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Harnessing Abundant Data to Create an Intelligent Manufacturing Enterprise
The manufacturing sector in many ways sowed the seeds for the
digital transformation of industries. Building on the automation of
production from the computing age, the fourth industrial
revolution brought digital technologies into the factory. This has
since pervaded all aspects of business leading to a radical
reimagination of economic activity. We at TCS termed this TMphenomenon as Business 4.0 .
At the center of this is the manufacturer, for whom data has
exploded at multiple levels – the product, enterprise, and
ecosystem. Even before the emergence of large-scale internet of
things (IoT)-based systems, aircrafts generated terabytes of data
for each �ight, process plants recorded production data captured
in ‘plant historians’ and control systems, and industrial equipment
captured performance data through their duty cycles. Sensor data
magni�ed this exponentially by turning terabytes into petabytes.
Today, automobile �rms, airlines, and factories process vast
amounts of data. The products and services they offer provide in-
depth information about their users by presenting behavioral
patterns. However, enterprises are still grappling with the best
strategies to harness this surfeit of data and are often left with
deep insights in siloed parts of their businesses. This paper looks
at how manufacturing companies can master this data abundance
to create a data-centric operating model and become an
intelligent enterprise.
The Challenge of Harnessing Abundant DataIn the traditional linear manufacturing value chain, each enterprise executes different
activities—from design, procurement, and production, to selling and servicing of
products, now bundled with services—to create differentiation in the products or drive
cost-based advantages. In the new data-driven economy, manufacturers can gain
competitive advantage through collaboration, not only within the enterprise but also
across the ecosystem, which is now part of the extended value chain. Smooth data �ow
across the ecosystem is an important enabler of collaboration, giving rise to the
creation of data products.
While data is available aplenty (see Figure 1), most organizations struggle to harness it
effectively. This is due to lack of data standardization, quality, harmonization, exchange
formats, regulatory rules, and well-de�ned and universally accepted data ownership
guidelines. Additionally, data from connected devices may contain sensitive, con�dential, or
personally identi�able information (PII). Organizations must safeguard such data to
protect privacy, adhere to regulations such as the General Data Protection Regulation
(GDPR) and the California Consumer Privacy Act (CCPA), and maintain con�dentiality.
Besides, organizations must factor in concerns regarding data security, costs, and
storage of huge volumes of data. There is a strong need to overcome these issues by
fostering faith among stakeholders across industries, agencies, and governments that
will harvest the abundantly available data. This will subsequently create innovative
services, cut down costs and effort, and create exponential value through partnership.
Figure 1: Various manufacturing enterprises generate not just terabytes, but petabytes of data
Industrial, automotive, and navigation devices
# of connected IoT devices
30GB30TB
Data produced by a single autonomous test vehicle per day
Data produced by a gas turbine per day844TB
Data produced by a twin engine aircraft on a 12-hour flight
-
of discrete manufacturing ecosystem participants will lead and shape those ecosystems by 2023
of manufacturers will be engaged in cross-industry collaboration by 2022
# of ecosystem partners for a typical European auto company (Source: BCG)
2018 2025
0.2 ZB16 bn
28 bn20%
25%
30+
6.2 ZB
IoT Data Created1Source: IDC's Global DataSphere, 2019
TB- Terabyte; GB - GigabyteSource: Aviation week, Wards Auto, Powermag
2Source: IDC FutureScape, Top 10 Worldwide Manufacturing 2019 Predictions
bn - BillionZB - Zettabyte
Connected devices lead to an exponential growth in data
Fueled by growth in collaborative ecosystems
8% CAGR
60% CAGR
1. IDC; Worldwide Global DataSphere IoT Device and Data Forecast, 2019–2023; May 2019.
2. IDC; IDC FutureScape: Worldwide Manufacturing 2019 Predictions; October 2018.
Enterprises are able to offer personalized products and services—an important facet of
the Business 4.0 era—by effectively harnessing abundantly available data. They are able
to do this by bringing together digital technologies and capabilities, thus ensuring they
gain competitive advantage. Besides, the ecosystem partnerships that manufacturers
have formed facilitate new business models, which allows them to deliver exponential
value to their customers.
