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Constraint Management

Chapter 5

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What is a Constraint?

Constraint

Any factor that limits the performance of a system and restricts its output.

Bottleneck

A capacity constraint resource (CCR) whose available capacity limits the organization’s ability to meet the product volume, product mix, or demand fluctuations required by the marketplace

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The Theory of Constraints

The Theory of Constraints (TOC)

A systematic management approach that focuses on actively managing those constraints that impede a firm’s progress toward its goal

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The Theory of Constraints

Table 5.1

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Key Principles of the TOC

The focus should be on balancing flow, not on balancing capacity.

Maximizing the output and efficiency of every resource may not maximize the throughput of the entire system.

An hour lost at a bottleneck or constrained resource is an hour lost for the whole system.

An hour saved at a nonbottleneck resource does not make the system more productive.

Inventory is needed only in front of bottlenecks and in front of assembly and shipping points.

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Key Principles of the TOC

Work should be released into the system only as frequently as needed by the bottlenecks.

Bottleneck flows = market demand

Activating a nonbottleneck resource is not the same as utilizing a bottleneck resource.

It doesn’t increase throughput or promote better performance.

Every capital investment must be viewed from the perspective of the global impact on overall throughput, inventory, and operating expense.

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The Theory of Constraints

Identify the System Bottleneck(s)

Exploit the Bottleneck(s)

Subordinate All Other Decisions to Step 2

Elevate the Bottleneck(s)

Do Not Let Inertia Set In

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Managing Bottlenecks in Service Processes

Throughput time

Total elapsed time from the start to the finish of a job or a customer being processed at one or more work centers

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Example 5.1

Managers at the First Community Bank are attempting to shorten the time it takes customers with approved loan applications to get their paperwork processed. The flowchart for this process is shown in the next slide.

Approved loan applications first arrive at activity or step 1, where they are checked for completeness and put in order.

At step 2, the loans are categorized into different classes according to the loan amount and whether they are being requested for personal or commercial reasons.

While credit checking commences at step 3, loan application data are entered in parallel into the information system for record-keeping purposes at step 4.

Finally, all paperwork for setting up the new loan is finished at step 5. The time taken in minutes is given in parentheses.

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Example 5.1

Which single step is the bottleneck? The management is also interested in knowing the maximum number of approved loans this system can process in a 5-hour work day.

Figure 5.1

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Example 5.1

We define the bottleneck as step 2, which has the highest time per loan processed.

The throughput time to complete an approved loan application is 15 + 20 + max(15, 12) + 10 = 60 minutes.

The actual time taken for completing an approved loan will be longer than 60 minutes due to nonuniform arrival of applications, variations in actual processing times, and the related factors.

The capacity for loan completions is derived by translating the “minutes per customer” at the bottleneck step to “customer per hour.” At First Community Bank, it is 3 customers per hour because the bottleneck step 2 can process only 1 customer every 20 minutes (60/3).

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Example 5.1

Step 2 is the bottleneck constraint.

The bank will be able to complete a maximum of only three loan accounts per hour, or 15 new loan accounts, in a 5-hour day.

Management can increase the flow of loan applications by increasing the capacity of Step 2 up to the point where another step becomes the bottleneck.

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Managing Bottlenecks in Manufacturing Processes

Identifying Bottlenecks

Setup times and their associated costs affect the size of the lots traveling through the job or batch processes.

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Example 5.2

Diablo Electronics manufactures four unique products (A, B, C, and D) that are fabricated and assembled in five different workstations (V, W, X, Y, and Z) using a small batch process. Each workstation is staffed by a worker who is dedicated to work a single shift per day at an assigned workstation. Batch setup times have been reduced to such an extent that they can be considered negligible. Figure 5.2 is a flowchart of the manufacturing process. Diablo can make and sell up to the limit of its demand per week, and no penalties are incurred for not being able to meet all the demand.

Which of the five workstations (V, W, X, Y, or Z) has the highest utilization, and thus serves as the bottleneck for Diablo Electronics?

