OnProcess recently organized a panel discussion on Service Supply Chain Optimization (SSCO) as part of a full-day Client Event in San Jose, CA. The panel members were distinguished service leaders from major technology companies. The scope of the discussion was based on challenges the technology companies are facing in optimizing their service businesses, how they are measuring the success of their operations and key learning that could be leveraged by others. The main components of service business – spare parts, labor, logistics and administration of the service process were discussed. Some key points from the discussion are highlighted below:
- End to end service functions can span across multiple groups (in medium to large companies) or may be all rolled into one group (in small companies). But regardless of size, there is need for end to end visibility and analytics across the service chain. While SLA fulfillment is the biggest metric for service management, there is a need to identify other key metrics and analytics to effectively manage the business. The panelists indicated that they would like to optimize all sub-processes from order capture and entitlement to the asset recovery at the end. There is a need to establish key “value-drivers” for each service business and then tracking them rigorously.
- Dispatch of parts can be made effective as one analyzes the transactions carefully and creates appropriate business rules. For example, a 4 hour SLA dispatch request created at 2 am in the morning may lead to a part dispatch immediately. In reality, that part may not be even received at that time as no one will be available at the customer site and will have to re-delivered resulting in higher cost. Setting up a process to catch such dispatches and follow an alternative workflow will result in savings without impacting SLAs. While this is one example of exception management, having a business rules engine that drive dispatches and integrates with the client and 3rd party systems will result in higher SLA fulfillment and creation of efficiency in the service lifecycle.
- Predictive analytics can be used to identify dispatches that are potentially “repeat dispatches” or “unnecessary dispatches”. The clients and OnProcess have encountered scenarios where a part has been ordered but not utilized within the expected timeframe. Preventing such scenarios will result in savings in inventory cost and logistics cost. Typically the recovery of the used part is more effective when such dispatches are prevented.
- OnProcess presented a predictive analytics model named PTR™. There is a strong desire to leverage the PTR™ methodology and data analytics in general to help drive asset optimization across the end-to-end life cycle.
- Putting controls around self-service RMAs is important to ensure part dispatches are made correctly. OnProcess shared an approach where self-service RMAs are handled through a centralized back-office equipped with business rules to ensure accurate and timely dispatches.
- One of the discussion items focused on how part dispatches without engineering team’s involvement can be made more effective. Can some diagnostics be incorporated at the helpdesk level so that a certain percentage of part dispatches be made without taking the bandwidth from the engineering team? Approaches for putting diagnostics in a Triage engine were discussed.
A very active set of participants made the panel discussion energizing and full of ideas. We welcome more thoughts from our clients, as well as others, on these topics.
