opc presentation
TRANSCRIPT
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Special Lecture on Manufacturing Special Lecture on Manufacturing
Planning & ControlPlanning & Control
Dr. SACHIN S KAMBLE,Dr. SACHIN S KAMBLE,
NITIE, MUMBAI NITIE, MUMBAI
11OPC Lecture OPC Lecture PPtsPPts
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Capacity Assessment Capacity Assessment
Particulars Bean
Cleaner
Roaster Winnower Melangeur Conche Temperin
g &
molding
Packagi
ng
Batch size (kg) 200 250 250 115 1400 140 200
Cycle Time (hrs) 0.25 1.5 - 1.25 50 1 1
# of Machines 1 1 1 1 2 1 1
Hours/day 8 8 8 16 24 16 16
Yield 0.96 1 0.74 1 1 1 1
22OPC Lecture OPC Lecture PPtsPPts
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Capacity Available Capacity Available Particulars Bean
Cleaner
Roaster Winnower Melangeur Conche Tempering &
molding
Packaging
Batch size (kg) 200 250 250 115 1400 140 200
Cycle Time (hrs) 0.25 1.5 - 1.25 50 1 1
# of Machines 1 1 1 1 2 1 1
Hourly Capacity
(kgs)
800 167 450 92 56 140 200
Hours/day 8 8 8 16 24 16 16
Daily intake
capacity (kgs)
6400 1333 3600 1472 1344 2240 3200
Yield 0.96 1 0.74 1 1 1 1
Daily output
Capacity (Kgs)
6144 1333 2664 1472 1344 2240 3200
Monthly output
capacity (kgs)
184320 39990 79920 44160 40320 67200 9600
33OPC Lecture OPC Lecture PPtsPPts
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Equivalent Capacity Equivalent Capacity Particulars Bean Cleaner Roaster Winnower Melangeur Conche Tempering &
molding
Packaging
Batch size (kg) 200 250 250 115 1400 140 200
Cycle Time (hrs) 0.25 1.5 - 1.25 50 1 1
# of Machines 1 1 1 1 2 1 1
Hourly Capacity
(kgs)
800 167 450 92 56 140 200
Hours/day 8 8 8 16 24 16 16
Daily intake
capacity (kgs)
6400 1333 3600 1472 1344 2240 3200
Yield 0.96 1 0.74 1 1 1 1
Daily output
Capacity (Kgs)
6144 1333 2664 1472 1344 2240 3200
Monthly output
capacity (kgs)
184320 39990 79920 44160 40320 67200 9600
Equivalent
capacity in terms
of Nib intake and
output (kgs)
For 62%
Chocolate
184320 x 0.74
=136396.8
29592.6 79920 44160 24998.4 41664 5952
44OPC Lecture OPC Lecture PPtsPPts
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Forecasting Forecasting
55OPC Lecture OPC Lecture PPtsPPts
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Patterns of Patterns of
DemandDemandQ
uan
tity
Qu
an
tity
TimeTime
(a) Horizontal: Data cluster about a horizontal line.(a) Horizontal: Data cluster about a horizontal line.
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Patterns of Patterns of
DemandDemandQ
uan
tity
Qu
an
tity
TimeTime
(b) Trend: Data consistently increase or decrease.(b) Trend: Data consistently increase or decrease.
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Patterns of Patterns of
DemandDemandQ
uan
tity
Qu
an
tity
| | | | | | | | | | | |JJ FF MM AA MM JJ JJ AA SS OO NN DD
MonthsMonths
(c) Seasonal: Data consistently show peaks and valleys.(c) Seasonal: Data consistently show peaks and valleys.
Year 1Year 1
Year 2Year 2
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Patterns of Patterns of
DemandDemandQ
uan
tity
Qu
an
tity
| | | | | |11 22 33 44 55 66
YearsYears
(c) Cyclical: Data reveal gradual increases and (c) Cyclical: Data reveal gradual increases and
decreases over extended periods.decreases over extended periods.
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Forecasting TechniquesForecasting Techniques
�� JudgmentsJudgments
��Causal methodsCausal methods
�� Time series Time series
1010OPC Lecture OPC Lecture PPtsPPts
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Causal MethodsCausal Methods
Linear RegressionLinear RegressionD
ep
en
de
nt
va
ria
ble
De
pe
nd
en
t va
ria
ble
Independent variableIndependent variableXX
YYEstimate ofEstimate of
Y Y fromfrom
regressionregression
equationequation
RegressionRegression
equation:equation:
YY = = aa + + bXbX
ActualActual
valuevalue
of of YY
Value of Value of X X usedused
to estimate to estimate YY
Deviation,Deviation,
or erroror error
{
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
SalesSales AdvertisingAdvertising
MonthMonth (000 units)(000 units) (000 $)(000 $)
11 264264 2.52.5
22 116116 1.31.3
33 165165 1.41.4
44 101101 1.01.0
55 209209 2.02.0
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
SalesSales AdvertisingAdvertising
MonthMonth (000 units)(000 units) (000 $)(000 $)
11 264264 2.52.5
22 116116 1.31.3
33 165165 1.41.4
44 101101 1.01.0
55 209209 2.02.0
aa = = YY –– bbXX bb = = ΣΣΣΣΣΣΣΣXYXY –– nnXYXY
ΣΣΣΣΣΣΣΣXX 2 2 –– nnXX 22
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
aa = = YY –– bbXX bb = = ΣΣΣΣΣΣΣΣXYXY –– nnXYXY
ΣΣΣΣΣΣΣΣXX 2 2 –– nnXX 22
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Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = YY –– bbXX bb = = ΣΣΣΣΣΣΣΣXYXY –– nnXYXY
ΣΣΣΣΣΣΣΣXX 2 2 –– nnXX 22
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = YY –– bbXX bb = = 1560.8 1560.8 –– 5(1.64)(171)5(1.64)(171)
14.90 14.90 –– 5(1.64)5(1.64)22
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = YY –– bbXX bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = 171 171 –– 109.229(1.64)109.229(1.64) bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = –– 8.1368.136 bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = –– 8.1368.136 bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
YY = = –– 8.136 + 109.229(8.136 + 109.229(XX))
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = -- 8.1368.136 bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
YY = = –– 8.136 + 109.229(8.136 + 109.229(XX))
Advertising (thousands of dollars)
| | | |1.0 1.5 2.0 2.5
300 —
250 —
200 —
150 —
100 —
50
Sa
les
(th
ou
san
ds
of
un
its
)
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = -- 8.1368.136 bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
YY = = –– 8.136 + 109.229(8.136 + 109.229(XX))
| | | |1.0 1.5 2.0 2.5
Advertising (thousands of dollars)
300 —
250 —
200 —
150 —
100 —
50
Sa
les
(th
ou
san
ds
of
un
its
)
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
aa = = -- 8.1368.136 bb = 109.229= 109.229
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
YY = = –– 8.136 + 109.229(8.136 + 109.229(XX))
