MBAC6080 Decision Modeling Midterm Exam Review Professor Stephen Lawrence.

MBAC6080 Decision Modeling

Midterm Exam ReviewMBAC6080 Decision Modeling

Midterm Exam Review

Professor Stephen Lawrence

AgendaPrepare for Midterm ExamDescribe midtermReview highlights of material to dateAnswer questions

Course OutlineIntroduction to OMProductivity

Linear programming

QualitySPC tools

TimelinessQueueing Theory

Flexibility & SCMInventory Theory

InnovationProject Management

Environmental OpsOperations StrategyWrap-up

Midterm Details & AdministrationClosed book75 minutes in lengthCalculators OKNo Computers1-page cheat sheetFair Game for Exam

Lectures & readingsHomework assignmentsCases

7 topics, 7 partsMultiple choiceTrue/False – ExplainProblemsProblem fragments

Focus on interpretation

Defining OperationsDefining Operations


TransformationProcesses

TransformationProcesses

GoodsGoods

ServicesServices

LaborLabor

KnowledgeKnowledge

CapitalCapital

MaterialsMaterials

What is Operations?

Transformation Definition

INPUTS OUTPUTS

Who are “operations” managers?

Manufacturing and Services Continuum of Characteristics

Mining (coal)

Automobiles

Fast Food

Banking

Consulting

ServiceOrientation

Manufacturing Orientation

Example:

Bicycle Manufacturing

Labor Used per unit produced

Capital Used(equipment) per unit produced

Huffy

Serotta

Schwinn ’80sLots of automationLots of labor / unit

Automated equipmentLittle labor per unit

Little automationLots of skilled laborModerate Automation

Moderate LaborTrek

Example:

Bicycle Manufacturing



Desirable

Desirable

Undesirable!

Desirable

EfficientFrontier



What is Operations?

Economic Definition

Added Value Model

adapted from Porter, Competitive Advantage, Free Press, 1985

Information SystemsInformation Systems

People and OrganizationPeople and Organization

FinanceFinance

AccountingAccounting

MarketingMarketing OperationsOperations

Profit!Profit!

CostCost

Added Value for CustomerAdded Value for Customer

Added Value Model

adapted from Porter, Competitive Advantage, Free Press, 1985

Suppliers Customers

Competitors

The Firm

BusinessEnvironment

Value Chain

Operations is the fundamental means by

which firms…

What is Operations?

Added-Value Definition

Add Value!

What is Operations?

How do Firms Add Value?Greater Productivity

Lower costs and expensesLower prices for the customer

Higher QualityBetter performanceGreater durability, reliability, aesthetics, ...

Better TimelinessFaster response and turnaroundOn-time delivery, meet promises

Greater FlexibilityGreater varietyCustomization for customer needs / desires

Useful InnovationFeatures, technologyBetter performanceNew capabilitiesOften unrecognized

The Value Equation

price

ePerformancValue

price

InnovationyFlexibilitTimelinessQualityValue

P

IFTQValue

Competing with Competing with ProductivityProductivityLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419


The Value Equation

price

yFlexibilitTimelinessQualityValue

How are productivity

and price related?

Inputs

OutputstyProductivi

Productivity Defined

Inputs: labor, materials, capital, …

Outputs: goods, services

Single Factor ProductivityMeasures increase in productivity in relation to a single factor of productionLabor, materials, capital, …Productivity = Output / Single InputExample:

Output LaborPeriod 1 1000 units 100 hrsPeriod 2 1100 105

Productivity10.0 units / hour10.6 units / hour

Total Factor ProductivityMeasures increased in productivity from all relevant factors of productionProductivity (TFP)

= all outputs / all factor inputsExample: product sales + internal services

TFP Index = ----------------------------------------------------------------------------- labor + material + services + depreciation + investment

4.94% IncreaseTFP Index

2002 20031.073 1.126

TFP LimitationsIssues affecting TFP measurement

inflation, currency exchange gains (losses)depreciation, inventory valuationproduct mix changes, choice of base period, output measures ...

