For the final post in this series, I thought I'd go back to basics -- the sourcing basics -- and talk about ways to apply optimization technology from vendors such as CombineNet, Trade Extensions, Emptoris, Iasta and BravoSolution to tackle commodity management challenges. Please note that the capabilities of vendors in the above list may vary, but from our vantage point (having seen demonstrations and worked with some of the tools ourselves), this group represents the deepest set of optimization generalists in sourcing. Regardless, advanced sourcing/optimization -- take your pick of moniker -- can be a key driver of a range of benefits including a more fluid RFI/RFP process, more flexible data collection, supplier creativity (i.e., the ability to express alternative specifications or alternative proposals), the ability to fully explore (vs. just weight) all price and non-price factors in a tender, the creation and application of constraints and an ability to run scenarios based on constraints and present a menu of outcomes for stakeholders to consider.
Why does this matter in commodity management? For one, it can transform the way we engage suppliers in the sourcing and change the basis of the negotiation process from one of specified price discovery to price exploration and a narrowing of a virtually unlimited number of constraints and options to the best possible solution. Consider, for example, the ability to look at different commodity scenarios through such an approach (e.g., who buys, from whom, how much, when, from what geographies/plants, on what terms, etc.) Or consider how such an approach can help organizations discover unforeseen potential advantages/disadvantages of specific suppliers (e.g., perhaps a mill that can run a shift for your production requirements at an off-peak time might enjoy cheaper electricity costs in a certain market, but the available skilled labor force for that shift might be less, resulting in smaller order quantities).
Advanced sourcing/optimization also enables procurement teams to better understand the cost of different commodity trade-offs. A real-life example that I've observed is how best to allocate awards to reduce the chance of a supply disruption in specific geographies. Granted, a constraint by nature is "limiting." In other words, it increases price on a unit cost level, and also perhaps total cost when one does not factor in variables that are not included in a typical landed costs model. Yet at the same time, constraints are typically how we operate our companies and our own lives. We would all love to stay an extra hour at the gym or the pub after work, but we have friends and family we must get home to visit (in this case, the constraint in the equation to factor into the time spent either lifting weights or pints is required/desired home time).
Above all, advanced sourcing/sourcing optimization approaches are far more collaborative in nature -- both internally and with suppliers -- than rigid 5, 7 or 9 step sourcing processes that rely on set steps and parameters (e.g., data collection first, structured RFI authoring, auction here, sealed bid there, etc.). And in a time when we need any advantage out there to come out on top of our competitors given rising and volatile commodity markets, isn't it worth exploring all the potential options on the table -- including the other table that you don't even know about until a supplier volunteers the idea?