Sa
les
(th
ou
san
ds
of
un
its
)
| | | |1.0 1.5 2.0 2.5
Advertising (thousands of dollars)
300 —
250 —
200 —
150 —
100 —
50
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Causal MethodsCausal Methods
Linear RegressionLinear RegressionSales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY XX 22 YY 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
nnΣΣΣΣΣΣΣΣXYXY –– ΣΣΣΣΣΣΣΣX X ΣΣΣΣΣΣΣΣYY
[[nnΣΣΣΣΣΣΣΣXX 22 –– ((ΣΣΣΣΣΣΣΣXX) ) 22][][nnΣΣΣΣΣΣΣΣY Y 22 –– ((ΣΣΣΣΣΣΣΣYY) ) 22]]rr ==
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
rr = 0.98= 0.98
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
rr = 0.98 = 0.98 r r 22 = 0.96 = 0.96
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
rr = 0.98 = 0.98 r r 22 = 0.96 = 0.96
Forecast for Month 6:Forecast for Month 6:
Advertising expenditure = $1750Advertising expenditure = $1750
YY == -- 8.136 + 109.229(1.75)8.136 + 109.229(1.75)
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Causal MethodsCausal Methods
Linear RegressionLinear Regression
Sales, Sales, YY Advertising, Advertising, XX
MonthMonth (000 units)(000 units) (000 $)(000 $) XYXY X X 22 Y Y 22
11 264264 2.52.5 660.0660.0 6.256.25 69,69669,696
22 116116 1.31.3 150.8150.8 1.691.69 13,45613,456
33 165165 1.41.4 231.0231.0 1.961.96 27,22527,225
44 101101 1.01.0 101.0101.0 1.001.00 10,20110,201
55 209209 2.02.0 418.0418.0 4.004.00 43,68143,681
TotalTotal 855855 8.28.2 1560.81560.8 14.9014.90 164,259164,259
YY = 171= 171 XX = 1.64= 1.64
rr = 0.98 = 0.98 r r 22 = 0.96 = 0.96
Forecast for Month 6:Forecast for Month 6:
Advertising expenditure = $1750Advertising expenditure = $1750
YY == 183.015 or 183,015 hinges183.015 or 183,015 hinges
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
WeekWeek
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
| | | | | |
00 55 1010 1515 2020 2525 3030
De
ma
nd
fo
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Actual SalesActual Sales
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivals
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivalsActual patientActual patient
arrivalsarrivals
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
SyringeSyringe
WeekWeek SalesSales
11 400400
22 380380
33 411411
Dem
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivalsActual patientActual patient
arrivalsarrivals
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
SyringeSyringe
WeekWeek SalesSales
11 400400
22 380380
33 411411
Dem
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Dem
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivals
WeekWeek
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
| | | | | |
00 55 1010 1515 2020 2525 3030
Syringe Syringe
WeekWeek SalesSales
11 400400
22 380380
33 411411
FF44 = = 411 + 380 + 400411 + 380 + 400
33
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivals
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
Syringe Syringe
WeekWeek Sales Sales
11 400400
22 380380
33 411411
FF44 = 397.0= 397.0
Dem
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivals
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
SyringeSyringe
WeekWeek SalesSales
11 400400
22 380380
33 411411
FF44 = 397.0= 397.0
Dem
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Dem
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivals
WeekWeek
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
| | | | | |
00 55 1010 1515 2020 2525 3030
syringesyringe
WeekWeek salessales
22 380380
33 411411
44 415415
FF55 = = 415 + 411 + 380415 + 411 + 380
33
De
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
Actual patientActual patient
arrivalsarrivals
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
Syringe Syringe
WeekWeek salessales
22 380380
33 411411
44 415415
FF55 = 402.0= 402.0
Dem
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TimeTime--Series MethodsSeries Methods
Simple Moving AveragesSimple Moving Averages
WeekWeek
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
| | | | | |
00 55 1010 1515 2020 2525 3030
Syri
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e d
em
an
d
Syri
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e d
em
an
d
Actual salesActual sales
33--week MAweek MA
forecastforecast
66--week MAweek MA
forecastforecast
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
Exponential SmoothingExponential Smoothing
αααααααα = 0.10= 0.10
FFt +1t +1 = = FFtt + + αααααααα ((DDtt –– FFt t ))
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
Exponential SmoothingExponential Smoothing
αααααααα = 0.10= 0.10
FF44 = 0.10(411) + 0.90(390)= 0.10(411) + 0.90(390)
FF3 3 = (400 + 380)/2= (400 + 380)/2
DD33 = 411= 411
Ft +1 = Ft + αααα (Dt – Ft )
Pa
tie
nt
arr
iva
lsP
ati
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t a
rriv
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
FF44 = 392.1= 392.1
Exponential SmoothingExponential Smoothing
αααααααα = 0.10= 0.10
FF3 3 = (400 + 380)/2= (400 + 380)/2
DD33 = 411= 411
FFt +1t +1 = = FFtt + + αααααααα ((DDtt –– FFt t ))
Dem
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
WeekWeek
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
| | | | | |
00 55 1010 1515 2020 2525 3030
FF4 4 = 392.1= 392.1
DD44 = 415= 415
Exponential SmoothingExponential Smoothing
αααααααα = 0.10= 0.10
FF44 = 392.1 = 392.1 FF55 = 394.4= 394.4
FFt +1t +1 = = FFtt + + αααααααα ((DDtt –– FFt t ))
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
WeekWeek
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —
| | | | | |
00 55 1010 1515 2020 2525 3030
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —Dem
an
d f
or
Syri
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es
Dem
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d f
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WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
Exponential Exponential
smoothingsmoothing
αααααααα = 0.10= 0.10
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TimeTime--Series MethodsSeries Methods
Exponential SmoothingExponential Smoothing
450 450 —
430 430 —
410 410 —
390 390 —
370 370 —Dem
an
d f
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Syri