Theoretically interesting, practically difficult

Why Productivity is Important

Agriculture Services

Mfg

Knowledge

0102030405060708090

100

1800

1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

Per

cent

of

the

labo

r fo

rce

Year

Allocative vs. Technical Efficiency Capital Used(equipment) per unit produced

AY

B

Labor Used per unit

C

EfficientFrontier

EfficientFrontier

CostLine

1 / c

ost o

f c a

pita

l

1 / cost of labor

Allocative inefficiency

Technical inefficiency

O

Inefficiency = BC÷OB

How Much Inefficiency Exists?

Tech efficiency based on gross output 63-95%

Tech efficiency based on value-added 28-50%

Mean levels of technical inefficiency for 365 U.S. industries:

Caves and Barton, Efficiency in U.S. Manufacturing Industries, MIT 1990.

Management FindingsOligopoly is detrimental to technical efficiencyImport competition is favorable to technical efficiencyEnterprise diversification is hostile to an industry’s technical efficiencyTechnical efficiency is negatively related to the presence of trade unions in industries that operate large plantsCapital vintage distributions have an important impact on technical efficiency -- importance of optimizing capital modernization and replacement decisions

Caves and Barton, Efficiency in U.S. Manufacturing Industries, MIT 1990.

Evolving Economic TheoryPosits a new factor of production: knowledge

Knowledge increases return on capital investmentKnowledge doesn’t just happen, it is paid for by foregoing current consumptionVirtuous cycle in which investment spurs knowledge and knowledge spurs investment

Four factors of productionCapital, unskilled labor, human capital (training), ideas (patents)

The Economist, Jan 4, 1992, pgs15-18

Introduction to Linear Programming

Professor Stephen LawrenceLeeds School of Business

University of Colorado

Boulder, CO 80309-0419

An LP Example

Product Mix LP. A potter produces two products, a pitcher and a bowl. It takes about 1 hour to produce a bowl and requires 4 pounds of clay. A pitcher takes about 2 hours and consumes 3 pounds of clay. The profit on a bowl is $40 and $50 on a pitcher. She works 40 hours weekly, has 120 pounds of clay available each week, and wants more profits.

Graphical LP SolutionsWorks well for 2 decision variables“Possible” for 3 decision variablesImpossible for 4+ variables

Other solution approaches necessary

Good to illustrate concepts, aid in conceptual understandingAn automated tool…

LP Standard Form

Max Z = c1x1 + c2x2 + … + cnxn

Subject to (s.t.)

a11x1 + a12x2 + … + a1nxn b1 a21x1 + a22x2 + … + a2nxn b2

…am1x1 + am2x2 + … + amnxn bm

x1 0, x2 0, …, xn 0

Assumptions of LPLinear objective function, constraints

ProportionalityAdditivity

DivisibilityContinuous decision variables

CertaintyDeterministic parameters

LP ConceptsDecision variablesObjective functionConstraintsFeasible solutionsFeasible region (convex polytope)Corner point solutionsOptimal solution“Constrained optimization”

Linear Programming ApplicationsProduct MixDietInvestmentCash flowEmployee SchedulingProductionMarketing

Competing with Competing with QualityQualityLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419


The Value Equation

price

yFlexibilitTimelinessQualityValue

Quality

Why Quality is CriticalQuality: Quality is the single most important thing you can work on to improve the effectiveness of your company. It's as simple as that. Things just cascade when you get control of your quality. John Young, CEO Hewlett Packard

Micro-economic interpretation:

Quantity

Price Demand Supply

Quality affects both!

Eight Dimensions of Quality

1. Performancethe primary operating characteristics of the product or service.

2. Featuresthe characteristics that supplement the basic functioning of the product or

service.

3. Reliabilityprobability of the product or service failing within a specified period of time.

4. Conformancethe degree to which a product or service meets acknowledged standards

Quality is not uni-dimensional, but has a numberof important dimensions:

David Garvin, “Competing on the Eight Dimensions of Quality,” Harvard Business Review, Nov-Dec 1987

Eight Dimensions of Quality

5. Durabilitya measure of product life (both technical and economic).

6. Serviceabilitythe speed, courtesy, competence, and ease of repair or recovery.

7. Aestheticshow a product or service looks, feels, sounds, tastes, or smells.

8. Perceived Qualityvarious tangible and intangible aspects of the product from which quality is

inferred.

