Why Scenario-based planning helps planners better manage risk and create better outcomes.
If you are reading this, you are probably a supply chain professional with responsibilities for demand forecasting, inventory management or both. If you live in the 21st century, you use software of some kind to help you do your job. But what, fundamentally, does your software do for you?
Traditionally, software has served as a delivery vehicle for equations. Even if you decided early on in life that you and equations don’t get along, they can still do something for you, and you can live with them—provided some software keeps all that math at a safe distance away.
This is fine, as far as it goes. But we at Smart Software think you would do better by trading in your equations for scenarios. Most often, the point of an equation is to give “the answer”, typically in the form of a number, as in “next month’s demand for SKUxxx will be 105 units.” Results like these are helpful, but incomplete.
Forecasting can be thought of as a computing problem, but it is more helpful to think of it as an exercise in risk management. The equation’s forecast of 105 units does not include any indication of the uncertainty in the forecast, though there is always some. It does not help you think about plausible contingencies: what if demand is for more than 105 units? What if it’s for fewer than 105? Could it get as high as 130 or as low as 80? Is 80 even remotely likely?
This is where scenario-based analysis shows its advantage. One definition of “scenario” is “a postulated sequence of events.” Our definition is more extensive: a scenario is “a postulated sequence of events and their associated probabilities of happening.” Scenarios are the ultimate what-if planning tool. Forecasting by equation will predict a demand for 105 units. Scenario forecasting produces a bundle of possible demand figures, some more likely and others less so. If there are few or no scenarios as low as 80, you can let that contingency go.
Plus-or-Minus How Much?
Those who are better versed in equation-based forecasting might protest that equation-based software sometimes provides indications of the “plus or minus” of a forecast, complete with a bell-shaped curve indicating the relative likelihood of various contingencies. However, when you see a perfect bell-shaped distribution, you know you are being asked to rely on a theoretical assumption that is only sometimes valid.
Scenario forecasts do not rely on that assumption. In fact, they need not rely on any pre-conceived mathematical assumption whose main selling point is that it simplifies analysis. You don’t need a simplified analysis–you need a realistic analysis based on facts.
Cutting-edge software produces scenario forecasts, not just for demand planning but also for inventory management. Demand is a key input to inventory software, along with supplier behavior as reflected in replenishment lead times. Both demand and supply need to be forecasted if you want to see the consequences of, for instance, choosing a reorder point of 15 and an order quantity of 25.
Inventory systems are what is called “path sensitive”, meaning that any particular sequence of demand values will yield different performance than the same demand values in a different order. For example, if all your highest demand periods come bunched up, one after another, you’ll have much more difficulty keeping stocked than if the same high demand periods are spaced apart with time to restock in between. Scenarios reflect these differences in sufficient detail to yield average performance metrics reflective of the various contingencies inherent in uncertain demand.
Figure 1 illustrates the difference between an equation-based forecast and forecast scenarios. The green cells hold 10 months of demand for a spare part. The blue cells hold an equation-based forecast that calls for average demand of 1.5 units in months 11, 12 and 13. The pistachio-colored cells hold eight scenario forecasts, though in practice our software would generate tens of thousands of scenarios. Now, the scenarios also average out to 1.5 units per month, but they go further and display the wide variety of ways that the next three months could play out. For instance, reading vertically, the monthly demand could range from 0 to 3. Reading horizontally, the three-month totals could range from 0 to 6, compared to the equation-based estimate of 4.5. Continuing with this toy example, if you have 5 units on hand and the replenishment lead time is greater than 3 months, the equation-based model says you will be ok over the next 3 months, but the scenario-based results say you have 1 chance in 8 (12.5%) chance of stocking out. Equivalently, you have an 87.5% service level. If the part is critical and you are aiming for a 95% service level, you are at risk of missing your item availability goal.
Remember, equation-based forecasting gives you information, but shallow information. Scenario-based forecasting can tell you not just what result is most likely but also how reliable any of a variety of predictions are—and this allows you to bring your judgment to bear on balancing risk and stocking expenses—all automated to scale to a vast catalog of items.