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Artificial intelligence, machine learning and retail — an extraordinary love affair
Retail offers enormous opportunities to achieve competitive advantages through data-based decisions. Whether online or stationary. But e-commerce in particular, where there is a high level of digitization and usually large amounts of data, is of course ideal for innovations in the areas of artificial intelligence (AI) and machine learning (ML). These options are not only reserved for large retail groups and e-commerce platforms. Even smaller retailers can use their data to increase value with the help of AI.
One key use case is forecasting. Regardless of whether it is sales planning, incoming goods forecasting or returns forecasting — a precise forecast forms the basis of every planning. Existing planning tools such as Excel spreadsheets, gut instinct or traditional planning software are quickly reaching their limits in view of growing complexity and volumes of data. The use of machine learning algorithms makes it possible to consider numerous data sources and large volumes of data, which leads to more consistent, accurate and transparent forecasts. Using five examples from the world of online and stationary retail, we will show you what this means in practice for typical forecasting applications in retail.
What is forecasting?
Forecasting Definition: Forecasting, or in German “forecasting,” comprises the process of estimating and predicting future events or developments based on current and historical data. The core of this process lies in the application of statistical models and analysis methods to Identify patterns in the data and derive future trends from them. This process enables companies to make strategic and well-founded decisions — future events should be predicted as precisely as possible in order to optimize future planning.
The use of forecasting ranges from predicting sales and demand to estimating prices, risks and operational processes. Through precise forecasts, companies can plan their resources — such as personnel, inventories and production capacities — more effectively and identify risks at an early stage. Forecasting is particularly reinforced by technologies such as machine learning and artificial intelligence, which enable more complex models and deeper analysis.
In our increasingly data-driven world, forecasting is therefore becoming a decisive competitive factor. As a result, organizations can react agilely to market dynamics and ensure their profitability in the long term.
Common forecasting methods
Let's dive a bit deeper into forecasting before we dive into practical examples. Because there are a number of exciting and different methods and approaches for effective forecasting. The right choice depends on the specific requirements, the data basis and the goals:
Time series analysis
This classic method analyses historical data from a time series in order to predict future values. Methods such as exponential smoothing or ARIMA models are used.
Regression analysis
By modelling the relationship of a dependent variable with one or more independent variables, predictions can be made. Linear and nonlinear regressions are common.
Machine learning
Machine learning algorithms automatically recognize patterns and relationships in data in order to make forecasts based on them. This includes decision trees, neural networks, etc.
Predictive analytics
It combines data mining techniques, statistical models and machine learning to predict probabilities of future events. Often used in predictive maintenance.
scenario analysis
Appropriate forecasts are prepared for various future scenarios in order to prepare for potential developments. Often used for risk analysis.
The methods can also be combined and supplemented with expert knowledge. It is crucial that methodology and models are continuously adapted and validated to new data. Enough theory, let's now discover 5 examples of how forecasting is used in retail.
1. The forecast of sales in the food trade
Which goods are needed in the individual sales branches on a specific day? In a large supermarket, which can carry up to 30,000 different items, a precise sales forecast (forecast) is required for each individual item. This task is made more difficult by the different sales frequencies (how often an item is purchased) of the many items. And then sales histories also vary, which can sometimes be very short — especially for newly launched products.
Especially in the food trade, machine learning-based sales forecasts (Demand forecasting) great potential. These not only make a significant contribution to retailers' purchasing and inventory planning, but also in terms of sustainability. By improving the prediction of daily buying behavior in individual stores, purchasing managers can determine exactly how many items need to be placed on the shelves. This not only prevents empty shelves and overcrowded warehouses, but also prevents perishable goods from being thrown away. The drastic reduction of food waste is another remarkable potential of intelligent sales forecasting. A study by Bitkom and BVE predicts that such technologies could theoretically reduce food waste to zero by 2030 — a concept known as”Zero Waste”.
Find out more about our successful collaboration with a supermarket chain in our joint success story on our blog — here we explain how we were able to reliably predict the sales of highly sought after and less popular products (Renner/Banners).
2. The forecast of inventories in textile retail
The main goal of inventory optimization is clear: We want to minimize inventory levels and at the same time ensure that you, as a customer, The right product in the right place at the right time receives. In view of constantly changing demand, short product life cycles and the growth of omni-channel models, this is a real challenge. According to a study by IHL Group, trade shortages alone cause sales losses of 634 billion US dollars worldwide every year, while excess inventory results in losses of 472 billion US dollars due to price reductions.
The purchasing department of a fashion house can help avoid exactly these problems through precise forecasting. By analyzing historical sales figures and price developments using machine learning algorithms, we can forecast future product demand. Taking current inventory levels into account, the optimal order quantity can then be recommended. This ensures that neither excess nor understocks arise and that the predicted demand is met precisely.
