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Quantitative Method ToolsQuantitative Method Tools
Group Project Group Project
Shunna Brown, Adrienne Gosnell, Ryan Bell,Shunna Brown, Adrienne Gosnell, Ryan Bell,Susie Martin, Marvin JoynerSusie Martin, Marvin Joyner
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QM Tools DiscussedQM Tools Discussed
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ForecastingForecasting the art and science of predicting future eventsthe art and science of predicting future events
Moving AveragesMoving Averages A forecasting method that uses an average of the A forecasting method that uses an average of then most recent periods of data to forecast the next periodn most recent periods of data to forecast the next period
ForecastingForecasting
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ForecastingForecasting
(Weighted Average)(Weighted Average)Weights Applied Period
4 Last Month
2 Two months ago
1 Three months ago
Period
Number of Cars
Sold
3 Month Weighted
Moving Average
Trend AnalysisRegression
Formula
(y=112.3+6.6x)
1 112
2 1283 1394 145 132 139
5 143 141 1456 149 143 152
7 156 147 159
8 167 152 1659 168 161 172
10 174 166 17811 183 171 185
12 199 178 192
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ForecastingForecasting
What is the probability that we will makeWhat is the probability that we will makemore than 178 cars in the next period?more than 178 cars in the next period?
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Break Even AnalysisBreak Even Analysis
The point at which cost or expenses andThe point at which cost or expenses andrevenue are equal; there is no loss or gainrevenue are equal; there is no loss or gain
Suppose the company needs to makeSuppose the company needs to make$1,000,000 profit next year in order to$1,000,000 profit next year in order tostay in business, how many cars must bestay in business, how many cars must be
sold?sold?
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BEP SolutionBEP SolutionDoors Roo s
Selling Price 150$ 250$Variable Cos 40$ 80$Con ibu ion Margin 110$ 170$Fixed Cos $20,000
*Mus sell 4 doors or every roo
600$ 250$ 850$160$ 80$ 240$
CM Per Car 610$
Pro i + FC 1,000,000+20,000 = 1,672.13 Roo sSP-VC 850-240 6,688.52 Doors
Revenue 1,421,311$
Variable Cos s 401,311$Con ribu ion Margin 1,020,000$Fixed Cos 20,000$Ne Income 1,000,000$
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Linear Programming
Linear Programming
Is an analytical technique in which linear algebraicIs an analytical technique in which linear algebraicrelationships represent a firms decisions, given arelationships represent a firms decisions, given abusiness objective, and resource constraintsbusiness objective, and resource constraints
Our problem:Our problem: We have two products that profit $110 for doors and $170We have two products that profit $110 for doors and $170
for roofs. Which must be made at a ratio of 4 doors tofor roofs. Which must be made at a ratio of 4 doors toevery 1 roof every 1 roof
It costs $40 for the doors to be produced and $80 forIt costs $40 for the doors to be produced and $80 forroofs at a maximum cost of $4000roofs at a maximum cost of $4000
We have 5000 lbs of metal to be used (40lbs/door andWe have 5000 lbs of metal to be used (40lbs/door and120lbs/roof)120lbs/roof)
We have a maximum of 40hrs time (20 min/door andWe have a maximum of 40hrs time (20 min/door and
30min/roof)30min/roof)
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Linear ProgrammingLinear Programming
The GOAL of our problem was how to best The GOAL of our problem was how to best utilize our resources and time to enable usutilize our resources and time to enable usto have the greatest profit (maximization)to have the greatest profit (maximization)
We found that we could produce 64 car doorsWe found that we could produce 64 car doors
and 16 roofs at a maximum profit of $9760and 16 roofs at a maximum profit of $9760
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Decision AnalysisDecision Analysis
Decision analysis deals with managersDecision analysis deals with managerslooking in depth at business problems andlooking in depth at business problems and
understanding how to solve and fix theunderstanding how to solve and fix theproblems, then making a decision based onproblems, then making a decision based ontheir findings.their findings.
