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How Predictive Analytics & Machine Learning is Revolutionizing Inventory Management?

In retail, manufacturing, e-commerce, and wholesale, inventory management efficiency is absolutely essential. Understanding demand across products, knowing your current inventory levels and how much you need to have at any given time, how much raw material you need to order, and understanding what resources and time-intensive processes need to be implemented will all play a role that impacts the bottom line.


The bigger your company is, the more complex these processes can be, particularly when your aim is to target multiple territories and outlets and have large scale operations. These operations are often dependent on external forces such as service providers, suppliers, and even weather — making getting them right all the more difficult.


In the realm of inventory management, AI adoption, specifically, the use of machine learning, is revolutionizing inventory agility — maximizing stock levels and reducing stock depletions. This is why manufacturers, wholesalers, and retailers — both big and smaller operations too — are keen adopters of predictive analytics and machine learning technology. Combining the strengths of supervised learning, unsupervised learning, and reinforcement learning, machine learning is proving to be very effective as it consistently seeks to find factors that impact your supply chain performance.


In 2016, a study done by Accenture found that the adoption of AI tech across all industries may double by the year 2035. Investing in AI is expected to increase labor activity by 40%. In fact, 70% of business owners and executives plan to increase their AI investments significantly.


These businesses aim to create efficiencies in their complex systems that involve multiple, compartmentalized processes, which is exactly the area where this technology excels. In hindsight, it’s about the machine’s ability to make lots of little savings and efficiencies for you, which compounds and adds up to much larger benefits.


Machine learning is proving to be very effective in taking account multiple factors that existing methods are incapable of quantifying or tracking over time. The adoption of predictive analytics and machine learning in inventory management allows businesses to keep track of their operations, augment business strategies, manage sales and business productivity and adopt a more proactive business approach rather than a reactive one. The growth of artificial intelligence in the supply chain industry is driven by several factors, such as increased awareness of AI and big data analytics and a widening implementation of machine learning across autonomous and semi-autonomous applications. Let’s learn more about why this approach is increasingly becoming important in all business sectors.


Machine Learning Helps Forecasting Accurate Stock Levels

One of the most challenging aspects of inventory management is forecasting production demand. Walmart, an obvious giant in the global supply chain industry, applied predictive analysis on their food items to see which ones will sell better; this ended up boosting their sales by 18%. They learned that they should have more steaks in stock when it’s cloudy, warm, windy, and dry outside. Also, they learned several other patterns such as the fact that berries get sold more on sunny days and salads get sold more when temperatures are higher. The reality is that you don’t have to be a giant in the retail sector to use AI and improve your inventory management.


Currently, existing forecasting techniques use baseline statistical analysis and simulation modeling to predict future production demands, which is not very effective nor is it accurate. In the retail and manufacturing sectors, not all stock that comes in will end up being sold to the end consumer. It’s inevitable that a certain amount will be lost due to inventory mismanagement, fraud, theft, stocktaking errors, damage, etc. Predictive analytics and machine learning solutions do the job of telling you when your warehouse needs restocking, what items are high in demand, and they discover sales patterns in your business that will influence what products you should buy, when you should buy them, and so much more. Discovering new information and patterns in inventory management has the potential to revolutionize your business.


Machine learning algorithms automate supply chain processes without any manual intervention or classification system to guide the overall analysis. When organizations use these advanced analytics in their operations, customer needs are detected more accurately and quickly, thereby increasing operational efficiencies and reducing unnecessary inventory during hectic periods. You no longer have to be concerned about idle or excess stock that is tying up your cash which could have otherwise been used for better purposes. Current data, provided by machine learning tracking features ensures optimal business performance, satisfied customers, and better inventory usage.


Also, let’s explore another use case for machine learning. Let’s consider a scenario where a new product enters your warehouse. It needs to be stored and organized. You’ll need some time to remove the original packaging of those items, keep track of their location, and place them carefully on the shelves. One of the major benefits of machine learning is that it can analyze the items’ real-time location in your warehouse. As new shipments come in, it matches them to the current location. This helps you reduce the time consumed in an otherwise manual and inefficient process. Machine learning will direct employees to where they need to go to find the product, thus saving a decent amount of time when you add up how much excess time is spent by each employee to go and search for items.


