Inventory is the most important section of a company, ranging from different industries, such as retail, manufacturing, and F&B. Proper inventory management strategies can help businesses control their assets and avoid shortage of products.
On the other hand, inventory can be considered as “liability”. A large warehouse contains risks from theft, damage, fees,etc. Plus, you will have to monitor it carefully for effective stockings and logistics services. With the help from data analytics, you can get real-time and detailed updates of the warehouses, market, customers and make more informed decisions.
What is Inventory Management?
It is one of the most important aspects as it can make or break a business. Inventory management ensures that the stocks are running as planned and monitors the products’ status, especially in retail sections when companies have to match the orders 24/7 with accuracy. Moreover, the development of inventory management needs to be attached to the company’s progress – larger companies need more extensive warehouses and strategies to manage.
The role of data analytics in inventory management
Customers now have accessibility to products more than ever. They can easily find information or even alternative products. This makes their behaviors more complex and disruptive for analysts to track. Consequently, companies need to observe their clients closely to pinpoint the demand as soon as possible before putting on new stocks.
As the competition is becoming challenging for retailers, inventory management requires more accurate tracking or automated processes. Modern controlling methods should apply data mining and analyzing functions to provide managers with insightful information before adjusting the shelves.
Implementing data analytics in managing inventory can give you key metrics on customers, stocks, supply/demand,etc. These data can answer critical questions such as:
- What is the amount of inventory needed to meet the demand while keeping the stock levels down?
- How can stock management be optimized?
- How can product recalls be reduced?
Moreover, advanced data analytics can solve and smoothen your process with 4 different models:
- Descriptive analytics: It gives retailers the summary of the inventory performance – movement of items, speed of replenishment, etc.
- Diagnostic analytics: It answers the why Why did the items stocked out? Why did the customer leave a bad review? Etc.
- Predictive analytics: It helps anticipate trends and shopper behavior on the basis of the inventory management history.
- Prescriptive analytics: It helps retailers make gradual adjustments in the anticipation of change in consumer emotion, supply shocks, demand, etc.
1. Forecasting the demand
On the one hand, customers’ shopping habits are getting more complex than ever, and their favors can change constantly. On the other hand, according to most managers, it can take up to 2 years to develop a brand new product. As the market is unstable, different trends can appear during a year, and the company should have an agile inventory to fit in diverse occasions.
How can data analytics benefit your inventory management?
With the help of data analytics, predictive data can help companies find the patterns in the market. This information is often extracted from inventory management history and points out the shopping behaviors or trends. Here are some of the most crucial figures you can identify:
- Target personas
- Customer behavior and buying patterns
- Customer preferences
- Identify location-based and seasonal trends
Data can also reveal the reasons behind each purchase or why certain products are returned. Thus, seasonality and patterns across the market can be tracked and predict similar trends in the future. As a result, managers will know what to be on the shelves or what to prepare for the next shopping season. Businesses can also learn what particular price should be used for a specific segment for each product as the data are often dynamic. Being one step ahead in the market would be a significant advantage for a company as it can satisfy customers’ demand as soon as it appears.
2. Optimizing performance
Trends and opportunities often come and go fast. If businesses are slow in adjusting their shelves, it will lead to poor performance as the supply doesn’t meet the demand. Furthermore, unsatisfied sales and out-of-date stocks can lead to higher maintenance or warehouse costs. Plus, manual processes in managing inventory are often sluggish and inaccurate as the availability of informative data is limited.
Data analytics can help them collect key metrics in retailing activities such as sales trends, what products are selling the fastest? or which is having poor performance? These questions can help managers set up their arrangements to generate profits consistently. For example, in a brick-and-mortar store, the owner can put in-trend items on the most crowded aisle and distribute poor sales products in unfrequented areas.
Another way to create profits from the data is replacing out-of-trend items with new commodities. In this option, businesses can distribute their resources such as manual force or capital on the right section to ensure their sales numbers.
Read about how IKEA set up their store, so customers are often attracted to specific items: How Ikea’s shop layout influences what you buy – BBC Worklife
3. Matching orders with speed and accuracy
The basics of retailing is selling what you have and can match orders as fast as possible. According to thegood.com, the average rate of running out of stocks for e-commerce businesses are 8% and can go up to 10% during promotions. Those numbers can be reduced when applying data analytics in inventory management. Data-driven plans can help managers prepare material/items for restocking and avoid missing out on demand.
Another thing to consider is effective logistics. The delivery process is even more critical for online retailers as their primary activity is selling remotely. Usually, the orders will get assigned immediately to specific warehouses so they can decrease the shipping time and other logistics costs.
Having a good database with data analytics will help you fasten up the process. Based on purchasing history, you can predict what is the most demanding item in different areas and make sure they are always on stock. Therefore, planning strategies can improve customer satisfaction with a faster shopping experience, lower fees, and the availability of products
|Interesting info: The Future of Retail Analytics (Top 10 Trends)|
4. Reducing operation costs and shrinkage
Proper inventory management can also help businesses save more money during activities. In contrast, poor monitoring of the warehouses and shelves often leads to severe impact. According to Prologis, the average cost for 5,000 square feet usually takes more than 5,000 USD, and many fees can come along. To save businesses from expensive storing services, data analytics will reduce wasted space and optimize the capacity. Some of the elements can be managed, including:
- Logistics and warehouse cost
- Service cost
- Storage cost
- Insurance cost
- Material handling cost
Identifying different costs and data from data analytics will give companies access to real-time demand/supply, types of products, etc., so that they can optimize space and calculate expenses financially.
In 2020, more than 15% of US retailers experienced shrinkage of 3% or higher. There are many reasons behind the shrinkage of products, such as theft, damaged stocks, calculating, etc. Fortunately, data analytics can limit this problem by accurately tracking transportation and who is responsible for certain products to avoid warehouse theft.
Efficient inventory optimization solutions can monitor huge proportions of past sales and predict future inventory demands by calculating seasonality and lead times. Moreover, with modern technologies combined, inventory optimization methods can provide knowledge about customer preferences, performance metrics, and other critical business elements.
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