Manufacturers have spent decades digitizing what happens inside the four walls of the enterprise. Manufacturing execution systems track production; sensors monitor equipment; warehouse management systems know where inventory sits, and enterprise resource planning ERP systems connect purchasing, production, inventory and fulfillment. Automation has transformed the factory floor, and visibility inside manufacturing operations has never been better.

But there’s a disconnect. Much of the information that determines what those systems need to do originates from outside the organization — from suppliers, distributors, logistics providers and customers. Purchase orders, shipping notices, forecasts and invoices, quality certificates, product specs, and shipment updates continuously cross boundaries, and that data doesn't arrive neat and clean.

One supplier sends an EDI, another replies on spreadsheets; a customer sends a PDF purchase order; the logistics provider uses an application programming interface. Another partner uses a proprietary file format configured years ago that has barely changed. The result is an increasingly complex, yet sophisticated operation being fed by a fragmented data supply chain.

It might be one of the most overlooked constraints on manufacturing today: The factory can only move as fast as the data coming into it.

Consider what happens when a customer changes an order. The physical operation might be able to react quickly, but only if the changes reach the right systems accurately and on time. If someone first has to open the email, read the attachment, rekey information, resolve an issue or determine why an EDI transaction failed, the automation downstream becomes almost irrelevant.

The same issue appears on the supply side. A delayed advance shipping notice can affect receiving and production scheduling. An inaccurate inventory file can change purchasing decisions. A mismatched product identifier can trigger manual investigation before an order can be processed.

Individually, these problems may look like small exceptions. But at scale, they become something bigger.

A manufacturer working with hundreds or thousands of trading partners effectively manages hundreds or thousands of different ways that external data enters the business. And unlike the systems inside the factory, manufacturers cannot simply standardize the technology used by every supplier and customer. That makes data inherently more variable.

Demand Exposes the Weak Points

These weaknesses become particularly visible during periods of high demand. As manufacturers and retailers prepare for the holiday season, forecasts change more frequently. Order volumes increase, inventory shifts, and transportation capacity tightens. Customers want more information about availability and delivery.

Every part of the operation becomes more dependent on timely, accurate information. A company may have excellent production capacity and sophisticated planning, yet still miss a delivery commitment because a critical transaction was delayed, incorrectly mapped or manually processed.

That’s why improving supply chain performance can’t just be about optimizing physical movement and requirements. There’s also a data movement challenge.

Traditional integration strategies often assumed that data would be relatively predictable. Connect System A to System B. Define the fields. Build the transformation, test the connection, and move to production.

That model works well when the endpoints and formats are stable. Modern supply chains are rarely that simple. New suppliers are added, customers introduce new requirements, companies make acquisitions and ERP systems change. Partners modify formats, new APIs appear alongside decades-old EDI connections, and a significant amount of business-critical information still lives in documents, spreadsheets and email attachments rather than structured transactions.

That means a problem is no longer about simply connecting applications. It’s managing variable, messy, and unstructured data. The most effective supply chain data architectures will increasingly be designed around this new reality.

Start With the First-Mile Data

Supply chain organizations spend considerable time discussing the last mile, the final stage of getting a product to a customer. The first mile for data is equally important. Before information can drive production, inventory, fulfillment and distribution, it has to enter the organization correctly. That sounds basic, but it’s where many operational problems begin.

The first question supply chain leaders should ask is not simply, “Do our systems have the data? It should be: “How much work does it take to get external data into a usable format?” That question often reveals processes that have quietly accumulated over years.

Operations teams downloading spreadsheets, customer service employees manually editing orders, IT teams maintaining one-off partner mappings, integrations specialists troubleshooting transactions — those processes function, but they can cause a lot of friction. And friction becomes expensive as volumes and partner ecosystems grow.

You don't need to replace every system to address the problem. A more practical approach is to identify where external data creates the most operational friction. Following are three places to look first:

• Partner onboarding. How long does it take to connect a new supplier, customer or logistics partner?  If every new relationship requires a custom integration project and IT involvement, integration becomes a constraint. Partner connectivity should be repeatable, with reusable files and mappings rather than starting from scratch each time.
• Manual intervention.  Are people effectively acting as middleware?  If employees routinely copy information between documents, spreadsheets and email, the organization has found a workaround for a data integration problem. These workflows are often good candidates for automation.
• Exception management. The goal is to identify exceptions quickly and prevent bad information from moving downstream unnoticed. Validation should happen as close as possible to the point where data enters the organization.

AI Raises the Stakes

The push toward artificial intelligence makes solving this problem even more important. Manufacturers are increasingly exploring AI for forecasting, procurement, supply chain planning, customer service and operational decision-making. But AI systems are only as reliable as the information they have to pull from.

If supplier information is incomplete, customer orders are inconsistently structured or logistics data arrives late, AI doesn’t eliminate the underlying problem. In some cases, it can even amplify it by allowing poor information to influence decisions.

AI therefore increases the value of getting the data foundation right. It can also help companies process increasingly variable data by interpreting documents, accelerating mappings and identifying inconsistencies before information reaches operational systems.

Automation should make complex data easier to process without making mission-critical workflows unpredictable.

There’s another reason manufacturers should pay attention to this problem. Data integration is increasingly becoming part of the customer and supplier experience.

Customers don’t care about the manufacturer's internal systems. They want to be able to place an order easily, receive accurate information, and get the product on time as promised. Suppliers want the same simplicity.

Companies that can connect to partners quickly, accommodate different ways of exchanging information and process transactions reliably create less friction. That makes better data connectivity less than an IT initiative and more of how manufacturers compete.

The next phase of manufacturing digitization will be to improve information flow across boundaries. Because ultimately, every factory still depends on the data that tells it what to do.

Tim Bond is chief product officer with Adeptia.