s7 - 1© 2011 pearson education, inc. publishing as prentice hall s7 capacity and constraint...
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S7 - 1© 2011 Pearson Education, Inc. publishing as Prentice Hall
S7S7 Capacity and Constraint Management
Capacity and Constraint Management
PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer and Render Heizer and Render Operations Management, 10e Operations Management, 10e Principles of Operations Management, 8ePrinciples of Operations Management, 8e
PowerPoint slides by Jeff Heyl
S7 - 2© 2011 Pearson Education, Inc. publishing as Prentice Hall
Process StrategiesProcess Strategies
The objective of a process strategy is The objective of a process strategy is to build a production process that to build a production process that meets customer requirements and meets customer requirements and product specifications within cost product specifications within cost and other managerial constraintsand other managerial constraints
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CapacityCapacity
The throughput, or the number of units a facility can hold, receive, store, or produce in a period of time
Determines fixed costs
Determines if demand will be satisfied
Determines if facilities remain idle
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Planning Over a Time Planning Over a Time HorizonHorizon
Figure S7.1
Modify capacity Use capacity
Intermediate-range planning
Subcontract Add personnelAdd equipment Build or use inventory Add shifts
Short-range planning
Schedule jobsSchedule personnel Allocate machinery*
Long-range planning
Add facilitiesAdd long lead time equipment *
* Difficult to adjust capacity as limited options exist
Options for Adjusting Capacity
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Design and Effective Design and Effective CapacityCapacity
Design capacity is the maximum theoretical output of a system Normally expressed as a rate
Effective capacity is the capacity a firm expects to achieve given current operating constraints Often lower than design capacity
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Utilization and EfficiencyUtilization and Efficiency
Utilization is the percent of design capacity Utilization is the percent of design capacity achievedachieved
Efficiency is the percent of effective capacity Efficiency is the percent of effective capacity achievedachieved
Utilization = Actual output/Design capacity
Efficiency = Actual output/Effective capacity
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
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Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
S7 - 9© 2011 Pearson Education, Inc. publishing as Prentice Hall
Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
S7 - 10© 2011 Pearson Education, Inc. publishing as Prentice Hall
Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
S7 - 11© 2011 Pearson Education, Inc. publishing as Prentice Hall
Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
S7 - 12© 2011 Pearson Education, Inc. publishing as Prentice Hall
Bakery ExampleBakery Example
Actual production last week = 148,000 rollsEffective capacity = 175,000 rollsDesign capacity = 1,200 rolls per hourBakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
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Bakery ExampleBakery Example: Estimating Output of : Estimating Output of a New Facilitya New Facility
They are considering adding a second production line and they plan to hire new employees and train them tooperate this new lineEffective capacity on this new line = 175,000 rolls whichis the same on the first lineHowever, due to new hires they expect that efficiency of this new line will be 75% rather than 84.6%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
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Capacity ConsiderationsCapacity Considerations
1. Forecast demand accurately
2. Understand the technology and capacity increments
3. Find the optimum operating level (volume)
4. Build for change
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Economies and Economies and Diseconomies of ScaleDiseconomies of Scale
Economies of scale
Diseconomies of scale
25 - room roadside motel 50 - room
roadside motel
75 - room roadside motel
Number of Rooms25 50 75
Av
era
ge
un
it c
os
t(d
olla
rs p
er
roo
m p
er n
igh
t)
Figure S7.2
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Managing DemandManaging Demand Demand exceeds capacity
Curtail demand by raising prices, scheduling longer lead time
Long term solution is to increase capacity
Capacity exceeds demand Stimulate market
Product changes
Adjusting to seasonal demands Produce products with complementary
demand patterns
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Complementary Demand Complementary Demand PatternsPatterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sal
