Improving planning accuracy with advanced analytics

Decision-makers are leveraging Big Data for important business decisions, but are missing out on true knowledge and insights that deliver business value. They still experience critical supply chain pain points such as inefficient planning, inaccurate estimates, pricing errors, delays, and even business instability. These companies have yet to discover the potential of advanced analytics technology. Having access to a wealth of data is not enough – efficient planning is built on the foundation of accurate knowledge extracted from your data.

The next level of supply chain maturity - the Self-Learning Supply Chain

For business decisions that hit the mark, predictions of future outcomes need to be more accurate. Identify patterns in historical data and select the optimal variables and ranges to further refine the patterns. Accurate knowledge extraction will increase the quality and efficiency of your plans.

The DELMIA Quintiq software’s self-learning process captures the actuals such as setup, processing and waiting times as input. This allows your planning system to adjust to the large and visible changes in your enterprise, while detecting subtle changes from constant improvement efforts. Accurate knowledge is automatically derived from the data, and stays up-to-date.

The continuous flow of real-world data entering your planning system helps to generate up-to-date predictions for new and unseen tasks, by generalizing over past tasks. With the Self-Learning Supply Chain capabilities, you’ll have the supporting knowledge to make future decisions based on actual data instead of hunches.
    • Self-learning intelligence
      Task durations of future, unseen tasks are predicted based on data of historical executed tasks. Each production task has certain properties or characteristics (e.g. length, width, and material). With self-learning technology, the relation between these properties and the task duration is learned. This relation is then used to generate predictions for new tasks.
    • On-time delivery
      Inefficient planning in logistics adds up, causing delays and affecting customer service. With the Self-Learning Supply Chain, dwell, travel and service times are predicted based on recent data, and therefore always up-to-date. 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 processes for the supply chain
      Inaccurate estimates lead to inefficiency and lower productivity, or even unrealized plans. The self-learning of setup times, processing times and more will deliver accurate estimates including variance information. Predictions based on actual data will increase productivity in the supply chain.