Charles Smart

Charles Smart

When a customer calls for that product item that almost no one ever asks for, do you have it?  If your answer is no, it could cost you a sale, or even a customer.  Equally important, if that item is only occasionally requested, do you have too many units of the product on-hand to avoid stocking out of it?  If your answer is yes, then excess inventory is probably costing you money.

Forecasting intermittently demanded, “slow-moving” product items or parts is a problem that is especially well-known to managers in service parts organizations and companies in the capital goods industries, among others.  There are two common mistakes these firms make when forecasting intermittent demand:  First, they focus on estimates of per period demand when they should really focus on estimates of the inventory stocking requirements necessary to meet their desired service levels.  Second, they use forecasting methods that are inappropriate for intermittent demand.

Not All Forecasting Methods Are Created Equal

Traditional forecasting methods, such as exponential smoothing and moving averages, that are designed for normal, high-volume demand just don’t work well with intermittent demand.  Even worse, methods that some companies use specifically for intermittent demand, such as Croston’s method and Poisson models, fail to provide accurate inventory stocking recommendations.  And judgmental, “gut feel” techniques used by some organizations are neither feasible nor adequate, especially when you are trying to forecast thousands of items at a time.

Here’s the problem:  Traditional forecasting methods, commonly used by ERP/SCM and other forecasting software systems, fail because they try to identify recognizable patterns in the demand data, such as trend and seasonality.  However, intermittent demand data don’t exhibit such regular patterns and tend to be characterized by a preponderance of zero values.

The older, traditional technologies ignore the special role of zero values when analyzing demand and tend to simply “smooth over” the zero and occasional non-zero values found in intermittent data.  But the zeroes are very important!  If you don’t properly account for the timing and frequency of zero values, you can’t generate an accurate demand forecast or properly estimate the required inventory level for an intermittent item.  However, new  empirical methods like statistical “bootstrapping” don’t make assumptions about patterns in the data, do account for the zero values found in intermittent demand, and can produce accurate forecasts and inventory stocking estimates.

 

Figure 1 below shows the challenge demand planners face. It plots the demand over 36 months for three intermittent part items (shown in red, blue and green),.  Many months had no demand at all (zero values), and when demand did appear, its value varied erratically.

Figure 1

Figure 1

Figure 1: Examples of Monthly Demand for Intermittently Demanded Parts

Evaluating Intermittent Demand Forecasting Software

When you evaluate forecasting software, it’s important to “look under the hood”, because how the software does its job is just as important as what it does.  Of course, most companies need software that handles a variety of forecasting situations, including forecasting high volume, frequently demanded products.  But, software that solves the intermittent demand forecasting problem is very specialized and should provide certain capabilities that enable it to detect intermittent demand patterns and create accurate forecasts which facilitate demand planning and cost-effective inventory management decisions.

 

When evaluating software for intermittent demand forecasting, here are three things you should look for:

  1. The forecasting method should accurately forecast the entire distribution of lead time demand values (i.e., total demand over a lead time), rather than produce just a single number representing the average demand per period.
  2. The solution should provide optimal service level inventory requirements for satisfying total lead time demand (for example, the minimum safety stock and inventory needed for a 90%, 95% or 99% likelihood of not stocking out of a product item).
  3. The solution should accurately reflect the asymmetrical (i.e., non-normal) nature of the lead time demand distribution—a phenomenon typical of intermittently demanded items, as illustrated in the demand distribution in Figure 2 below.
Figure 2

Figure 2

Figure 2: Distribution of Lead Time Demand for a Service Part Exhibiting Intermittent Demand

In addition, to make sure that you find a comprehensive demand forecasting solution that works for all of your company’s needs, you should:

  1. Investigate and understand the system’s capabilities to satisfy other needs you may have besides intermittent demand forecasting, such as promotion/event modeling, new product forecasts, multi-level forecasting at the item and group level, multivariate regression for causal analysis, and automatic forecasting of large-scale forecasting jobs, among other needs;
  2. Educate yourself about forecasting software options at events such as those sponsored by the Institute of Business Forecasting;
  3. Test drive solutions to compare results and confirm that you can achieve the accuracy and value you expect.  In particular, make sure that the inventory recommendations provided by a possible solution actually hit the service level targets you specify; this ensures that future decisions you make about your inventory investment will be the correct ones.

By making the right forecasting software decision, the payoff can be tremendous.  Depending on the size of your company’s inventory, an accurate solution for forecasting intermittent demand can lead to first-year inventory savings in the millions of dollars and service level improvements of 10% to 20% or more.  These kinds of results free up cash for other uses, reduce the number of lost sales, help to guarantee better customer service, and increase shareholder value.

Charles Smart
President and CEO
Smart Software, Inc.