OPC Presentation

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

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


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


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


Forecasting Forecasting


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.


DemandDemandQ

uan

tity

Qu

an

tity

TimeTime

(b) Trend: Data consistently increase or decrease.(b) Trend: Data consistently increase or decrease.


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


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.

Forecasting TechniquesForecasting Techniques

�� JudgmentsJudgments

��Causal methodsCausal methods

�� Time series Time series


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

{


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



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



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





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







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



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



aa = = YY –– bbXX bb = 109.229= 109.229



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



aa = = 171 171 –– 109.229(1.64)109.229(1.64) bb = 109.229= 109.229



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



aa = = –– 8.1368.136 bb = 109.229= 109.229



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



aa = = –– 8.1368.136 bb = 109.229= 109.229



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))



aa = = -- 8.1368.136 bb = 109.229= 109.229



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

)



aa = = -- 8.1368.136 bb = 109.229= 109.229



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


300 —

250 —

200 —

150 —

100 —

50

Sa

les

(th

ou

san

ds

of

un

its

)



aa = = -- 8.1368.136 bb = 109.229= 109.229



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


300 —

250 —

200 —

150 —

100 —

50


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





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 ==





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





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





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)





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

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

r S

yri

ng

es

De

ma

nd

fo

r S

yri

ng

es

Actual SalesActual Sales



Actual patientActual patient

arrivalsarrivals

450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

WeekWeek

| | | | | |

00 55 1010 1515 2020 2525 3030

Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es




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

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es




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

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es




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

Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es




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

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es




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

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es




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

ma

nd

fo

r S

yri

ng

es

De

ma

nd

fo

r S

yri

ng

es




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

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es



WeekWeek

450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

| | | | | |

00 55 1010 1515 2020 2525 3030

Syri

ng

e d

em

an

d

Syri

ng

e d

em

an

d

Actual salesActual sales

33--week MAweek MA

forecastforecast

66--week MAweek MA

forecastforecast


Exponential SmoothingExponential Smoothing

450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

WeekWeek

| | | | | |

00 55 1010 1515 2020 2525 3030


αααααααα = 0.10= 0.10

FFt +1t +1 = = FFtt + + αααααααα ((DDtt –– FFt t ))

Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es



450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

WeekWeek

| | | | | |

00 55 1010 1515 2020 2525 3030


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

en

t a

rriv

als



450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

WeekWeek

| | | | | |

00 55 1010 1515 2020 2525 3030

FF44 = 392.1= 392.1


αααααααα = 0.10= 0.10

FF3 3 = (400 + 380)/2= (400 + 380)/2

DD33 = 411= 411


Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es



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


αααααααα = 0.10= 0.10

FF44 = 392.1 = 392.1 FF55 = 394.4= 394.4


Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es



WeekWeek

450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

| | | | | |

00 55 1010 1515 2020 2525 3030

Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es



450 450 —

430 430 —

410 410 —

390 390 —

370 370 —Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es

WeekWeek

| | | | | |

00 55 1010 1515 2020 2525 3030

Exponential Exponential

smoothingsmoothing

αααααααα = 0.10= 0.10



450 450 —

430 430 —

410 410 —

390 390 —

370 370 —Dem

an

d f

or

Syri

ng

es

Dem

an

d f

or

Syri

ng

es

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

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 =

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%





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




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




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




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




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




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




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




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

AggregateAggregate

PlanningPlanning


58

Intermediate Planning in PerspectiveIntermediate Planning in Perspective

Overview of planning levels Overview of planning levels


http://www.baskent.edu.tr/~kilter59

Planning SequencePlanning Sequence


60

Aggregate planning inputs and outputs Aggregate planning inputs and outputs


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


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.


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


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


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

66 6666OPC Lecture OPC Lecture PPtsPPts

67

TrialTrial--andand--Error Techniques Using Graphs and Error Techniques Using Graphs and

SpreadsheetsSpreadsheets

A cumulative graph A cumulative graph


68

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.



