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Demand Forecasting for Retail Industry

December 11, 2019 | Divyansh Meena

Blog / Demand Forecasting for Retail Industry

Demand Forecasting for Retail Industry


Demand Forecasting is a crucial part of a retail company. Demand forecasts are basically estimates of expected consumer demand. Retailers rely on forecasts to plan the number of goods and services their customers will purchase in the future. Long Term strategic and operational plans like planning and scheduling production, acquiring inputs, making provisions for finances, formulating pricing, and planning marketing come from forecasts.


There are different theories and ways to forecast demand. There are qualitative and quantitative methods, as forecasting is seen as an art and a science. 

  • Passive forecasting is for stable businesses with conservative growth plans. 
  • Active forecasting is done for diversifying businesses with aggressive growth plans. 
  • Short term forecasts are easier to predict, they are up to any forecast that is up to 12 months. 
  • Long term forecasts are anywhere from 12 to 24 months and are more complex in nature.

The three most common techniques are the Trend projection method, the Barometric technique, and the Econometric forecasting technique. All three require meticulous calculation and are often off considerably as unplanned events occur.  


Retailers are facing challenges staying ahead of the competition with traditional demand forecasting methods. Some of the challenges they face are as follows: 

  • Predicting variables that affect demand like weather or consumer trends
  • Cost-effective buying for future seasons based on historical purchases, and other factors. 
  • Maintaining enough inventory to meet demand in a timely, efficient way without stockouts or overstocked inventory. 
  • Planning is usually a very manual process. 
  • Make fast decisions to compete with large retailers



Normally, when you want the computer to do something, you have to program it with explicit rules. For example, if you want to look at a product on a manufacturing line and figure out if it is faulty or not, you have to make sure to code the rules for when the product is bent, cracked, broken, the wrong color, etc. Machine Learning is completely different. Instead of coming up with rules for how the product might be faulty, you give the computer information on what a good product is and what a faulty product is. Then you let the computer figure out how to tell if a product is good or faulty so that the computer learns to make a decision based on the data. Similarly, with demand forecasting, the historical data, market trends, and other key indicators train the model to make decisions and predict the forecast with higher accuracy because they are do not have the same limitations that humans do.  


California Design Den is a manufacturer and wholesaler of high-quality bed linens. They are also an e-commerce retailer, selling their products directly to consumers online. A couple of years ago, CDD partnered with Pluto7 and Google Cloud to leverage machine learning and artificial intelligence to predict demand and balance it with supply. Previously, the CDD team had to dig through spreadsheets and run scenarios to get a sense of how a particular product had sold and manually find relevant data points.

With Machine Learning they are now able to make decisions quickly to optimize profitability, pricing, and inventory. CDD was able to reduce its inventory carrying costs by over 50%, improve the accuracy of demand planning quarter over quarter, and gain insights into how individual SKUs are performing. They were able to do all of this without an army of data scientists by leveraging Google Cloud Platform.

Click here to read the entire case study  


Looking to improve your demand forecast accuracy by more than 90%? Pluto7 has created an omnichannel analytics solution to drive effective sales and operations plans with exception-based demand forecasting methods. Demand ML™ leverages AI to manage complex and unpredictable fluctuations in demand volumes. It understands the pattern in your supply chain data to more accurately source the number of products to meet customer needs and deliver the best customer experience. This AI-Driven solution modernizes your supply chain systems and evolves your dynamic assortment planning. 

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