Steve Beeler

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

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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) managing by walking around quality audit in 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.

Quality improvement is a big part of my day job as a Professional Engineer.  Visit my Operations Engineering page for methods and case studies.

 

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. Variation is at the root of all waste.  Less variation, less waste.  To reduce variation, and effectively solve problems, its sources must first be understood as common cause vs special cause.

common cause vs special cause

Common 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

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.  Two examples:  an untrained operator or parts from an unapproved supplier suddenly appearing one day and generating defects at an otherwise stable and predictable work station.

Statistical Control

Statistical control is not a natural state. All processes are under relentless attack from special causes.  Confusing common cause vs special cause variation 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…

To provide a rational means to make the distinction between common cause vs special cause variation, Walter Shewhart invented control charts circa 1930. W. Edwards Deming took them to Japan after World War II and planted the seeds for the quality revolution.

This distinction is not often obvious. A machine alarm and subsequent shutdown could be due to common cause variation.  In the digital age, we have sensors on almost everything.  Don’t just assume that alarm or defect is due to a special cause.  Analyze data over time with a control chart.

Making the correct distinction between common cause vs special cause variation is a big part of my day job as a Professional Engineer.  Visit my Operations Engineering page for methods and case studies.

Best Constraint Location

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In my previous blog on Theory of Constraints, I defined the constraint (aka, the bottleneck) as the weak link in the chain. Every system has one. If there is a choice, where is the best constraint location?

best constraint location bottleneck

The Ugly. By far the worst place to have the constraint is in the marketplace. When the constraint is outside the four walls of a company’s operations, management’s control over it is very limited. Operational and financial performance is completely exposed to market turbulence: product and pricing actions by competitors, shifts in aggregate demand, changes in consumer tastes, and so on.

In a perfect world, annual demand will exceed capacity by one unit per year. Why? Operational and financial performance can be optimized by managing the internal constraint while having only one unhappy customer.

The Bad. An internal constraint should not be in a process that is unreliable, uncertain, or inflexible. The constraint is the “drum” that establishes the rhythm for the enterprise. If the constraint does not have a steady beat, then wastes of all types (especially inventory and waiting) will be incurred at non-constraints as they struggle to keep in step with the constraint.

Processes with low availability and/or low process capability are also bad places for the constraint. The opportunity costs of production losses and scrap at the constraint are huge.

An inflexible constraint is another bad idea. The entire organization will suffer if its constraint cannot quickly respond to shifts in consumer tastes or aggregate demand. Adding cost at the constraint (e.g., overtime, outsourcing, etc) to capture incremental profits is good business. Watching a more nimble competitor grab those dollars is not.

The Good. The best constraint location is inside the four walls of your operations and is reliable, certain, and flexible. Easy to say, harder to do.

Choose a familiar technology…the constraint is no place for a steep learning curve. Minimize planned maintenance during shift hours. Cross-train employees for “instant” capacity at the constraint. Design the constraint to be flexible across a broad range of mix and sequence scenarios. Adequately buffer the constraint upstream and downstream to minimize block and starve waiting losses.

At the constraint, all the little details matter.

5-Step Throughput Improvement Model

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In my previous blog, I used the “ten machine” manufacturing puzzle to establish the need to think systemically: in isolation each machine hit its performance target, in combination the system failed to reach its goal. The 5-Step Throughput Improvement Model is a proven process to solve this local optimization paradox.

5-Step Throughput Improvement Model

Theory of Constraints views an organization as a chain of dependent activities or functions all working towards a goal. The constraint is the weakest link in the chain…the link that most severely limits the organization’s ability to achieve higher performance (throughput) relative to goal. In business, that goal is usually to make more money now and in the future. The following five step process will continuously improve performance (increase throughput) to the goal.

5-Step Throughput Improvement Model

Step 0: Define the system. In this context, the “system” includes both the goal and the activities and functions that deliver the goal: Who and what contributes to making money?

Step 1: Identify the system’s constraint. Finding the constraint in a large, complex organization can be a challenge. A simple rule of thumb: If a link in the chain is blocked then the constraint is downstream. If a link is starved then the constraint is upstream. More on finding the constraint in subsequent blogs.

Step 2: Decide how to exploit the constraint. How can we get the most out of the constraint: Approve overtime? Reduce set up times? Improve scheduling? Increase in-coming inspection?

Step 3: Subordinate everything else to the decisions made in Step 2. What can non-constraints do to ensure that the constraint is as productive as possible: Cross-train people? Improve quality? Take lunch and breaks at different times?

Step 4: Elevate the system’s constraint. Add capacity if and only if the constraint’s performance has been truly maximized.

Step 5: If a constraint is broken in Step 4, go back to Step 1. Repeat process on the next constraint until the organization’s goal has been met. If the goal is open ended (make more money!), then this process never ends.

If the plant is starved for orders, the constraint (also known as the bottleneck), is outside the plant in the marketplace.  Does that invalidate this 5-step process?  Not at all…apply it to your sales funnel.

Sales Funnel

The late Dr. Eliyahu Goldratt has a series of books on Theory of Constraints. His first book, The Goal, applies TOC to a manufacturing plant. A later book, Its Not Luck, applies TOC to a conglomerate’s portfolio of businesses. Both books are novels, not textbooks, and they are very easy reads. I highly recommend them.

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Terminology

Bottleneck = same as constraint

Sales Funnel = customer journey from enquiry to order