Development of a business model for diagnosing uncertainty in ERP

int j prod res 2002 vol 40 no 13 3015plusmn3039

Development of a business model for diagnosing uncertainty in ERPenvironments

S C L KOHy and S M SAADz

It has been identireged from a comprehensive literature review and a subsequentindustrial survey that uncertainty within an Enterprise Resource Planning (ERP)-controlled manufacturing environment has not been tackled systematically andnot examined e ectively This has been shown in two areas (i) most identiregedresearch on uncertainty within an ERP environment focuses on regnding suitableapproaches to cope with uncertainty rather than tackling the underlying causesand (ii) most identireged simulation models on uncertainty controlled by ERPwhile purporting to represent such an environment do not truly model a multi-level dependent demand system with multi-products and controlled by PlannedOrder Release (POR) based on planned lead times The aim of this research is totackle these two areas simultaneously A business model that is aimed at enablingthe underlying causes of uncertainty within ERP environments is developedThis business model is verireged and validated through a comprehensive surveyinvolving ERP users operating in batch manufacture with mixed demandpatterns Validation of the business model is carried out via an extensive experi-mental programme within a simulation modelETHdeveloped using SIMAN VETHthattruly represents a multi-level dependent demand system in ERP environmentsdisturbed by uncertainty An experimental fractional factorial design is executedwhereby the simulation results are analysed using Analysis of Variance(ANOVA) The results indicated that late delivery from suppliers machinebreakdowns unexpected or urgent changes to the schedule a ecting machinesand customer design changes a ect Parts Delivered Late (PDL) signiregcantlyIt was found that the higher the level of uncertainties the higher the level ofPDL

AcronymsCDC Customer Design ChangesCRM Customer Relationship Management

FPDL Finished Products Delivered LateINST Insecure StoresLDFS Late Delivery From SuppliersMBD Machine BreakDownsMPS Master Production Schedule

MRPII Manufacturing Resource PlanningMTBF Mean Time Between FailuresMTTR Mean Time To Repair

International Journal of Production Research ISSN 0020plusmn7543 printISSN 1366plusmn588X online 2002 Taylor amp Francis Ltd

httpwwwtandfcoukjournals

DOI 10108000207540210140077

Revision received March 2002 University of She eld Management School 9 Mappin Street She eld S1 4DT UK She eld Hallam University School of Engineering City Campus She eld S1 1WB

UK To whom correspondence should be addressed e-mail SCLKohshe eldacuk

PDL Parts Delivered LatePOR Planned Order Release

PSTE Planned Set-up or change over Times ExceededUCSM Unexpected or urgent Changes to Schedule a ecting Machines

WFL Waiting For LabourWFT Waiting For Tooling

1 IntroductionModern manufacturing industry is facing increasing pressure to improve its

responsiveness to market dynamics Central to this are the issues addressed byproduction planning and control systems and philosophies Customer expectationsfor shorter delivery lead-times greater agility improved quality and reduced costshave made the e ective application of an appropriate system a signiregcant deter-minant of survival for many manufacturing enterprises

Within batch manufacture a wide variety of systems each with its own philo-sophy may be adopted These systems include Material Requirements Planning(MRP) Manufacturing Resource Planning (MRPII) Enterprise ResourcePlanning (ERP) Customer Relationship Management (CRM) Just-In-TimeOptimized Production Technology and Advanced Production Scheduling eitherused discretely or in combination Over the last 30 years many millions of poundsand considerable man-years of e ort have been invested in improving the perform-ance of MRP-based systems These systems include MRPII ERP and CRMBoston-based Advanced Manufacturing Research predicts the ERP market willreach $666 billion by 2003 at an estimated compound annual growth rate of 32(Angerosa 1999) However a contemporary study by Forrester Research identiregedthat 40 of enterprises believe ERP will not provide any optimization capabilitieswhile 46 expect them to play a partial role at best (Gormley 1998)

Whatever system is chosen it must be capable of performing within an uncertainworld As the growth of ERP is increasing even while the systems only operate bestin deterministic and predictable manufacturing environments (Dilworth 1996) thisresearch started from the basis that causes and e ects of uncertainty were not gen-erally well understood or managed within batch manufacture ERP environmentsUltimately underperformance of ERP was reported as the assessment was based onstochastic and unpredictable manufacturing environments

Cox and Blackstone (1998) deregned uncertainty as unknown future events thatcannot be predicted quantitatively within useful limits Thus the occurrence ofuncertainty is unpredictable and its e ect is di cult to quantify This implies thatuncertainty by nature is stochastic An example of this uncertainty is late delivery ofa purchased part The e ect of this would be a delay in the manufacture of the parentassembly that is di cult to quantify because the loading on the shop macroor when thepart ultimately arrives may be very di erent to that planned when it was due toarrive This may result in other planned work being displaced to make way for thedelayed assembly or the delay may be increased if a suitable production space cannotbe found

Without the existence of uncertainty the POR regle generated from the MRP runwould be a deregnitive workable plan that would guarantee time delivery Howevermodern manufacturing environments are subjected to continuous changes bothinternally and externally which require the ERP environments to be adaptive touncertainty in order to satisfy the customers

3016 S C L Koh and S M Saad

2 Literature reviewMany aspects of uncertainty within ERP environments and methods of dealing

with them have been researched over the 30 years since MRP was regrst introduced Acomprehensive review can be found in Koh et al (2002) This section relates a sampleof these aspects to typify the approaches that have been studied

