mbac6080 operations mgmt final exam review professor stephen lawrence
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MBAC6080 Operations Mgmt
Final Exam ReviewMBAC6080 Operations Mgmt
Final Exam Review
Professor Stephen Lawrence
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AgendaPrepare for Final ExamDescribe FinalReview course materialsRecommendations for further studyAnswer questionsFCQ’s
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Course OutlineIntroduction to OMProductivity
Linear programming
QualitySPC tools
TimelinessQueueing Theory
FlexibilitySCMInventory Management
InnovationProject Management
Operations Strategy
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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
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Defining OperationsDefining Operations
Professor Stephen Lawrence
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TransformationProcesses
TransformationProcesses
GoodsGoods
ServicesServices
LaborLabor
KnowledgeKnowledge
CapitalCapital
MaterialsMaterials
What is Operations?
Transformation Definition
INPUTS OUTPUTS
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Who are “operations” managers?
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Manufacturing and Services Continuum of Characteristics
Mining (coal)
Automobiles
Fast Food
Banking
Consulting
ServiceOrientation
Manufacturing Orientation
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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
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Example:
Bicycle Manufacturing
Labor Used per unit produced
Capital Used(equipment) per unit produced
Desirable
Desirable
Undesirable!
Desirable
EfficientFrontier
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Labor Used per unit produced
Capital Used(equipment) per unit produced
What is Operations?
Economic Definition
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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
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Added Value Model
adapted from Porter, Competitive Advantage, Free Press, 1985
Suppliers Customers
Competitors
The Firm
BusinessEnvironment
Value Chain
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Operations is the fundamental means by
which firms…
What is Operations?
Added-Value Definition
Add Value!
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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
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The Value Equation
price
ePerformancValue
price
InnovationyFlexibilitTimelinessQualityValue
P
IFTQValue
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Competing with Competing with ProductivityProductivityLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419
Professor Stephen Lawrence
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The Value Equation
price
yFlexibilitTimelinessQualityValue
How are productivity
and price related?
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Inputs
OutputstyProductivi
Productivity Defined
Inputs: labor, materials, capital, …
Outputs: goods, services
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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
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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
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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
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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
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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
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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.
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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
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Introduction to Linear Programming
Professor Stephen LawrenceLeeds School of Business
University of Colorado
Boulder, CO 80309-0419
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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…
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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.
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Assumptions of LPLinear objective function, constraints
ProportionalityAdditivity
DivisibilityContinuous decision variables
CertaintyDeterministic parameters
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LP ConceptsDecision variablesObjective functionConstraintsFeasible solutionsFeasible region (convex polytope)Corner point solutionsOptimal solution“Constrained optimization”
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Linear Programming ApplicationsProduct MixDietInvestmentCash flowProductionMarketing
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Competing with Competing with QualityQualityLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419
Professor Stephen Lawrence
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The Value Equation
price
yFlexibilitTimelinessQualityValue
Quality
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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!
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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
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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
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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:
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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
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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
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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
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Total Quality ManagementTotal Quality Management
TQMTQM
Commitmentto Quality
Commitmentto Quality
TotalInvolvement
TotalInvolvement
Scientific Toolsand TechniquesScientific Toolsand Techniques
ContinuousImprovementContinuous
Improvement
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Japanese Deming PrizeEstablished 1951Annual prizeAwarded for
development of quality tools, orquality improvement programs
Created by JUSA(Union of Japanese Scientists and Engineers
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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)
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International standards for business quality and control
ISO 9000
Management responsibilityQuality systemContract reviewDesign controlDocument ControlPurcasingTraceability
Process controlInspection / testingReject controlHandlingQuality recordsInternal auditsTraining Statistical techniques
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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
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Does Quality Matter?
Quality and total quality costnegatively correlated.
Quality and productivitypositively correlated.
Quality and profitabilitypositively associated.
Garvin, Managing Quality, The Free Press, 1988
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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
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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
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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
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Run Charts
time
mea
sure
men
t
Look for patterns and trends…
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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
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CAUSE & EFFECT DIAGRAMS
Employees
Proceduresand Methods
TrainingSpeed Maintenance
Equipment
Condition
ClassificationError
Inspection
BADCPU
Pins notAssigned
DefectivePins
ReceivedDefective
Damagedin storage
CPU Chip
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HISTOGRAMSA statistical tool used to show the extent and type of variance within the system.
Fre
qu
ency
of
Occ
urr
ence
s
Outcome
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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.
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Process Control
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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...
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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!
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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%
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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
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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
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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”
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Upper Control Limit
Lower Control Limit
6
3
Target Spec
Process Control Charts
Upper Spec Limit
Lower Spec Limit
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UCL
Target
LCL
Samples
Time
In control Out of control !
Natural variation
Look forspecial
cause !
Back incontrol!
Process Control Charts
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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
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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(
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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
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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
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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
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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
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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
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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.
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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
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The Value Equation
price
InnovationFlexTimeQualityValue
Timeliness
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Comparative Lead Times
Engineer to Order
Make to Order
Assemble to Order
Make to Stock
Customer LeadtimeInternal Leadtime
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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!
