Demand forecasters and inventory planners face the perpetual challenge of attempting to anticipate what customers will buy and when. When they factor in seasonality, geography and up-and-coming industry trends, this adds a whole new layer of complexity. Some items may be (fairly) easy to predict while others might catch even the seasoned pros off-guard.
You have the data, trends, market research, risk analysis and other factors to help you develop the forecast, but you may be overlooking key factors that could potentially have a big impact. In our experience, we have observed three critical factors that need to be considered and could have a significant impact on the numbers driving the forecast.
Seasonality is one of the most frequently used statistical patterns to improve the accuracy of demand forecasts. Seasonal demand is impacted not only by the seasonality of the product itself but also by typical seasonal pillars such as weather and holidays. It’s a safe bet that during the cold winter months, demand for hot soup will increase.
Quasi-seasonal events fall beyond the scope of classical seasonality when specific events or select time periods happen once a year and not always on the same date. In the QSR (Quick Service Restaurant) industry, sporting events and seasonal shifts in local population size have an immediate impact on demand. So how does one address quasi-seasonality?
Quasi-seasonal events play an important role in regional areas of the country and in our experience are critical to the forecast. Our team develops seasonality profiles of products known to have seasonal behaviors in key areas of the country which become the building blocks of the forecast development during promotional periods.
For instance, during the summer months, key areas in Alaska experience a big influx in their local population due to cruise line tourism. Our forecasting team takes in to account the additional foot traffic at the restaurants to ensure continuity of supply at the sandwich shops.
Industry experience, local knowledge and expert judgement provide critical input to assessing macro-level demand forecasting. A few of these indicators to keep in mind when building your forecast model are as follows:
❏ The macroeconomic environment
❏ Regulatory or government actions
❏ Level of consumer confidence
❏ Level of average real wages
❏ Demographic and social trends
❏ Regional preferences
Goodness of fit is improved in the forecasting model when you incorporate regional drivers. To illustrate the above indicators, in our experience, certain key pockets of the country have strong regional consumer preferences which play an important role in the forecast development. For instance, BBQ sauce is a local favorite in the south and is factored in when planning a nationally limited time offer featuring this sauce. Just as important is regional purchasing power. For instance, during the North Dakota oil boom, the local purchasing power grew and this is considered to be the chief driver of higher than average sales at the local sandwich shops.
A fundamental assumption in forecasting is that there are relationships driving demand in the past that will help predict future behavior. Being able to foresee shifts in consumer behavior, technology trends or identify up and coming market trends can make a big difference.
Our team is continuously asking questions to get a pulse on these shifts such as:
❏ What are seasonal trends that are likely to come back even stronger, are growing year over year or has had a sudden growth within the past months? Can these be identified as seasonal risers/decliners or sustained risers/decliners?
❏ What consumer behaviors are trending that will have an impact?
❏ Are there cultural connections that are impacting the landscape? US consumers are more than ever exposed to all kinds of international cuisine and QSR brands are catering to the growing demand by offering Asian, Indian, and Mexican infused flavors.
❏ What new or up and coming technology trends will impact demand? The increasing use of mobile coupons and digital media can quickly impact daily and in a few cases even hourly demand patterns.
❏ How is the product marketed to consumers compared to the industry standard? Is the product price point driven and appeals to a value consumer or focused on a key product attribute that appeals to a segmented market?
❏ What will be the consumer demand for a new or limited time offer product?
In conclusion, we recommend that considering the key factors of Seasonality, Geography and Industry Trends and incorporating them into your forecasting model, will greatly help in building a more robust forecast while minimizing risk.