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Example 5.2

Product A

Raw materials

Purchased parts

Product: A

Price: $75/unit

Demand: 60 units/wk

Step 1 at workstation V (30 min)

Finish with step 3 at workstation X (10 min)

Step 2 at workstation Y (10 min)

Product C

Raw materials

Purchased parts

Product: C

Price: $45/unit

Demand: 80 units/wk

Finish with step 4 at workstation Y (5 min)

Step 2 at workstation Z (5 min)

Step 3 at workstation X (5 min)

Step 1 at workstation W (5 min)

Product B

Raw materials

Purchased parts

Product: B

Price: $72/unit

Demand: 80 units/wk

Finish with step 2 at workstation X (20 min)

Step 1 at workstation Y (10 min)

Product D

Raw materials

Purchased parts

Product: D

Price: $38/unit

Demand: 100 units/wk

Step 2 at workstation Z (10 min)

Finish with step 3 at workstation Y (5 min)

Step 1 at workstation W (15 min)

Figure 5.2

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Example 5.2

Identify the bottleneck by computing aggregate workloads at each workstation.

The firm wants to satisfy as much of the product demand in a week as it can.

Each week consists of 2,400 minutes of available production time.

Multiplying the processing time at each station for a given product with the number of units demanded per week yields the workload represented by that product.

These loads are summed across all products going through a workstation to arrive at the total load for the workstation, which is then compared with the others and the existing capacity of 2,400 minutes.

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Example 5.2

These calculations show that workstation X is the bottleneck, because the aggregate work load at X exceeds the available capacity of 2,400 minutes per week.

Workstation	Load from Product A	Load from Product B	Load from Product C	Load from Product D	Total Load (min)
V
W
X
Y
Z

60 x 30 = 1800

1,800

80 x 5 = 400

100 x 15 = 1,500

1,900

60  10 = 600

80 x 20 = 1,600

80 x 5 = 400

2,600

60  10 = 600

80 x 10 = 800

80 x 5 = 400

100 x 5 = 500

2,300

100 x 10 = 1,000

1,400

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Drum-Buffer-Rope Systems

Drum-Buffer-Rope

A planning and control system that regulates the flow of work-in-process materials at the bottleneck or the capacity constrained resource (CCR) in a productive system

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Drum-Buffer-Rope Systems

The bottleneck schedule is the drum because it sets the beat or the production rate for the entire plant and is linked to market demand.

The buffer is the time buffer that plans early flows into the bottleneck and thus protects it from disruption.

The rope represents the tying of material release to the drum beat, which is the rate at which the bottleneck controls the throughput of the entire plant.

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Drum-Buffer-Rope Systems

Buffer

Drum

Market Demand 650 units/wk

Shipping Schedule

Shipping Buffer

Finished Goods Inventory

Non-constraint

PROCESS C Capacity 700 units/wk

PROCESS B Capacity 500 units/wk

CCR (Bottleneck)

Constraint Buffer

Time Buffer Inventory

Non-constraint

PROCESS A Capacity 800 units/wk

Material Release Schedule

Figure 5.3

Rope

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Applying the Theory of Constraints to Product Mix Decisions

Contribution margin

The amount each product contributes to profits and overhead; no fixed costs are considered when making the product mix decision

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Example 5.3

The senior management at Diablo Electronics (see Example 5.2) wants to improve profitability by accepting the right set of orders.

They collected the following financial data:

Variable overhead costs are $8,500 per week.

Each worker is paid $18 per hour and is paid for an entire week, regardless of how much the worker is used.

Labor costs are fixed expenses.

The plant operates one 8-hour shift per day, or 40 hours each week.

Currently, decisions are made using the traditional method, which is to accept as much of the highest contribution margin product as possible (up to the limit of its demand), followed by the next highest contribution margin product, and so on until no more capacity is available.

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Example 5.3

Pedro Rodriguez, the newly hired production supervisor, is knowledgeable about the Theory of Constraints and bottleneck-based scheduling. He believes that profitability can indeed be improved if bottleneck resources were exploited to determine the product mix.

What is the change in profits if, instead of the traditional method used by Diablo Electronics, the bottleneck method advocated by Pedro is used to select the product mix?

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Example 5.3

Step 1: Calculate the contribution margin per unit of each product as shown here.

When ordered from highest to lowest, the contribution margin per unit sequence of these products is B, A, C, D.

$75.00

$72.00

$45.00

$38.00

–10.00

–5.00

–10.00

$65.00

$67.00

$40.00

$28.00

	A	B	C	D
Price
Raw material and purchased parts
= Contribution margin

Decision Rule 1: Traditional Method

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Example 5.3

Step 2: Allocate resources V, W, X, Y, and Z to the products in the order decided in Step 1. Satisfy each demand until the bottleneck resource (workstation X) is encountered. Subtract minutes away from 2,400 minutes available for each week at each stage.

