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Douglas C. Fair

Quality Insider

SPC, the Big Picture

Published: Monday, June 23, 2008 - 22:00

A few weeks ago, I found myself and my family on a beach making a sand castle. It was the last day of our vacation and shortly after we began working the warm South Carolina sand, an official approached us and asked if we would like to be contestants in the weekly sand sculpture contest. Why not?

At first, we were just playing in the sand with no purpose. But since judges and bystanders were watching the progression of our work, we got serious. Soon we began incorporating architectural details that our red-plastic-bucket-toting competitors hadn’t contemplated. We got serious solely because others were evaluating our progress. With knees in sand, our actions were driven by the adage, “What gets evaluated gets improved.”

The same can be said about an SPC system. Yes, it’s important for operators to gather data. Yes, it’s important that control chart alarms are acted upon by process experts. And yes, it’s important for an operator to assess data from their shop-floor viewpoint. All of these actions support localized control of individual processes. And, of course, these are all actions necessary to sustain manufacturing consistency and control.

However, if no one actively oversees the big picture, the likelihood of reducing overall costs and improving macro quality will be minimal. The words “overall” and “macro” are meant to induce you into thinking much bigger than enhancement of a single processing line or machine. For some, “overall” might mean departmentwide. For others, it might equate to improving quality for an entire plant. Yet others might interpret “macro” in a global sense—throughout all company-owned plants, and across a multitude of suppliers. So whether you are thinking departmentwide or you are struck by the possibilities of improving quality across your entire supply chain, we must begin seriously viewing SPC systems in terms of the big picture.

That big picture should be carefully scrutinized by top management. Unless management is involved and overseeing an SPC system and its results, chances are that financial and quality results will be less than stellar. Again, what gets evaluated gets improved. If no one looks at the big picture, then bottom-line business results that an SPC system provides cannot be fully exploited.

I know of several different companies that carefully focused on the goal of large-scale improvement and cost reduction via SPC. Each of these companies was extraordinarily successful and the turnaround for each was stunning. These amazing and successful SPC deployments I witnessed were very similar in execution, and performed in different industries. Although the companies and their businesses were different, their focus was similar. The way they deployed SPC and how management dealt with the system was very similar.

In the beginning these companies adhered to the basics, with which most companies are familiar. They trained operators, provided improvement resources for the shop floor and helped users understand how to interact with the SPC technology. Each successful company also did two amazing things that I have rarely seen:

  1. Each day top-level management meetings were held to evaluate statistical information from the previous 24 hours
  2. Each month top-level management meetings were held to evaluate statistical information for the previous 30 days

Management leadership was intimately involved in the interpretation of data and results. Each meeting was performed in the early morning and was presided over by the highest ranking manager on site. That a plant or director-level manager was the meeting organizer helped emphasize the meeting’s importance and sent a clear and compelling message to the rest of the organization—quality data and their related statistics are exceedingly important.

Meetings were typically less than an hour in length. Based on hard statistics, information gleaned from the data helped steer specialists toward daily and monthly improvement activities. This allowed separate organizational disciplines to focus on the specific tasks that would best support their goals and those of overall plant operations.

In essence, each daily meeting provided strategic focus for short-term actions necessary to reduce costs and improve quality. Monthly meetings focused more on long-term actions needed to support operational goals. Based upon predictable process data and process capabilities, capital needs decisions could be deliberated using statistical data and defects information rather than hearsay and rhetoric. Although simple in execution, these meetings proved to be powerful organizational reminders of how important quality truly is.

The big picture was focused on by providing information such as:

      1. Unusual events (statistical alarms, indications of out-of-specification material) for an entire plant. Events were sorted and categorized to allow management to define strategic actions that could be specified by

        a. Production line

        b. Product family

        c. Shift

        d. Lot numbers

        e. Batches, etc.

        2. Process capability, defects and performance comparisons between

          a. Different production lines

          b. Different product codes

          c. Different plants

          d. Different shifts, batches, etc.

        3. Processes that are best for manufacturing certain products (or families of products)

      This short list is short provided information for better managing the businesses, including:

          1. Pinpointing where quality resources should be placed

          2. Identifying the most likely opportunities for cost containment

          3. Determining which processes were in need of maintenance and repair versus those that required an infusion of capital for corrective action

          4. Picking out product families that were causing the most defects and alarms

          5. Specifying shifts requiring additional engineering/process control support

          6. Identifying machine settings best for minimizing customer complaints and maximizing line speeds

          7. Allowing scheduling to identify the production lines best suited for running specific products

      These companies were successful because their top management was driving the system to see how best to improve financial and operational performance. Their deeply involved management staff treats SPC data as it should be treated—as an opportunity for making the overall business better. And isn’t that the whole idea? I mean, hey, I love statistical methods like the next guy, but I also know that statistical methods aren’t used for altruistic purposes. Use of SPC should be considered an operations and business decision for achieving ends that managers understand—minimize costs, maximize productivity, and drive profitability to the bottom line.

      The issues we focus upon most are those that have the highest probability for success. It’s no different for an SPC system. I have seen companies shocked by the wealth of information that an SPC system can provide. From ways to minimize overall defects to settings necessary to ensure the fastest run rates and the most efficient plant, big-picture statistics can transform a company’s performance and competitive position while helping it to minimize overall costs. Standing on that beach in Hilton Head, I understood this on a small scale. Those overseeing our plastic-shovel work motivated my family to focus on the big picture. I’m proud to say we won that sand sculpture contest. The grand prize? A large pepperoni pizza. I smiled and thought to myself that by focusing our efforts, we lowered our costs. Even if it was only lunch.


      About The Author

      Douglas C. Fair’s picture

      Douglas C. Fair

      A quality professional with 30 years’ experience in manufacturing, analytics, and statistical applications, Douglas C. Fair serves as chief operating officer for InfinityQS. Fair’s career began at Boeing Aerospace, and he worked as a quality systems consultant before joining InfinityQS in 1997. Fair earned a bachelor’s degree in industrial statistics from the University of Tennessee, and a Six Sigma Black Belt from the University of Wisconsin. He’s a regular contributor to various quality magazines and has co-authored two books on industrial statistics: Innovative Control Charting (ASQ Quality Press, 1998), and Quality Management in Health Care (Jones and Bartlett Publishing, 2004).