This document discusses how predictive analytics can accelerate and enrich product development. It begins by explaining how predictive analytics can help companies sharpen forecasts, better predict product performance and failures, and generate more value. It then discusses how companies can derive more value from the large amounts of data they collect. The document outlines how predictive analytics can be applied across the entire product development process. It concludes by stating that incorporating predictive analytics can enrich information quality, minimize mistakes, and inform better decisions throughout product development.
The Art and Science of Sales Forecasting: A Webinar for Sales Managers and Co...Birst
Overview
Sales forecasting is a science and an art. It is the combination of information and metrics, intuition and best practices. However, sales forecasting is most commonly associated to the standard grading methodology of the particular customer relationship system that is being used (Salesforce.com, Oracle, Microsoft, etc.). In reality, how do key sales leaders become high performing accurate sales forecasters? In addition, how do companies effectively utilize sales forecasting information to increase overall organizational performance?
Here’s what we’ll discuss in this session:
State-of-the-art forecasting strategies, best practices, and key metrics
The interconnection between product complexity, company lifecycle stage, and accurate forecasting
Mitigating downside risk and triangulation strategies to determine the truth
Deal inspection and vetting sales rep forecasts
The different types of sales forecasters; exaggerators, sandbaggers, and Heavy Hitters
The difference between snapshot, intra-department, and inter-department sales forecasting
A cross-discipline team at ASAE is inventing a new product development process for associations so that you don’t have to. Here's a summary of how ASAE evaluates new product proposals, who makes the decisions, and with what criteria. This presentation to the Kansas City Society of Association Executives (KCSAE) also explains how to encourage innovation, fill the idea pipeline, and analyze and balance an organization’s entire portfolio of programs, products, and services.
The final slides pose questions designed to help you create a process for making decisions about what programs, products, and services to offer—and which to discontinue, and when. This step-by-step process will help you put ASAE's new product development principles into practice at your own association - based on your size, resources, and needs.
By Mariah Burton Nelson, VP, Innovation and Planning, ASAE
A case study on how to improve forecast accuracy by incorporating market or business intelligence into statisitical forecasting and know whether it improves forecast accuracy or not.
Argus Analyzer personal toolkit for Smart Home Product ManagersJames (JD) Dillon
Developed specifically to help Smart Home Product Managers do their jobs, this SaaS platform requires no IT interactions, zero professional services, and minimal training. This presentation describes how the toolkit helps with marketing metric development, competitive analysis, executive & cross-functional presentations, and ultimately to define products and measure immediate impact on product launch. All with a simple subscription.
Business Case: Sales forecasting with SAS Advanced Analytics for the Pharmace...Claudio Menozzi
Goal: Predict New Product Market Penetration
Company: One of the world’s largest biotech companies, with more than 7,000 employees across six continents and a rapidly expanding product portfolio and a growing pipeline.
Business Area: The company provides medical product support across the Hospital Channel
Business Needs: Understanding the adoption of the existing products and predict adoption of new products allows to increase the customer service, adoption and eventually profitability
Project dealt by Achille Masserano, Enterprise Information Manager at Blue BI
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According to Nielsen Holdings, 40% of Consumer Goods trade promotion spending doesn’t drive the desired results. Even though the trade promotions spend take up a lion’s share of the organizational revenue, traditionally manufacturers have always struggled in optimizing their promotion mix for the maximum bang for the buck.
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What You Can Expect From The eBook?
1. Key Trade Promotion Optimization (TPO) challenges faced today
2. What is AI in the context of TPO?
3. How AI helps run profitable trade promotions?
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5. Case-studies
6. How you can get started right away!
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bank marketing professionals
understand the scope of marketing
analytics and also on how it can
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We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
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The use of Big Data is becoming a key basis of competition and growth for individual firms. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
The Art and Science of Sales Forecasting: A Webinar for Sales Managers and Co...Birst
Overview
Sales forecasting is a science and an art. It is the combination of information and metrics, intuition and best practices. However, sales forecasting is most commonly associated to the standard grading methodology of the particular customer relationship system that is being used (Salesforce.com, Oracle, Microsoft, etc.). In reality, how do key sales leaders become high performing accurate sales forecasters? In addition, how do companies effectively utilize sales forecasting information to increase overall organizational performance?
