The logistics sector is at a tipping point. Global supply chains face mounting pressure from volatile demand, rising costs, and customer expectations for near-instant fulfillment. Generative AI offers more than incremental fixes—it enables a fundamental redesign of how logistics networks think, learn, and respond. By unlocking capabilities in forecasting, inventory management, scheduling, last-mile delivery, and predictive maintenance, GenAI is helping early adopters move beyond efficiency gains to build Agile, Resilient, and Customer-Centric supply chains. As Dr. Sourabh Bhattacharya, Professor – Operations & Supply Chain Management, Institute of Management Technology, Hyderabad, and Janvi, IMT Hyderabad argue, this shift signals not just technological progress, but the dawn of a new logistics paradigm—Intelligent by Design and Adaptive by Necessity…
Dr. Sourabh Bhattacharya
Picture this... A major shipping port is facing a critical challenge, the challenge of congestion, which is a direct byproduct of global supply chains. This issue is a reason for an increase in costs, shipping delays, and decreased efficiency in the entire supply chain. As the global supply chains continue to expand and the demand for goods continues to surge, the port will be extremely overwhelmed with the quantum of cargo causing bottlenecks in the process.
Now this is an actual issue that was being faced by the ports of Rotterdam, and the remedial actions have been involving generative artificial intelligence. The port of Rotterdam is one of the most advanced ports on the global landscape, and they have now taken a stride further and embraced digitalization. The PortXchange platform provides real-time data on vessel arrival times, the availability of the berth, and the weather conditions, which can be quite unpredictable in the wake of climate chang. Deploying artificial intelligence and predictive analysis, the port has been able to ascertain the trafficand consequent resource allocation. Owing to the fully automated container terminals, there has been a considerable reduction in human error, an increase in cargo throughput, and the minimization= of idle time.
This is the kind of disruption that the world is looking at if generative artificial intelligence is adequately deployed. Everything around us is rapidly changing and so is the landscape of logistics and supply chain. With the advent of generative artificial intelligence, the way we imagine our traditional logistics systems has gone for a tumble.
The last five years have made certain tasks and jobs completely obsolete and yet there is evidence that suggests that the adaptation of generative artificial intelligence in logistics has been slow, and managers have not been able to fully tap into what GenAI can do for their logistics systems. However, there is research and theory that also speaks of the wondrous capabilities of GenAI’s abilities.
Janvi
The limitation that people have in mind is that AI is good enough for chatbots and handling customer or internal grievances, a sort of a frontline in CRM but what is being overlooked are the numerous other possibilities that GenAI can offer in terms of forecasting, inventory management, scheduling, just in time and lean operations because of its ability to parse through mounds of unstructured data and yielding actionable insights in the manner of seconds, generation of reports and so many other tasks that will be discussed below.
However, to be specific, this article is about the disruption GenAI can bring to the domain of logistics and streamlining that facet of operations management, reducing costs, streamlining scheduling of activities and achieving higher customer satisfaction with the overarching element of increasing efficiency across the whole process. The incorporation of technology in logistics has proven to increase transparency in the process, elimination of non-value activities, and early detection of problems for speedy redressal.
The Case of Digital Twins for Logistics Systems
Digital Twins are also a niche use case for artificial intelligence to augment and improve the way we look at logistics. A digital twin is exactly what the name suggests, a digital likeness of the model whose efficacy is to be improved nonvalue added activities to be reduced if not eliminated, what was supposed to be offline models has now been converted into real-time portrayal of situations and issues that may crop up.
Although there are numerous technologies that are involved in the creation of a digital twin, the way AI has transformed this is by leveraging historical data and real time data paired with machine learning frameworks to make future predictions in the context of the particular asset.
Comprehensive data on the movement of goods can help reduce unnecessary movement and reduce wastage in operations. The Singapore Port Authority is working with a consortium of partners, including the National University of Singapore, to develop a digital twin of the country’s new mega hub for container shipping. Effectively the usage of the data can bring about spatial models to life and thus provide actionable insights.
Dramatic improvement in the power and the usability of advanced analytics tools has led to a major change in the way that companies are extracting and analyzing data from big and complex datasets and hence the deploy ability in digital twins for logistic systems.
The Case of Last Mile Delivery Optimisation
Customers are increasingly expecting their deliveries to reach them quicker (withing 24 hours of placing the order if we are talking about the exact figures) and if logistics providers wish to rise to the occasion, then the implementation of artificial intelligence is paramount, predominantly to save costs and increase efficiency.
