Role of artificial intelligence in building resilient supply chains

Big data analytics has demonstrated the use of predictive analytics for identifying the sources of supply chain disruptions resulting in improved supply chain resilience (SCRes). Firms should develop analytical capabilities to enhance the SCRes by effectively utilizing the resident firm knowledge, thereby strengthening organizations’ existing information capabilities. Emerging technologies such as Artificial Intelligence (AI), industry 4.0, additive manufacturing, and advanced product tracking applications hold significant potential for supply chain risk analytics, thereby improving SCRes. Firms are needed to take a dynamic, innovative, and proactive approach in managing risks. Risks and disruptions should be opportunities to adapt and evolve in the face of hyper-connected networks, advanced cyber threats, highly competitive environments, and changing customer expectations to improve the supply chain performance. The resilient organizations must use the technological advancements in automation, AI, and the Internet of Things to increase collaboration and cooperation, both vertically and horizontally, internally, and externally. Developing AI-driven innovation is advantageous as it accelerates the decision process in identifying, prototyping, and testing novel solutions.

Sachin Kamble, Professor of Strategy, EDHEC Business School, identifies the urgent need for the companies to develop their artificial intelligence (AI) capabilities for building supply chain resilience in the present context of COVID-19. His team of reputed international researchers interacted with the leading supply chain practitioners from Morocco, France, and India, who are involved in AI implementation programs. They further elaborated that AI-based data-driven innovations promote proactive real-time coordination, enhancing supply chain collaborations and visibility. This will place the companies in a better position to design and implement innovative supply chain resilience strategies with increased pace and participation from their supply chain partners. They stressed that the AI-based Information processing capabilities should include self-adaptive algorithms, prediction, and learning techniques to improve adaptive skills. With its predictive and forecasting capabilities, the AI will support real-time sharing of forecasts, inventories, and production capacities and prepare the firms for future risks.

Managers should improve SCP in a sustained way through the development of SCRes to deal with uncertainties and unexpected events. In doing so, they should recognize the role of developing AI-based information processing capabilities to develop AC and collaboration among the supply chain partners, which leads to the creation of long-lasting SCP under dynamism and uncertainty. The firms that can recognize and acquire external knowledge to develop environmental competencies and capable of assimilating, transforming, exploiting the knowledge and develop environmental capabilities, results in improved performance.

Even though the focal firm develops the AI capabilities for building SCRes, these capabilities might not be used to its full potential unless all the supply chain partners participate in the AI-driven supply chain. Most of the suppliers at the lower tiers of the supply chain may not have access to the skilled resources and technological sophistication needed for developing AI capabilities or may possess inconsistent data or incompatible interface standards. The cooperation and value of information sharing amongst the supply chain partners are highly critical to mitigating the risks. A high level of supply chain uncertainty does not always lead to a high degree of collaboration with the supply chain partners but is mainly driven by organizational culture having an external orientation. The integration of AI capabilities in the different tiers of the supply chain is critical for SCC and AC to develop SCRes.

Sachin Kamble, Professor of Strategy, EDHEC Business School, France