Our problem:Our problem: Would making repairs to existing presses beWould making repairs to existing presses be
beneficial?beneficial?
New Presses?New Presses?
No changes?No changes?
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Decision AnalysisDecision Analysis
Our findings:Our findings:
We would be benefitted by the increasedWe would be benefitted by the increasedrevenues of repairing the presses and byrevenues of repairing the presses and byinvesting in new pressesinvesting in new presses
The problemThe problem
Project investments are costlyProject investments are costly The answerThe answer
We should continue operating without We should continue operating without
changes.changes.
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Decision AnalysisDecision Analysis
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Establish objectivesEstablish objectives
Define project Define project
Create work breakdown structureCreate work breakdown structure
Determine resourcesDetermine resources
Form organizationForm organization
Work Breakdown StructureWork Breakdown Structure
Project PlanningProject Planning
1.1. P roject
P roject
2 .2 . M ajor tasks in the project M ajor tasks in the project
3.3. S ubtasks in the major tasksS ubtasks in the major tasks
4.4. Activities (or work packages) Activities (or work packages)to be completed to be completed
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Install new equipmentInstall new equipment Project PlanProject Plan
Critical Path CalculationCritical Path Calculation
ActivityActivity Description / Project phaseDescription / Project phase DependencyDependency DurationDuration ESES EFEF LSLS LFLF FloatFloat
11 Project initiation, preparation etcProject initiation, preparation etc 1212 11--AprApr 1212--AprApr 11--AprApr 1212--AprApr 00
22 Hardware AssemblingHardware Assembling 11 22 1313--AprApr 1414--AprApr 1919--JunJun 2020--JunJun 6767
33 Test HardwareTest Hardware 22 88 1515--AprApr 2222--AprApr 2121--JunJun 2828--JunJun 6767
44 Software InstallationSoftware Installation 11 1010 1313--AprApr 2222--AprApr 1313--AprApr 2222--AprApr 00
55 Develop ProgramsDevelop Programs 44 4545 2323--AprApr 66--JunJun 2323--AprApr 66--JunJun 00
66 Debug programsDebug programs 55 2222 77--JunJun 2828--JunJun 77--JunJun 2828--JunJun 00
77 Install the application for usersInstall the application for users 3,63,6 88 2929--JunJun 66--JulJul 2929--JunJun 66--JulJul 00
88 User TrainingUser Training 77 33 77--JulJul 99--JulJul 77--JulJul 99--JulJul 00
99 User AcceptanceUser Acceptance 88 11 1010--JulJul 1010--JulJul 1010--JulJul 1010--JulJul 00
EF = ES+durationEF = ES+duration--11 ESES -- Early StartEarly Start
LS = LFLS = LF--duration+1duration+1 EFEF -- Early FinishEarly Finish
Float = LSFloat = LS--ESES LSLS -- Late StartLate Start
LFLF -- Late FinishLate Finish
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Critical Path AnalysisCritical Path Analysis
T he critical path is the longest pathT he critical path is the longest paththrough the network through the network
T he critical path is the shortest time inT he critical path is the shortest time inwhich the project can be completed which the project can be completed
Any delay in critical path activities Any delay in critical path activitiesdelays the project delays the project
Critical path activities have no slack Critical path activities have no slack timetime
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Critical ath of project i 101 day
Critical Path DiagramCritical Path Diagram
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Crosby 5 stepCrosby 5 step DEFINE THE PROBLEMDEFINE THE PROBLEM
QUICK FIX (IF QUICK FIX (IF POSSIBLE)POSSIBLE)
ROOT C AUSEROOT C AUSE ANALY SIS ANALY SIS
CORRECTIVE ACTIONCORRECTIVE ACTION(INCLUDE WHAT,(INCLUDE WHAT,WHO, B Y WHEN)WHO, B Y WHEN)
FOLLOWFOLLOW--UP (INCLUDEUP (INCLUDEWHAT, WHEN, WHO)WHAT, WHEN, WHO)
Over the past six months, weOver the past six months, wehave been losing an average of have been losing an average of 10% of our physical inventory10% of our physical inventorycompared to the sublegercompared to the subleger
Check inventory by handCheck inventory by hand
Brainstormed with productionsBrainstormed with productions
Checked with suppliers to confirmChecked with suppliers to confirminventory shipmentsinventory shipments
Watched employees handleWatched employees handleinventory when it arrivedinventory when it arrived
Discovered employees inDiscovered employees inproduction were not countingproduction were not countingscrapped inventory as inventoryscrapped inventory as inventoryused.used.
