Two years of relentless disruption demonstrate the importance of incorporating AI into the supply chain. Energy price increases, war in Eastern Europe, skills shortages, commodity and shipping cost inflation, port delays, congestion, container shortages, and Chinese Covid-19 lockdowns are all causing considerable disruption in many businesses.
Poorly stocked shelves and warehouses, declines in manufacturing output, delays in on-time delivery to consumers, and revenue loss are the results. As organisations try to meet demand, they may feel compelled to spend heavily on expedites such as air freight, adding to their cost concerns (and carbon footprint, a growing area of pressure for companies).
For example, planner efficiency can be boosted by automating boring procedures so that planners can focus on complicated exceptions. They can also utilise AI to estimate demand, improving the accuracy of the signal that initiates the rest of the supply chain planning stages.
Organizations that integrate AI and advanced analytics have advanced warning of potential events and can respond much faster. Consider demand sensing, which use AI to improve short-term forecasting by adding external cues in addition to the standard sales history inputs. Data from the downstream supply chain, market information, social media, and commodity price indexes are examples of external signals.
An AI programme will monitor disruption signals and prescribe recommendations based on what it has learnt from the efficacy of past interventions in a control tower method.
AI offers a number of approaches for dealing with a wide range of occurrences. For example, a machine learning approach known as clustering would aggregate sales patterns into groupings that can be used to improve forecasts. These clusters include instructions on how to order and renew.
The measures that firms must take to maximise the enormous potential of AI integration
However, integrating AI into existing supply chain management systems necessitates a number of cautious processes and broader concerns.
Data is one of the primary focus areas. AI requires enormous volumes of data to make predictions, and while supply chains often have enough of data, it must be available, ready for analysis, and carefully monitored to ensure its quality.
The human factor is still essential. AI research advances on a regular basis, but even as computers become smarter, human input is required. Instead of creating a “lights out” or totally autonomous supply chain that substitutes the human, AI applications and competent people should complement each other. We know AI can uncover patterns in enormous volumes of data, but it lacks the three C’s: it cannot derive meaning from context, participate by developing and maintaining relationships, or give a conscience.
Human oversight is also required as ESG (environmental, social, and governance) regulations become increasingly pressing. AI will do whatever we want it to do, but it has no conscience and is unconcerned about preventing contemporary slavery and deforestation or choosing the most environmentally beneficial energy consumption.
AI implementation should also prioritise transparency so that it does not become a mysterious and dangerous black box solution. AI models often sacrifice precision for interpretability, which means they may be more accurate but provide no explanation for how the findings were obtained. Planners require AI supported with tools to improve interpretability so that they can make choices regarding the decisions made by organisations.
To summarise, the last two years have shown how standard spreadsheet approaches to supply chain planning and operations fail when one set of severe and unexpected disruptions follows another. Businesses require AI to look ahead, respond faster and more efficiently, enabling supply chain leaders to make better decisions and grasp opportunities faster than competitors.