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es
Dem
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d f
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Syri
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WeekWeek
| | | | | |
00 55 1010 1515 2020 2525 3030
33--week MAweek MA
forecastforecast
66--week MAweek MA
forecastforecast
Exponential Exponential
smoothingsmoothing
αααααααα = 0.10= 0.10
![Page 47: OPC Presentation](https://reader034.vdocuments.net/reader034/viewer/2022052321/5451ccacb1af9f00078b4791/html5/thumbnails/47.jpg)
Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Measures of Forecast ErrorMeasures of Forecast Error
EEtt = = DDtt –– FFtt
ΣΣΣΣΣΣΣΣ||EEt t ||
nn
ΣΣΣΣΣΣΣΣEEtt22
nn
ΣΣΣΣΣΣΣΣ[[ ||EEt t | (100)| (100)]] //DDtt
nn
Cumulative Sum of Forecast Cumulative Sum of Forecast Error(CFEError(CFE) = ) = ΣΣΣΣΣΣΣΣEEtt
MSE = MSE =
MAD = MAD =
MAPE = MAPE =
![Page 48: OPC Presentation](https://reader034.vdocuments.net/reader034/viewer/2022052321/5451ccacb1af9f00078b4791/html5/thumbnails/48.jpg)
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 -25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
Choosing a MethodChoosing a MethodForecast ErrorForecast Error
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Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
Measures of Error
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Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
CFE = – 15
Measures of Error
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Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
CFE = – 15
Measures of Error
E = = – 1.875– 15
8
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Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
MSE = = 659.45275
8
CFE = – 15
Measures of Error
E = = – 1.875– 15
8
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Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
MSE = = 659.45275
8
CFE = – 15
Measures of Error
E = = – 1.875– 15
8
σσσσ = 27.4
![Page 54: OPC Presentation](https://reader034.vdocuments.net/reader034/viewer/2022052321/5451ccacb1af9f00078b4791/html5/thumbnails/54.jpg)
Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
MSE = = 659.45275
8
CFE = – 15
Measures of Error
MAD = = 24.4195
8
E = = – 1.875– 15
8
σσσσ = 27.4
![Page 55: OPC Presentation](https://reader034.vdocuments.net/reader034/viewer/2022052321/5451ccacb1af9f00078b4791/html5/thumbnails/55.jpg)
Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
MSE = = 659.45275
8
CFE = – 15
Measures of Error
MAD = = 24.4195
8
MAPE = = 10.2%81.3%
8
E = = – 1.875– 15
8
σσσσ = 27.4
![Page 56: OPC Presentation](https://reader034.vdocuments.net/reader034/viewer/2022052321/5451ccacb1af9f00078b4791/html5/thumbnails/56.jpg)
Choosing a MethodChoosing a MethodForecast ErrorForecast Error
Absolute Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
MSE = = 659.45275
8
CFE = – 15
Measures of Error
MAD = = 24.4195
8
MAPE = = 10.2%81.3%
8
E = = – 1.875– 15
8
σσσσ = 27.4
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AggregateAggregate
PlanningPlanning
5757OPC Lecture OPC Lecture PPtsPPts
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58
Intermediate Planning in PerspectiveIntermediate Planning in Perspective
Overview of planning levels Overview of planning levels
5858OPC Lecture OPC Lecture PPtsPPts
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http://www.baskent.edu.tr/~kilter59
Planning SequencePlanning Sequence
5959OPC Lecture OPC Lecture PPtsPPts
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60
Aggregate planning inputs and outputs Aggregate planning inputs and outputs
6060OPC Lecture OPC Lecture PPtsPPts
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61
Demand and Capacity OptionsDemand and Capacity Options
Demand OptionsDemand Options
Pricing (the degree of price elasticity for the product or Pricing (the degree of price elasticity for the product or service)service)
PromotionPromotion
Back OrdersBack Orders
New DemandNew Demand
Capacity OptionsCapacity Options
Hire and lay off workersHire and lay off workers
Overtime/Slack timeOvertime/Slack time
Part time workersPart time workers
InventoriesInventories
SubcontractingSubcontracting
6161OPC Lecture OPC Lecture PPtsPPts
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62
Basic Strategies for Meeting Basic Strategies for Meeting
Uneven DemandUneven Demand�� Maintain a level workforce.Maintain a level workforce.
�� Maintain a steady output rate.Maintain a steady output rate.
�� Match demand period by period.Match demand period by period.�� Use a combination of decision variables.Use a combination of decision variables.
level capacity strategylevel capacity strategy maintaining a steady rate maintaining a steady rate of regular time output while meeting variations in of regular time output while meeting variations in demand by a combination of options.demand by a combination of options.
chase demand strategychase demand strategy matching capacity to matching capacity to demand; the planned output for the period is set demand; the planned output for the period is set equal to the expected demand for the period. equal to the expected demand for the period.
6262OPC Lecture OPC Lecture PPtsPPts
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http://www.baskent.edu.tr/~kilter63
A varying A varying demand demand pattern pattern and a and a compariscomparison of a on of a chase chase demand demand strategy strategy versus a versus a level level strategy strategy
6363OPC Lecture OPC Lecture PPtsPPts
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64
Choosing a StrategyChoosing a StrategyTwo important factors are Two important factors are company policycompany policy and and costscosts
Comparison of reactive strategiesComparison of reactive strategies
Chase approachChase approach
Capacities (workforce levels, output rates, etc.) are adjusted tCapacities (workforce levels, output rates, etc.) are adjusted to o match demand requirements over the planning horizon.match demand requirements over the planning horizon.
