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      Harnessing traceability to support the factory of the future

      The word “traceability” can mean different things to different people or organizations. In manufacturing, it’s a powerful way to ensure quality by tracking raw materials and works-in-progress. When errors arise, there’s a comprehensive record of product history that can point out a root cause and minimize the impact of a recall.

    Traceability has evolved to include strategies that optimize productivity and quality within manufacturing operations. Barcode reading and vision inspection work together to separate good and faulty products in real-time, while data contained in barcodes, direct part marks (DPMs) or RFID tags automatically direct the work-in-progress towards next production steps.

    From the beginnings of traceability to Traceability 4.0

    To highlight the evolution of traceability from simple part tracking to an all-encompassing production strategy, we’ve coined the term “Traceability 4.0.” This term purposefully evokes the widely used phrase “Industry 4.0,” which in many cases is synonymous with fully integrated, next-generation technologies and solutions.

    Traceability 1.0, 2.0, and 3.0 refer to product visibility, supply chain visibility, and line item visibility, respectively. The fourth phase, process visibility, is the union of all three. It includes all machine and process parameters – such as overall equipment effectiveness (OEE) – that are necessary to reach the highest achievable level of manufacturing.

    Although some manufacturers are already employing Traceability 4.0, it represents the future for most. Facilitated by smart sensors, AI controllers, RFID and advanced data management software, Traceability 4.0 systems can make automatic decisions that optimize equipment and processes based on acquired data.

    Traceability 4.0: Where do we go from here?

    The first step for a broad swath of the manufacturing sector is to transition away from manual traceability processes and replace them with automated ones. Companies should then consider investing in Industrial Internet of Things (IIoT) technologies as well as network-based cloud solutions that integrate into ERP, MES, production analytics and production historian systems.

    The IIoT applications within traceability allow a variety of low-cost sensors to record operating conditions like humidity and temperature. The IIoT-enhanced traceability system can bring together data from all aspects of production to determine the root cause of quality issues that could otherwise remain a mystery.

    Artificial intelligence (AI) is yet another emerging technology that’s enhancing traceability. In essence, traceability and AI can work in tandem, feeding into each other via a cyclic relationship that continuously improves efficiency. Traceability information feed into AI algorithms, which in turn provide insight that can help a manufacturer tune their traceability system.

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