Reliable in the long term: Predicting spare parts requirements for the next decade

BSH Hausgeräte GmbH is one of the world's leading companies in the industry and the largest manufacturer of household appliances in Europe. BSH employs almost 60,000 people worldwide and maintains a correspondingly large sales and customer network, which also includes a number of central warehouses for spare parts. A key element of BSH's quality promise is the long service life of the appliances it produces. This includes guaranteeing the availability of spare parts so that repairs and maintenance can be carried out over the long term - even if the appliance is no longer in production. A major challenge for the company: Because at the end of the regular production run, dispatchers have to estimate how many spare parts for the respective products are likely to be in demand over the next few years and have corresponding quantities pre-produced and stored.

Estimating requirements from experience

Dispatchers at BSH and other manufacturers traditionally decide on the quantities of spare parts to be stocked based on detailed knowledge of the items and the associated empirical values. In doing so, they draw on their experience from previously recorded product life cycles and take into account previous sales of the respective spare part. However, this process is time-consuming, as all relevant information has to be gathered from different interfaces and systems. In addition, high safety stocks are traditionally calculated due to the high level of uncertainty in the estimation. BSH has therefore commissioned the Supply Chain Services working group to find a data-driven solution that can provide automated and reliable support for the decision.

»Clean« master data as the basis

During the project, it quickly became clear that the task could not be solved trivially, even in a data-driven manner, for various reasons: Firstly, there were too few consistent data sets available on which the analyses could be based, meaning that master data had to be supplemented through clever linking and heuristics. In addition, the classic methods for predicting time series were not applicable. These do not mathematically map the pattern of a demand curve, but rather extend the trend of spare parts requirements linearly. The BSH dispatchers therefore initially supported the AI experts from Fraunhofer with their specialist knowledge so that a "clean" database could be created together.
Reliable forecasting using clustering.

Reliable forecasting using clustering

Building on this database, the researchers developed a new forecasting method for spare parts by deriving typical sales curves from various clusters of similar spare parts. If the need or demand for a new spare part is to be predicted, it is assigned to a specific life cycle cluster based on its master data. This clustering was repeated iteratively so that the data-driven forecast is now a valuable aid for dispatchers.

Development of a data-driven forecasting tool

To make the forecasting process developed here easy to use, the researchers developed a graphical interface that displays the most important decision-relevant data for a specific spare part in addition to the forecast. This forecasting tool can also be used by dispatchers to generate reports on all spare parts. For example, the system can also identify potential for scrapping by identifying items that are no longer likely to be needed. This tool has proven its worth over the last two years and everyone involved is working on expanding it - however, the final decision on the spare parts inventory always remains with the dispatchers.

Optimization of the storage quantity

Based on a precise demand forecast, the question of a cost-optimized ordering and production strategy arises. The costs of an ordering strategy are made up of two conflicting components. On the one hand, a high stock level is associated with high storage costs or even scrapping costs if too many parts are stored. On the other hand, the unit costs decrease with the order quantity, so that large one-off orders are more cost-effective than many individual orders. Using mixed-integer programming techniques, the Fraunhofer Working Group for Supply Chain Services at Fraunhofer IIS calculates a cost-optimized ordering strategy that finds the best possible compromise between the two cost factors and at the same time covers the forecast requirements at all times. A particular challenge here is the erratic behavior of ordering costs with increasing quantities. However, thanks to clever modelling techniques, an optimized ordering strategy can be calculated in just a few seconds. This allows the optimization software to be used in real time. In a further research project, the optimization even takes into account uncertainties in the demand forecast in order to minimize the risk of uncovered demand.