Manufacturing business segments have long and complex value chains. Enterprises can
create value in this data-driven economy by obtaining information from multiple
sources—from smart intelligent products; from within the enterprise, as in the case of
enhancing safety inside plants; across the value chain, as in the case of connected cars;
and across industries, as in the case of airlines (as aircraft operators). This potentially
places manufacturing in a unique position among other comparable industries. To
better understand this, it is important to know how manufacturing �rms harness data
from the various information sources.
Figure 2: Prerequisites to harnessing data and the barriers preventing it
3In our recently conducted Business 4.0 research , we have identi�ed the factors critical for
harnessing data and the barriers that prevent it (see Figure 2 for manufacturing sector
speci�c �ndings). While a majority of respondents in the survey said data offers them
insights, there is still a need to overcome data security, risks, and regulatory boundaries.
Risks to data security
Data protection laws
Lack of senior leadership buy-in
I. Industry regula�onII. Outdated technologyIII. Lack of clear business caseIV. Dispersed data
12%
36%
27%
20%
19%
No insights
No skills in-house
Top barriers to monetize transaction data
20%
55%
24%VERYHIGH MODERATE
HIGHClear assurance
is necessary
Ability to derive insights from data % of respondents
12%
3. TCS.com; TCS Business 4.0 Study: Industry Focus – Manufacturing; Accessed October 15, 2019; https://www.business4.tcs.com/content/dam/tcs_b4/pdf/TCS-Business-4.0-Study-Manufacturing-Report.pdf
Intelligent Products
Intelligent Enterprise
At the heart of this new data-driven manufacturing economy lies the intelligent
product.
Manufacturers can generate business value by creating smart products that range from
jet engines and mining equipment to consumer and home products such as 4toothbrushes and home printers . What is changing the business models of firms is the
connectivity they offer. Sensors embedded in the products offer firms data-led insights
into usage and provide personalized services to end users. Moreover, the products and
the data they generate are deeply interconnected in the new economic model of value
creation.
While the traditional value chain captures enterprise data right from product design to
manufacturing and aftermarket services, data harnessing has to go beyond this and
capture information related to time-stamp production, asset performance, and end-user
and usage context.
Enterprises must look beyond efficiency and optimization and focus on growth
and transformation.
Democratization of data, or making data available to everyone, within the enterprise has
multiple benefits as it can be accessed from a single source. The connected enterprise
makes data available across various stakeholders, from suppliers to production and field
usage, which is the source of innumerable insights for planning and execution. It creates
value as it fuses enterprise systems data with real-time product data. To illustrate,
manufacturers now offer service contracts and extended warranties for a charge, which
create new revenue streams for them while ensuring that they deliver optimum
performance and significant business outcomes.
4. TCS Perspectives; Why Your Products Must be Smart and Connected; Accessed October 15, 2019; https://www.tcs.com/perspectives/articles/why-your-products-must-be-smart-and-connected
Intelligent Ecosystem: When Data Converges, Businesses GrowThe manufacturing ecosystem is increasingly becoming a part of the connected, purpose-driven value chain, and manufacturers can create value from the collaborative data-driven economy in multiple ways.
Collaborating partners need to factor in the business model, the target operating model, and commercial models when making decisions, as they will de�ne how data products are created and shared. This requires cross-industry partnerships to ensure all partners gain from the adequate surplus.
A collaborative ecosystem comprises the following stakeholders:
n Enterprise, extended enterprise, and industryn Supporting and controlling agencies—academia, startups, government agencies,
regulatory bodies, data aggregators, data-led service providers, and advisory organizations
n Consumers of connected and intelligent products
Harnessing data must happen at the intersection of the enterprise, the value chain, and across industries, as Figure 3 illustrates.
To overcome the challenges of harnessing data, such as lack of quality and standardization, and to monetize it effectively, members of the ecosystem, who until now were just 'tiered' participants in the value chain, can create value based on the new services they can provide. This leads to the creation of data products.