Put a system into place toPut a system into place to
reconcile weigh counted scrapreconcile weigh counted scrapwith virtually reported scrap on awith virtually reported scrap on adaily basisdaily basis
To count scrap as material usedTo count scrap as material used
Decided to follow up after 1Decided to follow up after 1month to see if employees aremonth to see if employees are
keeping an accurate list of keeping an accurate list of materials used.materials used.
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Fishbone DiagramFishbone Diagram (cause and effect)(cause and effect)
Machines
ProductionEmployees
Suppliers
ReceivingEmployees
MissingInventory
Failure to recordinventory received
Employeesstealing inventory
Uses morematerial than
recorded
Under estimatesmaterials on hand
Failure torecord scrap
Inaccurateorders
Poor shipping records
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Install new equipment Project Plan
Critical Path Calculation
Activity Description / Project phase Dependency Duration ES EF LS LF Float
1 Project initiation, preparation etc 12 1-Apr 12-Apr 1-Apr 12-Apr 0
2 Hardware Assembling 1 2 13-Apr 14-Apr 19-Jun 20-Jun 67
3 Test Hardware 2 8 15-Apr 22-Apr 21-Jun 28-Jun 67
4 Software Installation 1 10 13-Apr 22-Apr 13-Apr 22-Apr 0
5 Develop Programs 4 45 23-Apr 6-Jun 23-Apr 6-Jun 0
6 Debug programs 5 22 7-Jun 28-Jun 7-Jun 28-Jun 0
7 Install the application for users 3,6 8 29-Jun 6-Jul 29-Jun 6-Jul 0
8 User Training 7 3 7-Jul 9-Jul 7-Jul 9-Jul 0
9 User Acceptance 8 1 10-Jul 10-Jul 10-Jul 10-Jul 0
EF = ES+duration-1 ES - Early Start
LS = LF-duration+1 EF - Early Finish
Float = LS-ES LS - Late Start
LF - Late Finish
Project anage ent
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Critical Path of project i 101 day
Project Management Project Management
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Transportation ModelTransportation Model
A product is transported from a number of A product is transported from a number of sources to a number of destinations at thesources to a number of destinations at the
minimum possible cost.minimum possible cost. Each source is able to supply a fixed number of Each source is able to supply a fixed number of
units of the product, and each destination has aunits of the product, and each destination has afixed demand for the product.fixed demand for the product.
The linear programming model has constraintsThe linear programming model has constraintsfor supply at each source and demand at eachfor supply at each source and demand at eachdestination.destination.
