Certainly, Demand forecasting requires effort and accuracy. That is to say, it is not easy for stores to forecast accurately. As a result of inaccurate demand forecasting, stores result in disappointing customers. Further, this results in a bad impression and poor customer service. In simple words, demand forecasting means to know the demands of the customers and stocking the items relatively. Consequently, it helps businesses flourish by having smooth business operations. On the other hand, inaccurate demand forecastings leads to a waste of money and extra stock.

How to practice Demand Forecasting?

It is difficult to understand about demand forecasting in the beginning because as mentioned earlier it is complicated.

“Prediction is very difficult, especially if it’s about the future.”

–Nils Bohr

However, with the use of right tools, retailers can make this difficult process easy. In this article, we have discussed how retailers can effectively practice demand forecastings.

Tips to Implement Affective Demand Forecasting

Demand Forecasting for Beginners

For those retailers who are new to the practice, demand forecasting should make a baseline. Certainly, without data, it is difficult to get accurate results. Therefore for this purpose, retailers should analyze the data of the previous year. They can simply do so through their Point of Sale System. Consequently, retailers can predict which demands to expect from the customers in the coming years.     

Demand Forecasting requires understanding your customers

Certainly understanding customers is very important for demand forecasting. Further, it is important to monitor customer demands and buying patterns. Retailers should try considering the following questions.

  • What is the customer shopping behavior?
  • Is it that they shop frequently or do they shop seasonally?
  • What are the frequently bought sizes?
  • Which colors do the customers prefer?
  • What are the shoppers’ interests?
  • Do they adapt to new trends instantly or not?

“We are witnessing a seismic change in consumer behavior. That change is being brought about by technology and the access people have to information.”

–Howard Schultz

Certainly, retailers can have all this data through their Point of Sale system as mentioned earlier. That is to say, all this data can easily be gathered through past sales records. 

Accurate Demand Forecasting through technology

Certainly lack of accuracy in statistics is due to poor technological use. Further without the use of technology, there are more possibilities that the results will be inaccurate due to the high chances of human error. However, in this age where technology is everywhere, retailers must make the most of the technology.

So, firstly retailers should automate their tasks. Certainly, this is an effective way of reducing human error and securing data. Therefore retailers should use such technology that informs them about the products being sold and the stock levels. As a result, retailers will be able to have accurate demand forecasting. So retailers need to upgrade their technology so that they proficiently know the accurate demand forecasts.

Secondly, retailers should ensure that all the data is saved in one place. In other words, this helps retailers manage their business well especially if they are handling multiple stores. As a result, this will lead to accurate decision making regarding forecasts because retailers will have an accurate number of products sold. Certainly, this will help retailers in managing forecasts because it is one of the biggest setbacks that retailers do not have their data stored in one place. Further, it leaves a lot of room for human error because then things have to be managed manually. However, with the use of effective tools, retailers do need to worry about the data being inaccurate.

Frank Johnson has rightly said,

Right now we’re forecasting demand for that scenario, … We need to get an idea of the products that are required and the capabilities of our suppliers. The demand could be huge, and the contracts could be very big.”

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