By giving people the resources and knowledge, they need to understand data and make knowledgeable judgements, decision support and analytics are necessary to assist enterprises in making better decisions.
Data visualisation, statistical analysis, predictive modelling, and prescriptive analytics are some examples of this. These tools may assist firms in seeing trends, patterns, and insights that might otherwise be challenging to spot.
Additionally, automation and streamlining firms' data-driven decision-making processes may help them become more successful and efficient. Advancements in computing power and the requisite for real-time prediction and decision-making by analysing large-scale data have caused Artificial Intelligence (AI) proliferation in the last few decades.
Integrated project planning and scheduling is a popular study area that has offered a blueprint for project success based on on-time completion. In reality, each project is vulnerable to many hazards that might stymie development or result in significant quantitative or qualitative harm.
While no one knows exactly how AI will affect project risk management, almost everyone agrees that it will. Most AI-based risk management systems are confined to a few risk categories, limited data features, and small datasets, making them unworkable for real-world project settings.
Similarly, today's supply chain businesses need to be operating as normal. Advanced technologies can help firms establish an intelligent supply chain that forecasts and monitors the impact of every action, allowing them to balance three critical goals (i.e., relevance, resilience and responsibility). However, industry stakeholders still need to be aware of AI's potential, particularly in risk and uncertainty management, since the use of these techniques for Supply Chain Risk management is still in its infancy.
We’ve been creating different AI-based risk management systems for complex projects and have been proposing different advanced and hybrid AI-based approaches, which are being applied to supply chain, project management, feature extractions and other predictive analytic techniques.
Although AI models’ numerous qualities typically help them achieve higher performance, their complexity often makes their basic working principle difficult to comprehend. This lack of readability and clarity raises questions about their reliability and impartiality. Regardless of their accuracy, this effectively questions the classifiers’ reasoning and supports the mistrust.
The bulk of existing state-of-the-art AI models has comparable flaws, prompting the development of Explainable AI (XAI), aiming to create approaches that can improve the interpretability and explainability of AI models. Developing appropriate XAI methodologies for real-world project scenarios may successfully give insights into the variables that influence project risks and make the project environment accessible to stakeholders, allowing them to plan preventive maintenance and budget accordingly.
We’re working to close the research gap in developing acceptable XAI approaches for intelligent risk management systems for both supply chain and project management problems.
Our research is aimed at delivering the following impacts: