How IoT Data Can Cut Machine Part Safety Stock

How IoT Data Can Cut Machine Part Safety Stock

In the world of The Internet of Things supplier relationships can span many industries and business models.
New SCM research shows how the The Internet of Things can be used to predict machine failures

A Supply Chain Management (SCM) master’s program thesis research project carried out in collaboration with managed services provider OnProcess Technology, headquartered in Ashland, MA, found that by using Internet of Things (IoT) data to predict machine failures, companies can reduce spare part inventory volumes and improve their ability to meet service quality levels.

The thesis study, “The Impact of Installed Base and Machine Failure Prediction on Spare Parts Forecasting and Inventory Planning,” was carried out by SCM graduates Mike Brocks and Renzo Trujillo, and supervised by MIT CTL’s Dr. Chris Caplice, Dr. Daniel Steeneck, and Dr. Francisco Jauffred. It’s the first study to analyze how connected machine data affects this critical component of the post-sale supply chain, says OnPoint.

“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,” says 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 insights and methods developed by the full IoT-driven research may be used to:

  • 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.

The machine failure analyses is one component of a broader joint research effort between OnProcess and MIT CTL.

A summary of the thesis by Mike Brocks and Renzo Trujillo is published by Supply Chain Management Review here.

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