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B7801: Operations Management27 March 1998 - Agenda
•Mass Customization•National Cranberry Cooperative•Capacity Management•Queue and customer management
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Why is capacity management important?
ROA PROFIT MARGIN
ASSET TURNOVER = x
1) Driver of Financial Performance
2) Driver of Operating Performance
•direct labor•overhead costs•productivity
•facility utilization•equipment utilization•inventory turnover
delivery performance• fill rate• lead time
service levels• wait times• availability
Capacity Utilization
increasing
decreasing
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Matching demand and capacity
time
# units/hr.
demand
poor service / lost revenue
excess assets and costs
capacity
How do firms match capacity to demand?
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Key steps in capacity planning
STEP 1: Forecast demand– forecast quantities– forecast methods– understanding errors and uncertainties
STEP 2: Assess the options for meeting demand– capacity increases/decreases– capacity allocation– inventory– demand management
STEP 3: Construct and evaluate the plans– planning methodology– evaluation/robustness
• scenario analysis• simulation
What is demand for our product/service like?
What are its main characteristics?
How accurately can we predict it?
What options do we have available to meet demand?
What constraints do we face?
What is the relationship between capacity and service levels?
What is our cost structure?
How do we go about developing a plan?
What is the effect of forecast uncertainty on plan performance?
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A hierarchy of time scales
Long Term (1-10 yrs.)
Medium Term(3 mon. - 1 yr.)
Short Term(hourly, daily,wkly)
facility expansionhiring/firingtechnology investmentsmake/buy
capacity allocationhiring/firingovertimeinventory build-up
detailed prod. schedulingstaff schedulingdetailed allocation
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An example: National Cranberry Cooperative• Forecasting demand
– peak season same as previous year– no increase in total volume – increase to 70% wet
• Assessing options to meet demand– do nothing– overtime– capacity expansion (bins, dryers)
• Constructing and evaluating a plan– methodology (trial and error, incremental analysis)– process flow analysis to determine cost/performance
• overtime cost• truck backup
– evaluation/robustness• average cost/benefit estimates• worst-case performance (peak day) (also remember McDonald’s,BK!!)• simulation
• Time scales (med: add dryer, short: overtime on demand)
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Forecasting• What to forecast
– level of aggregation • one location vs. region• individual product vs. product family• daily, weekly or monthly
– trade-off: detail vs. forecast accuracy
• Forecast methodology– subjective methods (Delphi method)– time series (exponential smoothing)– causal methods (regression)
• Forecast errors– point estimate = “best guess”– magnitude of error
• MAD (mean absolute deviation)• MSD (mean square deviation)
– distribution of errors
Aggregate where possible, but keep enough detail to make your planning decisions.
If data is available and product or service is mature, use data intensive methods; otherwise, resort to subjective methods.
Try to quantify forecast errors as well as point estimates. Factor forecast uncertainty into your plans.
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Ex: Aggregate planning in an ice tea bottling plant
• demand forecast next 9 months:27, 20, 36, 45, 78, 97, 118, 121, 82 (x10,000 units (12-
oz.))• 20 workers required• capacity is 3,000 units/hour• wages:
– $15/hr regular time– $16/hr second shift (8 hr shifts)– $20/hr overtime
• hiring/firing– 16 hrs. of training @ $15/hr.– 80 hrs. severance pay @ $16/hr.
• 500,000 unit warehouse. Extra storage is $1/month per 100 units.
• unit revenue = $0.40, unit cost (material) = $0.20 • $2M working capital line of credit (18% per year). Current
balance is $1M.
