Decoding AI’s Role in Supply Management


Modern supply chains today must be intelligent, data-driven, and employ every advanced digital tool and automation application under the sun. At least that’s what today’s business pundits evangelize. But how? Artificial Intelligence (AI), particularly Generative AI, has created a multi-decibel buzz that is unclear, disruptive, inspiring, and annoying all at once. 

While many are growing weary of the seeming over-hype, fear of being left out keeps them plugged in to the roaring rhetoric. For supply chain leaders, the big question is about resilience—and headache prevention. How can AI eliminate shipment delays, reduce stock shortages, and end extended dwell times—incidents that can be extremely disruptive and costly. From the executive suite, questions tend to explore a more strategic line of questioning. How can AI improve profitability? Contribute to growth? 

Confusion clouds a true picture of benefits and opportunities of AI technology, creating a skeptical haze over fact-finding. How can a supply chain manager obtain a clear road map to competitive advantages? Generative AI use cases are being conflated with existing forms of AI, making practical implementations difficult to comprehend. If you’re confused, you’re not alone.  Both Generative AI and other forms of AI use advanced algorithms and machine learning to tackle complex business problems. Beyond that, each type of AI has unique capabilities and applications that are quite distinct. You should always consult experts in AI about specific applications. 

Existing AI Applications in Supply Chain 

Tech-savvy enterprises have been leveraging AI-enriched functionality for decades. It helps automate rules-based processes, identifies patterns and anomalies. It can be predictive leveraging machine learning to analyze historical data and predict future outcomes. Or, it can be prescriptive, taking the assigned task one step further and analyzing possible solutions, comparing possible solutions, applying relative constraints, and suggesting actions to match preset priorities.

Some examples of applications in action:

  • Planning: AI helps anticipate demand, based on analysis of historical trends. This allows organizations to forecast market demands and initiate necessary orders. Solutions can also suggest ways to optimize inventory, including appropriate safety stock levels and practical procurement plans. 
  • Transportation: AI algorithms create accurate estimated times of arrival of shipments based on previous patterns. The solution can consider other factors, such as weather and heavy traffic periods in shipping lanes and ports. Analytics can also help managers optimize sourcing, choose reliable shipping partners, and execute low-risk transportation plans. Solutions can determine when consolidating carriers makes sense or if diversifying suppliers is advantageous.
  • Warehousing: AI helps calculate the warehouse space needed to accommodate demand, including associated labor requirements. AI-powered solutions with predictive abilities can suggest optimal just-in-time or just-in-case strategies for stocking goods and how to avoid obsolete inventory. Prescriptive analytics help plan slotting optimization, distribution of goods between multiple warehouses, and how to optimize the labor force for efficient stocking. 

Generative AI breaks new ground

Generative AI creates new content such as text, images, audio, and video by training on diverse data sets, including real-time and external data sources and both structured and unstructured data. Following established patterns, it boosts productivity by automating routine tasks, like classifying information, summarizing data, identifying trends and anomalies, and retrieving information. It processes these tasks in milliseconds—much faster than manual handling. Generative AI also can monitor processes, trigger alerts, initiate workflows, and run scenarios. The result is enhanced efficiency, prompting users to follow best practices and make well-informed choices. 

In addition, Generative AI can augment and enhance predictive AI by creating synthetic data sets of possibilities for consideration. This enables predictive analytics to simulate a wider range of “out of the box” outcomes and encourages innovative problem-solving.

For supply chain planning, GenAI can be instrumental in projecting demand. Using historical data and overlaying real-time influences like market trends and other factors to simulate potential supply-and-demand scenarios. It can spot minute patterns and correlations that human analysis may miss. 

In transportation, GenAI can factor in weather and traffic data to identify delays and suggest alternative routes. It can be used to monitor dwell at ports, alerting users to potential demurrage charges or missed departures and can convert non-traditional data sources like email and PDFs into structured shipping documents and event updates.

In the warehouse, GenAI can assist in automation and help managers continually make the best use of limited space and inventory. The solution doesn’t just repeat historical models, but it also generates new solutions, including suggesting appropriate safety stock levels. It also can flag potential inventory shortages, even initiating processes to move inventory between locations.

Parting Insights

Although the topic of AI can be overwhelming and encumbered with hype, it’s important for supply chain professionals to remain informed and make smart recommendations for their organization. Understanding the differences in AI capabilities is key to distinguishing which use case applications match your needs. As you strive to optimize your supply chain operations, you can identify specific opportunities to leverage AI solutions. You can form a realistic vision for a supply chain of the future, one that is fact-driven, with super-charged productivity.

Heidi Benko is VP Product Marketing and Strategy at Infor.


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