Figure 3: Purpose-driven economic models at the intersection of the enterprise-extended value chain and industry ecosystem
ENTERPRISE
VALUE CHAIN CROSS INDUSTRY
ConnectedAsset Data
Context Data
Context Data
Context Data
Operational Data
Engaging enterprise systems along with external partners
The industrial equipment business, the distributors, and service providers
rely on product condition and performance data to enhance
uptime and productivity
Software, electronics, and arti�cial intelligence
lie at the heart of collaboration in the autonomous vehicle
development business
The airline industry brings OEMs, suppliers, airlines, MROs, regulators,
and airports onto a single data marketplace for mutual gain
Collaboration across the value chain
Partners for complimentary data driven offerings
Context Data
Maps, Weather, Location
Asset DataConnected ComponentsConnected ProductsConnected Asset Fleets
Operational Data
ERP, CRM, SRM, EHS, MES, PLM
Take, for example, the case of lightweight designs for electric cars. The material supplier
for the body of the car has to reduce the overall weight of the car without compromising
on its quality. Because of technology advancements, the parts can now be 3D printed,
instead of going through the traditional manufacturing process of stamping a metal
sheet to a desired shape. The performance of the materials under different operating
conditions can lead to a continuous improvement cycle, including sharing data on usage
patterns and operating environments. This value add not only reduces the cost of
materials but also de�nes the performance characteristics of a vehicle, which ultimately
impacts its market acceptance.
Figure 4 below demonstrates how emerging collaborative models can give rise to new services.
As enterprises begin to offer new services, they must ensure data is available across
the organization and industries. To understand how data �ows within an enterprise,
it is important to examine the operating models in which this data is organized.
Figure 4: Potential new services of a collaborative ecosystem
ENTERPRISE• Ecosystem-Led Services• Regulatory Ecosystems• Innovation & Academic Ecosystem• Connected Product Touchpoints (Update, Communicate, Alert, and Advise)
INDUSTRY & VALUE CHAIN PARTNERS
• Manufacturer & Supply Chain Collaboration
ENHANCED PARTNER ECOSYSTEMS• Partnering with Regulators for Open Data Exchange• Data Aggregators, Data-Led Service Providers• Collaboration across Competitors for Economy of Scale• Customer-Led Co-Innovation
Operating Models of the Intelligent EnterpriseOrganizations have a choice to make regarding their 'data centricity' and operating models, and this will depend on how strong their foundation is. Figure 5 depicts �ve key operating models, classi�ed basis the potential returns they can deliver.
Enterprises must take a leap of faith and shift from operating in silos to embracing cross-industry partnerships, but it is not that simple. Besides technology, organizations need to make choices on both the business and target operating models, which align with the construct of the intelligent ecosystem discussed earlier. Along with data, two other crucial elements that stand out are the abundance of capital and talent.
Currently, �rms are only dealing with individual assets or enterprise data, with data exchange and commercial monetization frameworks largely absent. In the connected ecosystem, �rms need real-time assets and sensor data, user and usage context data, and social data as well, which has to be shared across the wider stakeholder community. To make this data available, a framework to facilitate resource sharing (within the scope of abundant data), while maintaining the competitive advantage of individual players, is needed (see Figure 6).
