1 - 1 7 suppl capacity planning heizer and render principles of operations management, 8e powerpoint...
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1 - 3 © 2011 Pearson Education, Inc. publishing as Prentice Hall Planning Over a Time Horizon Figure S7.1 Modify capacityUse capacity Intermediate- range planning SubcontractAdd personnel Add equipmentBuild or use inventory Add shifts Short-range planning Schedule jobs Schedule personnel Allocate machinery * Long-range planning Add facilities Add long lead time equipment * * Difficult to adjust capacity as limited options exist Options for Adjusting CapacityTRANSCRIPT
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7Suppl Capacity Planning
Heizer and Render Heizer and Render Principles of Operations Management, 8ePrinciples of Operations Management, 8e
PowerPoint slides by Jeff Heyl
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Capacity The throughput, or the number of
units a facility can hold, receive, store, or produce in a period of time
Determines capital requirements and, therefore, fixed costs
Determines if demand will be satisfied Three time horizons
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Planning 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 Capacity
Design capacity is the maximum theoretical output of a system in a given period Normally expressed as a rate
Effective capacity is the capacity a firm expects to achieve given current operating constraints Often lower than design capacity
Measuring Capacity: maximum number of units produced in a specific time
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Utilization 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 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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Prentice Hall
Bakery 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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Prentice Hall
Bakery 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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Prentice Hall
Bakery 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 Strategy
Sustained profits come from building competitive advantage, not just from a good financial return on a specific process.
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 Considerations
1. Forecast demand accurately: whatever the new product, its prospects and the life cycle of existing products must be determined.
2. Understand the technology and capacity increments: Aid technology decisions by analysis of cost, human resources, quality, and reliability.
3. Find the optimum operating level (volume): If smaller, fixed cost is too
burdensome, if larger, the facility becomes more than one manger task1. Build for change: Build flexibility into facility and
equipment
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Economies 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 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 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 Demand
1. 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 the 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 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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Process Times for Stations, Systems, 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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A Three-Station Assembly Line
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.
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Process Times for Stations, Systems, 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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Capacity 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 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 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 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 Constraints Five-step process for recognizing and
managing limitationsStep 1:Step 1: Identify the constraintStep 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 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 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 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 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 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
Cos
t in
dolla
rs
Volume (units per period)Figure S7.5
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Break-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 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 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$10,000
4.00 - (1.50 + .75)
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Break-Even Example
50,000 –
40,000 –
30,000 –
20,000 –
10,000 –
–| | | | | |0 2,000 4,000 6,000 8,000 10,000
Dol
lars
Units
Fixed costs
Total costs
Revenue
Break-even point
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Break-Even Example
BEP$ =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 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 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
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 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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Expected Monetary Value (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 (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
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Expected Monetary Value (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
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Expected Monetary Value (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
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Strategy-Driven Investment
Operations may be responsible for return-on-investment (ROI)
Analyzing capacity alternatives should include capital investment, variable cost, cash flows, and net present value
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Net Present Value (NPV)
where F = future valueP = present valuei = interest rate
N = number of years
P = F(1 + i)N
F = P(1 + i)N
In general:
Solving for P:While this works fine, it is cumbersome for larger values of N
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NPV Using Factors
P = = FXF(1 + i)N
where X = a factor from Table S7.1 defined as = 1/(1 + i)N and F = future value
Portion of Table S7.1
Year 6% 8% 10% 12% 14%1 .943 .926 .909 .893 .8772 .890 .857 .826 .797 .7693 .840 .794 .751 .712 .6754 .792 .735 .683 .636 .5925 .747 .681 .621 .567 .519
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Present Value of an Annuity
An annuity is an investment which generates uniform equal payments
S = RXwhere X = factor from Table S7.2
S = present value of a series of uniform annual receiptsR = receipts that are received every year of the life of the investment
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Present Value of an Annuity
Portion of Table S7.2
Year 6% 8% 10% 12% 14%1 .943 .926 .909 .893 .8772 1.833 1.783 1.736 1.690 1.6473 2.676 2.577 2.487 2.402 2.3224 3.465 3.312 3.170 3.037 2.9145 4.212 3.993 3.791 3.605 3.433
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Present Value of an Annuity
$7,000 in receipts per for 5 yearsInterest rate = 6%
From Table S7.2X = 4.212
S = RXS = $7,000(4.212) = $29,484
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Limitations
1. Investments with the same NPV may have different projected lives and salvage values
2. Investments with the same NPV may have different cash flows
3. Assumes we know future interest rates4. Payments are not always made at the end
of a period