Forecast accuracy is a key metric by which to judge the quality of your demand planning process. (It’s not the only one. Others include timeliness and cost; See 5 Demand Planning Tips for Calculating Forecast Uncertainty.) Once you have forecasts, there are a number of ways to summarize their accuracy, usually designated by obscure three- or four-letter acronyms like MAPE, RMSE, and MAE.  See Four Useful Ways to Measure Forecast Error for more detail.

A less discussed but more fundamental issue is how computational experiments are organized for computing forecast error. This post compares the three most important experimental designs. One of them is old-school and essentially amounts to cheating. Another is the gold standard. A third is a useful expedient that mimics the gold standard and is best thought of as predicting how the gold standard will turn out. Figure 1 is a schematic view of the three methods.

Figure 1: Three ways to assess forecast error

The top panel of Figure 1 depicts the way forecast error was assessed back in the early 1980’s before we moved the state of the art to the scheme shown in the middle panel. In the old days, forecasts were assessed on the same data used to compute the forecasts. After a model was fit to the data, the errors computed were not for model forecasts but for model fits. The difference is that forecasts are for future values, while fits are for concurrent values. For example, suppose the forecasting model is a simple moving average of the three most recent observations. At time 3, the model computes the average of observations 1, 2, and 3. This average would then be compared to the observed value at time 3. We call this cheating because the observed value at time 3 got a vote on what the forecast should be at time 3. A true forecast assessment would compare the average of the first three observations to the value of the next, fourth, observation. Otherwise, the forecaster is left with an overly optimistic assessment of forecast accuracy.

The bottom panel of Figure 1 shows the best way to assess forecast accuracy. In this schema, all the historical demand data are used to fit a model, which is then used to forecast future, unknown demand values. Eventually, the future unfolds, the true future values reveal themselves, and actual forecast errors can be computed. This is the gold standard. This information populates the “forecasts versus actuals” report in our software.

The middle panel depicts a useful halfway measure. The problem with the gold standard is that you must wait to learn how well your chosen forecasting methods perform. This delay does not help when you are required to choose, in the moment, which forecasting method to use for each item. Nor does it provide a timely estimate of the forecast uncertainty you will experience, which is important for risk management such as forecast hedging. The middle way is based on hold-out analysis, which excludes (“holds out”) the most recent observations and asks the forecasting method to do its work without knowing those ground truths. Then the forecasts based on the foreshortened demand history can be compared to the held-out actual values to get an honest assessment of forecast error.

What do you do when you are forecasting an intermittently demanded item, such as a spare part, with average demand of less than one unit per month?  Most of the time the demand is zero, but the part is significant in a business sense; it can’t be ignored and must be forecasted to be sure you have adequate stock.

Your choices tend to center around a few options:

Option 1:  Round up to 1 each month, so your annual forecast is 12.

Option 2:  Round down to 0 each month, so your annual forecast is 0.

Option 3:  Forecast “same as same month last year” method so the forecast matches last year’s actual.

There are obvious disadvantages to each option and not much advantage to any of them.  Option 1 often results in a significant over forecast.  Option 2 often results in a significant under-forecast.  Option 3 results in a forecast that is almost guaranteed to miss the actual significantly since the demand isn’t likely to spike in the exact same period. If you MUST forecast the item, then we would normally recommend option 3 since it is the most likely answer that the rest of the business would understand. 

But a better way is to not forecast it at all in the usual sense and instead use a “predictive reorder point“ keyed to your desired service level. To calculate a predictive reorder point, you can use Smart Software’s patented Markov bootstrap algorithm to simulate all possible demands that could occur over the lead time, then identify the reorder point that will yield your target service level.

You can then configure your ERP system to order more when on-hand inventory breaches the reorder point rather than when you are forecasted to hit zero (or whatever safety stock buffer is entered). 

This makes for more common-sense ordering without the unneeded assumptions that are required to forecast an intermittently demanded, low-volume part.

