The research of supply chain risk management (SCRM), which includes risk identification, assessment, mitigation, and monitoring, is a fast expanding area of study. Identification, mitigation, and management of supply chain (SC) hazards have received a lot of attention.
In order to create a proactive and predictive intelligent risk management mechanism, supply chain managers have started to concentrate on decision-making based on a variety of data sources. These characteristics make machine learning (ML) and artificial intelligence (AI) techniques suitable in the SCRM industry.
With AI, problems are solved more quickly, more accurately, and with a larger variety of inputs than with conventional methods. However, only recent technological developments have shown that AI has several applications, including SCM. The majority of industries are changing their production planning methods from forecasting to prediction and from manual to automated operations. In addition to its expanding significance in business, AI is becoming more prominent and widespread, and it is currently being investigated from a more comprehensive angle. Additionally, by using predictive analytics, AI and ML techniques will transform the risk management process. Consequently, sophisticated and predictive data analytics have grown to be a crucial part of SCRM decision-making. Given this, it is more relevant to exploit AI using some powerful techniques such as ML, Deep learning (DL), and Natural language processing (NLP) in SCRM.
AI that was developed with biassed data will undoubtedly produce biassed findings. The five guiding principles listed below should be used when creating AI-based risk management principles.
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Transparency: Retaining transparency in how AI is utilised, how it functions, and providing oversight can help to reduce distrust of AI processes.
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Business Strategy: The corporation must have a strong strategy in place that is completely in line with its business strategy. The risk assessment and mitigation processes must take these solutions into account.
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Trust: Both the general public and employees must believe that AI is being created, used, and safeguarded in a way that is both ethical and secure for all parties.
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Security and privacy: While implementing AI models, businesses must be fully conscious of the need to protect the privacy of their customers, employees, and organisational data.
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Values and Social Impact: Are corporate, individual, and societal values being taken into consideration while developing AI and machine learning systems?
If created with a basic set of guiding principles, creating AI systems to optimise risk management can have exponentially positive benefits.
Join us on 30th - 31st may, 2023 for the Supply Chain Risk and Resilience Forum, in Berlin, Germany so you don't feel left out in the industry!
To register or learn more about the Forum please check here: https://bit.ly/3DsfWE4
For more information and group participation, contact us: [email protected]
