to
identify candidates
for rationalization
n Review and discuss
list of candidates
n Develop a
rationalization plan
n Determine the
business value
impact of executing
the plan
n Communicate plan
to key stakeholders
n Execute
rationalization plan
n Track and manage
business value of
the rationalization
efforts
n Implement an annual
rationalization process
n Ensure it is conducted
by a cross-functional
team
n Establish a supporting
communications plan
for all key
stakeholders

6
When properly executed, the first step of the rationalization process can provide the basis for a fact-based analysis
to identify SKUs that are the most likely candidates for retirement. A typical analysis of the profitability of SKUs, and
their relation to the overall profitability of a company, is given in Figure 2.
Figure 2: Sample Analysis of SKUs (Source: TCS Internal)
%ofProfitability
% of SKUs
Target
Margin
Product Line/
SKUs
0% 20% 40% 60% 80% 100%
Candidates for increased
investment in resources
and brand-building
activities
Candidates for marginal
improvement strategies
Candidates for rationalization
Margins

To make the right decision, it is not enough to only understand the actual costs incurred by an SKU (real
profitability data based on activity based costing can help with that). Understanding how a particular SKU fits –
from a value perspective – in a company's product portfolio, retail, and market (growth and market share) strategies
is also vital.
Once the appropriate strategic value has been taken into account, the company will be able to classify its SKUs into
three categories: (1) Candidates for rationalization, (2) Candidates for marginal improvement strategies, and (3)
Candidates for increased investment in resources and brand-building activities.
SKUs that are considered for marginal improvement should be evaluated for the possibility of:
n Reengineering the product to improve its cost structure
n Renegotiating raw material, sub-component, and manufacturing costs
n Renegotiating price and sales allowances
n Increasing their Direct-to-Consumer (D2C) business (by selling a product through the company-owned website)
Freed-up resources and working capital can then be directed to improve the competitive positioning and
performance of products that are doing well in the marketplace, as well as to conceptualize and develop new
products.
Having described how companies can implement an ongoing yearly process to streamline their product portfolios
by eliminating non-performing SKUs, the key question then is: Are there ways in which companies can leverage
data to improve their new product development success rate, and 'permanently' reduce the need to rationalize
SKUs at the rate they are doing today?
Optimization of NPD&I Processes
A number of products fail to succeed in the market due to the fact that they do not meet or satisfy the wants and
needs of the end customer. Others (especially those in the fashion or toy industries) are not successful simply
because NPD&I processes are not agile and robust enough to ensure the product makes it to the market at the right
place and at the right time, missing the required on-shelf availability date or quantity. This is particularly true for
product launches tied to seasons, holidays, and promotional events such as film releases.
7
Successful innovation not
only requires a well-
articulated strategy and a
clear understanding of
latent or future consumer
needs, it also demands the
flawless execution of NPD&I
processes.

Best practices in New Product Development (NPD) suggest, among other things, that companies leverage concepts
such as concurrent engineering, rapid prototyping, and virtual reality as part of a flexible stage-gate process to
successfully develop new products (see Figure 4).
8
The operational aspects of NPD&I are depicted in Figure 3.
Figure 3: An Overview of the Operational Component of Successful Innovation (Source: TCS Internal)
Well-Articulated
Innovation Strategy
Operational Component of NPD&I
Understanding of
(Future) Consumer Needs
Flawless Execution
of the NPD&I Processes
Successful
Innovation
n Objectives for achieving sustainable competitive
advantage
n Required cross-functional processes, organizational
skills, and underlying enabling technologies
n Social media to communicate directly with consumers
n Co-innovation networks to enhance new product
discovery or identification
n Formal Stage-gate process
n Cross-functional ownership
n Concurrent engineering, rapid prototyping, and virtual
reality
n Formal post-launch reviews
n Database of raw materials, formulas, specifications,
packaging, processes
n Database of best practices and regulatory mandates

However, we have observed that mastering this process can be elusive for companies. The lack of process discipline,
inability to recognize process variability due to product complexity, no formal post-launch reviews (PLRs), and
limited central repositories of reusable materials, components, and processes, are some of the challenges
companies face when executing NPD&I processes. All of this gets
compounded when shorter product lifecycles, higher number of yearly
seasonal and event-driven product introductions, rigorous licensing
approvals, and outsourced manufacturing are added to the mix.
To help companies deal with all of these challenges, we recommend an in-
depth analysis of their end-to-end NPD&I process data, including in-market
sales performance. In our engagements with industry players, we have
observed that such an analysis enables them to segment their products
(even within product lines), and determine how product characteristics drive
product development complexity and process variability.
9
Execution of Events,
Promotions , Allowances
Generate and
Manage Ideas
Define/ Develop
and Test
Concepts
Develop and
Test Product
Develop
Artwork and
Packaging
Manufacture
Product
Launch
Product
Manage
Product
Lifecycle
= Gate Reviews
Engineering/Production
Manufacturing, Supply Chain
Sales, Marketing &
Brand Management
Launch
Plan
Inventory and
Distribution
Plan
Product
Constraints, Test
Plans
Brand
Requirements,
Product DesignTechnical
Feasibility
Strategic Portfolio, Resource, and Project Management
Functions
Ideation
Mgmt.
Underlying
Capabilities
Integrated Data Analytics and Insight Development
(Includes Business Process Management)
Collaboration with Business Partners, Customers and Consumers
(Internal and External)
IT
Database of Components, Raw Materials, Formulas, Specifications, Packaging, and Processes
= Structured Post-Launch Review(s)
R&D
(Including Licensing Process Mgmt.)
Licensor (Internal and External)
Figure 4: Concurrent Engineering in the New Product Development Process
Understanding how product
design choices impact NPD&I
processes is key to a
company's success in
bringing new products to
market, on time, and in the
quantity necessary to meet
the required shelf-fill rate.
Database of Best Practices and Regulatory Mandates