When faced with uncertainty a variety of approaches can be used to cope withthe unwanted e ects eg safety stock safety lead-time overtime subcontract multi-skilling labour etc A study carried out by Mather (1977) examining changes to theMaster Production Schedule (MPS) and poor vendor performance suggestedremoval of uncertainty as a better approach than simply managing it The resultsderived from a planning heuristic suggested that rescheduling is the main cause ofuncertainty and by tackling the causes of rescheduling uncertainty could be reducedsigniregcantly

New and Mapes (1984) studied the e ect of process yield losses using the meas-ures of cost e ectiveness and customer satisfaction Their framework showed thatapplying a mean yield rate and regxed bu er stocks is appropriate in a continuousschedule Make-To-Stock environment In a continuous schedule Make-To-Orderenvironment mean yield rate regxed bu ers and a yield to regnish monitoring systemare suitable Using a similar methodology Ho et al (1995) developed a frameworkto dampen uncertainty for reducing system nervousness caused by external demandand supply and system uncertainties The framework suggested the use of safetystock safety capacity safety lead-time and rescheduling to bu er these uncertainties

Grasso and Taylor (1984) concluded that by allowing purchased parts to arrivelate more frequently than by allowing them to arrive early would be advantageousfrom a practical point of view since it results in the lowest total costs of the systemTheir simulation results showed that when bu ering against variations in supplytiming it is more prudent to use safety stock instead of safety lead-time Brennanand Gupta (1993) simulated uncertainties in external demand delivery and processlead-times ANOVA was applied to the simulation results and showed that lead-timeand demand uncertainties are individually and interactively signiregcant determinantsof performances the number of parts at a given level in the product structure and itsshape are signiregcant when lead-time and demand uncertainties were applied thechoice of lot-sizing rule has a signiregcant e ect on performances and the value ofthe ratio of set-up to holding cost has a signiregcant e ect on performances when lead-time and demand uncertainties occurred

Ho and Carter (1996) simulated static dampening automatic rescheduling andcost-based dampening to cope with external demand and systems uncertaintiesTheir simulation results indicated that the performance of dampening proceduresdepends on the operating environments within manufacturing enterprises Theresults also showed that a reduction in uncertainty as measured by reschedulingfrequency does not necessarily lead to better system performance Rather it is theappropriate use of dampening procedures and lot-sizing rules that results in systemimprovement Byrne and Mapfaira (1998) simulated system performance with uncer-tainty in capacity loading Their simulation results showed that performance of abatch manufacture environment is signiregcantly a ected when the system is over-loaded It was found that systems with low uncertainty outperform all others as aconsequence of positive knock-on e ects from normal lead-time variations

Through simulation modelling again Ho and Ireland (1998) identireged that fore-cast errors might not cause a high degree of scheduling instability and in any case

3017Diagnosing uncertainty in ERP environments

scheduling instability can be dampened using an appropriate lot-sizing rule Theirstudy concluded that applying Economic Order Quantity and Lot for Lot rulescreates signiregcantly more nervous systems than applying Silver Meal and PartPeriod Balancing rules Looking from the perspective of design for manufactureYang and Pei (1999) modelled the e ect of engineering changes on inventory level AStandard for Transfer and Exchange Product (STEP) model database integrationenvironment was developed to link design tasks with MRP activities For eachengineering change activity the Engineering Bill of Material data relating to thechange and stored in a Computer Aided Design database were extracted and trans-formed to a Manufacturing Bill of Material data stored in the MRP database Themodireged MRP record was then generated and compared with the original dataBased on this information the designer can determine an appropriate design alter-native such that the e ect on inventory level can be minimized

Murthy and Ma (1996) developed a mathematical model to measure the optimalover-planning factor required to cope with scrap resulting from both supply andprocess failures The optimal over-planning factor would then be used as a dampen-ing tool in the planning process Krupp (1997) proposed a statistical model thatexpresses deviations in units of time rather than quantity to provide safety stockcalculations that are responsive to trend andor seasonality in future forecasts Aforecast tracking signal was used to dampen forecast inaccuracy by adjusting safetystock calculations in cases where forecasts were consistently overoptimistic

Through an industrial survey of ERP users operating in batch manufacture in theUK Koh et al (2000a) identireged that industry applies these approaches with little orno discrimination and overtime and multi-skilling labour are the most robustapproaches used A review of this literature showed that uncertainty within ERPenvironments has not been studied systematically as the researchers mainly studieduncertainty by regnding suitable approaches that cope rather than by diagnosing thesigniregcant underlying causes of those uncertainties to the performance measuresused Hence ERP underperformance persists because the signiregcant underlyingcauses of uncertainty were not resolved

The review also revealed that simulation is the common method used for exam-ining uncertainty in ERP environments However most ERP-controlled simulationmodels while purporting to represent such an environment do not truly model amulti-level dependent demand system with multi-products and controlled by PORbased on planned lead times The simulation models identireged were either deregningdemand stochastically ie not driven by MPS and were hence not POR controlledcreating a simplireged matching assembly to mimic dependent demand within the Billof Materials (BOM) ie not multi-products and multi-level or releasing orders pre-maturely even when delay has occurred The ERP-controlled manufacturing envir-onment does not release the order earlier than planned If delay is encountered therelease date of parts at the upper level BOM and orders in the pipeline should beadvanced according to the amount of the delay and resources availability As theirsimulation model characteristics do not e ectively represent ERP environments theconclusions made are questionable Although some claim that their simulationmodel is representative no evidence could be found to prove the claim

3 Research methodology and models developmentTo close the identireged research gaps dual methods were deployed the regrst being

the development of a business model for diagnosing uncertainty in ERP environments


and the second being the development of a multi-products multi-level dependentdemand simulation model controlled by POR for validating the business model

