Inventory is an indispensable part of every distribution business. It involves stocking merchandise for future use and always having it readily available when the businesses place an order. It sounds pretty simple, but that’s not it. In addition to providing the products at the right time, the distributors also need to ensure that they don’t end up with stock much more than they can sell.
It’s a conundrum. Low inventory can lead to paying high shipping costs for rush orders, missing sales, and losing loyal customers.
On the contrary, excessive inventory can lead to potential insurance costs and storage costs, it also puts the inventory at the risk of going obsolete, and most importantly, it can tie up capital. All that invested money is just sitting in the warehouse, in the form of products. The products aren’t even paying for itself let alone bringing profits.
These are some of the main reasons why inventory management decisions should be based on deep analyzation of stock and the supply chains.
The goal of inventory management is to go neither understock nor overstock but maintain just the right balance. Both conditions can end up in catastrophes for distributors and intermediaries.
Unfortunately, overstocking and understocking occurs more frequently than most inventory managers would like to admit, therefore they struggle to strike that right balance.
Fortunately, technology has come to the rescue. By employing the best practices via Robotic Automation Tools and data analytics, we can never run out of inventory and never have too much of it.
MACHINE LEARNING, DATA ANALYTICS, AND INVENTORY MANAGEMENT
Market leaders, both in the distribution business and manufacturing business are seemingly enjoying endless possibilities as the result of embracing RPA and data analytics.
Knowing the worth of data and its role in supply management, Wal-Mart, a business that serves more than a million sales transactions every hour collects data from their suppliers as well as their customers, on both ends. They have their suppliers tag shipment with radio frequency identification system (RFID) and store the data that is generated hundred to thousand times more than the data collected by the conventional bar code.
Amazon has a network of 200 fulfillment centers around the world and to keep track of such a large amount of inventory – nearly 1.5 billion items – Amazon uses big data to scrutinize and secure them. Amazon then uses this data for predictive analytics and finds out when the wholesalers or the retailers would be placing an order and when would be the best time to ship the products to the nearest depot. In this manner, Amazon seems to never run out of inventory, even though it hardly ever overstocks. And the cash flow is brilliant without any blocked up capital
Striking the right balance
Let’s learn about some of the ways, big data and RPA helps distribution businesses maintain optimal inventory levels:
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Historical Data
Nothing will tell a business more about the demand and supply of their products, other than their own sales levels in the past. When RPA imports historical data into the database, along with the real-time data, predictions can be made about the future purchasing behavior of the clients. With such level of advance forecasting, inventory managers are able to take more accurate decisions regarding the stock and hardly ever end up understocking or overstocking.
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Data Analytics Providing Forecast for Appropriate Stock Levels
Stock levels cannot be predicted through historical sales data or buying patterns alone. Advanced data analytics platforms provide more accurate prediction by looking at numerous factors that could dictate the demand for a product. The holiday season, weather, political situations, etc. are used in predictive analytics to foretell the spike in products demands.
With such an accurate detection of customers’ demand, it becomes easier for distribution businesses to maintain just the right inventory, without ever coming up short, or with too many products in the warehouse. It even gains them a competitive advantage over their counterparts when they are able to run smoothly during the time when the products are in extreme demand. One of the major retail companies, Kroger has been leveraging their merchandising decisions via predictive sales analysis.
Based on the predicted trends and demands, Kroger has already started stocking up on healthier foods to meet up the rising demand for a healthier lifestyle.
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Removing Understocking Problems Caused by Operational Inefficiency
Various times, manufacturers end up digging a hole for themselves by delaying supplies to their distributors because of operational inefficiency. As soon as customers realize that the supply is not coming up from the company, they do not take long to shift to other products and that could mean suffering from major losses.
In 2015, an untimely strike by the American Shipping Industry ended up in magnanimous losses of the manufacturers and distributors only because the products couldn’t reach the shelves on time. While the reason for the delay wasn’t exactly a fault in the production line, the losses incurred by delay is undeniable. The Chinese companies found their competitor’s products on the shelves, the agricultural exports suffered from a loss as large as $1.75 Billion per month, and the North American Meat Institute estimate their loss at an average of $85 Million per week.
This is what happens when the products are delayed, and one of the main reasons they are delayed is when manufacturing companies suffer from operational inefficiency. These inefficiencies are often caused by hidden bottlenecks, not having the right parts at hand and other faulty processes that are often impossible to detect.
This is where data analytics comes in and saves the day. Data analytics platforms keep an eye on the entire operation process and detect glitches inconsistencies and hidden bottlenecks beforehand. It allows the manufacturers to enhance their operational performance and always supply their products on time.
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Keeping Tabs on Inventory
Where predicable analysis, operational bottlenecks, and historical data are all very important to manage inventory, one tiny little cog that can disbalance the entire venture is the unawareness of the current inventory levels.
A company needs to be constantly aware of their current inventory level in order to manage it smoothly. It is only when they are aware of it, would they employ the aforementioned methods to manage it. Relying on humans can be risky since the chances of error are high. On the other hand, RPA tools eliminate that danger by keeping a tab on the inventory levels and notifying them when the inventory is about to run out.
Matter of fact, RPA can even reorder the stock on its own before the inventory reaches a certain level. Robotic automation can read the predictive analysis, advanced analysis, and descriptive analysis and make decisions based on that.
RPA and data analytics provides enhanced insight to the distributors and intermediaries and a clear picture of their past, present and future experiences with sales. All that data allows them to make better decisions and never suffer from the stigma of overstocking or understocking.
Omnisys Solutions has a well-developed practice in RPA, which supports both data analytics initiatives as well as carry out automation driven initiatives. With a focus on business value, Omnisys leverages RPA to deliver tangible dollar benefits to its customers.