A Cordence Perspective

The Digital Odyssey of the Automotive Industry

Automotive Industry needs a transformation ‘Rx’The question of what next and what will the ‘car of the

future’ look like has been a perennially recurring question

in the auto industry and the question has never been more

relevant than it is now. While the car of today has made huge

leaps and bounds since the days of Ford’s Model T, the auto

industry is at the cusp of nothing short of a never before

witnessed revolution in the industry.

The consumer behaviour is evolving at a pace faster than

ever, be it choosing accessibility over ownership, evolving

lifestyles, digital adoption & connectedness, commuting

habits or the need for customised solutions. The car of

tomorrow is likely to be a product of convergence and

seamless integration of complex software and hardware

solutions. The Industry is witnessing not only stricter

government regulations on safety and environment, but also

societal changes with an inherent need to reduce pollution

and traffic.

Market players are likely to fight battles on multiple fronts

viz. over capacity, technological disruptions, compliance

norms, demanding customers, newer business models,

platforms, etc. which each one trying harder to outdo the

other to survive. Pricing will continue to remain under

pressure as the Chinese and Asian factories are graduating

beyond cost leadership to offering variety and choice of value

added features. Competition is likely to intensify further with

the non-automotive “technology” players like Google, Apple,

Microsoft etc. developing game changers like autonomous

cars, drones, connected systems etc. Access to financing too

will be challenging as companies struggle to improve their

return on capital.

The global automotive industry is expected to clock in ~95 million units of sales by end of the year 2017, and will breach the 100 million mark by 2019. While the numbers certainly look encouraging compared to the downslide the industry has been witnessing over the last few years, the automakers face looming threat of missing the growth should they fail to adapt themselves to the new trends. Moreover, these would not certainly be driven by the demand side (read: customers) alone.

Digital disruption in the auto industry 1

Digitalist,http://www.digitalistmag.com/digital-economy/2016/05/03/automotive-industrys-new-digital-transformation-04180968IBM, https://www-935.ibm.com/services/multimedia/IBMCAI-Digital-disruption-in-automotive.pdf

The digitization of the auto industry is already a work in

progress. The extent of the digitization of the auto industry

is evident from the fact that an average high-end car today

has roughly seven times more computer code than a Boeing

787 airplane. Not only has the manufacturing process gained

from the increased digital disruption in the auto industry

but also it has been of benefit in the targeted sales and

marketing efforts by the auto manufacturers. The ability

to analyze real-time road data is specifically improving the

efficacy of sales and marketing in the auto industry.

The digital disruption in the industry has essentially

transformed a traditional automobile to an ‘app-on-wheels’

or a ‘computer-on-wheels’. The companies such as Tesla have

overcome the initial scepticism pertaining to the scalability

and mass production capabilities and potential of this wave

of digital disruption in the auto industry. In the future, this

digitization can be expected to bridge the gap between

concept vehicles and mass produced vehicles.

The biggest change in the auto industry digital disruption is

the way data is being leveraged like never before. In order to

leverage the vast amounts of data from the users, suppliers,

production floor, engineering and design teams, and sales and

marketing teams, it is being aggregated and synthesized to

be fed directly into the production process. This data driven

manufacturing process is expected to deliver cars that are

closest to customer needs and car produced through best

possible utilization of available resources.

The digitization is going to help the industry on two fronts:

cost savings and revenue generation. While the increased

efficiency and optimization in the manufacturing process

offers cost savings in the long term, the ability to offer

enhanced user experience offers opportunities to generate

additional sources of revenue, even post sales.

Optimizing Manufac-turing

Revenue Generation

Manufacturing CostsIndustry Competition

Evolving Customer

Needs

Need for Digitization

Quicker Concept to Production Time

TREN

DSCo

nsum

er

beha

vior

Convergence

Compliance

CompetitionConnectivity

Automotive Industry Growth

Complexities (Manufacturing)

Capital

Cost Efficiency

IMPACT

All of the above leaves OEMs with very little room for error when it comes to developing future growth strategy and investment

plans.

2 3

The Industry 4.0 2

While Industry 1.0 was about mechanization, Industry

2.0 about mass production and Industry 3.0 focused on

automation and robots, the Industry 4.0 aims to revolutionize

manufacturing through end-to-end digitization of the

manufacturing operations.

