MBAC6080 Operations Mgmt

Final Exam ReviewMBAC6080 Operations Mgmt

Final Exam Review

Professor Stephen Lawrence

AgendaPrepare for Final ExamDescribe FinalReview course materialsRecommendations for further studyAnswer questionsFCQ’s

Course OutlineIntroduction to OMProductivity

Linear programming

QualitySPC tools

TimelinessQueueing Theory

FlexibilitySCMInventory Management

InnovationProject Management

Operations Strategy

Final Details & AdministrationClosed book, notes, & computersCalculators OK8.5x11 crib sheet OK120 minutes in lengthFair game for Final

Lectures & readingsHomework assignmentsCases

Format similar to MidtermMultiple choiceTrue/False – ExplainProblemsProblem fragments

Defining OperationsDefining Operations

Professor Stephen Lawrence

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

Labor Used per unit produced

Capital Used(equipment) per unit produced

Desirable

Desirable

Undesirable!

Desirable

EfficientFrontier

Labor Used per unit produced

Capital Used(equipment) per unit produced

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

Professor Stephen Lawrence

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.

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

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…

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.

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 flowProductionMarketing

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

Professor Stephen Lawrence

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

W. Edwards Deming

1900 to 1993Trained as a physicistMaster of Science -- CUTaught SQC during World War IIWent to Japan in 1946Brought SQC to JapanEnthusiastically adopted by Japanese

Total Quality ManagementTotal Quality Management

A program to focus all organizational activities on enhancing quality for customers

Its four components are:a commitment to make quality product for customersa commitment to continuous improvementa total involvement in the quality undertakingextensive use of scientific tools, technologies and methods

Total Quality ManagementTotal Quality Management

TQMTQM

Commitmentto Quality

Commitmentto Quality

TotalInvolvement

TotalInvolvement

Scientific Toolsand TechniquesScientific Toolsand Techniques

ContinuousImprovementContinuous

Improvement

Japanese Deming PrizeEstablished 1951Annual prizeAwarded for

development of quality tools, orquality improvement programs

Created by JUSA(Union of Japanese Scientists and Engineers

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

Professor Stephen Lawrence

Process Control Tools

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

Check sheetsCheck sheets Pareto analysisPareto analysis

Run ChartsRun Charts Scatter PlotsScatter Plots

Cause & effect diagramsCause & effect diagrams Control chartsControl charts

HistogramsHistograms

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,

3min

USLLSLC 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

Process Control Charts

Upper Spec Limit

Lower Spec Limit

UCL

Target

LCL

Samples

Time

In control Out of control !

Natural variation

Look forspecial

cause !

Back incontrol!

Process Control Charts

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

Over time, collect k samples of n observations eachConstruct R-chart with limits:

kRR /RDLCLRDUCL 34

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

x-bar = sample meank = number of observations of n samples

Construct x-bar-chart with limits:

kxx /

RAxLCLRAxUCL 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

Professor Stephen Lawrence

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

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

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

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

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

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

Ch 15 - 25

Reducing Setup Time

Preset desired settingsUse quick fastenersUse locator pinsPrevent misalignmentsEliminate toolsMake movements easier

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

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

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

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

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

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

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

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

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

Ch 15 - 32

Visual Control

Library shelfWork station

Visual kanbansTool board

Machine controls

BetterGood Best

30-50

Howto

sensor

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

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

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

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 ProcessService 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

Since 1/ = p

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.

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

Professor Stephen Lawrence

The Value Equation

price

InnovationFlexTimeQualityValue

Flexibility

Operations Flexibility

Perspective of the producer:

Flexibility

Perspective of the customer:

Responsiveness

Flex’i*bil’i*ty: capable and adaptable to change.

Wall Street Journal – Mar 04, 02

“[The US economy is] benefiting from a new flexibility woven into

its fabric over the last decade.

“[Greenspan] attributed the performance to the economy’s apparent increased flexibility

and resilience…”

Types of FlexibilityLike Quality, flexibility is not unidimensional1. Mix Flexibility2. Changeover Flexibility3. Modification Flexibility4. Volume Flexibility5. Rerouting/Program Flexibility6. Resource Flexibility7. Flexibility Responsiveness

Pitfalls of FlexibilityFocus vs. flexibilitySpecialists vs. generalistsCost/flexibility trade-offsFlexibility for whom and what?When does the market reward flexibility?Customization/responsiveness squeeze

Supply Chain Management

Professor Stephen R. LawrenceGraduate School of Business Administration

University of Colorado

Boulder, CO 80309-0419

Supply Chain ManagementSupply Chain Management = SCMSCM used to be called “logistics”

from the verb loger, “to lodge or quarter”

