This question is encased in a nut that’s quite tough to crack. We hinted at the importance of defining it three years ago in our post that asked how do you define Procurement success which noted that if you consider the art of the Strategic Sourcing Process, the Category Management Process, or the Contract Management Lifecycle, you [not only] see that they all start about the same at a high-level but that a key requirement of each step is an acceptable definition of success.
This means that if you want to be successful, you need a good definition of success but what should it be?
If you ask the CFO, she will say it should be cost savings! Reduce the outflow!
If you ask the Chief Engineer, it should be the best quality and reliability money can buy!
If you ask the Production Chief, it should be rock solid supply availability.
If you ask the CMO, it should have the most unique gee-whiz features on the market for the biggest marketing splash.
If you ask the VP of Sales, it should be the product that comes with the most value-adds so they can command the greatest price.
And so on.
On SI, we have repeatedly said the definition of procurement success should always be the outcome that brings the most value to the organization, but this can be hard to define when there are a number of competing viewpoints on what value is.
However, we can define Value as the outcome that balances the tradeoff between the goals of the respective stakeholders for maximum return against an agreed upon value scale that normalizes a dollar of savings (for the CFO) against a reliability metric (for the Chief Engineer) against an expected availability metric (for the Production Chief) against a feature differential against the market average (for the CMO) against a value-add differential (for the VP of Sales) [etc].
Now, you might be wondering how you do that? The answer is simple: define an expected dollar value. It’s not as hard to do as you think (as long as you have the [big] data and the model and the software to calculate it)!
The CFO metric is easy, a dollar of savings is a dollar of savings.
The reliability metric is not that much harder. A failure rate of 90% vs 93% during the warranty period has an incremental cost equal to 3% of the units times replacement cost (which is base product cost + processing cost if outside of supplier warranty or processing cost + return cost if inside supplier warranty) and this cost can be amortized per unit.
The supply availability metric is involved, but still easy to define. First you have to calculate an expected chance of disruption based on it. Once you do, the cost can be approximated as follows: (% chance of disruption * % length of disruption x cost per day of disruption) amortized by units. If there is 10% chance of disruption, then you expect one every 10 years, for the estimated length of time, at the estimated cost per day, and amortize that cost over each unit purchased each year. Not perfect, but a good approximation. To find the conversion from expected availability percentage to chance of disruption, you mine your data and extrapolate the multiplier. Easy peasy (with a modern cognitive or deep analytics platform).
The CMO metric is tricky. Just how much better is that gee-whiz feature? Probably not nearly as important as the CMO claims. To figure out an approximate dollar value per unit here, you will have to mine historical data to see the incremental marketing value from the company’s “most differentiated” or “feature rich” products compared to its “least differentiated” or “feature poor” products as compared to the estimated market share each product obtained. If “feature rich” products typically command an extra 10% of market share, each unit is valued at a premium of 10%.
The value-add is easy — mine the historical data to extract the dollar value of each “value-add” available to the company.
Then, to find the optimal trade-off during a sourcing event, build a multi-objective optimization model that maximizes the overall value generated from these goals.
In other words, what used to be downright impossible is now pretty straight forward with strategic sourcing decision optimization and cognitive sourcing.