Quality is not uni-dimensional, but has a numberof important dimensions:

David Garvin, “Competing on the Eight Dimensions of Quality,” Harvard Business Review, Nov-Dec 1987

Quality Costs

Prevention costs: process/product design, training, vendor relations;Appraisal costs: quality audits, statistical quality control;Correction costs (internal failure): yield losses, rework charges;Recovery costs (external failure): returns, repairs, lost business.

Costs associated with quality:Costs associated with quality:

Quality Costs

Quality costs escalate as value is added to product or service:

Supplier Inspection

Incoming Inspection

Fabrication Inspection

Subproduct Test

Final Product Test

Field Service

0.003

0.03

0.30

$3

$30

$300

Cost of finding and correcting a defective component

Quality MastersW. Edwards Deming

The basic cause of sickness in American industry and resulting unemployment is failure of top management to manage.Began consulting with Japanese in 1950Japanese Deming Prize

Joseph M. Juran“Fitness of Use”Runs the Juran InstituteLarge impact on Japanese quality

Phillip B. CrosbyStarted as an industrial inspectorRuns the Crosby Quality College“Zero Defects”

Total Quality ManagementTotal Quality Management

TQMTQM

Commitmentto Quality

Commitmentto Quality

TotalInvolvement

TotalInvolvement

Scientific Toolsand TechniquesScientific Toolsand Techniques

ContinuousImprovementContinuous

Improvement

Malcolm Baldridge Award

Stimulate companies to attain excellenceRecognize outstanding companiesDisseminate information and experienceEstablish guidelines for quality assessmentGather “how to” information from winners

U.S. Quality Award (patterned after Deming award)

International standards for business quality and control

ISO 9000

Management responsibilityQuality systemContract reviewDesign controlDocument ControlPurcasingTraceability

Process controlInspection / testingReject controlHandlingQuality recordsInternal auditsTraining Statistical techniques

Six Sigma“Invented” by MotoralaChampioned by GE and Jack WelchGoal of parts-per-million process defectsFour steps

1. Measure – new metrics; measure all processes2. Analyze – determine performance objectives3. Improve – wholesale changes, focus on results4. Control – monitor processes to maintain control

66

Does Quality Matter?

Quality and total quality costnegatively correlated.

Quality and productivitypositively correlated.

Quality and profitabilitypositively associated.

Garvin, Managing Quality, The Free Press, 1988

Statistical ProcessStatistical ProcessControl (SPC)Control (SPC)Leeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419


Process Control Tools

Process tools assess conditions in existing processes to detect problems that require intervention in order to regain lost control.

Run ChartsRun Charts

Check sheetsCheck sheets Pareto analysisPareto analysis

Cause & effect diagramsCause & effect diagrams ScatterplotsScatterplots

HistogramsHistograms Control chartsControl charts

Check SheetsCheck sheets explore what and where

an event of interest is occurring.

Attribute Check Sheet

27 15 19 20 28

Order Types 7am-9am 9am-11am 11am-1pm 1pm-3pm 3pm-5-pm

Emergency

Nonemergency

Rework

Safety Stock

Prototype Order

Other

Run Charts

time

mea

sure

men

t

Look for patterns and trends…

PARETO ANALYSISA method for identifying and separating

the vital few from the trivial many. P

erce

nta

ge o

f O

ccu

rren

ces

Factor

AB

CD

E F G IH J

CAUSE & EFFECT DIAGRAMS

Employees

Proceduresand Methods

TrainingSpeed Maintenance

Equipment

Condition

ClassificationError

Inspection

BADCPU

Pins notAssigned

DefectivePins

ReceivedDefective

Damagedin storage

CPU Chip

HISTOGRAMSA statistical tool used to show the extent and type of variance within the system.