es i
n u
nit
s
Time (months)
Jet ski engine sales
Figure S7.3
S7 - 18© 2011 Pearson Education, Inc. publishing as Prentice Hall
Complementary Demand Complementary Demand PatternsPatterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sal
es i
n u
nit
s
Time (months)
Snowmobile motor sales
Jet ski engine sales
Figure S7.3
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Complementary Demand Complementary Demand PatternsPatterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sal
es i
n u
nit
s
Time (months)
Combining both demand patterns reduces the variation
Snowmobile motor sales
Jet ski engine sales
Figure S7.3
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Tactics for Matching Tactics for Matching Capacity to DemandCapacity to Demand
1. Making staffing changes
2. Adjusting equipment Purchasing additional machinery
Selling or leasing out existing equipment
3. Improving processes to increase throughput
4. Redesigning products to facilitate more throughput
5. Adding process flexibility to meet changing product preferences
6. Closing facilities
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Demand and Capacity Demand and Capacity Management in the Service Management in the Service
SectorSector Demand management (scheduling
customers)
Appointment, reservations, FCFS rule
Capacity management
(scheduling workforce)
Full time, temporary, part-time staff
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Bottleneck Analysis and Bottleneck Analysis and Theory of ConstraintsTheory of Constraints
Each work area can have its own unique capacity
Capacity analysis determines the throughput capacity of workstations in a system
A bottleneck is a limiting factor or constraint
A bottleneck has the lowest effective capacity in a system
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Bottleneck ManagementBottleneck Management
1. Release work orders to the system at the pace of set by the bottleneck
2. Lost time at the bottleneck represents lost time for the whole system
3. Increasing the capacity of a non-bottleneck station is a mirage
4. Increasing the capacity of a bottleneck increases the capacity of the whole system
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Process Times for Stations, Process Times for Stations, Systems, and CyclesSystems, and Cycles
The process time of a stationprocess time of a station is the time to produce a unit at that single workstation
The process time of a systemprocess time of a system is the time of the longest process in the system … the bottleneck
The process cycle timeprocess cycle time is the time it takes for a product to go through the production process with no waiting
These two might be quite
different!
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Process Times for Stations, Process Times for Stations, Systems, and CyclesSystems, and Cycles
The system process timesystem process time is the process time of the bottleneck after dividing by the number of parallel operations
The system capacitysystem capacity is the inverse of the system process time
The process cycle timeprocess cycle time is the total time through the longest path in the system
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A Three-Station Assembly LineA Three-Station Assembly LineProcess time of stationsProcess time of stations: 2, 4 and 3 min/unit: 2, 4 and 3 min/unitProcess time for the systemProcess time for the system: 4 min/unit: 4 min/unitProcess Cycle TimeProcess Cycle Time= 2 + 4 + 3 = 9 min/unit= 2 + 4 + 3 = 9 min/unitSystem Capacity System Capacity = ¼*60(min)= 15 units/hour= ¼*60(min)= 15 units/hour
Figure S7.4
2 min/unit 4 min/unit 3 min/unit
A B C
QuickTime™ and a decompressor
are needed to see this picture.
S7 - 28© 2011 Pearson Education, Inc. publishing as Prentice Hall
Capacity AnalysisCapacity Analysis Two identical sandwich lines
Lines have two workers and three operations
All completed sandwiches are wrapped
Wrap
37.5 sec/sandwich
Order
30 sec/sandwich
Bread Fill Toast
15 sec/sandwich 20 sec/sandwich 40 sec/sandwich
Bread Fill Toast
15 sec/sandwich 20 sec/sandwich 40 sec/sandwich
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Capacity Capacity AnalysisAnalysis Wrap
37.5 sec
Order
30 sec
Bread Fill Toast
15 sec 20 sec 40 sec
Bread Fill Toast
15 sec 20 sec 40 sec
It seems that the toast work station has the longest processing time – 40 seconds, but the two lines work in parallel and each deliver a sandwich every 40 seconds so the process time of the toast work station is 40/2 = 20 seconds per sandwich.
Order: 37 seconds, Combined Assembly:20 seconds, Wrapping:37.5 seconds
With 37.5 seconds, wrapping station has the longest processing time and it is the bottleneck. So process time of the system is 37.5 sec.
