What Is Seasonality In Forecasting | 4 mins read

What Is Seasonality in Forecasting? Planning for Changes in Demand

what is seasonality in forecasting planning for changes in demand
Jin Hyun

By Jin Hyun

In a perfect world, every product in a warehouse would sell at exactly the same rate throughout the year. In reality, certain products naturally experience peaks and dips in sales due to factors like holidays and weather changes. This is a phenomenon known as seasonal variation.

For example, the fourth quarter of the year with its combination of Halloween, Black Friday, Cyber Monday, and Christmas is incredibly profitable for online retail sales, fueling $187.25 billion into the US eCommerce industry in 2019.

Seasonality in forecasting requires business owners and supply chain managers to identify which goods have seasonal patterns and which do not. And for the goods that do fluctuate in popularity, the challenge is in determining when they will see the highest and lowest demand.

The Importance of Seasonality in Forecasting

Understanding seasonal variation in forecasting is crucial to solving a two-fold business problem. On the one hand, when you don't have sufficient product inventory or enough employees to meet surges in consumer demand, you risk damaging the business' reputation and relationships with customers.

On the other hand, an excess in inventory and too many staff members working during slower periods result in inefficient use of labor and funds. Either way, the company can lose revenue and miss out on profitable opportunities as a result.

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Types of Seasonalities

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The first step to creating a demand forecast that reflects changes in trend and seasonality is understanding the typical cycles experienced by seasonal products. Generally speaking, there are three types of time-based seasonal patterns.

  1. Weekly seasonality - This usually applies to general product consumption on particular days of the week. For example, a Gallup poll found that Americans on average spent the most money on Saturdays and least on Mondays. This is likely the result of weekdays leaving little time for shopping and entertainment as a result of work or school.
  2. Monthly seasonality - This covers cyclical demand for goods and services over the course of a month. For example, you may find that your customers spend more when their paychecks arrive either at the beginning or the end of the month.
  3. Yearly seasonality - This seasonality sees a predictable and recurring demand for goods and services on an annual basis. As mentioned earlier, online retail sales surge during the fourth quarter of the year due to the holiday season. Likewise, the back to school' period also sees a consistent spike in demand for books and school supplies each year.
However, there are also seasonalities that aren't influenced by time per se. These include.

  • Man-made seasonality - This is demand caused by external factors. For example, the annual South by Southwest (SXSW) festival in Austin resulted in 12,000 individual hotel reservations booked during the event in 2019. Meanwhile, June has historically been associated with a spike in weddings, leading to a predictable increase in business for caterers, wedding venues, and wedding planners.
  • Natural seasonality - This seasonality is caused by natural factors, such as the weather or changing seasons. For example, vacation resorts see a surge in guests during the spring and summer months in the northern US, and autumn and winter in the south. Meanwhile, the winter months are generally a quieter period for the real estate sector.

How to Manage Seasonal Demand

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What makes managing seasonal demand tricky is how this phenomenon is influenced by multiple factors, such as, but not limited to, location, local calendars, industry, and product type.

Considering the following practices could help improve the accuracy of your seasonal forecasting model-

  • Determine which products are seasonal - To detect seasonality, find a recurring pattern in the demand of your products over time. Observe existing historical data within a specific time frame and ask yourself this question, Can you see a similar demand pattern year-over-year, month-over-month, or week-over-week? Next, find the correlation between each year, month, or week. If you see a pattern, you have a reliable seasonal demand.
  • Know why and when spikes in demand happen - Next, understand the correlation between spikes in demand relative to specific periods of time. Is it due to the weather? A holiday, perhaps? Is it caused by man-made or natural factors? Understanding how these variables influence your customers' buying habits will go a long way towards maximizing peaks in demand and accurately estimating seasonality.
  • Measure the size of these spikes relative to baseline demand - Once you know why and when popularity for certain goods fluctuate, you then need to measure seasonal peaks in demand and compare it with average (i.e., baseline) demand levels. Knowing this will help prepare both the inventory and labor needs accordingly.
  • Determine the reliability of your forecasts - The dynamic nature of the marketplace means that forecasting seasonal demand cannot always be 100% accurate. However, there are ways to improve the accuracy of your forecasts, such as by identifying demand outliers and understanding their effect on your calculations. This will help in determining the underlying causes and levels of error in previous forecasts and help in creating seasonally adjusted data. For products or companies who have been operating for several years, also consider going back as many years as you can to have as much historical data to work with as possible. For newer products or product lines, it may be necessary to cross-reference them with the syndicated market data of similar products.

Using Technology

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Many of the processes that go into analyzing and predicting seasonal demand can be automated with business forecasting software. While spreadsheet programs like Excel have been a staple of business forecasting for years, they're inadequate to crunch the vast volumes of data behind today's digitized supply chains.

Instead, it may be more productive to consider investing in a forecasting tool with robust seasonality features, such as filters for national holidays, weather changes, and peak seasons, as well as dashboards containing historical averages.

Creating a seasonal forecasting model can be challenging. But with the right data and the help of forecasting software, it becomes easier to have a solid baseline, which, in turn, makes the seasonal variation of products clear.

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