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Queueing Theory
Professor Stephen LawrenceLeeds School of Business
University of ColoradoBoulder, CO 80309-0419
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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
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Principal Queue ParametersArrival ProcessService ProcessNumber of ServersQueue Discipline
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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
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Exponential Distribution
m
etf
mt /
)(
Exponential Density
Mean = mStd Dev = m
f(t)1/m
tm
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M/M/1 Queues
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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
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M/M/1 Operating Characteristics
Utilization (fraction of time server is busy)
Average waiting times
Average number waiting
1W WWq
L LLq
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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
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Cost Trade-offs
= 0.0
Cost CombinedCosts
Cost ofWaiting
Cost ofService
Sweet Spot –Min Combined
Costs
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G/G/k Queues
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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
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General DistributionsTwo parameters
Mean (m)Std. dev. (s)
ExamplesNormalWeibullLogNormalGamma
f(t)
tm
s
Coefficient of Variationcv = s/m
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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
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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
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G/G/k Variance AnalyzedWaiting times increase with the square of the coefficient of variance
No variance, no wait!
Wq
c
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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?
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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.
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Competing with Competing with FlexibilityFlexibilityLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, CO 80309-0419Boulder, CO 80309-0419
Professor Stephen Lawrence
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The Value Equation
price
InnovationFlexTimeQualityValue
Flexibility
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Operations Flexibility
Perspective of the producer:
Flexibility
Perspective of the customer:
Responsiveness
Flex’i*bil’i*ty: capable and adaptable to change.
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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…”
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Types of FlexibilityLike Quality, flexibility is not unidimensional1. Mix Flexibility2. Changeover Flexibility3. Modification Flexibility4. Volume Flexibility5. Rerouting/Program Flexibility6. Resource Flexibility7. Flexibility Responsiveness
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Pitfalls of FlexibilityFocus vs. flexibilitySpecialists vs. generalistsCost/flexibility trade-offsFlexibility for whom and what?When does the market reward flexibility?Customization/responsiveness squeeze
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Supply Chain Management
Professor Stephen R. LawrenceGraduate School of Business Administration
University of Colorado
Boulder, CO 80309-0419
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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
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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
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SCM Information Flows
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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
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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
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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
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SCM FunctionsForecasting demandSelecting suppliersOrdering materialsInventory controlScheduling production
Shipping & deliveryInformation mgmtQuality managementCustomer Service
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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
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Global SCM OpportunitiesTrade barriers decliningRegional trans-national trading groups
NAFTAEuropean CommunityAsia
Improved transportation systemsImproved information systems
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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
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Inventory Management
Professor Stephen R. LawrenceLeeds School of Business
University of ColoradoBoulder, CO 80309-0419
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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)?
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Where Inventory is HeldINPUTS TRANSFORMATIONS OUTPUTS
Purchasing
Vendors
Receiving
Raw Materials
FGI
WIP
Shipping
Distributors
Customers
Customers
Customers
ConversionProcesses
WarehouseInventory
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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).
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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
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Independent vs Dependent Demand
Bicycle
HardwareWheelsFrame
RimHubSpokes Tire
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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
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Deterministic Inventory Theory
Professor Stephen R. LawrenceLeeds School of BusinessUniversity of Colorado at BoulderBoulder, CO 80309-7058
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EOQ Inventory Pattern
time
Inventory Level
t t t t
Q
Q /2average inventory
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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.
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EOQ Cost Trade-offs
TotalCosts
Stocking Costs
HoldingCosts
min TC
Q*
Costs
Quantity Q
QH /2
DS /Q
TC
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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*
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Stochastic Inventory Theory
Professor Stephen R. LawrenceLeeds School of BusinessUniversity of ColoradoBoulder, CO 80309-0419
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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( *
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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
)
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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
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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!
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Safety Stock Calculations
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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
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Continuous Review (CR) Stochastic Inventory Models
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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
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(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
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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)
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Periodic Review (PR) Stochastic Inventory Models
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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
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(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
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(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, …
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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)
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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
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The Value Equation
price
InnovationFlexTimeQualityValue
Product Innovation
Process Innovation
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Competing with Process Technology
Competing withMarketing
Capabilities
Competing withMarketing
Capabilities
Competing withTechnologicalCapabilities
Competing withTechnologicalCapabilities
Competing withOperationalCapabilities
Competing withOperationalCapabilities
ProcessEnhancementsProduct
Enhancements
Value toCustomer
PriceQualityTimelinessFlexibility
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Perf
orm
an
ce
Effort (funds)
Physical limit of technology
Foster, Innovation: The Attackers Advantage, Summit Books, 1986
Successive Tech Innovations
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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.
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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
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Evolution of
Product/Process Innovation
Product Innovation
Stage of Product Life-cycleearly late
Rate ofInnovation
high
low
ProcessInnovation
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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.
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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
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Project Scheduling
Professor Stephen LawrenceLeeds School of Business
University of ColoradoBoulder, CO 80309-0419
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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:
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Examples
Building a new airportDesigning a new computer productLaunching an advertising campaignConstruction projects of all typesMaintenance projectsCurriculum reviews
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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
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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
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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
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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
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Distribution AssumptionAssume a “Beta” distribution
activity duration
dens
ity
ma b
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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
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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%
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OperationsOperationsStrategyStrategyLeeds School of BusinessLeeds School of BusinessUniversity of ColoradoUniversity of ColoradoBoulder, COBoulder, CO
Professor Stephen Lawrence
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Course OutlineIntroduction to OMProductivity
Linear programming
QualitySPC tools
TimelinessQueueing Theory
FlexibilitySCMInventory Management
InnovationProject Management
Operations Strategy
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Good Luck on Final !
Questions?