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Strategy 1: Chase demand (production = demand)
20
40
60
80
100
120
140
Jan Feb Mar April May June July Aug Sept
Demand Prod
Monthly Demand/Productionx10,000 units/month
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Chase strategy financialsSeptAugJuly JuneMayAprilMarFebJan
Units82.00121.00118.0097.0078.0045.0036.0020.0027.00(x 10,000 units)Demand82.00121.00118.0097.0078.0045.0036.0020.0027.00(x 10,000 units)Sales32.8048.4047.2038.8031.2018.0014.408.0010.80(x $10,000 )Rev. (Cash In)
Labor Hrs. Avail32.0032.0032.0032.0032.0032.0032.0032.0032.00(x 100 hrs)Std32.0032.0032.0032.0032.000.000.000.000.00(x 100 hrs)2nd Shirt16.0016.0016.0016.0016.0016.0016.0016.0016.00(x 100 hrs)OT
Production Plan84.00120.00120.0096.0072.0048.0036.0024.0024.00(x 10,000 units)Prod. Output
0000020000New Hires2000000000No. Fired
32.0032.0032.0032.0032.0032.0024.0016.0016.00(x 100 hrs)Reg. Hours24.0032.0032.0032.0016.000.000.000.000.00(x 100 hrs)2nd Shift Hrs
0.0016.0016.000.000.000.000.000.000.00(x 100 hrs)OT Hours0.000.000.000.000.000.000.000.000.00(x 10,000 units)Ext. WH
Inventory18.0019.0017.0018.0024.0021.0021.0017.0020.00(x 10,000 units)Start20.0018.0019.0017.0018.0024.0021.0021.0017.00(x 10,000 units)End
(x 10,000 units)Units in WH20.0018.0019.0017.0018.0024.0021.0021.0017.00(x 10,000 units)Co.
0.000.000.000.000.000.000.000.000.00(x 10,000 units)ExternCash OutOperations
16.8024.0024.0019.2014.409.607.204.804.80(x $10,000 )Materials4.804.804.804.804.804.803.602.402.40(x $10,000 )Std Labor3.845.125.125.122.560.000.000.000.00(x $10,000 )2nd Shift Labor0.003.203.200.000.000.000.000.000.00(x $10,000 )OT Labor0.000.000.000.000.000.480.000.000.00(x $10,000 )Hiring Cost2.560.000.000.000.000.000.000.000.00(x $10,000 )Firing Cost0.000.000.000.000.000.000.000.000.00(x $10,000 )Ext. WH
Plant Financing Costs0.891.041.181.301.421.451.481.471.50(x $10,000 )Fin. Cost (prev. mon.)
28.8938.1638.3030.4223.1816.3312.288.678.70(x $10,000 )Total Cash Out-55.32-59.23-69.47-78.38-86.76-94.77-96.45-98.57-97.90(x $10,000 )Cash Balance
100.00%$249.60(x $10,000 )Total Plan Rev.77.40%$193.20(x $10,000 )Total Oper. Cst
4.70%$11.72(x $10,000 )Total Fin. Cst.17.90%$44.68(x $10,000 )Plant Earnings
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Strategy 2: Level production
20
40
60
80
100
120
140
Jan Feb Mar April May June July Aug Sept
Demand Prod
Monthly Demand/Productionx10,000 units/month
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Level strategy financialsSeptAugJuly JuneMayAprilMarFebJan
Units82.00121.00118.0097.0078.0045.0036.0020.0027.00(x 10,000 units)Demand82.00121.00118.0097.0078.0045.0036.0020.0027.00(x 10,000 units)Sales32.8048.4047.2038.8031.2018.0014.408.0010.80(x $10,000 )Rev. (Cash In)
Labor Hrs. Avail32.0032.0032.0032.0032.0032.0032.0032.0032.00(x 100 hrs)Std16.0016.0016.0016.0016.0016.0016.0016.000.00(x 100 hrs)2nd Shirt16.0016.0016.0016.0016.0016.0016.0016.0016.00(x 100 hrs)OT
Production Plan69.3369.3369.3369.3369.3369.3369.3369.3369.33(x 10,000 units)Prod. Output
0000000010New Hires000000000No. Fired
32.0032.0032.0032.0032.0032.0032.0032.0032.00(x 100 hrs)Reg. Hours14.2214.2214.2214.2214.2214.2214.2214.220.00(x 100 hrs)2nd Shift Hrs
0.000.000.000.000.000.000.000.0014.22(x 100 hrs)OT Hours0.000.0034.3383.00110.67119.3395.0061.6712.33(x 10,000 units)Ext. WH
Inventory32.6684.33133.00160.67169.33145.00111.6762.3320.00(x 10,000 units)Start20.0032.6684.33133.00160.67169.33145.00111.6762.33(x 10,000 units)End
(x 10,000 units)Units in WH20.0032.6650.0050.0050.0050.0050.0050.0050.00(x 10,000 units)Co.
