The Manufacturing Academy Article Series

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.

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.

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.

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.

My new book titled Measuring Manufacturing Effectiveness develops a practical framework for understanding manufacturing effectiveness through the lens of performance metrics including how they are defined, how they are interpreted, and how they influence real operational behavior.

Chapter 5 turns attention to the first of the three components of Overall Equipment Effectiveness (OEE): Availability.

Availability addresses a deceptively simple question: Can the equipment run when it is scheduled to run?
Behind that question sits a wide range of losses related to downtime, changeovers, failures, maintenance practices, and scheduling decisions.

This article is adapted from Chapter 4 of my book, Measuring Manufacturing Effectiveness.

The book is organized as a structured, multi-chapter examination of how manufacturing organizations define, measure, and interpret “effectiveness.” Rather than focusing on isolated metrics or individual tools, it treats measurement as a system, one that directly influences operational decisions, improvement priorities, and management behavior across manufacturing organizations.

Each chapter is written to stand on its own, while also contributing to a larger, integrated framework for understanding manufacturing performance.

Chapter 4 introduces the core components that make up Overall Equipment Effectiveness (OEE). While OEE is often presented as a single number, it is actually the product of three distinct elements, each representing a different category of loss and a different dimension of system performance.

This chapter provides an orientation to those components, explains what each is intended to capture, and establishes the conceptual foundation needed before OEE can be used meaningfully as a measurement or improvement tool.

This article is adapted from Chapter 3 of my book titled Measuring Manufacturing Effectiveness.

The book is organized as a structured, multi-chapter exploration of how manufacturing organizations define, measure, and interpret “effectiveness.” Rather than focusing on isolated metrics or tools, it examines measurement as a system; one that shapes decisions, priorities, and behavior across operations, quality, reliability, and management.

Each chapter is written to stand on its own, while also contributing to a larger, integrated framework for understanding manufacturing performance.

Chapter 3 focuses on three of the most commonly used, and most frequently misunderstood, manufacturing effectiveness metrics:

• Total Effective Equipment Performance (TEEP)
• Overall Operations Effectiveness (OOE)
• Overall Equipment Effectiveness (OEE)

Although these measures are often treated as variations of the same idea, they differ in important and meaningful ways. This chapter clarifies what each metric is designed to measure, the assumptions embedded in each definition, and the types of questions each metric is and is not capable of answering.

Measuring Manufacturing Effectiveness – Chapter 2: Manufacturing Time as a System

Over the past several years, I’ve been discovering a structured framework for understanding and improving how manufacturing organizations define, measure, and interpret “effectiveness.”

Not efficiency. Not productivity in isolation. Not isolated KPIs. But manufacturing effectiveness as a system that integrates operations, quality, reliability, capacity, flow, decision-making, and management behavior into a coherent measurement model. It may sound like a lofty goal. But please stay with me.

This article is drawn from Chapter 2 of my book, Measuring Manufacturing Effectiveness, which focuses on understanding time as a resource that is systematically reduced by cumulative and irreversible losses, at the end of which remains the time needed to manufacture good parts.

Finding Weaknesses Before the Customer Does

Co-authored with Mike Vella

One of the most effective analytical tools in reliability and quality engineering is Environmental Stress Screening (ESS).

ESS is a process designed to force latent defects to reveal themselves. It does this by applying controlled environmental stresses to hardware, accelerating the transition from hidden weakness to detectable failure.

The goal is not to test whether a product meets specifications. The goal is to flush out defects that already exist but have not yet developed into failures under normal conditions.

Why “Good Enough” Assumptions Often Are

Co-authored with Mike Vella

If you’ve spent any time working with real manufacturing, reliability, or field data, you already know an uncomfortable truth:

Most statistical models assume ideal conditions that rarely exist in practice.

Textbooks often begin with assumptions like perfectly normal distributions, clean random samples, and well-behaved processes. Meanwhile, engineers are dealing with skewed cycle times, mixed populations, censored failure data, process shifts, and the occasional mystery outlier that refuses to explain itself.

Turning Uncertainty into Informed Action

Co-authored by Mike Vella

At its core, risk management exists to protect us. It aims to reduce both the likelihood and the consequences of adverse events, whether those events affect safety, quality, cost, equipment, or project schedules.

Good risk management is not reactive. It is proactive. It assumes that future events can be influenced and, to some degree, controlled. That belief alone fundamentally changes how organizations plan, design, and operate.

Attribute inspection is one of the most widespread and difficult-to-control measurement methods in manufacturing. Whether inspecting machined surfaces for cosmetic defects, checking weld quality, reviewing molded parts, evaluating assembly completeness, or using go/no-go gauges, many operations depend on human inspectors to make subjective judgments. [Read more…]

In conversations about “Six Sigma level” performance, I hear people using DPPM and DPMO like they’re the same thing. And in fact, they’re close, but not interchangeable. Among manufacturing quality professionals though, this distinction matters critically when describing how good (or how bad) a process really is.

(Originally published in Quality Magazine, October 2025)

The term Measurement Systems Analysis (MSA) refers to a collection of experimental and statistical methods designed to evaluate the error introduced by a measurement system, and the resulting usefulness of that system for a particular application. In manufacturing, measurement system quality directly affects decisions about processes, products, and a manufacturer’s ability to meet their customer’s requirements.

In the push toward digital transformation, automated inspection, and AI-enabled quality systems, it’s easy to overlook the fundamentals that quietly hold everything together. Among them are two terms that are frequently confused, even by seasoned professionals: Calibration and Verification

These words live at the core of measurement assurance, and understanding the difference isn’t academic; it shapes risk decisions, audit outcomes, and ultimately, product quality and reliability.