Data suggests that for consumers, the speed of the delivery is the determining factor in their online purchase decisions, and a slower delivery time could potentially lose that customer. Hence, developing a robust logistics system is important. However, the implication of last-mile delivery for logistics companies is high operational costs, lack of real time data analysis, high costs of fuel consumption, static route planning which would and could cause unpredictable delays which could drastically alter the time of arrival.
There is also research that shows that the business owners who are embracing AI-driven solutions to personalize their offerings are experiencing higher engagement and conversion rates, reason being? AI can pull in information from multiple sources, including traffic databases and meteorological data, and analyze this very same data to streamline delivery routes and tracking packages, thus increasing the end-to-end visibility in supply chains. Predictive analytics can be used to forecast demands and fleet scaling. DHL uses AI insights to minimize unnecessary extra stops and trips, which means a decrease in fuel consumption and cost-cutting. The traceability that is generated because of the deployment of AI means better co-ordination between the stakeholders and thus provides better customer service and better solutions to meet the ever-increasing consumer demand.
Drivers for Companies to Use GenAI
These are the times of disruption and putting a spotlight on a common, flawed assumption about supply chain operations. That assumption being that traditional supply chain models were only designed to handle predictable patterns of demand and supply. Natural disasters, geopolitical conflict and the like caused sudden spikes in demand, and supply, leading to significant shortages and delays for lean supply chains. They aren’t equipped to meet customer expectations in a volatile and unpredictable world. As major black swan events become more frequent and intense, companies must reimagine their supply chains for a future filled with more pervasive disruption.
The enterprise customers and their customers expect a new normal from the supply chain industry. The customers want digital -first experiences in their enterprise transactions that mirror what they get in their personal commerce interactions, online, transparent, end-to-end, real time and mobile. There are multiple advantages of adopting artificial intelligence in the supply chain:
Route Optimization: Real time traffic updates and the integration of maps in AI systems and robust data driven insights can help with the optimization of routes for the fleet of vehicles.
Amazon uses artificial intelligence to optimize its routes to support fast delivery promises. They have deployed machine learning in their route planning models which analyse the delivery addresses, the historical data, driver availability, weather and traffic conditions to fine tune the delivery processes in real time.
Coming to the home grounds, DHL is making strides in implementing generative AI and IoT to optimise itineraries and reduce idle time. The trucks receive real-time traffic weather data and help drivers optimize their delivery routes, which has then led to a 20% decrease in the transit time which implies major cost savings and better vehicle maintenance.
Demand Forecasting: Past data, Present data and the movement and tracking of the inventory can help managers to better understand what and how are the goods moving and can thus help better plan and understand the production and save on costs. Walmart has not shied away from implementing AI technologies to streamline its supply chain, which is necessary if they want to maintain their position as the world’s largest retailer. The company has large pools of data which are comprised of point-of-sales systems, customer demographics, economic indicators, weather patterns etc., and the company has leveraged this data to churn out insights into the demands which help better inventory allocation. The AI can identify new patterns and trends; they can also predict the most efficient delivery routes. The company has been able to reduce logistics costs, increase revenue, and improve on-time delivery rates because of their AI initiatives.
Inventory and warehouse management: There can be severe issues with understocking and overstocking in the inventory, especially in a FMCG landscape where inventory management can be a real issue in cost cutting, AI can again with the help of past data help navigate these issues. Zara, the fast fashion giant, has changed the landscape of how we look at the apparel industry by combining its inventory management technology of RFID tags with AI. Items are tagged individually; the inventory is being tracked in real time, and the restocking is quicker as demand is being gauged automatically.
The inventory accuracy rate has reached a whopping 98% as the sales velocity is being tracked and so is the stock. This eventually results in lesser markdowns and minimizing inventory holding costs. According to a study, the implementation of RFID technology can lead to a 25% reduction in inventory holding costs and a 30% reduction in stockouts.
Supply Chain Automation: A lot of paperwork, verification, authorization eats up valuable time and with the help of AI, rote work can be outsourced to AI leaving people to focus on the more problem-solving aspect of the operations.
