Individual driving a futuristic car with overlaid text 'New Developments in AI and Supply Chain Management: The Future Is Now

New Developments in AI and Supply Chain Management: The Future Is Now

by LJM Group

While artificial intelligence tools such as ChatGPT and Google Bard promise to revolutionize every aspect of the economy, the challenge for managers now becomes: “How do we apply this technology in specialized areas, such as shipping and logistics management, where the collection and analysis of accurate and timely information is critical?”

According to Thomas Andersen, Partner and Vice President of Supply Chain Services at LJM, “LJM is a technology firm, we build data.”  In data-dependent selling environments, AI can be leveraged in many ways, promoting personalized shopping within the same day or next day footprint, targeted upselling, and friction-free retail.  On a larger scale, detailed data can provide a competitive logistical advantage, which includes:

Intelligent Inventory Management – By utilizing AI algorithms, sellers can optimize inventory management to ensure that popular products are stocked at distribution centers strategically located in proximity to major population centers. This allows for faster order fulfillment and facilitates same-day or next-day delivery.

Predictive Analytics – Customers can analyze browsing and purchase history, as well as demographic and behavioral data, to predict individual preferences and shopping patterns. This enables personalized recommendations and targeted promotions to provide incentives for immediate purchases.

Dynamic Pricing – AI algorithms can analyze real-time market data, competitor pricing, and customer demand to dynamically adjust prices. Sellers can offer time-sensitive discounts, flash sales, or personalized pricing incentives to encourage customers to make purchases within a short time frame.

Fast Delivery Optimization – AI-powered route optimization algorithms can efficiently plan and optimize delivery routes, taking into account various factors such as traffic conditions, weather, and even customer preferences, enhancing contract analysis. This helps sellers ensure quick and reliable deliveries and maximize the likelihood of same-day or next-day delivery.

Larger providers, like Amazon, have already begun to make use of chatbot assistance, where they employ AI-based virtual assistants to provide real-time assistance to customers, helping them find products, answer queries, and guide them through the purchasing process. This streamlined support can reduce decision-making time and encourage immediate purchases. AI is also being deployed to leverage image and voice recognition technologies that enable visual and voice search capabilities. Customers can now take a photo or use voice commands to search for products, simplifying the browsing and buying process and potentially leading to quicker purchases.

Elsewhere, AI algorithms are being used to analyze social media data to identify trends, preferences, and influencers, where sellers can leverage this information to target specific customer segments with relevant advertisements and promotions, urging them to make purchases within a short time frame.  One-click ordering can likewise integrate AI features to simplify the checkout process, allowing customers to make purchases with a single click. By combining this feature with AI-powered recommendations, sellers can entice customers to buy products immediately.

The Data Challenge

AI-driven strategies can enhance the overall customer experience, expedite order processing, and provide incentives for customers to buy within a same-day or next-day delivery footprint, thereby promoting faster transactions within the supply chain process.  In niche markets, however, the performance of AI models depends largely on relevance and data quality, which provides the foundation for training AI agents. In supply chain environments, such variables as supply chain layouts, available suppliers, lead times, demand scenarios, adjusted costs, or additional shipping restrictions must be incorporated into AI data models, which can empower logistics analysts to make better decisions for their clients.


Rolf, B. , Jackson, I.,  Muller, M., et. al., (2022). A review on reinforcement learning algorithms and

applications in supply chain management, International Journal of Production Research.

Acocella, A. March 2023. Exorcising ghost lanes from transportation procurement. Supply Chain Management Review.

Global Artificial Intelligence in Supply Chain Management Market Report 2023:  A $17.5 Billion Industry by 2028. April 2023.—Focus-on-Automation-Planning-Logistics-Inventory-Risk—

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