4 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Process Analysis 4 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.

4 – 1Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.

Process AnalysisProcess Analysis4

For For Operations Management, 9eOperations Management, 9e by by Krajewski/Ritzman/Malhotra Krajewski/Ritzman/Malhotra © 2010 Pearson Education© 2010 Pearson Education

PowerPoint Slides PowerPoint Slides by Jeff Heylby Jeff Heyl


Process AnalysisProcess Analysis

Processes may be the least understood and managed aspect of a business

A firm can not gain a competitive advantage with faulty processes

Processes can be analyzed and improved using certain tools and techniques

Process analysis can be accomplished using a six-step blueprint


A Systematic ApproachA Systematic Approach

Figure 4.1 – Blueprint for Process Analysis

Define scope

2

Identify opportunity

1

Implement changes

6

Evaluate performance

4

Redesign process

5

Document process

3


Documenting The ProcessDocumenting The Process

Three effective techniques for documenting and evaluating processes are

1) Flowcharts

2) Service blueprints

3) Process charts

They help you see how a process operates and how well it is performing

Can help find performance gaps


FlowchartsFlowcharts

No

Yes

No Yes

No

Yes

Line of visibility

Finish

Figure 4.2 – Flowchart of the Sales Process for a Consulting Company

Payment received?

Client billed by accounting,

sales, or consulting

Follow-up by accounting,


Approvalby

consulting?

Final invoice created by

accounting, sales, or consulting

Nested Process Client agreement

and service delivery

Is proposal

complete?

Follow-up conversation

between client and sales

Sales and/or consulting

drafts proposal

Sales: Initial conversation

with client

Marketing lead

Follow-up conversation

between client and consulting

Consulting drafts

proposal

Consulting: Initial

conversation with client

Consulting lead

Sales lead



Final invoice created by

accounting, sales, or consulting

Delivery of service by consulting

50% invoiced by accounting,


Letter of agreement

signed

Project manager assigned

Form completed by

sales or consulting

Verbal OK from client

Is proposal

complete?

Figure 4.3 – Flowchart of the Nested Sub-process of Client Agreement and Service Delivery


Credit and invoicing

Production Control and Manufacturing

Assembly and Shipping

PR

OD

UC

TIO

NF

INA

NC

ES

AL

ES

CU

ST

OM

ER


No

Yes No

Yes

Payment received

Paym

ent

Order stopped

Ord

er cancellatio

nOrder

cancelled

Payment sent

Pro

du

ct packag

esProduct

and invoice received

100% of credit

checked within 24 hours

Two scheduling errors per

quarter

Invoice sent

No

tice of sh

ipm

ent

Order shipped

Order pickedOrder

Packages assembled and

inventoried

`Items manufactured

Production scheduled

Inventory adjusted

Invoice prepared

Credit check OK?

New customer?

Order received

Ord

er

Order entered

Order completed

and submitted

Ord

er

Order generated

Figure 4.4 – Flowchart of the Order-Filling Process Showing Handoffs Between Departments


Process ChartsProcess Charts

An organized way to document all the activities performed by a person or group

Activities are typically organized into five categories Operation, Transportation, Inspection, Delay, Storage,


Step No.

Time (min)

Distance (ft) Step Description

1 X

2 X

3 X

4 X

5 X

6 X

7 X

8 X

9 X

10 X

11 X

12 X

13 X

14 X

15 X

16 X

17 X

18 X

19 X

0.50 15.0

10.00

0.75 40.0

3.00

0.75 40.0

1.00

1.00 60.0

4.00

5.00

2.00 200.0

3.00

2.00 200.0

3.00

2.00

1.00 60.0

4.00

2.00 180.0

4.00

1.00 20.0


Figure 4.5 – Process Chart for Emergency Room Admission

Sit down and fill out patient history

Enter emergency room, approach patient window

Nurse escorts patient to ER triage room

Nurse inspects injury

Return to waiting room

Wait for available bed

Go to ER bed

Wait for doctor

Doctor inspects injury and questions patient

Nurse takes patient to radiology

Technician x-rays patient

Return to bed in ER

Wait for doctor to return

Doctor provides diagnosis and advice

Return to emergency entrance area

Check out

Walk to pharmacy

Pick up prescription

Leave the building


Step No.