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Transportation ScheduleTransportation Schedule
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Minimize Z = $28x1A + 12x1B + 39x1C Minimize Z = $28x1A + 12x1B + 39x1C +25x1D+ 29x2A + 48x2B + 16x2C +30x2D+25x1D+ 29x2A + 48x2B + 16x2C +30x2D
subject to:subject to: x1A + x1B + x1C+1D = 60x1A + x1B + x1C+1D = 60 x2A + x2B + x2C+x2D = 40x2A + x2B + x2C+x2D = 40 x1A + x2A = 25x1A + x2A = 25 x1B + x2B = 15x1B + x2B = 15 x1C + x2C = 13x1C + x2C = 13 x1D + x2D = 50x1D + x2D = 50 xijxij uu 00
Shipping schedule for Roofs based uponTransportation Linear Programming Model
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Roofs A B C SupplyGreenville 28 12 39 25 60
Charle ton 29 48 16 30 40
emand 25 15 13 50
Shipping Schedule for RoofsShipping Schedule for Roofs
Optimal cost = $2298 A B CGreenville 15 45
Charle ton 25 13 2
Du y 3
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FromFrom To Ship Cost Ship costTo Ship Cost Ship cost
GreenvilleGreenville BB 1515 1212 180180
GreenvilleGreenville DD 4545 2525 11251125
CharlestonCharleston A A 2525 2929 725725
Charleston
Charleston
CC1313 1616 208208
CharlestonCharleston DD 22 3030 6060
DummyDummy DD 33 00 00
Shipping Schedule for RoofsShipping Schedule for Roofs
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Minimize Z = $28x1A + 12x1B + 39x1C Minimize Z = $28x1A + 12x1B + 39x1C +25x1D+ 29x2A + 48x2B + 16x2C +30x2D+25x1D+ 29x2A + 48x2B + 16x2C +30x2D
subject to:subject to: x1A + x1B + x1C+1D = 140x1A + x1B + x1C+1D = 140 x2A + x2B + x2C+x2D =260x2A + x2B + x2C+x2D =260 x1A + x2A = 100x1A + x2A = 100 x1B + x2B = 60x1B + x2B = 60 x1C + x2C = 52x1C + x2C = 52 x1D + x2D = 200x1D + x2D = 200 xijxij uu 00
Shipping schedule for oors based uponTransportation Linear Programming Model
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Shipping Schedule for DoorsShipping Schedule for Doors
Warehouse A B C SupplyGreenville 28 12 39 25 140
Charle ton 29 48 16 30 260
Demand 100 60 52 200
Optimal cost = $9692 A B C D
Greenville 60 80
Charle ton 100 52 108
Dummy 12
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From To Ship Cost Ship costFrom To Ship Cost Ship cost
GreenvilleGreenville BB 6060 1212 720720
GreenvilleGreenville DD 8080 2525 20002000 CharlestonCharleston A A 100100 2929 29002900
CharlestonCharleston CC 5252 1616 832832
CharlestonCharleston DD 108108 3030 32403240 DummyDummy DD 1212 00 00
Shipping Schedule for DoorsShipping Schedule for Doors
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Transportation Routes per dayTransportation Routes per day
Greenville
Charleston
Doors
Roofs
A
B
C
D
Roofs
Roofs
Doors
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DFM Check list
Data collected by: John Lee
ork
Cell: achine pre depart ent
Date:
11/12/201
0
Failue mode: 1- ov 2- ov 3- ov 4- ov 5- ov 6- ov 7- ovWeeklyTotals
Hydro line bur t I 1
Category 1 failure II I I 4
Exce ive te p 0
Da aged upper
Die I 1
Da aged lo er Die I I 2
Hydro pu p failure I 1
Clogged filter I I II II I I I 9
Broke lead cre I 1Ja ed upper
cylinder II I 3
Da pner error I 1
he Check Sheet i a i ple, organized ay of tracking the
frequency of particular event over ti e.
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The u e of a Pareto to offer vi ual identification of out tanding DF hour
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DFM Cost
$ (y) Hours (a) age(b)
Press 1 1200 13500 7Press 2 900 10300 3
Press 3 1890 18500 8
Press 4 1200 16320 7
Press 5 900 7500 3
Press 6 630 9100 3
Forecasting the Down for Maintenance (DFM)
Cost of Re-commissioning an Older Press Using Regression
Determine the intercept
and two variables:Cost=75.87+.06a+48.78b
And the correlation
coefficient:
.88
If the decommissioned press has 26500 hours and is 14 years old:
=75.87+(0.06*26500)+(48.78*14)
=$2349
88% of the time this data will correlate
with predicting maintenance cost
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.886415
R Square 0.785732
Adjusted R Square 0.642886Standard Error 259.4669
Observations 6
Coefficients
Intercept 75.87253221
X Variable 1 0.063182687
X Variable 2 48.77945373