Tuesday, January 21, 2020

Avoid Feast or Famine with Demand Planning

How do grocery stores know how much food to buy and when to buy it? This has been a long-standing problem for the retail food industry, but data analysis techniques are coming to the rescue. Demand planning is a process whereby data gathered on many variables can be analyzed to make a forecast of future sales.

Planning vs. Forecasting 

There is a difference between planning and making predictions. Demand planning is a process by which data are analyzed to make a forecast. If a retailer wants to know how much of an item to order, they can look at sales data from past years. For example, let's look at an item like butter. For most of the year, butter sales remain fairly constant, but at certain times, such as the weeks before Christmas, demand for butter goes up because of holiday baking. But what if there is a shortage of butter for some reason, or a viral news story about how eating too much butter is unhealthy? How will this affect the forecast?

Avoiding Empty Shelves 

One of a grocer's worst nightmares is a row of empty shelves and a crowd of unhappy customers who are not able to find what they want in that store. Today's business software pulls data from past sales, as well as market trends, weather events, media input and other variables to make accurate predictions of how much of an item consumer will want. These forecasts help to put the products on the shelves when customers want them. If running out of something is such a fear, why not just order extra? That leads to problems of its own.

Reducing Food Waste 

Overstocking leads to two major issues: loss of revenue and food waste. In the United States alone, food waste costs as much as $160 billion per year, with dairy products being the largest contributor to that figure. Not only does food waste cost money, it is also a growing environmental concern. The Food and Agriculture Organization of the United Nations estimates that as much as one-third of all the food produced in the world ends up in landfills.

Being able to make accurate forecasts for demand is crucial for business planning and today's analytical tools make that possible. Business intelligence analytics can take many variables and use this information to come up with predictions that are much more accurate than in the past. The next time you stop by the grocery store to pick up a package of butter, pause and think about how it came to be there when you needed it.

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