How Might Data Analytics Help Retailers Optimize Their Inventory?

According to a report by Research and Markets, the global market for retail analytics is anticipated to develop at an 18% CAGR between 2019 and 2025. This indicates that the market will soon be worth $9.5 billion.

The amount, however relatively remarkable, prompts the question, “How?” How can a technology that did not even exist five decades ago significantly impact a field that has been for 10,000 years? The fact that it fits can be a straightforward response to this. The capacity to use data analytics has been all that has been required to transform retail and inventory optimization. Check out the trending best data science courseonline to familiarize yourself with the cutting-edge tools used by data scientists and analysts. 

How does big data in retail work?

Big data and analytics are applied data-driven technologies used to describe business patterns and performance in the retail industry. Big data or data science in retail, on a more advanced level, refers to applying business analytics techniques to the retail sector.

For decision-making and to enhance inventory management, operational effectiveness, sales, and the entire customer experience, retailers use business intelligence and big data analytics.

Using Data science and Big data, retailers can: 

• Discover the target personas.

• Establish purchasing patterns and customer behavior

• consumer preferences are compared

• Determine seasonal and place-based trends.

• Considering that this article was about using big data in retail, let’s narrow our attention to inventory control.

The level of data analytics in inventory management today and its Role:

The market for inventory management solutions is expected to multiply, with a projected value of $3.82 billion by 2028. The possibilities of modern inventory management go beyond accurate inventory and business process automation, driven by a strong desire for competitive-level efficiency. Analytics, data mining, and smart data discovery are key to delivering corporate insights that support the data-backed decisions required for increased profitability and productivity.

Effective inventory optimization programs can examine a significant percentage of past sales data and, by factoring in seasonality and lead times, forecast future demand for the inventory. Additionally, in the era of big data, inventory optimization strategies can provide you with information on client preferences, product performance, and channel performance.

Using big data in inventory optimization strategies can assist in addressing issues like:

• What inventory quantity is required to meet demand while preserving low stock levels?

• How can inventory management be improved?

• How many product recalls are minimized?

• How can cross-selling be made possible to enhance slow-moving stock performance?

It is critical to understand the how and the use cases of data analytics in inventory management.

How is inventory management made more efficient by data analytics? 

Via the application of its four models.

1. Descriptive analytics: It gives retailers an overview of the inventory performance, including how quickly things are replenished and how they flow through the inventory.

2. Diagnostic Analytics: The why is addressed. Why did the supplies run out? Why did the client provide a poor review? Etc.

3. Predictive analytics: Using the history of inventory management helps predict trends and consumer behavior.

4. Prescriptive analytics aids merchants in gradually adjusting their strategies in anticipation of changes in consumer sentiment, supply shocks, demand fluctuations, etc. 

For a detailed explanation of types of analytics, refer to the bestdata science course online, designed for working professionals. After reviewing the broad advantages of big data and analytics for retail and inventory optimization, let’s get into the specifics.

What advantages do data analytics for inventory optimization offer?

Any retailer’s major responsibility is to find ways to improve inventory management. Big data and analytics use in retail makes it simpler. The various ways that data analytics enhance inventory management are listed below.

• Demand forecasting

Predictive analytics for inventory optimization is one of the important components of big data in the retail sector. Foreseeing changes in customer behavior can make inventory management much more effective.

Customers exhibit radically diverse purchasing behaviors at various times throughout the year. When a retail establishment struggles to identify a pattern in these shifting trends, they are stuck with goods they don’t need and no room for what their clients actually desire. They can gain knowledge about what to fill their inventory with and when by using data analytics. This not only addresses the issue of incorrect stocking but also spares them from the last-minute stress of purchasing products for their customers.

• Optimization of replenishment

Both customer satisfaction and revenue can be harmed by having a large inventory of slowly moving things or by not having an item that is now popular.

Employees have had to estimate how much of an item needs to be reordered for a long time based only on guesswork after manually checking the inventory. Data analytics now allows you to assess important business factors like sales trends, the rate at which a hot product runs out of stock, the rate at which a slow-moving item sells out, etc.

With all this knowledge at your disposal, it is simple to make the greatest replenishment optimization choice by keeping sluggish-moving products off the most popular shelf positions and switching them out for genuinely required items. Best of all, several inventory optimization solutions available on the market today alert businesses when a product needs to be restocked.

• Eliminate stock outs

Stockout avoidance is a continuation of replenishment optimization. The fact that consumers will rapidly move to other retail establishments if they can’t find the item they’re looking for presents a significant issue for retailers.

Calculating lead times, or the number of days it takes an item to arrive at your warehouse after you make an order, is something data analytics for inventory optimization may help with. The safety stock may then be estimated using this lead time and the most recent sales information, which will also let retailers know when to make reorder requests.

• Accelerated order fulfillment

The ability to improve order fulfillment speed lies in the retail company data. Although it is normal practice to distribute orders to the nearest warehouse to minimize shipping costs and expedite delivery, data analytics for inventory optimization can accomplish much more.

You can design a system to specify where the item should be housed in the warehouse based on its delivery timetable using the correct set of big data technology. It can also inform the personnel of the item’s precise position to cut down on the time it takes the staff to gather and pack the products.

• Rapid recalls

Recalls of products are not isolated incidents, albeit they are regrettable. These take place regularly. While these occurrences are expensive, if handled slowly, they may also damage a brand’s reputation.

Monitoring the sale information is now a significant component of item recall. This is where big data may help by tracking the product by number and its shipment information at every point in the supply chain. Big retailers like Amazon employ big data to monitor web pages, from social media to review websites, to identify customers to whom defective goods were sold and promptly fix the problem.

• Higher levels of customer satisfaction

By tracking the causes of product returns and streamlining the logistics involved in the retail process, data analytics-driven inventory management solutions improve the shopping experience.

Let’s examine how improved inventory management might lead to increased customer satisfaction.

If customers refuse to place future orders or return their purchases due to the delivery experience, you should change to a reputable carrier provider.

Another common problem in a retail firm is when a customer receives the wrong item. Something that can be resolved by quickly scanning a barcode. For instance, if a warehouse worker unintentionally selects the wrong item, a barcode scanner can alert them, allowing them to fix the problem before the item is dispatched.

It is really simple to steer clients into supplemental, add-on products when you have data about what they are buying or watching when they purchase at your disposal. Furthermore, assisting clients in making wiser purchases boosts the financial performance of the retail establishment.

• Reduced  costs

Few merchants are aware of how managing inventories can affect costs. Most of them frequently disregard the financial cost of carrying extra or inappropriate products. Finding a balance is just as crucial as stocking the items in demand if you don’t want to unintentionally squander any warehouse space.

So how do you make this sure? 

Based on the cost of the inventory. An inventory cost consists of costs like:

• Cost of distribution and storage

• Cost of storage and material handling

• Cost of capital

• Cost of Insurance

• Risk-holding expenses

To manage the inventory space effectively, it is crucial to understand and control the inventory cost. And the best way to do it is to draw conclusions from real-time inventory data, which will enable you to predict demand and determine the safety stock levels.

 

After looking at the main advantages of combining data analytics and inventory management, the question of how to do it now emerges. Investing in tools that address particular inventory management problems is the solution involved. If you want to work as a data science professional in leading retail companies, Learnbay has the best data science courses in India. Enroll and master the technologies to succeed in the real world.