The business model was conceptualized from the construction of an Ishikawadiagram structuring causes and e ects of uncertainty in ERP environments Theultimate performance measure used was Finished Products Delivered Late(FPDL) located at level zero which was found to be the industry preferred measure(Koh and Jones 1999) The business model consists of regve separate strands namelymaterial shortages labour shortages machine capacity shortages scraprework andregnished products completed but not delivered and three levels The link betweeneach uncertainty at each level shows the cause-and-e ect relationship The under-lying causes of uncertainty are located at level three These are the potential reasonscausing FPDL No further level decomposition was considered necessary because thebusiness model was designed mainly to operate within a single tier manufactureFigures 1 and 2 show the Ishikawa diagram and the business model respectively

With such a business model tackling the signiregcant underlying causes of uncer-tainty is enabled through diagnosis within each chain Data can be collected orestimated to quantify the e ect of the underlying causes of uncertainty

31 Business model veriregcationThis business model was verireged through a comprehensive survey involving ERP

users operating in batch manufacture with mixed demand patterns The question-naire was designed and structured to verify the business model of uncertainty as itsought responses according to the structure of causes and e ects developed Failureto provide comprehensive information would invalidate the structure Correct com-pletion would provide implicit veriregcation of the structure

An overall response rate of 5635 was achieved with telephone follow-up Thiswas considered an excellent response rate Nevertheless the majority of respondentswere unable to supply objective data choosing instead to estimate the percentagecontribution of causes to specireged e ects A wide range of results was observedprompting a statistical analysis to assess the signiregcance of each uncertainty

The intention of this veriregcation was to establish the existence or otherwise ofcause-and-e ect relationships between uncertainties and their outcomes The use ofANOVA was considered entirely appropriate for this purpose The data derivedfrom the survey were largely based on estimates and the estimates themselves weretaken after the application of approaches to cope with uncertainty Therefore aconregdence level of 80 (not ˆ 020) was set

ANOVA results identireged signiregcant evidence that a total of 23 underlyingcauses of uncertainty a ect FPDL within mixed demand pattern environments(Koh et al 2000b) However this does not mean that those uncertainties with pvalues not within 020 do not a ect FPDL instead it simply means that higherconregdence was gathered that those identireged to be signiregcant have a higher like-lihood of resulting in FPDL As the respondents have satisfactorily quantireged con-tributions of uncertainties at each level of the structure the cause-and-e ectrelationship of uncertainty within the business model was verireged

32 Simulation model developmentResponses from a survey are always subject to a certain degree of reliability To

increase the reliability of the results simulation studies were carried out to validatethe business model



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A multi-product multi-level dependent demand simulation model controlled byPOR has been developed in SIMAN V for this purpose The data used for thesimulation studies were gathered from a commercial transformer manufacturerwho uses a proprietary ERP system for production planning and scheduling Tenproducts were modelled which consist of three runners four repeaters and threestrangers deregned by Parnaby (1988) di erentiated in Time Between Orders for creat-ing a mixed demand patterns environment The products consist of up to regve BOMlevels and a total of 434 di erent parts were purchased and manufactured An MPScovering two yearsrsquo demand was prepared for these products and run through acommercial ERP system resulting in POR data for some 50 000 batches (parts)with 60 for purchase and 40 for manufacture

These POR data control all purchasing and manufacturing start times by usingthe planned release times of the batches As the POR were generated based on thedependency within each productrsquos BOM which implies that parent assembly cannotstart until all child parts are available the parent and child relationship has implicitlybeen modelled The only time when this dependency would be displaced is wheneverdelay (uncertainty) is applied Within the MRP logic of inregnite capacity this type ofdisplacement will never occur Within a regnite capacity environment (simulation) thistype of displacement will occur and hence this dependency has to be designed Thisimplies that work will be held in a queue and cannot be started when the resourcesrequired are not available A second constraint was incorporated in the simulationmodel to model the dependent demand logic within MRP work cannot be startedwhen the planned release time is not reached

The simulation model logic was derived from the concept of the parent andchild relationship within MRP Under this logic a parent part cannot be startedfor manufacture until all required child parts are available required resources areavailable and the planned release time is reached A child part cannot be started forpurchase or manufacture until required resources are available and the plannedrelease time is reached These are the rules governing work release on dependencyupon resources availability and MRP logic Figure 3 shows the simulation modellogic

The entire POR regle was ranked in ascending order by release time and then bypart number Each individual POR record was read into the simulation model inorder and was regrst recognized as either a child or parent part A parent part will berouted into a queue to wait for all required child parts A child part will be routed forprocessing either purchase or manufacture Whenever a child part is completed asearch algorithm will then be carried out at its parentrsquos queue verifying the parentand child relationship As it could be more than a child part required by a parentpart an evaluation will be made to assess whether all child parts are present Onlywhen this condition is met can the parent part be released otherwise it remains in thequeue Upon completion of operations for parent parts it was established whetherthe part is a regnished product A regnished product leaves the system otherwise itreturns to the queue

To illustrate the implementation of the simulation logic in SIMAN V reggure 4shows the use of relevant subroutines variables and attributes in a macrow diagramThe detailed simulation sub-models logic in SIMAN V is shown in reggure 5

For verifying the simulation model the dynamic technique devised by Whitnerand Balci (1989) was employed This technique was applied using a single chainwithin a BOM a product and regnally the entire POR regle for debugging top-down


testing bottom-up testing execution tracing stress testing and regression testing

