The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine learning for developing supply chain risk intervention models, and demonstrate its use with a case study in supply chain risk management in the maritime engineering sector. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions, allowing "what-if" scenario planning. We therefore propose different machine learning developmental pathways for for predicting risk, and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning.

Microsoft and the US Department of Energy grabbed headlines by announcing a partnership to develop AI-powered applications to help first responders reacting to natural disasters. One of the first AI prototypes in development will employ computer vision to detect and predict the frontiers of active wildfires and floods. The second application uses an AI tool that should be in every risk manager's toolbox: simulation. This simulator will aid teams in running mock scenarios to better plan and prepare for the next big natural disaster. Many companies were caught flat-footed by COVID-19, perhaps the largest disruption to global trade in a hundred years.

A person, utterly exhausted with seeing those two letters: AI. As we see from Deloitte's CPO Survey in 2018, AI is only fully deployed in 2% of procurement organizations and is far from making any real impact at scale within the digital ecosystems procurement teams are so eagerly trying to build. To top it off, there's only 27% percent of procurement leaders considering AI/Cognitive technology, and 55% who haven't considered it at all. Cut to 2019, Deloitte published its newest CPO Survey where they found that 81% of chief procurement officers with fully implemented solutions in the space of Supply Chain Risk & Compliance Management aren't satisfied with their solutions. That's a whole 25% higher than Donald Trump's disapproval rate when it reached its peak.

I've been researching and writing about supply chain management for 20 years. I've seen a goodly number of "revolutions" in SCM. In the first revolution, the concept of supply chain, as opposed to logistics, was put forth. Constraint based optimization tools for the extended supply chain were developed to support the new philosophy. As this was going on, Lean and Six Sigma approaches to improving capabilities, not just at the factory level, but in other internal departments, as well as across the supplier and 3PL base, were gaining in strength.

Last week's World Economic Forum in snowy Davos, Switzerland, brought a blizzard of proclamations about the disruptive impact of artificial intelligence, along with an avalanche of debate over its job-killing potential. The good news for us humans is that the current generation of AI technologies being used to automate data collection and processing -- such as machine-learning software that amasses more expertise as it analyzes data or neural networks modeled after the human brain -- are more likely to augment the human workforce rather than replace it. Indeed, almost two-thirds of the business executives responding to a survey released last week by IT consulting firm Infosys (PDF) said they believed AI would "bring out the best in their organization's people." The rise of AI, the Infosys poll respondents suggest, will place a premium on skills such as creativity and logical reasoning. Noted IBM chairwoman and CEO Ginni Rometty, in remarks Wednesday at Davos, said: "History has taught us many things. When you [have] powerful technologies, you have a responsibility that they're introduced in the right way." "This pivotal investment will empower our team to accelerate R&D and enhance our proprietary technology with the latest innovations in machine learning and natural language processing while broadening our expertise in CSR analysis to foster environmental, social and ethical performance at a global scale," said EcoVadis co-CEO Frederic Trinel, in a statement.