Services
IoT and Digital Engineering
Bringing Together IoT and Digital Engineering to Create Rich Experiences
Highlights
- The service lifecycle, comprising the aftermarket services essential for maintaining and operating large industrial equipment, accounts for a significant share of a product's carbon footprint.
- Artificial Intelligence and machine learning technologies can transform the maintenance-repair-upgrade processes by adopting a predictive and proactive approach.
- These processes present sustainable alternatives to traditional practices with immense potential for reducing the carbon footprint of aftermarket operations.
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Service evolution
Tapping aftermarket operations to meet sustainability goals.
In their pursuit of sustainability, manufacturers of large industrial equipment are increasingly focusing on the aftermarket value chain to reduce their carbon footprint. Beyond the design, development, and manufacturing phases, the aftermarket segment holds significant potential for lowering carbon emissions. Sustainable practices in the aftermarket operations can ensure products are maintained, repaired, and disposed of in an eco-friendly manner.
Service lifecycle management (SLM) systems have traditionally focused on delivering capabilities that support reactive strategies. Manufacturers invariably engage in maintenance only after a failure has occurred and respond to customer complaints as and when they arise. This reactive stance can cause longer downtimes and lead to higher costs, unsustainable practices, and reduced customer satisfaction.
Addressing these challenges requires transitioning to a proactive and predictive service management approach. By leveraging AI and machine learning, organizations can anticipate failure events well in advance, enable preemptive solutions, enhance service task efficiency, lower repair costs, and decrease carbon expenditures throughout the service lifecycle.
SLM reimagined
Harnessing a digital thread-powered service intelligence hub for sustainable and efficient aftermarket operations.
A complete digital reimagination of SLM can help establish sustainable practices and improve the quality and speed of aftermarket operations. This next-generation SLM must be driven by a service thread that connects the entire value chain, centered around a service intelligence hub. As shown in Figure 1, the service intelligence hub consolidates the complete data DNA of the asset, is cloud-enabled, and generates a digital trace via a knowledge graph. Once this foundation is established, the power of AI, ML, and GenAI can be harnessed to introduce intelligent interventions into individual processes within the value chain. A low-code/no-code platform can be deployed to empower business users further to develop applications that specifically address their contextual needs, consuming information from the service intelligence hub.
Figure 1
Next Generation Service Lifecycle
Figure 1
Future of service
Potential avenues for transformation using next-generation service lifecycle management.
With this framework in place, key processes in the service lifecycle can be radically transformed to gain significant outcomes. A few methods are described below:
- Predictive maintenance: ML models can be developed to process alerts from real-time data, facilitating the identification of faults, the prediction of potential failures, and the suggestion of appropriate solutions. With these recommendations, remote assistance can be provided ahead of any failure, effectively reducing asset downtime. This approach reduces the need for onsite visits, yielding significant cost savings, promoting sustainability, and boosting customer satisfaction.
- Service planning: When a remote resolution cannot be achieved, a service request can be created after assessing the customer's warranty entitlements. Two vital considerations during the visit planning phase are the availability of parts at the appropriate location and the scheduling of the service technician. ML models can assess historical demand patterns for service parts and suggest optimal sourcing strategies. The most suitable service technician can also be allocated to the service request, ensuring a more efficient resolution.
- Service execution: Service technicians need to be provided with precise and up-to-date information, including the service bill of materials, augmented and virtual reality instructions, repair protocols, and fault tree of the asset. This information can be integrated into a user-friendly platform accessible via handheld devices through the service intelligence hub. Additionally, machine learning algorithms can analyze images to detect faults that may not be easily identified by human inspection. A service request can be automatically generated based on the fault's severity.
- Remanufacturing: Products are often discarded prematurely due to emergency replacements. This contributes to rising waste levels and the depletion of natural resources. Businesses can significantly mitigate their environmental impact by emphasizing repair and refurbishment over replacement. Using AI/ML, defective components can be identified for remanufacturing, allowing used parts to be restored to a condition that meets the original specifications. The service intelligence hub can help orchestrate the entire remanufacturing value chain.
- Closed-loop feedback: The service intelligence hub can be crucial in establishing a feedback loop from service to design. Technicians can initiate change requests for persistent field problems through this hub. The relevant data from the hub is shared with the product design team, who can analyze the implications of the proposed change and address the issues with the support of a generative AI assistant.
Customer-first SLM
Creating a connected and intelligent service ecosystem.
AI, ML, and GenAI can play a significant role in introducing sustainable SLM practices. By predicting failures and facilitating proactive resolutions, these practices prioritize repair and remanufacturing over replacement, cut down on technician and parts logistics, and reduce waste. This approach will contribute to a lower carbon footprint in maintenance processes and ensure uninterrupted operations, thereby improving customer satisfaction and profitability. In the future, SLM is poised to merge even more tightly with the development phase of the product lifecycle and transform aftermarket services to follow a continuous improvement strategy.
TCS has defined a next-generation service lifecycle framework that helps establish the service digital thread and incorporates all these digital technologies. It provides the foundation for innovative use cases built to suit individual customer requirements.
