Although AI-powered supply chain management is increasingly being leveraged, several companies are still wary of its benefits and added risks
With the vision to replace crew members with intelligent machines that could load and unload cargo and navigate through the seas, Rolls Royce partnered with Google to build AI-enabled ships for their marine division. This was in 2017. Since then, it has been aiming to improve the speed of shipping and delivery further.
Extra labour, bigger workspace, and more human expertise is no longer needed. As 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.
Apart from the usual use cases, including inventory management, shipping process optimisation, workforce management, reduced customer response time, and supplier relationship management, AI-driven management solutions also help maintain the warehouse.
Supply chain company Lineage that has established itself 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 had increased their management efficiency by over 20 per cent.
AI-driven supply chain systems also maintain security in warehouses, which is enabled by facial recognition technology. Experts claim that such unmanned warehouses, powered with AI, are proving to be 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 in AI to their management solutions in the past few years. While the AI transformation has been rapidly activated in the span of months due to the new customer expectations. Experts reckon that the management process will grow substantially in the coming years with better quality control and optimisation.
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 alone 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 days.
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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 organisation lack the knowledge of AI and Machine Learning (ML), and such AI systems cannot be left unsupervised. In its present form, AI is not self-sufficient. With the responsibility to handle a massive amount of data, slips and lapses can happen. Also, in a cyberattack, the team must have the ability to override the bugs, create backups, and increase security. Some IT technology leaders believe that AI regulation is not 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 not look at it as a holistic solution. Experts believe that they focus on such 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.
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Companies might not see the benefits of AI development immediately, it might take a while. In the meantime, experts advise data quality initiatives and supply chain optimisation to be a top priority for an easy and smooth AI transition and implementation.
On the other hand, organisations that choose to remove AI from their system might fall far behind their competitors. Some research reflects only 12 to 30 per cent of supply chain companies using AI as a management tool. Although a trend, many are not implementing it. Experts reckon it might be the added expenses, risks or that the general mindset of the industry is yet to adjust to the data-driven change and the possible good outcomes that could come from it. Majority of the industry might be waiting for a few success stories before taking a step.