0.000.0034.3383.00110.67119.3395.0061.6712.33(x 10,000 units)ExternCash OutOperations
13.8713.8713.8713.8713.8713.8713.8713.8713.87(x $10,000 )Materials4.804.804.804.804.804.804.804.804.80(x $10,000 )Std Labor2.282.282.282.282.282.282.282.280.00(x $10,000 )2nd Shift Labor0.000.000.000.000.000.000.000.002.84(x $10,000 )OT Labor0.000.000.000.000.000.000.000.000.24(x $10,000 )Hiring Cost0.000.000.000.000.000.000.000.000.00(x $10,000 )Firing Cost0.000.000.340.831.111.190.950.620.12(x $10,000 )Ext. WH
Plant Financing Costs1.071.461.822.052.152.061.921.691.50(x $10,000 )Fin. Cost (prev. mon.)
22.0122.4023.1123.8224.2024.1923.8123.2523.37(x $10,000 )Total Cash Out-60.57-71.35-97.35-121.44-136.43-143.43-137.23-127.82-112.57(x $10,000 )Cash Balance
100.00%$249.60(x $10,000 )Total Plan Rev.77.91%$194.45(x $10,000 )Total Oper. Cst
6.30%$15.71(x $10,000 )Total Fin. Cst.15.80%$39.43(x $10,000 )Plant Earnings
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Strategy 3: Mixed
20
40
60
80
100
120
140
Jan Feb Mar April May June July Aug Sept
Demand Prod
Monthly Demand/Productionx10,000 units/month
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Mixed strategy financialsSeptAugJuly JuneMayAprilMarFebJan
Units82.00121.00118.0097.0078.0045.0036.0020.0027.00(x 10,000 units)Demand82.00121.00118.0097.0078.0045.0036.0020.0027.00(x 10,000 units)Sales32.8048.4047.2038.8031.2018.0014.408.0010.80(x $10,000 )Rev. (Cash In)
Labor Hrs. Avail32.0032.0032.0032.0032.0032.0032.0032.0032.00(x 100 hrs)Std32.0032.0032.0032.0032.000.000.000.000.00(x 100 hrs)2nd Shirt16.0016.0016.0016.0016.0016.0016.0016.0016.00(x 100 hrs)OT
Production Plan96.0096.0096.0096.0096.0048.0048.0024.0024.00(x 10,000 units)Prod. Output
0000020000New Hires2000000000No. Fired
32.0032.0032.0032.0032.0032.0032.0016.0016.00(x 100 hrs)Reg. Hours32.0032.0032.0032.0032.000.000.000.000.00(x 100 hrs)2nd Shift Hrs
0.000.000.000.000.000.000.000.000.00(x 100 hrs)OT Hours0.000.000.003.004.000.000.000.000.00(x 10,000 units)Ext. WH
Inventory6.0031.0053.0054.0036.0033.0021.0017.0020.00(x 10,000 units)Start
20.006.0031.0053.0054.0036.0033.0021.0017.00(x 10,000 units)End(x 10,000 units)Units in WH
20.006.0031.0050.0050.0036.0033.0021.0017.00(x 10,000 units)Co.0.000.000.003.004.000.000.000.000.00(x 10,000 units)Extern
Cash OutOperations
19.2019.2019.2019.2019.209.609.604.804.80(x $10,000 )Materials4.804.804.804.804.804.804.802.402.40(x $10,000 )Std Labor5.125.125.125.125.120.000.000.000.00(x $10,000 )2nd Shift Labor0.000.000.000.000.000.000.000.000.00(x $10,000 )OT Labor0.000.000.000.000.000.480.000.000.00(x $10,000 )Hiring Cost2.560.000.000.000.000.000.000.000.00(x $10,000 )Firing Cost0.000.000.000.030.040.000.000.000.00(x $10,000 )Ext. WH
Plant Financing Costs0.821.091.351.471.481.501.481.471.50(x $10,000 )Fin. Cost (prev. mon.)
