Tremendous cost-saving efficiencies can result from optimizing inventory stocking levels using the best predictions of future demand. Familiarity with forecasting basics is an important part of being effective with the software tools designed to exploit this efficiency. This concise introduction (the first in a short series of blog posts) offers the busy professional a primer in the basic ideas you need to bring to bear on forecasting. How do you evaluate your forecasting efforts, and how reliable are the results?

A good forecast is “unbiased.” It correctly captures predictable structure in the demand history, including: trend (a regular increase or decrease in demand); seasonality (cyclical variation); special events (e.g. sales promotions) that could impact demand or have a cannibalization effect on other items; and other, macroeconomic events.

By “unbiased,” we mean that the estimated forecast is not projecting too high or too low; the actual demand is equally likely to be above or below predicted demand. Think of the forecast as your best guess of what could happen in the future. If that forecast is “unbiased,” the overall picture will show that measures of actual future demand will “bracket” the forecasts—distributed in balance above and below predictions by the equal odds.

You can think of this as if you are an artillery officer and your job is to destroy a target with your cannon. You aim your cannon (“the forecast”) and then shoot and watch the shells fall. If you aimed the cannon correctly (producing an “unbiased” forecast), those shells will “bracket” the target; some shells will fall in front and some shells fall behind, but some shells will hit the target. The falling shells can be thought of as the “actual demand” that will occur in the future. If you forecasted well (aimed your cannon well), then those actuals will bracket the forecasts, falling equally above and below the forecast.

Once you have obtained an “unbiased” forecast (in other words, you aimed your cannon correctly), the question is: how accurate was your forecast? Using the artillery example, how wide is the range around the target in which your shells are falling? You want to have as narrow a range as possible. A good forecast will be one with the minimal possible “spread” around the target.

However, just because the actuals are falling widely around the forecast does not mean you have a bad forecast. It may merely indicate that you have very “volatile” demand history. Again, using the artillery example, if you are starting to shoot in a hurricane, you should expect the shells to fall around the target with a wide error.

Your goal is to obtain as accurate a forecast as is possible with the data you have. If that data is very volatile (you’re shooting in a hurricane), then you should expect a large error. If your data is stable, then you should expect a small error and your actuals will fall close to the forecast—you’re shooting on a clear day!

So that you can understand both the usefulness of your forecasts and the degree of caution appropriate when applying them, you need to be able to review and measure how well your forecast is doing. How well is it estimating what actually occurs? SmartForecasts does this automatically by running its “sliding simulation” through the history. It simulates “forecasts” that could have occurred in the past. An older part of the history, without the most recent numbers, is isolated and used to build forecasts. Because these forecasts then “predict” what might happen in the more recent past—a period for which you already have actual demand data—the forecasts can be compared to the real recent history.

In this manner, SmartForecasts can empirically compute the actual forecast error—and those errors are needed to properly estimate safety stock. Safety stock is the amount of extra stock you need to carry in order to account for the anticipated error in your forecasts. In a subsequent essay, I’ll discuss how we use our estimated forecasts error (via the SmartForecasts sliding simulation) to correctly estimate safety stocks.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

The destructive impact of Hurricane Sandy has been both staggering and instructive. Our thoughts and best wishes for rapid recovery go out to all who have suffered personal or economic loss or damage. Now, in Sandy’s aftermath, we find ourselves thinking about accelerating recovery and planning for the next unforeseen event.

Our work with clients in the heavily hit mass transit sector presented a sobering view of damaged infrastructure, heavy equipment, and losses of essential inventory. Those most affected have seen a crush of work as inventory managers take stock of what they have, what they need and procure a mountain of replacement parts and products. This uniquely massive replenishment cycle presents all sorts of opportunities and considerations. For those who are still in this phase, and to help our collective preparation for the Next Big Event, here are a few thoughts:

Opportunity to immediately “right size” inventory

You may be in a position to receive a large, one-time infusion of funding for replacement inventory. It could be insurance money, federal relief or rainy day funds from your own treasury. Use the funding to establish the best possible inventory mix. Do not order to previously established Min/Max levels. Doing so may simply repeat excesses and shortfalls of the past.

A major event like Sandy presents a rare opportunity to transform your inventory. Start with an accurate demand forecast over the replenishment period, and generate safety stocks and reorder points that would address your critical needs. This can be accomplished in a matter of hours or days. Ordinarily, implementing optimal inventory levels may occur over several years, as excess inventory is gradually depleted. Now, however, you have a one-time opportunity to jump to the right answer. This shift can substantially reduce replenishment spending, freeing hundreds of thousands of dollars for other, more critical recovery uses.

Prioritize classes to be replenished

Be clear on what you need for crucial operations, and prioritize your replenishment. Which parts have long lead-times, and which are readily available? Obviously short lead-time items can be acquired in stages—getting just enough now, making funds available for the longer lead-time items.

Determine how much is “just enough”

This is where an accurate demand forecast, safety stocks and reorder point calculations come into play. Consider the service level you require—the likelihood that products will be on the shelves when you need them—which is really your tolerance for risk. Do this for each item, or class of items. This will tell you how much safety stock, in addition to your expected lead time forecast, you should have on hand. Iterating on service level-driven requirements will enable you to maximize the value of the replenishment budget at hand.

Statistical forecasting for intermittent demand vs. ‘rule of thumb’ methods

Now is the time to shift from ‘the way we’ve done it’ to the most accurate demand forecasting and inventory optimization process available to you. Greater forecast accuracy requires less safety stock—again, making inventory dollars available for other users. The greatest single category for improvement is intermittent demand. Most organizations do not apply solid statistical methods to this, instead resorting to the “heavy hammer rule”—have lots on hand because no one knows. Here is an area where SmartForecasts is especially adept, with a patented solution for forecasting intermittent demand. The resulting safety stock recommendations hit the service level goal nearly 100% of the time. Getting this right will save lots of spending now, and help minimize the potential for excess, obsolete inventory in the future.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.