“Multi-echelon Production Control” SCM is an amalgam of traditional business and operations functions

SCM Definitions

The art of managing the flow of materials and products from source to user

Set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying service level requirements

SCM Information Flows

Why an Interest in SCM?$862 billion spent in 1997 on supply-related activities (US)Opportunities

3 months for a box of cereal to go from factory to supermarket shelf15 days for a new car to travel from factory to dealerNational Semiconductor replaced warehouses with air freight cut distribution costs by 2.5% decreased delivery time by 47% increased sales by 34%

Wal-mart’s growth is largely attributed to its distribution strategy – pioneering use of cross docking

Adapted from Professor Joe Geunes

SCM ComplexitySupply chains increasingly complex

Number of organizations, facilities, products, and customers growing rapidly

Matching right supply with right demand at the right time is particularly difficult

Boeing 1997 $2.6 Billion write off due to raw material shortages, internal and

supplier part shortages, and productivity inefficiencies

IBM stocks out of new Aptiva PC $$ millions in lost revenue

Adapted from Professor Joe Geunes

SCM ComplexityDynamic complexities of the supply chain.

Time varying costs and demands often make it impossible to create tractable system models

New challengesHigh tech advances make obsolescence a great riskIncreased customer focus higher product variety More products greater management complexity

Adapted from Professor Joe Geunes

SCM FunctionsForecasting demandSelecting suppliersOrdering materialsInventory controlScheduling production

Shipping & deliveryInformation mgmtQuality managementCustomer Service

Conflicting SCM ObjectivesManufacturing and transportation

Desire economies of scale Long production runsFull truckload shipments

Marketing and salesdesire flexibility and product varietyIncreased inventory for better service

Trade-off service levelsinventory levels

Global SCM OpportunitiesTrade barriers decliningRegional trans-national trading groups

NAFTAEuropean CommunityAsia

Improved transportation systemsImproved information systems

New Developments in SCM

Third party logistics“one-stop shopping” for logistics services combining warehousing, transportation, order picking, ...

Partnershipsnon-adversarial cooperation between shippers, carriers, and warehouses

Bar-coding & RF trackingexpedites automatic product and order tracking

Cross-dockingElectronic Data Interchange (EDI) via Internet

computer-to-computer order entry communication

Inventory Management

Professor Stephen R. LawrenceLeeds School of Business

University of ColoradoBoulder, CO 80309-0419

Inventory ManagementWhat is inventory?

materials, supplies, and goods held in excess of what is immediately needed for production or immediately demanded by customers

Purposes of inventoryProvide scale economiesBuffer against uncertainty

Types of inventoryRaw material inventories (RMI)Work in process (WIP) inventoriesFinished goods inventories (FGI)

Fundamental inventory questionsHow much to order (produce)? When to order (produce)?

Where Inventory is HeldINPUTS TRANSFORMATIONS OUTPUTS

Purchasing

Vendors

Receiving

Raw Materials

FGI

WIP

Shipping

Distributors

Customers

Customers

Customers

ConversionProcesses

WarehouseInventory

Why Inventories are HeldCycle Stock

inventory resulting from batch (rather than unit) ordering or production.

Safety (Buffer) Stock buffer against uncertain demand.

Anticipation Stock accumulation in anticipation of peak demand.

Pipeline (WIP) Stock goods in transit and in-between stages of production.

Decoupling Stock inventory used to seperate decision making at different production echelons (e.g. factory and warehouse).

ABC AnalysisObservation: 20% of SKU's account for 80% of total inventory costs (Pareto principal).Idea: Manage most important (costly) inventory items most closely.Use: First analysis to undertake when attacking inventories!

1.0

Fraction of Total Items

0.0 0.2 0.5

0.6

0.91.0

Fraction of $Annual Use

Independent vs Dependent Demand

Bicycle

HardwareWheelsFrame

RimHubSpokes Tire

Inventory System DesignRepetition

Single-order (one-time buy)Multiple orders

Supply sourceExternal supply (purchase)

Internal supply (produce)

Certainty of demandDeterministicStochastic

Pattern of demandConstant demandTime-varying demandDependent demand

Knowledge of leadtimeConstant (certain)Stochastic (random)

Inventory ReviewContinuous (perpetual)Periodic

Deterministic Inventory Theory

Professor Stephen R. LawrenceLeeds School of BusinessUniversity of Colorado at BoulderBoulder, CO 80309-7058

EOQ Inventory Pattern

time

Inventory Level

t t t t

Q

Q /2average inventory

EOQ AssumptionsDemand rate deterministic and constant (no variation);No quantity discounts;Cost factors do not change appreciably with time;All items treated independently of other items;Constant replenishment leadtime;No shortages/backorders allowed;Entire order quantity is delivered at same time.