32.5030.2130.4730.6230.6416.3815.888.678.70(x $10,000 )Total Cash Out-54.46-54.76-72.95-89.68-97.86-98.43-100.05-98.57-97.90(x $10,000 )Cash Balance
100.00%$249.60(x $10,000 )Total Plan Rev.76.89%$191.91(x $10,000 )Total Oper. Cst
4.87%$12.15(x $10,000 )Total Fin. Cst.18.24%$45.54(x $10,000 )Plant Earnings
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Components of the Queuing Phenomenon
Servers
Waiting Line
Servicing System
CustomerArrivals Exit
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Some Service Generalizations
1. Everyone is an expert on services.
2. Services are idiosyncratic.
3. Quality of work is not quality of service.
4. High-contact services are experienced, whereas goods are consumed.
5. We cannot inventory services (capacity becomes dominant issue)
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Capacity Management in Services
•You cannot store service output
•If you cannot store output, you store the demand
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Strategic Service Vision
• Who is our customer?• How do we differentiate our
service in the market?• What is our service package
and the focus?• What are the actual
processes, systems, people, technology and leadership?
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Service-System Design Matrix
Mail contact
Face-to-faceloose specs
Face-to-facetight specs
PhoneContact
Face-to-facetotal
customization
none some much
High
LowHigh
Low
Degree of customer/server contact
On-sitetechnology
SalesOpportunity
ProductionEfficiency
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Three Contrasting Service Designs
• The production line approach
• The self-service approach
• The personal attention approach
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Some Performance Measures
• Average time spent waiting in queue
• Average time in system• Average length of queue• Average number of customers in
system• Probability that a customer
waits before service begins• Server utilization
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Strategies for effective capacity management
• Maximize process flexibility– mix flexibility– volume flexibility
• Standardize the product/service reduce variety– risk pooling– reduced forecast error
• Centralize operations– risk pooling– reduced forecast error
• Reduce lead time– reduced forecast error– minimize overshooting/undershooting demand
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Some Service Generalizations1. Everyone is an expert on
services.
2. Services are idiosyncratic.
3. Quality of work is not quality of service.
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Some Service Generalizations4. High-contact services are experienced,
whereas goods are consumed.
5. Effective management of services requires an understanding of marketing and personnel, as well as operations.
6. Services often take the form of cycles of encounters involving face-to-face, phone, electromechanical, and/or mail interactions
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Characteristics of a Well-Designed Service System1. Each element of the service system is
consistent with the operating focus of the firm.
2. It is user-friendly.
3. It is robust.
4. It is structured so that consistent performance by its people and systems is easily maintained
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Characteristics of a Well-Designed Service System5. It provides effective links between the
back office and the front office so that nothing falls between the cracks.
6. It manages the evidence of service quality in such a way that customers see the value of the service provided.
7. It is cost-effective
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Components of the Queuing Phenomenon
Servers
Waiting LineCustomerArrivals Exit
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Customers arrivals to a bank• Average customers per minute =
10• Average service time = 30 seconds
– HOW MANY TELLERS ARE NEEDED?
Case I: No variabilityCase II: Variability in arrival processCase III: Variability in arrival & service processes
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How many tellers?: Variability in both arrival and service processes
# Tellers Avg. Delay Utilization
6 17.6 0.833
7 4.9 0.714
8 1.7 0.625
9 0.6 0.556
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Methods for reducing impact of variability• Demand
– better forecasting– pricing– appointment systems
• Process– standardization– training– automation– self-service– variable staffing– use of inventory
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Tools for capacity planning in service systems• Queueing models
– fast– little data needed
• Simulation– can handle complexity
• Linear programming– to allocate capacity over multiple facilities or
multiple locations– scheduling and other constraints can be readily
incorporated
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Line Structures
Single Channel
Multichannel
SinglePhase
Multiphase
One-personbarber shop
Car wash
Hospitaladmissions
Bank tellers’windows
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Degree of Patience
No Way! No Way!
BALK RENEG
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Key facts needed for a model•Average number of customer
arrivals per unit of time
•Average service time per customer
•The number of servers
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Assumptions in our models• FCFS• Events occur one at a time• We are interested in long run avg
performance• Unlimited storage• Utilization < 100%• No predictable variation• Unpredictable variation
– arrivals - Poisson processes– service - exponential distributed processing times
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Operating Focus
• Customer treatment
• Speed and convenience of service delivery
• Variety of services
• Quality of tangibles
• Unique skills
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Service-System Design Matrix
Mail contact
Face-to-faceloose specs
Face-to-facetight specs
PhoneContact
Face-to-facetotal
customization
Buffered core (none)
Permeable system (some)
Reactivesystem (much)
High
LowHigh
Low
Degree of customer/server contact
On-sitetechnology
SalesOpportunity
ProductionEfficiency
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Three Contrasting Service Designs• The production line approach
• The self-service approach
• The personal attention approach
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Example: Model 1Drive-up window at a fast food restaurant.Customers arrive at the rate of 25 per hour.The employee can serve one customer every two minutes.Assume Poisson arrival and exponential service rates.