Time (min)


1 X

2 X

3 X

4 X

5 X

6 X

7 X

8 X

9 X

10 X

11 X

12 X

13 X

14 X

15 X

16 X

17 X

18 X

19 X

0.50 15.0

10.00

0.75 40.0

3.00

0.75 40.0

1.00

1.00 60.0

4.00

5.00

2.00 200.0

3.00

2.00 200.0

3.00

2.00

1.00 60.0

4.00

2.00 180.0

4.00

1.00 20.0


Figure 4.5 – Process Chart for Emergency Room Admission

Sit down and fill out patient history

Enter emergency room, approach patient window

Nurse escorts patient to ER triage room

Nurse inspects injury

Return to waiting room

Wait for available bed

Go to ER bed

Wait for doctor

Doctor inspects injury and questions patient

Nurse takes patient to radiology

Technician x-rays patient

Return to bed in ER

Wait for doctor to return

Doctor provides diagnosis and advice

Return to emergency entrance area

Check out

Walk to pharmacy

Pick up prescription

Leave the building

Summary

Activity Number of Steps

Time (min)

Distance (ft)

Operation 5 23.00

Transport 9 11.00 815

Inspect 2 8.00

Delay 3 8.00

Store ― ―



The annual cost of an entire process can be estimated

It is the product of1) Time in hours to perform the process each

time

2) Variable costs per hour

3) Number of times the process is performed each year

Annual labor cost

Time to performthe process in hours

Variable costsper hour

Number of times processperformed each year=



If the average time to serve a customer is 4 hours

The variable cost is $25 per hour

And 40 customers are served per year

The total labor cost is

4 hrs/customer $25/hr 40 customers/yr = $4,000


Work Measurement TechniquesWork Measurement Techniques

Used to estimate the average time each step in a process would take

1) Time study method

2) Elemental standard data approach

3) Predetermined data approach

4) Work sampling method

Learning curve analysis is appropriate for new products or processes


Time Study of Revised ProcessTime Study of Revised Process

EXAMPLE 4.1

A process at a watch assembly plant has been changed. The process is divided into three work elements. A time study has been performed with the following results. The time standard for process previously was 14.5 minutes. Based on the new time study, should the time standard be revised?

SOLUTION

The new time study had an initial sample of four observations, with the results shown in the following table. The performance rating factor (RF) is shown for each element, and the allowance for the whole process is 18 percent of the total normal time.


Time Study of Revised ProcessTime Study of Revised Process

Obs 1 Obs 2 Obs 3 Obs 4 Average (min) RF Normal

Time

Element 1 2.60 2.34 3.12 2.86 2.730 1.0 2.730

Element 2 4.94 4.78 5.10 4.68 4.875 1.1 5.363

Element 3 2.18 1.98 2.13 2.25 2.135 0.9 1.922

Total Normal Time = 10.014

The normal time for an element in the table is its average time, multiplied by the RF. The total normal time for the whole process is the sum of the normal times for the three elements, or 10.01 minutes. To get the standard time (ST) for the process, just add in the allowance, or

ST = 10.014(1 + 0.18) = 11.82 minutes/watch


Work SamplingWork Sampling

Figure 4.6 – Work Sampling Study of Admission Clerk at Health Clinic Using OM Explorer


Learning CurvesLearning Curves

140,000

120,000 –

100,000 –

80,000 –

60,000 –

40,000 –

20,000 –

0

Lab

or

Ho

urs

per

Un

it

Cumulative Units Produced

| | | | | | |

0 20 40 60 80 100 120

Figure 4.7 – Learning Curve with 80% Learning Rate Using OM Explorer

4 – 18

Learning CurvesLearning Curves

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.

Prof. Dr. Ali ŞENProf. Dr. Ali ŞEN

• Definition of Leaning CurvesDefinition of Leaning Curves• Importance of LC.Importance of LC.• LC Fog Linear FunctionLC Fog Linear Function• Operational Application of a Operational Application of a Leaning CurveLeaning Curve

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19

Learning CurveLearning Curve

Learning Curve

Cumulative Production

Ho

urs

Re

qu

ire

d

to

Pro

du

ce

th

e M

ost

Re

cen

t U

nit

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ExampleExample

Consider a product with the following data about the hours of labor required to produce a unit:

Hours required to produce 1-st unit: 100

Hours required to produce 10-th unit: 48




As more and more units are produced, the hours of labor required to produce the most recent unit is lower and lower.