Fre

qu

ency

of

Occ

urr

ence

s

Outcome

SCATTERPLOTSV

aria

ble

A

Variable B

x x x x x x xx x x x x xx x x x x x xx x xx x x x x xx x x x x x xx x x x xx xxx x x x x xx xx x x x x xx x x x xxx xx x x x x xxx x x xx x x xx xx x x x x x xx x x xxx xx xx xxx x x xx xxx x x x x x x xx x x x x x x xx x x xx x x xx x x x

Larger values of variable A appear to be associated with larger values of variable B.

Process Control

Causes of Variation

Natural Causes Assignable Causes

What prevents perfection?

Exogenous to processNot randomControllablePreventableExamples

tool wear“Monday” effectpoor maintenance

Inherent to processInherent to process RandomRandom Cannot be controlledCannot be controlled Cannot be preventedCannot be prevented ExamplesExamples

– weatherweather

– accuracy of measurementsaccuracy of measurements

– capability of machinecapability of machine

Process variation...

Specification vs. Variation

Product specificationdesired range of product attributepart of product designlength, weight, thickness, color, ...nominal specificationupper and lower specification limits

Process variabilityinherent variation in processeslimits what can actually be achieveddefines and limits process capability

Process may not be capable of meeting specification!

Process CapabilityMeasure of capability of process to meet (fall within) specification limitsTake “width” of process variation as 6If 6 < (USL - LSL), then at least 99.7% of output of process will fall within specification limits

LSL USLSpec

6

3

99.7%

Process Capability Ratio

Define Process Capability Ratio Cp as

CpUSL LSL

6If Cp > 1.0, process is... capable

If Cp < 1.0, process is... not capable

Process Capability Index Cpk

3

,3

minUSLLSL

C pk

If Cpk > 1.0, process is... Centered & capable

If Cpk < 1.0, process is... Not centered &/or not capable

Mean

Std dev

Process Control Charts

Establish capability of process under normal conditionsUse normal process as benchmark to statistically identify abnormal process behaviorCorrect process when signs of abnormal performance first begin to appearControl the process rather than inspect the product!

Statistical technique for tracking a process anddetermining if it is going “out to control”

Upper Control Limit

Lower Control Limit

6

3

Target Spec


Upper Spec Limit

Lower Spec Limit

UCL

Target

LCL

Samples

Time

In control Out of control !

Natural variation

Look forspecial

cause !

Back incontrol!


Types of Control Charts

1. Attribute control chartsmonitors frequency (proportion) of defectives p - charts

2. Defects control chartsmonitors number (count) of defects per unit c – charts

3. Variable control chartsmonitors continuous variables x-bar and R charts

Using p-chartsFind long-run proportion defective (p-bar) when the process is in control.Select a standard sample size nDetermine control limits

p

p

pLCL

pUCL

3

3

n

ppp

)1(

Using c-chartsFind long-run proportion defective (c-bar) when the process is in control.Determine control limits

c

c

cLCL

cUCL

3

3

cc

3. Control Charts for Variablesx-bar and R chartsMonitor the condition or state of continuously variable processesUse to control continuous variables

Length, weight, hardness, acidity, electrical resistanceExamples

Weight of a box of corn flakes (food processing)Departmental budget variances (accountingLength of wait for service (retailing)Thickness of paper leaving a paper-making machine

Range (R) ChartChoose sample size nDetermine average in-control sample ranges R-bar where R=max-min

Construct R-chart with limits:

nRR / RDUCLRDLCL 43

Mean (x-bar) ChartChoose sample size n (same as for R-charts)Determine average in-control sample means x-double-bar where x-bar = sample mean