A) What is the average utilization of the employee?B) What is the average number of customers in line?C) What is the average number of customers in the system?D) What is the average waiting time in line?E) What is the average waiting time in the system?
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Example: CVS
Manager is considering two ways of using cashiers: ( Assume customers arrive randomly at a rate of 15 per hour)
• 1 fast clerk -- serves at an average of 2 minutes per customer
or
• 2 moderate clerks -- each serves at an average of 4 minutes per customer
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Some Performance Measures• Average time spent waiting in queue• Average time in system• Average length of queue• Average number of customers in
system• Probability that a customer waits
before service begins• Server utilization
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= 25 cust /hr
= 1 customer
2 mins (1hr /60 mins) = 30 cust /hr
= = 25 cust /hr
30 cust /hr = .8333
Example: Model 1A) What is the average utilization of the employee?
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13
Example: Model 1B) What is the average number of customers in line?
4.167 = 25)-30(30
(25) =
) - ( = Lq
22
C) What is the average number of customers in the system?
5 = 25)-(30
25 =
- = Ls
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14
Example: Model 1D) What is the average waiting time in line?
mins 10 = hrs .1667 = 25)-030(3
25 =
) - ( = Wq
E) What is the average waiting time in the system?
mins 12 = hrs .2 = 25-30
1 =
-
1 = Ws
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mms.xls M/M/s Queueing Formula Spreadsheet
Inputs: Definitions of terms:lambda 25 lambda = arrival ratemu 30 mu = service rate
s = number of serversLq = average number in the queueLs = average number in the systemWq = average wait in the queueWs = average wait in the systemP(0) = probability of zero customers in the systemP(delay) = probability that an arriving customer has to wait
Outputs:s Lq Ls Wq Ws P(0) P(delay)Utilization
1 4.1667 5.0000 0.1667 0.2000 0.1667 0.8333 0.8333
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Example: CVS
Manager is considering two ways of using cashiers: ( Assume customers arrive randomly at a rate of 15 per hour)
• 1 fast clerk -- serves at an average of 2 minutes per customer
or
• 2 moderate clerks -- each serves at an average of 4 minutes per customer
![Page 47: B7801: Operations Management 27 March 1998 - Agenda Mass Customization National Cranberry Cooperative Capacity Management Queue and customer management.](https://reader036.fdocuments.in/reader036/viewer/2022062321/56649e175503460f94b02526/html5/thumbnails/47.jpg)
mms.xls M/M/s Queueing Formula Spreadsheet
Inputs: Definitions of terms:lambda 15 lambda = arrival ratemu 30 mu = service rate
s = number of serversLq = average number in the queueLs = average number in the systemWq = average wait in the queueWs = average wait in the systemP(0) = probability of zero customers in the systemP(delay) = probability that an arriving customer has to wait
Outputs:s Lq Ls Wq Ws P(0) P(delay)Utilization
01 0.5000 1.0000 0.0333 0.0667 0.5000 0.5000 0.50002 0.0333 0.5333 0.0022 0.0356 0.6000 0.1000 0.2500
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mms.xls M/M/s Queueing Formula Spreadsheet
Inputs: Definitions of terms:lambda 15 lambda = arrival ratemu 15 mu = service rate
s = number of serversLq = average number in the queueLs = average number in the systemWq = average wait in the queueWs = average wait in the systemP(0) = probability of zero customers in the systemP(delay) = probability that an arriving customer has to wait
Outputs:s Lq Ls Wq Ws P(0) P(delay)Utilization
01 infinity infinity infinity infinity 0.0000 1.0000 1.00002 0.3333 1.3333 0.0222 0.0889 0.3333 0.3333 0.50003 0.0455 1.0455 0.0030 0.0697 0.3636 0.0909 0.3333
