Define audit requirements to evaluate data reporting and identify areas of improvement.
Step 3: Choose the right traceability technologies
Next, develop a technology architecture to support traceability goals. This is often easier said than done, as no single service offers both deep visibility at the operational level and granular traceability at the product level across the supply chain. Instead, IT teams will likely have to set up an interconnected network of technologies working together to provide the insights and control needed.
A few core technologies often form the foundation of supply chain traceability. These include the following:
* Tracking technology. Barcodes, QR codes, RFID tags, GPS and Bluetooth technologies work together to assign unique digital identifiers to each item in the supply chain and transmit data to track items in real time as they move — for example, from a warehouse to a delivery truck to a customer.
* Monitoring tools. In combination with the above, IoT devices and sensors can gather data on certain conditions, such as temperature, humidity and other environmental factors. This can help monitor and ensure product quality across locations.
* Data management tools. Data standardization, including data formatting and structure, can optimize supply chain traceability. Master data management (MDM) tools can organize and standardize data, improving its quality and completeness. Blockchain technology can also create secure, transparent ledgers for more accurate records throughout the product journey.
* Resource planning tools. In addition to data management, traceability software — like ERP tools, and inventory, warehouse and transportation management systems — offers a more complete picture of the supply chain. They also offer the control needed to predict future resource usage and optimize operations accordingly.
* Analytics and automation. AI and machine learning (ML) algorithms are fundamental supply chain tools. They can analyze supply chain data to evaluate performance, predict disruptions, detect anomalies and automate decisions to help operations run smoothly. Digital twins and predictive analytics can also simulate and model supply chain conditions to optimize operations.
All that said, designing and building architecture for traceability is complex. IT teams must consider the legacy technology they already use, the compatibility and interoperability of technologies they want to deploy and the architecture’s scalability. This may require extensive research, planning and strict vendor evaluations to determine which would make the best partner.
