We often come into contact with potential customers who claim that they cannot use a forecasting system since they are a “build-to-order” manufacturing operation. I find this a puzzling perspective, because whatever these organizations build requires lower level raw materials or intermediate goods. If those lower level inputs are not available when an order for the finished good is received, the order cannot be built. Consequently, the order could be canceled and the associated revenue lost.

I agree that in such an environment, forecasting the finished good is not always possible or particularly helpful. Sometimes it’s helpful, but not sufficient. In any case, it is critical to make sure that the underlying raw materials and intermediate goods that go into the finished good are available. Demand for these can certainly be forecasted.

The organization’s goal would be to maintain service level inventories for these intermediate goods that are high but not unaffordable. Planners will need to set optimal stocking levels for these materials, balancing service level requirements against available budget. Since a given intermediate good could serve as an input to more than one finished good, the volatility of the demand for the intermediate good would be less than the volatility of the demand for a specific finished good. Hence, the safety stocks necessary to keep high service level inventories of the intermediate goods would be relatively lean.

Three companies, all users of SmartForecasts, serve as interesting examples. The first is a chemical company, Bedoukian Research, which manufactures custom chemicals for various clients. Each of these “finished goods” is a unique combination of intermediate chemical compounds. Bedoukian begins its demand planning with a finished goods forecast, which drives the production schedule and allocation of essential production resources. This requires exercising considerable judgment, as finished goods demand changes dynamically.

Once these finished good forecasts are created, raw material requirements can be estimated via a bill of material disaggregation. Bedoukian combines these results with safety stock estimates, based on actual utilization rates and service level objectives to be achieved, to generate the complete, service level-driven forecast for raw materials. This has allowed Bedoukian meet its production requirements with significantly less inventory.

The second company manufactures the internal components for mobile phones, where finished goods are specialized combinations of these components. For example, an order may call for a certain number of phones with unique labels on the case. This is the finished good for this order. Everything that goes into that order, except for the label, is built out of standard components. Again, SmartForecasts will be used to keep lean, high service level inventories of the components. This company thought that the only way to manage component inventories was via bill of material aggregations. They are now looking at the actual utilization rate for the components and setting much leaner inventories while maintaining high component availability.

A third company, NKK Switches, which explored this topic in their recent webinar (see CFO Bud Schultz’ guest blog post), considered their products to be “unforecastable”. You can read more about it below, but overall NKK Switches was able to forecast components and meaningful aggregations of product families. By tracking forecast vs. actuals over several months, NKK was able to demonstrate the accuracy of its forecasts to its Asian factory suppliers, and convince them to shift from a “build-to-order” model to “build-to-forecast.” This change has resulted in dramatic reductions in lead times, in many cases cutting them in half, increasing customer satisfaction and the overall sales close rate.

The bottom line here is that there is a perfectly viable—I would say essential—method of demand forecasting for build-to-order businesses, setting high service levels for pivotal input resources. If you would like to know more, please drop me a note, at nelsonh at smartcorp dot com.

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.

Fluctuations in an inventory supply chain are inevitable. Randomness, which can be a source of confusion and frustration, guarantees it. A ship carrying goods from China may be delayed by a storm at sea. A sudden upswing in demand one day can wipe out inventory in a single day, leaving you unable to meet the next day’s demand. Randomness creates frictions that make it hard to do your job.

At first blush, it sometimes seems best to respond to randomness with the ostrich approach: head buried in the sand. You can settle on a prediction and proceed on the assumption that the prediction will always be spot on. The flaw in that approach is that it ignores statistical methods that allow us to make use of a wealth of knowledge about our knowledge itself—how confident we can be in our predictions, and what breadth of possibilities confront us. The efficient approach to tackling the problems that stem from randomness is not to ignore uncertainty, but to embrace it with eyes open.

As a fundamental tenet of Smart Software’s approach to forecasting, we will always provide you with an assessment of the level of uncertainty in forecasts. If you are expecting nothing more than an absolute figure—the demand for widgets in February will be 120 units—you may dismiss the added element of uncertainty as a negative, or lose faith in a forecast you had hoped would be definite. But we argue for what we consider the adult approach; you need to know what you are risking when you commit to a forecast and premise your decision-making upon it.

Your forecasts can have big consequences that go beyond inventory stocking levels. They can determine your raw materials needs or staffing levels—forecasts drive many important resource allocation decisions. If you have too much faith in the most likely outcome, without also specifically considering just how likely it is, you aren’t really understanding the risks you face, and you may put yourself in a precarious position.

The need to make fully informed decisions forces us to see, in a forecast, the plus/minus range of results with a certain likelihood of occurring. In the specific case of forecasts that are going into inventory systems, this is an important part of deliberately planning for contingencies. This is how you determine not only the inventory you need to maintain in order to satisfy typical demand, but also the additional inventory you need on hand to deal with most unexpected outcomes.

This importance only increases when you are trying to maintain a reliable store of critical spare parts. Between the cost of stocking additional inventory, and accounting for the degree of reliability in your forecasts, there is a balance that crystallizes when an airplane that you need in the air is grounded—because you don’t have the replacement for a damaged part.

(While stocking extra inventory relies on the high end of the uncertainty range, if cash flow is tight, it’s the low end of the range that becomes important. Treasury-minded users find value in this other side of uncertainty in scenarios where even minimal overstocking can be more of a problem than a missed sales opportunity, for example. Reliable information about the lowest likely outcomes pays off at this time.)

Inventory theory says that you need to think about the outer ends of likely possibilities and prepare to cope with more scenarios than just what is most likely. Randomness is a reality that can’t be ignored. The average is not the answer.

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.