Article
5 min read
Artificial intelligence benefits: Demand forecasting in retail
Nowadays, accurate and rapid demand forecasting is essential for the optimal operation of commercial companies, as it is the only way to compete with other industry branch players. Forecasting is also becoming increasingly emphasised in retail because any unusual, out-of-the-ordinary event, such as the vagaries of weather, can disrupt and profoundly upset supply chain processes. This is why companies at the forefront of digitisation have slowly but surely started to embrace next-generation technologies such as artificial intelligence and have gained a competitive edge in supply chain planning. But what exactly are the difficulties that can be overcome by using AI? In this article, we summarise the key facts!
Challenges in demand forecasting
The first difficulty companies have to face is the collection and retroactive analysis of data. In respect of a product that has been on the market for a long time, this means properly managing and sifting through an enormous amount of data, which can be prone to errors. Of course, the situation is no easier for a new product. In fact, the challenge is perhaps even greater, as in this case no prior information is available to analysts.
The quality and format of the data available is also a non-negligible factor to be taken into account. Demand planning must include all the information that had an impact on demand in the past or may affect it in the future, so that distortions do not lead to false results.
The process therefore consists of 3 important steps:
1. Data collection
2. Data cleaning
3. Modelling
At the data collection stage, already a number of difficulties can arise. Consider that the information is available in different formats, and some data may not even be digitised at all. So the first step is to organise and clean up the collected data, which is by no means a small amount of energy spent at the beginning of a project. Finally, one should not forget external factors such as the weather, which affects demand in many categories and whose unpredictable nature makes it impossible to forecast accurately without the proper technology.
AI in retail
Artificial intelligence is able to extract useful business information from continuously accumulating data assets through various algorithms, and can automate the processing, cleaning, and analysis of large amounts of data. This in turn can also enable retailers to anticipate changes in demand so big that could cause them problems in the supply chain - from ordering, through supplier selection, and pricing, to warehousing and delivery. Ultimately, supply problems can lead to dissatisfied customers.
French supermarket chain Intermarché tested the potential of AI for several months. They concluded that forecasting solutions using AI proved to be 15% more accurate than those obtained with conventional forecasting methods.
Trust the data!
By using AI to build on demand forecasting, retailers can gain tangible benefits. Increased speed of movement, reduced stock-outs, smaller warehouse capacity, and overall improved customer service. Furthermore, thanks to AI, logistics processes can be optimised and cooperation with suppliers improved.
It can therefore be stated that AI solutions not only accelerate the demand forecasting process, but also bring tangible benefits at multiple points in the corporate value chain by improving operational efficiency.
It goes without saying that there is still a lot of yet undiscovered potential in AI and data-driven decision-making. If you are interested, feel free to contact us and learn more about the Stratis service portfolio.
Source: logisticsit.com.
