B7801: Operations Management 27 March 1998 - Agenda Mass Customization National Cranberry...

B7801: Operations Management27 March 1998 - Agenda

•Mass Customization•National Cranberry Cooperative•Capacity Management•Queue and customer management

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

Matching demand and capacity

# units/hr.

demand

poor service / lost revenue

excess assets and costs

capacity

How do firms match capacity to demand?

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?

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

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)

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.

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.

Strategy 1: Chase demand (production = demand)

Jan Feb Mar April May June July Aug Sept

Demand Prod

Monthly Demand/Productionx10,000 units/month

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

Strategy 2: Level production

Demand Prod

Level strategy financialsSeptAugJuly JuneMayAprilMarFebJan

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

Strategy 3: Mixed

Demand Prod

Mixed strategy financialsSeptAugJuly JuneMayAprilMarFebJan

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

Components of the Queuing Phenomenon

Servers

Waiting Line

Servicing System

CustomerArrivals Exit

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)

Capacity Management in Services

•You cannot store service output

•If you cannot store output, you store the demand

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?

Service-System Design Matrix

Mail contact

Face-to-faceloose specs

Face-to-facetight specs

PhoneContact

Face-to-facetotal

customization

none some much

LowHigh

Degree of customer/server contact

On-sitetechnology

SalesOpportunity

ProductionEfficiency

Three Contrasting Service Designs

• The production line approach

• The self-service approach

• The personal attention approach

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

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

Some Service Generalizations1. Everyone is an expert on

services.

2. Services are idiosyncratic.

3. Quality of work is not quality of service.

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

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

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

Components of the Queuing Phenomenon

Servers

Waiting LineCustomerArrivals Exit

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

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

Methods for reducing impact of variability• Demand

– better forecasting– pricing– appointment systems

• Process– standardization– training– automation– self-service– variable staffing– use of inventory

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

Line Structures

Single Channel

Multichannel

SinglePhase

Multiphase

One-personbarber shop

Car wash

Hospitaladmissions

Bank tellers’windows

Degree of Patience

No Way! No Way!

BALK RENEG

Key facts needed for a model•Average number of customer

arrivals per unit of time

•Average service time per customer

•The number of servers

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

Operating Focus

• Customer treatment

• Speed and convenience of service delivery

• Variety of services

• Quality of tangibles

• Unique skills

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)

LowHigh

Degree of customer/server contact

On-sitetechnology

SalesOpportunity

ProductionEfficiency

Three Contrasting Service Designs• The production line approach

• The self-service approach

• The personal attention approach

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?

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

• 2 moderate clerks -- each serves at an average of 4 minutes per customer

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

= 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?

Example: Model 1B) What is the average number of customers in line?

4.167 = 25)-30(30

(25) =

) - ( = Lq

C) What is the average number of customers in the system?

5 = 25)-(30

- = Ls

Example: Model 1D) What is the average waiting time in line?

mins 10 = hrs .1667 = 25)-030(3

) - ( = Wq

E) What is the average waiting time in the system?

mins 12 = hrs .2 = 25-30

1 = Ws

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

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

• 2 moderate clerks -- each serves at an average of 4 minutes per customer

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

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

Inputs: Definitions of terms:lambda 7.5 lambda = arrival ratemu 15 mu = service rate

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

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

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

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

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

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

What did we learn?

B7801: Operations Management 27 March 1998 - Agenda Mass Customization National Cranberry...

Documents

Transcript of B7801: Operations Management 27 March 1998 - Agenda Mass Customization National Cranberry...

The Cranberry Pest Management Strategic Plan

Frost Tolerances UMass Cranberry Station of Cranberry Fruit · Frost Tolerances UMass Cranberry Station of Cranberry Fruit In the late summer and into the fall, cranberry frost tolerance

National Cranberry Cooperative Harvard Case Solution ... · National Cranberry Cooperative ... National Cranberry Cooperative Harvard Case Solution ... National Cranberry Cooperative

Integrated Cranberry Crop Cranberry Crop Management for ... · used flooding methods to control insects. Over time our industry has reevaluated the efficacy of such cultural methods

The Cranberry Pest Management Strategic Planipmcenters.org/pmsp/pdf/uscranberry.pdf · the Cranberry Pest Management Strategic Plan to be both economically and biologically ... insecticides

Cranberry Crop Management Journal Volume 31 • Issue 4 · Management Journal Volume 31 • Issue 4 Evaporative cooling to reduce heat stress in cranberry. UW Evaporative cooling

National Cranberry 1

Integrated Cranberry Crop Management Cranberry for Wisconsin … · 2017. 7. 17. · Record-breaking 2012 Spring: Why Degree-Days were Critical in Assessing Insect Development (Continued

P. 1 Mass Customization B7801: Operations Management 24 March 2000 Burger King & McDonald’s Tuffy Bike Queuing fundamentals.

Cranberry Scheduler

Cranberry Castle

Just-in Time Management Supplier Partnerships B7801 April 17, 1998

Deer Creek Winery...Cranberry Craze: Cranberry & Concord merge Cranberry Mist - 100% Cranberry wine for anytime ofyear! Deer Creek Raspberry: 100% Raspberry, our signature wine s@unts:

B7801 Operations Management Introduction · “As Coca-Cola Co. tries to regain its footing in Europe after a contamination scare that caused the biggest product recall in the company's

Decision-Making for Management of Cranberry Tipworm, · PDF fileDecision-Making for Management of Cranberry Tipworm, Dasineura oxycoccana: Responses of Tipworm and Parasitoid Populations

Cranberry Sustainable Resource Management Plan...Land Use Objectives for the Cranberry Sustainable Resource Management Plan Area Part 1 – General 1. Interpretation (1) Pursuant to

Cranberry Insect Pest Management and Karner Blue Butterfly ... Species Act and... · Cranberry Insect Pest Management and Karner Blue Butterfly Protection: A Wisconsin Case Study

The Cranberry plan

Cranberry craze

CRANBERRY PROMOTIONS 02