To succeed in a business economy shaped by uncertain demand and rapid market changes, companies must be able to sense and adapt quickly. Today, the supply chain is seen as a key enabler of this flexibility. To face this challenge your supply chain must forecast correctly and then modify the plan as new information is acquired.
To accomplish this, supply chain planners must use multiple forecasting methods tuned to different selling profiles that perform well at different phases of the product life cycle where each method is chosen to best exploit the available historical data and available market knowledge. The key is to pick the most effective and flexible models and shift between them as needed to keep forecast accuracy at its peak. Let’s review some of the methods available and the ideal scenarios for their use.
Forecasting models classically fall into three categories: qualitative, quantitative and hybrid. The primary differences between them include the type of input data and the mathematical and statistical methods employed to generate forecasts.
• Qualitative models are experience-driven, relying on subjective inputs from knowledgeable personnel, such as salespeople and account managers.
• Quantitative models are statistically driven, drawing heavily on historical performance data as the basic data input. The calculating logic is defined and operations are purely mathematical.
• Hybrid models typically draw on historical demand information as a starting point, then use empirical data to further refine the forecast.
Best-Fit Statistical Modeling
For most levels of management within an organization, aggregated demand history for product family, brand, category, country and/or selling region are strong predictors of future performance. Such demand history also serves as a baseline for effectively forecasting Stock Keeping Units (SKUs). When there are more than four-to-six periods of sales history, SKUs can be forecast with moving average and basic trend methods. SKUs with at least one year of sales history offer sufficient information to incorporate a seasonal profile into the projected trend.
A modified Holt-Winters decomposition model with best-fit analysis can generate forecasts based on demand history that incorporate trends and seasonal information. The method “senses” the amount of history available for each time series or segment to create a basic model that best fits the history. Then it uses the best combination of smoothing factors to enable the model to react to changing conditions going forward without over-reacting to anomalies in demand (such as unplanned seasonal events, transportation disruptions, and so on).
A powerful best-fit statistical method should include flexible features such as trend, seasonal-with-trend, moving average, and low-level pattern fitting, as well as trend models for products with sporadic, low-volume demand.
“Best fit” refers to the ability to change forecast methods as a product evolves. The process may start out as a demand profile method, evolve to a modified Holt-Winters method as the product becomes stable, and ultimately transition to a demand profile method again as the product life cycle comes to an end.
One method of generating new product forecasts is to use demand variations or extensions from existing products, families or brands. Consequently, they draw on the historical data of existing products or families. When combined with causal effects or management-selected overrides to accommodate introductory promotions, derived modeling can provide a realistic and dynamic forecast for new products.
Using this approach, new products are assigned a percentage of the parent, family and/or brand, enabling them to proportionately inherit a forecast that contains the base, trend and seasonal elements of the associated category. As the forecast for the associated category is adjusted to reflect changing conditions over time, so too is the derived product’s forecast. If the derived product’s point-of-sales (POS) or demand levels deviate from the forecast and exceed a user-defined tolerance, the system can generate a performance management alert to notify forecast analysts to take corrective action.
Slow-moving parts typically exhibit irregular demand that may include periods of zero or excessively lumpy demand. A Modified Croston Method handles low and lumpy demand that exhibits either a patterned variation or no pattern.
The patterned variation looks at available history and classifies each demand element relative to those around it. It measures the duration of plateaus and plains, as well as the severity of peaks and valleys and then conducts pattern-fitting analysis to find regularity over time. If no pattern is present, the unpatterned variation method attempts to use averaged highs and lows to create a step-change forecast for future demand.
Both techniques permit zero demand to reside in the history, and will acknowledge such in the future demand forecast.
What if lack of data, short life cycle, or other mitigating factors make it difficult to forecast using time series or qualitative techniques? Forecast creation for new product introductions, short-life or seasonal products, and end-of-life products calls for attribute-based modeling techniques.
The attribute-based model provides a wide variety of demand profiles by which to characterize the product, and can adjust the product’s plan dynamically in response to early demand signals. The method will analyze historical sell-in and/or sell-through data to develop a wide variety of demand and seasonal profiles. These profiles are assigned to individual planning records. Then, as actual demand information is captured, the current profile is validated or alternate profiles identified to dynamically adjust the product’s plan.
Through Relative-Error-Index (REI) calculations the planner can quickly see which demand profile now has the most accurate fit based on current demand trends and can change the current demand profile to the profile that has the lowest REI.
Causal Event Modeling
Causal modeling can specifically address the effects of promotional elements such as price discounts, coupons, advertising, and product placements. It supports input from multiple marketing groups, and aids the identification and reconciliation of potential conflicts or overlaps in promotional planning proposals.
Causal modeling enables planners to quickly simulate marketing program options and refine forecasts. Using pre-trained neural network technology in causal-based modeling is a unique best practice that lets planners quickly start to model the cause-and-effect relationship of different promotional elements.
Flexibility and Ease of Use are Vital to Good Forecasting
Optimal demand planning and forecasting requires comprehensive modeling capabilities plus the flexibility and ease-of-use to shift methods as life cycles progress and market conditions change. Advanced forecasting systems use a combination of qualitative and quantitative techniques to generate reliable forecasts.
As we have seen, attribute-based methods that use demand profiles are often suited to new product introduction and end of product life cycles, at times when reliable historical demand data is lacking or the available data is less relevant.
At the more mature stages of the product life cycle, five different time-series statistical models come into play. These models are used to create retrospective forecasts that cover prior periods (typically three years) of documented demand. The forecasts are then matched to actual demand history to determine which one best fits the real-world data. The best-fit winner is used to create an objective-based forecast. At this point, planners may include more qualitative calculations based on personal knowledge and experience with intangible market factors.
Causal methods are used throughout the life cycle to adjust forecasts in anticipation of promotional events. Causal methods allow planners to predict how discounting and other promotional factors will affect volume, and layer the impact of these events on top of the underlying base forecast.
Finally, derived models can be used to create a parent-child relationship in which forecasts for closely related products are driven as a percentage of the forecast for a ‘leader’ product. This ensures that when the forecast is modified for the ‘parent’ all the ‘child’ forecasts would be updated accordingly.
To prevail in a business economy shaped by uncertain demand and rapid market changes, all of these forecasting methods must be harnessed. Planners must have access to the best method, be able to spot trends and forecast demand signal changes more quickly, and sense the best time to change methods when a more appropriate approach is indicated. Superior predictive power and real-time reflexes are the keys to coming out ahead of the competition.
About the Author
Karin L. Bursa is a vice president at Logility, a provider of collaborative supply chain management solutions. Ms. Bursa has more than 25 years of experience in the development, support and marketing of software solutions to improve and automate enterprise-wide operations. You can follow her industry insights at www.logility.com/blog. For more information, please visit www.logility.com.