Expected responses were obtained hence verifying the internal logic of the simula-

tion model

To validate the simulation model a commercial ERP system was used Thevalidation was carried out in two stages regrst to validate the POR input and secondly

to validate the simulation results Release times between the ERP systems and aver-

age resources utilizations between the ERP system and the simulation model were

compared for a range of batches over the entire MPS period In all cases exact

matches were found A residual of some late delivery was identireged from the simula-

tion model before uncertainty was modelled even when overall utilizations appearedadequate To validate this outcome it was expected that this late delivery would be

remacrected in MRP resources overload An analysis was carried out to identify the

parts involved and the timing of the late delivery For each part identireged routings

were established From this a total of regve resources were found to recur which were

those with the highest overall utilizations A spreadsheet was then devised to plot the

late delivery against resources utilizations from the ERP system A regve-day moving


POR

Is part aParent

Route to purchaserouting

When completesearch Parent

queue

Yes

No

Route to Parentqueue and wait

Are all childrenpresent

Route tomanufacture

routingRemain in queue

Is this afinishedproduct

No

No

Yes Yes

Deliver tocustomer

Figure 3 Simulation model logic


ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

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end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

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tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

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AR

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end

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for

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y fr

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den

tQ

ue

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AR

CH

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ep

en

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tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

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ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV

average was then calculated This reggure was selected to remacrect typical industryplanning time buckets

The results broadly supported the expectation that the late delivery occurred inperiods when there were a number of consecutive days of resources overloadAlthough a very few anomalies occurred in the absence of the ERP system thatincludes regnite scheduling this analysis provided the highest level of validation possible

This simulation model was then used as the datum for modelling uncertainty byincorporating additional stochastic algorithms The same veriregcation technique wasdeployed and was equally successful A further validation exercise was not consid-ered necessary for two reasons First that the stochastic simulation model simplyintroduced verireged algorithms to an already validated simulation model andsecondly that no appropriate simulation model could be found for validationpurposes

4 Uncertainty screening sensitivity studies and pilot experimentsThe 23 identireged underlying causes of uncertainty were screened to ensure they

could be simulated and whether di erent uncertainties could use similar simulationmodelling techniques Table 1 shows the simulation groups and the uncertainties towhich they relate


Simulation technique Uncertainty

Cycle time increment Late delivery from suppliersInsecure storesPlanned set-up or change-over times exceededWaiting for laboursWaiting for toolingWaiting for inspection laboursWaiting for materials internally suppliedUnexpected demand for particular skills (Labour)

Batch size increment Unexpected or urgent changes to schedule a ectingmachinesUnexpected or urgent changes to schedule a ecting labours

Change to queuing rule Schedules or work-to-lists not controlledSchedules or work-to-lists not produced

Mean Time Between Machine breakdownsFailuresMean Time ToRepair (MTBFMTTR)

Alternative routing Customer design changes

Probability (Passfail) Labour errorsDefective raw materialsMachine errorsItems on-hold (Financial)Unavailability of transportsSeeking concessionsAwaiting balance of orders

Use of commercial ERP MRP planned overload (Inregnite scheduling of machines)systems MRP planned overload (Inregnite scheduling of labours)

Table 1 Groups of simulation techniques with associated uncertainties

A total of ten uncertainties either do not a ect the manufacturing cycle as they

occur after product completion were not considered suitable for modelling within

the practical limits of this research or else were identireged as a proxy for other un-

certainties already simulated and could be discarded from further detailed study Afurther two uncertainties had already been modelled within the ERP system and could

also be excluded A total of 11 uncertainties remained to be simulated both discretely

and in combination Table 2 lists all 13 uncertainties modelled and their codes

Sensitivity studies were performed to identify suitable settings for simulating each

uncertainty These were carried out on the basis that the settings were chosen based

upon their measurability and realismDual performance measures namely FPDL and PDL were examined FPDL

measures as a percentage of regnished products delivered late from all products due

in any time period while PDL measures as a percentage of parts delivered late from

all parts due in any time period FPDL is a remacrection of the e ects of all uncertainties

combined and hence it is purely cumulative and less sensitive in analysing the con-

tribution made by particular uncertainties PDL provides a more sensitive indicator

of the e ects of uncertainty on individual piece parts Figure 6 shows a simple BOMas an example for the use of FPDL and PDL


Code Uncertainty

LDFS Late delivery from suppliersINST Insecure storesSWTLNC Schedules or work-to-lists not controlledUCSL Unexpected or urgent changes to schedule a ecting laboursUCSM Unexpected or urgent changes to schedule a ecting machinesPSTE Planned set-up or change-over times exceededMBD Machine breakdownsWFL Waiting for laboursWFT Waiting for toolingCDC Customer design changesWFIL Waiting for inspection laboursPOL MRP plan overload (Inregnite scheduling of labours)POM MRP plan overload (Inregnite scheduling of machines)

Uncertainty modelled in an ERP system

Table 2 Uncertainties modelled with associated codes

Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)

Part No description (quantity per unit)

10001 Heavy duty transformer (1)

10002 Case subassembly (1) 10003 Ballast subassembly (1)

10008 Rivet kit (1) 10009 Enclosure (1) 10004 Ballast thermal fuse (1) 10005 Coil (2)

FPDL

PDL

Figure 6 A simple BOM showing the use of FPDL and PDL

If part number 10006 is delivered late and no slack exists to recover the latenessthen it will also cause part numbers 10005 10003 and the regnal product 10001 to belate The e ect will be 444 PDL (as there are nine parts) If FPDL is measured thee ect is 100 Thus irrespective of the number of parts late in a single BOM noincrease in FPDL can result FPDL is therefore seen to be a dampened measure andhence misleading when establishing which uncertainties are signiregcant PDL on theother hand measures the precise e ect of individual or combined uncertainties to amuch higher level of sensitivity and is therefore adopted in the simulation studies

