Regardless of your position in the food supply chain, if you are reading this, your business can almost certainly benefit from more accurate demand forecasting.
That certainly isn’t new or unknown among industry professionals: the Journal of Farm Economics was quite concerned, even as far back as 1926, with topics like, “Studies of Market Supply, Price, and Sales as a Basis for Control of Distribution of Perishables,” the title of one early published study on the matter. And for good reason: accurate, timely demand planning processes and demand forecasts help optimize business operations, improve labor productivity, and minimize loss while maximizing profit. In the case of the food supply chain, perishables in particular, there is also the potential to reduce food waste while maximizing profit, a multifaceted problem that producers, suppliers, and retailers alike have power to, if not “solve,” certainly mitigate.
If everyone understands the great value—monetary and otherwise—of accurate forecasting, why is it still such a pain point in the industry?
Put simply, knowing is one thing, but doing—and doing accurately—is quite another. First, the terms “sales forecasting” and “demand planning” often have different connotations to different individuals, different businesses, and throughout different supply chains. Here’s a quick breakdown of the distinction between demand planning and demand (or sales) forecasting:
According to the Institute of Business Forecast and Planning, demand planning refers to the use of a combination of forecasts and experience to estimate demand for products at various points in the supply chain—and then tailoring a strategy around the results. Demand planning requires a wealth of information (i.e. timely, as accurate as possible, useable, qualitative and quantitative) in order to adequately predict product demand (and corresponding sales). The result of effective demand planning is highly accurate demand forecasts that should deliver on agreed strategies.
Demand forecasting, on the other hand, refers to the process of predicting product demand (note: “demand” is often used interchangeably with “sales” in this context). Accurate sales forecasting is an output of demand planning and accounts for a number of other factors, including qualitative measures, the possible impact of upcoming promotions or relevant events, and actual sales data (which may then require adjusting the forecast accordingly). In a nutshell, demand planning is a process, whereas demand forecasts are the result of the demand planning process.
Why is better demand forecasting so important?
There are innumerable systems in place to aid in forecasting, many of which rely on trusty ol’ Excel sheets that, while they certainly have had their value as systems have evolved, are time-consuming to manage and also reliant on human input, thus making them vulnerable to human error. Basically, the current food industry forecasting infrastructure and supporting methodologies are often cumbersome, prone to human error, and disjointed.
What’s a better solution?
Advanced sales forecasting technology that uses smart algorithms, artificial intelligence, and machine learning applied to not only historical trends, but also to all the factors with the potential to influence demand (weather, promotions, seasonality, shifts in holidays, social sentiment, etc.), along with the wisdom and knowledge that come from firsthand experience.
That’s where Crisp comes in. We believe in using every byte of data available to suppliers, food brands, retailers and distributors to optimize forecasting, production efficiency and ultimately, profitability. Interested in learning more? Book a demo, or contact us for information on how Crisp consolidates everyone byte of your data for optimal forecasting and ideal production.
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