DELMIA Quintiq Self-Learning Supply Chain captures actuals such as setup, processing and waiting times, and feeds them into your planning system. This continuous process equips your system with the real-time data it needs to generate up-to-date predictions for new and unseen tasks, and generalize over past tasks. Easily adjust to large and visible changes throughout your enterprise while detecting subtle changes from constant improvement efforts. Identify patterns in historical data and select the optimal variables and ranges to further refine the patterns. The self-learning supply chain, powered by advanced analytics, represents the next level in supply chain planning maturity.
Key features and benefits include:
Predict task durations based on historical data. Each production task has certain properties or characteristics (e.g. length, width, and material). Self-learning technology learns the relationship between these properties and the task duration. This data is then used to generate predictions for new tasks.
Predict dwell, travel and service times based on updated data. By using more accurate estimates of travel and service times, plans can actually be executed as planned. This improves adherence-to-plan and reduces business disruptions.
Improved Supply Chain Processes
Self-learning capabilities, when applied to setup times, processing times and more, help to deliver accurate estimates including variance information. Make predictions based on actual data to increase productivity throughout the supply chain.