Pilot experiments were then carried out to establish the signiregcance of the 11uncertainties ANOVA results from the pilot experiments showed that there is sig-niregcant evidence at 95 conregdence level that late delivery from suppliers (LDFS)insecure stores (INST) planned set-up or change over times exceeded (PSTE)machine breakdowns (MBD) waiting for labours (WFL) waiting for tooling(WFT) unexpected or urgent changes to schedule a ecting machines (UCSM) andcustomer design changes (CDC) a ect PDL Therefore these uncertainties would beexamined in detail and hence the simulation results used for validating the businessmodel

5 Uncertainty modelling algorithmsThe modelling algorithms together with the settings used for the eight uncertain-

ties identireged from the pilot experiments will be discussed in this section Table 3shows the settings for each level of uncertainty simulated

LDFS was modelled with a discrete probability distribution applied to a randomselection of purchased parts only The probability distribution was programmed interms of frequency and magnitude Frequency specireges the percentage of batchesthat were subjected to LDFS while magnitude specireges the times delay (minutes) foreach a ected batch The algorithm then o sets the planned release time by theminutes delay experienced The e ect of this algorithm was that all a ected pur-chased parts exceed their due date with subsequent delays for parent parts Formodelling INST an almost identical algorithm was used with the exception thatboth purchased and manufactured parts were subjected to this uncertainty

PSTE only directly a ects resources that include a set-up time and it was mod-elled with a discrete probability distribution to randomly delaying operations carriedout on those resources The e ect of this algorithm was that most a ected partsexceed their due date with subsequent delays for parent parts although it waspossible that some slack exists within the lead-time resulting in no overall delay

MBD was modelled by deregning Mean Time Between Failures (MTBF) andMean Time To Repair (MTTR) for assigned machines This results in stoppage ofcurrent work and hence late delivery causing subsequent delays to parts in the queueof the a ected resources and all associated parent parts Time-based distributionswere applied to model MTBF and MTTR and each was modelled with exponentialand gamma distributions respectively Only those resources with high utilizationswere subjected to MBD in order to increase the responses of the simulation modelLow utilizations resources would have slack time available and hence such break-downs would not result in late delivery

Again a discrete probability distribution was applied to model WFL as well asWFT The same rules applied for modelling INST were applied here with theexception that only manufactured parts were subjected to WFL WFT uses the


same algorithm apart from the fact that only batches require tooling would be

randomly subjected

UCSM was modelled using a batch size multiplier coe cient applied to manu-

factured parts from a selection of orders for repeaters products with machines con-

tent Only repeaters products were subjected to this uncertainty as runners would be

very tightly controlled and strangers being irregular in nature were assumed not tobe subjected to volume changes after MRP had been run The algorithm regrst iden-

tireges selected order numbers and associated part numbers having machines content

The batch size multiplier coe cient was then executed to increase the planned batch

size by a chosen factor

CDC was modelled with a discrete probability distribution without specifying

any magnitude of delay at the outset Alternative routings were designed for a ected

manufactured parts that corresponding to the additional operations required whensuch changes occur It was assumed that purchased parts subjected to such changes

would be available when required A frequency was modelled to decide which route


Levels settingsUncertainty

code 1 2

LDFS Discrete probability distributionFrequencyˆ 2 Frequencyˆ 2

Magnitudeˆ 480 min Magnitudeˆ 1440 min

INST Discrete probability distributionFrequencyˆ 2 Frequencyˆ 2


PSTE Discrete probability distributionFrequencyˆ 5 Frequencyˆ 5


MBD MTBF Exponential distributionBRKPRS ˆ 60 000 min

CLR ˆ 24000 minWELD ˆ 30 000 min

MTTR Gamma distributionBRKPRS ˆ (300 min 2) BRKPRS ˆ (1200 min 2)

CLR ˆ (120 min 2) CLR ˆ (1200 min 2)WELD ˆ (150 min 2) WELD ˆ (1200 min 2)

WFL Discrete probability distributionFrequencyˆ 5 Frequencyˆ 5


WFT Discrete probability distributionFrequencyˆ 5 Frequencyˆ 5


UCSM Batch size multiplier coe cient ˆ 100Orders a ected ˆ 1 Orders a ectedˆ 10

CDC Discrete probability distributionFrequencyˆ 4 Frequencyˆ 10

Table 3 Uncertainty levels settings

should be taken Only repeaters and strangers were directly a ected as it wasassumed that changes to runners would occur as part of a formal design changeprocedure and not have immediate e ect

6 Experiments results analysis and discussionsUsing the uncertainty modelling algorithms and settings experiments were

designed and run A fractional factorial design with resolution VIII conreggurationwas used which has resulted in 128 experiments for eight uncertainties each with twolevels Pegden et al (1995) stated that with data that may not be normally distrib-uted increasing the number of replications to ten would ensure that results would beapproximately normal This reggure was chosen for an initial pilot run for all experi-ments The distributions resulting from the pilot run were then analysed to establishthe conregdence intervals achieved as measured by the half-widths (h) of the student tdistribution The formula applied to achieve this is shown in equation (1)

h ˆ t1iexclnot=2niexcl1

Shellipxdaggern

p hellip1dagger

where

h is the distribution half-widtht1iexclnot=2niexcl1 is the standard deviate in t-distribution for not conregdence level