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Graph for ExampleGraph for Example

1 10010 4825 3575 25200 18

Hours Required to Produce

Most Recent UnitCumulative Production

Learning Curve

0102030405060708090

100110

0 25 50 75 100 125 150 175 200 225


Ho

urs

Re

qu

ire

d

to

Pro

du

ce

M

ost

Re

cen

t U

nit

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Reasons for Continual Decease in Reasons for Continual Decease in the Number of Hours Required to the Number of Hours Required to

Produce the Most Recent UnitProduce the Most Recent Unit

On the previous slide, we observed that, as more and more units are produced, the hours required to produce the most recent unit is lower and lower.

What are some potential reasons why this occurs?

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What happens whenWhat happens whencumulative production doubles?cumulative production doubles?

The concept of a Learning Curve is motivated by the observation (in many diverse production environments) that, each time the cumulative production doubles, the hours required to produce the most recent unit decreases by approximately the same percentage.

For example, for an 80% learning curve,

If cumulative production doubles from 50 to 100, then the hours required to produce the 100-th unit is 80% of that for the 50-th unit.

If cumulative production doubles from 100 to 200, then the hours required to produce the 200-th unit is 80% of that for the 100-th unit.

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The Functional FormThe Functional Formof a Learning Curveof a Learning Curve

To model the behavior described in the previous slides, we proceed as follows:

Let x = cumulative production

y = hours required to produce the x-th unit

Then, y = ax-b

where a and b are parameters defined as follows:

a = hours required to produce the 1-st unit

b = a value related to the percentage associated with the Learning Curve

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25

An 80% Learning CurveAn 80% Learning Curve

Assume that production of the first unit required 100 hours and that there is an 80% Learning Curve.

Again, let

x = cumulative production


Then, mathematicians can show that the Learning Curve is

y = 100x-0.322

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An 80% Learning CurveAn 80% Learning Curve(continued)(continued)

Hours RequiredCumulative to ProduceProduction Most Recent Unit

x y = 100x -0.322

1 100.0002 80.000

--- --- 4 64.000

--- --- 8 51.200

--- --- 16 40.960 --- --- 25 35.478 --- --- 32 32.768 --- --- 50 28.383 --- --- 64 26.214 --- --- 100 22.706 --- --- 128 20.972 --- --- 200 18.165

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A 70% Learning CurveA 70% Learning Curve

Assume that production of the first unit required 100 hours and that there is an 70% Learning Curve.

Again, let

x = cumulative production


Then, mathematicians can show that the Learning Curve is

y= 100x-0.515

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A 70% Learning CurveA 70% Learning Curve(continued)(continued)


x y = 100x -0.515

1 100.0002 70.000

--- --- 4 49.000

--- --- 8 34.300

--- --- 16 24.010 --- --- 25 19.083 --- --- 32 16.807 --- --- 50 13.358 --- --- 64 11.765 --- --- 100 9.351 --- --- 128 8.235 --- --- 200 6.546

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The Relationship BetweenThe Relationship Betweenb and pb and p

The table below shows the relationship between the exponent b and p, the percentage associated with the Learning Curve:

Recall that the functional form for a Learning Curve is

y = ax-b

b 0.000 0.074 0.152 0.234 0.322 0.415 0.515 0.621 0.737 0.862 1.000 1.322 1.737 2.322 3.322p 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 40% 30% 20% 10%

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The Relationship BetweenThe Relationship Betweenb and p (continued)b and p (continued)

There is a direct mathematical relationship between the exponent b in the equation y = ax-b and (p/100)%, where p is the percentage associated with the learning curve:

)2ln(*)%100/()2ln(

)100/ln( beppb ly,equivalentor,

For example, if p=75%, then 415.0)2ln(

)75.0ln( b

For example, if b=0.737, then 60.0)2ln(*737.0)%100/( ep

NOTE: e=2.7183… (never ending, like ¶)

ln(x) is the exponent of e that yields x.

That is, eln(x)=x

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Operational ApplicationOperational Applicationof the Leaning Curveof the Leaning Curve

Assume that production of the 1-st unit required 100 hours, and assume that there is an 80% learning curve. Then, y = 100x-0.322.

Also, assume that cumulative production to date is 150 units.

The learning curve can be used to provide estimates of answers to questions about the production of the next 100 units.

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Operational Application of a Leaning Operational Application of a Leaning Curve Curve (continued)(continued)

Question 1: To produce the next 100 units, how many hours of labor will be required?

Question 2: With a labor force of 6 workers each working 40 hours per week, how long will it take to produce then next 100 units?

Question 3: To produce 100 units in 5 weeks with each worker working 40 hours per week, what should be the size of the labor force?