Advantages: Investment in inventory is low, Labor utilization isAdvantages: Investment in inventory is low, Labor utilization iskept highkept high
Disadvantage: The cost of adjusting output rates and/or Disadvantage: The cost of adjusting output rates and/or workforce levelsworkforce levels
Level approachLevel approach
Capacities (workforce levels, output rates, etc.) are kept constCapacities (workforce levels, output rates, etc.) are kept constant ant over the planning horizon.over the planning horizon.
Advantage: Stable output rates and workforce levelsAdvantage: Stable output rates and workforce levels
Disadvantages: Greater inventory costs, Increased overtime and Disadvantages: Greater inventory costs, Increased overtime and idle time, Resource utilizations that vary over timeidle time, Resource utilizations that vary over time
6464OPC Lecture OPC Lecture PPtsPPts
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65
Techniques for Aggregate PlanningTechniques for Aggregate Planning
Informal trialInformal trial--andand--error techniques error techniques
A general procedure for aggregate planning consists of the A general procedure for aggregate planning consists of the
following steps:following steps:
1.1. Determine demand for each period.Determine demand for each period.
2.2. Determine capacities (regular time, overtime, subcontracting) Determine capacities (regular time, overtime, subcontracting)
for each period.for each period.
3.3. Identify company or departmental policies that are pertinent Identify company or departmental policies that are pertinent
(e.g., maintain a safety stock of 5 percent of demand, (e.g., maintain a safety stock of 5 percent of demand,
maintain a reasonably stable workforce).maintain a reasonably stable workforce).
4.4. Determine unit costs for regular time, overtime, Determine unit costs for regular time, overtime,
subcontracting, holding inventories, back orders, layoffs, and subcontracting, holding inventories, back orders, layoffs, and
other relevant costs.other relevant costs.
5.5. Develop alternative plans and compute the cost for each.Develop alternative plans and compute the cost for each.
6.6. If satisfactory plans emerge, select the one that best satisfiesIf satisfactory plans emerge, select the one that best satisfies
objectives. Otherwise, return to step 5.objectives. Otherwise, return to step 5.6565OPC Lecture OPC Lecture PPtsPPts
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66 6666OPC Lecture OPC Lecture PPtsPPts
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TrialTrial--andand--Error Techniques Using Graphs and Error Techniques Using Graphs and
SpreadsheetsSpreadsheets
A cumulative graph A cumulative graph
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ExampleExamplePlanners for a company that makes several models of skateboards Planners for a company that makes several models of skateboards are are
about to prepare the aggregate plan that will cover six periods.about to prepare the aggregate plan that will cover six periods. They They
have assembled the following information: have assembled the following information:
They now want to evaluate a plan that calls for a steady rate ofThey now want to evaluate a plan that calls for a steady rate of regularregular--
time output, mainly using inventory to absorb the uneven demand time output, mainly using inventory to absorb the uneven demand but but
allowing some backlog. Overtime and subcontracting are not used allowing some backlog. Overtime and subcontracting are not used
because they want steady output. They intend to start with zero because they want steady output. They intend to start with zero
inventory on hand in the first period. Prepare an aggregate planinventory on hand in the first period. Prepare an aggregate plan and and
determine its cost using the preceding information. Assume a levdetermine its cost using the preceding information. Assume a level el
output rate of 300 units (skateboards) per period with regular toutput rate of 300 units (skateboards) per period with regular time (i.e., ime (i.e.,
1,8001,800÷÷6 = 300). Note that the planned ending inventory is zero. There 6 = 300). Note that the planned ending inventory is zero. There
are 15 workers, and each can produce 20 skateboards per period. are 15 workers, and each can produce 20 skateboards per period.
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ExampleExample
After reviewing the plan developed in the After reviewing the plan developed in the preceding example, planners have decided preceding example, planners have decided to develop an alternative plan. They have to develop an alternative plan. They have learned that one person is about to retire learned that one person is about to retire from the company. Rather than replace from the company. Rather than replace that person, they would like to stay with that person, they would like to stay with the smaller workforce and use overtime to the smaller workforce and use overtime to make up for the lost output. The reduced make up for the lost output. The reduced regularregular--time output is 280 units per time output is 280 units per period. The maximum amount of overtime period. The maximum amount of overtime output per period is 40 units. Develop a output per period is 40 units. Develop a plan and compare it to the previous one. plan and compare it to the previous one.
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Mathematical TechniquesMathematical Techniques
Linear programmingLinear programming
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ExampleExampleGiven the following information set up the problem in a Given the following information set up the problem in a
transportation table and solve for the minimumtransportation table and solve for the minimum--cost plan: cost plan:
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75
Transportation solution Transportation solution
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76
Simulation modelsSimulation models
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77
Disaggregating the Aggregate Disaggregating the Aggregate
PlanPlanMoving from the aggregate plan to a master schedule Moving from the aggregate plan to a master schedule
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Disaggregating the aggregate planDisaggregating the aggregate plan
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79
Master SchedulingMaster Scheduling
�� The duties of the master scheduler generally includeThe duties of the master scheduler generally include
�� Evaluating the impact of new orders.Evaluating the impact of new orders.
�� Providing delivery dates for orders.Providing delivery dates for orders.
�� Dealing with problems:Dealing with problems:
Evaluating the impact of production delays or late deliveries Evaluating the impact of production delays or late deliveries
of purchased goods.of purchased goods.
Revising the master schedule when necessary because of Revising the master schedule when necessary because of
insufficient supplies or capacity.insufficient supplies or capacity.
Bringing instances of insufficient capacity to the attention of Bringing instances of insufficient capacity to the attention of
production and marketing personnel so that they can production and marketing personnel so that they can
participate in resolving conflicts.participate in resolving conflicts.
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The Master Scheduling ProcessThe Master Scheduling Process
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Weekly forecast requirements for industrial pumps. Weekly forecast requirements for industrial pumps.
