CAMX 2021 exhibit preview: Plataine

Plataine (Waltham, Mass., U.S.) recently released AI algorithm Practimum-Optimum Scheduler as part of its Total Production Optimization (TPO) cloud-based suite of interconnected applications. Practimum-Optimum is said to optimize high levels of KPIs within a practical, robust planning application.

The patent-pending Practimum-Optimum algorithm is designed as a solution for search algorithm-based existing software solutions, which the company says can require a high level of manual involvement to make schedule changes based on customer priorities or other considerations not addressed by the software. Practimum-Optimum is said to build optimal schedules that already integrate practical considerations and support the user in further integrating subjective changes.

Production planners can set target values for business goals such as on-time delivery, overall equipment efficiency (OEE), or make-span. 

With built-in machine learning algorithms, the Practimum-Optimum Scheduler teaches itself insights such as trade-offs between competing goals, clusters of similar schedules, patterns of demand sets and best attainable values for each individual KPI score. Historical schedules are useful but not mandatory. These insights are used by the Practimum-Optimum algorithm to produce schedules that are both optimal and practical to execute, Plataine says, and include a visual, interactive Gantt chart and graphic indicators.

In addition, Plataine’s intelligent Digital Assistants accompany users throughout schedule execution, alerting them to deviations in the planned schedule and offering recommendations and insights before a crisis occurs. Over time, as the algorithm is used and real-life schedules are generated, the learning algorithm adapts its planning capabilities and improves its performance.  

Beyond Practimum-Optimum, Plataine’s TPO suite offers Industry 4.0 solutions to solve production challenges, including Material & Asset Tracker, WIP Optimizer and Tool Manager applications, to offer optimized scheduling decisions based on a rich digital thread.