Fujitsu tech eases supply chain management
Fujitsu Laboratories Ltd. has developed what it describes as a model predictive control technology aimed at supply chain management. With this technology, it is now possible to make successive revisions to plans based on multiple long-term forecasting scenarios and to respond to sudden changes in demand, indicated the firm.
Big data has made it possible to forecast demand with increasing accuracy, but there is still a need to make better decisions for production quantities and order quantities in situations where demand often changes due to factors such as discount sales promotions or new product introductions. In such situations forecasting is considered to be unreliable and uncertain.
Fujitsu Laboratories has developed a model predictive control (MPC) technology, based on multiple forecasting scenarios, that looks ahead a certain amount of time to generate optimal ordering and production plans. Furthermore, by revising the forecasting model itself, it is possible to generate highly accurate planning made in response to sudden changes in demand.
This technology can be used, for example, to optimize inventories based on demand forecasts for retail store operations, or to generate ordering plans that optimize profits even when demand changes suddenly. When verified with actual customer data, profits increased by an average of roughly 16 per cent.
In recent years, there has been a sudden emergence of tools for real-time collection, compilation and analysis of huge volumes of data such as data from product sales as well as social media and sensor data. The use of big data has come to be regarded as an important way to grow business and establish competitive advantage.
This includes putting machine learning, data mining and other predictive and analytic technologies to work together with optimization technologies to deal with enormous volumes of data. Technologies employing big data are increasingly seen as a way to assist with decision making in operations and management.
Although the accuracy of demand forecasting is improving thanks to the use of point-of-sale data, it is not easy to make accurate forecasts that take into account changes in demand that may arise from special sales or new product introductions. In such situations, it has been challenging to determine how best to use big data to make optimal decisions.
While it is possible to improve forecasting accuracy by using more sophisticated forecasting models that explicitly account for the factors underlying changes in demand, there is no way to completely eliminate the uncertainty in the forecast. Accordingly, it is important to make the best possible decisions while understanding the risks involved because of the inherent uncertainty of forecasts.
Fujitsu Laboratories has researched and developed optimal control methods for relevant models in the manufacturing domain, and, taking into account the unpredictability of models, has moved forward on the development of model predictive control technology that achieves optimal control in response to conditions that change minute-by-minute. This has been applied to fields such as energy management and engine control, where it has resulted in more efficient use of resources and improved performance.
In this instance, Fujitsu Laboratories applied its acquired model predictive control technology to retail supply chains. Responding to rapid changes in demand, and taking into account opportunity loss due to the cost of maintaining inventory and the cost of running out of stock, this technology achieved ordering plans that minimized total costs.