The Benefits and Drawbacks of a Supply Chain With Artificial Intelligence

AI in Supply Chain Management

Businesses are currently using artificial intelligence to overcome any supply chain difficulties in supply chain management. Machine learning, artificial intelligence (AI) and big data advancements have propelled the start of a new, algorithm-based age. Companies may now automate a wide range of processes, reducing mistakes and the downtime and costs associated with them. AI has the potential to assist supply networks in particular greatly. It can help companies improve demand projections, control the entire supply chain, reduce waste, increase efficiency and increase profitability.

AI is commonly mistaken for machine learning. While they are inextricably linked, and experts frequently call machine learning a subset of AI, it yields self-generated decision-making skills. In other words, AI cannot only collect data, learn about patterns in data and produce intelligent suggestions; it can also formulate and execute a plan of action on its own.

However, like with any new technology, there are both advantages and disadvantages. It's critical for companies using AI in supply chain to grasp the advantages and downsides of using AI for supply chain optimization to ensure they are deploying the correct solutions for their specific needs.

Benefits of AI in supply chain

AI applications in supply chain such as robotics, autonomous vehicles, smart warehouses and automated predictive analytics (for example, forecasting) help improve workplace safety, save costs, and simplify systems and procedures.

For example, AI may be used to collect extensive data that may impact delivery schedules, such as weather patterns, GPS data and reroutes. This can assist the sales team in forecasting more precise product delivery timeframes while also informing consumers of real-time inventory changes. As a result, businesses are better equipped to deliver optimal customer service to both new and existing clients.

Disadvantages of AI in Supply Chain

AI is continually growing, with several research and development activities taking place worldwide. However, when algorithms begin to generate new algorithms, which are then auto-executed, a "black box" scenario emerges. Researchers and AI developers may struggle to disentangle the complexities of these AI-generated systems. Consider attempting to comprehend as well as forecast the "what," "when," "where," and "how" of human creativity and behavior.

Self-driving or autonomous delivery vans, for example, are driven by incredibly complicated systems, incorporating sensors that input into an AI algorithm, allowing for monitoring of surrounding traffic while anticipating and accounting for the behavior of adjacent human drivers. In these cases, a single wrong prediction by the AI program might prove disastrous for human drivers. Of course, we cannot overlook the many threats posed by inadequate cybersecurity practices.


Businesses may use artificial intelligence and machine learning in supply chain management to automate low-value processes, giving them more time to focus on strategic and high-impact business activities. The difficulty will be in properly integrating this technology throughout the supply chain. After all, for technology to flourish, you must share information across departments inside the firm. This innovation provides substantial competitive benefits for firms hoping to work more effectively.

AI comes with certain inherent obstacles and hazards, but that doesn't imply that you shouldn't use it. On the other hand, AI in the supply chain should be carefully examined within a thorough risk, contingency and mitigation matrix. Remember that AI is a tool that you should use in conjunction with human talents and decision-making processes, not as a replacement for human work.

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