You’ve seen the stats: Every day, 5.5 million new things get connected, and by 2020, Gartner says that 20.8 billion Internet of Things (IoT) devices should be in use around the world. This connected tsunami is creating a huge opportunity to enhance the notoriously complex and costly-to-operate service supply chain by tapping into machine data.
You may not have thought of it this way, but your connected products are talking to you. Through their log files, they tell you every day how to improve your business. We call this Voice of Product, and it provides essential information about what’s happening with each product in the field, pinpointing current and potential issues with software, infrastructure capacity, configuration, hardware and more.
On its own Voice of Product has limited value. But, when analyzed alongside other critical post-sale supply chain data—including Voice of the Customer, Voice of the Process, real-time and historical operational data—VoP can have a significant impact on your ability to predict machine failures so you’re better equipped to handle, and even avert, issues; shift from reactive to proactive modes across traditionally fragmented post-sale supply chain functions; and improve overall supply chain health and outcomes.
For example, imagine…
- Using machine failure predictability to reduce expensive spare parts inventory
Through joint research with OnProcess Technology, Massachusetts Institute of Technology recently developed a new model for spare parts forecasting and inventory planning that incorporates machine failure predictability into the equation. It found that by using IoT data, you can significantly reduce both costly inventory stock and stock-outs — even with relatively low predictive power. The higher the failure predictability, the greater the reductions.
- Replacing soon-to-fail parts before they fail
Instead of waiting for failures to happen, monitor the product’s log files to predict why and when a part is likely to break down. Program the log data to trigger alerts, telling your outbound calling agents, for example, that a particular customer’s product has a part that needs attention. With this knowledge, you can inform the customer of the pending problem and proactively ship a replacement part via slower and much less-expensive means than the typical rush shipment that happens after products fail.
- Reducing No Trouble Found returns
When customers complain that products either aren’t working and need to be fixed/replaced, or aren’t performing as expected and, therefore, don’t fulfill their needs and should be returned, IoT-enhanced analytics can signal whether or not there’s an actual problem—before anything is replaced or returned. If the IoT data doesn’t turn up any issues, then it’s likely the cause is a gap in vendor-to-consumer education.
It’s easy to see why so many companies have a sense of excitement around connected device potential, but there’s also tremendous confusion about how to leverage connected info. Where do you start? How extensively should you incorporate it throughout your service supply chain? How can you tell if it’s benefiting your process?
OnProcess created a framework for leveraging IoT data, based on our expertise in post-sale supply chain operations and machine analytics. Here’s a high-level look, divided into Planning and Implementation phases:
Of course, it’s all about mastering the details. For instance, to keep your IoT analytics project from being unwieldy and improve its success, we recommend starting in a focused way. Pick a target segment, typically a combination of the product plus geography, type of failure mode, post-sale function (i.e., inventory, transportation, service triage, etc.). Treat this as a sort of Proof of Concept, which you can then fine tune and make repeatable so that you can later leverage IoT analytics in other segments.
In the execution stage, it’s important to obtain broad and granular visibility into the all relevant data points (Voice of Product, Voice of the Customer, Voice of the Process, real-time and historical operational data), and funnel that data into an alerting system so that, when defined rules are triggered, it specifies certain actions that should be immediately taken to avoid, alleviate or fix a particular customer’s product-related problem.
The more visibility you have into what’s happening with your connected products in the field, and the more you integrate those log files into your post-sale analytics processes, the better you’ll be able to predict product failures and turn what could be negative, costly events into positive experiences for your customers, and money-saving outcomes for your business.
If leveraging IoT data to optimize your post-sale supply chain sounds intriguing to you, download our white paper on the subject and contact us to learn more.