Here’s what we’ll discuss in this session:
State-of-the-art forecasting strategies, best practices, and key metrics
The interconnection between product complexity, company lifecycle stage, and accurate forecasting
Mitigating downside risk and triangulation strategies to determine the truth
Deal inspection and vetting sales rep forecasts
The different types of sales forecasters; exaggerators, sandbaggers, and Heavy Hitters
The difference between snapshot, intra-department, and inter-department sales forecasting
A cross-discipline team at ASAE is inventing a new product development process for associations so that you don’t have to. Here's a summary of how ASAE evaluates new product proposals, who makes the decisions, and with what criteria. This presentation to the Kansas City Society of Association Executives (KCSAE) also explains how to encourage innovation, fill the idea pipeline, and analyze and balance an organization’s entire portfolio of programs, products, and services.
The final slides pose questions designed to help you create a process for making decisions about what programs, products, and services to offer—and which to discontinue, and when. This step-by-step process will help you put ASAE's new product development principles into practice at your own association - based on your size, resources, and needs.
By Mariah Burton Nelson, VP, Innovation and Planning, ASAE
A case study on how to improve forecast accuracy by incorporating market or business intelligence into statisitical forecasting and know whether it improves forecast accuracy or not.
Argus Analyzer personal toolkit for Smart Home Product ManagersJames (JD) Dillon
Developed specifically to help Smart Home Product Managers do their jobs, this SaaS platform requires no IT interactions, zero professional services, and minimal training. This presentation describes how the toolkit helps with marketing metric development, competitive analysis, executive & cross-functional presentations, and ultimately to define products and measure immediate impact on product launch. All with a simple subscription.
Business Case: Sales forecasting with SAS Advanced Analytics for the Pharmace...Claudio Menozzi
Goal: Predict New Product Market Penetration
Company: One of the world’s largest biotech companies, with more than 7,000 employees across six continents and a rapidly expanding product portfolio and a growing pipeline.
Business Area: The company provides medical product support across the Hospital Channel
Business Needs: Understanding the adoption of the existing products and predict adoption of new products allows to increase the customer service, adoption and eventually profitability
Project dealt by Achille Masserano, Enterprise Information Manager at Blue BI
How An AI-Powered Trade Promotion Optimization Software Can Improve Consumer ...Gina Shaw
Artificial Intelligence (AI) will happen in both TPx and Retail Execution sooner than you probably think – Promotion Optimization Institute
According to Nielsen Holdings, 40% of Consumer Goods trade promotion spending doesn’t drive the desired results. Even though the trade promotions spend take up a lion’s share of the organizational revenue, traditionally manufacturers have always struggled in optimizing their promotion mix for the maximum bang for the buck.
With the advancements in AI technologies, it is now possible to powerfully harness data and run high-yield trade promotions.
What You Can Expect From The eBook?
1. Key Trade Promotion Optimization (TPO) challenges faced today
2. What is AI in the context of TPO?
3. How AI helps run profitable trade promotions?
4. What an AI-Powered analysis looks like?
5. Case-studies
6. How you can get started right away!
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
The use of Big Data is becoming a key basis of competition and growth for individual firms. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
Visual Analytics combines human intuition and data science to derive knowledge from the data in a very efficient, effective and easy way. Visual Analytics empowers your people to interact with the data and generate new insights.
Optimizing New Product Development and Introduction (NPD&I) Through AnalyticsWill Ruiz
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Lessons from highly innovative companies. 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.
Understanding how product design choices impact NPD&I
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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
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Leveraging Analytics to Manage Complexity in Highly Innovative Companies - A ...Will Ruiz
Nothing drives true top-line growth and competitive advantage more than innovation. It’s therefore not a surprise to see the topic rank high in any survey of senior executives of consumer goods companies. But with the continued drive to introduce new products into the market, many companies are realizing that managing a growing and significantly larger set of SKUs is adding complexity and cost to the business. This is especially true in highly creative companies where, on a yearly basis, new products with a relatively short lifecycle constitute a high percentage of the total saleable product portfolio. This whitepaper explores how consumer companies can leverage analytics to optimize their new product development and introduction processes, and proactively manage supply chain complexity.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
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Predictive Analytics: Accelerating & Enriching Product Development
1. Predictive
Analytics:
Accelerating
& Enriching
Product
Development
While product developers are
familiar with predictive analytics,
they often lack clarity when it comes
to understanding how these tools
can contribute to the success of new
product concepts.
