AI Spotlight

AI-powered methods for optimal initial inventory levels in Supply Chain Management 

AI optimizes processes at a container terminal

Learn how neural networks enable an initial inventory strategy in supply chain management. This use case illustrates how artificial intelligence, as generated by a neural network, opens up new, reliable planning possibilities.

Key Benefits

  • Objective Initial Demand Estimates: Neural networks allow for the calculation of optimal order quantities, even in the absence of sales history.
  • Optimized Planning: Warehouse costs are reduced, stockouts during the launch phase are avoided, and ordering processes are streamlined.
  • Robust Decision Support: The optimal use of data through computer-based methods complements human expertise, helps minimize sources of error, and ensures the right decisions are made regarding (initial) inventory levels. 

Use Case

Summary

  • For manufacturing companies, the introduction of new products comes with the expectation that customers will be adequately supplied with replacement parts in the future. This applies to our use case as well, involving a motorcycle manufacturer that has introduced a new model.
  • Using AI-powered methods and neural networks, enables the company to generate reliable demand forecasts for spare parts by using computer-assisted comparisons with models that have similar attributes. Based on these forecasts, the company can determine optimized order quantities.
  • The neural networks were trained on the current product portfolio and used historical sales and repair data from comparable products (digital twin), seasonal fluctuations, and current market factors (e.g., prolonged rainy periods despite summer) to efficiently analyze various demand and inventory patterns and generate optimal recommendations for action.

How It's Done

  • For its recommendations, the AI not only analyzes historical sales data for various items but also, if necessary, incorporates numerous additional factors that influence purchasing behavior. 
  • By recognizing similar items based on their attributes, our AI-powered software can infer demand for other products and even predict demand when there is no continuous demand at the individual item level or no sales history at all. Neural networks make it possible to transfer insights from similar scenarios to new situations, such as initial inventory levels.
  • This makes the use of neural networks particularly suitable for initial demand forecasting. They enable sales predictions, allowing companies to optimally align their inventory strategies, even for new items or changing assortments.

Did You Know?

  • According to a study by the digital association Bitkom in June 2024, only 6 percent of the companies surveyed believe they are fully utilizing the potential of the data available to them.
  • Neural networks can extract value from the most complex data sets and assist users, especially when they reach the limits of their expertise due to complexity.

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