As a common practice, companies identify a list of spare parts for different processes, assign a spare part number to them and manage their stocks separately. What they fail to focus on most of the times, specially with Tooling Spares is identifying common spare part families, clubbing them together and leveraging that for massive cost savings and inventory optimization.
COPIT (Common Parts Identification Technique) is a specialized service provided by Dainsta where we make use of Machine Learning and Data Analytics technologies to identify common spare parts based on the parametric features and categorize them into families to take maximum advantage of bulk & MOQ based orderings.
Let us explain this with an example. A manufacturing company named ABC corporation holds 50,000 spare parts across their 3 global locations. A lot of the tooling spares among these are “Stainless Steel L brackets” with slightly different sizes and holes at different locations. As a common practice, ABC corp. place separate orders with different MOQs and hold separate inventories for all these L brackets resulting into huge spare part costs.
When Dainsta performs a COPIT analysis on all their spare parts, our algorithm identifies all these common L Brackets and categorize them into on single family. Based on the yearly consumption, we recommend bulk ordering quantities and the optimum pricing. Now even though the parts are slightly different from each other, but the manufacturing and setup process for them remains the same and hence when ordered in bulk, ABC corporation is able to get the same parts at a more economical price, taking advantage of the cost saved in Setup, material ordering and bulk quantities. As a result, ABC corporation is able to save up to 45% cost for a single family of spare parts.