Optimizing Data Chains

How Accurate, Unified Data Enhances Efficiency, Forecasting and Resilience

The frantic scramble for toilet paper and hand sanitizer during the COVID‑19 pandemic just a few years ago was likely the first time many consumers paid attention to supply chains. The world saw firsthand that supply chains aren’t just logistics puzzles; they are essential systems working to ensure commerce and even humanity’s survival.

“The average person had no idea what supply chain management was until COVID‑19 hit, which was really unfortunate on so many levels,” Dr. Nada Sanders, distinguished professor of supply chain management at Northeastern University, said. “Supply chains are what make everything go around today, from biopharma and pharmaceutical drugs to semiconductors, bottle caps and ordinary things that everyone uses and we take for granted.”

Today, amid the tariff rollercoaster and geopolitical turmoil, the most resilient supply chains are fortified not only by physical networks but also by invisible, intricate data chains—the accurate, real‑time digital threads that determine what, when and how product moves.

“You probably could not run a modern organization without data,” Chris Burchett, senior vice president for generative AI at MHI member Blue Yonder, said. “It’s so true, because you would quickly lose complete control of your supply chain if you didn’t have those data feeds coming.”

The types of data that feed into data chains can include:

Inventory and Orders

• What’s on each shelf in each store
• Orders placed by customers and suppliers
• Reorder quantities for item locations
• Supply orders and demand forecasts

Transportation and Logistics

• Shipment and load tracking
• Truck locations
• Traffic patterns and disruptions

Warehouse and Equipment Telemetry

• Idle time, pedestrian alerts, safety checks and engine hours from forklifts and other warehouse equipment
• Telematics from Internet of Things (IoT) sensors on trucks and containers
• Temperature monitoring for goods

External and Environmental Information

• Real‑time weather and weather situations, such as wildfires and earthquakes
• Traffic disruptions
• Port closures

Sentiment

• Consumer sentiment
• Social media trends

Operational and Calendar

• Supply chain milestones like holidays or return‑to‑school
• Historical sales and seasonal patterns
• Expiration dates

All these elements are part of interconnected flows—physical, financial and informational—that serve as strong anchors and foundations, explained Gerald Jackson, vice president of supply chain management product strategy at Oracle.

“The data is not the point, so to speak,” Jackson said. “You can’t fully understand your supply chain if you don’t understand how the information and the data flow, migrate, transform and aggregate as your product moves from producer to consumer.”

Every function in a supply chain generates its own data chain. On their own, each of these data streams can provide a snapshot of what’s happening in a specific corner of the operation. It’s an isolated, limited perspective.

The real power lies in connecting these chains. Patterns emerge that weren’t visible before, like in the case of a warehouse where a forklift operator was flagged for performance concerns. Initially, a poor work ethic was thought to be to blame. However, a closer look at combined data chains revealed that the worker’s shift began at the shift change, and the forklift’s starting location was in the middle of the foot traffic getting on and off the floor. The layout caused congestion in the aisleways.

“You might actually try to reprimand an operator for being inefficient when really, that’s not right. It’s your inefficiency as a facility manager in terms of managing the flow inappropriately or not as efficiently as you could,” Michael Bloom, director of connected solutions for MHI member Mitsubishi Logisnext Americas, said. “You wouldn’t know that unless you’re able to look at multiple data sources that you bring together, and that’s where you are able to see it. It’s the classic concept that context brings greater clarity, right?”

Telemetry revealed the solution: a redesign of the warehouse, not disciplinary action to the employee.

Data Chains Turn Chaos into Coordination

Eleven of the largest Coca‑Cola bottlers in North America had a supply chain challenge. Each had its own manufacturing and distribution to stores and vending machines, information technology services and inventory. That meant each bottler had to manage its problems on its own—that is, until the bottlers’ data chains were connected through a platform provided by Cona Services and supported by Blue Yonder. Now, the IT services for all 11 are centralized. The bottlers deal with demand, transportation planning and warehouse management in the same way. They learn from each other and can apply lessons across the board. It saves money, and collectively, their operations are more efficient, reliable and precise.

“It’s all consistently managed, and in that way, they can respond to disruptions faster and more consistently without each individual bottler having to learn the lessons themselves,” Burchett said.

More than ever before, data chains rely on information that’s steady, structured and clean. Context and human engagement make the difference, according to Sanders. For example, at the end of 2024 and into the beginning of this year, Apple shipped a lot of iPhones. A glance at the numbers might suggest large iPhone sales at the beginning of 2025. But the reality, Sanders said, is that the shipments represented a stocking up in anticipation of new tariffs.

“Every company needs to have a system in place that scrubs that data. As an example, a company in aerospace had an issue where one of the managers looked at a huge spreadsheet of data, and there was an item that was flagged, saying they had missed a shipment. But the problem was really that the date on one of the items in the shipments was incorrect,” Sanders said. “Was it a human error? They didn’t know. But basically, the spreadsheet said it was missed for 2024, when it was actually for 2025, and the item was flagged by the algorithm. It was simply a data error. In this day and age, we can still have data errors where dates are missed or quantities are missed. That doesn’t happen a lot, but it can still happen.”

Many companies are using artificial intelligence (AI) and large language models to collect and process vast amounts of data. Jackson considers it one of the most significant innovations in supply chain management.

“When I’m talking to chief supply chain officers, and we’re discussing agentic AI, in my 30 years, I haven’t seen anything as transformational as this. Ever. And I’ve been through Y2K to client servers, to the introduction of the internet,” Sanders said. “Agentic AI is huge, much different than the IoT big data analytics/algorithm development from just five years ago. What I’m seeing is that companies have the ability to look at their core supply chain processes and identify those necessary but non‑value‑added tasks. They’re the ones that have a very clear picture for how AI agents can help them drive radical productivity in their organization.”

The demand to feed more and more data into one place, make informed decisions and take action is critical for supply chain leaders, Burchett said. For example, it can help a major retailer that sells 30 million items track what’s on every shelf in every store and know when to reorder. It can use forecasting models that adjust to the seasons, holidays and the type of store and merchandise (e.g., apparel versus groceries). AI can analyze day‑to‑day traffic patterns for inventory that’s moving from Austin to Dallas. If there’s a weather‑related traffic jam, inventory can be rerouted from a different state. In some cases, the system might recommend air shipping instead of trucking.

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