EightEight--week schedule showing forecasts, customer orders, and beginning week schedule showing forecasts, customer orders, and beginning
inventoryinventory
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Projected onProjected on--hand inventory is computed week by week until it hand inventory is computed week by week until it
becomes negative becomes negative
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Determining the MPS and projected onDetermining the MPS and projected on--hand inventoryhand inventory
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Projected onProjected on--hand inventory and MPS are added to the master hand inventory and MPS are added to the master
scheduleschedule
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85
The availableThe available--toto--promise inventory quantities have been added to promise inventory quantities have been added to
the master schedulethe master schedule
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86
Time fences in an MPS Time fences in an MPS
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Material Material Requirement Requirement
Planning Planning (MRP I)(MRP I)
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Demand PatternsDemand Patterns
| | | | | | | | | |11 55 1010
DayDay
2000 2000 —
1500 1500 —
1000 1000 —
500 500 —
0 0
Bic
ycle
sB
icycle
s
Figure1.1Figure1.1
(a)(a) (b)(b)
Reorder pointReorder point
OrderOrder
1000 on1000 onday 3day 3
OrderOrder1000 on1000 on
day 8day 8
Rim
sR
ims
Rim
sR
ims
2000 2000 —
1500 1500 —
1000 1000 —
500 500 —
0 0 | | | | | | | | | |
11 55 1010DayDay
2000 —
1500 —
1000 —
500 —
0
Rim
sR
ims
| | | | | | | | | |1 5 10
Day
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Bills ofmaterials
Engineeringand process
designs
Material Requirements Plan Material Requirements Plan
OutputOutput
Figure 1.2Figure 1.2
Inventorytransactions
Inventoryrecords
Othersources
of demand
Authorizedmaster production
schedule
Materialrequirements
plan
MRPMRPexplosionexplosion
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Bill of MaterialsBill of Materials
Figure 1.3
Seat cushion
Seat-frame boards
Front legs A
Ladder-back chair
Back legs
Leg supports
Back slats
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Bill of MaterialsBill of Materials
J (4)Seat-frame
boards
I (1)Seat
cushion
H (1)Seat
frame
G (4)Backslats
F (2)Backlegs
C (1)Seat
subassembly
D (2)Frontlegs
B (1)Ladder-backsubassembly
E (4)Leg
supports
AA
LadderLadder--backbackchairchair
Figure 1.3
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Master Production ScheduleMaster Production Schedule
Ladder-back chair
Kitchen chair
Desk chair
1 2
April May
3 4 5 6 7 8
Aggregate production plan for chair family
Figure 1.4Figure 1.4
200
670670
200
150
120
200
150
200
120
670670
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Inventory RecordInventory Record Figure 1.5Figure 1.5
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements
1 2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
Week
150150
230230
00
00
00
00
120120
00 00
150150
00
120120
00 00
37
0000
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Inventory RecordInventory Record Figure 1.5Figure 1.5
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
Explanation:Gross requirements are the total demand for the two chairs. Projected on-hand inventory in week 1 is 37 + 230 – 150
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Inventory RecordInventory Record
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
Explanation:Gross requirements are the total demand for the two chairs. Projected on-hand inventory in week 1 is 37 + 230 – 150 = 117 units.
Figure 1.5Figure 1.5
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Inventory RecordInventory Record Figure 1.5Figure 1.5
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
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Inventory RecordInventory Record Figure 1.5Figure 1.5
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
Projected on-hand inventory balance at end of week t
Inventory on hand at end of
week t - 1
Gross requirements
in week t
Scheduled or planned receipts in
week t
= + –
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Inventory RecordInventory Record
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 –– 33 –– 33 ––153153 ––273273 ––273273
Figure 1.5Figure 1.5
Projected on-hand inventory balance at end of week t
Inventory on hand at end of
week t - 1
Gross requirements
in week t
Scheduled or planned receipts in
week t
= + –
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 ––33 –– 33 ––153153 –– 273273 –– 273273
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Without a new order in week 4, there will be a shortage of three units: 117 + 0 + 0 – 120 = – 3 units.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Adding the planned receipt brings the balance to 117 + 0 + 230230 – 120 = 227 units.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Adding the planned receipt brings the balance to 117 + 0 + 230230 – 120 = 227 units.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Offsetting for a two-week lead time puts the corresponding planned order release back to week 2.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Offsetting for a two-week lead time puts the corresponding planned order release back to week 2.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
227227 7777 ––4343
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:The first planned order lasts until week 7, when projected inventory would drop to – 43.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
227227 7777
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Adding the second planned receipt brings the balance to 77 + 0 + 230230 – 120 = 187.
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
227227 7777
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:Adding the second planned receipt brings the balance to 77 + 0 + 230230 – 120 = 187.
187187
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Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
227227 7777
230230
187187
230230
Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Explanation:The corresponding planned order release is for week 5.
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Planned OrdersPlanned Orders Figure 1.6Figure 1.6
Item: CDescription: Seat subassembly
Lot Size: 230 unitsLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
00
2
00
00
3
00
120120
4
00
5
00
150150
6
00
120120
7
00
8
00Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
0000
117117 117117 227227
230230
230230
227227 7777
230230
187187
230230
187187
109109OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQItem: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements
1 2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
Week
150150
230230
117117
120120 150150 120120
37 117117 117117
110110OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
POQ lot
size
Gross requirements for weeks 4, 5, and 6
Inventory at end of week 3= –
111111OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
112112OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0 + 150)
113113OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0 + 150) – 117
114114OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0 + 150) – 117 = 153 units
153153
115115OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0 + 150) – 117 = 153 units
153153
116116OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0 + 150) – 117 = 153 units
153153
150150
117117OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0 + 150) – 117 = 153 units
153153
150150
153153
118118OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
(120 + 0) – 0 = 120 units
153153
150150
153153
150150 00 00 00
120120
120120
119119OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ
Item: CDescription: Seat subassembly
Lot Size: P = 3Lead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
153153
150150
153153
150150 00 00 00
120120
120120
120120OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– POQPOQ Figure 1.7Figure 1.7
121121OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements
1 2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
Week
150150
230230
117117
120120 150150 120120
37 117117 117117
122122OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
L4L lot
size
Gross requirements in week 4
Inventory balance at end of week 3= –
123123OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
= 120 – 117 = 3L4L lot
size
124124OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
3
= 120 – 117 = 3L4L lot
size
125125OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
3
3
0
= 120 – 117 = 3L4L lot
size
126126OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
3
3
0 00
127127OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
3
3
0
150
00
150
128128OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
3
3
0
150
00
150
0
120
120
129129OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L
Item: CDescription: Seat subassembly
Lot Size: L4LLead Time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
3
3
0
150
00
150
0
120
120
0
130130OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rules Sizing Rules –– L4LL4L Figure 1.8Figure 1.8
131131OPC Lecture OPC Lecture PPtsPPts
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LotLot--Sizing Rule ComparisonSizing Rule Comparison
•• The FOQ rule generates high average The FOQ rule generates high average
inventory because it creates remnants.inventory because it creates remnants.
