The document discusses strategies for optimizing costs across the product realization lifecycle. It identifies factors that can lead to cost overruns at each stage, from requirements to post-market, and recommends best practices. These include adopting integrated requirements management, knowledge-based engineering to reduce design iterations, optimization tools for manufacturing planning, and an integrated quality management system to address issues early. Implementing cost analytics can provide visibility across the organization to analyze and communicate ROI of optimization efforts.

1. Optimizing Product Realization Costs
Across the Value Chain
By leveraging various cost optimization enablers, global automotive,
aerospace, discrete manufacturing and medical device companies
can identify and optimize costs holistically across the product
lifecycle, thereby improving safety, environmental and regulatory
compliance, sustainability and quality of products.
Executive Summary
Automotive, aerospace, discrete manufacturing
and medical devices companies face numerous
business challenges daily. Shrinking profit
margins (due to unrelenting competition) and the
movement of manufacturing to lower-cost regions
are among the stiffest such challenges. Moreover,
amid these escalating pressures, companies need
to produce high-quality, differentiating products,
while remaining cost-effective.
Each product development phase uniquely
contributes to the company’s cost equation.
Howeover, most product development companies
apply cost optimization inititiaves to individual
silos. To get more bang from these efforts, they
need to embrace a holistic view of cost optimiza-
tion across the product realization value chain.
This white paper identifies and addresses the
various pain areas or factors contributing to cost
overruns across the product realization lifecycle.
The paper addresses the challenges within the
cognizant 20-20 insights | may 2016
• Cognizant 20-20 Insights
four major phases of product realization in the
value chain:
• Requirements and feasibility study.
• Design, verify and validate.
• Approve, manufacture and launch.
• Post-market observation.
Apart from the enablers/best practices in each
product development phase, we also highlight
key industry trends. In our view, early adoption of
emerging solutions would help optimize product
costs and enhance growth prospects.
Product Cost Optimization Challenges
Based on our domain and engagement experience,
and discussions with multiple stakeholders across
industry functions, we discovered the full sweep
of factors that contribute to cost overruns
across the product development lifecycle, and
corresponding best practices to avoid such cost
overruns (see Figure 1, next page).

2. cognizant 20-20 insights 2
Cost Overrun Factors and Avoidance Best Practices
• Inaccurate or
Incomplete
determination
of VOC or VOB.
• Inadequate time spent
on requirements
management.
• Insufficient exploration
of concepts and
prototypes.
• Multiple iterations in
design and testing.
• Inadequate attention
to DFx requirements.
• Lack of focus on
cost/value aspect
of design.
• Initiating V&V efforts
after design is nearly
complete.
• Manual approval/wet
signature for design and
test artifact approvals.
• Test all for CSV/COTS
validation.
• Single source or long
lead items in BOM.
• Increase in raw
materials costs.
• Poor data quality in
device trails and on
road testing.
• Insufficient
post-production
monitoring.
• Ineffective root cause
analysis and incomplete
issue resolution before
releasing the product
after a recall.
• Spend adequate time on
VOC/VOB and
requirements cascade.
• Complete requirements
analysis before
proceeding to design.
• Enable
knowledge-based
engineering to reduce
iterations.
• Adopt DFx checklist
for analysis.
• Risk-based CSV/COTS
validation.
• Early involvement
of V&V to identify
requirements defects
early.
• Adoption of electronic
document management
systems and test
management systems.
• Avoid single source
or long lead items; if
unavoidable, plan for
adequate inventory.
• Plan and approach
manufacturing phase
using optimization
tools.
• Usage of quality control
techniques like Six
Sigma and Lean
manufacturing.
• Working with contract
manufacturers by
outsourcing work.
• Integrated quality
management system
that stresses active
vigilance.
• Use analytics to
preempt failures.
• Thorough root-cause
analysis before
re-releasing the product
to the field after
a recall.
Requirements &
Feasibility Study
Design, Verify & Validate
Approval Manufacture &
Launch
Post-Market Observation
Social Media
PLM Integration
Latest
Thinking
CAE PLM Integration
3-D Shape Search
Optimize Product Cost
Using Big Data Analytics
Connected Vehicles and
Internet of Things
CostOverrunFactors
BestPracticestoAvoid
CostOverruns
Figure 1
Product Realization Requirements
Figure 2
Typical Product Realization Requirements
Design & Risk
Management
Sourcing &
Manufacturing
Interface Performance
Product &
Data Security
Service &
Installation
Labelling
Documentation
Packaging &
Shipping
Usability Regulatory
Ideation: Requirements Management Stage
The successful realization and marketing of
any product is dependent on capturing correct
requirements as early as possible in the lifecyle.
Faulty requirements during product development
constitutes nearly 40% to 60% of total errors.
In our experience, cost overrun factors at the
ideation stage include the following:
• The inability to accurately identify the voice of
customer (VoC) and voice of business (VoB) at
the start of the project, and inadequate time
spent on requirements due to strict timeline
commitments.
• Poor requirements and document-manage-
ment practices.
• Neglecting process and documents to
accelerate working product development.
• Lack of adequate dialogue with a regulatory
authority at an earlier stage of product devel-
opment – such as with the Federal Drug Admin-
istration (FDA) in cases concerning medical
device development.

