The Manufacturing Academy Article Series

A machine run-off, refers to the process of testing and adjusting a new or modified machine or piece of equipment before it is put into regular use. When a run-off is performed prior to shipping to the customer, it is called a Factory Acceptance Test (FAT), and when it is performed after installation at the customer’s facility it is called a Site Acceptance Test (SAT). 

Both types of machine run-offs are common with large, complex, and/or expensive equipment. And both have the same goal of ensuring the equipment is safe and reliable, and meets the customer’s requirements and functional criteria before it is launched into production where repairs and corrections become much more expensive. The SAT is largely a repeat of the FAT expect it additionally verifies that no damage was incurred during shipment and that the unit is correctly installed.

A common first step in the learning journey of a quality engineer is mastering a set of data visualization tools called the “Seven Basic Quality Tools.” Popularized by an architect of the modern quality movement, Karou Ishikawa, the seven basic quality tools allow practitioners to readily extract useful insights from raw data and build a foundation for further statistical analysis. These tools stand as pillars of systematic problem-solving, aiding in the identification, analysis, and improvement of processes.

These seven tools, in order of complexity, are: Check Sheets, Flowcharts, Cause and Effect Diagrams, Histograms, Pareto Diagrams, Scatter Plots, and Control Charts.

In the ever-evolving landscape of engineering and design, strategic frameworks play a pivotal role in deciphering the intricate dynamics of customer satisfaction. Consider, for example, the Kano Model of Customer Satisfaction, brainchild of management science professor, Noriaki Kano. This model offers a nuanced approach to understanding the diverse aspects of customer satisfaction, making it a valuable tool for engineers and design professionals as they transition from problem definition to ideation and prototyping. [Read more…]

Welcome to the realm where reliability engineering meets product design—a landscape where innovation thrives on understanding and addressing customer pain points. In this guide, we will explore the intricate dance of empathy within the design thinking process, offering engineers and designers a roadmap to navigate the complexities of customer-centric problem-solving.

At the heart of the design thinking process lies the empathize phase—a journey that begins with a courageous act: wiping the slate clean of preconceived notions and biases. For seasoned professionals, this is akin to stepping into the unknown. Design thinking demands a reset—a departure from the comfort of accumulated solutions and a return to the essence of the problem.

with co-author Dr. Jay Desai

Steel forging holds significant importance in developed economies around the world. It is used to shape raw materials into precisely designed components that often have critical performance requirements. Due to their strength, durability, and reliability in harsh environments, forged steel components are utilized in a wide range of industries including aerospace, automotive, defense, and construction.

In today’s competitive business landscape, ensuring the reliability of products is paramount for any company aiming to maintain a strong market presence and satisfy customer expectations. One essential tool that plays a pivotal role in achieving this goal is Advanced Product Quality Planning (APQP). Understanding the major components of APQP and how it is intertwined with reliability provides design and engineering professionals with a framework for assuring early success in a product launch.

In the world of reliability and quality engineering, technical expertise and domain knowledge have always been highly valued. Engineers are expected to possess a strong foundation of technical skills to excel in their roles. However, in today’s rapidly evolving workplace, it has become increasingly evident that technical ability alone is not enough to guarantee success. The modern engineering professional must recognize and embrace the importance of soft skills to complement their analytical skills. 

In this blog post, we will dive into the significance of soft skills in the workplace and how they can enhance the effectiveness and success of reliability and quality engineering professionals.

As engineering professionals, we’re no strangers to complex problem-solving scenarios that demand rigorous analysis, innovative thinking, and efficient decision-making. Whether it’s designing new components, optimizing processes, or addressing infrastructure challenges, our success hinges on our ability to approach problems with a structured and creative mindset. 

One method that can significantly boost our problem-solving prowess is the “Six Hats Method” – a systematic approach that allows us to don different thinking hats, unlocking diverse perspectives for comprehensive and effective solutions. Let’s see how the Six Hats Method can empower engineering professionals to navigate challenges with clarity, focus, and ingenuity.

In the world of reliability engineering, ensuring the long-term dependability and safety of components is of paramount importance. Nondestructive Testing (NDT) technologies have emerged as indispensable tools for reliability professionals in various industries, including aerospace, automotive, manufacturing, and power generation. By enabling the inspection and evaluation of materials and components without causing damage, NDT techniques play a crucial role in enhancing short and long-term reliability. 

In this blog post, we will explore several NDT technologies and how they contribute to improving component reliability.

In 1956, Harvard Business Review published a landmark article by economist and business leader Armand Feigenbaum titled “Total Quality Control” that summarized the quality control system he developed during his long tenure at General Electric and gave prominence to many concepts still used in quality management today. One of those concepts was cost of quality measurement.

The goal of a cost of quality measurement system is to provide manufacturing leaders with a tool that can help drive process improvements. By understanding the magnitude and sources of their quality costs, manufacturing executives, managers, engineers, and technicians can more effectively direct their efforts, improvement strategies, and capital budgets toward reducing them.

In the early 1700’s, English mathematician and Presbyterian minister Thomas Bayes derived the eponymous mathematical theorem that allows us to calculate the probability of an event occurring based on prior knowledge of conditions that might be related to the event. 

With U.S. annualized inflation rates exceeding 5.0% each month since May of 2021¹ – the longest stretch this century – the need for sustainable cost improvements has rarely been greater. And one source of cost reductions available to nearly every manufacturer is the elimination of waste and quality defects within their own facilities. Understanding the major continuous improvement (CI) strategies may help manufacturing leaders find a path toward lowering their costs and creating healthier margins for their organizations.

One of the most commonly asked questions about quality engineering is “What is the difference between quality assurance and quality control?”

[Read more…]

Among students beginning their examination of reliability engineering, one question pops up repeatedly: Where did all these reliability life models come from? On one hand, reliability engineering is deeply entrenched in statistical models … Weibull, exponential, etc. But these models alone, do not fully explain the product life models. There is still a missing piece: the Physics of Failure (PoF).

PoF and reliability models are closely connected concepts, as they both relate to the ability of products, processes, and systems to perform their intended function consistently over time. [Read more…]

Having worked in manufacturing quality for the great majority of my career, with a few tentacles into the field of reliability, I’ve considered many comparisons between the two fields, with of course, my unconscious biases favoring quality. One interesting comparison, for instance, is between job postings for similar positions in these related fields.