Businesses are actively pursuing tangible supply chain improvements through the adoption of innovative AI solutions.

24 April 2024

Businesses are actively pursuing tangible supply chain improvements through the adoption of innovative AI solutions.

Businesses are increasingly embracing artificial intelligence (AI) to enhance their supply chain operations, aiming to reduce costs, expedite distribution, and mitigate potential disruptions.

The initial strides indicate specific areas where logistics operators foresee immediate benefits from generative AI, an emerging technology capable of swiftly analyzing vast datasets, making predictions, and engaging in human-like interactions. Since its emergence in late 2022, companies across various sectors, from law firms to manufacturers, have been exploring its potential advantages. Early applications have focused on expediting tasks like decision-making, software development, and report generation. In the logistics realm, initial implementations have included the deployment of chatbots to manage customer support tasks such as shipment tracking and load booking.

Today, companies are integrating this technology into their day-to-day logistics operations. For instance, German software company Celonis is collaborating with snack-food giant Mars to leverage generative AI in consolidating truckloads, thus reducing shipping expenses and enhancing delivery speed.

According to Celonis CEO Alex Rinke, Mars previously relied on manual assessments, considering factors like weather conditions to determine optimal shipment consolidation and the necessity for refrigerated trucks. With AI, Celonis can proactively identify opportunities for load consolidation, significantly reducing manual interventions by 80% while simultaneously enhancing efficiency. Rinke also highlighted another company utilizing the technology to cross-reference supplier contracts with final bills to ensure the realization of rebates or discounts, a process that was previously labor-intensive.

The increasing adoption of generative AI represents the latest phase in companies' ongoing efforts to integrate machine-learning tools into their supply chain management practices. Secondhand apparel retailer ThredUp, for instance, has leveraged AI in its distribution centers to boost throughput and productivity. CEO James Reinhart emphasized the technology's evolving capabilities over the past year, particularly in optimizing operational efficiency and margin profiles.

However, despite its potential, generative AI has its limitations. Its effectiveness is contingent on the quality of training data, and it may occasionally provide erroneous responses, caution experts. Matthias Winkenbach, director of research at MIT's Center for Transportation and Logistics, highlighted these limitations, suggesting that the technology is currently confined to specific segments of supply chains to mitigate potential risks.

Looking ahead, there is optimism about broader applications for generative AI, including order management and supplier tracking. Rinke mentioned Celonis's exploration of aggregating data from various clients anonymously to offer comprehensive insights into potential risks and cost savings across supply chains without compromising proprietary information.

By Liz Young