Efficiency in Order Management Can Lead to Happier Customers


Organizations spend plenty of time optimizing their inventory management techniques and figuring out how they can make the process as organized and smooth as possible. An inefficient picking process in inventory management results in higher lead times, which often leads to unhappy customers. Many manufacturers and retailers use manual processes and traditional ERP systems, failing to comprehend the importance of proper inventory management. Optimal picking order and proper inventory management are paramount to running a successful and smooth warehouse. Machine learning can optimize the process for you in a variety of ways. For example, it lowers the opportunities for damages or errors to take place by reducing the number of steps involved in the inventory management process.

Efficient picking of multiple orders can sometimes lead to inefficiencies and consume more time than usual if the right system is not in place.


Machine learning also helps businesses analyze their orders & arrange the direction path while separating orders simultaneously. Using machine learning, businesses can improve their inventory optimization, especially when they have multiple warehouses. The technology takes into account many different independent variables that might cause delays or errors and provides solutions and suggestions on how to manage stock levels. Businesses that use machine learning and predictive analytics to automate a bulk of their work often focus their efforts and energy on improving customer experience & product quality which leads to happier customers and more sales.


Machine Learning Can Direct You to Keep the Right Parts On Hand

Manufacturers require appropriate components for assembling products in different facilities and they normally include contingency plans in case of a potential equipment breakdown. If your plant doesn’t have the necessary replacement parts readily accessible, it could substantially hinder business operations. If necessary, you must communicate requirement changes with your suppliers in connection with an increase or decrease in demand.


Warehouse workers also need to pay significant attention to items with expiration dates. If these components or items are not organized, stored, or handled on time, they can get spoiled and cause a ton of waste and investment loss.


Machine learning and predictive analytics can significantly mitigate the likelihood of not having enough components or having too many components. It also adds an added layer of intelligence to the overall inventory management process. In one case study involving a part supplier for an airplane manufacturer, the performance of the supplier was one of the key predictors of fleet reliability. After the implementation of predictive analytics, the company saw substantial cost savings of $1.8 to 3.8 million due to a quicker issue resolution system.


Machine Learning Can Help You Engage in Suggestive Selling, increasing your AOV per customer


All retailers have products that are more popular than others, more in demand at different times, etc. In inventory management, there is a common practice called the “ABC method.” What this method says is that products in group A are the most popular, they need the tightest inventory control. The “ABC method” suggests that group B items don’t bring in as much profits since they are not selling rapidly and group C items generate the least profits and make up the smallest percentage in inventory. The ABC approach is effective in the sense that it informs retailers what their strongest sellers are but it lacks a ton of sophistication to maximize margins on Group B, C, etc.


In this regard, predictive analytics is a very propelling practice that follows the notion of suggestive selling. The practice sheds light over the most wanted items you have in your inventory and takes into account what other products customers might be interested in as a result of their interest in the most wanted products. Perfect examples of this sort of practice are Netflix and Spotify. They offer regular suggestions to their viewers based on their past interactions and content preferences. It allows them to encourage their customers to consume more of their content and increase engagement on their platforms. Large e-commerce brands such as Amazon & Alibaba also have these sophisticated recommendation engines built which will recommend items to you based on your shopping cart + previous purchase behavior. This smart recommendation engine is used to increase AOV + LTV for these e-commerce brands.


Similarly, retailers can also identify these hidden opportunities to sell products to customers who buy product X who might also be inclined to buy product Y. Many retailers try to put together similar products that are bought together right next to each other in any given store. However, they lack the sophistication to do this for thousands of items at a time and they don’t have real-time knowledge of how things are performing, where to make changes, etc. All of this can be solved via the power of predictive analytics. You can increase your customer’s AOV by strategically recommending items to them they are most likely to purchase by studying their purchase history, the frequency of purchases, etc. Predictive analytics allows business owners to make informed product decisions based on facts and figures without the guesswork.