EOQ Cost Trade-offs

TotalCosts

Stocking Costs

HoldingCosts

min TC

Q*

Costs

Quantity Q

QH /2

DS /Q

TC

Intuitive Solution for Q*

Total costs minimized when holding costs = stocking costs

QH

D

QS

2

Solving for Q gives optimal order quantity Q*

H

SDQ

2*

Stochastic Inventory Theory

Professor Stephen R. LawrenceLeeds School of BusinessUniversity of ColoradoBoulder, CO 80309-0419

Solving Single-Period Problems

Where Pr(x≤Q*) is the “critical fractile” of the demand distribution.

ExampleU = cost of unmet demand (understock) U = 12 - 6 = 6 profitO = cost of excess inventory (overstock) O = 6 - 3 = 3 loss

Produce/purchase quantity Q* that satisfies the ratio

Optimal Solution:

OU

UQx

)Pr( *

Solving Single-Period Problems

D

0.00

0.20

0.40

0.60

0.80

1.00

1000 2000 3000 4000 5000 6000

Quantity (Q)

Pro

bab

ilit

y P

(x<Q

)

Demand over LeadtimeMultiply known demand rate D by leadtime L

Be sure that both are in the same units!

ExampleMean demand is D = 20 per dayLeadtime is L= 40 daysd(L) = D x L = 20 x 40 = 800 units

Demand Std Deviation over LeadtimeMultiply demand variance 2 by leadtime LExample

Standard deviation of demand = 4 units per dayCalculate variance of demand 2 = 16Variance of demand over leadtime L=40 days is

L2 = L 2 = 40×16=640

Standard deviation of demand over leadtime L is L = [L 2]½ = 640½ = 25.3 units

RememberVariances add, standard deviations don’t!

Safety Stock Calculations

Safety Stock AnalysisThe world is uncertain, not deterministic

demand rates and levels have a random componentdelivery times from vendors/production can varyquality problems can affect delivery quantitiesMurphy lives

safety stock

RL

time

Inventory Level

Q

0stockout!

SS

Continuous Review (CR) Stochastic Inventory Models

Always order the same quantity QReplenish inventory whenever inventory level falls below reorder quantity RTime between orders variesReplenish level R depends on order lead-time LRequires continuous review of inventory levels

RQ Q

Q

Q

L

time

Inventory Level

(CR) Continuous Review System

(CR) Implementation

Implementation Determine Q using EOQ-type modelDetermine R using appropriate safety-stock model

PracticeReserve quantity R in second “bin” (i.e. a baggy)Put order card with second binSubmit card to purchasing when second bin is openedRestock second bin to R upon order arrival

Total Inventory Costs for CR PoliciesTAC = Total Annual CostsTAC = Ordering + Holding + Expected Stockout Costs

1004430073442400

)05.0(200

2400500

2

20020624

200

2400200

)Pr(2

TAC

TAC

QdQ

DB

QSSH

Q

DSTAC

TAC = $10,044 per year (CR policy)

Periodic Review (PR) Stochastic Inventory Models

Multi-Period Fixed-Interval SystemsRequires periodic review of inventory levelsReplenish inventories every T time unitsOrder quantity q (q varies with each order)

T TT

L L L

I

II

Inve

nto

ry L

evel

time

q

q

q

(PR) Periodic-Review SystemPeriodic review (often Class B,C inventories)Review inventory level every T time unitsDetermine current inventory level IOrder variable quantity q every T periodsAllows coordinated replenishment of itemsHigher inventory levels than continuous review policies

(PR) ImplementationImplementation

Determine Q using EOQ-type model;Set T=Q/D (if possible --T often not in our control)Calculate q as sum of required safety stock, demand over leadtime and reorder interval, less current inventory level

PracticeInterval T is often set by outside constraintsE.g., truck delivery schedules, inventory cycles, …

Total Inventory Costs for PR PoliciesTAC = Total Annual CostsTAC = Ordering + Holding + Expected Stockout Costs

14718150133681200

)05.0)(6(5002

40035724)6(200

)Pr(2

TAC

TAC

QdQ

DB

QSSH

Q

DSTAC

TAC = $14,718 per year (PR policy)

Technology, New Products,Technology, New Products,and Innovationand InnovationLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419

Professor Stephen Lawrence

The Value Equation

price

InnovationFlexTimeQualityValue

Product Innovation

Process Innovation

Competing with Process Technology

Competing withMarketing

Capabilities

Competing withMarketing

Capabilities

Competing withTechnologicalCapabilities

Competing withTechnologicalCapabilities

Competing withOperationalCapabilities

Competing withOperationalCapabilities

ProcessEnhancementsProduct

Enhancements

Value toCustomer

PriceQualityTimelinessFlexibility

Perf

orm

an

ce

Effort (funds)

Physical limit of technology

Foster, Innovation: The Attackers Advantage, Summit Books, 1986

Successive Tech Innovations

Technology in HistoryUnorderly

not an orderly process of research and development; few elements of planning or cost-benefit analysis.

