An article by Olivier Sirey, J2 procurement expert.
Artificial intelligence is gradually establishing itself as an indispensable aspect of the business world. Yet when it comes to its concrete impact on businesses, few functions are likely to undergo as profound a transformation as the supply chain.
In recent years, supply chains have been under constant pressure: unstable transportation costs, raw material shortages, geopolitical tensions, inflation, new customs regulations, and volatile demand. This reality has exposed a significant weakness in many organizations: despite increasingly complex operations, many companies still operate with highly reactive processes and limited predictive capabilities.
It is precisely in this context that artificial intelligence becomes a strategic lever.
Planning and Predictability
According to McKinsey & Company, companies that integrate advanced analytics and artificial intelligence tools into their operations can significantly improve the accuracy of their forecasts while reducing inventory costs and stockouts.
One of the most advanced applications today involves demand planning. Historically, many companies based their forecasts primarily on sales history and manual adjustments. Today, machine learning models make it possible to simultaneously incorporate hundreds of variables: seasonality, promotions, weather, consumer behavior, economic fluctuations, and supplier lead times.
The goal is no longer simply to forecast average demand, but rather to anticipate variations and the risk of discrepancies. In manufacturing or distribution environments where lead times are long and margins are tight, a few extra points of accuracy can have a major financial impact on inventory and working capital.
We are also seeing significant developments in supplier risk management. Artificial intelligence now makes it possible to simultaneously analyze OTIF (On Time In Full) performance, actual delivery times, suppliers’ financial stability, fluctuations in material costs, and certain geopolitical risks.
Some platforms are now capable of generating alerts even before an actual disruption impacts operations. In a context where many companies are currently reviewing their sourcing and nearshoring strategies, this visibility becomes extremely strategic.
In real time
The impact of AI is also becoming very tangible in procurement and customs compliance functions. With the increase in trade regulations and measures such as Section 232 in the United States, many organizations must manage vast amounts of regulatory data.
Some companies are already using artificial intelligence to assist with tariff classification, detect inconsistencies in HS codes, analyze risks related to countervailing duties (ADD/CVD), or automate certain document validations. In environments where a compliance error can result in significant costs, these tools are becoming increasingly relevant.
The recent emergence of generative AI is further accelerating this transformation. Many organizations are beginning to integrate internal assistants capable of querying their ERP systems and operational data in natural language to quickly obtain complex analyses, without having to manually extract multiple reports.
Data Quality: The Key Factor!
According to Gartner, organizations currently investing in AI tools for the supply chain are primarily seeking to improve their decision-making speed, operational resilience, and predictive capabilities. However, despite the enthusiasm surrounding artificial intelligence, many companies still underestimate a fundamental issue: data quality.
Effective AI depends directly on reliable, standardized, and well-structured data. Yet, in many organizations, supply chain information remains fragmented across various Excel files, ERP systems, or logistics platforms. Before even considering artificial intelligence, many companies must first strengthen their digital foundations: data governance, process standardization, and information centralization.
Artificial intelligence will not replace supply chain experts. On the other hand, companies that can intelligently integrate these tools into their operations will have a major competitive advantage in the coming years.
References :
McKinsey & Company. (2024). Supply Chain AI: The Next Frontier in Operations Performance. https://www.mckinsey.com/capabilities/operations/our-insights
Gartner. (2024). Artificial Intelligence in Supply Chain: Driving Predictive and Autonomous Decision-Making. https://www.gartner.com/en/supply-chain
KPMG. (2024). Intelligent Supply Chain: Unlocking Value Through Data and AI. https://kpmg.com/xx/en/home/insights/artificial-intelligence.html
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