70

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.



72

Mathematical TechniquesMathematical Techniques

Linear programmingLinear programming



74

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:


75

Transportation solution Transportation solution


76

Simulation modelsSimulation models


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


78

Disaggregating the aggregate planDisaggregating the aggregate plan


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.


80

The Master Scheduling ProcessThe Master Scheduling Process


81

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


82

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


83

Determining the MPS and projected onDetermining the MPS and projected on--hand inventoryhand inventory


84

Projected onProjected on--hand inventory and MPS are added to the master hand inventory and MPS are added to the master

scheduleschedule


85

The availableThe available--toto--promise inventory quantities have been added to promise inventory quantities have been added to

the master schedulethe master schedule


86

Time fences in an MPS Time fences in an MPS


Material Material Requirement Requirement

Planning Planning (MRP I)(MRP I)


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


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


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



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


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


200

670670

200

150

120

200

150

200

120

670670


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





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


Planned receipts


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


Inventory RecordInventory Record




1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


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.







1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000






1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


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

= + –


Inventory RecordInventory Record




1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 –– 33 –– 33 ––153153 ––273273 ––273273


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

= + –





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


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.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117






1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230


Explanation:Adding the planned receipt brings the balance to 117 + 0 + 230230 – 120 = 227 units.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230


Explanation:Adding the planned receipt brings the balance to 117 + 0 + 230230 – 120 = 227 units.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230


Explanation:Offsetting for a two-week lead time puts the corresponding planned order release back to week 2.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230


Explanation:Offsetting for a two-week lead time puts the corresponding planned order release back to week 2.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230

227227 7777 ––4343


Explanation:The first planned order lasts until week 7, when projected inventory would drop to – 43.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230

227227 7777

230230


Explanation:Adding the second planned receipt brings the balance to 77 + 0 + 230230 – 120 = 187.





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230

227227 7777

230230


Explanation:Adding the second planned receipt brings the balance to 77 + 0 + 230230 – 120 = 187.

187187





1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230

227227 7777

230230

187187

230230


Explanation:The corresponding planned order release is for week 5.






1

230230

117117

00

2

00

00

3

00

120120

4

00

5

00

150150

6

00

120120

7

00

8



Planned receipts


37

Week

0000

117117 117117 227227

230230

230230

227227 7777

230230

187187

230230

187187


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


Planned receipts


Week

150150

230230

117117

120120 150150 120120

37 117117 117117


LotLot--Sizing Rules Sizing Rules –– POQPOQ




1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

POQ lot

size

Gross requirements for weeks 4, 5, and 6

Inventory at end of week 3= –






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0 + 150)






