In demand planning, seasonality is used to project cycles observed in the past in order to anticipate future fluctuations.
Incorporating seasonality into demand forecasting and planning is often recommended to demand planners as a best practice. When properly identified, seasonality makes it possible to anticipate regular increases or decreases and to adjust decisions accordingly.
However, this approach assumes two essential conditions: the availability of reliable historical data over a sufficiently long period, and the ability to distinguish a true seasonal signal from simple occasional variations. Without meeting these conditions, it becomes difficult to trust one's planning.
Thus, in certain product planning contexts, it may instead be preferable to start from a neutral basis, without any seasonality assumptions, in order to gradually build a more accurate understanding of demand and to preserve the quality of decisions.
Seasonality relies on observing repeated cycles over time. However, some products simply do not have the necessary time horizon to identify such patterns. This is particularly the case for new products whose market behavior remains uncertain, in contrast to those that, although recent, already follow an expected seasonality because they belong to a stable or well-known category. This situation is also found for products with short life cycles, whose duration of commercialization does not allow for observation of multiple complete cycles.
In these contexts, any attempt to introduce seasonality is based on external assumptions or sometimes rough analogies with other products or categories. These assumptions may be useful for exploratory purposes, but there is no guarantee that they will truly reflect the demand dynamics of the product in question.
Adopting a non-seasonal approach allows demand to be observed as it actually occurs, and planning assumptions can then be gradually adjusted as more data becomes available.
The presence of historical data does not guarantee its usability. Incomplete, noisy, or data heavily influenced by exceptional decisions can lead to the identification of false seasonal signals. In such cases, seasonality is based on fragile foundations and artificially strengthens weak trends.
Under these conditions, planning without seasonality favors robustness and clarity by enabling decisions to be built on signals that, while less sophisticated, have the advantage of being reliable.
Strategic decisions made by a company can fundamentally alter demand dynamics. Product repositioning, a change in price, target, or distribution channel transforms the way the market responds to the offer. Likewise, a product line overhaul, the introduction or withdrawal of key products, or inter-product cannibalization phenomena may modify the overall structure of the portfolio.
In these situations, historically observed seasonality relies on customer behaviors that were relevant in another market context and for another value proposition.
Non-seasonal planning allows for a more neutral starting point and provides time to evaluate the real impact of recent strategic choices, without constraining demand by inherited patterns.
Some developments can permanently change the context in which demand is expressed. Health crises, regulatory changes, new standards, or societal transformations can profoundly affect purchasing behaviors, distribution channels, or usage patterns. The COVID-19 pandemic is a prime example: for many companies, 2020–2021 disrupted sales, product mix, and market positioning, effectively forcing a reset of their historical data.
In these contexts, historical data no longer reflect a “normal” or reproducible functioning of the market. The cycles observed in the past are often the result of exceptional constraints or behaviors, not intended to be repeated. Applying seasonality based on these periods amounts to integrating structurally obsolete dynamics into the future.
To neutralize these disruptive effects and allow the emergence of new patterns more consistent with the current market reality, it may be relevant to resume planning without seasonality.
Deliberate hypotheses may be favored during phases of learning, testing, or transformation.
Planning without seasonality can then serve as a clear reference point, facilitating comparison between different scenarios and interpretation of the real impacts of operational or commercial decisions. It enhances the readability of the plan and supports alignment between teams.
Sometimes, the best way to improve planning is not to add complexity, but to know when to do without it.The next step is therefore to understand when, how, and why to adjust seasonality.
For further details, you may refer to the articles below regarding the management of seasonality and forecasting in Pawa.
How to configure seasonality
This article provides a step-by-step guide to calculating seasonality and understanding the concept in greater depth.
How allocation and seasonality of forecasts work
This article describes how PAWA manages forecast decomposition using the various allocation methods available, as well as the application of seasonality curves. It also presents the product substitution (Supersede) options.
Demand Forecasting Process – S&OP Monthly Cycle
This process guides the organization from an objective baseline forecast to a plan approved by management.
