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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
4 – 2Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 3Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 4Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 5Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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,
sales, or consulting
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
4 – 6Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
FlowchartsFlowcharts
Final invoice created by
accounting, sales, or consulting
Delivery of service by consulting
50% invoiced by accounting,
sales, or consulting
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
4 – 7Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Credit and invoicing
Production Control and Manufacturing
Assembly and Shipping
PR
OD
UC
TIO
NF
INA
NC
ES
AL
ES
CU
ST
OM
ER
FlowchartsFlowcharts
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
4 – 8Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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,
4 – 9Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
Process ChartsProcess Charts
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
4 – 10Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
Process ChartsProcess Charts
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 ― ―
4 – 11Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Process ChartsProcess Charts
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=
4 – 12Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Process ChartsProcess Charts
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
4 – 13Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 14Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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.
4 – 15Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 16Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Work SamplingWork Sampling
Figure 4.6 – Work Sampling Study of Admission Clerk at Health Clinic Using OM Explorer
4 – 17Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 19
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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
4 – 20
20
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
Hours required to produce 25-th unit: 35
Hours required to produce 75-th unit: 25
Hours required to produce 200-th unit: 18
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
Cumulative Production
Ho
urs
Re
qu
ire
d
to
Pro
du
ce
M
ost
Re
cen
t U
nit
4 – 22
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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?
4 – 23
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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.
4 – 24
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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
4 – 25
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
y = hours required to produce the x-th unit
Then, mathematicians can show that the Learning Curve is
y = 100x-0.322
4 – 26
26
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
4 – 27
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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
y = hours required to produce the x-th unit
Then, mathematicians can show that the Learning Curve is
y= 100x-0.515
4 – 28
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A 70% Learning CurveA 70% Learning Curve(continued)(continued)
Hours RequiredCumulative to ProduceProduction Most Recent Unit
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
4 – 29
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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%
4 – 30
30
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
4 – 31
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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.
4 – 32
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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?
Hours RequiredCumulative to ProduceProduction Most Recent Unit
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
4 – 33
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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
4 – 34
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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
Cumulative Production
Hou
rs R
equi
red
to P
rodu
ce
M
ost R
ecen
t Uni
t
Learning Curve Firm A Firm B Firm C
4 – 35
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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.
4 – 36
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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
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
For a given set of data, how do we determine the specific values of a and b?
4 – 37
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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
Cumulative Production
Hours Required to Produce
Most Recent Unit
Learning Curve Data
0500
10001500
20002500
30003500
40004500
0 50 100 150 200
Cumulative Production
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
4 – 38
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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
Cumulative Production
Hours Required to Produce
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.
4 – 39
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Example Example (continued)(continued)
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(Cumulative Production)
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
4 – 40
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Example Example (continued)(continued)
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
4 – 41
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Example Example (continued)(continued)
Learning Curve
0500
10001500200025003000350040004500
0 50 100 150 200
Cumulative Production
Ho
urs
Re
qu
ire
d
to
Pro
du
ce
th
e M
ost
Re
cen
t U
nit
Data Learning Curve
y = 8.29642 x-0.23694
4 – 42
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Example Example (continued)(continued)
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.
4 – 43
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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)]
4 – 44
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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.
4 – 45
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A “Not So Nice” ExampleA “Not So Nice” Example(continued)(continued)
1 37217 271325 174765 1593
180 1099
Cumulative Production
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
Cumulative Production
Hours Required to Produce Most
Recent Unit
Learning Curve Data
0500
1000150020002500300035004000
0 50 100 150 200
Cumulative Production
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(Cumulative Production)
ln(H
ou
rs R
eq
uir
ed
)
4 – 46
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A “Not So Nice” ExampleA “Not So Nice” Example(continued)(continued)
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(Cumulative Production)
ln(H
ours
Req
uire
d)
ln(Data) Best Linear Fit
Learning Curve
0500
1000150020002500300035004000
0 50 100 150 200
Cumulative Production
Hou
rs R
equi
red
to P
rodu
ce
th
e M
ost R
ecen
t Uni
t
Data Learning Curve
4 – 47
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A “Not So Nice” ExampleA “Not So Nice” Example(continued)(continued)
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(Cumulative Production)
ln(H
ou
rs R
eq
uir
ed
)
4 – 48
Tools & TechniquesTools & Techniques
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
4 – 49Copyright © 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
4 – 50Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 51Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 52Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 53Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Pareto Chart for a RestaurantPareto Chart for a Restaurant
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).
4 – 54Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Pareto Chart for a RestaurantPareto Chart for a Restaurant
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
4 – 55Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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.
4 – 56Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
4 – 57Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
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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.
4 – 59Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Causes of Headliner Process FailuresCauses of Headliner Process Failures
Defect type Tally Total
A. Tears in fabric 4
B. Discolored fabric 3
C. Broken fiber board36
D. Ragged edges 7
Total 50
|
|
|
|
| |
|
| ||
||
|
| | | | || | | || | |
| | | || | | || | | ||
|| ||
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
4 – 60Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Causes of Headliner Process FailuresCauses of Headliner Process Failures
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
4 – 61Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
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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?
4 – 63Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Generating IdeasGenerating Ideas
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
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Generating IdeasGenerating Ideas
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
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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
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BenchmarkingBenchmarking
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
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BenchmarkingBenchmarking
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
Figure 4.13 – Illustrative Benchmarking Metrics by Type of Process
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BenchmarkingBenchmarking
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
Figure 4.13 – Illustrative Benchmarking Metrics by Type of Process
4 – 69Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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
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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
4 – 71Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
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.
4 – 72Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Figure 4.14 – Flowchart of Telephone Ordering Process
Solved Problem 1Solved Problem 1
SOLUTION
4 – 73Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Solved Problem 1Solved Problem 1
Figure 4.14 – Flowchart of Telephone Ordering Process
SOLUTION
4 – 74Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Solved Problem 2Solved Problem 2
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.
4 – 75Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Solved Problem 2Solved Problem 2Step No.
Time (min)
Distance (ft) Step Description
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
4 – 76Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Solved Problem 3Solved Problem 3
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.
4 – 77Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Solved Problem 4Solved Problem 4
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.
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Solved Problem 4Solved Problem 4
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%
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Solved Problem 4Solved Problem 4
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
4 – 80Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
Solved Problem 4Solved Problem 4
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 – 81Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.