Steve Beeler

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

Theory of Constraints

Variation and Waste

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Variation is at the root cause of almost all waste in manufacturing and business systems.  It is no coincidence then that variability reduction is the foundation for the Toyota Production System (Lean Thinking) and its success in continuously reducing waste.  Two games of chance with dice and cards illustrate the linkage between variation and waste.

The first game is a single piece flow system with five machines.  The output of each machine is represented by the total of two dice.

The average of two dice, of course, is 7.  However, our five-machine system only averages 4.3…a 38% loss!  Why?  Variation and waste.  In single piece flow, no machine can outproduce another.  Production is lost through waiting losses (blocks and starves) when the machines interact with each other.  Balanced and dependent systems are surprisingly common.  They never work as expected.  In isolation yes, in combination no is a key lesson from Theory of Constraints.

The second game is a system of three machines feeding an assembly area.  The output of each machine is represented by the draw from a deck of cards.  In order to assemble a product, all three cards must match.

Variation and Waste

Each machine is produces one card per time period.  Jokers represents a defective product and cannot be matched.  The decks are shuffled so the three output sequences are independent.

Variation and Waste

Quite a few cards collect (to the right of the decks) before there are matches for the assembly machine to assemble (to the left of the decks).  Work in process inventory (WIP) and lead time are horrendous.  Throughput suffers as the assembly area waits for matches.  In isolation, each machine is successfully producing cards.  In combination, the system is performing poorly.  Variation and waste again.  This time the variation is in sequence, but the waste is equally dramatic.

Variation and waste.  Not exactly like peanut butter and jelly.  While variation is always present, robust systems can be designed through discrete event simulation to minimize the linkage between variation and waste.

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.

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)