An area where the internet of things (IoT) can live up to its hype is the post-sales or service supply chain. Sensors and product log files can be used to track and analyze the performance of installed products ranging from computer equipment and storage devices to production machinery.
IoT-enabled product monitoring provides much greater precision and granularity than traditional methods, and a more efficient service supply chain increases device productivity with a reduced spare parts inventory. The potential for companies that go down this path is to reap huge cost savings, service improvements, and a much-enhanced customer experience.
Companies are understandably eager to reap these rewards — but there’s a catch. Before this revolution can move forward, both the quality and collection of performance data need to be greatly improved.
Traditionally, machine-monitoring programs manage spare parts inventory largely on the basis of historical demand data. Companies use algorithms combined with simple rules of thumb to determine how many spare parts they should stock. While this was best-practice methodology prior to the IoT’s arrival, it does not deliver a high level of accuracy. Companies are prone to overstocking parts at considerable cost, and often underachieve when it comes to reducing machine or device downtime. The understocking of parts has a negative impact on service quality and customer goodwill.
In other words, the post-sales supply chain is ripe for change.
Looking Toward Big Upsides
A recent research project at the MIT Center for Transportation & Logistics (MIT CTL) carried out in collaboration with OnProcess Technology underlines the potential for fresh approaches. MIT CTL researchers reformulated the way a leading computer manufacturer can generate spare parts forecasts. Simulated scenarios in the revised model — the first to incorporate machine-failure predictability in the service supply chain — showed a potential to reduce average inventory requirements by 5% to 7%. Inventory reductions on this scale could translate into tens of millions of dollars in cost savings.
Better monitoring and forecasting methods enable companies to be more proactive when reacting to machine failures, which increases equipment uptime and hence productivity. When IoT sensor networks are introduced, dramatic improvements to service operations are within reach. Companies can construct detailed breakdowns of how specific components perform and evaluate the likelihood of failures with greater accuracy. Aligning service operations with detailed maps of parts demand patterns increases the responsiveness of service supply chains.