“Despite what you’ve seen in your Saturday morning cartoons, elephants can’t jump, and there’s one simple reason: They don’t have to. Most jumpy animals—your kangaroos, monkeys, and frogs—do it primarily to get away from predators.”  — Patrick Monahan, Science.org, Jan 27, 2016.

Now you know why the largest ERP companies can’t develop high quality best-of-breed like solutions. They never had to, so they never evolved to innovate outside of their core focus. 

However, as ERP systems have become commoditized, gaps in their functionality became impossible to ignore. The larger players sought to protect their share of customer wallet by promising to develop innovative add-on applications to fill all the white spaces.  But without that “innovation muscle,” many projects failed, and mountains of technical debt accumulated.

Best-of-breed companies evolved to innovate and have deep functional expertise in specific verticals.  The result is that best of breed ERP add-ons are easier to use, have more features, and deliver more value than the native ERP modules they replace. 

If your ERP provider has already partnered with an innovative best of breed add-on provider*, you’re all set! But if you can only get the basics from your ERP, go with a best-of-breed add-on that has a bespoke integration to the ERP. 

A great place to start your search is to look for ERP demand planning add-ons that add brains to the ERP’s brawn, i.e., those that support inventory optimization and demand forecasting.  Leverage add-on tools like Smart’s statistical forecasting, demand planning, and inventory optimization apps to develop forecasts and stocking policies that are fed back to the ERP system to drive daily ordering. 

*App-stores are a license for the best of breed to sell into the ERP companies base –  being listed  partnerships.

​If you had your head up lately, you may have noticed some additional madness off the basketball court: The failure of Silicon Valley Bank. Those of us in the supply chain world may have dismissed the bank failure as somebody else’s problem, but that sorry episode holds a big lesson for us, too: The importance of stress testing done right.

The Washington Post recently carried an opinion piece by Natasha Sarin called “Regulators missed Silicon Valley Bank’s problems for months. Here’s why.” Sarin outlined the flaws in the stress testing regime imposed on the bank by the Federal Reserve. One problem is that the stress tests are too static. The Fed’s stress factor for nominal GDP growth was a single scenario listing presumed values over the next 13 quarters (see Figure 1). Those 13 quarterly projections might be somebody’s consensus view of what a bad hair day would look like, but that’s not the only way things could play out.  As a society, we are being taught to appreciate a better way to display contingencies every time the National Weather Service shows us projected hurricane tracks (see Figure 2). Each scenario represented by a different colored line shows a possible storm path, with the concentrated lines representing the most likely.  By exposing the lower probability paths, risk planning is improved.

When stress testing the supply chain, we need realistic scenarios of possible future demands that might occur, even extreme demands.   Smart provides this in our software (with considerable improvements in our Gen2 methods).  The software generates a huge number of credible demand scenarios, enough to expose the full scope of risks (see Figure 3). Stress testing is all about generating massive numbers of planning scenarios, and Smart’s probabilistic methods are a radical departure from previous deterministic S&OP applications, being entirely scenario based.

The other flaw in the Fed’s stress tests was that they were designed months in advance but never updated for changing conditions.  Demand planners and inventory managers intuitively appreciate that key variables like item demand and supplier lead time are not only highly random even when things are stable but also subject to abrupt shifts that should require rapid rewriting of planning scenarios (see Figure 4, where the average demand jumps up dramatically between observations 19 and 20). Smart’s Gen2 products include new tech for detecting such “regime changes”  and automatically changing scenarios accordingly.

Banks are forced to undergo stress tests, however flawed they may be, to protect their depositors. Supply chain professionals now have a way to protect their supply chains by using modern software to stress test their demand plans and inventory management decisions.

Figure 1: Scenarios used the Fed to stress test banks.

Figure 2: Scenarios used by the National Weather Service to predict hurricane tracks

Figure 3: Demand scenarios of the type generated by Smart Demand Planner

Figure 4: Example of regime change in product demand after observation #19