•• The POQ rule reduces The POQ rule reduces
average onaverage on--hand inventory hand inventory
because it does a better because it does a better
job of matching order job of matching order
quantity to requirements.quantity to requirements.
•• The L4L rule minimizes The L4L rule minimizes
inventory investment inventory investment
but maximizes the number of orders placed.but maximizes the number of orders placed.132132OPC Lecture OPC Lecture PPtsPPts
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Safety StockSafety Stock Figure 1.9Figure 1.9
133133OPC Lecture OPC Lecture PPtsPPts
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MRP OutputsMRP Outputs
Figure 1.10Figure 1.10
Material requirements plan
Action notices• Releasing new orders• Adjusting due dates
Priority reports• Dispatch lists• Supplier schedules
Capacity reports• Capacity requirements planning• Finite capacity scheduling• Input-output control
Manufacturing resources plan
Performance reportsCost and price data
Routings and time
standards
MRP MRP
explosionexplosion
134134OPC Lecture OPC Lecture PPtsPPts
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Bill of MaterialsBill of Materials
Figure 1.11Figure 1.11
J (4)Seat-frame
boards
H (1)Seat
frame
I (1)Seat
cushion
C (1)Seat
subassembly
135135OPC Lecture OPC Lecture PPtsPPts
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MRP ExplosionMRP Explosion
Item: Seat subassemblyLot size: 230 units
Lead time: 2 weeks
Gross requirements 150150
1
230230
117117
2 3
120120
4 5
150150
6
120120
7 8
Scheduled receipts
Projected on-hand inventory
Planned receipts
Planned order releases
37
Week
117117 117117
0 00 0
00 00 000 00 0
227 227 77 187 187
230230
230230
Figure 1.12Figure 1.12 136136OPC Lecture OPC Lecture PPtsPPts
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Figure 1.12Figure 1.12
Item: Seat subassemblyLot size: 230 units
Lead time: 2 weeks
Gross requirements
150150
1 2 3
120120
4 5
150150
6
120120
7 8
Planned receipts
Planned order releases
Week
0 00 0
230
230
230
230
MRP ExplosionMRP Explosion
137137OPC Lecture OPC Lecture PPtsPPts
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Item: Seat subassemblyLot size: 230 units
Lead time: 2 weeks
Gross requirements
150150
1 2 3
120120
4 5
150150
6
120120
7 8
Planned receipts
Planned order releases
Week
0 00 0
230
230
230
230
MRP ExplosionMRP Explosion
Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
1
00
2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
40
Week
00 00 00300 00 0
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
1
00
2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
0
Week
00 00 000 00 0
Figure 16.12Figure 16.12 138138OPC Lecture OPC Lecture PPtsPPts
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Item: Seat subassemblyLot size: 230 units
Lead time: 2 weeks
Gross requirements
150150
1 2 3
120120
4 5
150150
6
120120
7 8
Planned receipts
Planned order releases
Week
0 00 0
230
230
230
230
Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
0
1
0
2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
40
Week
230
0 0 0300 00 0
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
0
1
0
2 3 4 5 6 7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
0
Week
230
0 0 00 00 0
Usage quantity: 1Usage quantity: 1 Usage quantity: 1Usage quantity: 1
MRP ExplosionMRP Explosion
Figure 16.12Figure 16.12 139139OPC Lecture OPC Lecture PPtsPPts
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Item: Seat subassemblyLot size: 230 units
Lead time: 2 weeks
Gross requirements
150150
1 2 3
120120
4 5
150150
6
120120
7 8
Planned receipts
Planned order releases
Week
0 00 0
230
230
230
230
Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
00
1
00
2 3
00
4 5 6 7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
40
Week
230 2300
00 00 00300 00 0
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
00
1
00
2 3
00
4 5 6 7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
0
Week
230 2300
00 00 000 00 0
Usage quantity: 1Usage quantity: 1 Usage quantity: 1Usage quantity: 1
MRP ExplosionMRP Explosion
Figure 16.12Figure 16.12 140140OPC Lecture OPC Lecture PPtsPPts
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Item: Seat subassemblyLot size: 230 units
Lead time: 2 weeks
Gross requirements
150150
1 2 3
120120
4 5
150150
6
120120
7 8
Planned receipts
Planned order releases
Week
0 00 0
230
230
230
230
Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
00
1
00
4040
2 3
00
4 5
00
6
00
7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
40
Week
110110 110110
230 2300 0
00 00 00300 00 0
110 180 180 180 180
300
300
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
00
1
00
00
2 3
00
4 5
00
6
00
7 8
Scheduled receipts
Projected on-hand
inventory
Planned
receipts
Planned order releases
0
Week
00 00
230 2300 0
00 00 000 00 0
0 0 0 0 0
230
230
230
230
MRP ExplosionMRP Explosion
Figure 1.12Figure 1.12 141141OPC Lecture OPC Lecture PPtsPPts
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Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
300
300
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
230
230
230
230
00 00 00 00230 2300 000 00 00 00230 2300 0
MRP ExplosionMRP Explosion
Figure 1.12Figure 1.12 142142OPC Lecture OPC Lecture PPtsPPts
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Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
300
300
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
230
230
230
230
00 00 00 00230 2300 000 00 00 00230 2300 0
Gross requirements
1
00
2 3 4 5 6 7 8
Scheduled receipts
Planned
receipts
Planned order releases
200
Week
00 00 000 00 0
Projected on-hand
inventory
Item: Seat-frame boardsLot size: 1500 units
Lead time: 1 week
Figure 1.12Figure 1.12
MRP ExplosionMRP Explosion
143143OPC Lecture OPC Lecture PPtsPPts
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Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
300
300
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
230
230
230
230
00 00 00 00230 2300 000 00 00 00230 2300 0
Gross requirements
00
1
00
2 3
12001200
4 5
00
6
00
7 8
Scheduled receipts
Planned
receipts
Planned order releases
200
Week
0 00 0