3. 3cognizant 20-20 insights
Ideation Cost Optimization Enablers
Maintaining complete, correct, feasible and pri-
oritized requirements within a requirements
management system can help optimize costs (see
sidebar below). Product lifecycle management
(PLM) systems, for example, typically come
equipped with requirements management
capability, which helps to:
• Improve product quality and provide better
traceability and risk management.
• Improve compliance to regulatory standards.
• Avoid rework and recalls, and ensure product
is on time to market.
A PLM accelerator for socio-interactive
automotive product development helps car
makers acquire and apply social media feedback
in a structured and meaningful way in their PLM
systems during car platform development.1
Integrating systems-engineering capabili-
ties into PLM improves collaboration between
various departments of product development
(mechanical, electrical and software design).2
Design/Verify/Prototype Stage
Design is another critical phase in the product
realization value chain. Approximately 70% of
the product costs are fixed during its design.3
Typical cost overrun factors faced by product
development companies during the design, verify
and validation phase include:
• Time wasted in iterations done during design,
design prototyping and prototype testing.
• Inadequate attention to design for x or DFx
(where x = manufacturing, testing, assembly,
etc.) requirements.
• Designers unaware of how to focus on costs or
their value attributes.
• Consideration for verification and validation
activities after the requirements and design
activities are completed.
Design Stage Cost Optimization Enablers
Product companies can adopt our knowledge-
based engineering and VA-VE framework
(see Figure 5, page 5) to effectively:
• Improve integration between computer-aided
design (CAD), computer-aided manufacturing
(CAM) and computer-aided engineering (CAE)
systems.
• Optimize products using “what-if” scenarios.
• Improve time to market.
• Reduce, rework and reuse design knowledge.
Quick Take
Integrated Requirements Management for a Global Automotive Major
• Business situation: A global automotive major
was using legacy systems to manage require-
ments. As a result, its engineers were spending
an inordinate amount of time fetching data and
managing requirements.
• Solution: A bidirectional integration of the PLM
tool with the legacy system was developed.
Earlier, the company used two-plus Java-based
applications for requirements gathering,
supplier management, manufacturing bill of
materials control and CAD management. Using
Teamcenter PLM, we were able to integrate all of
those functions into a single tool and reduce the
number of applications and approvals/signoffs,
with improved visibility and transparency.
• Benefits: As a result of the integration, the
automotive major could:
>> Easily maintain, manage and fetch the re-
quired information with regard to standards
and regulations.
>> Conduct root-cause analysis with reduced
lead time.
>> Reduce the number of applications.
>> Increase operational visibility and transpar-
ency.
>> Manage regulatory and standard-process-re-
lated information in a centralized repository.
3cognizant 20-20 insights

4. 4cognizant 20-20 insights
Quick Take
Automated Design Verification and Validation at a Global
Automotive Major
• Business situation: A global automotive major
was using a traditional and manual process for
design verification and validation. As a result
of this traditional process:
>> Time was wasted in repeatable work during
design and prototyping.
>> Design knowledge was lost when employees
left the organization.
>> There was an absence of design verification
and validation standards.
• Solution: A proven, knowledge-based engi-
neering framework was implemented that
automated the CAD-based verification and
validation process.
• Benefits: As a result of automating the verifi-
cation and validation process, the automotive
major has:
>> Increased efficiency and productivity, and
improved quality via repeatability and re-
producibility.
>> Re-stratified and effectively used engineer-
ing manpower.
>> Effectively integrated its CAD and enter-
prise information systems.
>> Separated decision support from decision-
making activity.
>> Made product creation process automation
the primary driver.
• Create design, validation and verification
standards.
• Improve functional focus and value.
• Create sustainability and reduce costs:
>> Improve safety, ergonomics, usability and
process efficiency.
>> Reduce the total number of parts in an
assembly.
Traditional and Knowledge-Based Engineering (KBE) Design
Figure 3
Design Inputs
Design Outputs
Design Verify,
Validate & Launch
Production
Redesign
Loops
Post-Production
Maintenance
Design Inputs
Empirical & Analytical
Knowledge About the System
Design Outputs
Design Verify,
Validate & Launch
Production
Source of
Knowledge
Knowledge-
Based Design
Aid
Post-Production
Maintenance
4cognizant 20-20 insights

