Industry
Manufacturing
TCS Neural Manufacturing: Driving Agility & a Competitive Edge
Highlights
- Automotive and industrial manufacturers lose billions of dollars in warranty claims, annually. Integrating new-age technologies such as AI and ML with traditional warranty management tools can help tackle the problem.
- Manual intervention, longer turnaround, a plethora of rules, and varying standards across dealership services are some of the reasons why warranty management in industrial and automotive companies is a complex task.
- Using AI, ML, and advanced analytics will make the process structured, data-driven, and systematic, ensuring faster claims settlement and better customer experience.
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Warranty management – a critical function
Automotive and industrial manufacturing companies are constantly working to improve product quality to reduce warranty costs, enhance customer satisfaction, and better financial performance.
But the demand for new products, competition, new technologies, and supply chain disruptions often stymies those efforts. Quality problems pervade across industries and there can be weak spots even in the best quality management systems. On an average, warranty claims expenses for automotive and industrial companies range between 1.5-2.5% of their annual revenue, translating into billions of dollars of revenue loss and poor customer experience. These companies often offer prompt warranty service and replacement of parts to minimize the discomfort to customers and retain their loyalty.
It is therefore critical for automakers and industrial manufacturers to overlap their traditional warranty management systems with new-age technologies such as artificial intelligence (AI) and machine learning (ML). These technologies help create a more robust, connected, agile, and sustainable quality framework, which will largely reduce process inefficiencies.
A bumpy road
Warranty claims management in automotive and industrial manufacturing sectors is a complex task.
This is due to the presence of multiple stakeholders in the value chain, several rules and regulations, a variety of analytical models, and varying standards of dealership services, among others. As a result, organizations end up with higher warranty claims rate and are unable to keep their costs in check.
Key challenges include:
Manual intervention – The existing warranty lifecycle in automotive and industrial sectors involves disparate processes, siloed systems, and legacy rules. As a result, multiple manual interventions are required to successfully close a claim, right from creation and processing through supplier chargeback. This, often, leads to human error and delays, resulting in warranty cost spillage.
Time and resource – Original equipment manufacturers (OEMs) usually take two to three weeks to settle a normal warranty claim. But that time can get protracted in cases of damage, improper documentation, poor image legibility, wrong data entry, and supplier chargeback.
A connected system
Modern warranty management systems use AI and ML technologies to drive intelligence across the warranty lifecycle.
Since siloed systems and legacy processes will only offer limited positive outcomes, Figure 1 shows what capabilities manufacturers need to develop to effectively process warranty claims.
Figure 1: The next-gen warranty business model – connected, simplified, energized
Let us examine the building blocks of this next-gen warranty business model:
Foundational technologies:
AI, ML, and advanced analytics can help make the process structured, data-driven, and systematic. A US trucking company, for example, rationalized its unused warranty business rules and leveraged foundational digital technologies for warranty information flow within the organization. This helped them to streamline the warranty processes, reduce the overall processing time, and respond faster to dealers and customers for claims settlement.
API-fication – APIs help with building innovative digital solutions that allow customers to have access to data anywhere, anytime, and from any device. Choosing the right API infrastructure to match the requirements at hand, and ensure a return on investment (RoI), is vital for any manufacturer. Most enterprises are creating an API infrastructure to expose warranty services to application and integration layers.
Enterprise service bus (ESB) – An ESB architecture facilitates increased organizational agility by reducing the time to market for new initiatives by providing a simple and well-defined pluggable system that scales well. It also provides a way to leverage existing systems and expose them to new applications using its communication and transformation capabilities.
Agile frameworks – Agile frameworks help in requirements gathering, iteration planning, progress tracking, and reporting development work.
Master data management – It is important to have a master data of the organization’s customers, products, and sellers and their inter-relationships. This would help in connecting everything, providing seamless information across multiple channels, building trust in the data, and enabling actionable insights.
Cloud – Automotive and industrial players need to be more responsive by being able to scale-up or scale-down any application as needed, and that too, in a quick and cost-effective manner. This fundamentally requires a robust cloud infrastructure setup.
