The development of one shared spare parts strategy is an important step to clearly define the target state and the detailed way to get there. The roadmap should always be developed in cross-functional workshops (warehouse, maintenance, purchasing, finance), which breaks down silo thinking of individual departments and allows common goals to be pursued. Common goals could be to reduce inventories, increase availability, or reduce complexity and parts diversity through standardization. With the continuation of the project, the participants of the workshops should – in addition to implementing – also work on acceptance in the company as a whole and thus increase their likelihood of implementation.
“Data is the gold of the 21st century” – This or something similar is often said. The quality of the data has a major influence on the findings and actions that can be derived from this data. Due to specific aspects (type designations, dimensions, article codes…) and their susceptibility to errors, in the spare parts area there are often duplicates of the same items, which often differ in the system only by individual letters, numbers or symbols. The solution is to use algorithms to identify and clean up such duplicates. This also includes consolidation across different languages. At the same time, incomplete master data should be enriched with additional information (so-called attributes, e.g. layer thickness, material, composition, hazard class, …). This information is also essential in procurement in order to identify the right articles in catalogs and price lists and ultimately find suitable suppliers and thus to purchase correctly.
The next essential step to achieve measurable results is to structure and analyze the existing information. Essential for this are the actual consumption and inventories in order not to treat all spare parts the same. Taking actual consumption into account helps to avoid losing focus and to derive material-specific consumption ratios. After the derivation of consumption key figures (=”VBK”), the individual optimization of the respective category takes place.