The pursuit of sustainability has risen to the forefront of concern for supply chain organizations, driven by escalating demands from consumers for eco-conscious products and processes.
Yet, the path to sustainability is full of challenges when the journey of materials and products remains obscured. The prevailing role of visibility and traceability in advancing sustainability agendas cannot be overstated. To tackle this, companies have turned to innovative technologies like Radio Frequency Identification (RFID), Internet of Things (IoT) sensors, and QR Codes. However, the influx of data generated by these technologies presents a new obstacle—making sense of this data to extract valuable business insights.
What can be of help in this situation?
Enter generative AI as a potent solution. Leveraging AI tools enables the streamlining of logistics operations, optimization of routes, and fine-tuning of inventory levels. These endeavours hinge upon the availability of high-quality, standardized data that can be seamlessly shared across trading partners—precisely where AI shines in ensuring data integrity and extracting optimal insights.
The advent of IoT devices has significantly improved the visibility of product journeys within supply chains. Advanced data carriers like 2D barcodes and sensor-based technologies such as RFID collect invaluable information about products, assets, and locations, facilitating informed decisions to uphold sustainability and Environmental, Social, and Governance (ESG) requirements.
The imperative for accurate tracking information extends beyond organizational benefits—it caters to evolving consumer expectations. As mentioned by Bob Czechowicz, Senior Director of Innovation at GS1 US, "Consumer expectations are like a ratchet, but they only ratchet up—never down."
Ensuring the accuracy of the provided information is crucial, given pressures from consumers and governmental bodies to substantiate sustainability claims. Here lies the promise of generative AI. By augmenting existing data, AI facilitates critical decision-making tasks such as sourcing and route optimization, thereby advancing sustainability objectives. Establishing parameters around sustainability goals and leveraging AI to generate recommendations based on augmented data empowers organizations to pinpoint areas for improvement.
Realizing the full potential of generative AI necessitates confidence in the quality and consistency of data. "Unless you can feel confident that you have quality and consistent data," Czechowicz emphasizes, "you're probably going to struggle to solve challenges like sustainability and ESG requirements."Thus, the initial step towards bolstering visibility to support sustainability initiatives entails cleansing data—an age-old adage reiterated in the digital era: garbage in, garbage out.
Conclusion
Adherence to standardized systems fosters confidence in internal data accuracy and facilitates efficient information exchange with organizational stakeholders. With a unified approach, organizations can gain a comprehensive understanding of their entire supply chain landscape. Armed with this insight, they can identify inefficiencies, optimize sourcing and routes, and forge a path towards a more sustainable business paradigm.