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0 + 150) – 117






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0 + 150) – 117 = 153 units

153153






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0 + 150) – 117 = 153 units

153153






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0 + 150) – 117 = 153 units

153153

150150






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0 + 150) – 117 = 153 units

153153

150150

153153






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

(120 + 0) – 0 = 120 units

153153

150150

153153

150150 00 00 00

120120

120120






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

153153

150150

153153

150150 00 00 00

120120

120120


LotLot--Sizing Rules Sizing Rules –– POQPOQ Figure 1.7Figure 1.7


LotLot--Sizing Rules Sizing Rules –– L4LL4L


Lot Size: L4LLead Time: 2 weeks

Gross requirements

1 2 3 4 5 6 7 8

Scheduled receipts


Planned receipts


Week

150150

230230

117117

120120 150150 120120

37 117117 117117






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

L4L lot

size

Gross requirements in week 4

Inventory balance at end of week 3= –






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

= 120 – 117 = 3L4L lot

size






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

3

= 120 – 117 = 3L4L lot

size






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

3

3

0

= 120 – 117 = 3L4L lot

size






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

3

3

0 00






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

3

3

0

150

00

150






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

3

3

0

150

00

150

0

120

120






1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


37

Week

117117 117117

3

3

0

150

00

150

0

120

120

0


LotLot--Sizing Rules Sizing Rules –– L4LL4L Figure 1.8Figure 1.8


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

Safety StockSafety Stock Figure 1.9Figure 1.9


MRP OutputsMRP Outputs


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




J (4)Seat-frame

boards

H (1)Seat

frame

I (1)Seat

cushion

C (1)Seat

subassembly


MRP ExplosionMRP Explosion

Item: Seat subassemblyLot size: 230 units

Lead time: 2 weeks


1

230230

117117

2 3

120120

4 5

150150

6

120120

7 8

Scheduled receipts


Planned receipts


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



Lead time: 2 weeks

Gross requirements

150150

1 2 3

120120

4 5

150150

6

120120

7 8

Planned receipts


Week

0 00 0

230

230

230

230




Lead time: 2 weeks

Gross requirements

150150

1 2 3

120120

4 5

150150

6

120120

7 8

Planned receipts


Week

0 00 0

230

230

230

230


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


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


0

Week

00 00 000 00 0



Lead time: 2 weeks

Gross requirements

150150

1 2 3

120120

4 5

150150

6

120120

7 8

Planned receipts


Week

0 00 0

230

230

230

230


Lead time: 1 week

Gross requirements

0

1

0

2 3 4 5 6 7 8

Scheduled receipts

Projected on-hand

inventory

Planned

receipts


40

Week

230

0 0 0300 00 0


Lead time: 1 week

Gross requirements

0

1

0

2 3 4 5 6 7 8

Scheduled receipts

Projected on-hand

inventory

Planned

receipts


0

Week

230

0 0 00 00 0

Usage quantity: 1Usage quantity: 1 Usage quantity: 1Usage quantity: 1




Lead time: 2 weeks

Gross requirements

150150

1 2 3

120120

4 5

150150

6

120120

7 8

Planned receipts


Week

0 00 0

230

230

230

230


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


40

Week

230 2300

00 00 00300 00 0


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


0

Week

230 2300

00 00 000 00 0

Usage quantity: 1Usage quantity: 1 Usage quantity: 1Usage quantity: 1




Lead time: 2 weeks

Gross requirements

150150

1 2 3

120120

4 5

150150

6

120120

7 8

Planned receipts


Week

0 00 0

230

230

230

230


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


40

Week

110110 110110

230 2300 0

00 00 00300 00 0

110 180 180 180 180

300

300


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


0

Week

00 00

230 2300 0

00 00 000 00 0

0 0 0 0 0

230

230

230

230




Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


Week

300

300


Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


Week

230

230

230

230

00 00 00 00230 2300 000 00 00 00230 2300 0




Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


Week

300

300


Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


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


200

Week

00 00 000 00 0

Projected on-hand

inventory

Item: Seat-frame boardsLot size: 1500 units

Lead time: 1 week





Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


Week

300

300


Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


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


200

Week

0 00 0

00 00 000 00 0

Projected on-hand

inventory


Lead time: 1 week

Usage quantity: 4Usage quantity: 4





Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


Week

300

300


Lead time: 1 week

Gross requirements

1 2 3 4 5 6 7 8

Planned receipts


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


200

Week

200200 200200

0 00 0

00 00 000 00 0

500 500 500 500 500

1500

1500

Projected on-hand

inventory


Lead time: 1 week




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


Scheduling Scheduling

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


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


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

given timegiven time 150150OPC Lecture OPC Lecture PPtsPPts

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

151151

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.

152152

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

153153

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

154154

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

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)


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


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


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

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

160160

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

161161

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

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

Waxing A A C C C C B B B B B D D D D E E163163OPC Lecture OPC Lecture PPtsPPts

Thank you Thank you


Date post:	30-Oct-2014
Category:	Documents
Upload:	prakash-sethu
View:	29 times
Download:	0 times

OPC Presentation

Documents