Our Cost Reduction Programs start with your current purchasing spend data-set.
If your product categories are product facing e.g. Wheels, Brackets, Housings - we may recommend changing them to ones that are cost driver related e.g. 5 axis Milling, Slide-head Turning, Transfer Stamping...etc. This ensures parts are grouped in an efficient manner and end up with the most optimum supplier.
For suitable commodities we identify cost drivers and perform Multiple Regression Analysis to identify outliers from a long list of part numbers. The example (right) is based on Nitrile Rubber O-Rings. In this example, the cost drivers: ID and cross section were easily taken from the description field in the PLM system. For more complex products with multiple cost drivers and where there are 100's of part numbers, the coding of these parameters can be outsourced to a low cost region like India. In this example, there are two outliers with significant cost gap i.e. opportunity. If appropriate, a should cost estimate can be created for the outliers to confirm the opportunity before approaching the supplier.
Although, in this example the part price is low, the volume is high and since the analysis takes short amount of time - the ROI is high!
A should cost estimate is created by researching material, labour, overheads, profit and other costs associated with the manufacturing location to create an accurate estimate. Also known as bottom-up estimation or clean sheet costing. We have a detailed understanding of cost drivers associated with the various manufacturing processes and with a high level of accuracy can determine the optimum /fair price for a product. At times the should cost estimate can be at a more granular level than the supplier and this is the basis for successful fact-based negotiations and cost reduction delivery.