5. cognizant 20-20 insights 5
Problem
Statement
Measure Analyze
SMART
Reengineer Test &
Validate
Our VA/VE Framework: SMART
Figure 5
Understand
customer needs.
Define “as is” and
“to be” condition in
measurable terms.
Project charter.
Define the defects
or issues.
Gather baseline
information.
Critical to quality
customer
requirements.
Process map.
Analyze info/data
collected for
evaluation.
Identify top
contributors for
defects or issues.
Set goal for
improvements.
Develop
concepts/ideas for
better design.
Finalize ideas and
perform detailed
design engineering.
Perform cost
analysis.
Validate design
using CAE analysis.
Build functional
prototypes.
Measure
performance of
new design.
Our Approach to KBE-Design Automation: CFAD
Figure 4
» »
KBE System for
Design Automation
Input
• Customer
• Specification
Product Data
• Knowledge
acquisition through
reports, design,
manufacturing,
performance and
maintenance
functions.
• Captured knowledge
from business to
maintenance
activities & modeling
techniques.
• Use of spreadsheet
for capturing
knowledge by
implementing
equations or rules
that enable
knowledge recycling
• Interpretation of
acquired knowledge
into rules and
formalization to a
computer-implement-
ation-friendly format.
• Formalization using
a partner-company
approach. During
this activity the
implementation
structure – i.e., a
hierarchical class
structure – takes
shape.
• Implementation
captured process,
rules & activities to
develop a KBE tool.
• Follow iterative
process between the
capturing of
engineering
knowledge and the
automation of
engineering
activities.
• Testing of tool
and/or application by
allowing engineers
to design the
lifecycle properties
of the product.
• Packaging &
deployment of
the tool.
Product Model
• Geometry
• Configuration
• Product Knowledge
• Engineering
Knowledge
External Data
• Catalogues
• Tables
• Materials
• Analysis
Output
• Reports
• Drawings
• Costs
• BOM
• Manufacturing Plans
• CAD Models/Assemblies
Capture of
Engineering Knowledge
Knowledge
Formalization
Automation of
Engineering Activities
Deploy Quality Control
of Engineering Activities

6. cognizant 20-20 insights 6
PLM integrated 3-D shape search helps designers
in identifying parts of similar shape in the
enterprise databases. As a result, companies can
save time by avoiding rework.4
In the medical
device industry, 3-D printing is another instant
prototyping technology that has proven to be rev-
olutionary. With the use of 3-D printing, surgeons
can design implants and instrumentation specific
to each patient. When doctors need to create a
new device on demand for rare, unpredictable
conditions, 3-D printing is applied.
Manufacture
Manufacture and launch is another critical phase
in the device lifecycle. While requirements and
design set the foundation for a successful product
launch, costs incurred on manufacturing and the
supply chain can make or break a product launch.
Hence, cost optimization in manufacturing is of
prime importance. The factors that contribute to
cost overruns in this phase inclde:
• Increase in raw materials cost.
• Selecting single source for long-lead items.
• Poor data quality in device trials and on road
testing.
Manufacturing Stage Cost Optimization
Enablers
Three enablers can be implemented for cost opti-
mization at the manufacturing stage.
• Optimize: Planning and approaching the man-
ufacturing phase using optimization tools for
raw materials optimization as well as costing
and supply chain optimization.
• Control: Use of quality control techniques
such as Six Sigma and Lean manufacturing to
effectively reduce quality-related issues of the
devices produced.
• Produce: Working with contract manufac-
turers by sourcing design, manufacturing or
delivery to OEMs.
The integration of manufacturing with product
development using analytics and big data helps
designers to take corrective and preventive
actions based on inputs from manufacturing
engineers.
Post-Market Stage
Product development companies spend billions
of dollars5,6
on product recalls (warranty- or
compliance-based). The cost of poor quality
affects the image of companies and erases profit
earned - due to litigation costs. Poor quality also
endangers the lives of customers.
Companies face huge warranty-, compliance- and
safety-related issues, while automotive companies
regularly face warranty and safety issues with
regard to various components. The causes:
• Inefficiency and a lack of proactive vigilance.
• Ineffective root-cause analysis.
• Incomplete addressing of issues.
Figure 6
Cost Optimization Enablers: Manufacturing Phase
Raw Material Optimization
Real-time Should-costing
Supply Chain Optimization
Optimize Control Produce
Six Sigma
Poka Yoke
Lean
SPC
Contract
Manufacturing

