Supply Chain Illustrated

woman-in-middle-of-moving-boxes

One-Size-Fits-Some

In traditional MRP, we start with a prediction of demand called a forecast, which is typically a range. We are forced to choose one single number to represent that range which we then input into a complex calculator. Included in those calculations are other (possibly inaccurate) inputs. The end result is another single number prediction as to how much we should order and when we should place that order. Hopefully, the order placement date isn’t yesterday or earlier. Very few people trust that single number, so they manipulate the result and update the prediction.

Imagine I need to arrive for a meeting between 9:30 and 10 am. I expect it will take between an hour and an hour and a half to get there (forecast). So, I choose to leave home using the mid-point of each, which means I calculate using 1 hour, 15 minutes travel time and a planned arrival of precisely 9:45 am (execution based on the forecast). I can already hear some of you thinking, that will work some of the time, but often, you’ll be late. Some are thinking it would be better to define 9:30 am as the arrival time and allow the full hour and a half for travel. Others of you are thinking let’s gamble and cut it closer because you don’t ever want to wait the extra hour you’ve just added to the calculation. Still, others are happy with the mid-point method. Each of you will be right some of the time. But if three of us are riding together, you’ll have to be extra clever to make sure that your choice is the applied solution.

A word about impact. In the above scenario, we have three travelers attending a conference. And let’s say their choices have made them late to the conference. If they are merely audience members, perhaps an acceptable risk. If they are panelists in the same timeslot, the program schedule is a factor. But if one is the first speaker on the program, then perhaps only that individual should determine the arrival time and the subsequent risk of counting on absolute precision throughout the journey.

Let’s translate this to our demand forecast and inventory management question. Your CFO will not accept the worst-case assumptions. Too much inventory is held. Your COO will not accept the best-case assumptions. Too many times you’ll be short of inventory. So, you’re forced to choose the middle ground, which will result in sometimes you win, sometimes you lose.

Add the crazy complication that your CFO is looking at the total inventory value and subsequently focuses on the high dollar items, while the COO is concerned with the quantity of individual parts, but really only the parts that are close to the low end of their quantity range.

There is no way to win this game until you change the rules.

We insist on trying to apply clever thinking to our faulty assumptions rather than fix the faulty assumptions. We feel there are no other options because we fail to take a step back and ask the right questions. We’re approaching this with a one-size-fits-all solution, which has always actually been one-size-fits-some.

Let’s have a conversation about the right questions and the right approach. Then, together, we can arrive at the right answers and the best solution.

John Melbye

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