Steve Beeler

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

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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.

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.

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…

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

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.

Best Constraint Location

The Good. A good constraint 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.

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.

Terminology

Bottleneck = same as constraint

Theory of Constraints

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“In isolation yes, in combination no” is a key concept in Theory of Constraints.

Consider the ten machine manufacturing process in the schematic below. Each machine had a team of manufacturing engineers managing their design and development. All ten teams hit their performance target: 50 units per hour and 98% availability. A separate team of plant engineers developed the plant layout. Their objective was to minimize work-in-process inventory. By carefully arranging the ten machines, they achieved perfect single-piece flow.

Theory of Constraints

Before the new manufacturing process went into production, the teams briefed senior management on the status of the project. Everyone had met or exceeded their objectives. Waste had been minimized. All the lean metrics looked great. Optimism for a successful launch was very high, and why not?

When the new manufacturing process was launched, production was only 42 units per hour not the 49 units per hour that was expected. If everyone met or exceeded their objectives, what went wrong?

The interactions between the machines were not considered. When Machine #6 is down, Machines #1 through #5 are immediately blocked and Machines #7 through #10 are immediately starved. In isolation, each machine could produce 49 units per hour. In combination, they could not.

Theory of Constraints

This manufacturing puzzle can be solved by inspection. Now replace the ten machines with manufacturing departments and functional organizations (marketing, sales, scheduling, purchasing, manufacturing, distribution, customer support, etc). No longer are interactions (blocks and starves) easily observed. In fact, they are likely going unnoticed as the multiple activities work hard to improve their local metrics. This is the first lesson in Theory of Constraints: In isolation yes, in combination no.

And that’s the Theory of Constraints opportunity. Think of your business not as individual silos but as a dependent system…the connections matter!

Terminology

Availability = percentage of time ready to work
Blocked = waiting with nowhere for completed work to go
Starved = waiting for work from the previous activity

Seven Wastes =

  1. Over-production (making more than customer demand)
  2. Motion (human or machine)
  3. Waiting (human or machine)
  4. Conveyance
  5. Over-processing (making features not valued by the customer)
  6. Inventory (raw materials or finished goods)
  7. Correction (scrap and rework)