Continuous improvement is a building block of quality control in the manufacturing industry. Read this blog post from Job Shop Blog to learn how 1factory brings a continuous improvement approach to software development.

(Part 3 of 3) Arena Solutions recently spoke with 1factory’s President Nipun Girotra to get his thoughts on the world of product design and manufacturing.

In this three-part blog post series, you’ll discover the quality control challenges facing most global manufacturers and the keys to success.

(Part 2 of 3) Arena Solutions recently spoke with 1factory’s President Nipun Girotra to get his thoughts on the world of product design and manufacturing.

In this three-part blog post series, you’ll discover the quality control challenges facing most global manufacturers and the keys to success.

(Part 1 of 3) Arena Solutions recently spoke with 1factory’s President Nipun Girotra to get his thoughts on the world of product design and manufacturing.

In this three-part blog post series, you’ll discover the quality control challenges facing most global manufacturers and the keys to success.

As part of our consulting engagements, we are frequently asked about industry best practices in structuring supplier quality teams.

And often, the discussion turns to the evolution of the organization from supplier quality management to supplier development. Here are our thoughts on this subject.

Earlier this week, we met with a prospective customer, and discussed challenges with their first article process. The customer told us that they were going "back-to-the-basics" with First Article Inspections.

Nearly 20 years ago, this company eliminated receiving inspections, and parts began to flow directly from the receiving dock into stock and on to the production line.

A recall for faulty ignition switches that could impact as many as 2.6 million cars made by General Motors has captured the headlines for the last few months.

The number of deaths, the years of accidents, the perceived inaction by the NHTSA, the relatively inexpensive fix, and the staggering size of the recall, have all contributed to the media frenzy.

A few weeks ago, I came across a video produced by the Ford Motor Company back in the 1980s. In this video, Ford executives from the Batavia transmission plant in the US compare and contrast their own performance against that of a Japanese supplier making an identical transmission.

GD&T characteristics such as runouts are specified with a single value: a not-to-exceed value. For example, the specification for runout could be 0.0005 inches. In this case, the Nominal = LSL = zero. Our goal is to have a manufacturing process with a runout as close to zero as possible.

Many companies have decided to require their suppliers to only ship lots to them if the process capability index Cpk ≥ 1.33. They hope that the vast majority of material received will conform to their specifications. But many pitfals await.

Successful Process Capability Analysis requires more than a series of calculations. It's important that prerequisites including Process Stability, Measurement System Resolution, Order-of-Production, Sub-Group Sizing are fully understood and addressed before attempting to calculate process capability.

My friend, Jon, was asked to review a chart similar to the one shown here during an interview for a position in supplier quality management. The histogram showed the incoming inspection data for a large number of parts from a supplier.

The curve was supposed to show that each part was built to spec, and show that the supplier had no quality issues.

In Part 1 we discussed the key differences between Process Capability analysis and Process Performance analysis. In this post, we’ll cover the basics of the Cp and Pp Indices, understand the pre-requisites, and learn how to interpret the values.

One of the most common questions that comes up during my discussion with manufacturing and quality managers has to do with the difference between Process Capability Analysis (Cp, Cpk) and Process Performance Analysis (Pp, Ppk), and how these techniques relate to Statistical Process Control.

In The Checklist Manifesto, best-selling author and surgeon, Dr. Atul Gawande, demonstrates how professionals in a wide variety of occupation - from surgeons in operating rooms to airline pilots landing a plane in an emergency - rely on checklists to prevent errors and ensure safe and successful outcomes.

Manufacturing companies across the world use Non-Conforming Material Reports (NCMRs) or Discrepant Material Reports (DMRs) to identify and track supplier parts that do not fit or function as desired. Surprisingly, this is often the first selection a technician is required to make, setting off a finger-pointing exercise.

Standard work plays a very important role in controlling product quality in make-to-order environments. Effective use of standard work can free-up assembly capacity, assembly capacity, make cross-training easier, and allow a new worker to perform a task with the same level of quality as an experienced worker.

Successful Supplier Quality Management requires four key control elements: Foundational Processes, Mfg Process Control, Inspection of Finished Products, and Performance Monitoring. A supplier quality team must verify the existence of the first three groups of controls at each supplier for each part.

Disruptions due to defective parts result in long highly-variable manufacturing cycle-times. By reducing the number of defects discovered during the manufacturing process, you can reduce cycle-time, improve labor productivity, free-up capacity, and improve your return-on-assets.

How do you apply lean manufacturing techniques to a make-to-order-environment? What can you hope to achieve? Here's a case study from my experience implementing lean in the semiconductor capital equipment industry.

As supply chain complexity increases, the ability of a firm to control manufacturing processes across the supply chain diminishes. As a result, a large portion of total defects now originate in the supply chain. In fact, there is a direct correlation between supply chain complexity and the number of defects observed in a product.

"Many companies have tried to upgrade their quality, adopting programs that have been staples of the quality movement for a generation: cost of quality calculations, inter-functional teams, reliability engineering, or statistical quality control. Few companies, however, have learned to compete on quality. Why?"

"Do you have leptokurtophobia? The symptoms of leptokurtophobia are (1) routinely asking if your data are normally distributed and (2) transforming your data to make them appear to be less leptokurtic and more "mound shaped".

If you have exhibited either of these symptoms then you need to read this article.?"

"Statistical quality control can effectively control process variation, but it cannot detect or prevent most mistakes.

Because mistakes or blunders are frequently the dominant source of nonconformities, we conclude that statistical quality control by itself is not effective."

"Based on some recent inquiries there seems to be some need to review the four capability indexes in common use today. A clear understanding of what each index does, and does not do, is essential to clear thinking and good usage.

To see how to use the four indexes, to tell the story contained in your data, and to learn how to avoid a common pitfall, read on."

"In this two-part column, I will review the gage repeatability and reproducibility (R&R) study in the Automotive Industry Action Group (AIAG) manual1 for its ability to determine the true capability of different parts of a measurement system.

I’ll use a geometrical approach to describe the components of the total measurement variance."

"It is important to have a valid quality measurement study beforehand to ensure the part or product is accurate and the power of statistical process control and design of experiments is fully used.

Accuracy—in other words, the absence of bias—is the function of calibration, which is performed before the precisions of the gage and its operators are measured."

"Every quality professional is concerned about the improvement of processes. By making processes better, we get less waste, lower costs, and happier customers.

Why has it taken so long to understand that processes need analytic methods, not enumerative ones?"