Challenge
- Development and operation of a system to support the planning process of vehicle properties (equipment characteristics)
- Weekly provision of a model to forecast the demand quantities on component level with a 12-month time horizon
- The model is to consistently integrate the following existing data:
- Rules defined by sales and logistics
- History of vehicles built in the past
- Specifications based on capacities, orders, forecasts etc.
Solution
Markov networks integrate structural and probability information:
- The structure of the Markov network is determined based on logic and sales rules
- The initial distribution of the network is “learned” based on the history of built vehicles (dependencies, relevance)
- Planning specifications are consistently incorporated through changes to the probability distribution
Benefit
- Forecasting demand quantities on component level is possible with high quality
- Component requirement bottlenecks are detected at an early stage
- Inconsistencies within the rules or between rules and specifications are detected
- Inconsistencies in the planer specifications are automatically compensated for and can be analyzed
- Order planning can be based on a consistent model
AI from msg – Made in Europe
With artificial intelligence, we want to drive forward the use of AI in organisations faster and more successfully. What sets us apart: Forward-looking consulting services and products to enable value creation through data.