More Data Brings More to Manage

The rise of Big Data has helped take the guesswork out of inventory management. However, when real-time data meets the supply chain, more customer touch points mean more data to manage along with your regular inventory.

In today’s global economy, technology trends like the Internet of Things (IoT) and Big Data have become an indispensable part of everyday business. Before cloud computing and the era of Big Data, collecting inventory information required more resources and extra manpower. Plus, manual data entry often left inventory management susceptible to human error.

Now, thanks to real-time data and the ever-expanding Internet of Things, it has never been easier or more important for businesses to have clear visibility into their inventory across all channels. As supply chains continue to expand around the globe, keeping track of inventory in real-time is key to keeping your customers loyal and happy. With fulfillment of web orders coming from multiple physical locations, it’s essential that every time a SKU is scanned in-store, the item quantity is simultaneously updated with each purchase and shows in real time to the online customer.

Efficient inventory management can make or break your business. According to the latest eCommerce research by the Baymard Institute, more than 69% of online shoppers will abandon their online purchase when one or more of their items are not in stock. And the problem extends beyond one lost order because only 17% of shoppers will return to that same retailer after having a poor ordering experience.

Big Data and Inventory Management

“Big Data” refers to the large amount of data generated by sales transactions, mobile phones, social media and more. In the past, inventory management relied on historical sales and stock-out data to help make important business decisions. Now, the Internet of Things turns everyday objects into smart objects that collect and transmit data across the internet in real time. When this data gets combined with various devices and platforms, it becomes Big Data.

All this real-time data gives businesses the foresight to determine when, what and how much inventory they need to maintain appropriate stock levels. Computer algorithms can help uncover patterns and relations between various data points, helping to direct key business decisions. Real-time data can also give retailers and businesses valuable insight into product performance, consumer behavior, supplier relationships, business planning and more.

But too much data without the right technology, infrastructure and personnel can bring its own set of challenges. According to the latest supply chain trends, here are 7 ways businesses can effectively use Big Data to optimize their inventory and operations for 2020 and beyond.

1. Improved Pricing and Sales Forecasting

Machine learning and real-time data can help businesses determine an average price for their products by quickly evaluating a variety of factors including available supplies, cost, competitor pricing and the overall product value. Big Data also gives retailers and warehouse managers the ability to predict the demand for specific products. Knowing which products are bestsellers and which aren’t performing well gives businesses the opportunity to address any potential issues in real time. Retailers and businesses alike are now using Big Data to cross-sell more products and advance the practice of suggestive selling.

2. Real-time Supply Chain Visibility

Big Data gives businesses the ability to monitor products with laser-like focus as they move through their supply chain in real time. Businesses no longer have to guess where their items are or whether their shipments will arrive on time. Big Data tracks every detail, from where it is to the status of each shipment.

3. Better Inventory Planning and Development

Multiple data points can be combined to help forecast optimal stock levels. Real-time data gives businesses the ability to predict future sales for products that have no prior sales history. Big Data provides valuable insights that can help businesses use related product details to predict sales potential for a new item, seasonal product or help uncover new merchandising opportunities.

4. Improved Order Management

Real-time data helps businesses reduce the number of items on back order. Big Data uses predictive analytics to predict consumer demand. These insights help improve planning and give manufacturers more peace of mind and confidence communicating with their customers.

5. Reduce Shrinkage

Manufacturers and retailers have to deal with shrinkage that happens when inventory doesn’t get sold due to problems with theft or damage during shipment. Research into loss prevention data mining shows that Big Data can help reduce shrinkage by helping to predict risk factors that workers might not notice until it is too late to be proactive or take action.

6. Ensure the Right Parts Are on Hand

Manufacturers need to make sure they have the right assembly parts available in their facility, and they should always have complete visibility into potential equipment breakdowns that could shut down operations. Big Data and predictive analytics are helping transform the supply chain by reducing the likelihood manufacturers are stocking too many parts or not enough.

7. Monitor Vendor Performance

The real-time data gathered from payments and other electronic transactions can be used to form strategic vendor partnerships. Big Data helps provide important real-time information about orders, shipments and backorders so businesses can glean valuable performance and financial insights. Understanding how to leverage Big Data in your vendor relationships can lead to better customer service and more satisfied customers.

Big Data has brought more for supply chain leaders to manage than ever before. While managing all this real-time data brings challenges, leaders have options. Cloud services have now given businesses the ability to outsource part or all of their data management and analytics. As systems continue to integrate, data will become more precise, enabling businesses to continuously improve their order management processes, equipment, vendor relationships and workforce.

Businesses that learn how to effectively leverage real-time data in their operations can more accurately understand consumer demand, helping them reduce the need to carry unnecessary inventory, allowing them to operate more efficiently during peak periods.