Inventory plays a key role in every retail and e-commerce company. This aspect of running a trading business significantly influences cash flow, financial performance, and growth possibilities. That’s why more and more retail and e-commerce companies decide to opt for inventory management using machine learning. What are the benefits of such a solution? And why can we say that inventory management using machine learning is definitely this industry’s future? Let’s find out!
Inventory management is a complex and difficult task, especially for companies operating more than one warehouse and stores selling thousands of products per month. Retail/e-commerce companies frequently struggle with order mix-ups, dead stock issues, insufficient stock levels, and warehouse disorder. On top of that, the human factor can also be a source of problems. Even if your WMS (Warehouse Management System) works correctly, your warehouse staff can, for example, put a given product on the wrong shelf. Such an initially unnoticeable mistake can lead to severe consequences, just to mention order mix-ups.
If you run a retail company or an online store, we believe that inventory management using machine learning is an answer to your problems. In this article, we are going to show you how inventory management using machine learning can improve your business. Today, machine learning in inventory management is used to optimize stock levels, prevent dead stock, and improve customer experience (CX). How does it work in practice?
Inventory management using machine learning: Applications
We want to tackle five crucial aspects of running a warehouse with the support of machine learning. As you know, this technology’s applications can be very broad and by no means are limited just to the following list.
We’ve already tackled this issue. Order and product mix-ups is a real thing that happens even in large and experienced companies. Partly the solution lies in the WMS system that’s effective and fully tailored to your needs. However, in many instances, that’s only a partial solution. If you’re struggling with warehouse-related mistakes, think of adopting machine learning.
For instance, you can teach your machine learning algorithms to understand the way your warehouse operates, mainly–where the products from each category are and should be stored. This way, it’s much simpler to track all of your products and orders. Thanks to machine learning, every parcel can be verified for compliance with the order, minimizing the order mix-up issue. And this aspect of your inventory management also improves CX!
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OPTIMIZING INVENTORY MANAGEMENT
Inventory optimization plays an essential role in every retail/e-commerce company operating in various locations and running more than one warehouse. Inventory management using machine learning can help you apply various optimization strategies that can be adjusted to take into account your specific company’s conditions and needs. After all, you have to remember that there are many different variables that delay product delivery:
- Where the products are stored and how
- What’s your picking and packing strategy
- How shipping works in your company
- How many employees are engaged in preparing orders
Machine learning can help you with each one of these questions. In fact, we can say that inventory management using machine learning is, by far, the most efficient way to manage your inventory.
Over and under-stocking can be a severe problem. If you have too many products, your inventory piles up without a purpose. On the other hand, if you have an insufficient amount of products, your customers are dissatisfied, your cash flow is endangered, and your image is exposed to risk. Machine learning can help you predict demanded stock levels that will match the number of orders. How is that possible? Thanks to historical data and predictive analytics. Machine learning algorithms can analyze your past orders and assess future sales levels.
As a result, you don’t have to worry about inaccurate stock levels. And predictive analytics even can determine when extra supplies will be needed at certain times of the year. For example, there are two busy periods in the retail/e-commerce industry–Black Friday/Cyber Monday and Christmas. During these commerce holidays, the sales levels in online stores are typically much higher than during the rest of the year.
In 2019, Cyber Monday was the record-breaking online spending day in history, jumping 19.7% to 9.4 billion USD. Your online store has to be ready for such an occasion, and machine learning algorithms can help you predict sales level during Black Friday or Christmas.
Furthermore, stock predicting can help you avoid the dead stock problem. Dead stock is a serious threat! It can ruin your performance and cash flow. What is dead stock all about? When you order too many products that you can’t sell, they start piling up in your warehouse. If these products have a short expiration date (e.g., FMCG products) or get quickly outdated (e.g., electronics, sports equipment) shortly, they become unsellable. They become dead stock. Stock predicting can help you avoid this problem.
IMPROVING USER EXPERIENCE (UX)
Actually, it’s a resultant of the aforementioned aspects of inventory management using machine learning. When your warehouse operates flawlessly, your supply chain is effective, and there are no order mix-ups, your customers are satisfied. They get their products on time, and they receive exactly what they ordered. This seemingly obvious aspect of running a retail company can significantly improve the user experience in your company!
Inventory management using machine learning: Smart warehouses
Soon enough, inventory management using machine learning will be a global standard, allowing companies all over the world to reach a new level of development in retail. This new standard will pave the way for smart warehouses. The warehouses of the near future will operate almost 100% automatically, using advanced AI and machine learning algorithms. Just imagine 24/7 efficiency, 365 days a year, no delays, no sick leaves, no vacations. What can we expect to see in warehouses of tomorrow?
They are already prevalent and used in many aspects of the business. Drones will shortly be irreplaceable in warehouses, facilitating the quick detection of individual items and preventing already mentioned inventory mismatches. Thanks to sensors and cameras, drones can locate the requested item, scan a barcode, and even relocate products within warehouses.
PICKING AND PACKING CONDUCTED BY ROBOTS
Again, this happens even today. Large production plants have separate lines that are used exclusively for packing products with no human assistance. Of course, such a line is a massive financial investment, but you can expect a significant workforce and time savings in the long run, making the entire endeavor beneficial.
According to mhlnews.com, smart glasses used in one warehouse allowed to shorten the picking time by five to seven seconds per item. This means that efficiency has been improved by over 30%!
And yes, all of these novelties are based on advanced AI and machine learning algorithms. What’s even more exciting, it’s all possible even today! We are not talking here about some distant, Sci-Fi future. It’s just a matter of months to see them applied even in medium-sized warehouses. Now, let’s consider some of the examples of companies conducting inventory management using machine learning.
Inventory management using machine learning: Examples
Inventory management using machine learning: LOWE’S
It’s a home improvement retail chain with stores located all over the United States. In mid-2016, they introduced LoweBots–autonomous retail robots. Although their primary goal is to help customers locate products they need, LoweBots do more. They gather real-time data by using computer vision and machine learning algorithms to scan inventory and look for patterns in product or price discrepancies. As a result, they are extremely helpful both for customers and the company itself.
Inventory management using machine learning: AMAZON
This company needs no introduction, as they are a global e-commerce leader. What you may not have known, they also have their own warehouses and logistics services (Amazon Fulfillment). In 2012, Amazon bought Kiva Systems (currently known as Amazon Robotics), a robotics company, for 775 million USD. This investment was not in vain! Today they have tens of robots in their warehouses worldwide. These robots are helping the company pick and pack parcels and fill orders more quickly and efficiently.
See Amazon robots in action:
[link do video: https://www.youtube.com/watch?v=Ox05Bks2Q3s&ab_channel=EyadNofal]
Inventory management using machine learning: IBM WATSON
IBM’s Watson supercomputer, thanks to machine learning algorithms, can be used in modern warehouses. This platform can combine and analyze both visual and system-based data to track, report, and make real-time recommendations regarding inventory management. For instance, Watson can determine if a shipping container was damaged, classify it by damage time, and recommend the best corrective action to repair the assets.
As you can see, inventory management using machine learning is a true game-changer. All the aforementioned examples confirm this fact. This technology allows you to operate more effectively, curb parcel mix-ups, deal with the dead stock problem, and improve user experience. Soon, warehouses all over the world will be filled with autonomous machine learning-based robots that will do the vast majority of work. Your role will be limited just to overseeing their work and managing it.
If you are interested in machine learning and wonder how this technology could be utilized in your retail or e-commerce company, feel free to drop us a line! We are an experienced AI/ML consulting company, and we’ll gladly guide you through this fantastic world. It’s time to take your retail company to the next level!