The Industry 4.0 project envisages factories where

components travel through the production floor on small

computer-controlled carts looking for free machines

to process them - without human input. The tools

and equipment repair themselves and order their own

replacement parts automatically. However, the machines

will not just be locally controlled; they will be networked

and thus independent. The digital factory would also be

integrated with suppliers and sales. All the production stages

Volkswagen Annual Report, http://annualreport2014.volkswagenag.com/group-management-report/sustainable-value-enhancement/digitization.htmlSociety of Motor Manufacturers and Traders (SMMT) UK, https://www.smmt.co.uk/industry-topics/digital-manufacturing/NTT Data, https://emea.nttdata.com/fileadmin/web_data/publications/nttdata_Management_Summary_Online_Version_engl_vs2_cj.pdfForbes, https://www.forbes.com/sites/bernardmarr/2016/06/20/what-everyone-must-know-about-industry-4-0/#2e1fb420795f

will be comprehensively equipped with sensors and flexible

manufacturing technologies to make it possible to deal with

capacity fluctuations in an even more rapid and resource-

efficient manner. Under Industry 4.0, customer feedback and

demands can be implemented with even more customization

as a result of integration of the customer data with the

production process.

The Industry 4.0 paradigm also poses the following challenges that need to be tackled in order to be able to achieve maximum

value

• Data security – The issues pertaining to data security

are significantly increased by integrating new systems

and greater access to those systems. Moreover,

proprietary production knowledge becomes an IT security

problem as well. The companies need to be able to

achieve complete data security and safety

• Systems stability and reliability - A high degree of

system stability and reliability are needed for successful

cyber-physical communication that can be difficult to

achieve and maintain in a large interconnected cyber-

physical network

• Production process integrity – To maintain the

integrity of the production process with less human

oversight could become a barrier. Hence, it is important

to have good backup systems in place to avoid technical

problems that could cause expensive production outages

The following features are the cornerstones of any Industry 4.0 facility

Information Transparency• Information transparency effectively implies that

the systems create a virtual copy of the physical

world through sensor data in order to contextualize

information and leverage it effectively

Interoperability • Interoperability means that machines, devices,

sensors, robots and people are interconnected and

communicate with one another

Technical Assistance• Technical assistance refers to both the ability of the

systems to support humans in making decisions and

solving problems and the ability to assist humans with

tasks that are too difficult or unsafe for humans

Decentralized Decision-Making • Decentralized decision-making is the ability of cyber-

physical systems to make simple decisions on their

own and become as autonomous as possible

Production Process Integrity

Systems Stability and

Reliability

Industry 4.0 Challenges

Data Security

Industry 1.0 Mechanization

Industry 2.0Mass Production

Industry 3.0Automation and

Robots

Industry 4.0Digitization and

Data Driven Manufacturing

4 5

So what would the new age Digital factory look like? 3

Harvard Business Review, https://hbr.org/2016/06/german-manufacturing-is-leading-a-digital-industrial-revolutionSAP, https://www.sap.com/documents/2016/03/d21496d0-627c-0010-82c7-eda71af511fa.htmlSAP, https://www.sap.com/documents/2014/12/82f89b3c-6c7c-0010-82c7-eda71af511fa.html#I-Scoop, https://www.i-scoop.eu/industry-4-0/

A digital factory requires heavy investment in connectivity

and automation, advanced algorithms for managing

workflow, scheduling jobs, creating supply-side and

customer-side information sets. The investment is also

required in technologies that enable virtualization of design

and testing to achieve faster time-to-market and lower

physical prototyping and testing costs. Another outstanding

feature of the digital factory is feedback from predictive

asset maintenance, which helps more accurately anticipate

and pinpoint machine and part failures. Hence, a digital

factory is not just about a comprehensively connected and

integrated manufacturing operations and supply chain but

also about managing outbound logistics.

Below is an illustration of the essential components of a

digital factory

Features of a digital factory

1) Data Driven Manufacturing

While the manufacturers have always had access to

large tranches of data from a plethora of sources in

a number of different forms and formats, one of the

most critical contributions of the digital factory is the

ability to aggregate and streamline data gathering and

processing. In a digital factory, a range of data from

various technical disciplines – engineering, production

planning, manufacturing, procurement, finance, sales

& marketing and data directly from customers can be

collected and culled to gather valuable insights. By

having a single unified approach to data collection and

analysis, manufacturers are able to more efficiently

apply scenarios, such as changes in customer demands,

to the whole production process.

