Before we get started, let’s go over the basics. Machine learning is an automatic computer analysis that uses data to find patterns and create forecasts. Machine learning uses data to create a program, the program learns from experience and then decides based on that experience. This is just like humans – we gain knowledge, learn from experience, and then make decisions based on the knowledge and experience we have. One of the most common example of machine learning is the “Compare to similar items” on Amazon.
Increased amounts of data and progress in software technologies has resulted in machine learning gaining a lot of popularity recently – but data is essential to being able to use machine learning techniques. Which is exactly what most of our customers have – a lot of data!
Adding Correlation to AGR Inventory
The correlation model looks over the sale history and identifies positive correlation/halo effect between different products. A typical example of this is hot dogs and hot dog bread. If the sale of hot dogs increases, for example because of a price cut or because of the weather, then the sale of hot dog bread will increase by almost the same percentage. The data we needed for this analysis is simple transactional data which all our customers generally have on hand.
The following examples of identified correlations come from one of our customers. When looking at their data, we found that the following two items, a Culinary Torch and Butane fuel, have a rule of 80% correlation. That means that 80% of the time that a customer purchases a culinary torch, they will also buy a small tank of Butane.
In order to input this technology into our software, we used our powerful planning engine. Within the AGR Inventory module, we used the planner to display the results of the correlation model in a simple and useful manner, as seen below. This view shows a list of your products, with the first two columns containing their numbers and names, and the last two columns show a link to possible correlated items or similar items. When we click on the ‘correlated items’ link for one of the products, a new view opens. The following product is a magnifying glass with a light and its number and name is in the first two columns (red box). In the next two columns we then see the number and name of the products that are correlated to the magnifying glass (green box), and in the last column, we see the correlation percentage. We can therefore see that this magnifying glass has correlation with two products: a 4-pack and a 10-pack of AA batteries, which are exactly the batteries used for the magnifying glass. Approximately 29 percent of customers buy a 4-pack of batteries when they buy the magnifying glass, and 11 percent buy a 10-pack of batteries.
More Opportunities Abound
Using this machine learning technology within your AGR setup can open a lot of new opportunities for your business. First, knowing the connection between items can be incredibly useful when planning promotions, since an increased sale of a certain product also affects the products in which it is correlated. When the user is planning a promotion of, for example, the magnifying glass with the light, they would be able to see the butane fuel tank listed as a correlated item, informing them that the sale of the magnifying glass will also increase the sale of the batteries. While this may seem like common sense, when a company is working with thousands of SKUs, having the software do the work for you will save a lot of time and money for your business. Planning promotions with this information with AGR Inventory can help the user see the true overall effect of the promotion.
This information can also be used for pricing and store layout. If we think again about the culinary torch, 80% correlation is of course a considerably high correlation, however we can assume the fuel does not come with the torch. The customer will need to buy the butane fuel to use the torch, so this would be an opportunity to do make more sales and increase the correlation of these two products closer to 100 percent by examining the products‘ layout in the store, pricing, etc.
Knowing the rates of correlation in online retailing is also incredibly useful, as when the customer adds the magnifying glass to his basket, then we can point out the fact that other customers often buy AA batteries at the same time and thus encourage him to do the same, just like the first Amazon example ‘Frequently bought together
Embracing New Technology
Machine learning can be used in various ways in sales and inventory planning. We at AGR Dynamics believe that this technology can incredibly valuable for our customers, and we strive to stay on top of new technology changes and incorporate them into our supply chain management software. These new developments can make work a lot easier for our customers, by both giving them suggestions and informing them the causes of the decisions they are making. These two new functionalities will help give our customers a better overall view of their product and increase their effectiveness in inventory management.