Global supply chains face many challenges due to the dependency on interconnected activities and the increased complexity of balancing supply and demand over the years. A successful supply chain process must manage costs at the lowest possible level while keeping customer satisfaction at the highest possible level. The implementation of a Duty Drawback program is one of the best tools companies possess to minimize tariff costs.

A middle-ground approach that integrates machine learning into existing operations research (O.R.) methods and techniques appears to be a promising path forward. Let’s look at different examples of how this can be done in practice.

In the world of robotics, large world models (LWMs) play a role analogous to large language models (LLMs) in language processing. LWMs are developed to understand and manipulate the physical world. We will need to rethink the business model for robotics for LWMs to make an impact. As such, the journey to create LWMs, although challenging, is both feasible and vital to unlock the full potential of robotics.

A recent study asked: “Can an artificial intelligence chatbot assistant, provide responses to patient questions that are of comparable quality and empathy to those written by physicians?” In light of the rise of virtual healthcare, there’s been a rise in patient messages, putting more pressure on already burned-out healthcare professionals, and the idea that tools such as ChatGPT and Google’s Bard could improve not only the patient experience but also the lives of healthcare professionals offers a glimmer of hope. What if AI is the answer to better care for both patients and healthcare providers (HCPs)?

Artificial intelligence (AI) has a quality problem. And it’s big. According to Gartner, 54% of machine learning (ML) models never make it into production, primarily because of poor quality. Sometimes, the models don’t work as intended and developers can’t identify the cause to fix it. Other times, the models work but aren’t explainable, so they can’t be deployed for compliance reasons. Sometimes, even when the model’s results are explainable, stakeholders don’t trust them and they aren’t deployed. There are four important ML model testing criteria and developers need all four to ensure their models make it over the finish line.

Just-in-time logistics (JIT) is an inventory management system that allows companies to optimize inventory levels, reduce waste and improve efficiency by synchronizing the flow of materials with production and consumer demand. Organizations around the world have adopted JIT, which is particularly effective in the automotive, electronics, retail, pharmaceutical, food and beverage and e-commerce (product fulfillment) industries. One of the most effective ways to unleash the full potential of JIT is with data analytics.