The automated creation of an individual order quantity forecast significantly simplifies the purchasing process. This saves valuable time. Time with which buyers at the fashion house can once again dedicate themselves more to evaluating the quality of the goods. This guarantees the best possible quality of their products.
3. The forecasting of incoming goods quantities in retail logistics
Let's stick to the topic of fashion: A logistics service provider for a well-known German fashion house is faced with the challenge of optimizing incoming goods in warehouses — because it wants to make personnel planning more precise. But delivery dates and incoming goods are often difficult to predict, particularly in the fashion industry. Instead of fixed delivery dates, there are usually arbitrary time windows. When delivery companies do not meet their deadlines and deliveries are made unexpectedly, efficient planning of personnel and warehouse utilization becomes a real burden. As a result, there is often overstaffing or understaffing in the warehouse, which results in high personnel costs and losses in productivity.
Here too, forecasting using machine learning offers a valuable solution. By analyzing the delivery history of individual suppliers through machine learning, the arrival time of the goods can be predicted with high precision. These forecasts make it possible to plan personnel more efficiently and make optimal use of warehouse capacities.
This intelligent combination of evaluation of delivery companies and advanced forecasting models not only results in improved personnel planning, but also maximizes warehouse utilization efficiency.
4. The forecast of retail visitors
Fluctuating visitor numbers also often pose major challenges for retailers — particularly in stores with a high need for advice. An optician, for example, must be able to plan exactly how many customers are expected at different times of the day and week. The goal is, of course, optimal customer service at all times. For this purpose, opticians use frequency meters at the entrances to their branches to record the flow of customers. This consists of walk-in customers and customers with appointments.
With the help of machine learning and based on historical visitor data, a precise forecast of the number of customers can now be made for each branch. These forecasts make it possible to make automated recommendations for personnel requirements. When training the models, additional influencing factors such as the weather and information about special opening times (public holidays, holidays, etc.) are taken into account. These factors are analysed for their impact on the number of customers.
Thanks to these forecasts, personnel planning can continue to be effective and customer care can be optimized. This not only results in cost savings, but also has a positive effect on the customer experience.
5. The forecast of return quantities in fashion e-commerce
Returns are an unavoidable part of online retail — particularly in the fashion industry. Returns are also the biggest nuisance for all parties — retailers, customers and the environment. Because processing these returns is a costly, resource and labor-intensive process. A logistics company that is responsible for handling returns from a fashion retailer is therefore faced with the challenge of precisely predicting the volume of returned packages.
Machine learning is once again making the decisive contribution here. By predicting return quantities, returned packages can be predicted efficiently. This enables optimal planning of personnel deployment and targeted management of logistics resources in order to speed up returns processing — and to minimize costs. The returns forecast therefore facilitates capacity planning in logistics, warehouses and personnel, particularly in view of fluctuating package volumes.
Would you like to learn more about how these forecasting models work in practice? Then take a look at our joint success story with Mode Logistik GmbH & Co. KG, which operates logistics for Fashion ID (the online shop of Peek & Cloppenburg KG, Düsseldorf).
Conclusion
Regardless of whether it is B2B or B2C, stationary retail or online business — the possible uses of forecasting in retail are extremely diverse. The examples given are only a selection of what is possible. They should serve as a source of inspiration to give you an idea of which forecasts you can implement in your company with artificial intelligence and machine learning.
Have you become curious? Our Experts in forecasting will be happy to assist you find out how your individual forecast can be designed.
FAQ: Forecasting in retail
Q: What is forecasting in retail?
A: Forecasting comprises the process of estimating and predicting future events or developments based on current and historical data. In retail, it is often used to make more informed decisions about sales volumes and inventories.
Q: What is the role of artificial intelligence and machine learning in forecasting?
A: AI and machine learning make it possible to analyze large amounts of data from various sources and identify patterns that would be too complex for human analysts. This results in more accurate and consistent forecasts that improve operational efficiency and responsiveness to market demands.
Q: Can smaller retailers also benefit from forecasting?
A: Yes, even smaller retailers can use their data to make forecasts using AI/ML. Technology has become more accessible, and many solutions are scalable, making them financially viable even for smaller companies.
Q: What are some specific examples of forecasting applications in retail?
A: Some applications include predicting sales in grocery stores, optimizing inventory in textile retail, forecasting incoming goods quantities, estimating visits to retail stores, and predicting return quantities in e-commerce.
Q: How can I find out which forecasting methods are best for my business?
A: Choosing the right forecasting methods depends on many factors, including the type of data, specific business requirements, and existing technology resources. Ours Forecasting experts offer individual adviceto identify and implement the most appropriate techniques and models for your specific needs