Construct x-bar-chart with limits:

kxx / RAxUCLRAxLCL 22

x & R Chart Parametersn d(2) d(3) A(2) D(3) D(4)2 1.128 0.853 1.881 0.000 3.2693 1.693 0.888 1.023 0.000 2.5744 2.059 0.880 0.729 0.000 2.2825 2.326 0.864 0.577 0.000 2.1146 2.534 0.848 0.483 0.000 2.0047 2.704 0.833 0.419 0.076 1.9248 2.847 0.820 0.373 0.136 1.8649 2.970 0.808 0.337 0.184 1.81610 3.078 0.797 0.308 0.223 1.77711 3.173 0.787 0.285 0.256 1.74412 3.258 0.778 0.266 0.284 1.71616 3.532 0.750 0.212 0.363 1.63717 3.588 0.744 0.203 0.378 1.62218 3.640 0.739 0.194 0.391 1.60919 3.689 0.734 0.187 0.403 1.59720 3.735 0.729 0.180 0.414 1.58621 3.778 0.724 0.173 0.425 1.57522 3.819 0.720 0.167 0.434 1.56623 3.858 0.716 0.162 0.443 1.55724 3.895 0.712 0.157 0.452 1.54825 3.931 0.708 0.153 0.460 1.540

Control Chart Error Trade-offsSetting control limits too tight (e.g., ± 2) means that normal variation will often be mistaken as an out-of-control condition (Type I error).Setting control limits too loose (e.g., ± 4) means that an out-of-control condition will be mistaken as normal variation (Type II error).Using control limits works well to balance Type I and Type II errors in many circumstances.

Competing with TimeGraduate School of Business University of ColoradoBoulder, CO 80309-0419

Competing with TimeGraduate School of Business University of ColoradoBoulder, CO 80309-0419


The Value Equation

price

InnovationFlexTimeQualityValue

Timeliness

Comparative Lead Times

Engineer to Order

Make to Order

Assemble to Order

Make to Stock

Customer LeadtimeInternal Leadtime

Just-In-Time & Lean ConceptsJIT

produce only what is needed only when it is needed!Goal of Just-In-Time Systems: SIMPLIFY!

Reduce inventories;Reduce setup times;Reduce information flows;Fewer, more reliable suppliers;Design products for manufacturability

Reduce WASTE of all types!

Ch 15 - 3

Basic Elements of Lean Ops & JIT

Flexible resourcesCellular layoutsPull production systemKanban controlSmall-lot production

Quick setupsUniform productionQuality at the sourceTotal productive maint.Supplier networks

© 2000 by Prentice-Hall Inc, Russell/Taylor Operations Management 3/e

Ch 15 - 5

Flexible Resources

Multifunctional workers

General purpose machines

Study operators & improve operations


Ch 15 - 7

Cellular Layouts

Group dissimilar machines into a manufacturing cell to produce family of partsWork flows in one direction through cellCycle time adjusted by changing worker paths


Ch 15 - 10

Kanban Production Control

Kanban card indicates standard quantity of productionDerived from two-bin inventory systemKanban maintains discipline of pull productionProduction kanban authorizes productionWithdrawal kanban authorizes movement of goods


Ch 15 - 21

Small-Lot Production

Requires less space & capital investmentMoves processes closer togetherMakes quality problems easier to detectMakes processes more dependent on each other


Ch 15 - 25

Reducing Setup Time

Preset desired settingsUse quick fastenersUse locator pinsPrevent misalignmentsEliminate toolsMake movements easier


Ch 15 - 26

Uniform Production

Results from smoothing production requirementsKanban systems can handle +/- 10% demand changesSmooths demand across planning horizonMixed-model assembly steadies component production


Ch 15 - 28

Quality At The Source

Jidoka is authority to stop production lineAndon lights signal quality problemsUndercapacity scheduling allows for planning, problem solving & maintenanceVisual control makes problems visiblePoka-yoke prevents defects


Ch 15 - 29

KaizenContinuous improvementRequires total employment involvementEssence of JIT is willingness of workers to

spot quality problemshalt production when necessarygenerate ideas for improvementanalyze problemsperform different functions