00 00 000 00 0
Projected on-hand
inventory
Item: Seat-frame boardsLot size: 1500 units
Lead time: 1 week
Usage quantity: 4Usage quantity: 4
Figure 1.12Figure 1.12
MRP ExplosionMRP Explosion
144144OPC Lecture OPC Lecture PPtsPPts
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Item: Seat framesLot size: 300 units
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
300
300
Item: Seat cushionLot size: L4L
Lead time: 1 week
Gross requirements
1 2 3 4 5 6 7 8
Planned receipts
Planned order releases
Week
230
230
230
230
00 00 00 00230 2300 000 00 00 00230 2300 0
Gross requirements
00
1
00
200200
2 3
12001200
4 5
00
6
00
7 8
Scheduled receipts
Planned
receipts
Planned order releases
200
Week
200200 200200
0 00 0
00 00 000 00 0
500 500 500 500 500
1500
1500
Projected on-hand
inventory
Item: Seat-frame boardsLot size: 1500 units
Lead time: 1 week
Figure 1.12Figure 1.12
MRP ExplosionMRP Explosion
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MRP IIMRP II Figure 1.15Figure 1.15
Manufacturing resource planCost and
financial data
Purchasing reports
Financial/ accounting
reports
Sales and marketing
reports
Human resource reports
Manufacturing reports
Inventory records Inventory transactions
Bills of materialsRoutings
Time standards
MRPMRPexplosionexplosion
Master production schedule
Customer orders Forecasts
Material requirements plan
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Scheduling Scheduling
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Scheduling DefinitionsScheduling Definitions
�� Routing:Routing:–– The operations to be performed, their sequence, the work The operations to be performed, their sequence, the work centers, & the time standardscenters, & the time standards
�� Bottleneck:Bottleneck:–– A resource whose capacity is less than the demand placed A resource whose capacity is less than the demand placed on iton it
�� Due date:Due date:–– When the job is supposed to be finishedWhen the job is supposed to be finished
�� Slack:Slack:–– The time that a job can be delayed & still finish by its due The time that a job can be delayed & still finish by its due datedate
�� Queue:Queue:–– A waiting lineA waiting line
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HighHigh--Volume OperationsVolume Operations
�� HighHigh--volume, also called flow operations, volume, also called flow operations, like automobiles, bread, gasoline can be like automobiles, bread, gasoline can be repetitive or continuousrepetitive or continuous–– HighHigh--volume standard items; discrete or volume standard items; discrete or continuous with smaller profit marginscontinuous with smaller profit margins
–– Designed for high efficiency and high utilizationDesigned for high efficiency and high utilization
–– High volume flow operations with fixed routingsHigh volume flow operations with fixed routings
–– Bottlenecks are easily identifiedBottlenecks are easily identified
–– Commonly use lineCommonly use line--balancing to design the balancing to design the process around the required tasksprocess around the required tasks
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LowLow--Volume OperationsVolume Operations
�� LowLow--volume, job shop operations, are volume, job shop operations, are
designed for flexibility. designed for flexibility.
––Use more general purpose equipment Use more general purpose equipment
–– Customized products with higher margins Customized products with higher margins
–– Each product or service may have its Each product or service may have its
own routing (scheduling is much more own routing (scheduling is much more
difficult)difficult)
–– Bottlenecks move around depending Bottlenecks move around depending
upon the products being produced at any upon the products being produced at any
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LowLow--Volume Tool Volume Tool –– Gantt ChartsGantt Charts
�� Developed in the early 1900Developed in the early 1900’’s by Henry s by Henry GanttGantt
�� Load charts (see below Figure 15Load charts (see below Figure 15--1) 1) –– Illustrates the workload relative to the capacity Illustrates the workload relative to the capacity of a resourceof a resource
–– Shows todayShows today’’s job schedule by employees job schedule by employee
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Gantt Chart Gantt Chart (continued(continued))
�� Progress charts:Progress charts:–– Illustrates the planned schedule compared to actual Illustrates the planned schedule compared to actual performanceperformance
–– Brackets show when activity is scheduled to be finished. Brackets show when activity is scheduled to be finished. Note that design and pilot run both finished late and Note that design and pilot run both finished late and feedback has not started yet.feedback has not started yet.
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Scheduling Work Scheduling Work -- Work LoadingWork Loading
�� Infinite loading:Infinite loading:–– Ignores capacity Ignores capacity constraints, but helps constraints, but helps identify bottlenecks in identify bottlenecks in a proposed schedule a proposed schedule to enable proactive to enable proactive managementmanagement
�� Finite loading:Finite loading:–– Allows only as much Allows only as much work to be assigned work to be assigned as can be done with as can be done with available capacity available capacity ––but doesnbut doesn’’t prepare t prepare for inevitable slippagefor inevitable slippage
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Other Scheduling TechniquesOther Scheduling Techniques
�� Forward SchedulingForward Scheduling –– starts processing starts processing
immediately when a job is receivedimmediately when a job is received
�� Backward SchedulingBackward Scheduling –– begin scheduling the jobbegin scheduling the job’’s s
last activity so that the job is finished on due datelast activity so that the job is finished on due date
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How to Sequence JobsHow to Sequence Jobs
�� Which of several jobs should be scheduled Which of several jobs should be scheduled
first?first?