Question 4: To produce 100 units in 5 weeks using a work force of 60 workers, how many hours per week should each worker work?


x y = 100x -0.322

1 100.000 Cumulative --- --- Hours Required100 22.706 from 151-st Unit --- --- through Most Recent Unit150 19.928151 19.885 19.885152 19.843 39.728153 19.801 59.529154 19.759 79.288155 19.718 99.007 --- --- --- 200 18.165 948.644 --- --- --- 246 16.994 1755.483247 16.972 1772.455248 16.950 1789.404249 16.928 1806.332250 16.906 1823.238

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Effect of Sales’Effect of Sales’Annual Growth RateAnnual Growth Rate

Assume that:

Three firms have the same 80% learning curve: y=100x-0.322

During Year 1, all three firms sold 5000 units.

The three firms have respective annual growth rates in sales of 5%, 10%, and 20%.

Compare the three firms at the end of Year 4.

Conclusion?

Cummulative Production At End of Year 4Hours Required to Produce

Most Recent Unit

x y =100 x -0.322

A 5% x = [1.00+(1.05)+(1.05)2+(1.05)3](5000) = 15,764 4.453

B 10% x = [1.00+(1.05)+(1.05)2+(1.05)3](5000) = 16,551 4.384

C 20% x = [1.00+(1.05)+(1.05)2+(1.05)3](5000) = 18,202 4.252

Firm

Annual Growth Rate

in Sales

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Effect of Sales’Effect of Sales’Annual Growth Rate Annual Growth Rate (continued)(continued)

Learning Curve

0

2

4

6

8

5,000 10,000 15,000 20,000


Hou

rs R

equi

red

to P

rodu

ce

M

ost R

ecen

t Uni

t

Learning Curve Firm A Firm B Firm C

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Strategic ApplicationsStrategic Applicationsof a Learning Curveof a Learning Curve

Frequent Decreases in Selling Price.

Each decrease in selling price increases your market share, which in turn leads to a “faster ride” down the learning curve, which in turn makes it tougher for your competitors.

Reinvest Increased Profits

As the hours required to produce the most recent unit continually decreases, the cost to produce the unit continually decreases. Therefore, your profits increase. You can reinvest the incremental profit to improve the product or the production process, or you can reinvest the incremental profit in another area of the firm.

As the hours required to produce the most recent unit continually decreases, the cost to produce the unit continually decreases. Therefore, you can frequently decrease the selling price without decreasing total profit.

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How do we determine the How do we determine the parameters of a Learning Curve?parameters of a Learning Curve?

From previous slides, we know that, to model a learning curve, we proceed as follows:

Let x = cumulative production


Then, y = ax-b

where a and b are parameters defined as follows:

a = hours required to produce the 1-st unit

b = a value related to the percentage associated with the learning curve

For a given set of data, how do we determine the specific values of a and b?

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ExampleExample

For the Learning curve y=ax-b, how do we determine the specific values of a and b?

We begin by taking the natural logs of both sides of y=ax-b.

1 4000

7 255025 185065 1500180 1170



Most Recent Unit

Learning Curve Data

0500

10001500

20002500

30003500

40004500

0 50 100 150 200


Ho

urs

Re

qu

ire

d

to P

rod

uce

the

Mo

st

Re

ce

nt

Un

it

Note the linear relationship between ln(x) and ln(y).

This suggests taking the natural logs of the data.

)*ln()ln( xbaybaxy

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Example Example (continued)(continued)

Natural Log Natural Log

0.000 8.2941.946 7.8443.219 7.5234.174 7.313

5.193 7.065



Most Recent Unit

Note the approximate linear relationship between ln(Cumulative Production) and ln (Hours Required).

Natural Log of Learning Curve Dataln(Cumulative Production) versus ln(Hours Required)

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6

ln(Cumulative Production)

ln(H

ou

rs R

eq

uir

ed

)

We can use the statistical technique of Regression to determine the straight line that “best fits” the data.