Predictive Maintenance: Predictive maintenance uses IOT, past data and automation to conduct predictive maintenance and with the analytical skills of AI and its ability to store and map abundant data in a manner of seconds, predictive maintenance becomes easier and helps reduce downtime. Tesla has been using AI in its predictive maintenance; the vehicles they manufacture are complete with a wide range of sensors that collect data in real-time which analyses the performance of the vehicle and if the vehicle is deviating from the normal operating standards, the system alerts the consumer that it’s time to take the vehicle to be serviced before a breakdown occurs. This minimizes downtime and enhances the overall customer experience. This proactive approach from the company gravitates customers towards the product as well. This data collected can also provide insights into the performance of the product at large to the company.
Real-time tracking and Visibility: Real time tracking can give managers enough data for decision making, providing a more accurate delivery time to customers and improving customer satisfaction. There are multiple companies that are using artificial intelligence in their tracking technologies and freight management, Decathlon being one of them, they have completely automated and digitalized their final transaction processes for customers owing to their robust RFID technology that has been implemented end-to-end, which also ultimately provides managers insights into how the flow of inventors is occurring.
Customized Logistics Solutions: AI plays an important role in personalization by tailoring services and experiences to meet individual business and customer needs. By analyzing diverse data sources, AI can uncover patterns in customer preferences and behaviors, enabling businesses to offer more relevant and timely services. For instance, a 2021 McKinsey report found that over 90% of consumers consider two- to three-day delivery as the standard, while 30% now expect same-day delivery. To meet these rising expectations, AI enables personalized routing, optimized delivery schedules, dynamic pricing models, and deeper insights into return behaviors and customer feedback. These capabilities not only boost customer satisfaction and loyalty but also help businesses stand out in an increasingly competitive market.
BARRIERS FOR COMPANIES TO USE AI
The limitations of GenAI come into the picture the moment we shift the focus on the nomenclature, since it is limited to generative data and cannot predict any anomalies in demands forecasting or even unprecedented changes in weather patterns. Other than that, other challenges have been listed below.
High Initial Investment: One of the barriers that AI has is that the initial cost is very high and requires significant cost analysis to even understand if the incorporation is a wise choice for the business or not, the process of onboarding AI systems could also potentially mean uprooting all the prior systems that have been in place for a long time and may require extensive personnel training which would also further drive up the cost.
Complexity: The systems can be complex to understand by the personnel and incorporating these changes can be difficult and there could be resistance among the employees to change.
Job Disruption: There are many jobs that have disappeared ever since the advent of AI and at the same time many jobs have been created. AI can take over repetitive tasks and optimize processes and outperform people in certain avenues and requires certain technical skills to be able to work with AI at an extensive level.
WHY ARE COMPANIES FAILING TO IMPLEMENT AI?
In an interview with Joannes Vermorel, the founder of Lokad, Conor Doherty, the host, delved into why AI initiatives tend to fail in supply chain management despite high expectations. Vermorel argued that the issue is not AI but deep-rooted systemic issues that need to be addressed at grassroot level instead of expecting AI to act like a band-aid on these issues. Vermorel contends that mainstream practices which are rooted in methods from decades ago are fundamentally flawed. These include Request for Proposals (RFPs), traditional time-series forecasting, safety stock formulas, and service level targets- all of which fail to capture the economic complexity of real-world supply chains. These issues cannot be rectified by the implementation of artificial intelligence.
RFPs tend to be exhaustive and assume the organizations understand what it is that they need, which makes what could otherwise be a simple streamlined procurement process a bureaucratic nightmare laced with red tape. Similarly, Time series forecasting has been used as a tool for demand forecasting, but it too has its limitations as it tends to rely on aggregated data and fails to capture the nuances of variability in customer demands, this is increasingly dangerous in the present volatile markets.
The calculation methods for safety stocks are also flawed. The concept of safety stock is to mitigate the risks in inventory management, but the models followed to arrive at that are deterministic and simplistic which again fail to understand the actual economics of inventory decisions and can increase costs and inefficiencies.
Vermorel emphasized eliminating certain geriatric practices from the supply chain after acknowledging the outdated paradigms on which companies are operating. The adoption of AI can be a supplemental solution, but it cannot fix systemic issues. The results that AI will produce will be misguided as it does work on historical data.
FINAL THOUGHTS
Gen AI is here to stay and the best foot forward for management is to work in tandem with these tools and implement them in the various ways possible and leverage the advantages that they are providing. As the world moves forward, meeting consumer needs in saturated markets will require cutting-edge technology and data-driven decisions of which one of the major tenets is going to be generative artificial intelligence lest the businesses render obsolete.
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