Our analysis of the NPD&I process at leading industry players reveals that even within a major product segment (for
instance, fashion or toys) and its underlying product lines, there is room for multiple variations of the process. This is
above and beyond the typical time variations attributed to minor modifications, line extensions, and completely
new products. Figure 5 provides an overview of how a company can develop models for each type or segment of
their products.
10
Figure 5: Leveraging Analytics to Optimize the NPD&I Processes (Source: TCS Internal)
By following this approach, a company can gain granular insights into its NPD&I processes, by product line or
segment. Additionally, with insights on which product features or process steps are most likely to contribute to
project delays, companies can more closely monitor their performance against the standards for the new NPD&I
processes. The result of such a comparison can serve as the 'early predictor' of potential success or failure of a
newly introduced product.
These early predictors will allow companies to make timely course-corrections during product development, or
make a decision on the viability of continuing with a project that is likely to not deliver a successful market
introduction outcome (thereby reducing the proliferation of non-performing SKUs).
The Importance of Data
In order to make NPD&I analytics work, it is vital to have ready access to clean data, with the ability to display and
analyze it in a way that enables business decisions. It is equally important to address the organizational change
management considerations related to understanding how the new NPD&I process should be executed in the
light of new data and models.
Identify and collect
SKU data, and
perform SKU
segmentation
Review data
for errors, gaps,
completeness,
and so on
-
Aggregate data to
provide full view of
SKU lifecycle
n Identify data
sources
n Collect and stage
the data
n Segment SKUs
(classification
criteria could
include seasons,
major promotional
events, and so on)
n Review the data to
identify and correct
errors
n Fill in the gaps in
information,
standardize formats
where required
n Create (if
appropriate)
attributes based on
certain SKU
characteristics
n Create a database
containing
aggregate SKU
information from
concept generation
to in-market sales
performance
n Develop hypotheses
to be tested
n Perform analysis to
determine key
factors driving
success/failure of
new product
introductions
n Determine NPD&I
process duration by
product line or
segment
n Leverage models and
analyses to predict, at
an early stage, if an
NPD&I project is in
trouble
n Develop and
implement a process
health and early-
warning dashboard
n Leverage learnings to
optimize models and
the NPD&I process (by
segment)
Develop hypotheses
and perform statistical
analyses by segment
Incorporate insights
into the NPD&I processes
and implement a process
health dashboard

Successful 'full lifecycle' SKU analysis requires integrating large sets of data from disparate (internal as well as
external) source systems such as product lifecycle management (PLM), enterprise resource planning (ERP), point of
sale (PoS), supply chain management (SCM), and manufacturing applications and databases (see Figure 6).
11
Figure 6: Information and Data Sources (Source: TCS Internal)
We have observed that all of the typical data sources tend to have limitations and contain data inconsistencies, as
well as errors. These typically stem from manual data entry, inadequate process discipline, incomplete or incorrect
master data, and outdated SKU information residing in the systems. Before companies attempt to automate this
type of analysis, it is advisable to work with a set of pilot data. The pilot phase will allow companies to fully
understand any shortcomings the data might have. It will highlight when business resources at the R&D
department or the supply chain and brand management division may need to 'systematically' intervene to review
and correct the data, or inject business context into the analysis.
n Product Strategy
n Geography/Market Strategy
n Sold-to/Retailer
Considerations
n Sales/Revenue Targets
n Variable Margin Targets
n Rationale for Product Variations
PLM Data ERP Data
Demand & Supply
Planning Data
Manufacturing
Data
“Authoring” “Execution”
n NPD&I Data
n Product Characteristics
n BOM
n Initial Forecasts
n Complexity of NPD
process by SKU
n Adherence to NPD
process/milestone timing
n Design changes by SKU
and by NPD process step
n Costs for each type of
change
n Product Hierarchy
n Product/Item
Master Data
n BOM
n Financial Data
n Actual Product
Costs
n Materials
n Manufacturing
n Distribution,
Logistics
n Marketing/
Promotion
n Sales
n Sales and Sold-to
Data (by Market)
n Inventory Data
n SKU Forecasts, Orders
n Market Introduction Date
n Promotional/Event Calendar
n Sourcing, Lead Times
n Capacity
n Quality
n Ramp Up
n Shipment Data

The Road Ahead: Reimagining Business Performance
with an Analytics-driven Approach
In a world where consumer tastes, needs, and preferences are changing rapidly, new product introductions
continue to accelerate, while product lifecycles shrink drastically. This is especially true in segments such as fashion
(apparel, footwear, and so on), toys, and consumer electronics, but increasingly so in other consumer goods
segments (such as food, beverage, and household items) as well.
To succeed, companies will need to revamp and continuously evaluate and
optimize their NPD&I processes. With analytics, companies can tailor and
fine-tune their innovation processes to ensure they use the best-fit product
development and launch strategy, and execute the plan flawlessly in order
to maximize revenues in today's fiercely competitive marketplace. Those
that do this will have a notably better chance of winning over their
competition, and delivering superior shareholder value.
12
To survive and thrive in an
environment marked with
rapidly changing consumer
preferences and shrinking
product lifecycles, CPG
companies need to
continually reinvent their
NPD&I processes. Analytics
can be of great help in
achieving this.

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