OnProcess Technology, a global pioneer in service supply chain management and optimization, announced results of a joint research project with the Massachusetts Institute of Technology (MIT) Supply Chain Management (SCM) Program, which found that by using Internet of Things (IoT) data to predict machine failures, companies can reduce costly inventory stock while improving their ability to meet service levels. The study, “The Impact of Installed Base and Machine Failure Prediction on Spare Parts Forecasting and Inventory Planning,” is the first to analyze how connected machine data affects this critical component of the post-sale supply chain.
“Companies tend to overstock inventory so that when customers’ products break down, they have replacement parts readily available. But purchasing and storing all that extra ‘safety stock’ is very costly. With the proliferation of connected products, we saw an opportunity to analyze each product’s machine signals to predict when components may fail and, thus, develop a more sophisticated forecasting model,” said Mike Wooden, CEO of OnProcess Technology. “We were thrilled when MIT’s prestigious Supply Chain Management Program agreed to conduct this research. Based on the results, it’s clear that the more accurately you can predict failures, the lower average inventory you need. This has the potential to save companies millions of dollars every year.”
Traditionally, supply chain experts have used mathematical models to calculate the right inventory based on factors such as past demand, variations in demand, the amount of stock in the market and lead time from suppliers. MIT students and research staff developed a new inventory model that incorporates machine failure predictability. They found that even seemingly poor machine failure predictability tests can lead to a significant reduction in inventory levels. It can also enable a superior ability to predicate part demands, which leads to improved service levels.
According to Dr. Chris Caplice, Executive Director, MIT Center for Transportation & Logistics, “Improving the demand forecast for repair parts can lead to significant inventory reductions but it is notoriously difficult. This project has shown that utilizing machine data proactively can lead to better forecast accuracy and, in turn, potentially result in higher service levels with less inventory. We look forward to continue working with OnProcess on this research project.”
The machine failure analyses is one component of a broader joint research effort between OnProcess and MIT CTL. The insights and methods developed by the full IoT-driven research may be used to:
- Reduce inbound customer issues
- Reduce redundant shipment of hardware stemming from inaccurate diagnoses (thus reducing No-Fault-Found)
- Increase the percent of successful repairs (also reducing No-Fault-Found)
- Shorten time-to-resolve a customer issue
- Proactively place inventory in stocking locations
- Make inventory routing more efficient
- Drive product improvements.
For more information and news please visit: www.onprocess.com.