7. 7cognizant 20-20 insights
• Aging complaints and corrective actions
preventive actions (CAPAs).
Q
Adverse Event Noncomplianc
e
M
Recall
Audit
Integrated QMS and PDLC
Figure 7
Product
Realization
Value Chain
Aspects
Complaints
FMEA
S
CAPA
Training
Support Ideate
Design,V&V
L
aunch Manufac
ture
(between the design and quality departments, for
example). As a result, quality issues at the design
level can be addressed very early in the process.
Other benefits of IQMS include improved organi-
zational effectiveness, successful new product
implementation (NPI), increased compliance,
reduced internal and external failure costs,
and reduced quality prevention and quality
assurance costs.
Automotive companies are now looking at an
integrated approach to collecting vehicle infor-
mation using Internet of Things (IoT) technolo-
gies and connected vehicles to actively monitor
and advise customers on likely quality issues.8
Going Beyond
Any cost optimization inititiative without the
ability to view cost metrics is of little use. Enter-
prises typically do not visualize the benefits and
ROI of a cost optimization drive in a holistic way.
A single source of truth is necessary for analyzing
or viewing product costs, which can be obtained
through a cost analytics and reporting tool that
can communicate insights holistically across the
organization or enterprise.
An enterprise cost reporting system should be
able to:
• Interact with all enterprise systems and
departments.
Post-Market Cost Optimization Enablers
In our view, an integrated quality management
system (IQMS) can bring additional organiza-
tional effectiveness. Companies that have imple-
mented IQMS achieve better on-time shipment
rates.7
Moreover, integration adds quality control
from conception through post market, and brings
more effective cross-functional colloboration
Quick Take
Product Cost Management at a Global Automotive Major
• Business situation: A global automotive
major was using a traditional system of product
costing:
>> CAD data moved between various depart-
ments such as design, sourcing and costing
through e-mails.
>> Engineers did not have the most recent CAD
data for costing purposes.
>> Delays in design changes due to cost over-
runs led to product rollout delays.
• Solution: A CAD-based costing tool was
developed that automatically generates tool
and manufacturing costs based on recognition
of CAD features.
• Benefits: As a result of an integrated PLM/CAD
costing tool, the automotive major now can:
>> Get real-time cost information at every stage
of the product lifecycle.
>> Broadcast a single source of truth with re-
gard to cost.
>> Negotiate better with vendors with regard to
parts costs.
>> Help designers know the cost implications of
their designs, instantly.
7cognizant 20-20 insights

8. cognizant 20-20 insights 8
• Track and analyze cost for various product
configurations.
• Report cost data across the enterprise.
• Search and identify various cost components
with ease.
Looking Forward
Any effective product cost optimization program
is an amalgamation of effective people using
efficient tools to drive effective processes.
Product cost optimization is a by-product of
efficiency and a never-say-die attitude embedded
in an organization’s culture.
Post-Market-Activity Categories
Figure 8
Proactive Vigilance & Reporting Reactive Vigilance
Competitor-Initiated Product Recall,
MAUDE Database
Regulator-Initiated Recall
Competitor-Related Quality Incidents Quality Incidents
Recall Report Analysis Adverse Event
Today’s product teams focus not only on
products’ features and their performance,
but also on cost, safety, environmental and
regulatory compliance, sustainability, quality and
other factors that impact the design process.
Continuous monitoring of cost across the product
realization value chain can help identify key cost
drivers. With this knowledge, a product design
team can deploy control measures to optimize
costs through multiple tools and methodologies,
as discussed in the paper. Enterprise product cost
optimization brings the focus on creating a win-win
outcome for product development companies and
their suppliers, at an optimum cost for customers.
Enterprise Cost Reporting
Figure 9
Source: http://resources.apriori.com/h/
Product Lifecycle
Management (PLM)
Enterprise Resource
Planning (ERP)
Supply Chain
Management (SCM)
Material Requirements
Planning (MRP)
PRODUCT COST
MANAGEMENT
ENGINEERING
COST REPORTING
COSTTRACKING
COSTDATAMINING
COST ANALYSIS
MANUFACTURING
RESEARCH &
DEVELOPMENT
SOURCING

