OBER – Optimal inventory planning under uncertainty

Know forecast uncertainties, reduce safety stock

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Managing inventory levels is a key challenge for SMEs in the wholesale sector. In some cases, forecasting models are already used to predict possible sales. However, these are often not state of the art and also only provide point forecasts: i.e. only a single value is predicted. The uncertainty factor, i.e. the question of how likely it is that the predicted sales volume will be exceeded or fallen short of by a certain value, is either not known or is not taken into account in decisions due to a lack of specialist knowledge. The disadvantage here is that users such as dispatchers must decide for themselves how much they trust the forecast and plan inventory accordingly. This leads to companies holding unnecessarily large safety stocks because they do not correctly assess the risk of out-of-stock costs. AI can help remedy this situation. Together with its project partners, the Center for Applied Research on Supply Chain Services at Fraunhofer IIS is therefore developing and implementing a method that quantifies the uncertainty of a forecast and, based on this, automatically determines the optimal inventory level.

Optimize inventory planning in wholesale with AI

A reliable point forecast of goods sales is already a great help in planning inventory levels. However, such a forecast is never perfect. It is therefore of crucial importance to know the risk with which the forecast overestimates or underestimates actual sales at a given point in time. This risk is also called forecast uncertainty. In the case of sales forecasts, this means, for example, not being able to meet a demand or, on the other hand, holding too many goods in stock. In practice, companies mainly try to avoid out-of-stock situations. However, since they do not know the exact risk of this, they often keep too many goods in stock, tying up capital or even having to dispose of these goods in the end. The research project therefore explores various AI approaches that can be used to determine the uncertainty in forecasting sales in wholesale. The goal is to build on this to develop a mathematical optimization model that can process forecasts together with the forecast uncertainty and thus calculate the optimal inventory levels for the user.

Ensemble forecasts and Bayesian models in comparison

Together with Trevisto, the researchers of the Center for Applied Research on Supply Chain Services at Fraunhofer IIS are exploring two basic approaches for determining the uncertainty of a forecast. The first approach is so-called ensemble forecasts. Here, neural networks, other machine learning methods or modern statistical methods such as ARIMAX are used to generate a large number of different forecasts for the same value. Each individual forecast is referred to as a scenario. These scenarios are combined into an ensemble and their dispersion is used to model the forecast uncertainty. The second approach is to use Bayesian models, where the uncertainty of the forecast is implicitly modeled. Various scenarios can also be generated from this.

Expertise flows in

The researchers in the Center for Applied Research on Supply Chain Services at Fraunhofer IIS then develop a stochastic optimization model that determines the order quantity that is optimal across all the scenarios considered. For a perfectly fitting solution, both the forecast and the optimization model incorporate the expertise of the wholesale experts involved. To explore the transferability of the method, the developed models are then evaluated on the data of other wholesale companies.

From the forecast to the software solution

For a further exploitation of the results, especially for small and medium-sized enterprises, the embedding of the process in easily usable software is of crucial importance. For this reason, Trevisto and FIS are developing prototypes of both an independent software solution and the embedding in the SAP landscape in the form of demonstrators and piloting them at the project partner from the wholesale sector, Eisen-Fischer.

Project partners

  • Trevisto AG, Nuremberg
  • Eisen-Fischer GmbH, Noerdlingen
  • FIS Information Systems and Consulting GmbH (FIS), Grafenrheinfeld

Our core competences at our Center for Applied Research on Supply Chain Services at Fraunhofer IIS

In pursuit of our vision, we focus on three core competences.


Data spaces and IoT prototypes

Freeing data from application and system constraints and making it portable


Cutting-edge data analytics methods

Extracting the unique assets in companies’ data using analytics methods and artificial intelligence


Strategy and organization in the digital transformation

Bringing new perspectives to companies and guiding them through the challenges of digital transformation

Find out more about the Center for Applied Research on Supply Chain Services at Fraunhofer IIS


Business units

Based on the insights from our fields of research, we devise concrete advisory services for specific sectors and client groups. Find out more about our range of services in the business units »Logistics, Transport and Mobility«, »Wholesale and B2B Markets«, »Digital Production« and »Smart Services«.


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Scientific expertise in the reference process

The methodological expertise developed in our fields of research is informed by our specially developed Reference Process for Digital Transformation. Read about what this initiative means to us, and how we can use our expertise to comprehensively support companies.