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Best Linear Fit (via Regression)ln(Cumulative Production) versus ln(Hours Required)

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6


ln(H

ours

Req

uire

d)

ln(Data) Best Linear Fit

Using Excel’s Regression Tool, we obtain

ln(y) = 8.29642 – 0.23694 ln(x)

Intercept=8.29642

Negative of Slope = 0.23694

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From the previous slide, we know

ln(y) = 8.29642 – 0.23694 ln(x)

So,

eln(y) = e[8.29642 – 0.23694 ln(x)]

or, equivalently, the equation for the Learning Curve is

y = 8.29642 x-0.23694

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Learning Curve

0500

10001500200025003000350040004500

0 50 100 150 200


Ho

urs

Re

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d

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th

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Data Learning Curve

y = 8.29642 x-0.23694

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y = 8.29642 x-0.23694

b 0.000 -0.074 -0.152 -0.234 -0.322 -0.415 -0.515 -0.621 -0.737 -0.862 -1.000 -1.322 -1.737 -2.322 -3.322p 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 40% 30% 20% 10%

So, in our example, we have a Learning Curve that is close to but just below an 85% learning curve.

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Excel TemplateExcel Templatefor a Learning Curvefor a Learning Curve

A B C D E F G H I2

3 Hours Required LN of Regression4 Cumulative to Produce Cumulative LN of Estimate of5 Production Most Recent Unit Production Hours Required Hours Required6 1 4000 0.000 8.294 4009.57 7 2550 1.946 7.844 2528.48 25 1850 3.219 7.523 1870.19 65 1500 4.174 7.313 1491.2

10 180 1170 5.193 7.065 1171.4

1112131415 SUMMARY OUTPUT1617 Regression Statistics18 Multiple R 0.99987412719 R Square 0.9997482720 Adjusted R Square 0.9996643621 Standard Error 0.00876481622 Observations 52324 ANOVA25 df SS MS F Significance F26 Regression 1 0.915298765 0.915298765 11914.53938 1.69521E-0627 Residual 3 0.000230466 7.6822E-0528 Total 4 0.9155292312930 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%31 Intercept 8.296416964 0.007427526 1116.982525 1.58245E-09 8.272779238 8.320054689 8.272779238 8.32005468932 X Variable 1 -0.236941604 0.002170714 -109.1537419 1.69521E-06 -0.243849792 -0.230033415 -0.243849792 -0.23003341533 4009.48134

Rows 15-32 generated using the menu selection "Tools, Data Analysis, Regression" with Input X-Range of E6:E10 Input Y-Range of F6:F10 Output Range of A15

=LN(B8) =LN(C8) =$B$33*(B8^$B$32)

=EXP(B31)

NOTE: Regression output in cells B31 and B32 shows that LN(Hours Required) = 8.296 - 0.237*LN(Cumulative Production) or, equivalently, (Hours Required) = 4009.5*[(Cumulative Production)^(-0.237)]

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A “Not So Nice” ExampleA “Not So Nice” Example

In our example, there was a very close linear relationship between

ln(Cumulative Production) and ln(Hours Required)

This is NOT the typical situation.

A more typical situation is shown on the next slide.

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A “Not So Nice” ExampleA “Not So Nice” Example(continued)(continued)

1 37217 271325 174765 1593

180 1099


Hours Required to Produce Most

Recent Unit

Natural Log Natural Log0.000 8.2221.946 7.9063.219 7.4664.174 7.3735.193 7.002


Hours Required to Produce Most

Recent Unit

Learning Curve Data

0500

1000150020002500300035004000

0 50 100 150 200


Ho

urs

Re

qu

ire

d

to P

rod

uce

the

Mo

st R

ece

nt

Un

it

Natual Log of Learning Curve Dataln(Cumulative Production) versus ln(Hours Required)

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6


ln(H

ou

rs R

eq

uir

ed

)

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Although the linear relationship in this example is not as strong as in the previous example, we proceed in the same manner.

Best Linear Fit (via Regression)ln(Cumulative Production) versus ln(Hours Required)

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6


ln(H

ours

Req

uire

d)

ln(Data) Best Linear Fit

Learning Curve

0500

1000150020002500300035004000

0 50 100 150 200


Hou

rs R

equi

red

to P

rodu

ce

th

e M

ost R

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t Uni

t

Data Learning Curve

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An approximate linear relationship such as the one below occurs for many products and services.

Natual Log of Learning Curve Dataln(Cumulative Production) versus ln(Hours Required)

7.0

7.5

8.0

8.5

0 1 2 3 4 5 6


ln(H

ou

rs R

eq

uir

ed

)

4 – 48

Tools & TechniquesTools & Techniques

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.