Executive Summary
Product development is all about accelerating
innovation, strengthening quality, speeding time
to market, and keeping costs in line. By incor-
porating predictive analytics into the process,
companies can sharpen their forecasts; better
predict product performance, failures, and down-
time; and generate more value for the business
and its customers.
A digital mock-up of 3D geometry is no longer
enough because products are no longer just 3D
mechanical creations. While new aspects of prod-
uct development (“idea to launch”) continue to
garner a lot of attention, integrating predictive
analytics into the process can be challenging —
requiring companies to thoroughly assess their
strategic goals, their appetite for investment, and
their willingness to experiment.
This paper explores scenarios that are relevant to
predictive analytics in product development and
presents an approach for applying them.
Cognizant 20-20 Insights | January 2018
COGNIZANT 20-20 INSIGHTS
2. Cognizant 20-20 Insights
Predictive Analytics: Accelerating & Enriching Product Development | 2
DERIVING MORE VALUE
FROM DATA
Today, companies must rely on insights gleaned
from the massive amounts of data they accumu-
late from various channels. While the Internet of
Things (IoT) offers a trove of real-time informa-
tion via smart, connected devices and “things,”
challenges (detecting failure patterns, modeling
correlations, predicting failures, prescribing rem-
edies, and prioritizing recommendations against
cost constraints, for example) remain.
While organizations worry about the cost of new
products and the expected ROI, consumers care
about a product’s value/price ratio, its level of
innovation and, of course, its quality. Predictive
analytics can help mitigate these concerns and
meet the expectations of both the business and
its customers.
Questions for Executives
Executives responsible for product develop-
ment face a bevy of critical questions every day.
Among them:
• What factors and attributes will determine the
company’s success in product development?
• What external dynamics (customer needs and
behaviors, market and technology trends)
and internal considerations (capabilities and
culture) will contribute to our products’ per-
formance in the marketplace?
• How do we leverage the technologies, skills,
and knowledge that will optimize customer-
centric product breakthroughs?
Aside from these concerns, many companies have
limited tools at their disposal, and must rely heav-
ily on experience, guesswork, and trial and error.
ANALYTICS IN PRODUCT
DEVELOPMENT
Organizations have long relied on traditional prod-
uct-development tools and approaches, including
FMEA, CAD simulations, design of experiments,
and value stream analysis, to heighten efficien-
cies, eliminate waste, and optimize costs.
However, given the ever-increasing volumes of
data that flow into and through companies, con-
ventional product-development technologies and
tactics are no longer sufficient. (See Figure 1).
Traditional Tools & Approaches
3D Computer-Aided
Simulation (CAE),
Virtual Reality
Products are becoming
more complex with the
inclusion of software
Failure Mode &
Effect Analysis (FMEA)
Based on experience rather
than data
Design of
Experiment (DOE)
Analysis of influences and
responses methodologies can
produce sub-optimal results
Value Stream Analysis
Gives a retrospective,
rather than predictive, view
Figure 1
3. Cognizant 20-20 Insights
Predictive Analytics: Accelerating & Enriching Product Development | 3
Although product developers continually look for
better ways to handle the abundance of data at
their disposal, most don’t have the right tools to
manage it, make sense of it, or apply the insight
it provides to support future product initiatives.
Innovative companies know that data-driven
insights and decisions can help improve all
aspects of product development. According
to McKinsey’s global survey, many are already
applying big data/analytics to:
• Improve research and development (R&D)
• Develop new product strategies
• Identify new market segments
• Deepen customer knowledge/relationships
• Improve customer segmentation and targeting
• Develop differentiating and dynamic pricing
strategies
Making the Case for Predictive Analytics
Predictive analytics applies across the prod-
uct-development value chain. (See Figure 2).
Predictive Analytics Across the Value Chain
Crowdsourcing and
social media play a
significant role in
collaborative product
development.
Insights from suppliers’
manufacturing process-
es and materials can aid
in better design and
accelerate time to
market.
