Even though I've long been a vocal supporter of supplier performance development and management initiatives focused on joint cost take-out in various forms -- from lean supplier development initiatives to demand aggregation programs that offer a rebate back to suppliers -- for many companies, it's hard to calculate the ROI of focused SPM initiatives. This means it's also often challenging to get funding to support such programs in the first place. Yet there is hope. My colleague Sherry Gordon, who is an expert on the topic, wrote a Wiki-paper (whitepaper) for the Iasta Wiki earlier this year. While the paper is a great general SPM primer for companies at all stages of supplier development and collaboration maturity, it also features a section on how to calculate the SPM return on investment question specifically.
Here, Sherry suggests that there are two specific approaches to create a hard-cost model savings/cost avoidance calculation (and hence, the ROI for labor and technology to deploy programs). The first involves estimating the cost of supplier failures, including "the costs associated with poor supplier quality (such as defective materials or late delivery, etc.)." Under this ROI-driven model, organizations should "interpolate how much these costs could be reduced by implementing SPM." One manufacturer of environmental care products used this model to calculate "its total failure costs and then estimated how much a supplier performance management system could potentially reduce costs." The results of the program "far exceeded its initial goals and proved the ROI for the SPM project" and "it continues to reap additional savings and benefits as it extends the use of the SPM program."
One of the major challenges I've personally observed over the years when it comes to creating these types of ROI models is the chicken/egg approach to gathering and combining the right sets of information before an official SPM system and program is in place. After all, the real benefit of SPM technology is to bring together both qualitative and quantitative datasets and help the right set of individuals prioritize not the sum of the information, but the intelligence contained within (the two are not the same). Before this happens, sorting through systems data can feel like a needle in a haystack exercise where 80% of the needles end up dissolving once you pick them up (e.g., a late or delayed shipment or SLA non-conformance can often be the result of the internal organization or a third-party -- such as a carrier -- besides the supplier, or simply incorrect information entered into the system).
Stay tuned as we look at the second model for calculating SPM ROI.