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mms.xls M/M/s Queueing Formula Spreadsheet
Inputs: Definitions of terms:lambda 7.5 lambda = arrival ratemu 15 mu = service rate
s = number of serversLq = average number in the queueLs = average number in the systemWq = average wait in the queueWs = average wait in the systemP(0) = probability of zero customers in the systemP(delay) = probability that an arriving customer has to wait
Outputs:s Lq Ls Wq Ws P(0) P(delay)Utilization
01 0.5000 1.0000 0.0667 0.1333 0.5000 0.5000 0.50002 0.0333 0.5333 0.0044 0.0711 0.6000 0.1000 0.25003 0.0030 0.5030 0.0004 0.0671 0.6061 0.0152 0.1667
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M/M/s Queue with Priority
Poisson arrivals, high priority arrival rate = low priority arrival rate = 2
Exponential service time, service rate at each server =
s servers, one line, priority (high or low)
Performance measures (high and low):utilization,probability of delayaverage number of customers in system average throughput timeaverage queue lengthaverage waiting time
===> On-line queueing spreadsheets
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mms_priority.xls M/M/s Priority Queueing Formula Spreadsheet
Inputs: Definitions of terms:
lambda HIGH 0.16667 lambda HIGH= arrival rate of high priority class
lambda LOW 0.16667 lambda LOW= arrival rate of low priority class
mu 0.25 mu = service rate (assumed the same for both HIGH and LOW)s = number of serversLq = average number in the queueLs = average number in the systemWq = average wait in the queueWs = average wait in the systemP(0) = probability of zero customers in the systemP(delay) = probability that an arriving customer has to wait (same for both HIGH/LOW)
High Priority Low Priority Both ClassesOutputs:
s Lq Ls Wq Ws Utilization Lq Ls Wq Ws Utilization P(0) P(delay) Total Util.01 infinity infinity infinity infinity 0.6667 infinity infinity infinity infinity 0.6667 0.0000 1.0000 1.00002 0.2667 0.9333 1.6000 5.6000 0.3333 0.8000 1.4667 4.8000 8.8000 0.3333 0.2000 0.5333 0.66673 0.0517 0.7183 0.3099 4.3099 0.2222 0.0930 0.7596 0.5579 4.5579 0.2222 0.2542 0.1808 0.44444 0.0104 0.6770 0.0621 4.0621 0.1667 0.0155 0.6822 0.0932 4.0932 0.1667 0.2621 0.0518 0.33335 0.0019 0.6686 0.0116 4.0116 0.1333 0.0026 0.6693 0.0159 4.0159 0.1333 0.2634 0.0126 0.26676 0.0003 0.6670 0.0020 4.0020 0.1111 0.0004 0.6671 0.0025 4.0025 0.1111 0.2636 0.0026 0.22227 0.0001 0.6667 0.0003 4.0003 0.0952 0.0001 0.6667 0.0004 4.0004 0.0952 0.2636 0.0005 0.19058 0.0000 0.6667 0.0000 4.0000 0.0833 0.0000 0.6667 0.0001 4.0001 0.0833 0.2636 0.0001 0.16679 0.0000 0.6667 0.0000 4.0000 0.0741 0.0000 0.6667 0.0000 4.0000 0.0741 0.2636 0.0000 0.1481
10 0.0000 0.6667 0.0000 4.0000 0.0667 0.0000 0.6667 0.0000 4.0000 0.0667 0.2636 0.0000 0.1333
M/M/s-Priority Queueing SpreadsheetM/M/s-Priority Queueing Spreadsheet
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Suggestions for Managing Queues• Do not overlook the effects of perceptions
management. • Determine the acceptable waiting time for
your customers. • Install distractions that entertain and
physically involve the customer. • Get customers out of line. • Only make people conscious of time if
they grossly overestimate waiting times
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Perceptions of waiting times• Unoccupied delays feel longer than
occupied delays• Pre-process delays feel longer than
in-process delays• Anxious delays feel longer than
relaxed delays• Unacknowledged delays feel longer
than acknowledged delays• Waiting alone vs. waiting with others
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Suggestions for Managing Queues• Modify customer arrival behavior.
• Keep resources not serving customers out of sight.
• Segment customers by personality types.
• Adopt a long-term perspective.
• Never underestimate the power of a friendly server
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What did we learn?