Transforming traditional production environments into agile, data-driven ecosystems is the goal for many manufacturers. Known as “connected manufacturing,” this approach incorporates machines, technology systems, people and data into real-time operational ecosystems, replacing siloed, manual workflows. When executed successfully, leadership has the visibility needed to make precise, proactive decisions across the entire supply chain.
However, many factories still struggle with disconnected workflows, manual processes and fragmented operational data. According to a 2024 study from Zebra Technologies, a provider of enterprise data capture and visibility technology, although manufacturers say digital transformation is a strategic priority, achieving a fully connected manufacturing environment remains elusive. Visibility is key to optimizing efficiency, productivity, and quality on the plant floor, but only 16% of manufacturing leaders globally report that they have real-time, work-in-progress (WIP) monitoring across the entire manufacturing process.
Barriers often limit visibility across production, inventory and assets, but once eliminated, manufacturers can create a foundation for automation, operational excellence and future AI initiatives.
1 Disconnected Systems and Data Silos
Manufacturers generate large amounts of data, but not all of it necessarily translates into operational visibility. Systems such as ERP, warehouse management and manufacturing execution systems often operate independently, creating fragmented environments that make it difficult to develop a complete view of operations.
This fragmentation is particularly evident in warehouse workflows, where a single inbound shipment can involve barcode scanners, warehouse management software, forklifts, handheld devices, vehicle-mounted tablets, conveyor systems and programmable logic controllers. Each technology generates valuable data, but connecting these systems is essential to turning that information into actionable insight. Barcodes, RFID and mature standards within frameworks from RAIN RFID and GS1 are expanding data-capture capabilities, but manufacturers still need effective strategies for integration and management.
2 Manual Data Collection and Reporting Processes
Capturing data from everyday tasks is evolving from traditional line-of-sight technologies toward RFID, Internet of Things (IoT), vision systems and other automated approaches. However, operations still rely heavily on manual data collection, which can introduce delays, inaccuracies and human error, limiting the ability to achieve true real-time visibility.
But manual processes are not expected to disappear entirely. Certain supply chain activities, particularly last-mile delivery and proof of delivery, will continue to require human involvement, aided by emerging new technology use cases.
The focus is therefore shifting toward determining which processes can and should be automated, and where human intervention remains necessary.
AI, wearable devices and increasingly sophisticated process-control technologies, for example, are helping to bridge the gap. Rather than eliminating workers, automation can enhance workers’ capabilities, empower them, improve overall throughput, and make better use of limited resources.
3 Limited Real-Time Tracking of Materials, Assets and Inventory
Real-time location systems (RTLS) are becoming an increasingly important tool for improving visibility across transportation, logistics and warehouse operations. While such technology has traditionally been associated with tracking people and high-value assets, its applications are expanding to pallets, raw materials, servers, returnable transport items (RTIs), containers, reverse logistics and other logistics needs.
Sensors, for example, can provide both location and condition data, including temperature, shock and moisture exposure.
“Am I reaching the threshold of temperature? Did I have a shock value? Did it get wet? Those types of things now are a consideration and are driving a very complex ecosystem to create real-time locationing,” says Kent Landry, head of intelligent automation RFID sales at Zebra Technologies.
Additionally, shipments moving between ports, ships, cranes, trucks and distribution facilities require reliable connectivity through technologies such as cellular and wi-fi alongside identification and data-capture technologies such as RFID.
At the same time, lower-cost solutions, including passive RFID and overhead readers, enable manufacturers to verify shipments, track movements, and identify discrepancies at critical points.
4 Lack of End-to-End Traceability
End-to-end traceability provides manufacturers with more than a record of where products are located. It also traces the flow of raw materials through production and distribution, helping manufacturers understand what happened, why it happened and what may happen next.
Indeed, the need for traceability extends across suppliers, production facilities, distributors and downstream customers.
Returnable transport items (RTIs) also require traceability. These assets often move through closed-loop networks but can become lost, underutilized or unnecessarily held in inventory.
North America general manager for Lyngsoe, Carl Kallen, estimates that approximately 15% of RTIs go missing each year, leading to additional costs and operational inefficiencies, often due to a lack of visibility.
Technology can help by digitizing physical assets and tracking them throughout their lifecycle. Barcodes, RFID, sensors and automated data capture provide the foundation, but the real value comes from connecting that information to workflows and operational systems.
5 Poor Data Quality Limiting Automation and AI Adoption
AI is becoming increasingly important in manufacturing operations, but its effectiveness depends on the quality of the data it relies on. Deploying AI without reliable location, inventory and process data, for example, can produce inaccurate insights and undermine automation initiatives.
“AI will only be as effective as the operational data feeding it,” says Kallen.
Manufacturers should therefore address data integrity and AI adoption simultaneously. Automation can also further improve the consistency and reliability of the information feeding AI systems.
As AI evolves toward more agentic capabilities, it can increasingly help empowered frontline workers make faster decisions and determine the next best action within a workflow. The objective is not necessarily to replace employees, but to improve productivity, consistency and predictability.
The Lyngsoe-Zebra Solution
Lyngsoe delivers end-to-end visibility solutions combining Zebra Technologies’ RFID, printers, fixed-reader and mobile data-capture technologies with Lyngsoe’s software, integration expertise and workflow automation. The combination enables companies to move beyond simply collecting data to create a connected operational environment in which information can be captured, contextualized and acted upon across the enterprise.
“We can architect the different data flows and make sure that the relevant data is married with other relevant data in more meaningful ways,” says Kallen.
Indeed, rather than deploying technologies in isolation, Lyngsoe’s solutions help manufacturers to build an integrated data infrastructure that supports trusted, real-time information and evolves with their operations by connecting assets, inventory, materials and production processes.
A major benefit to such connectivity is visibility across complex, highly automated operations, operational efficiency and greater end-to-end traceability.
But most important, is more informed decision making in day-to-day operations, identifying bottlenecks, improving workflows, responding more quickly to exceptions and formulating forecasts and strategies.
Resource Link: https://lyngsoesystems.com/logistics
