on Tools & Techniques

It’s been a wonderful privilege to share a few steps in the learning journeys of more than 130,000 students and professionals who have enrolled in my quality and operations courses over the past nine years on the Udemy platform, which recently became part of Coursera.

As a result, I read thousands of comments and questions every year from manufacturing, engineering, and management professionals around the world in all stages of their careers. This volume of feedback helps me identify trends in the marketplace, common gaps in knowledge, and recurring challenges faced by professionals.

Last week, I received a DM from Domingo, a quality engineering professional who is one of more than 4,600 students enrolled in my Certified Lean Six Sigma Green Belt (2026) class. He’s asking a question I hear frequently in one form or another: “Which Six Sigma certification should I get?”

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This article is adapted from Chapter 11 of my book, Measuring Manufacturing Effectiveness.

The book explores how manufacturing effectiveness is shaped not only by equipment performance and process capability, but also by how organizations respond when things do not go as planned. While the chapters form an integrated framework, each one is written to stand on its own for readers encountering the work at different points.

Most manufacturing systems experience stops. Equipment fails, materials are delayed, quality issues arise, and plans change. What differentiates high-performing organizations is not the absence of disruption, but how they recover when disruptions occur. [Read more…]

This article is adapted from Chapter 10 of my book, Measuring Manufacturing Effectiveness.

The book examines how manufacturing organizations define, calculate, and use effectiveness metrics, and how those choices influence decisions, priorities, and behavior throughout the organization. While the chapters form a coherent framework, each is written to stand on its own for readers entering the series at different points.

By the time effectiveness metrics are calculated, the hardest work is often assumed to be complete. In practice, the opposite is usually true. Numbers that are technically correct can still be misunderstood, misapplied, or used to justify the wrong conclusions.

Chapter 10 focuses on the interpretation of effectiveness metrics rather than their calculation. It examines how context, assumptions, system boundaries, and organizational incentives shape what these metrics actually mean, and how the same number can support very different decisions depending on how it is interpreted.

The goal of this chapter is to help readers move beyond treating effectiveness metrics as objective scores and toward using them as diagnostic signals within a broader manufacturing system.

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This article is adapted from Chapter 9 of my book, Measuring Manufacturing Effectiveness.

The book examines how manufacturing organizations define and apply performance metrics, and how those choices influence decisions, priorities, and outcomes across operations. While the chapters collectively form a structured framework, each chapter is written to address a specific dimension of manufacturing effectiveness and can be read independently.

Quality loss is often discussed in terms of defects and scrap. While those outcomes matter, they represent only the most visible expressions of a deeper issue: the ability of a manufacturing system to produce stable, predictable, and usable output.

Chapter 9 reframes quality loss through the lenses of yield, stability, and usable output. Rather than treating quality as a pass/fail condition, this chapter examines how variation influences what portion of production can actually be counted as usable output.

By viewing quality loss as a system property rather than a simple defect rate, this chapter aims to clarify why many quality metrics fail to reflect real operational performance, and why improving quality often requires changes beyond the quality function itself.

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This article is adapted from Chapter 8 of my book, Measuring Manufacturing Effectiveness.

The book explores how manufacturing organizations define and use performance metrics, and how those definitions influence operational decisions, improvement efforts, and management behavior. While the chapters form a connected framework, each is written to address a specific aspect of manufacturing effectiveness and can be read independently.

Performance loss is often described in overly simple terms—the equipment is running slower than it should. While speed is certainly part of the story, this narrow view hides a much broader set of losses that affect output, flow, and stability.

Chapter 8 expands the discussion of performance loss beyond basic speed shortfalls. It examines how interruptions, minor stops, micro-downtime, variability, and operating practices contribute to lost performance—even when equipment appears to be running continuously.

By broadening how performance loss is defined and observed, this chapter aims to improve how organizations diagnose problems and select effective improvement actions.

[Read more…]

This article is adapted from Chapter 8 of my book, Measuring Manufacturing Effectiveness.

The book explores how manufacturing organizations define and use performance metrics, and how those definitions influence operational decisions, improvement efforts, and management behavior. While the chapters form a connected framework, each is written to address a specific aspect of manufacturing effectiveness and can be read independently.

Performance loss is often described in overly simple terms—the equipment is running slower than it should. While speed is certainly part of the story, this narrow view hides a much broader set of losses that affect output, flow, and stability.

Chapter 8 expands the discussion of performance loss beyond basic speed shortfalls. It examines how interruptions, minor stops, micro-downtime, variability, and operating practices contribute to lost performance—even when equipment appears to be running continuously.

By broadening how performance loss is defined and observed, this chapter aims to improve how organizations diagnose problems and select effective improvement actions.

[Read more…]

This article is adapted from Chapter 6 of my book, Measuring Manufacturing Effectiveness.

The book examines how manufacturing organizations define and use performance metrics, and how those measurement choices influence decisions, priorities, and behavior on the shop floor and in management. While the chapters are organized as a cohesive framework, each one is written to address a specific aspect of manufacturing effectiveness and can be read independently.

Chapter 6 focuses on a critical but often oversimplified topic: downtime.

Most organizations track downtime in some form, but far fewer distinguish meaningfully between how often downtime occurs and how long it lasts. These two dimensions — frequency and duration — are frequently combined, averaged, or summarized in ways that mask important operational realities.

This chapter explores why separating downtime frequency from downtime duration matters, how each dimension points to different underlying causes, and how failing to distinguish between them can lead to ineffective or misdirected improvement efforts.

[Read more…]