Are you in retail industry? Deep learning may soon be solving your forecasting challenges

Accurate forecasts are critical for retailers and the industries that rely on them for distribution. These predictions go a long way in determining revenue generation and operational management of the business. While forecasting too much demand may leave you with excess inventory, a short-sighted forecast will mean you’ll not be able to fulfil the demands of your consumers.

But, the most important question is how to get your forecasts precise and accurate? Gains in the accuracy of forecasts have widespread positive effects on your retail business, but factors like seasonal fluctuations in demand of certain products, short product life-cycles, and high value of stock keeping unit (SKU) may sometimes make predicting future sales a tough job.

 

Why traditional forecasting models are of little help?

Traditionally, business enterprises relied on structured data sets alone, like sales figures and market queries, etc., for their predictions. But these forecasting models are outdated since it fails to consider unstructured data sets and can handle only numerical data. These linear models can’t handle the complexities and uncover subtle patterns in the data. Also, this process requires lengthy and labour-intensive method to input data into the machines.

 

How deep learning and artificial intelligence help solve these problems?

Deep learning, which is a subset of machine learning that mimics the human brain, can be tipped as a primary solution to these common forecasting challenges. Whereas traditional machine learning algorithms require continuous human intervention, the deep learning models with artificial intelligence can learn and train without extensive manual efforts, while producing results with unprecedented accuracy.

• Deep learning has replaced the traditional forecasting models with sophisticated and customized prediction process that incorporates unstructured retail data sets. It can evaluate and process complex data patterns since it relies on Graphic Processing Units (GPUs).

• Deep learning can support a large number of SKUs at the same time. This enables it to learn from the similarities and differences between the sales pattern of various products and discover correlations for marketing and promotions. For example, increased sales of printers may also boost the sales of ink cartridges whereas launch of big-screen mobile phones may bring down the sales volume of similar-sized tablets.

• Artificial intelligence can be of great help in tracking browsing details on the online shopping platforms by the consumers and discover the demand of certain products among them. It can also cut the time and efforts involved in gathering a large amount volume of data and processing it for further projections.

Therefore, in addition to data collection and processing to produce accurate and precise results, use of artificial intelligence or deep learning can also save a significant amount of time, efforts and computational resources that can be used for other applications.

Many retailers have already deployed product recommendation software based on deep learning capabilities that allows the computer to make better predictions and offer smart choices in real time, ensuring happy customers, improved sales, and maximum profits.

 

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