2) Manufacturing process streamlining

A smart factory uses advanced algorithms and AI to

perform tasks like creating schedules and managing

workflow, and has robots working alongside humans on

assembly lines. These connected technologies increase

productivity and decrease defect rates. This has been

well document across multiple industries. In the design

process, the introduction of big data simulations and

virtual modeling has played a role in lowering product

development costs and speeding up time to market. The

ideas go from drawing board to production in months

rather than years or decades as earlier. The phenomenon

of ‘collaborative robots’, the robots that can safely and

effectively work alongside humans on the factory floor

have managed to overcome the inhibitions that the

presence of robots alongside humans on the factory floor

might be unsafe.

German car maker BMW is already successfully using

collaborative robots in its manufacturing process. In

BMW’s Spartanburg, South Carolina assembly plant,

robots work with humans to insulate and water-seal

vehicle doors. The combination of human dexterity

and robot strength makes the task easier. Humans

doing this alone could work for only about two hours

before resting. Now humans can work a full shift

without rotating workers. In 2014, BMW expanded this

technology to its factories in Germany.

3) Better inventory and resource management

In a digital factory with connected and integrated

systems, the material demand for spares etc. needed

is automatically sourced and delivered in time to the

point of manufacturing, thus reducing inventory levels

and supply chain costs and optimizing uptime. A digital

factory essentially strives to provide the production line

with the right material at the right time while saving

costs. The next step in this optimization process is to

better manage delivery schedules through a digital

integration of production and outbound logistics.

The productivity gains through optimal utilization of

resources occur across the value chain beginning with

engineering and product development (e.g. through

virtual prototyping and testing or 3D printing), in supplier

management, and outbound logistics.

4) Reduction in downtime and maintenance costs

Given that the machines in a digital factory are

interconnected and a huge tranche of data from

machines is collected and analyzed daily, it is easy to

anticipate and respond to any machine failures. The

preventive and diagnostic nature of response that a

digital factory affords translates into a reduction in

machine downtime and also reduced plant maintenance

costs. The data driven nature of maintenance means any

problems or deviations from the normal functioning of

the machines are captured right as they arise.

5) Higher degree of product customization

As manufacturers get a better understanding of

customer requirements through more real time data,

manufacturers can offer a truly bespoke and made-to-

order vehicle fulfilling exactly the requirements laid out

by customers. Digital factory that can make customized

production cheaper and the ability of digital factory to

incorporate feedback from data can aid manufacturers

achieve a higher degree of customization.

About 75% of global automotive production now follows

a built-to-stock logic based on dealers’ judgments.

Showcased cars rarely match the preferences of

customers, who are reluctant to pay for options they

Autonomus or Collaborative

RobotsInternet of Things and Cyber Security

Use of Simulations and Additive

Manufacturing

Cloud Computing and System Integration

Big Data and Data Driven

Manufacturing

The move from the existing factories to digital factories is likely to be a three-step process:

01. In the first step, pilot programs and retrofitting can be

expected for existing factories.

02. The second stage is going to involve a broader

adaptation of digital solutions and a gradual

replacement of most existing machinery with an

integrated hardware-software digital set up

03. The final step will involve a paradigm shift and a full

transition to completely digital factories

03

Pilot programs and retrofitting (~ 3-5 years)

Partial integration through hardware-software digital set up (~5-7 years)

End-to-end digital factory (~7-10 years)

6 7

do not require. And built-to-stock cars have extended

turnover times, making dealers hesitate to order

expensive options only to be forced to sell cars at

discounts. Hence, ability of manufacturers to offer

customized products at the required scale and desired

cost is a win-win for both manufacturers and customers.

6) Greater consumer engagement in the manufacturing

process

A smart factory capable of constantly leveraging the

data obtained from users back into the manufacturing

process is going to make the changes and improvements

in the auto industry much more real time. A smart

factory that can access and incorporate data obtained

from Internet of Things functionality devices in the cars

will revolutionize the auto industry like never before. It

isn’t far-fetched to expect updates in auto software and

even hardware systems on the lines of periodic updates

that we are so used to on our other smart electronic

devices such as smart phones, tablets, computers,

laptops, refrigerators, televisions, washing machines

and so on and so forth. The advanced diagnostic

systems can provide a feedback loop to the smart

factory thereby making it easier for the smart factory to

decipher improvement areas and implement necessary

improvements.