Shellipxdagger is an unbiased estimator of the standard deviationn is the number of replications

From the analysis it could be established whether any further replications wererequired to ensure an acceptable level of h value considered to be less than 5 of thesample mean (Saad 1994) The conregdence level was set in all cases to 95 Tocalculate the number of further replications required equation (2) was applied

n para nh

h

sup3 acute2

hellip2dagger

where

n is the total replications requiredn is the initial number of replicationsh is the initial calculated half-width for n replications

h is the desired distribution half width

This has resulted in a total of 254 additional replications which together with allsimulation results were then analysed To visualize the responses of the levels set-tings the average values of FPDL and PDL against the low and high levels settingsfor these uncertainties were plotted Figures 7 and 8 show the plots

It can be seen that changing the levels of MBD resulted in the biggest changes ofresponses to FPDL and PDL This has given a strong inclination that the e ect fromMBD to delivery performance might be signiregcant However only small incrementalresponses were observed from di erent level settings of INST PSTE WFL WFTand CDC Their e ect might not be signiregcant Larger incremental responses toFPDL and PDL from LDFS and UCSM were found suggesting that their e ectcould be signiregcant


To establish a clear di erentiation between the e ects of the uncertainties from

these responses ANOVA was carried out to identify whether there is any signiregcantevidence that the uncertainties modelled a ect delivery performance PDL was used

in ANOVA because as already discussed in section 4 it is a sensitive indicator A

95 conregdence level was adopted which implies all uncertainties with p values less

or equal to 005 being signiregcant Experiment design in resolution VIII conreggurationensures up to three-way interactions are not confounded with each other Therefore

higher-order interactions were excluded from the analysis


Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties

Figure 7 Responses of uncertainties to PDL

Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties

Figure 8 Responses of uncertainties to FPDL

Uncertainties a ecting PDL with signiregcant evidence were asterisked in allANOVA tables Table 4 shows the header and footer summary output fromANOVA of the simulation results A total of 1534 replications for the experimentswere run and analysed

61 Main e ectsThe main e ects analysis reveals the individual e ect of uncertainty to PDL The

main e ects results from ANOVA are shown in table 5 There was signiregcant evi-dence that LDFS MBD UCSM and CDC a ect PDL

Whenever any delay a ects a part within a BOM it would be propagated upthrough the BOM chain to cause additional lateness unless slack exists to recover thedelay The extent of lateness and the number of parts a ected would depend uponthe BOM level at which the uncertainty applied The term knock-on e ects wascoined to explain this phenomenon Where a delay occurs on a resource it a ectsnot only the batch being processed but also every batch held in the queue for theresource This queue could contain batches from a number of di erent BOMs thusdelays could be propagated across products which would create consequent knock-on e ects in the products concerned unless slack exists to recover the delay The termcompound e ects was coined to explain this phenomenon Details of both e ects canbe found in Koh et al (2001)

When LDFS transpires the purchase part a ected directly will be recorded asPDL Within a multi-level dependent demand ERP-controlled manufacture delay atthe lower level in the BOM chain will have a knock-on e ect to the higher level Thegreater is the rsquowhere-usedrsquo of an a ected part the larger is the knock-on e ect andhence the higher is the PDL Within any typical BOM purchase parts tend to be at


Sum of square Degree of Mean square F p

Source (SS) freedom (df) (MS) (MSMSerrordagger (Ftable lt Fobserveddagger

Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534

Corrected total 66 328159 1533

Table 4 Header and footer summary output from ANOVA

Source SS df MS F p

LDFS 3705849 1 3705849 586626 0000INST 19618 1 19618 3105 0078PSTE 6953 1 6953 1101 0294MBD 50881286 1 50881286 8054365 0000WFL 0240 1 0240 0038 0846WFT 0532 1 0532 0084 0772UCSM 378375 1 378375 59896 0000CDC 53634 1 53634 8490 0004

Signiregcant uncertainty at p lt 005

Table 5 Main e ects results from ANOVA

lower levels resulting in a maximum knock-on e ect When a delayed part even-tually arrives it could a ect subsequent processing depending upon resource loadingat that time causing a secondary compound e ect Thus LDFS is found to besigniregcant

MBD results in machine stoppage Although the direct e ect is on parts pro-cessed at the a ected machine parts from several di erent products that are in thequeue when the event occurs will be delayed As prediction on parts that are going tobe in the queue of a broken machine in the future is di cult the consequence is moreimmense than from a knock-on e ect MBD produces compound then knock-one ects to PDL Those parts either in-processed andor in the queue of the brokenmachine will be recorded as PDL In addition their parent parts will also be delayeddue to the knock-on e ect causing multiple parts to be recorded as PDL Thispersists until the backlog is cleared These e ects have signiregcantly resulted inhigh PDL and therefore MBD was found to be signiregcant

Batch size increment for UCSM causes an extended stay within all resourcesvisited This primarily results in unexpected resources unavailability for otherbatches requiring the same resources thus inducing compound e ects When thesee ects take place it will consequently induce secondary knock-on e ects as parentparts will be started late The e ects are identical with MBD but the level of sig-niregcance was not as strong as with MBD

Additional operations of CDC have resulted in a signiregcant e ect to PDLalthough only some small incremental responses were identireged This reinforcedthe importance of statistical analysis to clarify this doubt As some resource capacityis unexpectedly consumed resource unavailability has delayed the scheduled orderand is therefore creating a queue Parts in the queue are unpredictable hence result-ing in compound e ect Consequently the timeliness of parent parts of the delayedparts will also be a ected In short CDC produces compound then knock-on e ectsto PDL

