Steve Beeler

You have a goal…I have a way to get you there.

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Balanced and Dependent Systems

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The ten machine puzzle in my Theory of Constraints blog post is a simple example of the balanced and dependent systems that are surprisingly frequent in the real world. Balanced because all elements have the same capacity. Dependent because events at one element affect the performance of other elements. Frequent because lean thinking drives people and organizations towards them.  None work very well.

balanced and dependent systems

How does this happen? Inventory, conveyance, motion, and over production are wastes that are relatively easily recognized and reduced. When these wastes are removed, waiting losses (blocks and starves) can replace them. In the extreme, system performance deteriorates as lean “improvements” are made. In isolation yes, in combination no is a primary lesson from Theory of Constraints.

There are three options to improving balanced and dependent systems. The first is to improve the reliability of all of its dependent elements. That is lean thinking, but perfection is a high hurdle. In the ten machine puzzle, each machine’s reliability must be improved from 98% to 99.8% to achieve the 98% system availability target.

Cumulative probability predicts that the perfection hurdle gets even higher for larger balanced and dependent systems. Take a process with 100 dependent steps, not unusual in manufacturing or business. If each element has a 98% reliability, the system will only be available 13% of the time. To achieve 98% system availability, the reliability requirement for each element is 99.98%. Ouch!

The second option is to oversize each of the process elements. In the ten machine puzzle, oversizing each machine from 50 to 60 units per hour does the trick. With 100 process steps, each machine would have to be oversized by almost a factor of three…now that is expensive waste!

The third (and by far the best) option is to decouple process elements with buffers and to unbalance capacities to create a distinct constraint. This option trades inventory and conveyance waste against overproduction and waiting. The trick is to find the optimum balance. Is the trick magic? No, not with discrete event simulation…the next blog’s topic.

8D Team Oriented Problem Solving

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The 8D team oriented problem solving method is a great bolt-on to that control chart that is telling you that you have a problem. An 8D is a structured method that reinforces team work, encourages a bias for action, and delivers robust and permanent solutions across functions.

8d team oriented problem solving

Here are the eight disciplines in an 8D:

(1) Form a team. Select a small group of people with the product/process knowledge to understand the problem as well as the authority to implement solutions.

(2) Describe the problem. In quantifiable terms, specify the problem: who, what, when, where, why, how, and how many (5W2H).

(3) Implement and verify interim actions. Put containment actions (typically 100% inspection) in place to protect the customer until the root causes have been identified and eliminated.

(4) Define and verify root causes. Identify and prioritize all potential causes for the problem. A fishbone diagram is an excellent tool for this. Use the “5-Why” method to identify, isolate and verify potential root causes. Develop corrective action(s) for each root cause.

(5) Verify corrective actions. Run trials to confirm that corrective actions have indeed eliminated the problem. If the containment net finds no defects, then then corrective actions are successful.

(6) Implement permanent corrective actions. Choose the best corrective actions and implement on-going controls to ensure that they remain in place. The control chart that started the 8D is a great tool for on-going process monitoring.

(7) Prevent recurrence. Where else could this problem occur? Put procedures in place to prevent the problem from coming back on the next product, the next program, next factory, etc.

(8) Congratulate your team. Thank everyone for their great work!

As the 8D as evolved over the last 30+ years, the concept of “escape points” has been added to the fourth discipline. This is a really clever idea that drives improvements in control plans, process controls, error proofing, etc.

Please click HERE with any questions or comments about 8D team oriented problem solving.

Managing by Walking Around Quality Audit

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Managing by walking around is a great idea…if you are looking for the right things.  With just ten questions, you can perform a quick (but far reaching) quality audit while walking around your plant or office.

The ISO 9000 family of quality standards have received a little bit of bad publicity from over-zealous interpretations of document control and traceability of test equipment to national standards. With a little common sense, what is left is a comprehensive standard that can be used to assess the robustness of any company’s quality system and operating practices.

Managing by Walking Around Quality Audit

From ISO 9000, here are the ten managing by walking around quality audit questions:

(1) Management Responsibility. What is our quality policy?

(2) Customer Satisfaction. How do we determine and track customer satisfaction?

(3) Contract Review. How do we verify that all customer requirements can be met before accepting orders?

(4) Quality Planning. How do we ensure quality in new products and/or new services?

(5) Purchasing. How do we ensure the quality of purchased products and/or services?

(6) Process Control. How do we know production, inspection, and maintenance activities are being performed as planned?

(7) Inspection and Test Status. How do we clearly identify and segregate defective materials and parts?

