Disregarding Demand Forecasting Technologies during Tough Economic Times Can Be a Costly Mistake Caught up in the general disillusionment with IT in a downturn has been demand forecasting (DFi technologies. Many companies blame DF technologies for supply chain problems such as excess inventory. Pinning the blame on and discontinuing DF technologies is the equivalent of throwing out the baby with the bathwater. The BF misunderstanding stemsfrom the fact that, despite sophisticated mathematical models and underlying technologies, the output from these systems is, at best, an educated guess about the future. A forecast from these systems is only as good as the assump- tions and data used to build the forecast. Even the best forecast fails when an unexpected eventsuch as a recessionclobbers the underlying assumptions. However, this doesn't imply that DF technologies aren't delivering the goods. But, unfortunately, many DF and supply chain technology implementations have recently fallen victim to this mindset. OP is part science and part art (or intuition)having the potential to signicantly impact a company's bottom line. In this column. you'll nd an overview of how UP is supposed to work. which you can compare with how most com- panies actually practice it. I'll conclude with suggestions on how to avoid common mistakes when implementing and using this par- ticular class of technologies. The Need for DF Systems DF is crucial to minimizing working capital and associated expenses and extracting maximum value from a company's capi- tal investments in property, plant. and equipment (PPE). It takes a manufacturing company a lot of lead time to assemble and stage the raw materials and components to manufacture a given number of products per day. The manufacturing company, in turn. generates its sales forecast numbers using data from a variety of sources such as distribution channels, factory outlets, value- added resellers, historical sales data, and general macroeconomic data. Manufacturing companies can't operate without a demand forecast because they won't know the quantities of nished goods to produce. The manufacturing company wants to make sure all or much of its nished product moves off the store shelves or dealer lots as quickly as possible. Unsold products represent millions of dollars tied up in inventory. The ip side of this equation is the millions of dollars invested in PPE to manufacture the nished products. The company and its supporting supply chain must utilize as close to 100 percent of its PPE investments. Some manufacturing plants make products in lots of 100 or 1,000. Generally. it's cost prohibitive to have pro- duction runs of one unit. So how do you extract maximum value from your investments and avoid having money tied up in unsold inventory? DF and supply chain management (SCM) technologies try to solve this problem by generating a production plan to meet forecasted demand and extract maximum value from PPE. while reducing the amount of capital tied up in inventory. Usually, the demand forecast is pretty close to the actual outcomes, but there are times when demand forecasts don't match the outcomes. In addition to unforeseen economic events. a new product introduc tion may be a stellar success or an abysmal failure. In the case of a phenomenal success. the manufacturing plant may not be able to meet demand for its product. Consider the case of the Chrysler PT Cruiser. It succeeded way beyond the demand forecast's projections. Should it have started with manufacturing capacity to fulll the runaway demand? Abso- lutely not. Given the additional millions of dollars of investment in PPE necessary to add that capacity, it would've backred if the PT Cruiser had been a op. The value provided by DF and sup- porting SCM technologies in this instance was the ability to add capacity to meet the amended forecast based on actual events. Demand forecasts can and do frequently miss their targets. The point to underscore here is that the underlying DF and supporting SCM technologies are critical to a company's ability to react and respond in a coordinated manner when market conditions change. The manufacturing company and its supply chain are able to benet from sharing information about the changed market con- ditions and responding to them in a coordinated manner. Despite best practices embedded in UP and SCM technologies to support this manner of collaboration, it plays out differently in the real world. How It Works in Real LifeWorst Practices A company prepares its forecast by taking into account data about past sales, feedback from distribution channels, qualita- tive assessments from eld sales managers. and macroeconomic data. DF and SCM technologies take these inputs and add existing capacities within the company and across the supply chain to gen- erate a production plan for optimum nancial performance. There's been incredible pressure on executives of publicly traded companies to keep up stock prices. This pressure. among other reasons. may cause manufacturing company executives to make bold projections to external nancial analysts (or Wall Street) about future sales without using the demand forecast generated from the bottom up. When the company realizes this diSparity between the initial projection and the forecast, the fore- cast is changed to reect the projections made by the company's ofcers, negating its accuracy. The company arbitrarily sets sales targets for various regions to meet Wall Street numbers that are totally out of sync with input provided by the regional sales managers for the DF process. Even though the regional sales managers' input may have a qualitative element (art), they tend to be more accurate, given their proximity to the customers in the region. Unfortunately, the arbitrary sales targets make their way back to the supply chain, and the result is often excessive inventory buildup starting at the distribution channels to the upstream suppliers. Seeing the inventory pile up, the manufacturing company may decide to shutdown a production line. This action affects upstream suppliers who had procured raw materials and components to meet the executive-mandated production numbers, which may cause them to treat any future forecasted numbers with suspicion. Most cost efciencies that could be obtained through planned procurement of raw materials and components go out the win- dow. It's very likely that the companies try to blame DF and SCM technologies for failing to provide a responsive and efcient sup- ply chain. even though the fault may lie in the company's misuse of the technologies and not the technologies themselves. Guarding against the Extremes Earlier in this column, I said that DF is part art or intuition and part science. The artlintuition part comes in when subjectmatter experts (SMEs) make educated estimates about future sales. These SMEs could range from distribution outlet owners to sales and marketing gurus and economists. Their intuition is typically combined with data [such as historical sales gures) to generate the forecast for the next quarter or year. During a recession. the SMEs tend to get overly pessimistic. The demand forecasts gener- ated from this mindset iead to inventory shortages when the econ- omy recovers. Similarly, during an economic expansion. the SMEs tend to have an overly rosy picture of the future. This optimism leads to inventory gluts when the economy starts to slow down. In both instances, blaming and invalidating DF and SCM technolo- gies is counterproductive in the long run. It's very rare that a demand forecast and the actual outcome match 100 percent. If it's close enough to avoid lost sales or create an excess inventory situation, it's deemed a success. DF and supporting SCM technologies are supposed to form a closed loop, with actual sales at the cash register providing a feedback mechanism. This feedback is especially essential during economic upturns or downturns. It provides the necessary information to a company and its supply chain to react in a coordinated and effi- cient manner. Don't let the current disillusionment with DF and SCM technol- ogies impede the decision-making process within your company. The intelligent enterprise needs these technologies to effectively utilize its capital resources and efficiently produce to meet its sales forecasts.Questions 1. What is DF and why is it important? 2. Why might a company executive make bold predictions about future demand to Wall Street analysts? 3. How might an executive's conullelrls to Wall Street analysts affect demand forecasts, and what are the consequences 0! doing so