The Manufacturing Academy Article Series

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.

In manufacturing, variation is the enemy of quality. Left unchecked, it erodes reliability, increases waste, and drives up costs. One of the simplest but most effective tools for controlling variation is Layered Process Auditing (LPA).

Of the many constructive soft skills, active listening is surely one of the most helpful in the design thinking process. More than just listening to collect information, active listening is a mindset that fosters empathy for the speaker. In business and engineering, it’s our customer that’s speaking. Active listening seeks to understand not only the other person’s words, but also their attitudes and motivations.

For many people, even the idea of “being a better listener” seems ludicrous. Once you’re listening, how can you listen better? It’s like swallowing better or breathing better. [But the truth is, you can.] Even the best listeners periodically fall into a handful of traps that obstruct their understanding of their employer’s, customer’s, and loved one’s words.

Early in my quality management career, while working at a small extrusion and fabrication company, I learned something important: bosses pay attention to the money. And if I focused on cost savings projects, I could stay on their good side.

Most of my cost savings efforts at that time focused on eliminating specific types of defects. After all, even a low-frequency defect—especially one that reaches a customer—can drive substantial savings once resolved. Other projects looked inward, targeting inefficiencies in our systems and practices. Lab procedures, control plans, and audit schedules tend to drift out of sync with the products and processes they’re supposed to control. So every now and then, a little system hygiene—an organized cleanup—can free up resources and allow you to reallocate attention to where it’s needed most.

It was during one of those hygiene projects that I stumbled into something I’ve since come to call The Paradox of Invisible Discipline.

A Practical SPC Method for High-Capability Processes

By Ray Harkins and Michael J. Vella

Manufacturing professionals who regularly apply Statistical Process Control (SPC) methods, such as X̄ and R charts, know the power of SPC to maintain control of both quality and cost. The standard practice emphasizes attention to variation, with operators trained to take corrective action whenever a process signals an out-of-control condition. Typically, this means adjusting a process input, verifying the correction, and resuming production.

However, not every out-of-control signal requires intervention, especially in processes where inherent variation is extremely small and product quality is consistently high. In some cases, such as tooling wear or small material variations, process center shifts may occur outside of the operator’s ability to correct. Responding to these shifts with standard SPC rules can lead to overcorrection, lost productivity, and increased variation.