(8) Corrective and Preventive Action. What are our processes and methods to identify, correct, and prevent quality problems?

(9) Handling, Storage, Packaging, Preservation, and Delivery. How do we protect our raw materials and finished products from damage and/or deterioration?

(10) Training. How do we proactively assess training needs and deliver training when required?

There, in just ten questions, is the foundation for a robust quality system. Ask one of these quality audit questions a day as you walk around, and in two weeks you’ll have a good idea of what you have in place and what you need to be working on.

Any questions or comments?  Please click HERE to send me an e-mail.

Control Charts

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To provide a rational means to categorize (common cause vs special cause) and measure variation, Walter A. Shewhart invented control charts circa 1925 – 1931. W. Edwards Deming took these basic statistical concepts and tools with him to Japan after World War II and planted the seeds for the quality revolution. The rest is history.

Control charts are best maintained at the job station by the production team to provide those closest to the process guidance on when and when not to take action. Once a process is in statistical control (a very fine achievement in itself), a control chart can verify actions taken to reduce variability.

The primary signal of an out-of-control process is a point outside the control limits. Given constant probability (an requirement for process control), the chance of a point outside the control limits is very, very small. Other out-of-control signals include seven points in a row above or below the mean and runs of seven up or down. If these signals are present, then a special cause has visited the process. Look for it right away, before the trail grows cold!

Control Charts

Here is an example of an X-bar & R chart for variables data. An assembly plant body shop was requiring significant overline overtime to make production and the management team was attributing the volume shortfalls to “catastrophic” breakdowns at its constraint. A control chart for hour-to-hour production counts was in control (no special cause events) but with an unacceptably high level of common cause variation. The management team was “recalibrated” to focus their effort on the many small production loss causal factors occurring every hour: speed losses, absenteeism, operator pacing, untrained operators, material deliveries, weld faults, tip changes, etc.

Control Charts

Control charts also work with attribute data when variables data is not available. Here is an example of a U-chart of incomplete operations at an inspection and repair station. This chart has been out of control for quite awhile: the mean number of repairs have shifted up. In retrospect, the team was slow in finding the special cause in this case a defective bumper shield clip.

Control Charts

Out-of-control signals can be good. Let’s say an adjustment has been made to a process running above the specification target. A run of seven points below the averages chart mean is verification that the adjustment has moved the process mean. Or maybe a material spec change has been made. A run of seven points below the range chart mean is verification that the material change has reduced common cause variation. In both cases, new control limits must be calculated to reflect the process changes.

Terminology

Variables Data = quantitative value that can be measured (572 feet, 2403 lbs, 59.6 seconds, etc)
Attributes Data = qualitative value that can be counted (pass or fail, black or white, etc)

Common Cause vs Special Cause

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All manufacturing and business processes contain many sources of variation. The differences may be so small as to be difficult to detect, but they are there. To successfully reduce variation, its sources must first be understood (common cause vs special cause). Less variation, less waste, more profits.

common cause vs special cause

Common Cause vs Special Cause

Common cause variation is a constant system of chance. The sources of common cause variation are many in number but small in size. A process characterized by common cause variation is in a state of statistical control: its output is stable and predictable. The magnitude of common cause variation can be measured using control charts. The sources of common cause variation can be identified and quantified using Design of Experiments, regression analysis, and other statistical methods. The reduction of common cause variation requires actions on the system.  Example:  the total of two die.

Special cause variation affects processes in disruptive and unpredictable ways. The sources of special cause variation are relatively few in number but are large in size. A process driven by special cause variation is neither stable nor predictable. Special causes can be detected using control charts through out-of-control signals. The elimination of special causes requires local action on the process. Although special causes account for less than 20% of total variation in most processes, they must be removed before common cause variation can effectively be reduced.  Likely example:  a machine breakdown.

Statistical control is not a natural state. All processes are under relentless attack from special causes.  Confusing common causes and special causes results in one of two mistakes:

(1) The first mistake is to assume variation to be a special cause when it is in fact common cause. The mistake of over adjustment leads to more variation and time wasted looking for a reason for a defect when there is no single assignable cause.

(2) The second mistake is to assume variation to be common cause when it is in fact special cause. The mistake of under adjustment is a lost opportunity to find and eliminate a special cause. Special causes are not always present so it is best to start looking for them right away before the trail grows cold…

This distinction is not often obvious. A machine breakdown could be common cause. Walter Shewhart invented control charts to measure common cause variation and detect special cause events.