PTC Announces Release of Servigistics 13.1 & PAI 5.0

Author photo: Craig Resnick
ByCraig Resnick
Company and Product News
Service Lifecycle Management

PTC’s Service Lifecycle Management (SLM) business announces new product releases, Servigistics 13.1 and Performance Analytics and Intelligence (PAI) 5.0.  More Servigistics capabilities have been augmented with AI/ML to help customers forecast, optimize, and improve the sustainability of their service parts supply chain performance with greater ease and accelerated performance.

An overview of what has been enhanced with the release of both Servigistics 13.1 and PAI 5.0. :

  • Forecasting – Machine Learning-based forecast algorithms are now fully integrated in Servigistics forecasting; a new forecast parameter has been introduced to help reduce churn in forecast method changes.

  • Inventory Optimization – New time-phased metrics were added to support Sales, Inventory, and Operations Planning (SIOP) including support for comparing multiple scenarios; improved scenario modeling now can simulate forecast, price, and carbon tax changes, in addition to SKU data, such as lead times, wash rates, and costs.

  • Supply Planning and Planner Workflow – Monthly limits can be incorporated for vendors, multiple vendors can be utilized in priority order, and new guidance is available to suggest best practices and root causes for common planning issues, such as resolving shortages, vendor issues, and demand misses.

  • Service Parts Pricing – Introduction of the ability to generate new prices based on user-defined offsets; kit pricing update includes the use of all price buckets, incorporation of new component part prices, and a review reason to alert users of outdated part kit prices.

  • Performance Analytics and Intelligence (PAI) – A new ML time series forecasting algorithm was added; new ML-based Remaining Useful Life or “time to failure” prediction capability of serialized items based on utilization and/or sensor data; ML-based recommendations are now available to help prevent potentially detrimental forecast overrides.


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