To what extent is the future Automotive manufacturing likely to be Data Driven 5

Hewlett Packard, https://www.hpe.com/h20195/v2/GetPDF.aspx/4AA6-3032ENW.pdfThe Data Team, http://www.thedatateam.in/data-and-the-automotive-industry/Global Manufacturing, http://www.manufacturingglobal.com/technology/how-big-data-driving-technology-and-innovation-automotive-manufacturing

anagement styles have emphasized data driven decision

making in the automotive industry more than in many other

industries. For decades, quality management movements

have influenced decision making on the manufacturing shop

floor. There is a strong culture of quality and a focus on

data driven quality approaches in many of these companies.

Approaches like Six Sigma and approaches based on the

Toyota Production System have been institutionalized in

many automotive manufacturing companies and such

organizations often have extensive data sampling, collection

and hypothesis validation processes in place, for quality data

and for other data analysis. Warranty analysis and warranty

cost rationalization is another key area within the automotive

industry where statistical engineering principles find use. In

this sense, the automotive industry is uniquely positioned to

gain from the revolutions in digital data collection, storage

and representation and analytics.

The whole process of aggregation of data from sources

across the value chain and using big data platform tools to

leverage the data to make decisions and deliver solutions are

the core elements of data driven manufacturing.

The automotive industry’s many challenges can be offset

at least partially by the deployment of data-based decision

making, and expert systems that rely on advanced data

analytics to deliver value to the manufacturers and results.

Customer delight of new kinds is now within reach of

the automotive design and manufacturing teams in this

consolidated industry. New challenges of up selling, cross

selling and maintaining high quality in products and services

are all enabled by using advanced analytics. Increasingly, the

decision for automotive manufacturers seeking to improve

sales, quality, profitability and customer and employee

delight, is not whether they choose analytics and data driven

approaches such as advanced machine learning and IOT, but

when they choose it.

Case Study 4 Faurecia, a France headquartered global automotive parts

manufacturer has established a data-driven manufacturing

facility in Columbus, Indiana with an estimated investment

of USD 64 billion. The facility produces a high-tech emissions

control product for the commercial vehicle industry. The

plant employs a combination of interconnectedness,

automation, data processing and hardware to advance the

manufacturing process and is truly an Industry 4.0 facility.

The facility has collaborative robots that work alongside

450 employees in the plant. To ensure quality control, laser

scanning is employed for early detection of any variations or

deviations in the manufacturing process.

Digital Operations at Faurecia – Core standards

4 Advanced Manufacturing, http://advancedmanufacturing.org/faurecia-goes-industry-4-0/

Self-learning autonomous intelligent vehicles (AIVs) are

used to transport component parts from warehouse to

the assembly line. In order to predict and suitably respond

to equipment failures, continuous data collection from

equipment is done. A network of 1.3 miles of optic fibre

connects all the machines in the plant to the central network.

The terabytes of data that is collected daily is continually

mined, insights are culled and forecasts made in advance of

any issue before it occurs. The facility is replete with wireless

access points and back-up systems to reduce fail time in case

of any failure.

Data Driven Manufacturing

Big Data Platform Solutions

Data Aggregation

Automation: AGVs, Robots/

Cobots etc.

Traceability: RFID and

alternatives

Logistics Network Optimization

(LNO)

Plant Maintenance Monitoring

(PMM)

Light Guided System (LGS)

Digital Management Control (DMC): paperless shop

floor

Machine Intelligence and Predictive Maintenance

(MIPM)

8 98

The Power of Connected Smart Supply Chain6

Automotive Logistics, https://automotivelogistics.media/intelligence/bmw-shaping-self-steering-supply-chainDHL, http://www.delivered.dhl.com/en/articles/blog-posts/2015/the-connected-car-implications-on-the-automotive-supply-chain.htmlIBM, https://www-07.ibm.com/sg/manufacturing/pdf/manufacturing/Auto-industry.pdf

One vital element of the complete digital ecosystem is the

connected smart supply chain networks that supplement

the digital factory. A connected supply chain has four key

elements: inbound, inbound to manufacturing, outbound and

final vehicle distribution, and aftermarket.

Inbound

Inbound to Manuf- acturing

Connected Smart Supply

Chain

Outbound and

Delivery

Aftermarket

Inbound- The connected smart supply chain, owing

to the data driven nature of manufacturing through

digitization, aims to make the inbound logistics more

robust. The interconnection between the parts supplier

and OEM offers to overcome any supply disruptions and

achieve desired result through data sharing.