System capacity per hour is (1/37.5)*3,600 seconds = 96 sandwiches per hour
Process cycle time is 30 + 15 + 20 + 40 + 37.5 = 142.5 seconds
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Capacity AnalysisCapacity AnalysisExample S4, pg.322Example S4, pg.322
Standard process for cleaning teeth
Cleaning and examining X-rays can happen simultaneously
Customerpays
6 min/unit
Customer Checks in
2 min/unit
A Lab Ass Develops
X-ray
4 min/unit 8 min/unit
Dentistre-processes
A Lab Ass.TakesX-ray
2 min/unit
5 min/unit
DentistExaminesX-ray andprocesses
HygienistCleans
the teeth
24 min/unit
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Capacity Capacity AnalysisAnalysis All possible paths must be compared
Cleaning path is 2 + 2 + 4 + 24 + 8 + 6 = 46 minutes, X-ray exam path is 2 + 2 + 4 + 5 + 8 + 6 = 27 minutes
Longest path involves the hygienist cleaning the teeth, so the process cycle time is 46 min. The patient will be out of door after 46 min.
Bottleneck is the hygienist at 24 minutes which is the process time of the system
System capacity is (1/24)*60 min = 2.5 patients/per hour
Checkout
6 min/unit
Check in
2 min/unit
DevelopsX-ray
4 min/unit 8 min/unit
DentistTakesX-ray
2 min/unit
5 min/unit
X-rayexam
Cleaning
24 min/unit
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Break-Even AnalysisBreak-Even Analysis
Technique for evaluating process and equipment alternatives
Objective is to find the point in dollars and units at which cost equals revenue
Requires estimation of fixed costs, variable costs, and revenue
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Break-Even AnalysisBreak-Even Analysis Fixed costs are costs that continue
even if no units are produced Depreciation, taxes, debt, mortgage
payments
Variable costs are costs that vary with the volume of units produced Labor, materials, portion of utilities
Contribution is the difference between selling price and variable cost
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Break-Even AnalysisBreak-Even Analysis
Costs and revenue are linear functions Generally not the case in the
real world
We actually know these costs Very difficult to verify
Time value of money is often ignored
AssumptionsAssumptions
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Profit corri
dor
Loss
corridor
Break-Even AnalysisBreak-Even AnalysisTotal revenue line
Total cost line
Variable cost
Fixed cost
Break-even pointTotal cost = Total revenue
–
900 –
800 –
700 –
600 –
500 –
400 –
300 –
200 –
100 –
–| | | | | | | | | | | |
0 100 200 300 400 500 600 700 800 900 10001100
Co
st in
do
llars
Volume (units per period)Figure S7.5
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Break-Even AnalysisBreak-Even Analysis
BEPx = break-even point in unitsBEP$ = break-even point in dollarsP = price per unit (after all discounts)
x = number of units producedTR= total revenue = PxF = fixed costsV = variable cost per unitTC= total costs = F + Vx
TR = TCor
Px = F + Vx
Break-even point occurs whenBreak-even point occurs when
BEPx =F
P - V
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Break-Even AnalysisBreak-Even Analysis
BEPx = break-even point in unitsBEP$ = break-even point in dollarsP = price per unit (after all discounts)
x = number of units producedTR= total revenue = PxF = fixed costsV = variable cost per unitTC= total costs = F + Vx
BEP$ = BEPx P
= P
=
=
F(P - V)/P
FP - V
F1 - V/P
Profit = TR - TC= Px - (F + Vx)= Px - F - Vx= (P - V)x - F
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Break-Even ExampleBreak-Even Example
Fixed costs = $10,000 Material = $.75/unitDirect labor = $1.50/unit Selling price = $4.00 per unit
BEP$ = =F
1 - (V/P)$10,000
1 - [(1.50 + .75)/(4.00)]
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Break-Even ExampleBreak-Even Example
Fixed costs = $10,000 Material = $.75/unitDirect labor = $1.50/unit Selling price = $4.00 per unit
BEP$ = =F
1 - (V/P)$10,000
1 - [(1.50 + .75)/(4.00)]
= = $22,857.14$10,000
.4375
BEPx = = = 5,714F
P - V$10,000
4.00 - (1.50 + .75)
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Break-Even ExampleBreak-Even Example
50,000 –
40,000 –
30,000 –