62 Two-way interactionsTwo-way interactions analysis reveals dual uncertainties that result in additional

e ect to PDL when interacting with one another It is important not only to under-stand that PDL will be increased due to their interactive e ects but also to be awareof the condition when they could interact The two-way interactions results fromANOVA are shown in table 6 It was identireged with signiregcant evidence that onlytwo two-way interactions namely LDFSMBD and MBDUCSM result in addi-tional e ects to PDL

Having identireged these interactions it was required to consider whether theycould logically be correct or whether they were the result of a statistical macruke

Interactions between LDFS and MBD could only happen when the former werefollowed by the latter On its own LDFS would result in knock-on e ects primarilybut the a ected parent batch could also experience MBD which would occur in thesame BOM chain and hence create additional compound and knock-on e ectsAlthough LDFS only a ects the purchase part directly changes in the resourcesloading proregle could result in its interaction with MBD When the a ected partultimately arrives the resources loading proregle will be di erent to what it used tobe MBD could occur in the new proregle subsequently resulting in their parent partsbeing delayed again This condition has occurred frequently and hence it was foundto be signiregcant to PDL


Interactions between MBD and UCSM are logically possible when the samebatch in the same BOM chain is directly a ected at a di erent time Parts thatwere a ected by UCSM would have been delayed If they were routed to a machinewhich was broken then a second delay was applied

63 Three-way interactionsThree-way interactions analysis reveals uncertainty that results in an

additional e ect to PDL when there is an interaction with two otheruncertainties If there was no signiregcant evidence to show that the main e ects ofthe uncertainties are likely to a ect PDL then this does not imply that inter-actions with other uncertainties are not likely to occur Thus it is possible tohave signiregcant evidence of interactions between uncertainties that are themselvesnot likely to result in PDL The three-way interaction results from ANOVA areshown in table 7 This was identireged with signiregcant evidence that there arefour three-way interactions namely LDFSPSTEWFL INSTMBDWFLINSTPSTEUCSM and WFLWFTUCSM result in an additional e ect onPDL

The delay of parent parts of the purchase parts that were a ected by LDFS couldeasily be delayed again by other uncertainties As the time zone changes PSTE was


Source SS df MS F p

LDFS INST 3741 1 3741 0592 0442LDFS PSTE 3500 1 3500 0554 0457LDFS MBD 238565 1 238565 37764 0000LDFS WFL 2790 1 2790 0442 0506LDFS WFT 7019E-02 1 7019E-02 0011 0916LDFS UCSM 18429 1 18429 2917 0088LDFS CDC 0304 1 0304 0048 0826INST PSTE 6159 1 6159 0975 0324INST MBD 0441 1 0441 0070 0792INST WFL 18540 1 18540 2935 0087INST WFT 8060 1 8060 1276 0259INST UCSM 7424 1 7424 1175 0279INST CDC 3170 1 3170 0502 0479PSTE MBD 0743 1 0743 0118 0732PSTE WFL 8382 1 8382 1327 0250PSTE WFT 1695 1 1695 0268 0605PSTE UCSM 7012 1 7012 1110 0292PSTE CDC 0857 1 0857 0136 0713MBD WFL 2185 1 2185 0346 0557MBD WFT 14844 1 14844 2350 0126MBD UCSM 38791 1 38791 6140 0013MBD CDC 6730 1 6730 1065 0302WFL WFT 5890E-02 1 5890E-02 0009 0923WFL UCSM 12627 1 12627 1999 0158WFL CDC 12763 1 12763 2020 0155WFT UCSM 5120 1 5120 0810 0368WFT CDC 3107 1 3107 0492 0483UCSM CDC 13499 1 13499 2137 0144


Table 6 Two-way interactions results from ANOVA


Source SS df MS F p

LDFS INST PSTE 3746 1 3746 0593 0441LDFS INST MBD 2905 1 2905 0460 0498LDFS INST WFL 7418 1 7418 1174 0279LDFS INST WFT 1787 1 1787 0283 0595LDFS INST UCSM 17826 1 17826 2822 0093LDFS INST CDC 0504 1 0504 0080 0778LDFS PSTE MBD 1922 1 1922 0304 0581LDFS PSTE WFL 25076 1 25076 3970 0047LDFS PSTE WFT 0120 1 0120 0019 0890LDFS PSTE UCSM 7984 1 7984 1264 0261LDFS PSTE CDC 2925 1 2925 0463 0496LDFS MBD WFL 2285 1 2285 0362 0548LDFS MBD WFT 3499E-02 1 3499E-02 0006 0941LDFS MBD UCSM 5894 1 5894 0933 0334LDFS MBD CDC 4889 1 4889 0774 0379LDFS WFL WFT 1390 1 1390 0220 0639LDFS WFL UCSM 4289 1 4289 0679 0410LDFS WFL CDC 4337 1 4337 0687 0407LDFS WFT UCSM 1072 1 1072 0170 0680LDFS WFT CDC 5557 1 5557 0880 0348LDFS UCSM CDC 2810 1 2810 0445 0505INST PSTE MBD 1149E-02 1 1149E-02 0002 0966INST PSTE WFL 5699 1 5699 0902 0342INST PSTE WFT 6791E-03 1 6791E-03 0001 0974INST PSTE UCSM 43305 1 43305 6855 0009INST PSTE CDC 3237 1 3237 0512 0474INST MBD WFL 29125 1 29125 4610 0032INST MBD WFT 1551 1 1551 0245 0620INST MBD UCSM 0309 1 0309 0049 0825INST MBD CDC 4253 1 4253 0673 0412INST WFL WFT 8392 1 8392 1328 0249INST WFL UCSM 14411 1 14411 2281 0131INST WFL CDC 7622E-02 1 7622E-02 0012 0913INST WFT UCSM 16836 1 16836 2665 0103INST WFT CDC 5130 1 5130 0812 0368INST UCSM CDC 7289E-03 1 7289E-03 0001 0973PSTE MBD WFL 9729 1 9729 1540 0215PSTE MBD WFT 2459 1 2459 0389 0533PSTE MBD UCSM 8607 1 8607 1363 0243PSTE MBD CDC 0474 1 0474 0075 0784PSTE WFL WFT 1353 1 1353 0214 0644PSTE WFL UCSM 17624 1 17624 2790 0095PSTE WFL CDC 14235 1 14235 2253 0134PSTE WFT UCSM 1964 1 1964 0311 0577PSTE WFT CDC 5912 1 5912 0936 0334PSTE UCSM CDC 7584 1 7584 1201 0273MBD WFL WFT 1766 1 1766 0280 0597MBD WFL UCSM 0779 1 0779 0123 0726MBD WFL CDC 6426 1 6426 1017 0313MBD WFT UCSM 2190 1 2190 0347 0556MBD WFT CDC 5017 1 5017 0794 0373MBD UCSM CDC 0179 1 0179 0028 0866WFL WFT UCSM 30846 1 30846 4883 0027WFL WFT CDC 9779 1 9779 1548 0214WFL UCSM CDC 8887 1 8887 1407 0236WFT UCSM CDC 2675 1 2675 0424 0515


