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Role of Digital Twin in Automotive Industry

17 December 2018 #Technology & Innovation #Technology and Innovation Articles

Article written by Munish Sharma who is heading Customer Experience Management (CEM) value engine of the Innovation and Transformation Group at TCS UK & Ireland. He has over 17 years of automotive industry experience focusing on digital transformation of manufacturing industry.

Introduction

In the past few decades mass production, lean adoption and globalisation were the key enablers for the automotive industry to drive growth (profit margins). The future growth of the industry is expected to be fuelled from ‘data led manufacturing’ (Industry 4.0) where enterprise data across the product life cycle will be further leveraged to build faster, cost effective and high quality products.

The role of digital twin in realising the perceived benefits from ‘data led manufacturing’ is the topic handpicked for this article. This article will be addressing some key points (listed below) which will help the readers to understand the concept of digital twin and its potential applications to solve existing problems in vehicle product design, manufacturing, sales and service.

Key points addressed in the article:

  1. Definition of ‘Digital Twin’
  2. View of product life cycle data (Automotive Industry)
  3. Role of digital twin to address the current challenges of automotive industry

Definition of Digital Twin:

The simple definition of digital twin is a pairing of virtual and physical worlds; which allows analysis of data and monitoring of systems to solve the problems before they even occur. The key technologies i.e. IoT (Internet of things), 3D simulation tools and predictive analytics sits at the core of digital twin.

The digital twin is composed of three components i.e. physical entities in the real world, their virtual models and the connected data/view that tie the two worlds. The below figure (1.0) elaborates the concept of digital twin further. The left hand side picture represents the physical road ahead and its virtual image on the satellite navigator (SatNav). In this scenario, driver needs to do three things: (1) View the SatNav for direction (2) View the actual road (3) overlay the SatNav direction mentally into the actual road to take the right turn. This requires mental effort, some degree of driver experience and sense of timing to reach to your destination flawlessly. In the right hand picture of the vehicle, vehicle is using Augmented Reality (AR) capability, where driver has converged view of digital and physical world to take turns on the road. This prevents mental effort, distraction, minimizes driver error and freeing driver to focus on the road. This concept can be applied across the value chain to perform operations efficiently by leveraging different technology capabilities (IoT, Big Data Analytics, Simulation techniques).

Figure 1.0 represents the concept of digital twin (convergence between physical and digital world)

View of data (automotive enterprise) across product life cycle:

The product life cycle of a vehicle involves multiple stages (concepts, design, procure, build, stock, sell, service and recycle). At each stage of the life cycle, there is an enormous amount of data that gets generated. Leveraging this data to build faster, cost effective and high quality products is the ultimate aim of all the organisations. However, automotive manufacturers are at different maturity levels today; in terms of effective utilisation of the data. The below section uncovers the view of data generated at each stage (refer figure 1.0) and the list of activities performed during each stage of the product life. In the subsequent section (3), we will detail out the challenges at each stage to deal with the data and role of digital twin to address some of these challenges.

Figure 2.0 represents the view of product life cycle data (automotive)

Role of digital twin to address current challenges of automotive industry

The role and importance of digital twin in product development, manufacturing and service life cycle of the vehicle is detailed out in the below sections.

Product Development (Vehicle):

The automotive product development is a long and complex process. Typically a new car model takes 5-6 years of time1 from a design to launch stage. In fact, this stage is the key to the success and long term sustainability of the organisation. A slight oversite in the product design can erode the company’s brand value and profitability. For instance one of the OEM launched their model in early 2000 with product development cost 1.5 $ billion2. This model failed in a Moose test which resulted in recall of 2500 new cars. Later on, OEM added stability control and redesigned the car’s suspension. The cost to implement the change costed approximately 250 million ($)2 to the company.

In order to understand what challenges design engineers and product engineering team faces during this stage, please refer below figure 3.0 which summarises the key activities performed during product development life cycle, challenges faced and the role of digital twin in addressing these challenges.

Figure 3.0 represents product development life cycle, its challenges and role of digital twin

Vehicle Manufacturing:

More than a century ago, Henry Ford’s innovation reduced the time it took to build a car from more than 12 hours to two hours and 30 minutes. Since then, the industry has seen multiple disruptions and innovations and now a car is coming out of the assembly line every 30 seconds. The machine under the hood has evolved from a modest mechanic marvel to a complex and intelligent system comprising of an array of technologies, electronics and materials.

A fast and smooth manufacturing execution depends on the robustness of resource management, production plan and process control. Models and variants in production have increased manifold and customisations on the vehicle have also gone up significantly. The bigger view of entire vehicle manufacturing cycle and pressure on improving the OEE parameters like ‘first time through’, has put an emphasis on digital manufacturing among all the  automotive manufacturers these days.

Well executed digital adoption is now emerging as a critical success factor for the industry. This means gathering and analysing more data in a virtual context so that better decisions and, in many cases, predictive decisions can be made. Let us look at how Digital twin come into picture and address the classic challenges in manufacturing cycle.

Figure 4.0 represents manufacturing value chain, its challenges and role of digital twin

Vehicle Sales and Service:

The innovation in research, engineering finesse, network planning, marketing campaigns and a colossal effort of over 5 years for a new vehicle introduction comes down to the actual sales cycle at a Retail which translates these investments into revenue (topline) for the Manufacturer. The aftersales revenue from parts, accessories and services is also dependent on the actual sales.

Modern Auto sales floor is witnessing various trends and paradigm shifts with emerging model of servitization, customer demand for superior and personalized user experience at retail and omni channel experience for every transactional interaction, regulatory compliance like GDPR etc. Auto Manufacturers operating at a Global scale, have an even bigger challenge of dealing with macro environmental factors and geographical peculiarities. OEMs are keen to leverage the operational insights from the customers, vehicle (product) and channel partners to continuously improve product performance. But due various inefficiencies, constraints and external factors these valuable insights get eroded. How digital twin can help OEMs tackle these challenges in a better, faster and efficient way is explained in below figure (refer fig 5.0).

Figure 5.0 represents Vehicle Sales and Service value chain, its challenges and role of digital twin

Conclusion:

Automotive product life cycle depends on data inputs from various stakeholders in the value chain to manage the end to end life cycle of the product. Most of the data used or generated at each stage remains isolated and barely integrated in the subsequent stages of the product lifecycle. This situation leads to wider gaps between the physical products and their digitalised versions (virtual products). The convergence of physical and their virtual products has the potential to address many challenges which exists in the automotive value chain today. The ‘digital twin’ in automotive industry can enable convergence of existing gaps between physical and virtual versions of product prototypes, shop floor and actual vehicle on the road.

References

  1. CNBC.com https://www.cnbc.com/2018/09/10/is-fast-fast-enough-for-vinfast-vietnams-first-automaker.html
  1. Nottingham Business School https://www4.ntu.ac.uk/nbs/document_uploads/73575.pdf
  1. BCG (Flexible Cell Manufacturing) https://www.bcg.com/publications/2018/flexible-cell-manufacturing-revolutionize-carmaking.aspx
  1. Forbes.com https://www.forbes.com/sites/maryjosephs/2017/03/15/u-s-manufacturing-labor-shortage-how-to-make-your-company-a-happy-exception/#190cfda17e13
  1. Thomas insights https://news.thomasnet.com/companystory/downtime-costs-auto-industry-22k-minute-survey-481017
  1. IBM https://www.ibm.com/blogs/internet-of-things/iot-data-that-keeps-your-workers-safe/
  1. Warranty Week https://www.warrantyweek.com/archive/ww20180405.html

 

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