Industrial Internet of Things (IIoT) technologies can lead to a dramatic increase in production quality and throughput but they're often not the plug-and-play solutions that many companies in the manufacturing sector may expect. To get the most value from an IIoT solution, manufacturers need to thoroughly understand the nature of their operations and invest in a robust, real-time traceability system to collect relevant data in a proactive and systematic way.

Traceability systems make use of identification methods like barcoding and radio-frequency identification (RFID) to gather and analyze data on the movement of works-in-process and finished goods throughout the plant and supply chain. Once a relatively simplistic method for tracking products and components, traceability has now evolved into a powerful strategy for optimizing productivity, quality, and brand reputation within the manufacturing operation by tying products to process parameters and raw material inputs.

From Simple Product Tracking to Comprehensive Process Visibility

Some traceability systems use RFID systems for traceability, with each part or bin being given a reusable and unique tag that is read and written to by the reader/writer.

The transformation of traceability over time — from basic barcode reading of individual parts and products to systems that enable the in-depth investigation of bottlenecks and quality issues — offers a variety of ways to envision this ubiquitous manufacturing practice. Omron has broken these changes down into four general phases, culminating with the Traceability 4.0 phase that merges lower-level track-and-trace solutions with advanced Industry 4.0 and IIoT technologies.

Traceability 1.0 is about automatically identifying products to drive accuracy and efficiency. The ability to mark a part and then track it using barcode readers was groundbreaking and this strategy has improved manufacturing efficiency and accuracy during the processing of large numbers of discrete items or transactions.

Traceability 2.0 is about managing inventory and meeting the needs of society. Manufacturers recognized additional uses for barcodes — particularly the ability to track materials within the manufacturing facility and throughout the supply chain. This strategy has enabled targeted product recalls, reduced the cost of quality improvements, and increased consumer confidence.

Traceability 3.0 is about the optimization of manufacturing and supply chain security by focusing on all of the raw material components and subcomponents needed to build a product as well as the finished product with an encoded serial number. This helps ensure product authenticity and provides a strong foundation for anti-counterfeiting programs.

Traceability 4.0 is the union of all of the above, along with machine and process parameters to achieve the highest level of quality, productivity, and overall equipment effectiveness (OEE). Although some manufacturers have embraced Traceability 4.0, it represents the future for most. Those who adopt the strategy are ascending to the forefront of manufacturing and brand protection.

Barcoding is a highly popular and cost-effective method for traceability, with compact, industry-ready barcode readers featuring advanced decoding algorithms serving as an essential capability of these systems.

It is this final — and cumulative — stage of traceability where IIoT becomes fully supported and functional. With the types of data that Traceability 4.0 brings in, manufacturers can easily answer a variety of production-related questions such as which machine worked on which product at what time and who was operating the machine at that time. The potential diagnostic and process analytic scenarios are virtually limitless and substantial improvements arise in many areas when the relevant machine and process data is collected systematically.

Driving Manufacturing Decisions

IIoT solutions, in effect, build a bridge between the lower-level processes happening on the plant floor and the overarching business goals. The key ingredient in this holistic view of a company's manufacturing operations is data, which is acquired, organized, and utilized by means of a traceability system. When implementing a traceability system, the following questions should be considered in order to help define requirements.

  • How will upstream components or raw materials be confirmed to be compliant based on information encoded in a barcode, RFID tag, or other identifier?

  • Through what process does a particular part move during production?

  • What production tooling, process parameters, and testing scripts should be used when performing a specific process step for a given item in a flexible manufacturing environment?

  • Which components are used on a specific subassembly?

  • What data should be collected at each process step and how should that data be made available to higher-level MES or Historian applications?

  • What real-time decisions can be made based on collected data?

When manufacturers implement a traceability system that considers the above factors, they'll be able to support increasingly complex and delicate processes. Ultimately, even the most basic components — like door switches or proximity sensors — will be network-capable. Assembly verification, quality assurance, and bill of material (BOM) control can all be effectively optimized with a Traceability 4.0 strategy that employs smart manufacturing technologies like IO-Link-enabled sensors.

IO-Link is a recent innovation that underpins many “smart” devices to provide a connection between the sensor/ actuator and an interface module that helps garner more information from the sensors themselves beyond a basic ON/ OFF reading. Process values, parameters, and diagnostic messages can now be exchanged, broadening the pool of available information and allowing for a wide range of process options.

In addition to providing more data to work with, smart components also help cut the cost of machine construction and overall maintenance by shifting from a traditional direct wire solution to a network solution for their equipment's individual components. With smart components on a network, replacement of failed devices is literally plug-and-play and some OEMs have reported up to a 38% reduction in wiring costs.

What's Next for Traceability and IIoT?

A high-performance barcode reader scans sample-specific information on barcodes applied to laboratory test tubes.

Artificial intelligence (AI) is increasingly being used to support new aspects of manufacturing. Employing these algorithms within the cloud to monitor and support processes isn't a new thing but manufacturers are starting to pull AI out of the cloud and push it onto the machine to impact manufacturing on a specific machine in real time. As part of a traceability system, it can identify trends when there are too many variables to allow for explicit programming.

That said, it's important to keep in mind what AI does and what it doesn't do. It's basically an advanced way to crunch data and for that reason, it requires human expertise to determine which data to use and how to use it. Letting algorithms function as a “black box” without a solid grasp of the intricacies of the production line may not be a recipe for disaster but it's also not a recipe for success. Manufacturers need to understand what type of information they're collecting for each process and why that information matters.

Essentially, this is why a clear trace-ability strategy should be taken into account for any manufacturer's adoption of IIoT technologies. Traceability, by definition, is a means of collecting and organizing factory floor data in real time. If this data is being collected haphazardly with minimal understanding of its importance, that's not effective trace-ability and it's not a workable foundation for implementing smart manufacturing solutions. IIoT-enabled smart manufacturing demands a well-organized traceability solution.

The more insight manufacturers have into their processes, the closer they'll be to the ultimate goal of plug-and-play IIoT solutions based on specific, targeted needs. These are the type of gaps that AI fills most effectively. Although building a robust, real-time Traceability 4.0 system that truly reflects the architecture of the production line can be a daunting task, it's not a thankless one. The immense value of such an undertaking will be seen in the ease with which data can be manipulated to offer insights.

This article was written by Felix Klebe, Marketing Manager – Sensor and Advanced Sensing, Omron Automation Americas, Hoffman Estates, IL. For more information, visit here .


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This article first appeared in the March, 2021 issue of Tech Briefs Magazine.

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