Figure 5: Data centricity and the operating models of enterprises
Data-Centric Value Chain Collaboration
Functional Silo Centric Enterprise Cross-Industry Ecosystems
Enterprise Data Democratization Industry Partner Ecosystems
Descrip�ve & Prescrip�ve
Analy�cs
Asset Condi�on Monitoring
Func�onal Data
Management
Proac�ve Engagement &
Preven�ve Maintenance
Predic�ve Opera�ons & Diagnos�cs
Factory Models of Prototyping
Efficiency & Op�miza�on
Ini�a�ves Social
Collabora�on & Workplace tools
Prescrip�ve Analy�cs & Prognosis
Value Chain Throughput
Enhancement
Data Life-cycle Management
Co-innova�on for Product Development
Crowd Source Product Ideas
Data Exchange Pla�orms
Partner Process Integra�on
Crea�on of Data Products
Usage & Transac�on Based Models
Startup Ecosystem Engagement
Data Mone�za�on Pla�orms
Gain-share & Outcome Based Models
Pla�orm Play in Services
Strategic Business Units Around Data
Business Services Around Data
New Lines of Business Intelligent Product
Figure 6: The connected ecosystem
ObservedCharacteristics
Growth RateLow to Negative Growth Stable Growth Exponential Growth
Typical Focus Asset-Centric Data Services Enterprise Centric Data Services Platform Centric Services
Condition MonitoringIllustrative Application Supply Chain Optimization Data Monetization
High Risk Propensity
Ecosystem Collaboration
Risk Pro�le
Data Centricity
Low Risk Propensity Medium Risk Propensity
Value Chain Data
Mixed Talent Management
RoI Based Investments
Value Chain Collaboration
Localized Data
Abundant Talent
Abundant Data
Talent Management Internal Talent
Capital Management Limited Capital
Intra Organizational Collaboration
Abundant Capital
Collaborative Approaches
The potential anchors of a connected manufacturing ecosystem are as follows:
n Industry leader as an ecosystem anchor: An existing stakeholder in the emerging
ecosystem has the advantage of understanding the market dynamics and drivers as
well as what value data can create for the customer. By acting as the anchor, the
player can attract complementary partners, including technology players, who can
help develop new data-sharing platforms, evolve commercial models and market
offerings, and ensure shorter time to market. As trust and faith develop, the
participants grow and prosper. Such an ecosystem faces the challenge of attracting
competitors on the same platform despite fears of eroding the business value
proposition of the competing entities. However, industrial enterprises, which need
considerable support for 'data interpretation' from original equipment
manufacturers (OEMs), would certainly gain from having them as anchors.
n Neutral players forming a 'nucleus': This is typical of a startup-led incubation,
which has no speci�c existing stake in the ecosystem, and is able to attract
competing partners. Organizations can create value by forming a neutral platform
involving multiple stakeholders. Typically, these platforms prosper when the data
exchange is not bound by proprietary know-how.
n Hybrid options: Anchor players, seeking to form neutral open platforms, can create
subsidiaries or spin off entirely new businesses, which can harness the best
capabilities and degrees of freedom of both operating models.
Case in point: The automotive industry probably offers the best insight into how various
ecosystems are shaping up. All three models are at work, with existing OEMs forming
collaborative clusters, technology giants forming their own neutral platforms, and
competitors collaborating with one another. This is especially so in the case of shared
mobility, where the operating models can reach a critical mass quickly enough. The
three models can also be used to harness the scarce resource pool of data scientists
across the landscape and optimize the capex investment for standard features without
reinventing the wheel.
Drivers of the Data-Centric EnterpriseThe operating models that ensure data products are shared in the connected, data-led,
insight-driven, and intelligent manufacturing enterprise are based on numerous
motivations and opportunities (see Figure 7). In this complex and hyper-competitive
environment, it is difficult not to adopt any of these models.
The varied sources of data in the manufacturing value chain are driving collaboration,
thus ensuring that a framework exists for creating, sharing, mining and serving data. This
refers to a 'digital highway'. Besides ensuring that data �ows freely, securely, and in real-
time, this highway must be able to handle structured and unstructured data.
Case in point: Airlines do not operate in isolation but through an ecosystem of OEMs,
engine suppliers, the maintenance repair and overhaul (MRO) industry, airports,
regulators, air traffic controllers, service providers, ticketing agents, ground service
personnel and more. Each entity has access to data or is generating it. Consolidating this
data can create tremendous value, bringing all partners onto the same page.
Figure 7: Developing an intelligent manufacturing enterprise
Enterprise
Value Chain
Cross Industry
Regulatory / Legal
Cultural / Organizational
Technical / Operational
Barriers
CategoriesMotivation for Choice How to Build & Leverage
Ÿ Product and service innovation –Time and cost to market
Ÿ Market access and dominationŸ Supplier base broadening Ÿ Stay ahead of the curve -
Sustainable competitive advantage
Ÿ Aspiration for scaleŸ Manage skill de�cit Ÿ Portfolio managementŸ Complimentary ‘bundling’ for
customer needsŸ CAPEX spreadŸ Risk mitigation
Ÿ Incubate and acquire start-ups
Ÿ Crowdsourcing and accelerators
Ÿ Spin-off an ‘agnostic open platform’
Ÿ Reconstruct value chain
Ÿ JVs with competitors
Ÿ Corporate restructuring
Ÿ Academic interface
A collaborative ecosystem of partners will accelerate time to market
Emerging Opportunities in the Business 4.0 EraTraditionally, the manufacturing ecosystem operated under the hierarchy of enterprise, extended enterprise (value chain), and industry. Every manufacturing �rm had been associated with academia, nonpro�t advisory organizations, and government and regulatory bodies as supporting and controlling agencies. However, with the emergence of the data-led economy, data aggregators, big data managers, and data-led service providers have become stakeholders to each enterprise. The 'customer' or 'consumer', who now demands smart products bundled with services, propels this entire industrial ecosystem. This drives the need for more innovation and collaboration. This is also pushing manufacturers to create personalized products and services for their customers, which leads to higher pro�tability. The triangulation of assets and enterprise, customers and consumption, and ecosystem into a collaborative engagement model drives growth.