Inbound to Manufacturing- In the digital factories

with connected smart supply chains, the logistics for

ensuring delivery of parts from storage to production

floor is completely automated and is based on the data

fed in from the production floor. This has meant that

manufacturing process is smoother and there is cost and

time savings.

Outbound and Delivery- The delivery lead times in the

auto industry have increased in the past two decades.

One of the main root causes behind this deterioration is

the poor visibility and monitoring through the process

in times of high customer demand for certain models.

The connected car holds the key to an affordable and

highly effective solution. A connected car eliminates the

need to invest in certain hardware like scanners, people

for data entry and the back-up infrastructure set up for

connected cars could be expanded to monitor and plan

finished vehicle distribution. Given billions of working

capital tied up in finished vehicles and consumers need

for a “right here, right now” service, there is a huge

savings and commercial potential by leveraging the

connected smart supply chain.

Aftermarket- A connected smart supply chain offers

an opportunity to better utilize pre-diagnosis data.

This would allow manufacturers to offer just-in-time

solutions instead of just-in-case solutions for the

aftermarket support. The ability of manufacturers to

effectively serve customers is greatly accentuated by

leveraging on the connected supply chain phenomenon.

While the manufacturers will still have to maintain local

distribution centres, better access to information as to

the requirements and schedules for parts and spares will

help manufacturers optimize inventory.

The central theme behind a connected supply chain is to

create a self-driving and self-correcting ecosystem for a

seamless end-to-end delivery and service.

Cost and efficiency advantages of a connected smart supply chain

• The smarter automotive supply chain takes

full advantage of increased instrumentation,

interconnectivity and intelligence to reduce cost.

Using these capabilities, it maintains the optimum

inventory of incoming materials, finished products,

spare parts and accessories spread across distribution

centers and thousands of dealer repair shops

• Intelligent forecasting, inventory management and

dynamic pricing of parts are particularly important –

not just for efficiency, but because they position the

smarter supply chain to capture the expected growth

in the service segment as consumers hold onto their

vehicles longer

• In a smarter supply chain, spare parts forecasting

and planning is done at a lower level of detail. Even

with hundreds of thousands of parts and millions of

part-location combinations, it can simulate different

scenarios and tailor replenishment strategies for each

individual part. Sophisticated algorithms help to better

predict sporadic demand fluctuations. The entire value

chain saves money by carrying fewer inventories and

reducing obsolescence through better parts utilization

• The smarter automotive supply chain also uses

analytics to optimize distribution networks. For

example, by analyzing production and customer

locations, order quantities, transportation costs

and delivery times, a manufacturer can determine

the right number of distribution centers and their

ideal locations. The optimized network reduces

warehousing and transportation costs, while still

maintaining high service levels.

Key Take Aways : The Cordence Perspective• Evolving customer preferences, fierce industry

competition, necessity to innovate with the times

and a requirement to cater through a wide range

of product offering are all factors that have

made digitization an inevitable need in the auto

manufacturing

• Manufacturers are poised to leverage this new

wave of digitization and create innovative solutions

throughout the value chain- inbound logistics,

manufacturing, outbound logistics and delivery, after

sales and new product development

• Digitization offers multifold benefits through

cost savings through optimization of the whole

manufacturing value chain, revenue generation

through enhanced customer experience and shorter

time frame from concept to market

• Building upon innovative technologies from the

automotive & telecommunications industries and

data-driven technology, the potential of a connected

car virtually limitless

• Manufacturers that invest in digitization and make the

best use of data to manufacture and sell connected

cars are the ones that will emerge as victors from this

new wave of digitization in the auto industry

• The automotive industry is at an early stage in making

full use of the wealth of information available to it.

There is a long way to go for the industry to fully

leverage the available data

• Prepare for digitization by first thoroughly reviewing

and streamlining your processes, Or what one may

end with up would be just digitized waste

• Setting necessary measures and counter checks

is essential to realize the complete and continuous

benefits of digitization

• Workers need to be prepared for changing work

environment and their new roles in the future

manufacturing system. This will ensure that people

are not just capable but also willing to assume their

place in the new digital world order

• All said and done, Digitization should not be treated

as the ultimate goal, but a powerful tool to attain

future growth goals

10 11

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