Revolutionizing Supply Chain Management: The Role of AI in Streamlining Operations

Although AI-powered supply chain management is increasingly being leveraged, several companies are still wary of its benefits and added risks

Rolls Royce partnered with Google to build AI-enabled ships for their marine division with the vision to replace crew members with intelligent machines that could load and unload cargo and navigate through the seas. This was in 2017. Since then, it has been aiming to improve the speed of shipping and delivery further. 

Extra labor, bigger workspace, and more human expertise are no longer needed. AI has taken over the workload and promises a better supply chain management system with automation, decision support, demand forecasting, and predictive to prescriptive AI-based analytics. AI-enabled solutions also include problem-solving abilities from delayed deliveries to weather-related complications. 

Besides the usual use cases, including inventory management, shipping process optimization, workforce management, reduced customer response time, and supplier relationship management, AI-driven management solutions also help maintain the warehouse.

Supply chain company Lineage, established in over 200 locations globally, uses AI in its warehouses. The AI-driven system uses an algorithm that predicts which items will stay in the warehouse longer and which are expected to be bought sooner. Using the results, the employees keep the goods that will leave early in the front and push the rest to the back of the shelves. Lineage claims that the AI solution has increased management efficiency by over 20%.

AI-driven supply chain systems also maintain security in warehouses, which is enabled by facial recognition technology. Experts claim that such unmanned warehouses, powered by AI, are more efficient than human management. 

Unbeknownst to them, AI has been part of companies’ management systems much before the active installation, using it to forecast product demand, manage customer service, and maintain transportation, as many technology vendors have been quietly slipping AI into their management solutions in the past few years. At the same time, the AI transformation has been rapidly activated in months due to new customer expectations. Experts reckon the management process will grow substantially with better quality control and optimization in the coming years.

Moreover, cloud-based applications like manufacturing execution systems (MES), warehouse management systems (WMS), and enterprise resource planning (ERP) have also been taken over by AI and AI-driven analytics. Capgemini‘s research predicts that smart manufacturing platforms will grow exponentially in the next three years. Despite the optimism, experts warn supply chain companies to be wary of the AI program as it is still early.

Pitfalls and Loopholes

Major complications concerning AI-driven management systems include legacy software integration and the skills gap. Apart from data analysts and a minimal number of employees, most of the working staff in any supply chain organization need to gain knowledge of AI and Machine Learning (ML), and such AI systems must be supervised. In its present form, AI is not self-sufficient. 

With the responsibility to handle massive data, slips and lapses can happen. Also, in a cyberattack, the team must be able to override the bugs, create backups, and increase security. Some IT technology leaders believe that AI regulation needs to be on par with the speed of AI adoption in supply chain management. 

Another problem lies with the perception of supply chain companies that entirely depend on AI for random projects and do not consider it a holistic solution. Experts believe they focus on isolated projects instead of creating business management strategies for engineering and scalability. This might result in a scattered set of AI projects that do not provide substantial ROI.

AI being the future of management systems, industry leaders suggest companies launch a Centre of Excellence (CoE) that will focus on education and understanding AI. It will also work on data readiness digital tool integration and help create an improved supply chain environment. 

Companies might wait to see the benefits of AI development; it might take a while. In the meantime, experts advise data quality initiatives and supply chain optimization to be a top priority for an easy and smooth AI transition and implementation.  

On the other hand, organizations that choose to remove AI from their system might fall far behind their competitors. Some research reflects only 12% to 30% of supply chain companies use AI as a management tool. Although it is a trend, many are not implementing it. Experts reckon it’s the added expense risks or that the general mindset of the industry is yet to adjust to the data-driven change and the possible good outcomes that come from it. Most of the industry might wait for a few success stories before taking a step.