Insights from previous
product performance
help in maintaining the
right product portfolio.
Analysis of intellectual
property provides
crucial information to
design a legally sound
product.
Product data provides
information about how a
particular component
was designed, plus
insights into challenges
that were encountered.
Logics and rules from
this data will help design
new parts and assem-
blies and promote
standardization by
harvesting old parts
from existing databases.
Insights from manufac-
turing equipment and
processes help improve
“Design for
Manufacturing.”
BOM (bill of materials)
analysis helps set the
right product cost.
Insights concerning
hazardous materials,
legally restricted
substances and small
components, for
example, help assure
regulatory compliance
and aid in faster
product development.
Data on transport
conditions (weather,
humidity, etc.) helps in
developing the right
packaging contents.
Insights from quality
inspections and
third-party lab testing
can provide vital
information for product
design.
Information on local
laws helps assure
appropriate product/
packaging/labeling
specifications.
Insights from local
markets help in
launching customized
variants and fine-tuning
products to suit various
consumer segments.
Cost insights from
product development
aid in launching the
product at the right
price.Customer data
analytics help refine
existing designs and
develop specifications
for new models and
variants.
Best-in-class manufac-
turers can capture the
data generated from
warranty claims, spares,
and service, and use it to
develop better
products.
Product recalls,
although very expensive,
provide crucial insights
into flaws in the product
development process
and provide opportuni-
ty for correction.
Ideation &
Concept
Engineering &
Design
Development &
Validation
Pre-Production/
Commercialization
Launch &
After-Market
Figure 2
4. Cognizant 20-20 Insights
Predictive Analytics: Accelerating & Enriching Product Development | 4
Quick Take
The Potential of Predictive
Analytics in Product Development
Predictive analytics brings together advanced analytic capabilities spanning
ad-hoc statistical analysis, predictive modeling, data mining, optimization,
machine learning and more to help companies:
• Transform volumes of data (internal and external) into measurable, action-
able information
• Improve the speed and quality of decision making
• Develop forward-looking — rather than retrospective — strategies
• Evolve into a data-driven, insight-based organization
• Shift decision making from an executive-level task to an all-employee
pursuit
• Use digital capabilities to deliver insights and knowledge across the
organization.
5. Cognizant 20-20 Insights
Predictive Analytics: Accelerating & Enriching Product Development | 5
Navigating Critical Phases of Data
Modeling
Data modeling in the concept and feasibility
stages of product development incorporates the
following steps:
1. Gather past and present product attributes
and properties.
2. Model the relationship and correlation
between the above.
3. Capture product performance from market
and test data; identify features that are most
desired/in demand.
4. Establish an algorithm to determine the per-
formance of future products based on these
attributes. Let the model predict the combi-
nation of attributes.
5. Balance the above with business logic and
organizational capabilities.
Figure 3 illustrates the phases of high-level data
modeling.
The Stages of High-Level Data Modeling
DESCRIBE
Understand the
business problem
INTEGRATE
Collect data
on product
performance (KPIs)
ENRICH
Mine the historical
data on internal/
external factors
ANALYZE
Create a model
to predict
performance
VALIDATE
Use the business
logic to optimize and
finalize the model
Figure 3
It is important to note that data modeling is a
highly collaborative process that engages all
stakeholders, including design partners and com-
ponent vendors. Given that predictive analytics
is applicable at every stage of product develop-
ment, “one-step, one-stop” approaches are likely
to fail. Hence, data analytics capabilities should
be embedded in every phase of product devel-
opment to gather insights and drive innovation
from various perspectives.
Data models should remain in sync with product
models to ensure that simulation and testing
reflect real-world scenarios at every stage of
development, and build confidence that a prod-
uct will meet customers’ expectations.
Predictive Analytics & Product Lifecycle
Management
Predictive analytics speaks to the key aspects of
product development: time to market, cost, qual-
ity, and compliance. By embedding this capability
in Product Lifecycle Management (PLM) systems,
companies can significantly enhance innovation
by using quantitative data to shape decisions and
outcomes and sharpen their competitive advan-
tage. Lately, many PLM service providers have
started to build robust predictive analytics capa-
bilities. PTC (formerly Parametric Corporation)
acquired big data machine learning and predic-
tive analytics leader ColdLight. Siemens released
Simcenter, an end-to-end simulation platform.
