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Inventory management optimisation and forecasting

Introduction

In the course of operation of a retail business, managing the right inventory levels is a critical and challenging function. This involves a forecast of the future purchases that the customers will make and ensuring that only the optimal amount of stock is maintained so that the demand is met while at the same time avoiding over stocking. With the integration of Machine Learning (ML) and Artificial Intelligence (AI) in the retail sector, the approach to inventory management and forecasting has been completely changed. These technologies are helping the retailers to process large data, optimize trends and make future projections which has otherwise been a challenging and expensive task to achieve.

Challenges

There are several challenges that retailers face when it comes to inventory management. Overstocking causes high expenses for storing goods and the increased risk of goods being phased out or damaged; understocking leads to the loss of sales and customers’ complaints. It is hard to determine the customer demand because of the variability of the demand, seasonality, trends, and unpredictability. The process of inventory management is rather labor-consuming and can be rather inaccurate. They also have a problem with the accuracy of their forecasts, particularly for new items that have not been on the market for a while. They also have to deal with multi-channel sales, which includes sales channels such as online, physical stores, etc. and they must deal with returns as well.

AI Solutions

There are various ways in which AI and ML solutions are addressing these challenges. Predictive analytics models can predict the demand by analysing the past sales trends, customer behaviour, seasonal trends, weather conditions, and other such factors. The models are developed using historical data and as the data in the model gets updated over a period of time, so does the accuracy of the model. The boring tasks can be handled by AI and this limits the chances of errors by humans. It is also possible to use AI to control the multi-channel inventories and to update the stock data in real time. In addition, with the help of AI, it is possible to set the optimal price and carry out effective promotions depending on the demand forecast. In this way, for new products, it is possible to make predictions looking at other similar products or the market trends. Some of the companies that offer AI solutions for retail inventory management include IBM, Blue Yonder and Antuit. ai.

Benefits

There are many advantages of AI in inventory management. It enhances the accuracy of the forecasting process thus minimizing on stock outs and maximization of on hand inventory. This results to cost efficiency and enhanced sales. The AI automation reduces time and errors thereby enhancing efficiency of the operations. Real time inventory updates of channels help in minimizing stock differences and enhance customer satisfaction. By using AI, retailers are also able to make better decisions since they are provided with insights from data analyses. Also, AI can help with the implementation of sustainable retailing since it can help in minimizing the amount of inventory that goes wasted.

Return on Investment

AI can generate a high ROI in inventory management as will be shown below. Retailers can minimize on the inventory holding costs, improve on sales through increased product availability and reduce on labour costs through automation. According to a study by McKinsey, AI can enhance forecasting by reducing errors by as much as 50% and inventory reductions by 20-50%. The ROI will also differ based on several factors such as the size of the company, the accuracy of the AI in the system, and the extent of using AI in the business.

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