Retailers constantly face the challenge of managing inventory in a way that supports customer needs without wasting valuable storage space. The expenses associated with cooling, heating, and managing the moisture levels of storage areas, not to mention staffing and maintaining them, can push your budget beyond its limits.
The key is to use data to predict demand. When you know what customers want, optimizing your inventory management system is far easier. This is why data-driven retail is transforming the industry.
The Power of AI in Retail Inventory Management
AI algorithms can predict demand with high levels of accuracy. You can then use this data to optimize your pricing strategies and automate your replenishment system. The systems work by:
- Taking historical sales data and analyzing it to identify patterns
- Factoring in data from external factors, such as economic indicators, social media trends, and weather forecasts
- Adjusting the models according to seasonal trends, such as holiday purchasing or back-to-school buying
Then, data engineers use machine learning to understand the relationships between demand for products and various factors, such as those described above.
For example, Walmart has built machine learning models into its systems that enable it to use historical data to predict demand during the holiday season. Walmart’s system factors in weather patterns and demographic stats to forecast demand and use it to adjust its supply chain.
With the help of AI, Walmart keeps its shelves stocked even during the busiest holiday seasons.
Advanced Analytics for Demand Forecasting
Analytics tools reveal hidden patterns in sales data through a process of collecting data, cleaning and transforming it, and then using different models to derive insights. Some of these include:
- Predictive analytics, which uses historical data to predict future sales trends
- Descriptive analytics, which involves taking sales data, pinpointing trends, and then visualizing it via charts and graphs
- Association rules, which identify relationships between products that tend to be purchased together, such as razors and shaving cream, chips and dip, or beer and ice
AI systems also use these and other models to analyze customer buying patterns and then provide personalized recommendations. For instance, a machine learning system could identify groups of customers that tend to purchase fresh produce, bread, and dairy products all in the same trip to the grocery store.
The system can then:
- Recommend products that are either on sale or have high inventory levels via a customer-facing app
- Suggest combinations of products to purchase together, such as a certain kind of bread and artisan butter
- Show each customer products that may soon expire, decreasing the chance of dead stock impacting the store’s overhead expenses
Retailers can also use scenario planning to approximate inventory levels based on likely future conditions. This involves drawing up a variety of scenarios, such as best-case, worst-case, and statistically most likely situations using historical data.
For instance, a summer buying rush at a new car dealer may reveal that, according to historical data, in July, the company will sell:
- 72 cars in a worst-case scenario
- 112 cars in a best-case scenario
- 87 cars in a statistically most likely scenario
Decision-makers can then adjust their inventory levels accordingly.
The Role of Reliable Technology Infrastructure
The only way to take full advantage of AI and analytics for inventory optimization is to have a reliable, effective digital infrastructure. At the center of a winning ecosystem lies high-speed internet. This gives you the ability to process large amounts of data from multiple sources and geographic locations.
You can also use high-speed internet to help decision-makers and strategists collaborate to solve pressing inventory challenges.
Cloud services are also an essential facet of AI and analytics platforms that optimize inventory levels. By using cloud-based tools, you create systems that people can access from anywhere, as long as they have an internet connection. You also gain the ability to build scalable, versatile systems with advanced security features protecting your data.
For instance, when you use a cloud-based infrastructure, you can have several business applications sharing data at the same time — all within interconnected data centers. As the information streams through your analytic systems, you can derive insights in real time.
Scaling is straightforward because all you have to do is ask your cloud provider for additional resources. And because you can choose to pay only for what you need, the scalability of the cloud can also result in considerable cost savings.
Figuring out how to make the best use of cloud-based tools and high-speed internet is far easier with an expert technology provider, such as Cox Business. Cox’s team uses its experience to customize a solution for your business model while ensuring you have the tools needed to stay a step ahead of others in your industry.
Case Studies and Success Stories
One of the most successful implementations of AI algorithms comes from coffee giant Starbucks. The company analyzes the ordering patterns of customers, as well as their preferences, using historical data. Starbucks can predict what customers will order and make buying recommendations. As a result, Starbucks can predict inventory levels and buy ingredients and products in bulk, saving money in the process. The company can also reduce the amount of time customers have to wait for their lattes, which improves customer satisfaction and boosts repeat business.
Fashion retailer Zara also uses AI to analyze sales data in real time. In addition, the company’s AI can factor in social media trends and existing inventory levels. Armed with this data, Zara can quickly adjust to changing customer preferences. For example, they can reduce the expense associated with wasted stock by changing the products they order as customers ride a new fashion wave into the spring season.
Looking Ahead: The Future of Data-driven Retail
Blockchain has been emerging as a tool for supply chain transparency. Using blockchain tech, you can track inventory from the moment it’s manufactured, seeing where it goes and when all the way to your storeroom. Since the data hashed in the blockchain is immutable, you get unadulterated, accurate information that both improves your analytics solution and assures the genuineness of the inventory you’re stocking.
As machine learning models get more and more sophisticated, they’ll be able to make increasingly accurate and sophisticated predictions. For example, retailers will be able to use no-code machine learning solutions they can customize with a few clicks. With these kinds of solutions at their disposal, everyday employees can build systems that predict buying behavior and corresponding inventory needs months or years in advance.
Embrace AI and Analytics Now for More Efficient Inventory Management
By combining sales, ordering, and inventory data, you can predict what customers are most likely to purchase and when. By feeding this information into a machine learning system, you can automatically optimize your inventory management solution.
The key is to embrace AI and analytics now so you can start exploring their potential for your organization. At the heart of a successful inventory optimization solution is the connectivity, speed, and dependability you get from a reliable technology infrastructure.