Automation and intelligence:
Smart automation of warranty process improves customer service and reduces operational costs. AI algorithms are leveraged to automate sub-processes across the warranty lifecycle, such as pre-authorization, approval of claims flagged by predefined rules, dynamic rule update, contact center associate education, dealer education, and supplier chargeback.
A global engine manufacturer leveraged AI and automation - image processing and business rules rationalization – to detect fraudulent warranty claims and reduce the processing time. Consequently, the company was able to recover more than $5 million in claims processing.
Some of the technologies associated with this are:
Intelligent process automation – This removes process inefficiencies and automates repetitive tasks and services. The end goal is to reduce manual intervention significantly, to lower cost and the chance of human error.
Machine and deep learning algorithms – These algorithms look at patterns of warranty data, draw inferences, trigger actions, and subsequently self-improve. Such algorithms can be embedded across the warranty lifecycle, helping industry players drive growth and become cost efficient.
Computer vision – Manufacturers can leverage computer vision to assess images of damage and repairs for quality analysis, engineering, returns, and supplier chargeback processes.
New age technologies
Automotive and industrial manufacturers put sensors in vehicles and equipment to monitor fault codes for predictive maintenance.
However, the complexity that needs to be addressed is tying it back to the warranty system for proactive countermeasures. A Japanese automotive manufacturer leveraged IoT for early warning indications of parts failure and warranty recall scenario. That helped identify new warranty- related issues which typical claims didn’t expose and made the company deploy countermeasures that ultimately enhanced customer satisfaction.
Advanced analytics and connected data are leveraged to generate actionable insights toward early detection of parts or product failure, anomalies in claims, claim scoring, warranty waste reduction, and opportunities for supplier recovery based on root cause failure analysis. The following technologies can be the enablers:
IoT – Companies should develop and deploy an IoT platform that connects vehicles, equipment, sensors, and devices to warranty systems and applications to get leading indicators of potential failure and ensure proactive intervention well in time.
Generative AI – It can be applied to various functions and processes across the warranty lifecycle. Various enterprise solutions can be developed or deployed to augment warranty operations through proactive insights and support.
Software-over the-air (SOTA) – SOTA are wireless technologies to communicate with vehicles and equipment and download and manage software updates and upgrades without being connected physically. This enables automotive and industrial manufacturers to reduce warranty costs by quickly predicting the failure of parts or providing critical safety updates.
Efficiencies through contact center:
Companies should route all customer-facing warranty and technical service processes through their contact center to ensure consistent outcomes in claim adjudication.
For example, a US auto major transformed its contact center to streamline its customer service and warranty support operations which reduced the claims processing time. This led to an annual $1.5 million savings in warranty processing, $3.5 million savings in information technology cost, and a 15% improvement in agent productivity.
Some of the technologies that can be leveraged are:
Warranty platform – Handling customers, especially from a warranty perspective, requires moving to future platforms that can provide omni-channel visibility and are with integrated capabilities like cloud-based integrated calls, recording, and voice input analysis.
Chatbots – AI algorithms can be deployed to interact with customers through voice or text channels. Such bots should be integrated for automotive and industrial firms that are still operating on legacy platforms.
What lies ahead
It is imperative for automotive and industrial players to reimagine the conventional warranty business model.
The re-imagined model, leveraging new-age technologies, provides the following opportunities:
Services – It provides automated claim creation, real-time claim status, and helps with registration of vehicles and equipment.
Marketing – The model provides enterprises with cross selling opportunities, helps in parts-marketing based on real time data monitoring, creates effective campaigns, and enables customer feedback and warranty related sentiments.
Claim administration – It helps in warranty waste reduction, fraud detection, minimizing human error, while also processing claims.
Returns and supplier recovery – With this model. manufacturers can ensure quick recovery from suppliers and efficient logistics routing.
Reporting – The new model will also help in predicting failure of parts, claims scoring, and improving the repair profile.
These will help enterprises win new businesses, leverage newer opportunities to maximize value for their customers, and gain competitive advantage.
