A comprehensive portfolio of business intelligence, advanced analytics, predictive analytics, financial performance and strategy management, and operational planning and forecasting gives manufacturers clear, immediate and actionable insights into current performance and the ability to predict future outcomes.

1. BUSINESS ANALYTICS
FOR MANUFACTURING
MARKET UNDERSTANDING
&
AN ENTRY STRATEGY

2. Overview
 Manufacturing has evolved from being mechanical of things to
internet of things
 Manufacturing is still the largest contributor to world economy
– 80% of world trade is goods 20% is services, within WTO
regions
 Manufacturing has been the path to development – for
developed economies – majority of services industries serves
manufacturing
 The game is now be played on the basis of agility,
responsiveness and innovation, with the fundamental support
of technology and people skills.
 This has made the traditional MES/MOM to evolve and
embrace BI and data driven practices
 IOT, Big Data, Cloud, Mobility and Analytics are current trends
and helps to respond to the organization’s need to update
production operations and, at the same time, determine the
direction in which the technological evolution heads.

3. Overview
Need of the hours for manufacturing companies is;
Knowledge of future occurrence of peaks and troughs
To be globally competitive leveraging on improved process efficiencies – product engineering, manufacturing,
procurement, supply chain & distribution
To optimize resources – getting more from less
Deciding the right mixes of products, promotions and pricing
Understanding customer behaviors, forecasting future needs and demands
The solutions to above challenges can be found in the DATA….
“A comprehensive portfolio of business intelligence, advanced analytics, predictive analytics, financial
performance and strategy management, and operational planning and forecasting gives manufacturers
clear, immediate and actionable insights into current performance and the ability to predict future
outcomes.”

4. Manufacturing Analytics Market
$
3.14B
Global
Manufacturing
Analytics Spend
2016
IOT Market: 2015:
1.7Trillion
2025 Market Size
Smart Factories:
$1.2T-$3.7T
Vehicles: $210B –
$740B
CAGR
21.9%
Source:
MarketsandMarkets$
8.45B
Global
Manufacturing
Analytics Spend
2021
• Inventory management is estimated to
have the largest market share in 2016
• Food and beverages manufacturing
industry vertical is expected to witness
the highest CAGR during the forecast
Source
http://www.reportlinker.com
http://www.marketsandmarkets.com
IoT : Manufacturing market size
2015: $4.11B
2020: $13.49B
CAGR
26.9%

5. Industry Size
• Manufacturing (27%),
• Retail trade (11%),
• Information services (9%), and
• Finance and insurance (9%) are the four
industries that comprise more than half the
total value of the projected $14.4T market.
• The remaining 14 industries range between
7% percent and 1%.
Source: Internet of Everything To Capture Your Share of $14.4 Trillion, white paper published by
Cisco.

6. Manufacturing analytics Market Size by geography
• "North America is expected to hold the
largest market share“
• North America, followed by Europe, is
expected to continue being the largest
revenue generating region for the
manufacturing analytics vendors for the next
five years.

7. Business Analytics application
Customer Behavior
Analytics
• Customer Acquisition
and retention strategies
• Effective marketing and
customer engagement
Campaigns
• Post Purchase analysis
and better
understanding of the
customer behavior
• Differentiated product
offering and value
proposition for each
segment at each stage
of lifecycle
Predictive Analytics
• can mitigate risk,
transform business
processes and predict
outcomes with greater
certainty.
• Predict when and how an
asset is going to fail
• Predict appropriate
inventory levels
throughout the supply
chain
• Predict product quality
failures
• Quickly determine root-
cause analysis
• Predictive analytics help
manufacturers anticipate
change so that they can
plan and carry out
strategies that improve
results.
Marketing & Spend
Management
• Targeting specific
group of customers
with customized
messages and
offerings
• Helps in effectively
managing the fixed
and variable
marketing spend
• Helps in quantifying
the marketing
activities
contribution on
sales by evaluating
Supply Chain
Analytics
• forecast, optimizing
inventory and
distribution
• supply chain
efficiency and
logistic planning

8. Manufacturing and the data conundrum :
This Economist Intelligence Unit study, commissioned by Wipro, examines how manufacturers now
collect, analyse and use the complex, real-time data generated in production processes.

9. Respondents for the research
 Key findings from the survey include:
 Manufacturers have significantly
ramped up their shop floor data
collection.
 A minority of manufacturers has an
advanced data-management strategy
 Manufacturers find it difficult to
integrate data from diverse sources—
and to find the skilled personnel to
analyse it.
 Data is delivering stellar quality and
production-efficiency gains
 …but collecting data doesn’t
automatically yield benefits.

10. Manifold Data Sources
96
90
88
88
86
82
78
74
66
62
52
42
34
4
6
10
10
12
10
18
24
18
20
16
8
14
0 50 100
Customer feedback system-Compliance/incidents management…
Manufacturing Execution system (MES) process historian
Enterprise data (ERP)
Accounting / finance data
Supply chain management system/ supplier data
After sales failure data
Supplier provided test data
Demand forecasts
Sensor-generated data from external sources for comparative…
Sensor-generated data from networked machines
Operator logs
Sendor-generated data from individual Machines
RFID
Data Sources
Use Now Plan to use
What sources of data are used by your company to lower the cost of quality and improve
manufacturing efficiency? Select all that apply.