Breaks constraints. Technological change involves an attack by an individual on a constraint that is taken as a given by everyone else.

Unexplained timing.Often no good reason why an invention was made at a particular time and not centuries earlier (e.g. wheelbarrow and stirrup in Medieval times).

Moykr, The Lever of Riches: Technological Creativity andEconomic Progress, Oxford University Press (NY), 1990.

Chaos TheoryPath dependent and self-reinforcingInitial conditions are critical

Small perturbations in initial environment can have a large subsequent in eventual technological evolution

Inherent randomness (unpredictable)Positive feedback reinforces an evolutionary pathExample: Beta vs. VHS video tapesExample: PC operating system

Evolution of

Product/Process Innovation

Product Innovation

Stage of Product Life-cycleearly late

Rate ofInnovation

high

low

ProcessInnovation

Normal vs. Revolutionary InnovationNormal Innovation

innovation with an accepted “paradigm”incremental in natureincreasing specialization required

Revolutionary Innovationoften a response to “intellectual crisis”often proceeded by competing theories and ideaschanges the world view of a disciplineestablishes a new paradigm

Kuhn, T.S., The Structure of Scientific Revolutions, Univ of Chicago Press, 1962.

ParadigmsParadigm – a set of rules and regulations (written or unwritten) that does two things:

Establishes or defines boundariesGoverns how to behave inside the boundaries in order to be successful

Project Scheduling

Professor Stephen LawrenceLeeds School of Business

University of ColoradoBoulder, CO 80309-0419

Project Management

Management complex projectsMany parallel tasksDeadlines and milestones must be metDifficult to know “what to do first”Difficult to know when project is in troubleOften have competition for limited resources

When to use:

Examples

Building a new airportDesigning a new computer productLaunching an advertising campaignConstruction projects of all typesMaintenance projectsCurriculum reviews

Project Mgmt TechniquesCritical Path Method (CPM)

Developed by DuPont (1950’s)Plan and control maintenance of chemical plantsCredited with reducing length of maintenance shutdown by 40%

Project Evaluation and Review Technique (PERT)Developed by Navy (early 1960’s)Plan and control the Polaris missile projectCredited with speeding up project by 2 years

A

Finding the Critical Path

D

C

B

4

3

5

20 4

4

4

7

9

9 11

119

9

9

6

4

40

S=0 S=0

S=0

S=2

CPM TerminologyCritical Path: the chain of activities along which the delay of any activity will delay the projectEarly Start Time (ES): the earliest that an activity could possibly start, given precedence relationsLate Start Time (LS): the latest that an activity could possibly start without delaying the projectEarly Finish Time (EF): the earliest that an activity could possibly finishLate Finish Time (LF): the latest that an activity could possibly finish without delaying the projectActivity Slack: the amount of “play” in the timing of the activity; slack = EFT-EST = LFT-EFT

PERTSimilar to Critical Path Method (CPM)Accounts for uncertainty in activity duration estimatesProvides estimates of project duration probabilitiesBest used for highly uncertain projects

new product developmentunique or first-time projectsresearch and development

Distribution AssumptionAssume a “Beta” distribution

activity duration

dens

ity

ma b

Expected Duration & Variance

ET =

Var =

a + 4m + b6

(b - a)2

36

=2+4(3)+10

6= 4.0

=(10-2)2

36 = 1.778

Activity A

Using Project Statistics

What is the probability that the ad campaign can be completed in 18 weeks? 20? 24?

18 weeks: Z = -1.61 Prob(x<18) = 5.4%

20 weeks: Z = 0.00 Prob(x<20) = 50%

24 weeks: Z = 3.21 Prob(x<24) = 99.3%

OperationsOperationsStrategyStrategyLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, COBoulder, CO

Professor Stephen Lawrence

Course OutlineIntroduction to OMProductivity

Linear programming

QualitySPC tools

TimelinessQueueing Theory

FlexibilitySCMInventory Management

InnovationProject Management

Operations Strategy

Good Luck on Final !

Questions?