Evaluating PerformanceEvaluating Performance

Chapter 6, Capacity Planning; Supplement C, Waiting Lines; Supplement H, Measuring Output Rates; Supplement I, Learning Curve Analysis

Processing time

Total time from start to finish (throughput time)

Setup time

Operating expenses

Capacity utilization

Average waiting time

Average number of customers or jobs waiting in line

Chapter 5, Quality and Performance

Customer satisfaction measures

Error rate

Rework or scrap rate

Internal failure costs

Figure 4.8 – Metrics for Flowcharts, Process Charts, and Accompanying Tables


Evaluating PerformanceEvaluating Performance

Chapter 8, Lean Systems

Setup time

Average waiting time

Total time from start to finish (throughput time)

Waste

Chapter 7, Constraint Management

Cycle time

Idle time

Figure 4.8 – Metrics for Flowcharts, Process Charts, and Accompanying Tables


Data Analysis ToolsData Analysis Tools

Help identify causes of problems

1) Checklists

2) Histograms and bar charts

3) Pareto charts

4) Scatter diagrams

5) Cause-and-effect diagrams

6) Graphs


Pareto Chart for a RestaurantPareto Chart for a Restaurant

EXAMPLE 4.2

The manager of a neighborhood restaurant is concerned about the smaller numbers of customers patronizing his eatery. Complaints have been rising, and he would like to find out what issues to address and present the findings in a way his employees can understand.

SOLUTION

The manager surveyed his customers over several weeks and collected the following data:

Complaint Frequency

Discourteous server 12

Slow service 42

Cold dinner 5

Cramped table 20

Atmosphere 10



50 –45 –40 –35 –30 –25 –20 –10 –5 –0 –

Fa

ilu

res

Discourteous server

Slow service

Cold dinner

Cramped tables

Atmosphere

Failure Name

Figure 4.9 – Bar Chart

Figure 4.9 is a bar chart and Figure 4.10 is a Pareto chart, both created with OM Explorer’s Bar, Pareto, and Line Charts solver. They present the data in a way that shows which complaints are more prevalent (the vital few).



Figure 4.10 – Pareto Chart

100% = 69.7%(42 + 20)

89

– 100.0%

– 80.0%

– 60.0%

– 40.0%

– 20.0%

– 0.0%

45 –

40 –

35 –

30 –

25 –

20 –

10 –

5 –

0 –

Fa

ilu

res

Discourteous server

Slow service

Cold dinner

Cramped tables

Atmosphere

Failure Name

Pe

rce

nt

of

To

tal


Analysis of Flight Departure DelaysAnalysis of Flight Departure Delays

EXAMPLE 4.3

The operations manager for Checker Board Airlines at Port Columbus International Airport noticed an increase in the number of delayed flight departures.

SOLUTION

To analyze all the possible causes of that problem, the manager constructed a cause-and-effect diagram, shown in Figure 4.11. The main problem, delayed flight departures, is the “head” of the diagram. He brainstormed all possible causes with his staff, and together they identified several major categories: equipment, personnel, materials, procedures, and “other factors” that are beyond managerial control. Several suspected causes were identified for each major category.


Analysis of Flight Departure DelaysAnalysis of Flight Departure Delays

Delayed flight departures

Weather

Air traffic delays

Other Aircraft late to gate

Mechanical failures

Equipment

Passenger processing at gate

Late cabin cleaners

Unavailable cockpit crew

Late cabin crew

Personnel

Poor announcement of departures

Weight/balance sheet late

Delayed check-in procedure

Waiting for late passengers

Procedures

Late baggage to aircraft

Late fuel

Late food service

Contractor not provided with updated schedule

Materials

Figure 4.11 – Cause-and-Effect Diagram for Flight Departure Delays


Data Analysis ToolsData Analysis Tools

Tools can be used together for data snooping to analyze data and determine causes

Simulation can show how a process changes over time

Process simulation is the act of reproducing the behavior of a process using a model that describes each step


Causes of Headliner Process FailuresCauses of Headliner Process Failures

EXAMPLE 4.4

The Wellington Fiber Board Company produces headliners, the fiberglass components that form the inner roof of passenger cars. Management wanted to identify which process failures were most prevalent and to find the cause.

SOLUTION

Step 1: A checklist of different types of process failures is constructed from last month’s production records.

Step 2: A Pareto chart is prepared from the checklist data.

Step 3: A cause-and-effect diagram for identifies several potential causes for the problem.

Step 4: The manager reorganizes the production reports into a bar chart according to shift because the personnel on the three shifts had varied amounts of experience.



