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

the National Stock Exchange and Bombay Stock Exchange in India.

For more information, visit us at www.tcs.com

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