Living up to the hype: Lessons from IoT supply chain wins
In this podcast, Temple University professor Subodha Kumar explains trends in using IoT in supply chains, deployment challenges to expect and why AI boosts IoT demand and value.
The applications of IoT in supply chain management have always made a lot of sense, at least in theory.
The relatively affordable, internet-connected sensors IoT makes possible seem tailor-made for the day-to-day realities and challenges of supply chains. Much of the work of supply chain management (SCM) involves tracking and optimizing the movement of millions of goods around the globe, from raw materials through manufacturing, distribution, sales and returns. IoT sensors are ideal vehicles for collecting and transmitting the data companies need for a host of supply chain processes, including traceability that verifies a product's country of origin, cost and quality controls in procurement, inventory optimization in warehouses and timely shipping to stores.
But has IoT lived up to its promise a decade after its initial growth spurt and hype?
"We are at the stage where we can safely say that we are past the hype and we are seeing some real wins right now," especially in warehouses, transportation, cold chain management and machine maintenance, said Subodha Kumar, a professor at Temple University in Philadelphia. "We have seen fewer stockouts, less spoilage. We have seen less maintenance downtime."
In this episode of Enterprise Apps Unpacked, Kumar explains the challenges typically faced by companies implementing IoT in their supply chains, the hurdles that remain for the broader IoT industry and how the widespread availability of inexpensive AI has made IoT more useful.
AI spurs IoT growth
Subodha Kumar
Kumar is an expert in the complex interactions between supply chains and digital technology. He is a chair of statistics, operations, data science and information systems at Temple and directs its Ph.D. program in operations and SCM. He is also active in professional associations and journals focused on operations management and IT. For example, a 2025 journal article he co-wrote explores promising applications and research in using IoT in intralogistics: the logistics of moving and storing goods within facilities, such as factories, warehouses and ports.
Kumar said there are two major ways generative and agentic AI are changing how IoT is used in supply chains. "They have converted these IoT data into real time decision making," he said, giving the example of the pharmaceutical supply chain. The AI can analyze temperature and humidity data and decide whether to discard the product lot or modify its expiration date. "You can make these decisions in real time. You don't have to wait."
Recalls and the ability to trace products are another area of AI use cases that's taking off. Kumar cited the major push by large retailers, including Walmart, to use IoT and blockchain to trace the movement of spinach and other vegetables through the supply chain. AI can initiate a recall and then investigate the issues that triggered it.
AI and IoT are showing synergies in another "very interesting" way, he said. "These AI-based systems would not work unless we have very granular data. That is also working as a catalyst to grow more IoT-based systems," Kumar said. "More and more companies are deploying these sensors in different places that can pull data in real time."
Other topics discussed in the podcast include the following:
- Which IoT supply chain applications have the quickest ROI.
- Where IoT fits in the ecosystem of supply chain applications, including ERP systems.
- Future applications of AI and IoT in supply chains.
David Essex is an industry editor who covers enterprise applications, emerging technology and market trends, and creates in-depth content for several TechTarget websites.