Ch 15 - 32

Visual Control

Library shelfWork station

Visual kanbansTool board

Machine controls

BetterGood Best

30-50

Howto

sensor


Ch 15 - 34

Benefits Of Lean Ops & JIT

1. Reduced inventory 2. Improved quality 3. Lower costs 4. Reduced space

requirements 5. Shorter lead time 6. Increased productivity

7. Greater flexibility8. Better relations with

suppliers9. Simplified scheduling

and control activities10. Increased capacity11. Better use of human

resources12. More product variety


Benefits of Fast ThroughputThroughput time reduction encourages

Improved qualityReduced inventoriesProcess rationalizationAttention to and reduction of bottlenecksDiminished chaos and confusionOverhead eliminationFaster response to the marketImproved capital appropriations

Queueing Theory

Professor Stephen LawrenceLeeds School of Business

University of ColoradoBoulder, CO 80309-0419

Service

Queuing Analysis

Arrival

Rate ( Average Number

in Queue (Lq )

Avg Time in System (W )

Avg Number in System (L )

Average Wait

in Queue (Wq )

Rate (Departure

Principal Queue ParametersArrival ProcessDeparture ProcessNumber of ServersQueue Discipline

Queue NomenclatureX / Y / k (Kendall notation)X = distribution of arrivals (iid)Y = distribution of service time (iid)

M = exponential (memoryless)Em = Erlang (parameter m)G = generalD = deterministic

k = number of servers

Exponential Distribution

m

etf

mt /

)(

Exponential Density

Mean = mStd Dev = m

f(t)1/m

tm

M/M/1 Queues

M/M/1 Assumptions

Arrival rate of Poisson distribution

Service rate of Exponential distribution

Single serverFirst-come-first-served (FCFS) Unlimited queue lengths allowed“Infinite” number of customers

M/M/1 Operating Characteristics

Utilization (fraction of time server is busy)

Average waiting times

Average number waiting

1W WWq

L LLq

Managerial Implications

Low utilization levels provide better service levelsgreater flexibilitylower waiting costs (e.g., lost business)

High utilization levels provide better equipment and employee utilizationfewer idle periodslower production/service costs

Must trade off benefits of high utilization levels with benefits of flexibility and service

Cost Trade-offs

= 0.0

Cost CombinedCosts

Cost ofWaiting

Cost ofService

Sweet Spot –Min Combined

Costs

G/G/k Queues

G/G/k AssumptionsGeneral interarrival time distribution with mean and std. dev. = sa

General service time distribution with mean and std. dev. = sp

Multiple servers (k)First-come-first-served (FCFS)“Infinite” calling populationUnlimited queue lengths allowed

General DistributionsTwo parameters

Mean (m)Std. dev. (s)

ExamplesNormalWeibullLogNormalGamma

f(t)

tm

s

Coefficient of Variationcv = s/m

G/G/k Operating Characteristics

Average waiting times (approximate)

Average number in queue and in system

m

sc

k

ccW

kpa

q

)1(2

1)1(222

WL qq WL

1

qWW

Alternative G/G/k Formulation

pk

cc

k

ccW

kpa

kpa

q)1(2)1(2

1)1(2221)1(222

pk

ccW

kpa

q

)1(2

1)1(222

VarianceTerm

CongestionTerm

Service TimeTerm

G/G/k Variance AnalyzedWaiting times increase with the square of the coefficient of variance

No variance, no wait!

Wq

c

Other Queueing Behavior

Server

Queue(waiting line)Customer

Arrivals

CustomerDepartures

Customer balks(never enters queue)

Customer reneges(abandons queue)

Wait too long?Line too long?

Waiting Line Psychology

1. Waits with unoccupied time seem longer2. Pre-process waits are longer than process3. Anxiety makes waits seem longer4. Uncertainty makes waits seem longer5. Unexplained waits seem longer6. Unfair waits seem longer than fair waits7. Valuable service waits seem shorter8. Solo waits seem longer than group waits

Maister, The Psychology of Waiting Lines, teaching note, HBS 9-684-064.

Good Luck!

MBAC6080 Decision Modeling Midterm Exam Review Professor Stephen Lawrence.

Documents

customer slide

unrecognized slide

interpretation slide

value equation slide

economic definition

value definition

bicycle manufacturing

added value model