Data is playing an ever-increasing role in inventory management. Respondents to Grainger’s 2023 Supply Chain and Inventory Management survey confirmed that real-time data, along with platforms and people to manage it, is on their minds.

Fifty-nine percent of the 301 professionals surveyed said real-time data is the trend most likely to affect their work in the coming year. Tracking inventory at a granular level can help managers become agile decision-makers, responding immediately to changes in demand. Instead of scrambling to restock empty shelves, a manager equipped with real-time data could catch an unexpected demand surge early and accelerate the resupply process. 

But supply chain experts caution that taking advantage of real-time data requires sophisticated systems. Alan Amling, a distinguished fellow at a major university's supply chain institute, said, “The challenge with real time data is: what do you do with it? I find a lot of companies are gathering real-time inventory data, but they don’t have the right systems to turn that data into actionable information.

“Your systems have to be able to integrate different sources of data to make decisions,” Amling said. “That’s where AI-driven systems come in. They can make recommendations for inventory placement and shipping prioritization.” A powerful AI-driven system can use real-time data to organize the warehouse and maximize picking efficiency based on each SKU’s current velocity.

Deeper into Data Analysis 

A substantial portion of survey respondents are also interested in implementing sophisticated data analysis systems that can make sense of real-time data. Thirty-five percent of inventory managers said they would make more use of cloud computing services over the coming year, 26% are interested in deploying machine learning tools and 21% plan to expand the use of Internet of Things (IoT) technologies in their facilities.

Cloud computing offers inventory management software that can streamline purchasing, tracking, and shipping operations. The software and data storage are handled offsite “on the cloud,” with a third-party vendor maintaining, updating and securing the system. This outsourcing can be beneficial for small operations that need outside expertise to handle cybersecurity.   

Machine learning is a type of artificial intelligence that combs through past data to identify patterns and make predictions about the future. For example, a machine learning algorithm may discover that an order for ammonium hydroxide detergent is typically followed by a rapid drawdown in disposable 8 mil glove stocks. The machine learning algorithm doesn’t know that the cleaning staff is about to don thick gloves for a deep cleaning project, but it can identify a link between the products’ usage and anticipate a surge in demand.   

Internet of Things is a network of sensors and RFID tags connected to the internet that can streamline inventory movement and scan for issues. Instead of manually tracking MRO needs and stocking parts, a network of sensors and scanners can anticipate problems. For example, a networked IR thermometer could flag an overheating fan belt on the production line, automatically order a replacement belt and use RFID sorting to slot the part in a convenient bin for a maintenance tech to pick up the next day.     

An Emphasis on Upskilling 

Implementing new technologies will require new skills, and inventory managers are searching for ways to prioritize retraining current workers over recruiting new talent. Over half of respondents (53%) anticipate changing the mix of skills in their staff, including 60% of businesses with a large MRO budget ($600k+). Continuing education for existing staff significantly outweighed the plans to recruit new workers differently.

Amling confirms these findings, adding that upskilling current workers will remain the priority. Automation and robotics will not be implemented to replace workers, he says, but will instead allow them to transition from heavier manual work to more rewarding, higher-skilled jobs through that reskilling.