20,000 –
10,000 –
–| | | | | |
0 2,000 4,000 6,000 8,000 10,000
Do
llars
Units
Fixed costs
Total costs
Revenue
Break-even point
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MultiproductMultiproduct Break-Even Break-Even AnalysisAnalysis
BEP$ =F
∑ 1 - x (Wi)Vi
Pi
Each product’s contribution is weighted by Each product’s contribution is weighted by its proportion of salesits proportion of sales
where V = variable cost per unitP = price per unitF = fixed costs
W = percent each product is of total dollar salesi = each product
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Multiproduct ExampleMultiproduct Example
Annual ForecastedItem Price Cost Sales UnitsSandwich $5.00 $3.00 9,000Drink 1.50 .50 9,000Baked potato 2.00 1.00 7,000
Fixed costs = $3,000 per month
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Multiproduct ExampleMultiproduct Example
Annual ForecastedItem Price Cost Sales UnitsSandwich $5.00 $3.00 9,000Drink 1.50 .50 9,000Baked potato 2.00 1.00 7,000
Fixed costs = $3,000 per month
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 .248Drinks 1.50 .50 .33 .67 13,500 .186 .125Baked 2.00 1.00 .50 .50 14,000 .193 .096 potato
$72,500 1.000 .469
Annual WeightedSelling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
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Multiproduct ExampleMultiproduct Example
Annual ForecastedItem Price Cost Sales UnitsSandwich $5.00 $3.00 9,000Drink 1.50 .50 9,000Baked potato 2.00 1.00 7,000
Fixed costs = $3,000 per month
Sandwich $5.00 $3.00 .60 .40 $45,000 .621 .248Drinks 1.50 .50 .33 .67 13,500 .186 .125Baked 2.00 1.00 .50 .50 14,000 .193 .096 potato
$72,500 1.000 .469
Annual WeightedSelling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
BEP$ =F
∑ 1 - x (Wi)Vi
Pi
= = $76,759$3,000 x 12
.469
Daily sales = = $246.02
$76,759312 days
.621 x $246.02$5.00 = 30.6 31
sandwichesper day
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Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes
(a) Leading demand with incremental expansion
Dem
and
Expected demand
New capacity
(c) Attempts to have an average capacity with incremental expansion
Dem
and
New capacity Expected
demand
(b) Capacity lags demand with incremental expansion
Dem
and
New capacity
Expected demand
Figure S7.6
S7 - 46© 2011 Pearson Education, Inc. publishing as Prentice Hall
Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes
(a) Leading demand with incremental expansion
Expected demand
Figure S7.6
New capacity
Dem
and
Time (years)1 2 3
S7 - 47© 2011 Pearson Education, Inc. publishing as Prentice Hall
Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes
(b) Capacity lags demand with incremental expansion
Expected demand
Figure S7.6
Dem
and
Time (years)1 2 3
New capacity
S7 - 48© 2011 Pearson Education, Inc. publishing as Prentice Hall
Reducing Risk with Reducing Risk with Incremental ChangesIncremental Changes(c) Attempts to have an average capacity
with incremental expansion
Expected demand
Figure S7.6
New capacity
Dem
and
Time (years)1 2 3
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Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
Determine states of nature Future demand
Market favorability
Analyzed using decision trees Hospital supply company
Four alternatives
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Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
-$90,000Market unfavorable (.6)
Market favorable (.4)$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant
$0
Do nothing
S7 - 51© 2011 Pearson Education, Inc. publishing as Prentice Hall
Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
-$90,000Market unfavorable (.6)
Market favorable (.4)$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant
$0
Do nothing
EMV = (.4)($100,000) + (.6)(-$90,000)
Large Plant
EMV = -$14,000
S7 - 52© 2011 Pearson Education, Inc. publishing as Prentice Hall
Expected Monetary Value Expected Monetary Value (EMV) and Capacity Decisions(EMV) and Capacity Decisions
-$90,000Market unfavorable (.6)
Market favorable (.4)$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant
$0
Do nothing
-$14,000
$13,000
$18,000