Demand Forecasting in the Retail Industry

The retail industry is focused towards helping customers procure the desired merchandise form retail stores. The retail industry thrives to ensure customers easily find their required products and are satisfied by their shopping experience. Retailers need to manage their inventory effectively to make sure that their customer’s buying needs are always met.

The inventory accuracy of an average retail store is about 65%. This raises the question of how a retailer knows when to stock an item and in what quantities. Over time, retailers have spent a lot of resources for planning sales and managing inventory. However, if the demand for a product is unknown, all the resources employed to plan the retail merchandising cycle are of little help. This is where demand forecasting plays an important role.

What is Demand Forecasting?

Demand forecasting is a scientific method when used along with proper judgment can correctly predict demand for a product or service in the future. It collects data from various aspects of the market, such as, historical data on sales, buying patterns, product designs, and the purchasing power of the consumers. All this information is processed using scientific models to forecast demand of a product.

Demand forecasting is optimizing the retail industry and their merchandising lifecycle to meet customer demands and enhancing the shopping experience. It is crucial in planning inventory, understanding trends, saving time on reordering, and reducing stock-outs. More accurate forecast leads to the business running more efficiently by buying the right inventory at the right time. This ultimately lowers inventory storage costs, improves customer service, and increases profitability.

Knowing Consumer Patterns

Data collection today, is a known challenge in the massive data generating retail industry; however, the bigger challenge is to make this data meaningful. To collect meaningful data, you need to know what kind of data is needed to make the forecasting systems work efficiently.

To predict demand, you need to know what the customers tend to buy, when they are more likely to make purchases, and what factors would affect their spend patterns. To know what the customers would buy or to know what pattern your customers follow, you need to know more about user trends. Big data from the retail industry can help reveal spend patterns and purchase trends, especially related to customer behavior. For the retail industry, this means understanding customer shopping preferences and creating recommendations based on purchase history.

Knowing the purchase and spend patterns of retail customers, you can work towards inventory optimization.

Working towards Inventory Optimization

The retail industry is driven by customer requirements for finished products. Over 70% of online shoppers would search for an item elsewhere if it was unavailable, rather than wait any length of time for it to come back in stock. Retailers have to stock inventory efficiently to meet the purchase demands of their customers.

Questionable inventory planning decisions cost U.S. retailers $300 billion in revenues in 2018. In a turbulent market, to ensure maximum profit margins, it is essential to keep inventory investments to the minimum. Demand forecasting when used to optimize inventory can help in reducing the cost to retail business in a couple ways.

Firstly, it can reduce the amount of capital consumed by inventory surplus and the lesser stock on hand, lower are the holding costs. Secondly, retailers can grab every sale opportunity by not disappointing customers with stock-outs.

Future of Demand Forecasting

Is every method of forecasting demand equally efficient?

A variety of algorithms that use cutting-edge methodologies can be used while building a demand forecasting system. Irrespective of the methodology used, all the analysis is ultimately based on historical data and trends that can predict demand in the future. These super-sized data sets also help with forecasting trends and making strategic decisions based on market analysis.

Data Scientists develop sophisticated algorithms that involve learning from large amounts of data, such as prices, promotions, similar products, and a product’s attributes, in order to forecast the demand. These forecasts are used to order billions of dollars worth of inventory weekly and predict the company’s financial performance.

Demand forecasting when done using machine learning algorithms that consider multiple variables including seasonal variations, promotions, discounts and holidays can produce the closest demand prediction. This allows businesses to automate thousands of inventory decisions across stores.

Limitations of the AI-based System

There never existed a system without any drawbacks, so what is the drawback of smart machine learning based demand forecasting systems. Machine learning based systems need massive amounts of data input to produce a meaningful output/prediction. This means a fair amount of investment in setup and maintenance. The results of such a system could be very complicated and would require specialized skills to interpret correctly.

Identifying the Right Demand Forecasting System

Demand forecasting methods can be broadly classified into Qualitative and Quantitative methods.

Qualitative method uses more variable factors such as, market forces, economic demand, and potential demand. It is more like an art learned by inventory planners over years of practice. Usually short-term forecasts use qualitative methods.

Quantitative method is based on statistical analysis of historical data. It requires more data to ensure accurate predictions. While it provides a basis for forecasting, it does not account for variable market conditions or product seasonality. Spikes in demand can result in stock-outs and quiet periods may result in over stocked product.

Based on the specific requirements of your business, a customized Demand Forecasting model can be developed. Such a model is an extension or combination of various Qualitative and Quantitative Methods of Demand Forecasting.

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