Defect type Tally Total

A. Tears in fabric 4

B. Discolored fabric 3

C. Broken fiber board36

D. Ragged edges 7

Total 50

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|

|

|

| |

|

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||

|

| | | | || | | || | |

| | | || | | || | | ||

|| ||

C

D

A B

50 –

40 –

30 –

20 –

10 –

0 –

– 100

– 80

– 60

– 40

– 20

– 0

Nu

mb

er o

f F

ailu

res

Cu

mu

lati

ve P

erce

nta

ge

Defect Failure

SOLUTION

Figure 4.12 shows the sequential application of several tools for improving quality

Step 1. Checklist

Step 2. Pareto Chart

Figure 4.12 – Application of the Tools for Improving Quality



SOLUTION

Figure 4.12 shows the sequential application of several tools for improving quality

Step 3. Cause-and-Effect Diagram

Step 4. Bar Chart

Humidity

Schedule change

Other

Out of specification

Not available

Materials

Training

Absenteeism

Communication

People

Machine maintenance

Machine speed

Wrong setup

Process

Broken fiber board

20 –

–

15 –

–

10 –

–

5 –

–

0 –

Nu

mb

er o

f B

roke

n F

iber

Bo

ard

sShift

First Second Third

Figure 4.12 – Application of the Tools for Improving Quality


Redesigning the ProcessRedesigning the Process

After a process is documented, metrics are collected, and disconnects are identified, the process analyst determines what changes should be made

People directly involved in the process are brought in to get their ideas and inputs


Generating IdeasGenerating Ideas

Ideas can be uncovered by asking six questions

1. What is being done?

2. When is it being done?

3. Who is doing it?

4. Where is it being done?

5. How is it being done?

6. How well does it do on the various metrics of importance?



Brainstorming involves a group of people knowledgeable about the process proposing ideas for change by saying whatever comes to mind

After brainstorming the design team evaluates ideas and identifies those with the highest payoff



Benchmarking is a systematic procedure that measures a firm’s processes, services, and products against another firm

Competitive benchmarking is based on comparisons with a direct competitor

Functional benchmarking compares areas with those of outstanding firms in any industry

Internal benchmarking compares an organizational unit with superior performance with other units


BenchmarkingBenchmarking

There are four basic steps Step 1. Planning Step 2. Analysis Step 3. Integration Step 4. Action

Collecting data can be a challenge

Some corporations and government organizations have agreed to share data



Customer Relationship Process

Total cost of “enter, process, and track orders” per $1,000 revenue System costs of processes per $100,000 revenue Value of sales order line item not fulfilled due to stockout, as percentage of

revenue Average time from sales order receipt until manufacturing logistics is

notified Average time in direct contact with customer per sales order line item

Order Fulfillment Process

Value of plant shipments per employee Finished goods inventory turnover Reject rate as percentage of total orders processed Percentage of orders returned by customers due to quality problems Standard customer lead time from order entry to shipment Percentage of orders shipped on time

Figure 4.13 – Illustrative Benchmarking Metrics by Type of Process



New Service/Product Development Process

Percentage of sales due to services/products launched last year Cost of “generate new services/products” process per $1,000 revenue Ratio of projects entering the process to projects completing the process Time to market for existing service/product improvement project Time to market for new service/product project Time to profitability for existing service/product improvement project

Supplier Relationship Process

Cost of “select suppliers and develop/maintain contracts” process per $1,000 revenue

Number of employees per $1,000 of purchases Percentage of purchase orders approved electronically Average time to place a purchase order Total number of active vendors per $1,000 of purchases Percentage of value of purchased material that is supplier certified




Customer Relationship Process

Systems cost of finance function per $1,000 revenue Percentage of finance staff devoted to internal audit Total cost of payroll processes per $1,000 revenue Number of accepted jobs as percentage of job offers Total cost of “source, recruit, and select” process per $1,000 revenue Average employee turnover rate



Managing ProcessesManaging Processes

Failure to manage processes is failure to manage the business

Seven common mistakes1. Not connecting with strategic issues

2. Not involving the right people in the right way

3. Not giving the design teams and process analysts a clear charter and then holding them accountable


Managing ProcessesManaging Processes

Seven common mistakes

4. Not being satisfied unless fundamental “reengineering” changes are made

5. Not considering the impact on people

6. Not giving attention to implementation

7. Not creating an infrastructure for continuous process improvement


Solved Problem 1Solved Problem 1

Create a flowchart for the following telephone-ordering process at a retail chain that specializes in selling books and music CDs. It provides an ordering system via the telephone to its time-sensitive customers besides its regular store sales.

