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THE THEORY OF CONSTRAINTS (Sample of Full-Text)
Summary

The theory of constraints is a philosophy that has a lot in common with Just-in-Time but also has some critical differences. There are two basic differences. The first is that the theory of constraints accepts the existence of a constraint, at least temporarily, and focuses the improvement effort on the constraint and related workstations. The second is that the theory of constraints uses overlapped production (transfer batch not equal to the process batch) to schedule work through a batch production environment, while Just-in-Time provides no scheduling mechanism for a batch environment. Thus, the theory of constraints scheduling approach has wider applicability than Just-in-Time (although Just-in-Time's continuous improvement philosophy and quality emphasis clearly is applicable to batch production environments).

There are five steps to the theory of constraints: identify the constraint, exploit it, subordinate everything else to it, elevate the constraint, and avoid inertia when the constraint shifts. In exploiting the constraint, the drum-buffer-rope scheduling technique and buffer management are used. In finding ways to elevate the constraint, the techniques of effect-cause-effect and the cloud diagram often are useful.

What is TOC? / Why is Theory Of Constraints Important?

Thus far we have examined two approaches to production planning and control system design, material requirements planning (MRP) and Just-in-Time (JIT). In this section we examine a third approach, the theory of constraints (TOC). Developing a production planning and control system would be simple except for the existence of seemingly random problems (machine breakdowns, tool breakage, worker absenteeism, lack of a component, scrap, rework, customers who change their order timing or quantity, etc.) and the fact that operations are linked, with Operation A dependent on Operation B (the output of Operation B is all or part of the input into some Operation A). Henceforth, we shall refer to these problems as the problem of random fluctuations and dependent events.

The traditional, or MRP, approach to the problem of random fluctuations and dependent events is to eliminate the dependence by having a large inventory buffer at every workstation. The JIT approach is to eliminate the random problems by seeking out the root cause of each problem and correcting it. For example, machine breakdown may be eliminated by the use of preventive maintenance.

Both MRP and JIT practitioners believe that an ideal plant is a balanced plant, i.e., one in which every resource has the same output capability relative to the plant's need. The TOC approach is to accept the existence of an un­balanced plant, one in which some resource has less relative output capability than the others. The most limited resource is called the constraint. TOC breaks dependencies by creating a material buffer, but TOC buffers only the constraint. Non-constraint stations have a capacity buffer, i.e., excess capacity. Non constraints usually do not need a material buffer in addition to the existing capacity buffer. To add inventory to a non constraint station causes lead time to increase (a cost) and work-in-process inventory to increase (a cost) while providing little tangible benefit. TOC thus agrees with JIT that inventory is waste, if the inventory is planned at a non constraint station. However, by buffering the constraint from random problems at other stations, therefore permitting the constraint to work all the time, an inventory buffer at the constraint does add value and hence is not waste.

TOC does not try to eliminate all problems, only those that threaten the constraint in spite of the constraint's inventory buffer. To use JIT terminology, excessive effort in problem elimination is a waste. There must come a point when it is much less expensive to provide a small buffer against a problem at the constraint than it is to eliminate the problem. The constraint buffer also frees management time to solve problems against which no buffer can be provided,

SIMULATION MODEL: COMPARING MRP, JIT, AND TOC

The following discussion is intended to illustrate the differences in the traditional, JIT, and TOC approaches. An extremely simple shop was simulated so that it is easier to explore the implications of each approach.

Consider a shop that has a two-station assembly line and produces one product. Station 1 can produce either 2, 3, or 4 units of product each day; each outcome is equally likely. Station 2 has an identical capacity. There can be a maximum of 2 units of work in process between Station 1 and Station 2. Station 1 has an unlimited supply of raw material. There are presently 2 units of WIP between the stations. The shop is to operate for 200 days. What is the expected output of this system? How can the system be improved?

It might surprise you to learn that the expected output from this system is 2.806 units per day. You can estimate this result using Lotus 1-2-3 to simulate the shop. When we simulated this shop, we obtained approximately 2.80 units per day, averaged over the 200 days. Results of 10 replications of 200 simulated days are shown in Table 1. Single replication results varied from a low of 2.72 units to a high of 2.91 units and averaged 2.80 units. Since each station is capable of averaging 3 units per day of output, our results are about 7 per cent below what we expect to produce. Traditional thought, JIT, and TOC have different approaches to solving this problem. The traditional approach would add WIP between stations, JIT would reduce variability at each station, TOC would unbalance the line, add WIP at one station, and reduce variability slightly. Let's explore the implications of each of these approaches.

Table 2 shows a portion of the spreadsheet used to perform the Monte Carlo simulation. This particular spreadsheet was used only to prepare this example and is not one of the 10 replications reported. Random numbers deter­mine the production output for each station. A random number between 0 and 0.33 yields an output of 2, one between 0.33 and 0.67 yields a 3, and one between 0.67 and 1 yields a 4. In the first row of the main body of the table, Station 1 drew a random number of 0.519239, so Station 1 has a possible production of 3. There are 2 units of inventory available (beginning condition). Station 2, therefore, has 5 units of inventory potentially available. Station 2 drew a random number of 0,006+, yielding an output of 2 (because it is between 0 and 0.33). The output of the line is 2, the output of Station 2 is 2, and the actual output of Station 1 is limited to 2 since there is not room to store the additional potential unit between the stations. However, for collecting data for the Station 1 column, the value 3 is used. The unused inventory available for the next trial is 2. You may wish to verify the next line, in which Station 1's output is 3, Station 2's is 4, and the output of the line is 4. The inventory available for the third trial therefore drops to 1.

To understand how Station 2's output may be limited, assume at a given time no WIP exists between stations and Station I draws a potential of 2 while Station 2 draws a potential of 4. Then the output of the WIP would be 2, be­cause Station 2 is furnished only 2 units to process, despite its own potential to process 4 units. The output of the line would be 2, the value recorded for Station 1 would be 2, and the value recorded for Station 2 would be 4.

Observe in Table 1 that, in isolation, both Station 1 and Station 2 produced the theoretical average of 3 units per day, with some minor fluctuation around these averages for most replications of the simulation. However, the output of the line was consistently about 2.8 units per day because of the interaction of random fluctuations and the station dependencies... (there's much more!)

eBook: Theory-of-Constraints — $3.00 Buy Now

eBook Topics: An Integrated Set of Useful Articles
How to Test and Fine-Tune Strategic and Tactical Plans so that Resources are Only Expended on Achievable Results.
Making and Keeping Promises that are Realistic and Achievable is a Vital Skill Needed at Every Organizational Level.
Available-To-Promise Inventory can be Strategically Projected Using the Tools of Master Scheduling by Aligning Production Plans and Sales Plans.
Projected Resource Constraints are used to Modify Production Schedules to Assure that Available-To-Promise Inventory will Meet Customer Demands.
Detailed Capacity and Material Plans become Actionable, Unanticipated Constraints are Communicated Immediately to the Scheduler.
When the Goal is Zero Inventory, the Method is JIT. Only Buy or Build to Match Real Orders. Inventory Backlog must be Zero as well!
TOC can be seen as Similar to JIT but has a much Wider Application. Once Identified, Constraints are Exploited until it is Aleviated.
Developing Effective Work Relationships is Essential when Communication is central to the Success of Management Metrics.

 

 

 

   
ERP-MRP Evolution…  
ERP & Hoshin Kanri…  
ERP Implementations…   
Profit-Ability Improvement...  
Profit-Ability Management Principles...  
People, Empowerment & Profit-Ability…  
Hoshin Kanri & Deming's Plan-Do-Check-Act...  
     
     



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