The best product mix according to this traditional approach is then 60 A, 80 B, 40 C, and 100 D.

Work Center	Minutes at the Start	Minutes Left After Making 80 B	Minutes Left After Making 60 A	Can Only Make 40 C	Can Only Make 100 D
V
W
X
Y
Z

2,400

800

1,600

600

200

1,000

2,200

600

700

300

1,200

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Example 5.3

Step 3: Compute profitability for the selected product mix.

Manufacturing the product mix of 60 A, 80 B, 40 C, and 100 D will yield a profit of $1,560 per week.

(60  $75) + (80  $72) + (40  $45) + (100  $38)

$15,860

Profits
Revenue
Materials
Labor
Overhead
Profit

(60  $10) + (80  $5) + (40  $5) + (100  $10)

–$2,200

(5 workers)  (8 hours/day)  (5 days/week)  ($18/hour)

–$3,600

	=	–$8,500
	=	$1,560

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Example 5.3

Decision Rule 2: Bottleneck Method

Select the best product mix according to the dollar contribution margin per minute of processing time at the bottleneck workstation X. This method would take advantage of the principles outlined in the Theory of Constraints and get the most dollar benefit from the bottleneck.

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Example 5.3

When ordered from highest to lowest contribution margin/ minute at the bottleneck, the manufacturing sequence of these products is D, C, A, B, which is reverse of the earlier order. Product D is scheduled first because it does not consume any resources at the bottleneck.

$65.00

$67.00

$40.00

$28.00

10 minutes

20 minutes

5 minutes

0 minutes

$6.50

$3.35

$8.00

Not defined

	Product A	Product B	Product C	Product D
Contribution margin
Time at bottleneck
Contribution margin per minute

Step 1: Calculate the contribution margin/minute of processing time at bottleneck workstation X:

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Example 5.3

Step 2: Allocate resources V, W, X, Y, and Z to the products in the order decided in step 1. Satisfy each demand until the bottleneck resource (workstation X) is encountered. Subtract minutes away from 2,400 minutes available for each week at each stage.

The best product mix according to this bottleneck based approach is then 60 A, 70 B, 80 C, and 100 D.

Work Center	Minutes at the Start	Minutes Left After Making 100 D	Minutes Left After Making 80 C	Minutes Left After Making 60 A	Can Only Make 70 B
V
W
X
Y
Z

2,400

900

1,400

500

1,000

2,400

900

1,900

2,400

2,000

1,500

500

1,000

600

1,400

500

200

1,000

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Example 5.3

Step 3: Compute profitability for the selected product mix.

Manufacturing the product mix of 60 A, 70 B, 80 C, and 100 D will yield a profit of $2,490 per week.

(60  $75) + (70  $72) + (80  $45) + (100  $38)

$16,940

(60  $10) + (70  $5) + (80  $5) + (100  $10)

–$2,350

(5 workers)  (8 hours/day)  (5 days/week)  ($18/hour)

–$3,600

Profits
Revenue
Materials
Labor
Overhead
Profit

	=	–$8,500
	=	$2,490

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Application 5.1

O’Neill Enterprises manufactures three unique products (A, B, C) that are fabricated and assembled in four different workstations (W, X, Y, Z) using a small batch process.

Each of the products visits every one of the four workstations, though not necessarily in the same order.

Batch setup times are negligible.

O’Neill can make and sell up to the limit of its demand per week, and there are no penalties for not being able to meet all the demand.

Each workstation is staffed by a worker dedicated to work on that workstation alone, and is paid $12 per hour.

Variable overhead costs are $8000/week. The plant operates one 8-hour shift per day, or 40 hours/week.

Which of the four workstations W, X, Y, or Z has the highest total workload, and thus serves as the bottleneck for O’Neill Enterprises?

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Application 5.1

Product B

Raw materials

Purchased part

Product: B

Price: $85/unit

Demand:70 units/

Finish with step 4 at workstation Z (13 min)

Step 2 at workstation W (10 min)

Step 3 at workstation Y (10 min)

Step 1 at workstation X (12 min)

Product A

Raw materials

Purchased part

Product: A

Price: $90/unit

Demand:65 units

/wk

Finish with

step 4 at workstation Z (16 min)

Step 2 at workstation Y (15 min)

Step 3 at workstation X (9 min)

Step 1 at workstation W (10 min)

Product C

Raw materials

Purchased part

Product: C

Price: $80/unit

Demand: 80 units/wk

Finish with step 4 at workstation Z (10 min)

Step 2 at workstation X (10 min)

Step 3 at workstation W (12 min)

Step 1 at workstation Y (5 min)

$10

Flowchart for Products A, B, and C

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Application 5.1

Identify the bottleneck by computing total workload at each workstation. The firm wants to satisfy as much of the product demand in a week as it can. Each week consists of 2400 minutes of available production time. Multiplying the processing time at each station for a given product with the number of units demanded per week yields the capacity load. These loads are summed across all products going through that workstation and then compared with the existing capacity of 2400 minutes.

