OnProcess Technology has introduced its Dynamic Parts Planning Service, an IoT data-driven offering that predicts spare parts demand. By feeding historic and real-time machine signal data into its OPTvision microanalytics and visibility platform, OnProcess determines what parts will be needed and when they will be needed.
The solution is based on a proprietary spare parts planning algorithm that incorporates machine failure predictability, developed by OnProcess in partnership with MIT’s Center for Transportation & Logistics.
OnProcess’ service consumes the user’s base data and demand-side machine signals, and concurrently runs OnProcess’ highly predictive algorithm alongside traditional algorithms that include the user’s data inputs. It then compares the outputs using OPTvision. Users access OPTvision to view recommendations on how to reduce their inventory and new buys. These recommendations are aligned to the user’s process and review period, whether weekly, bi-weekly or monthly.