Findings from our Business 4.0 research have evinced the need for cross-industry collaborations across manufacturing enterprises. Industry leaders who have embraced more than one Business 4.0 behavior (driving mass personalization, creating exponential value, leveraging ecosystems, embracing risk) have experienced higher pro�tability, increased customer interactions, and an enhanced ability to develop innovative products and services, while being able to plan ahead to de-risk their business models (see Figure 8).
To be successful in this competitive context, �rms would need to choose their data-centric operative models early on, align them to their prime motivation and growth drivers, and then carefully evaluate the environment to build and leverage the most appropriate collaborative ecosystem constellation.
Figure 8: Manufacturing industry-specific findings from the TCS Business 4.0 research
AGILE CLOUD
AUTOMATION INTTELLIGENT
Embracing Risk When Planning Ahead
Ability to plan a 3-year cyclewith finite resources
Ability to plan a 5-year cycle with finite resources
Ability to plan 1 year ahead with flexible resources per market conditions
Ability to adapt and transformcontinuously to market conditions
47%
16%
12%
8%
Exponential Business Model
Higher profitability
Expand geographical marketplace
Ability to target more potential customers
Higher revenues
54%
52%
66%
64%
Mass Personalization
Increased volume of customer transactions
Increased value of customer transactions
Higher customer profitability
Reduced customer churn
54%
30%
60%
63%
Leveraging Ecosystems
Ability to develop innovativeproducts/services
Access to new markets
Higher revenues
Ability to act faster to satisfycustomer demand
52%
42%
41%
39%
EMBRACE RISK
CREATE EXPONENTIAL
VALUE
ABUNDANCE
MASS CUSTOMIZE
LEVERAGE ECOSYSTEMS
Sreenivasa Chakravarti, Vice President and Head, Manufacturing Business
Practice, TCSSreenivasa heads the business practice of the manufacturing business unit at TCS. With
27 years of cross-functional experience in consulting, IT, and manufacturing, he has
played roles in the areas of strategy planning and execution, sales and marketing,
corporate planning, innovation, and business consulting for TCS' manufacturing
industry clients. Sreenivasa has worked extensively in the area of connected,
autonomous, shared and electric (CASE) automotive business and has authored several
thought papers on digital-led transformation of the manufacturing enterprise.
Geeta Rohra, Industry Advisor, Manufacturing Business Practice, TCS Geeta leads the digital practice of the manufacturing business group at TCS. Her focus
is to develop digital business strategies for manufacturing enterprises across the world.
She brings over two decades of experience in developing innovative domain-speci�c
solutions and leverages TCS' platforms and new-age digital technologies for driving
digital strategy initiatives for the group.
About the Authors
All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark
and other applicable laws, and could result in criminal or civil penalties. Copyright © 2019 Tata Consultancy Services Limited
About Tata Consultancy Services Ltd (TCS)
Tata Consultancy Services is an IT services, consulting and business solutions
organization that delivers real results to global businesses, ensuring a level of
certainty no other firm can match. TCS offers a consulting-led, integrated portfolio
of IT and IT-enabled, infrastructure, engineering and assurance services. This is TMdelivered through its unique Global Network Delivery Model , recognized as the
benchmark of excellence in software development. A part of the Tata Group,
India’s largest industrial conglomerate, TCS has a global footprint and is listed on
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