According to the Bureau of Economic Analysis, consumer spending has seen some interesting trends over the first half of 2021. May was flat, April was at 0.9 percent, March was 5.0 percent, and February was at 1.0 percent. With varied consumer spending statistics as the nation comes out of the pandemic, businesses need to get demand forecasting as accurate as possible.
According to The University of Tennessee, Knoxville, demand forecasting is “a method for predicting future demand for a product.” It’s a calculated method to plan for inventory and helps prepare the supply chain for the future.
Demand forecasting helps businesses forecast their future sales, which is based primarily on historical data. However, relying exclusively on historical data is not generally recommended.
Historical data provides an incomplete picture because it does not factor in economic trends, seasonal ordering, or consumer behaviors. Multiple analyses are also recommended because young companies don’t have enough of their own data to perform such analyses.
It’s recommended to run through more than one method to forecast sales. It’s important to ensure that data is as accurate as possible and to consider factors beyond inventory. Such factors include how external players – shippers, material suppliers, etc. – will work with the company’s internal functioning.
It’s important to be mindful of the time frame of the different analyses. Short-term refers to the next quarter to four quarters (3 to 12 months) and helps businesses adapt to changes in consumer demand and market variations. Real-time sales data is used to manage just enough inventory. Long-term refers to at least 12 to 24 months, but sometimes 36-48 months, and is used for things related to the long-term business vision. Examples include creating a more reliable supply chain, capital expenditures, advertising campaigns, etc.
Similarly, demand forecasts run by a business can be done regarding intrinsic or extrinsic factors. External forecasts evaluate how the broader economy and systemic changes in commerce shifts future demand. Recommended indicators include exploring how much retail consumers spend, what they are interested in, and whether the economy is expanding or contracting. Internal demand forecasts look at the organization’s employee makeup and where and how the business can divert resources to help deal with additional capacity, if necessary.
Passive demand forecasting relies exclusively on historical data and is usually geared toward established companies with generally reliable sales histories.
Active demand forecasting is geared more toward startup businesses looking to scale and diversify their portfolio. It can be variable because it factors in changing trends of the fluid economy and how companies, especially startups, plan to accelerate growth. However, active demand forecasting also may be useful in order for businesses to work around fluid inventory and logistic network overview. Startup businesses are better geared for real-time demand planning, mainly due to a lack of historical data.
With the quantitative approach focusing on crunching data, oftentimes with complex “big data” processes, the qualitative method takes a more balanced approach with some data, but also cognitive-based analyses, including some of the following tactics:
While demand forecasting is individual to each company and each industry, the more businesses that understand the approach to demand forecasting, the more able they’ll be to react to any type of consumer trend.