How might data analytics aid in retail inventory optimization?

Table of Content : 

-Market size for retail analytics 
-Big data in retail generates information that allows merchants to: 
1. Descriptive analytics: 
2. Analytical diagnostics:
3. The use of predictive analytics: 
4. Prescriptive analytics:
-What are the advantages of using data analytics to optimize inventory? 
-Retail analytics and big data 
-But how can you make sure this happens? Knowing the cost of inventories. 
-How does Syoft integrate inventory management and data analytics? 

Introduction : 

According to Research and Markets, the global retail analytics market is predicted to develop at an annual rate of 18 percent between 2019 and 2025. This means the market will soon be worth $9.5 billion. While the figure is amazing, it begs the question, "How?" How has a technology that was not even invented five decades ago had such a significant impact on an industry that has existed for 10,000 years? The fact that it fits could be a simple answer. Data analytics, as a capability, has been all it took to transform inventory optimization and retail in general. 

Market size for retail analytics 

What is big data in the retail industry? 

Big The use of data-driven technologies for articulating business trends and performance is known as data and analytics for retail. Big data or data science in retail, on a higher level, is the application of business analytics techniques in the retail sector. Business intelligence and big data analytics are utilised by retailers to gather important insights that may be used to improve inventory management, operational efficiency, sales, and the entire consumer experience. 

Big data in retail generates information that allows merchants to: 

Find the identities you're looking for. 

Create customer purchase habits and trends. 

Compare the preferences of your customers. 

Determine seasonal and location-based trends. 

Let us narrow our attention to inventory management while we're on the subject of big data in retail. 

What is the minimum quantity of inventory required to meet demand while maintaining low stock levels? 

How can inventory management be improved? 

What can be done to reduce product recalls? 

How may slow-moving stocks' performance be improved through cross-selling? 

While understanding the applications of data analytics in inventory management is critical, answering the how is equally significant. 

What role does data analytics play in inventory management? Through the application of its four models. 

1. Descriptive analytics: 

It provides retailers with a summary of inventory performance, including item movement, replenishment speed, and so on. 

2. Analytical diagnostics:

It clarifies why. Why did the things sell out so quickly? What caused the client to post a negative review? Etc. 

3. The use of predictive analytics: 

On the basis of inventory management history, it may predict patterns and shopper behaviour. 

4. Prescriptive analytics:

It aids merchants in making progressive modifications in anticipation of shifts in consumer sentiment, supply shocks, and demand, among other things. 

We can assist you. 

Let's go into the details now that we've looked at the high-level benefits of big data and analytics for retail and inventory optimization. 

What are the advantages of using data analytics to optimize inventory? 

Any retailer's responsibility includes identifying strategies to improve inventory management. It is made easier by the introduction of big data and analytics in retail. Here are some of the various ways that data analytics may help with inventory management. 

Retail analytics and big data 

Prediction of requirements 

One of the most important  Predictive analytics for inventory optimization is one of the most important features of big data in the retail business. Inventory management can be made more efficient by anticipating changes in customer behaviour. 

Customers have radically diverse shopping habits at different times of the year. When a retailer fails to spot a pattern in these shifting trends, they are left with excess inventory and no room for what their customers really want. They can use data analytics to figure out what to stock in their inventory at what time of year. This not only solves the issue of inaccurate stocking, but it also eliminates the last-minute scramble to get things for their clientele. 

Optimization of replenishment 

Customer happiness and revenues might be harmed by a large number of slow-moving items or the lack of availability of an in-demand item. Employees have had to manually inspect inventories and then estimate how much of an item should be reordered for a long time - all based on guesswork. When you add data analytics to the mix, you can now examine crucial business components like sales trends, the speed at which a hot product sells out, the speed at which a slow-moving item sells out, and so on. With all of this data at your fingertips, making the greatest replenishment optimization option is as simple as removing slow-moving items from the most frequently visited shelf locations and replacing the What's more, there are a number of inventory optimization solutions on the market today that alert businesses when a product needs to be refilled. 

Avoid stockouts. 

Stockout prevention is an extension of replenishment optimization. It is a significant difficulty for merchants because if a shopper cannot discover the product they require, they will immediately switch to another retailer. Here, data analytics for inventory optimization can assist in calculating lead times - the time it takes for an item to arrive at your warehouse after you make an order. This lead time can then be used with current sales data to calculate the estimated cost with items that are genuinely required.   

Increase order fulfillment speed 

Retail business data has the potential to improve order fulfillment times. While it is typical for orders to be routed to the nearest warehouse to save money on transportation and expedite delivery, data analytics for inventory optimization can accomplish much more. You can develop a system where you can dictate where an item should be housed in the warehouse based on its delivery timeline using the correct set of big data technology. It can also inform employees of the item's exact position, reducing the time it takes for them to collect and pack the goods. 

Quick recalls 

Recalls of things are not uncommon, despite their terrible nature. They happen all the time. 

Tracking the selling details is becoming a key aspect of item recall. Big data can assist with this by tracking products by number and shipment details along the supply chain. Big merchants, such as Amazon, employ big data to monitor web pages, ranging from social media to review sites, in order to identify consumers who were sold defective merchandise and promptly correct the situation. 

Customer satisfaction has improved. 

By keeping track of product return reasons and streamlining the logistical side of the retail process, data analytics-powered inventory management solutions play a huge part in improving the shopping experience. 

Let's look at several ways that effective inventory management might help you achieve customer happiness. 

If customers are claiming poor shipping experiences as a cause for not ordering again or returning things, switch to a dependable transportation provider. 

Another common issue in the retail industry is customers receiving the wrong item. Something that can be resolved by scanning a barcode. For example, if a warehouse employee picks the wrong item by accident, a barcode scanner can alert them, allowing them to remedy the problem before the item is dispatched. 

When you have data on what clients are buying/viewing when they buy a product, it's a lot easier to persuade them to buy add-ons and additional items. This not only assists customers in making better purchasing selections, but it also benefits the retailer's bottom line. 

Cost savings 

Only a few stores exist. Few retailers are aware of the financial implications of inventory management. The bulk of them overlooks the financial consequences of carrying too many or inappropriate products. While it is critical to stock what is in demand, it is also critical to strike a balance so that warehouse space is not wasted in an unforeseen manner. 

But how can you make sure this happens? Knowing the cost of inventories. 

The following expenses make up an inventory cost: 

Costs of logistics and storage 

Cost of material handling 

Cost of storage 

Cost of capital 

Cost of Insurance 

Risk-aversion costs 

Understanding and managing inventory costs is critical to effectively managing inventory space. And one method to achieve that is to use real-time inventory data to gain insights.  

Now that we've looked at the major advantages of combining inventory management and data analytics, the next question is how. The complicated solution is to invest in tools that address specific inventory management challenges. The reasonable solution is to invest in a data analytics services business such as Syoft and delegate the task to them. 

How does Syoft integrate inventory management and data analytics? 

Our team of data analysts and engineers at Syoft specializes in developing retail-specific solutions that assist businesses with inventory management. While we specialize in customizing solutions, the following are the features that we typically include in all data analytics packages: 

Inventory data and warehouse, sales channel, POS system, and 3PLs synchronization Inventory stock tracking and reporting automation 

Algorithms for turning inventory data into reports that tell you when you're overselling or about to run out of something. 

Tracking system for outstanding orders, delivery dates, and billing information, among other things. 

These are only a few of the features available in a typical Syoft inventory management solution. Are you seeking a forward-thinking inventory optimization solution as well? Allow us to assist you.