Logistics Firms Must Embrace Technology, Not Just Tangible Assets
Translated & summarized from Calcalist by baba
Logistics companies must integrate technological advancements like AI to improve efficiency, moving beyond a reliance on physical assets, according to Amos Dayi, CEO of Gold Bond Contrem. While AI is increasingly used in business processes, its application in core logistics operations like cargo handling is crucial for optimizing resource use and reducing costs. Proactive issue resolution through AI can prevent delays and improve customer service, though human oversight remains essential for safety and accountability.
The story in 5 lines · by baba
- Logistics firms must adopt AI and technology, not just physical assets, to improve efficiency.
- Amos Dayi, CEO of Gold Bond Contrem, urges serious investment in technology alongside physical equipment.
- AI can optimize cargo handling by proactively resolving issues like document verification delays.
- Current AI use in Israel is more prevalent in administrative tasks than in core service delivery.
- Human oversight remains critical in logistics despite technological advancements for safety reasons.
The logistics industry, often perceived as traditional and reliant on physical assets like cranes, warehouses, and trucks, needs to adopt technological advancements, including artificial intelligence (AI), to improve efficiency. While not every logistics company needs to become a tech startup, its management must prioritize understanding and investing in technology with the same seriousness they apply to acquiring physical equipment, according to Amos Dayi, CEO of Gold Bond Contrem. Dayi, drawing on his experience managing the Israeli Navy's equipment and transport base, notes that while adding resources like more trucks is a common response to capacity issues, it's often more effective to optimize the use of existing resources.
He argues that a key question for logistics companies considering AI is whether they can increase operational scope without a proportional rise in expenses. Current data from Israel's Central Bureau of Statistics indicates that while 63% of businesses using AI in Israel apply it to administrative and business processes, only 35% use it in production or service delivery. Dayi stresses the need for AI to be integrated into the core logistics processes of receiving, handling, and dispatching cargo.
Dayi illustrates this with the example of a truck arriving to pick up a container. Even with the driver and container present, delays occur due to manual document checks, verification, and communication with ground staff. AI systems capable of reading documents, understanding natural language queries, and cross-referencing information could identify and resolve such issues proactively, preventing driver waiting times and optimizing truck availability. This approach saves time, reduces errors, and improves customer service.
While not every problem requires AI, and sometimes procedural adjustments or system synchronization are sufficient, Dayi emphasizes the necessity of starting the adoption process. He cautions that managers cannot simply delegate AI implementation to IT departments; they must be prepared to modify existing procedures and listen to employees who understand where bottlenecks occur. Furthermore, he highlights that human oversight remains crucial in logistics, as sole reliance on machines can lead to safety risks, with liability not transferring to software vendors. The core message is that experience should not prevent managers from evaluating whether current work methods are still optimal, especially when physical assets like trucks are idle due to information gaps.