Do You Have A Love-Hate Relationship with Demand Forecasting?
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:
-Demand frequency can be erratic, intermittent, lumpy, or seasonal, and could be a mixture of all of these. It can also be continuously shifting or evolving in rate and pattern due to trends, product lifecycle stages, or simply just having a previously less complete picture of expected demand occurrences.
-Demand levels can be unpredictable and/or seasonal. They can also, of course, be influenced by trends, product lifecycle stages, and/or demand frequency continuity and patterns.
-Other external factors such as planned demand, promotions, and holidays (especially those that move from year to year) have significant effects.
-Technical support often involves periodic updates that normally have to finish in a very short time window of nightly processing and are in the critical path to inventory optimization and merchandise planning. This requires innovative processing and architecture to be useful to the business.
-Operational issues come into play for retailers and wholesale distributors. They both can deal with very large SKU (forecast entity) volume on which the forecast update, seasonal profile, trend, and demand frequency analysis have to be performed.
Single-Method Forecasting Can Break Your Heart
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.
Don't Swoon over the "Best-Pick" Approach
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:
-Lack of scalability;
-A chance of suboptimal performance if the analyst is not vigilant;
-Although this approach leads to crisp and discrete mapping of a demand signal to a single given method, if the dynamics of the signal change enough, then forecast analysts must begin looking to adjust parameters or switch to a different method; and
-Finally, the best-pick method also leads analysts to prioritize and focus on the entities they deem as "most" important, forgoing the benefit of having global and complete coverage of demand forecasts.
Take Heart
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.
(This article was contributed by Manhattan Associates)
-Demand frequency can be erratic, intermittent, lumpy, or seasonal, and could be a mixture of all of these. It can also be continuously shifting or evolving in rate and pattern due to trends, product lifecycle stages, or simply just having a previously less complete picture of expected demand occurrences.
-Demand levels can be unpredictable and/or seasonal. They can also, of course, be influenced by trends, product lifecycle stages, and/or demand frequency continuity and patterns.
-Other external factors such as planned demand, promotions, and holidays (especially those that move from year to year) have significant effects.
-Technical support often involves periodic updates that normally have to finish in a very short time window of nightly processing and are in the critical path to inventory optimization and merchandise planning. This requires innovative processing and architecture to be useful to the business.
-Operational issues come into play for retailers and wholesale distributors. They both can deal with very large SKU (forecast entity) volume on which the forecast update, seasonal profile, trend, and demand frequency analysis have to be performed.
Single-Method Forecasting Can Break Your Heart
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.
Don't Swoon over the "Best-Pick" Approach
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:
-Lack of scalability;
-A chance of suboptimal performance if the analyst is not vigilant;
-Although this approach leads to crisp and discrete mapping of a demand signal to a single given method, if the dynamics of the signal change enough, then forecast analysts must begin looking to adjust parameters or switch to a different method; and
-Finally, the best-pick method also leads analysts to prioritize and focus on the entities they deem as "most" important, forgoing the benefit of having global and complete coverage of demand forecasts.
Take Heart
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.
(This article was contributed by Manhattan Associates)