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S7 - 1
S7 Capacity PlanningPowerPoint presentation toPowerPoint presentation to accompany Heizer, Render, accompany Heizer, Render, and Al-Zu’biand Al-Zu’biOperations Management, Operations Management, Arab World EditionArab World Edition
Original PowerPoint slides byJeff HeylAdapted by Zu’bi Al-Zu’bi
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OutlineOutline
Capacity Design and Effective Capacity Capacity and Strategy Capacity Considerations Managing Demand Demand and Capacity
Management in the Service Sector
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Outline – ContinuedOutline – Continued
Bottleneck Analysis and Theory of Constraints Process Times for Stations,
Systems, and Cycles Theory of Constraints Bottleneck Management
Break-Even Analysis Single-Product Case Multiproduct Case
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Outline – ContinuedOutline – Continued
Break-Even Analysis Assumptions Graphic Approach Algebraic Approach Single-Product Case Multiproduct Case
Reducing Risk with Incremental Changes
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Learning ObjectivesLearning Objectives
When you complete this supplement, you should be able When you complete this supplement, you should be able to:to:
1. Define capacity2. Determine design capacity, effective
capacity, and utilization3. Perform bottleneck analysis4. Compute break-even analysis
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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
Three time horizons
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Planning Over a Time HorizonPlanning Over a Time Horizon
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 CapacityDesign and Effective Capacity
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
Utilization = 148,000/201,600 = 73.4%
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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
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
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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 shiftsEfficiency = 84.6%Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 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 shiftsEfficiency = 84.6%Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
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Capacity and StrategyCapacity and Strategy
Capacity decisions impact all 10 decisions of operations management as well as other functional areas of the organization
Capacity decisions must be integrated into the organization’s mission and strategy
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Capacity ConsiderationsCapacity Considerations
1. Forecast demand accurately
2. Understand the technology and capacity increments
3. Find the optimum operating size (volume)
4. Build for change
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Economies and Diseconomies of ScaleEconomies and Diseconomies 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
Ave
rage
uni
t cos
t(d
olla
rs p
er ro
om p
er n
ight
)
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
S7 - 20© 2011 Pearson Education
Complementary Demand PatternsComplementary Demand Patterns
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
Sale
s in
uni
ts
Time (months)
Jet ski engine sales
Figure S7.3
S7 - 21© 2011 Pearson Education
Complementary Demand PatternsComplementary Demand Patterns
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
Sale
s in
uni
ts
Time (months)
Snowmobile motor sales
Jet ski engine sales
Figure S7.3
S7 - 22© 2013 Pearson Education
Complementary Demand PatternsComplementary Demand Patterns
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
Sale
s in
uni
ts
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 Capacity to DemandTactics for Matching Capacity to Demand1. Making staffing changes2. Adjusting equipment
Purchasing additional machinery Selling or leasing out existing equipment
3. Improving processes to increase throughput4. Redesigning products to facilitate more
throughput5. Adding process flexibility to meet changing
product preferences6. Closing facilities
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Demand and Capacity Management in Demand and Capacity Management in the Service Sectorthe Service Sector
Demand management
Appointment, reservations, FCFS rule
Capacity management
Full time, temporary, part-time staff
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Bottleneck Analysis and Theory of Bottleneck Analysis and Theory of ConstraintsConstraints
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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Process Times for Stations, Systems, and Process Times for Stations, Systems, and CyclesCycles
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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Process Times for Stations, Systems, and Process Times for Stations, Systems, and CyclesCycles
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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A Three-Station Assembly A Three-Station Assembly LineLine
Figure S7.4
2 min/unit 4 min/unit 3 min/unit
A B C
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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 AnalysisCapacity Analysis 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
Toast work station has the longest processing time – 40 seconds
The two lines each deliver a sandwich every 40 seconds so the process time of the combined lines is 40/2 = 20 seconds
At 37.5 seconds, wrapping and delivery has the longest processing time and is the bottleneck
Capacity per hour is 3,600 seconds/37.5 seconds/sandwich = 96 sandwiches per hour
Process cycle time is 30 + 15 + 20 + 40 + 37.5 = 142.5 seconds
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Capacity AnalysisCapacity Analysis
Standard process for cleaning teeth Cleaning and examining X-rays can happen
simultaneously
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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Capacity AnalysisCapacity Analysis
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 Bottleneck is the hygienist at 24 minutes Hourly capacity is 60/24 = 2.5 patients Patient should be complete in 46 minutes
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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Theory of ConstraintsTheory of Constraints
Five-step process for recognizing and managing limitations
Step 1:Step 1: Identify the constraintsStep 2:Step 2: Develop a plan for overcoming the
constraintsStep 3:Step 3: Focus resources on accomplishing Step 2Step 4:Step 4: Reduce the effects of constraints by
offloading work or expanding capabilityStep 5:Step 5: Once overcome, go back to Step 1 and find
new constraints
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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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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 corrid
or
Loss
corridor
Break-Even AnalysisBreak-Even Analysis
Total 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
Cos
t in
dolla
rs
Volume (units per period)Figure S7.5
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Break-Even AnalysisBreak-Even Analysis
BEPx= break-even point in unitsBEP$= break-even point in dollarsP = price per unit (after all discounts)
x= number of units producedTR = total revenue = PxF= fixed costsV= variable cost per unitTC = total costs = F + Vx
TR = TCor
Px = F + Vx
Break-even point occurs whenBreak-even point occurs when
BEPx =F
P - V
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Break-Even AnalysisBreak-Even AnalysisBEPx= break-even point in unitsBEP$= 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 + VxBEP$ = 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 = $4.00 per unit
BEP$ = =F1 - (V/P)
$10,0001 - [(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 = $4.00 per unit
BEP$ = =F1 - (V/P)
$10,0001 - [(1.50 + .75)/(4.00)]
= = $22,857.14$10,000.4375
BEPx = = = 5,714FP - V
$10,0004.00 - (1.50 + .75)
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Break-Even ExampleBreak-Even Example
50,000 –
40,000 –
30,000 –
20,000 –
10,000 –
–| | | | | |0 2,000 4,000 6,000 8,000 10,000
Dol
lars
Units
Fixed costs
Total costs
Revenue
Break-even point
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Break-Even ExampleBreak-Even Example
BEP$ =F
∑ 1 - x (Wi)Vi
Pi
Multiproduct CaseMultiproduct Case
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$76,759
312 days
.621 x $246.02$5.00 = 30.6 31
sandwichesper day
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Reducing Risk with Incremental ChangesReducing Risk with Incremental 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
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Reducing Risk with Incremental ChangesReducing Risk with Incremental Changes
(a) Leading demand with incremental expansion
Expected demand
Figure S7.6
New capacity
Dem
and
Time (years)1 2 3
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Reducing Risk with Incremental ChangesReducing Risk with Incremental Changes
(b) Capacity lags demand with incremental expansion
Expected demand
Figure S7.6
Dem
and
Time (years)1 2 3
New capacity
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Reducing Risk with Incremental ChangesReducing Risk with Incremental 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