9. 9cognizant 20-20 insights
Footnotes
1 http://www.cognizant.com/InsightsWhitepapers/a-PLM-accelerator-for-socio-interactive-automotive-
product-development-codex1204.pdf.
2 http://www.plm.automation.siemens.com/en_us/products/teamcenter/systems-engineering-soft-
ware/.
3 “Design determines 70% of cost? A review of implications for design evaluation.” J.A. Barton, D.M.
Love and G.D. Taylor.
4 http://www.plm.automation.siemens.com/en_us/products/open/geolus/.
5 http://www.catlin.com/flipbook/product-recall/files/inc/382055422.pdf.
6 “The Business Case for Medical Device Quality,” by Ted Fuhr, Katy George, Janice Pai,
www.mckinsey.com.
7 “Network Polymers and IQMS: A Mixture of Success,” http://www.iqms.com/company/network-polymers/.
8 “Design for Manufacturing’s Internet of Things,” https://www1.cognizant.com/content/dam/Cognizant_
Dotcom/article_content/Services/Designing-for-Manufacturings-Internet-of-Things.pdf.
Reference
• Requirements Management: The Interface Between Requirements Development and All Other
Systems Engineering Processes, by Colin Hood, Simon Wiedemann, Stefan Fichtinger, Urte Pautz;
Springer, 2008.
9cognizant 20-20 insights
Quick Take
Proactive Vigilance in Medical Device Quality Management Systems
A working QMS is part of reactive vigilance which
triggers a chain of events back to the PDLC only
when quality issues are discovered in the product.
Medical device companies should be encouraged
to go a step further and be proactive in following
product recalls or incidents of other competitors
in the same product category.
Such vigilance would provide valuable learning
that would ensure quality issues are addressed
proactively.

10. About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business
process outsourcing services, dedicated to helping the world’s leading companies build stronger
businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satis-
faction, technology innovation, deep industry and business process expertise, and a global, collaborative
workforce that embodies the future of work. With over 100 development and delivery centers worldwide
and approximately 221,700 employees as of December 31, 2015, Cognizant is a member of the NASDAQ-
100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing
and fastest growing companies in the world.
Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.
World Headquarters
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Phone: +1 201 801 0233
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Email: inquiry@cognizant.com
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Phone: +44 (0) 20 7297 7600
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Codex 1611
About the Authors
Madan Unde is a Senior Manager of Projects within the Product Engineering Practice of Cognizant’s
Engineering and Manufacturing Solutions business unit. He has over 13 years of engineering industry
experience, working in both the engineering OEM as well as engineering services spaces. Madan’s
work experience spans industries, from life sciences (medical devices), high-technology, and industrial
automation, through machine tools. In these roles, he has provided solutions across various technical
areas such as product design and development, innovation (front end), continuous improvement,
complaints investigation and resolution, CAPA management and product sustenance engineering.
Madan is well acquainted with medical devices standards such as ISO13485 and ISO14971, and practices
such methods as Six Sigma, DFx, risk management, and value engineering/value analysis and problem
solving. Madan holds a mechanical engineering and industrial engineering post graduate degree from
the University of Pune and is a certified Six Sigma Black Belt Professional (MSME India) and Value Engi-
neering Associate Value Specialist (SAVE & INVEST). He can be reached at Madan.Unde@cognizant.com.
Murali Krishna is a Manager of Projects within Cognizant’s Engineering and Manufacturing Solutions
business unit. He has 11-plus years of experience in building CAD-to-CAE and CAD-to-CAM data interop-
erability solutions, product cost management, geometric search and knowledge-based engineering
solutions. Murali has vast experience in creating frameworks for automotive design verification and
validation. He has worked extensively with automotive, high technology and manufacturing companies
to provide the best solutions for their data interoperability needs. Murali has authored technical papers
in the fields of CAD, CAM and CAE. He has a bachelor’s degree in mechanical engineering from College
of Engineering, Guindy. Murali can be reached at Muralikrishna.AV@cognizant.com.