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December 29, 2020

The financial reports of a luxury car maker in the UK for last three years revealed that it had spent nearly $3.20 billion in product warranty, recalling thousands of vehicles for faulty brake systems, flawed rearview camera systems, defective air bags, and other smaller oversights. The last three annual reports of yet another Europe-based luxury car maker revealed it had spent close to $7.3 billion recalling products for faults ranging from dysfunctional door locks to batteries, fuse boxes, fuel tanks, and wheel speed sensors. In an era when auto manufacturers are seeking to drive the adoption of autonomous and semi-autonomous vehicles, such oversights can hardly be ignored.

So, why are warranty costs, product recalls, and counterfeit parts on the rise? And just how ‘smart’ can a vehicle be if its individual components prove to be faulty?

Identifying the fault line

The auto manufacturing industry has carved for itself a reputation of proactively harnessing the latest technologies to build more effective vehicles. The entire network—right from suppliers to logistics partners, manufacturers, and dealers—follows stringent quality control processes. Despite this, as the cases above demonstrate, lapses continue to occur.

One of the root causes for the dilemma can be explained by the decentralized quality processes followed along the network in which individual stakeholders lack visibility into the quality processes and data of other players. As manufacturers cannot inspect every part sourced through their supplier network individually due to costs and logistical constraints, they rely on the test reports of a particular batch production. Similarly, dealers rely on the manufacturer’s pre-delivery inspection (PDI) report, which does not provide visibility into the quality impact on the vehicle while it is in transit.

Quality control towers with cognitive capabilities

To overcome the challenges of decentralized quality processes, manufacturers can adopt cloud-based control towers powered by cognitive capabilities like AI, the internet of things (IoT), machine learning, and analytics. These technologies can help firms sense and contextualize information from different stakeholders and prescribe actionable insights or decisions in near real time (see Figure 1). A quality control tower provides visibility across an interconnected network of partners, aligned to the common goals of improving product quality across the value chain and reducing errors. It can also monitor and control the quality of data from every stakeholder, ensuring that product design, supply chain, and operations remain resilient and adaptable.


Figure 1: An integrated view of the entire automotive network

Fostering a culture of continuous feedback

With the help of digital technologies, the data received is processed by the control tower to generate proactive decisions and insights. For example, if a supplier is manufacturing exhaust systems, the quality control tower would receive the data and detect anomalies using algorithms. The supplier will then check the quality of the faulty exhaust lot and stop production if required. The logistics partner will withhold delivery and the manufacturer will be alerted to defective products being assembled on any vehicle. This process arrests defects at the point of generation; reduces internal and external customer complaints; brings down costs associated with repairs, rejection, scrap, warranty, and product recalls; and builds brand equity.

Data can also be leveraged for use cases like production planning assistance, conditional monitoring of equipment, logistics cost optimization, changes in transportation routes, packaging and handling, supplier chargebacks, improvements in product design, identification of potential warranty issues, early warning of field vehicles, counterfeit parts assembly, and a host of other related functions. Over time, data can help build a culture of continuous, real-time feedback, and improvement.

A step-by-step approach to ensuring value chain quality and effectiveness

Adopting quality control towers will depend on multiple factors like the complexity of the supply chain, logistics, and the size of the dealer’s network, along with security of the data. Organizations will intentionally have to work on creating an interconnected ecosystem involving suppliers, manufacturers, logistics partners, dealers, and system integrators.

As deep learning, ML, and AI go mainstream, the auto industry will need to become future ready, boosting its operational efficiency and freeing up capital for more meaningful investments. In a study on the top 10 predictions for manufacturing product and service innovation for 2021, IDC found that by 2024, 75% of manufacturers will embed quality management across the value chain, including the supply chain and field service, reducing the overall cost of quality by 25%. The quality control tower could be key to streamlining business in the auto industry, automating and optimizing manufacturing processes, and ultimately ‘smartening’ up the entire automotive value chain.

Devashish Das is Senior Business Consultant with the Manufacturing Excellence, Growth & Transformation Group for the UK & Ireland Manufacturing Business Unit at Tata Consultancy Services. With 15 years of industry experience, he leads consulting engagements in business process re-engineering and digital transformation for global manufacturing clients. His earlier responsibilities included development of new automotive components and establishing their manufacturing facilities with an auto OEM. He has a bachelor’s degree in Mechanical Engineering from NIT, Durgapur.


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