Do you play with the idea of taking your demand planning to a new level with artificial intelligence to
On this page, we will inform you about what Demand Forecasting is all about. We will also introduce the relevant terminology, provide methodical insights, and more.
A company produces a large number of items of different types. This includes articles with regular and seasonal demand as well as articles with sporadic demand. The portfolio contains both new items and long-standing blockbusters, trendy and stable items. What is the expected quantity for each article group in the next few weeks?
The ideal solution would be a (partially) automated system producing forecasts at article group level for the next weeks and thus doing justice to the different types of planning objects. Ideally, methods from statistics as well as machine learning and pattern recognition methods are used to meet the special challenge of having both sporadic demand and regular demand patterns in the data. In addition, existing open orders are included in the forecast. A classification mechanism determines which article groups can be predicted by artificial intelligence and how well, and for which additional validation by experts make sense. An analysis at different hierarchical levels determines whether the requirements of the various article groups can be better predicted at the level of interest or, instead, top-down or bottom-up. The data is taken from the company’s ERP system, and the results of the sales planning are fed back there. The company uses its own reporting tools for the display and visualization of the results. A dashboard visualizes interim results for planners and points them specifically to those article groups for which it is worth taking an expert look at the forecasts again and adjusting them if necessary.
The following blog articles are provided in German:
Demand Planning with AI?
Inconsistent ordering behavior between customers & plants creates for us an incredible effort in manual planning. Therefore, a while ago we formulated the targets to
- Decrease the manual effort,
- Improve the planning quality,
- Recognize trend changes.
Thanks to our collaboration with prognostica GmbH, we were able to build a solution based on Artificial Intelligence featuring
- Market indicator clustering,
- Indicator selection per material number,
- Efficient interaction between planners and AI
Currently, the comparison between AI forecasts and our manual planning is in full swing and we already observe a substantial improvement of our planning and are able to put check marks behind the initial targets. Because an AI likes to be fed new data and ideas continuously, we are positive that we will be able to improve the AI solution even more day by day. Thank you to our colleagues Dirk Thomas and Joachim Vogel from Bosch Rexroth for enabling the collaboration with prognostica GmbH and for their visionary thinking to make our lives easier using this state-of-the-art technology.”
Are you interested in one of our use cases and would like to discuss it with Tina Geisberger? Contact her to find out how we can assist you.
Senior Account Manager - Tina's passion and expertise are use cases and she is eager to work with you to specify which of our use cases fit your situation. Through her years of professional experience, she knows how important it is to listen carefully to find out how our predictive analytics solutions can simplify your day-to-day work. She is extremely solution-oriented and welcomes any challenge - what does yours look like?