11. Where data can make the difference
In which of the following areas do you see greater volumes of data yielding the biggest
gains?
Select the top three.
(% respondents)

12. Geography & The type of Manufacturing

13. Analytics Spectrum for manufacturing industry

14. Careful analysis, data can be used to identify, analyse, and foster growth
opportunities by helping manufacturers:
 Staffing readiness
 Identify new geographic regions to target
 Raw resources in stock
 Spare parts inventory
 Supply Chain Optimization
 Expand into niche/micro-markets
 Tap into a customer base
 Foster customer intimacy
 Innovate
 Improve product lifecycle
 Increase value addition
 Improve profit margins
 Predict trends

15. Manufacturing Analytics deployment across various
manufacturing industry
 Automotive & aerospace manufacturing
 Electronics equipment manufacturing
 Food & beverages manufacturing
 Chemicals & materials manufacturing
 Machinery & industrial equipment manufacturing
 Pharma and life sciences
 Paper, pulp, plastic and rubber manufacturing
 Others

16. Manufacturing analytics key players
 SAS ,
 Tableau Software ,
 Tibco Software ,
 Oracle Corporation ,
 IBM Corporation ,
 Computer Science Corporation ,
 Dell Statsoft ,
 SAP SE ,
 Zensar Technologies Ltd. ,
 1010Data ,
 Alteryx .

17. Common pain points of manufacturers when undergoing data
analytics
 Needing to crunch more data in less time
 Ensuring the right people have access to big data
results.
 Effectively handling data quality and performance
 Needing big data solutions that scale to fit your
business
 Being able to expand your company’s data handling
strategy

18. Research conduct at USA
• More than 200 North American manufacturing executives participated

19. 67 pc of US manufacturing executives rely on big data analytics to
steer tough business conditions: study
 Survey of 200
executives indicates
most manufacturers
plan to increase
investments in data
analytics over next
year – even while
delaying other
technology
investments
Survey
Participants
• More than 200
North American
manufacturing
executives
Who conducted
the Survey?
• Honeywell Process
Solutions (HPS)
and KRC Research
Inc., from May 23
to June 8, 2016
The Main reasons for
investment
• A key component of the Industrial
Internet of Things (IIoT) - as a viable
solution to a cycle of problems that
lead to downtime and lost revenue.
• "Executives need to keep their
businesses running smoothly and
safely, and they're banking on IIoT
technologies to help navigate
challenges, even during cash-
strapped times,"
Key findings of the Survey;
• Some companies are feeling pressure to continue
working under the threats of unscheduled downtime
and equipment breakdowns
• Majority of companies says they are already investing
in data analytics technology
• More than a quarter said they don't plan to invest in
data analytics in the next year; of that group, not
understanding the benefits of data analytics and
inadequate resources are among the most cited
reasons for this lack of investment.

20. 67 pc of US manufacturing executives rely on big data analytics
Key Takeaway
DATA ANALYTICS AS A VIABLE SOLUTION
• Data analytics is a key component of a successful IIoT
implementation for manufacturers.
• Most respondents had favourable views of the benefits of
data analytics as a solution.
 The executives said they agreed data analytics can reduce
the occurrences of:
 Equipment breakdowns (70 percent)
 Unscheduled downtime (68 percent)
 Unscheduled maintenance (64 percent)
 Supply chain management issues (60 percent)
 More than two-thirds of respondents (68 percent) said they
are currently investing in data analytics
 50 percent said they believe their companies are right on
track in their use of data analytics
 Fifteen percent said they believe their companies are
ahead of the curve as it relates to data analytics usage.
NOT EVERY ONE IS SOLD
• While the majority of respondents said they are already
investing and/or planning to increase their investments in data
analytics in the coming year
• 32 percent said they are not currently investing in data
analytics.
• Meanwhile, 33 percent said their companies are not planning
to invest in data analytics in the next 12 months, or are
unaware of any plans to do so.
• Of those who currently have no plans to invest:
• 61 percent believes their organizations already have
systems in place to ensure safety, yield and success
• 45 percent said their companies have seen some growth
without data analytics
• 42 percent said they don't fully understand the benefits
of big data
• 35 percent believes people are overstating the benefits
of big data
http://cio.economictimes.indiatimes.com/news/business-analytics/67-pc-of-us-manufacturing-executives-rely-on-big-data-analytics-to-steer-tough-business-
conditions-study/54342481

21. Segmentation, Targeting & Positioning

22. Goto market : Middle Markets
• OEM dependent, have to
follow OEM footprint
• Agility and flexibility
• Local engineering players
have similar challenges to
expand
“More for less”
Challenges
Key challenges for Middle Market
• Customers create competition – margin pressures
• Uncertainty of Economy – Burden of fixed cost
• Products for different market – Geography penetration
• Fluctuating Work load – Fixed Labour Cost burden for variable loads
• Burden of regulatory Compliance – Cost of compliance and staff for regulatory
work
• Limited access to low cost, reliable suppliers and specialized expertise (dictated
by economies of scale)
– Limited talent available to manage complexities of the business
Sub $1-2 Billion companies is a good market….
Generally not dominant in the market and are affected by actions of their largest customer
Geographies
• North America
• Europe
• APAC
Choose Segment
• SME’s
• Middle Market
• Big Players / OEM
Industry
• Automotive & aerospace
manufacturing
• Electronics equipment manufacturing
• Food & beverages manufacturing
• Chemicals & materials manufacturing
• Machinery & industrial equipment
manufacturing
• Pharma and life sciences
• Paper, pulp, plastic and rubber
manufacturing
• Others
Business
Intelligence,
Current
Stethosco
Descriptive &
Predictive
analytics
Prescriptive
Analytics
(Decision
making insights)
Big Data
Analytics

23. Value Differentiation Lever:: Positioning
 Creation of sense of value in the minds of the customer
 Capability statements
Execution Body
Proof of
Concept,
POC
Differentiate with
domain
competency and
business models
Four Pillars of Solution
COST
QUALITY
DELIVERY
INNOVATION
Customer Delight
• Are we going to
develop product?
• Or solution based out
of existing platform

24. Thank You !!