How can Machine Learning solve inventory challenges for Retailers

As a retailer, the shift to today’s online paradigm imposes that online shops are equipped with dynamic content, prices, and customer recommendations.

How can Machine Learning solve inventory challenges for Retailers

As a retailer, the shift to today's digital paradigm imposes that online shops are equipped with dynamic content, prices and customer recommendations. Oddly enough, in the stationary sales channels, decisions are still made based on manual reports, planning tools and even partly intuitively, without the use of a management system.

The inventory challenge in retail

Overstocking, understocking, as well as a missing overview of sales techniques, such as the promotion of top sellers, can be challenges that lead to a lack of scalability and unnecessary costs.

The main inventory challenges in retail are:

  • Tracking inventory

  • Maintaining seasonal inventory

  • Overstocks or understocks

  • Stocking a new store

  • Inefficient merchandising management processes

  • Changing customer demands and expectations

  • Limited data visibility and centralization of essential data

  • Increasing Competition

Over time, merchandise planners must keep up with the trends in sales and even foresee how the clients' expectations will change over the course of a whole year. This has become increasingly harder, especially with the unexpected situations that the industry was confronted with. Decisions about merchandising management and inventory stocks can only be based on KPIs and comprehensive reports. Even then, decisions made intuitively rather than data-driven can be a dead end.

How can Machine Learning help

Implementing a new management system that meets the needs of merchandise planners seems to be a pervasive solution. However, after implementation, many retailers find that such a tool provides limited help to their store and area managers. Although KPI displays and reports are technically integrated, the application is unable to offer any decision suggestions about inventory issues, making human decision still critical.

This is where Machine Learning comes into play. When historical data from a management system is made available, human experience can be replaced by empirical values and machine learning from the system. If trends are taken up in addition, intelligent recommendations could be given to the planners.

With the help of a 3-step principle, Accesa has managed to offer personalized solutions for retail clients that faced inventory issues. By combining a management system with the power of machine learning technologies, the extracted data can be transformed into valuable information and generate smart predictions for the managers.

The 3-step principle encompasses:

  • A discovery phase to make the definition of needs clear. For example, the receipt of a weekly trend forecast based on historical (monthly, annual) sales data is used to measure inventory management planning. Based on that, we proceed to analyze the data and come up with the right method for the client.

  • A project study offers analysis and validation of existing data and decides over methods and technologies that will be used to create a prototype for the solution.

  • Implementation is the next step of the principle, where the solution is tested and validated, after which it is enrolled company-wide.

From raw data to machine learning solution

The basic prerequisite for a short-term, successful, and problem-free project is the availability and validity of the data. We use comprehensive and structured data analysis in 4 steps to identify sales trends:

  • Summary of the data: data stored in different formats with no intuitive descriptions for tables and metrics must be collected, centralized, and labelled.

  • Determination of project-relevant data: Based on the existing historical data, certain data categories are selected and summarized for the project purpose (e.g. product types, colour, size, price, season, year etc.).

  • Correlation and resampling: the different product information is correlated, and mutual influences are analyzed. Important conclusions could be identified (e.g.: The correlation of price to sale had a much lower value than expected).

  • Machine Learning: determining a suitable data aggregation method for training the machine learning model is one of the most time-consuming processes. The chosen model must be complex enough to allow the pairing with learning algorithms.

Results and next steps

The Machine Learning solutions can use forms of linear difference equations and map linear stochastic processes or approximate more complex processes. The combined application can manage a variety of processes, such as allocation of goods between central warehouses and other locations or control the retail stores.

The solution makes decisions based on existing data. Provided that this data is clean - no duplicates or outdated information - the machine makes suggestions that can be validated by operators, whereas the human factor has the last word.

Another notable result of such a solution is dynamic pricing. The mechanism can promote sales in the stores and optimize turnover at the same time. However, there are several things that need to be considered: Customers expect the price of a product to remain stable from one day to the next. Therefore, the machine only recommends the next price reduction in the system based on sales data.

The expected sales figures based on sales reports can also be used to optimize the distribution between the central warehouse and the stores, as well as between the stores themselves.

Final thought

Because of cultural and regional conditions, products sell differently from region to region and from time to time. This is not always foreseeable in the long term, but a Machine Learning Solution can help identify trends in the ongoing sales process and provide continuous optimization.