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Application 5.1

These calculations show that workstation Z is the bottleneck, because the aggregate work load at Z exceeds the available capacity of 2400 minutes per week.

Work Station	Load from Product A	Load from Product B	Load from Product C	Total Load (minutes)
W
X
Y
Z

(65x10)=650

(70x10)=700

(80x12)=960

2310

(65x9)=585

(70x12)=840

(80x10)=800

2225

(65x15)=975

(70x10)=700

(80x5)=400

2075

(65x16)=1040

(70x13)=910

(80x10)=800

2750

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Application 5.2

The senior management at O’Neill wants to improve the profitability of the firm by accepting the right set of orders.

Currently, decisions are made to accept as much of the highest contribution margin product as possible (up to the limit of its demand), followed by the next highest contribution margin product, and so on until no more capacity is available.

Since the firm cannot satisfy all the demand, the product mix must be chosen carefully.

Jane Hathaway, the newly hired production supervisor, is knowledgeable about the Theory of Constraints and bottleneck based scheduling. She believes that profitability can indeed be approved if bottleneck resources were exploited to determine the product mix.

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Application 5.2

Decision rule 1: Traditional method

Step 1: Calculate the profit margin per unit of each product as shown below

When ordering from highest to lowest, the profit margin per unit order of these products is ABC.

$90.00

$85.00

$80.00

–13.00

–14.00

–15.00

	A	B	C
Price
Raw Material & Purchased Parts

= Contribution Profit Margin


	$77.00			$71.00			$65.00

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Application 5.2

Step 2: Allocate resources W, X, Y, and Z to the products in the order decided in step 1. Satisfy each demand until the bottleneck resource (workstation Z) is encountered. Subtract minutes away from 2400 minutes available for each week at each stage.

The best product mix is 65 A, 70 B, and 45 C

Work Center	Starting	After 65 A	After 70 B	Can Only Make 45 C
W
X
Y
Z

2400

1750

1815

1425

1360

1050

975

725

450

510

525

500

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		Profits
Revenue
Materials
Overhead
Labor
Profit

Application 5.2

Step 3: Compute profitability for the selected product mix.

Manufacturing the product mix of 65 A, 70 B, and 45 C will yield a profit of $2980.

$15400

–$2500

		–$8000
		–$1920
		$2980

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Application 5.2

Decision rule 2: Bottleneck-based approach

Step 1: Calculate the contribution/minute of processing time at bottleneck workstation Z:

When ordering from highest to lowest contribution margin/minute at the bottleneck, the manufacturing sequence of these products is CBA, which is reverse of the traditional method order.

$77.00

$71.00

$65.00

16 minutes

13 minutes

10 minutes

4.81

5.46

6.50

	Product A	Product B	Product C
Contribution Margin
Time at Bottleneck
Contribution Margin per minute

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Application 5.2

The best product mix is 43A, 70B, and 80C

Work Center	Starting	After 80 C	After 70 B	Can Only Make 43 A
W
X
Y
Z

2,400

1,600

1,440

2,400

2,000

1,600

740

760

1300

690

310

373

655

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		Profits
Revenue
Materials
Overhead
Labor
Profit

Application 5.2

Step 3: Compute profitability for the selected product mix. The new profitability figures are shown below based on the new production quantities of 43A, 70B, and 80C.

Manufacturing the product mix of 43 A, 70 B, and 80 C will yield a profit of $3561.

$16220

–$2739

	–$8000
	–$1920
	$3561

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Managing Constraints in a Line Process

Line Balancing

The assignment of work to stations in a line so as to achieve the desired output rate with the smallest number of workstations

Precedence Diagram

A diagram that allows one to visualize immediate predecessors better

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Example 5.4

Green Grass, Inc., a manufacturer of lawn and garden equipment, is designing an assembly line to produce a new fertilizer spreader, the Big Broadcaster. Using the following information on the production process, construct a precedence diagram for the Big Broadcaster.