Machine Learning Can Reduce Shrinkage and Minimize Supplier Risk

Both retailers and manufacturers deal with shrinkage problems frequently. This happens when the actual or the expected inventory doesn’t get sold due to problems like customer theft, breakage during shipment, etc. Research shows that using data analysis, shrinkage could be made less problematic by identifying risk factors that business owners may not notice at all or can proactively be identified before it’s too late.


Predictive analytics allows manufacturers to undertake a proactive approach and gain insights on which of the supplies are more likely to reach the plant in a damaged or unusable condition. If there is a pattern that’s identified, the company could bring that concern to the suppliers immediately so they can resolve future issues.


According to a study by Global Trade Mag, 65% of cargo ends up getting damaged due to incorrect packaging in freight containers. Predictive analytics can be used to generate alerts when a certain product received is prone to breakage, allowing the manufacturer to raise complaints and cite data from their analysis to strengthen their case.


Shoplifting is another major source of shrinkage for many retailers. This is sometimes very difficult to detect and needs concrete evidence before any concern is raised. Employee theft can occur, manual processes such as paperwork errors could also be blamed, and much more. Using historical data, you can use predictive analytics to identify prominent causes of inventory shrinkage and proactively develop processes to tackle them.


The reasons behind shrinkage are not identical for all retailers or manufacturers. And that’s why business owners must use predictive analytics to gather the knowledge necessary to act decisively rather than resorting to trial and error to curb it.


Current ERPs don’t offer an Integrated Inventory Management System


Supply chain processes are known for generating giga-tons of data, just waiting to be analyzed. Current ERP solutions aren’t sophisticated enough to incorporate predictive analytics because they don’t integrate all the right data sources, and they don’t have the expertise to apply machine learning to get you more accurate inventory levels. In these modern times, you need an inventory management tool that uses constraint-based modeling with the greatest predictive accuracy. Using machine learning and predictive analytics, you can make sense of all the data at hand. Freshly analyzed and updated data then builds a robust foundation when it comes to the real-time flow of information. Every player in the supply chain sector is empowered with the best data at hand to make the best decisions.


Many ERPs don’t connect well with many sources including warehouse software, accounting software, purchase order PDFs, spreadsheet data, etc. This can be a major issue because if these data sources aren’t speaking to each other, you don’t have the full information you need to make the best inventory related decisions for your business.


It’s absolutely imperative that your ERP connects to all relevant systems so they can communicate in synergy so you have a 360 overview and understanding of issues that are happening or about to take place. The problem right now is that organizations are paying for different systems + resources that handle different tasks/functions that don’t speak to each other.


Such accurate data integration enables quality data collection as the data is continuously updated in a single location. You can use this to generate real time reports to track business performance, plan for future growth, and apply machine learning and predictive analytics to particular business problems.


Machine learning is no longer a “nice to have” innovation but rather a necessity in the industry. With the rise of real-time customer expectations and the partial erosion of the brick and mortar model, inventory management practices must embrace technologies that far outperform human actions and thoughts. To better comprehend the speed of supply chain transactions, consider these stats from a 2017 report from MHI Industry. The report showed one e-tailer getting 426 orders per second equating to more than 36 million order transactions in a day; not to forget the millions of scans on every stage of the process and generating tons of data around it. To integrate big data analytics to make the smartest business decisions for this particular e-tailer is not a nice to have, but critical for business survival and growth. Your organization might not be as big as a major retailer, but


How should you, as a business leader, be responding to these challenges? The 2017 report provides a clear answer to this conundrum. Supply chain companies should embed reasoning and data analysis into their decision making process. Most importantly, leaders need to position analytics as a core competency across the entire organization.


What is PredictiveDemand90 all about?


Given the challenges and complexities of inventory management, you need a solution that can help you make the best decisions on when to order inventory, how much to order, etc, so you can meet customer demand so you can meet/exceed sales targets without having stock outs & excess inventory while shortening your lead times.


At Lovers of Data, we have a system called PredictiveDemand90. In 90 days or less, we leverage predictive analytics on top of your existing ERP solution to identify the optimal levels of inventory to meet demand with a high degree of accuracy. You’ll be able to maximize revenues, reduce holding costs, and increase customer satisfaction like never before without worrying about switching costs or investing in new software. If you’d like to chat about this, feel free to book a call here: https://bit.ly/3k6FSdk


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