Lost sales, increased costs and poor customer service due to inaccurate forecasts can wreak havoc with a company's bottom line. Not too many companies can remain profitable by writing off hundreds of thousands of dollars worth of merchandise on an annual basis or sitting idly by as their customer bases erode due to inadequate service. But in honor of Valentine's Day, this first installment in the Demand Forecasting 2.0 series of webzines talks to the love-hate relationship that businesses have with demand forecasting. Like a longtime lover, many businesses find it difficult to live with it, or without it!
Given the costly and detrimental effects of poorly forecasted demand, are you giving this aspect of your supply chain the attention it deserves? If you're like most companies, then probably not. It's grown into a complex process with a number of challenges that come into play:
Reliance on a forecasting method which handles just one component (for example, seasonal) will not be effective in handling other components or changing lifecycle characteristics of a SKU. This approach can be very risky because demand patterns vary significantly based on both the type of SKU and where the SKU is in its lifecycle.
Even in a business with some of the most enduring products, SKU types are highly diverse. The toy industry provides an excellent example. Standard techniques will suffice for steady sellers such as traditional board games, but new, fad-sensitive, short lifecycle toys, such as Zhu Zhu Pets, call for new forecasting approaches.
Additionally, SKUs that are part of promotions or other special events require causal techniques to predict the expected demand lifts as well as the cannibalization or halo effects on non-promoted SKUs that have affinities to the target SKU.
Since methods focused on single demand forecasting components have become antiquated as supply chains have grown, most demand forecasting, demand planning, or inventory management vendors now provide multiple methods. These are standard textbook methodologies that are available to everybody, yet they require a lot of time, attention and engagement from experts.
These solutions are typically built around assumptions and user-defined classifications, forcing companies to use a "best pick" approach, which, in reality, is often a "best guess." A best-pick approach offers analysts the flexibility to choose the method they feel best forecasts a given entity. This is a nice capability, but has some inherent shortcomings. For example, the best-pick approach usually requires companies to staff forecasting experts who then must diligently monitor the health of their selected methods across a predefined set of entities.
Additionally, companies must vigilantly monitor demand signals in order to swap and tune methods. So, customers with large SKU populations are forced to segment how they forecast demand. This approach causes major problems: lost operational efficiency and, most notably, a continually oscillating loss of forecast accuracy.
Other problems surfacing with current solutions that incorporate the use of multiple forecasting methods include:
These problems are being addressed head-on by the development of more comprehensive solutions, such as the Unified Forecasting Method (UFM), that continuously handle transitions and mixtures of demand pattern classifications in a self-adjusting and optimal manner.