In the last few years, the growing sophistication of artificial intelligence has brought unprecedented levels of precision, efficiency and innovation to supply chain management. It is reshaping the way tasks are performed, data is managed, and customer service is delivered.
The future of AI in the supply chain is even more exciting. For brands and retailers, mere visibility is no longer enough. They want to know more than what happened previously, or even what’s happening now. They want to be able to predict the future, as accurately and as rapidly as possible, as they strive to balance supply and demand while exceeding customer expectations.
Following are three key areas where AI is set to make a significant impact on the supply chain industry.
Warehousing and Transportation
Smarter routing decisions. AI algorithms have already proved instrumental in helping to streamline delivery routes, cut costs and raise customer satisfaction. Able to learn from data and feedback, the system becomes even smarter in determining the least amount of travel time and fuel consumption, accounting for such variables as traffic, weather, delivery schedules, building layouts and vehicle capacity.
Inventory and movement. One of the critical aspects of warehouse management is optimizing the movement of goods within the facility. AI algorithms take into account factors such as product demand, warehouse layout, and real-time data on inventory levels. AI-driven tools utilize machine learning algorithms to predict demand patterns, so that warehouses can anticipate which products will be needed more frequently, and can position them for quicker access. Such systems continuously adapt to changing conditions, ensuring that the decisions remain optimal even in dynamic environments.
Analytics. In the realm of supply chain management, data is king. AI-powered analytics provide warehouses with invaluable insights into their operations, enabling data-driven decision-making. These tools analyze vast amounts of data generated by sensors, radio frequency identification tags, and internet-of-things devices within the warehouse.
Predictive analytics, for instance, can forecast inventory needs and potential bottlenecks. Descriptive analytics offer a comprehensive overview of historical data, helping warehouses identify trends and areas for improvement. The combination of these approaches not only enhances precision in inventory management, but also contributes to more effective resource allocation and planning.
AI-powered systems can analyze historical data to predict demand patterns, optimize inventory levels and reduce the likelihood of stockouts. These advances also result in job transformations, allowing workers to engage in more intellectually stimulating and strategic roles, such as overseeing and optimizing automated processes.
Fulfillment and packing. In recent years, AI and automation have been revolutionizing the way that tasks are performed in the warehouse. Routine and labor-intensive activities, such as order picking, packing and inventory management, are being automated. Systems powered by machine learning algorithms can efficiently organize and pack items, reducing the time required for fulfillment. These systems take into account factors such as package size, weight and fragility, to ensure optimal packing configurations, minimize the need for space and reduce the risk of damage during transit.
Cross-docks. AI algorithms can analyze a myriad of factors, including weather conditions, traffic patterns and delivery times, enabling dynamic adjustments to routing plans. This results in more responsive and adaptable cross-docking operations. AI’s ability to process vast amounts of information ensures that cross-docks can make informed decisions based on the current state of the transportation network.
Manual Processes and Workflow
Scan events. Manual data entry has long been a labor-intensive and error-prone aspect of supply chain management. Many companies today still rely on handwritten time stamps when a product arrives or departs a location. Automated scanning not only accelerates data entry, but also virtually eliminates errors associated with manual input. Scanning available at every stage of a pallet or parcel’s journey allows for greater consolidation opportunities up and down the chain. By capturing and processing data in real time, these systems contribute to accurate inventory tracking, order fulfillment, and overall supply chain visibility. Location in real-time is relayed directly to the stakeholder via an easy-to-use application or platform.
Appointment scheduling. AI algorithms are being increasingly employed to optimize appointment scheduling, taking into account factors such as warehouse capacity, dock congestion, carrier availability and delivery windows. These systems can dynamically adjust schedules in response to unforeseen events, such as weather disruptions or traffic delays. By automating appointment scheduling, companies can minimize wait times, reduce transportation costs, and enhance overall supply chain efficiency.
Track and trace technology. AI-guided track and trace systems provide end-to-end visibility, all the way to the end consumer. In the event of a recall or quality issue, companies can swiftly trace the affected products, minimizing the impact on both consumers and the overall supply chain.
Personalized Customer Service
Chatbots and conversational AI. The customer service landscape has undergone a significant transformation with the integration of AI-powered chatbots and conversational AI. In the supply chain industry, chatbots offer a scalable solution for handling customer inquiries and providing real-time updates. Chatbots draw on natural language processing (NLP) and machine learning to understand and respond to customer queries. Whether customers are inquiring about order status, shipment tracking or product information, chatbots can provide instant and accurate responses. Human workers, meanwhile, can focus on handling complex and emotionally nuanced interactions.
Troubleshooting issues. AI identifies and addresses issues in the supply chain before they escalate. Predictive analytics and machine learning algorithms can proactively identify potential disruptions, such as delays in transportation, inventory shortages or production bottlenecks. Through real-time data analysis, AI systems can identify the root cause of problems and suggest appropriate solutions.
Research into claims. AI-driven systems can analyze and cross-reference data from multiple sources to investigate the validity of claims efficiently. This includes examining shipping records, inventory data and communication logs to provide a comprehensive and accurate understanding of the situation. y automating the research process, companies can expedite the resolution of claims.
Jobs such as data entry, inventory management, routing and basic analytics are increasingly becoming automated, freeing up human workers to focus on more complex and strategic roles. But this doesn’t have to be a negative for workers in the field of logistics. While automation may displace certain jobs, it simultaneously creates new opportunities. The rise of AI has led to the emergence of new roles such as AI system trainers, maintenance technicians for robotic systems, and data analysts specializing in supply chain optimization.
As the industry evolves, there’s a growing demand for individuals with expertise in managing and maintaining automated systems, underlining the importance of upskilling and reskilling programs. As much as machine learning continues to improve and adapt, there will always be a need for a human touch when emotions are involved.
As companies continue to embrace these technologies, they position themselves not only to meet the challenges of today, but also to thrive in the dynamic and ever-evolving landscape of the future. The ongoing development and adoption of AI in the supply chain underscore its pivotal role in driving innovation and ensuring the resilience of global supply networks.
Daniel Sokolovsky is chief executive officer and co-founder of WARP.
