The Manufacturing Academy Article Series

Favorite Questions and Answers from my Course “Statistical Process Control (SPC) Using Microsoft Excel”, Pt 2

In this second installment of my three-part article series, I am again showcasing some of my favorite student questions from the past seven years since first launching my online class titled “Statistical Process Control (SPC) Using Microsoft Excel.”

Each exchange represents not only the question in that student’s mind, but the type of question that lingers the minds of countless professionals trying to advance their skillsets. Each exchange – some of which I edited for clarity – become a permanent part of the class itself that future students can read and learn from. Perhaps you’ve had some of these questions as well.

Co-authored with Mike Vella

Leaders and managers play many roles: planning, scheduling, coaching, teaching, supervising, hiring, and sometimes firing. While much of this work is routine, it often involves making decisions. Some decisions are low-risk with clear facts and limited options. Others are made with murky details, unknown options, and high risks if incorrect. Regardless, decision-making often falls to leaders and managers. Theodore Roosevelt, the 26th President of the United States, famously said: “In any moment of decision, the best thing you can do is the right thing, the next best thing is the wrong thing, and the worst thing you can do is nothing.”

Co-authored by Mike Vella and Ray Harkins

How many times have we heard that we must tolerate poor process performance because it’s “the nature of the beast” or its excess variability is considered “normal”? How much does financial performance suffer due to allowances for alarming levels of scrap, low yields, or losses considered “best practice” for the industry? Improving these situations requires a great team, supportive management, and cooperative suppliers. Beyond the support, operations and quality leaders must know a systematic method of problem-solving and have access to statistical tools to drive the data through to a solution.

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…]