6. Cognizant 20-20 Insights
Predictive Analytics: Accelerating & Enriching Product Development | 6
There are numerous areas where predictive ana-
lytics can play a significant role in PLM:
• Feature-based search
• Cost analytics
• Regulatory compliance
• Program management
• Configurable dashboards
• Organizational KPIs
• Environmental compliance
• Product portfolio analysis
• Product quality
Incorporating predictive analytics into PLM sys-
tems helps derive and deepen insights during the
product development process across multiple
functions. (See Figure 4).
CONCLUSION
Increasingly, product developers rely on analytics
to improve every stage of product development
— from concept to launch. Incorporating predic-
tive analytics in the process can enrich the quality
and delivery of information, minimize mistakes,
sharpen efficiencies, and inform better decisions.
By embedding predictive analytics in advanced
PLM systems, organizations can create a “one-
stop shop” for product development — with more
confidence, better information, and better results.
Predictive Analytics in Business Processes
COST MANAGEMENT
Provide cost estimates and cost rollups for different
configurations and BOMs. Help simulate, analyze, and
optimize product costs to assure that the right
decisions are made at the right time and to ensure
product profitability.
CHANGE MANAGEMENT
Analyze change requests to identify deviations
and to assess the impact on design and
manufacturing. Provide insight on historical
changes to help effect changes in a faster and
more efficient manner.
SUPPLIER MANAGEMENT
Improve visibility into supplier data
(approved suppliers, quality, perfor-
mance, delivery mechanism, material
availability, cost, etc.) by combining silos
of data from multiple sources.
REGULATORY COMPLIANCE
Perform what-if analyses on product variants
to understand how design changes affect
compliance status. Provide methods and
controls to ensure regulatory compliance.
SERVICE ISSUE MANAGEMENT
Resolve customer complaints by
obtaining a 360° view of issues and
performing root cause analyses.
QUALITY MANAGEMENT
Analyze quality data from manufacturing, customer
support, adverse events/non-compliance issues to gain
insights for initiating corrective and preventive actions.
PROJECT MANAGEMENT
Set up and utilize multiple KPIs to
understand project performance. Analyze
schedules, costs, and resources to ensure
optimal product development.
PRODUCT DATA MANAGEMENT
Find and reuse existing parts and data to
accelerate innovation and time to market.
Figure 4
7. Cognizant 20-20 Insights
Predictive Analytics: Accelerating & Enriching Product Development | 7
Amit Joshi
Associate Director,
Cognizant’s Intelligent
Products & Solutions
Practice
Amit Joshi is an Associate Director in Cognizant’s Intelligent Prod-
ucts & Solutions Practice. He has over 14 years of experience in
new product introduction, quality, and manufacturing transfor-
mation programs. Amit has advised various organizations on
the development and implementation of strategic, technology,
and process improvement initiatives. Amit is a Six Sigma Black
Belt, a Lean Manufacturing professional, and holds an MBA
from the Indian Institute of Management. Amit can be reached at
amit.joshi2@cognizant.com | www.linkedin.com/in/amitjoshi01
ABOUT THE AUTHOR
REFERENCES
1. Lana Klein, “Predictive Analytics as an Engine of R&D and New Product Launches.”
2. Tom Davenport and Andrew Spanyi, “Improve New Product Development with Predictive Analytics.”
3. Digital Engineering, “The Cornerstone of Siemens PLM Software’s Predictive Engineering Strategy.”
4. Michelle Boucher, Aberdeen Group, “Working smarter with analytics in product development.”
5. Stefan Rudolf and Christian Doelle, “Predictive analytics boosts product development.”
Hardik Kansupada
Senior Director,
Cognizant’s Intelligent
Products & Solutions
Practice
Hardik Kansupada is a Senior Director in Cognizant’s Intelligent
Products & Solutions Practice. He has over 20 years of experience
in leading and managing strategic engagements, working with
C-level executives and end users. Hardik has a master’s degree
in computer information systems from the University of Houston.
He can be reached at hardik.kansupada@cognizant.com |
www.linkedin.com/in/hardik-k