Integration of a cloud-based platform with SAP’s ERP system aimed at Spare Parts Inventory optimisation. The Luigi Lavazza S.p.A. case study

Efficient spare parts inventory management is paramount to many companies, from capital-intensive manufacturers to service organizations, such as businesses in the Food and Beverage Industry. Different from Work-In-Process and finished product inventories, spare parts are kept in stock to support maintenance operations and to protect companies against the risk of equipment failures. Many firms face the challenge of keeping on stock large inventories of spare parts, with excessive associated holding and obsolescence costs, because of the difficulty in defining a good spare parts management strategy, and that is due to the intrinsic nature of these items, normally very slow-moving, with highly stochastic and erratic demand. The problem stems from the fact that most companies treat spare parts like other inventory types, and they apply to the former supply chain techniques that do not work for them. Understanding this could help spare parts managers and companies save a lot of money and gain a competitive advantage over their competitors. This paper addresses this issue by analysing the spare parts inventory management of one of the most important coffee companies in the world, and certainly in Italy: Luigi Lavazza S.p.A. Lavazza spare parts inventory has always been managed through SAP’s Enterprise Resource Planning (ERP) system and a standardised use of the Reorder Point logic for all items regardless of their characteristics, such as lead time, demand trend, component cost or criticality, etc. However, it would be more productive to analyse and categorise spare parts into classes, based on these and other features, and apply different forecasting models to the various groups in order to set optimal replenishment parameters for each of them. Nevertheless, it would be impossible for Lavazza Spare Parts planners to periodically do this job for thousands of components, hence the integration with the existing ERP system of a platform that analyses demand trends and automatically generates reorder points could be an optimal solution, both operationally and economically. This thesis briefly analyses the method currently used by Lavazza to manage its spare parts inventory. To this end, a heterogeneous sample of spare parts, supplied over a 21-month period, was examined, whereby the forecast accuracy was determined, on a rough basis, by computing two forecast error metrics, the Mean Absolute Deviation and the Root Mean Squared Error. Finally, the new platform was shortly described. This will automatically analyse spare parts’ demand trends, over a predefined time-period, with the aim of dividing them into classes, it will then assign the best forecast model, and will ultimately determine optimal supply parameters for each of them, thus facilitating the work of Lavazza planners. Although it had not been launched yet, and therefore no comparisons with the current method could be made at the time of writing, considerable benefits could already be expected, first and foremost an increased forecast accuracy, which would presumably lead to a reduction in inventory, hence a decrease in operational costs, while increasing customer service level. It would certainly be very interesting, in a few months’ time, to compare these two methodologies and see if the latter actually brings the anticipated and desired improvements.