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Theory of Constraints (TOC)

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In the 1970s, a new industrial approach has been developed by Goldratt and Cox, initially called OPT (Optimized Production Technology) now trade mark for its software, then TOC (Theory Of Constraints) for the methodology itself.

This TOC concept focuses on the distinction of bottlenecks resources and non-bottleneck resources to develop a new production management by industrial constraints.


The goal of a company is to make sustainable profit now and in the future. Thus proposing to customer better quality products is not the top priority, not the primary goal of a firm.


A constraint is anything that limits an organization, operation or a system from maximizing its output or meeting its goal. Constraints may be physical in nature such as insufficient plant capacity, labor, capital, raw material or land. But it can be also non physical and arise because of poorly motivated employees, absenteeism, lack of training, bad scheduling, poor operating procedures…

The market itself can be a constraint when the economy is flat that limits the throughputs as the production system would be over capacity. The material supplies could also be a constraint when delays in deliveries or in case of long leadtimes.

Every company has at least one constraint; otherwise it could sell unlimited products quantities and so would make unlimited profits.
Generally speaking, a constraint is a scarce or expensive resource (otherwise buying a new one will solve the issue right away) that limits an organization.

The following figure summarizes the TOC approach:


Drum, buffer and rope

The Theory of constraints (TOC) is sometimes referred to as the drum, buffer and rope concept. As a result of the bottleneck in the system, a factory or operation is obligated to beat to the rhythm of the drum – the bottleneck which sets the pace for the factory and for all the non-bottleneck systems.

It is at this pace than units are pulled through the system by a rope. As the bottleneck drives the system it should always be working at full capacity and thus inventory buffers should always be immediately be upstream to ensure that the bottleneck is never starved for work.

This synchronization (Drum-buffer-rope) is mandatory to meet customer demand on-time, not too soon, not too late. A good synchronization allows maximizing the throughputs with minimum operating expenses.

The bottleneck is the constraint that limits the throughputs. Increasing the capacity of the others won’t increase the throughput rate.
So in the TOC approach the focus should be right on the most important bottleneck. This seems obvious but so many companies are not looking at their real issues.

Bottleneck and non-bottleneck interactions

A bottleneck is a resource where capacity is less than the downstream demand.
A non-bottleneck is a resource where capacity is greater than or equal to the downstream demand.

In a manufacturing process, successive operations are performed to get to the final product value added.
If a bottleneck feeds a non-bottleneck, the non-bottleneck throughput cannot exceed the bottleneck capacity. It is then mandatory to make sure the bottleneck run at full capacity.
As a reverse, if a non-bottleneck feeds a bottleneck, a buffer stock grows in front of the bottleneck.

There are 16 possible configurations with bottleneck and non-bottleneck, some are convergent others are divergent, but it is easier to map your current operation flows to get a clearer picture.

TOC Indicators

The theory of constraints (TOC) focuses on maximizing the profits, and some classical accounting measures are not appropriate.

That’s why the TOC proposes the following key performance indicators:

  • Throughputs (T): These are coming from the sales revenues, not the value of finished goods inventory. It is equal to the sales revenues minus the raw material costs.
  • Inventory (I): The cash invested in making inventories, such as raw materials, equipments and other investments. Inventories are valued at their buying cost.
  • Operating Expenses (OE): The expenses related to the operations to add value on the product itself. It represents all expenses except the purchases of raw materials (invested in stocks)


Here the goal consists in maximizing the throughputs while reducing the inventories and the operating expenses.

TOC implementation

The theory of constraints proposed a framework to ease its implementation:

Phase 1: Analyzing the imbalances

  • Identify the main weaknesses (deliveries, accounting, leadtimes,…)
  • Find out the root causes of weaknesses
  • Look for solutions to increase the performance of constraints
  • Determine parameters for synchronization (lots size, buffer stocks)
  • Determine the demand ordering management (MPS, real time,…)

Once this first analysis done, following actions should take place:

  • Synchronize the production
  • Follow-up buffer stocks
  • Start continuous improvement workshops
  • Adjust lots size if necessary
  • Identify the non-bottlenecks moving to constraints and try to increase their capacity without any investment

Phase 2: Look for the right imbalance

  • Analyze the work load profiles by resource (setup, breakdown, idle,…)
  • Evaluate inventory carrying costs and extra-capacity resources. Find out investments that can reduce them
  • Forecast the long term demand in order to evaluate the future capacity required
  • Find out the constraints that require investments
  • Determine the company strategy with some possible scenarios, and its related investments
  • Find out the opportunities (push-pull border, production process,…)

The 9 TOC rules


  • Rule #1: Balance material flow rather than capacity
  • Rule #2: Use of a non-bottleneck is determined by other system constraints
  • Rule #3: Utilization and full employment of a resource are not synonymous
  • Rule #4: An hour lost on a bottleneck is an hour lost on all the system
  • Rule #5: An hour saved on a non-bottleneck is a mirage
  • Rule #6: Bottlenecks govern both throughput and inventory accumulation
  • Rule #7: Transfer batch and process batch need not be equal
  • Rule #8: Lot sizes should be variable and not fixed
  • Rule #9: Establish schedules by considering all system constraints

In summary, the sums of local optimums are not equal to the system optimum!

Last modified on Friday, 11 May 2012 08:11

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