Connected Service Life-Cycle Management – A data-driven approach to service operations

The immense potential of aftermarket services for the manufacturing industry is a no-brainer. As per industry standards.

1. Aftermarket services comprise 25-30% of the revenues, with a profitability of up to 55%
2. Service parts management is around 15-20% of the revenue, with profitability of up to 50%
3. Service contracts are high margin businesses with a potential to earn anywhere between 30 to 50%

According to a recent Deloitte study, the role of aftermarket services in driving customer lifetime value (CLTV) and sustainable profits has become more profound post-COVID-19. With supply chains being disrupted, the service level expectations of customers, especially for complex products, manufacturing, construction machinery, and transport vehicles, have risen manifold. Customers are willing to pay a premium for uninterrupted services and longer-term contracts that can predict support or replacement proactively before their equipment becomes inoperative. It is a new win-win for both OEMs and customers.

Deciphering the aftermarket SLM ecosystem of a manufacturer

The case for aftermarket services sounds promising, but does it manifest? Does the transition to SLM translate into tangible business gains? What do OEMs need to realize the true potential of their aftermarket services?

Currently, a manufacturer’s aftermarket SLM tech stack can have one or many of these components, independent of each other.

1. Service Parts Management: Covers the spectrum of aftermarket parts sales, from direct customer sales, dealer sales, and service centers to custom programs.
2. Warranty Management: End-to-end management of product warranty processes involving product registration, claims processing, contract management, service plans, returns control, and warranty analytics.
3. Field Service Management (FSM): Provide resources to support products in operation at the customer’s point of use. Capabilities span asset management, mobile workforce management, customer portals, service request management, and contract management to ensure the right resources are delivered at the right time.
4. Service Knowledge Management: Manage, collect, and report on every aspect of customer interactions, including online portals, call center operations, training programs, and product health monitoring.
5. Service Network Management: Plan, manage, and expand service operations through organic capabilities to transform service strategies across MRO operations, component repair & exchange, product modifications, and service delivery.
6. Technical Information Management: Technical information storage about design, bill of materials (BOM), reliability data, parts information, configuration data, maintenance data, and production data to lay the foundation for the life-cycle and performance management of a product.

On a standalone basis, these systems are certainly helping manufacturers transform their processes. Still, this siloed approach is incapable of value creation as it tends to ignore the complementarities and interdependencies across the ecosystem – OEMs, suppliers, dealers, customers, and service centres.

Not only that, but the multiple system approach also leads to a growth slump, as it cripples OEMs’ ability to see complete and accurate data and deploy that data to build a seamless experience for their customers and gain a competitive advantage.

As modern enterprises focus heavily on keeping track of their customers’ needs and aim for proactive service delivery to meet their satisfaction levels and drive customer lifetime value over the life-cycle, the need to implement connected SLM has become more pronounced than ever.

From SLM to connected SLM – A case for manufacturing

Using AI and analytics to create a 360-degree view of the service life-cycle processes for manufacturers, their channel partners, and customers

Let’s look at an industrial equipment manufacturer that faced challenges across its service supply chain. The manufacturer wanted to eliminate inefficiencies and ensure maximum service parts availability across its global operations. This required evolution from a location-based inventory model to a centralized inventory management model, which could predict parts requirement, intelligently analyze parts availability, and automatically allocate resources per customer demand.

The journey began with designing, building, and implementing SLM solutions to serve use-cases built around industry-specific challenges. The next step was integrating SLM with existing ERP and SAP systems and using analytics and AI to leverage real-time orders and feed them to SLM systems to ensure optimal inventories. This helped the manufacturer drive inventory turnover by 18-20%, increase parts availability by 3-5%, and save inventory costs by millions.

Manufacturers must explore the integration of artificial intelligence (AI), the internet of things (IoT), and analytics tools across processes. IoT devices, or connected devices, help automate data collection from operational equipment to gauge product performance and uptime and diagnose problems. AI and analytics deliver capabilities to derive insights across system uptimes, inventory, service needs, and other functional areas.

Unlocking the value of the Convergent SLM Strategy

A connected SLM strategy can help build end-to-end interconnected systems that drive optimization across all manufacturing operations.

A transition from a pure-play SLM strategy to a connected SLM one enables manufacturers to collect data from field assets, warranty systems, parts management systems, and FSM. This data can be utilized to implement service updates, manage complex technical information, and drive a seamless service experience for end customers.

Some other benefits include:

1. Streamlined workflows: Connected SLM solutions can enable organizations to streamline workflows with smart connected products, reduce downtimes, reduce service response times, enhance first-time fix rates, optimize price and parts availability, and reduce costs.
2. Building new service models: Connected SLM solutions can also deliver insights into how products are performing at the customer’s point of use, which can be leveraged to build new service models.
3. Personalizing communication: The connected SLM solutions enhance communication channels by ensuring detailed information is available and curated as per stakeholder needs to perform reactive and proactive service activities. Implementing a feedback loop across the digital thread enables manufacturers to leverage data that serves as input to increase product serviceability and reliability.

Manufacturers must explore new revenue streams from real-time engagements with end customers. Smart devices and connected SLM systems will provide capabilities for manufacturers to deliver value-added services, reduce service and parts costs, and adopt a data-driven approach to decision-making.