Quality Analytics & Cost of Quality

You are part of the leadership team at a manufacturing company known for reliable products—but recently, customer complaints and internal scrap rates have begun to rise. The issue isn’t obvious. The production line hasn’t changed, suppliers appear consistent, and equipment is functioning within expected parameters.

Senior management has turned to you with a clear mandate:
“Use the data. Find the problem. Fix it—without driving up costs.”

You are handed detailed quality and process data from the factory floor. Some variation is expected—but how much is too much? Is the problem systemic or random? Should you adjust the process now or wait for more evidence? Every intervention has a cost, and unnecessary adjustments could make things worse.

As production continues, new data arrives. Patterns begin to emerge—or do they? You must decide whether what you’re seeing is a real signal or just noise. Acting too early could disrupt operations; acting too late could damage customer trust.

By the end of the simulation, participants confront a reality faced by operations leaders every day: Quality problems rarely announce themselves clearly—only careful analysis, sound judgment, and disciplined decision making reveal the right path forward.