News Room

The Biden administration’s response has made key mistakes in handling the emerging threat. We hope this situation serves as a wake-up call to rethink and re-tool emergency response policies and practices.

In the middle of the ongoing coronavirus pandemic, we're facing a second global, viral crisis: monkeypox.

If “the forecast is always wrong,” is improving forecast accuracy even the solution to our demand planning woes? In times that continue to defy our ability to predict them, the words of famous statistician George Box have never been more right: “All models are wrong, but some are useful.” So what can we do to make models more useful? Artificial intelligence and machine learning (AI/ML) can improve forecast accuracy, but a bigger problem is the failure to set accurate expectations around forecasting models, not the accuracy of the models themselves. For supply chains to get more use from their models, we need to “trust the box;” recognize that models are not the holy grail; and remember that a forecast is an input into making better decisions, not an end in and of itself.

It seems like we’re waiting now more than ever. The COVID-19 pandemic, which we were initially told would take just a few weeks to get under control, is now in its third year. Getting back to life as we knew it was more challenging than expected. Supply chain issues have extended lead times and severely disrupted operations across vital industries, including retail, travel, and healthcare.

In late July, South Korean electronics giant Samsung revealed plans to construct 11 new semiconductor manufacturing plants in Texas. This development is good news for America on a number of levels and represents a nearly $200 billion investment in U.S.-based chip manufacturing.