IoT Failure Analysis Improves Inventory Costs, Portends Seismic Shift in Inventory Planning and …

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ASHLAND, Mass.–()–OnProcess Technology, a global pioneer in service supply chain management and optimization, today revealed that using Internet of Things (IoT) data to predict machine failures could reduce costly spare parts inventory stock by up to 10%, according to joint research with the Massachusetts Institute of Technology’s (MIT) Center for Transportation & Logistics (CTL). The research also points to a shift from static to more dynamic inventory planning and the favorable impact that systematic machine data collection and monitoring could have on a company’s post-sale profitability.

“Inventory planning has long been a static, ‘review-and-stock’ endeavor. It hasn’t accounted for variabilities resulting from failures of parts in the field. As a result, it’s often inaccurate and leads to overstocking, which is expensive for business, and under-stocking, which hurts customers,” said Dan Gettens, Chief Analytics Officer, OnProcess Technology. “The new dynamic, IoT-based inventory model developed as part of the MIT research is incredibly promising, as it provides a way for companies to anticipate and accommodate for failures. We believe it will also require a seismic shift in the way supply chain practitioners view service parts inventory planning.”

Many companies have started taking steps to leverage IoT data. However, data collection, which has primarily been designed to respond to signal failures, is often haphazard and subject to the willingness of buyers to participate. More systematic collection of machine data provides a sound baseline for analyzing machine performance and predicting failures, and ultimately improves quality of service and profitability. By improving data collection, businesses can reduce average inventory requirements and increase service levels. Simulations showed a reduction of 4% to 10%, which could translate into tens of millions of dollars in cost savings.

“We found that even relatively weak signals that are not strong predictors of individual machine failure could provide useful and significant information when aggregated,” added Dr. Chris Caplice, Executive Director of MIT CTL. “These insights could be used to improve the inventory levels and positioning for service and repair networks.”

To learn more about the OnProcess/MIT research and how it can affect IoT data collection and inventory planning, attend the complimentary webinar “How New IoT-Based Models Could Reduce Service Parts Inventory” on February 22, 2017 at 11:00AM ET. Click here to register.

About MIT Center for Transportation & Logistics
Launched in 1973, MIT CTL is one of the world’s leading centers for supply chain education and research. Part of the MIT School of Engineering, MIT CTL coordinates more than 100 supply chain research efforts across the MIT campus and around the globe. The center also educates students and corporate leaders in the essential principles of supply chain management, and helps organizations to increase productivity and improve their environmental performance. For more information, please visit: http://ctl.mit.edu, and visit the MIT CTL blog: Supply Chain @ MIT

About OnProcess Technology
OnProcess Technology is a managed services provider specializing in complex, global service supply chain operations – the flow of people, parts and services following the sale of a product. The company’s deep expertise, purpose-built technology delivery and embedded, analytics-based process improvement, enable clients to quickly optimize and scale operations, grow revenue and profitability, and deliver superior customer experiences. OnProcess provides services in 23 languages and operates in six global facilities, including its Massachusetts headquarters and facilities in Maine, Costa Rica, India and Bulgaria. www.onprocess.com

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