�� Techniques are available to do shortTechniques are available to do short--term term
planning of jobs based on available capacity planning of jobs based on available capacity
& priorities& priorities
�� Priority rules:Priority rules:
–– Decision rules to allocate the relative priority of Decision rules to allocate the relative priority of
jobs at a work centerjobs at a work center
–– Local priority rules: determines priority based Local priority rules: determines priority based
only on jobs at that workstationonly on jobs at that workstation
–– Global priority rules: also considers the remaining Global priority rules: also considers the remaining
workstations a job must pass throughworkstations a job must pass through 155155OPC Lecture OPC Lecture PPtsPPts
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Commonly Used Priorities RulesCommonly Used Priorities Rules
�� First come, first served (FCFS)First come, first served (FCFS)
�� Last come, first served (LCFS)Last come, first served (LCFS)
�� Earliest due date (EDD)Earliest due date (EDD)
�� Shortest processing time (SPT)Shortest processing time (SPT)
�� Longest processing time (LPT)Longest processing time (LPT)
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Example Using SPT, EDDExample Using SPT, EDD
Example Using SPT and EDD at Jill's Machine Shop-Work Center 101
Job Time Days to SPT Rule EDD Rule
Job Number (includes Setup & Run Time) Due Date Sequence Sequence
AZK111 3 days 3 EZE101 AZK111
BRU872 2 days 6 BRU872 EZE101
CUF373 5 days 8 AZK111 DBR664DBR664 4 days 5 DBR664 BRU872EZE101 1day 4 FID448 CUF373FID448 4 days 9 CUF373 FID448
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How to Use Priority RulesHow to Use Priority Rules
1.1. Decide which priority rule to useDecide which priority rule to use
2.2. List all jobs waiting to be processed List all jobs waiting to be processed
with their job timewith their job time
3.3. Using priority rule determine which Using priority rule determine which
job has highest priority then job has highest priority then
second, third and so onsecond, third and so on
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Measuring Scheduling PerformanceMeasuring Scheduling Performance
�� Job flow time:Job flow time:–– Time a job is completed minus the time the job was first Time a job is completed minus the time the job was first available for processing; available for processing; avg. flow time measures avg. flow time measures responsivenessresponsiveness
�� Average # jobs in system:Average # jobs in system:–– Measures amount of workMeasures amount of work--inin--progress; progress; avg. # measures avg. # measures responsiveness and workresponsiveness and work--inin--process inventoryprocess inventory
�� MakespanMakespan::–– The time it takes to finish a batch of jobs; The time it takes to finish a batch of jobs; measure of measure of efficiencyefficiency
�� Job lateness:Job lateness:–– Whether the job is completed ahead of, on, or behind Whether the job is completed ahead of, on, or behind schedule; schedule;
�� Job tardinessJob tardiness::–– How long after the due date a job was completed, How long after the due date a job was completed, measures due date performancemeasures due date performance 159159OPC Lecture OPC Lecture PPtsPPts
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Scheduling Performance CalculationsScheduling Performance Calculations
�� Calculation mean flow time:Calculation mean flow time:
–– MFT= (sum job flow times)/ # of jobsMFT= (sum job flow times)/ # of jobs
= (10+13+17+20)/4 = 60/4 = = (10+13+17+20)/4 = 60/4 = 15 days15 days
�� Calculating average number of jobs in the system:Calculating average number of jobs in the system:
–– Average # Jobs =(sum job flow times)/ # days to complete Average # Jobs =(sum job flow times)/ # days to complete
batchbatch
= (60)/20 = = (60)/20 = 3 job3 job
�� MakespanMakespan is the length of time to complete a batchis the length of time to complete a batch
–– MakespanMakespan = Completion time for Job D minus start time for Job = Completion time for Job D minus start time for Job
AA
= 20 = 20 –– 0 = 20 days0 = 20 days
Job A finishes on day 10 Job B finishes on day 13
Job C finishes on day 17
Job D ends on day 20
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Performance Calculations Performance Calculations (Cont.)(Cont.)
�� Lateness and Tardiness are both measures Lateness and Tardiness are both measures related to customer servicerelated to customer service
�� Average tardinessAverage tardiness is a more relevant is a more relevant CustomerCustomer ServiceService measurement as measurement as illustrated belowillustrated below
Example 15-5 Calculating job lateness and job tardiness
Completion
Job Date Due Date Lateness TardinessA 10 15 -5 0
B 13 15 -2 0
C 17 10 7 7
D 20 20 0 0Average 0 1.75
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Sequencing Jobs through Two Work Centers Sequencing Jobs through Two Work Centers ––
JohnsonJohnson’’s Rules Rule
�� JohnsonJohnson’’s Rule s Rule –– a technique for a technique for minimizing minimizing makespanmakespan in a twoin a two--stage, stage, unidirectional processunidirectional process–– Step 1Step 1 –– List the jobs and the processing time List the jobs and the processing time for each activityfor each activity
–– Step 2Step 2 –– Find the shortest activity processing Find the shortest activity processing time among the jobs not yet scheduledtime among the jobs not yet scheduled�� If the shortest Processing time is for a 1If the shortest Processing time is for a 1stst activity, activity, schedule that job in the earliest available position in schedule that job in the earliest available position in the job sequencethe job sequence
�� If the shortest processing time is for 2If the shortest processing time is for 2ndnd activity, activity, schedule that job in the last available position in the schedule that job in the last available position in the job sequencejob sequence
�� When you schedule a job eliminate it from further When you schedule a job eliminate it from further considerationconsideration
–– Step 3Step 3 –– Repeat step 2 until you have put all Repeat step 2 until you have put all activities for the job in the scheduleactivities for the job in the schedule 162162OPC Lecture OPC Lecture PPtsPPts
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JohnsonJohnson’’s Rule Example:s Rule Example: VickiVicki’’s Office Cleanerss Office Cleaners does the annualdoes the annual
major cleaning of university buildings. The job requires moppingmajor cleaning of university buildings. The job requires mopping (1(1stst
activity) and waxing (2activity) and waxing (2ndnd activity) of each building. Vicki wants to activity) of each building. Vicki wants to
minimize the time it takes her crews to finish cleaning (minimizminimize the time it takes her crews to finish cleaning (minimize e
makespanmakespan) the five buildings. She needs to finish in 20 days.) the five buildings. She needs to finish in 20 days.
Activity 1 Activity 2 Johnson's Activity 1 Activity 2
Hall Mopping (days) Waxing (days) Sequence Mopping (days) Waxing (days)
Adams Hall 1 2 Adams Hall (A) 1 2
Bryce Building 3 5 Chemistry Building (C) 2 4
Chemistry Building 2 4 Bryce Building (B) 3 5
Drake Union 5 4 Drake Union (D) 5 4Evans Center 4 2 Evans Center (E) 4 2
Activity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Mopping A C C B B B D D D D D E E E E
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Thank you Thank you
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