The automated system greets customers, asks them to choose a tone or pulse phone, and routes them accordingly.

The system checks to see whether customers have an existing account. They can wait for the service representative to open a new account.

Customers choose between order options and are routed accordingly.

Customers can cancel the order. Finally, the system asks whether the customer has additional requests; if not, the process terminates.


Figure 4.14 – Flowchart of Telephone Ordering Process


SOLUTION



Figure 4.14 – Flowchart of Telephone Ordering Process

SOLUTION



An automobile service is having difficulty providing oil changes in the 29 minutes or less mentioned in its advertising. You are to analyze the process of changing automobile engine oil. The subject of the study is the service mechanic. The process begins when the mechanic directs the customer’s arrival and ends when the customer pays for the services.

SOLUTION

Figure 4.15 shows the completed process chart. The process is broken into 21 steps. A summary of the times and distances traveled is shown in the upper right-hand corner of the process chart.

The times add up to 28 minutes, which does not allow much room for error if the 29-minute guarantee is to be met and the mechanic travels a total of 420 feet.


Solved Problem 2Solved Problem 2Step No.

Time (min)


1 0.80 50.0 X Direct customer into service bay

2 1.80 X Record name and desired service

3 2.30 X Open hood, verify engine type, inspect hoses, check fluids

4 0.80 0.30 X Walk to customer in waiting area

5 0.60 X Recommend additional services

6 0.70 X Wait for customer decision

7 0.90 70.0 X Walk to storeroom

8 1.90 X Look up filter number(s)

9 0.40 X Check filter number(s)

10 0.60 50.0 X Carry filter(s) to service pit

11 4.20 X Perform under-car services

12 0.70 40.0 X Climb from pit, walk to automobile

13 2.70 X Fill engine with oil, start engine

14 1.30 X Inspect for leaks

15 0.50 40.0 X Walk to pit

16 1.00 X Inspect for leaks

17 3.00 X Clean and organize work area

18 0.70 80.0 X Return to auto, drive from bay

19 0.30 X Park the car

20 0.50 60.0 X Walk to customer waiting area

21 2.30 X Total charges, receive payment

Summary

Activity Number of Steps

Time (min)

Distance (ft)

Operation Transport Inspect Delay Store

Figure 4.15 – Process Chart for Changing Engine Oil

7 16.50

8 5.50 420

4 5.00

1 0.70

1 0.30



What improvement can you make in the process shown in Figure 4.14?

SOLUTION

Your analysis should verify the following three ideas for improvement. You may also be able to come up with others.

a. Move Step 17 to Step 21. Customers should not have to wait while the mechanic cleans the work area.

b. Store small inventories of frequently used filters in the pit. Steps 7 and 10 involve travel to the storeroom.

c. Use two mechanics. Steps 10, 12, 15, and 17 involve running up and down the steps to the pit. Much of this travel could be eliminated.



Defect Frequency

Lumps of unmixed product 7

Over- or underfilled jars 18

Jar lids did not seal 6

Labels rumpled or missing 29

Total 60

Vera Johnson and Merris Williams manufacture vanishing cream. Their packaging process has four steps: (1) mix, (2) fill, (3) cap, and (4) label. They have had the reported defects analyzed, which shows the following:

Draw a Pareto chart to identify the vital defects.



SOLUTION

Defective labels account for 48.33 percent of the total number of defects:

100% = 48.33%29

60

Improperly filled jars account for 30 percent of the total number of defects:

The cumulative percent for the two most frequent defects is

100% = 30.00%18

60

48.33% + 30.00% = 78.33%



10% + 90% = 100.00%

Defective seals represent of defects; the

cumulative percentage is

6

60 100% = 10.00%

The Pareto chart is shown in Figure 4.16

78.33% + 11.67% = 90.00%

7

60 100% = 11.67%Lumps represent of defects; the

cumulative percentage is



40 –

36 –

32 –

28 –

24 –

20 –

16 –

12 –

8 –

4 –

0 –

– 100

– 90

– 80

– 70

– 60

– 50

– 40

– 30

– 20

– 10

– 0

Fre

qu

enc

y o

f D

efe

cts

Label Fill Mix Seal

Cu

mu

lati

ve P

erce

nta

ge

of

Def

ect

s

100%90%

78%

48%

Figure 4.16 – Pareto Chart


4 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Process Analysis 4 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.

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