Work Element	Description	Time (sec)	Immediate Predecessor(s)
A	Bolt leg frame to hopper	40	None
B	Insert impeller shaft	30	A
C	Attach axle	50	A
D	Attach agitator	40	B
E	Attach drive wheel	6	B
F	Attach free wheel	25	C
G	Mount lower post	15	C
H	Attach controls	20	D, E
I	Mount nameplate	18	F, G
		Total 244

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Example 5.4

Figure 5.5

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Managing Constraints in a Line Process

Desired output rate

Ideally is matched to the staffing or production plan

Cycle time

Maximum time allowed for work a unit at each station

where

c = cycle time in hours

r = desired output rate

c =

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Managing Constraints in a Line Process

Theoretical Minimum (TM)

A benchmark or goal for the smallest number of stations possible

TM =

t

where

t = total time required to assemble each unit

c = cycle time

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Managing Constraints in a Line Process

Idle time

The total unproductive time for all stations in the assembly of each unit

Idle time = nc – t

where

n = number of stations

c = cycle time

t = total time required to assemble each unit

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Managing Constraints in a Line Process

Efficiency

The ratio of productive time to total time, expressed as a percent

Efficiency (%) = (100)

t

Balance delay (%) = 100 – Efficiency

Balance Delay

The amount by which efficiency falls short of 100 percent

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Example 5.5

Green Grass’s plant manager just received marketing’s latest forecasts of Big Broadcaster sales for the next year. She wants its production line to be designed to make 2,400 spreaders per week for at least the next 3 months. The plant will operate 40 hours per week.

a. What should be the line’s cycle time?

b. What is the smallest number of workstations that she could hope for in designing the line for this cycle time?

c. Suppose that she finds a solution that requires only five stations. What would be the line’s efficiency?

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Example 5.5

a. First convert the desired output rate (2,400 units per week) to an hourly rate by dividing the weekly output rate by 40 hours per week to get units per hour. Then the cycle time is

c = 1/r =

b. Now calculate the theoretical minimum for the number of stations by dividing the total time, t, by the cycle time, c = 60 seconds. Assuming perfect balance, we have

TM =

t

244 seconds

60 seconds

= = 4.067 or 5 stations

1/60 (hr/unit) = 1 minute/unit = 60 seconds/unit

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Example 5.5

c. Now calculate the efficiency of a five-station solution, assuming for now that one can be found:

Efficiency = (100) =

t

244

5(60)

(100) = 81.3%

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Managing Constraints in a Line Process

Finding a Solution

The goal is to cluster the work elements into workstations so that:

The number of workstations required is minimized

The precedence and cycle-time requirements are not violated

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Managing Constraints in a Line Process

Table 5.3

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Managing Constraints in a Line Process

The theoretical minimum number of workstations is 5 and the cycle time is 60 seconds, so this represents an optimal solution to the problem.

Figure 5.6

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Application 5.3

A plant manager needs a design for an assembly line to assembly a new product that is being introduced. The time requirements and immediate predecessors for the work elements are as

follows:

Work Element	Time (sec)		Immediate Predecessor
A		12	―
B		60	A
C		36	―
D		24	―
E		38	C, D
F		72	B, E
G		14	―
H		72	―
I		35	G, H
J		60	I
K		12	F, J
Total =		435

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Application 5.3

Draw a precedence diagram, complete I, F, J, and K

Work Element	Time (sec)		Immediate Predecessor
A		12	―
B		60	A
C		36	―
D		24	―
E		38	C, D
F		72	B, E
G		14	―
H		72	―
I		35	G, H
J		60	I
K		12	F, J
Total =		435

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Application 5.3

If the desired output rate is 30 units per hour, what are the cycle time and theoretical minimum?

c = =

(3600) = 120 sec/unit

TM =

t

= = 3.6 or 4 stations

435

120

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Application 5.3

Suppose that we are fortunate enough to find a solution with just four stations. What is the idle time per unit, efficiency, and the balance delay for this solution?

Idle time = nc – t

Efficiency (%) = (100)

t

Balance delay (%) = 100 – Efficiency

= 4(120) – 435 = 45 seconds

= 100 – 90.6 = 9.4%

= (100) = 90.6%

435

480

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Application 5.3

Using trial and error, one possible solution is shown below.

H, C, A

120

B, D, G

E, F	110	10
I, J, K	107	13
A fifth station is not needed

Station	Work Elements Assigned	Cumulative Time	Idle Time (c = 120)
1
2
3
4
5

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Rebalancing the Assembly Line

Managerial Considerations

Pacing is the movement of product from one station to the next as soon as the cycle time has elapsed

Behavioral factors such as absenteeism, turnover, and grievances can increase after installing production lines.

The number of models produced complicates scheduling and necessitates good communication. (Think Assignment #2)

Cycle times are dependent on the desired output rate or sometimes on the maximum workstations allowed.

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Solved Problem 1

Bill’s Car Wash offers two types of washes: Standard and Deluxe. The process flow for both types of customers is shown in the following chart. Both wash types are first processed through steps A1 and A2. The Standard wash then goes through steps A3 and A4 while the Deluxe is processed through steps A5, A6, and A7. Both offerings finish at the drying station (A8). The numbers in parentheses indicate the minutes it takes for that activity to process a customer.

Figure 5.7

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Solved Problem 1

a. Which step is the bottleneck for the Standard car wash process? For the Deluxe car wash process?

b. What is the capacity (measured as customers served per hour) of Bill’s Car Wash to process Standard and Deluxe customers? Assume that no customers are waiting at step A1, A2, or A8.

c. If 60 percent of the customers are Standard and 40 percent are Deluxe, what is the average capacity of the car wash in customers per hour?

d. Where would you expect Standard wash customers to experience waiting lines, assuming that new customers are always entering the shop and that no Deluxe customers are in the shop? Where would the Deluxe customers have to wait, assuming no Standard customers?

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Solved Problem 1

Step A4 is the bottleneck for the Standard car wash process, and Step A6 is the bottleneck for the Deluxe car wash process, because these steps take the longest time in the flow.

The capacity for Standard washes is 4 customers per hour because the bottleneck step A4 can process 1 customer every 15 minutes (60/15). The capacity for Deluxe car washes is 3 customers per hour (60/20). These capacities are derived by translating the “minutes per customer” of each bottleneck activity to “customers per hour.”

The average capacity of the car wash is (0.60  4) + (0.40  3) = 3.6 customers per hour.

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Solved Problem 1

d. Standard wash customers would wait before steps A1, A2, A3, and A4 because the activities that immediately precede them have a higher rate of output (i.e., smaller processing times). Deluxe wash customers would experience a wait in front of steps A1, A2, and A6 for the same reasons. A1 is included for both types of washes because the arrival rate of customers could always exceed the capacity of A1.

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Solved Problem 2

A company is setting up an assembly line to produce 192 units per 8-hour shift. The following table identifies the work elements, times, and immediate predecessors:

Work Element	Time (sec)		Immediate Predecessor(s)
A		40	None
B		80	A
C		30	D, E, F
D		25	B
E		20	B
F		15	B
G		120	A
H		145	G
I		130	H
J		115	C, I
Total 720

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Solved Problem 2

a. What is the desired cycle time (in seconds)?

b. What is the theoretical minimum number of stations?

c. Use trial and error to work out a solution, and show your solution on a precedence diagram.

d. What are the efficiency and balance delay of the solution found?

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Solved Problem 2

b. The sum of the work-element times is 720 seconds, so

TM =

t

= = 4.8 or 5 stations

720 sec/unit

150 sec/unit-station

which may not be achievable.

a. Substituting in the cycle-time formula, we get

8 hours

192 units

(3,600 sec/hr) = 150 sec/unit

c = =

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Solved Problem 2

Work Element	Immediate Predecessor(s)
A	None
B	A
C	D, E, F
D	B
E	B
F	B
G	A
H	G
I	H
J	C, I

c. Precedence Diagram

Figure 5.8

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Solved Problem 2

115

130

145

120

110

120

D, E, F

145

E, F, G	G	120	120	30
E, F	E	20	140	10
F, H	H	145	145	5
F, I	I	130	130	20
F	F	15	145	5
C	C	30	30	120
J	J	115	145	5

Station	Candidate(s)	Choice	Work-Element Time (sec)	Cumulative Time (sec)	Idle Time (c= 150 sec)
S1


S2

S3
S4

S5

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Solved Problem 2

d. Calculating the efficiency, we get

Thus, the balance delay is only 4 percent (100–96).

Efficiency (%) = (100)

t

720 sec/unit

5(150 sec/unit)

= 96%

(100)

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