Table 7 Three-way interactions results from ANOVA

found to be interactive because the set-up or changeover routine will be distorted andwill therefore result in a further delay at the machine-oriented resources This con-dition invited WFL to occur because labour that was scheduled to work on the partswill now not be available as it will be working on other parts that are on scheduleInteractions between LDFS PSTE and WFL were found to be frequently occurringand hence producing signiregcant additional e ects on PDL Note that both PSTE andWFL are themselves not signiregcant but create a signiregcant interactive e ect

INST was found to be not signiregcant in the main e ects analysis INSC has thesame type of knock-on e ect as LDFS with the extension that it also a ects man-ufactured parts Delays resulting from INST at the parts a ected or parent partswere found to be exacerbated by PSTE and UCSM The same condition for theabove discussion of interactive e ects from LDFS and PSTE can be applied here Itwas found that if the delayed parts whether they have been a ected directly orindirectly are a ected also by UCSM a signiregcant additional PDL wouldbe recorded As UCSM itself is signiregcant and produces a compound e ect thesigniregcant interactions found between INST PSTE and UCSM is not surprising

Delays resulting from INST at parts a ected or parent parts were found to beexacerbated this time by MBD and WFL When the delayed parts are routed to abroken machine or are being processed by a not-yet broken machine then additionalPDL will be recorded These delays will result in labour that was scheduled to beconsumed now being allocated to other on-scheduled work therefore producing theWFL e ect The interactions between INST MBD and WFL were found to producesigniregcant additional e ects on PDL

When WFL and WFT a ect the same part twice at a di erent time additionalPDL will be recorded This could easily happen because completions of all manu-facture orders require inputs from labour and tools If UCSM also a ects the partsthen further delay will result due to resource unavailability Since the frequency ofthis condition was found to be high interactions between WFL WFT and UCSMproduced a signiregcant additional e ect on PDL

The former three interactions each consist of primary knock-on e ects of LDFSor INST and secondary compound e ects In all cases it was logically possible foreach uncertainty to occur on the same batch within the same BOM chain whichultimately resulted in additional compound and knock-on e ects whereas the lastinteractions only consist of primary compound e ects of each and therefore thelikelihood of additional e ects on PDL would be high

64 Business model validationValidation of the business model was achieved in two stages by showing that

those underlying causes of uncertainty do a ect PDL and the higher the level ofuncertainty the worse the PDL The regrst stage sought to validate the structure of thebusiness model by proving the existence of cause-and-e ect relationships of uncer-tainties The second stage sought to validate the relationship between levels ofuncertainties and delivery performance

Since the simulation results showed that the eight uncertainties induced latedelivery the cause-and-e ect relationships were proven and hence the general struc-ture of the business model is validated However ANOVA revealed some signiregcantevidence of interactions between uncertainties These regndings do not imply that theinteractions negate the cause-and-e ect relationship rather they mean that addi-tional late delivery will occur as a result of the interactions


To show that delivery performance is dependent upon the levels of uncertaintiesFPDL and PDL from the above experiments were plotted for four scenarios alluncertainties set to low levels all uncertainties set to high levels signiregcant uncer-tainties set to low with non-signiregcant uncertainties set to high levels and signiregcantuncertainties set to high with non-signiregcant uncertainties set to low levels Figure 9shows the plot for the four scenarios A proportional relationship between uncer-tainty levels and FPDL and PDL does exist hence satisfying the second stage ofvalidation It can be seen that high levels of signiregcant uncertainties produce deliveryperformances as bad as when all uncertainties were set too high

In spite of the fact that not all identireged uncertainties were modelled an ex-amination of the behaviour of the business model through simulation and validationled to a high degree of conregdence that the results and validations obtained areextendable to the entire business model

7 ConclusionsA business model to enable the underlying causes of uncertainty to be diagnosed

within an ERP-controlled manufacturing environment has been developed Thisbusiness model verireged through a comprehensive survey involving ERP users oper-ating in batch manufacture with mixed demand patterns that the structure of thecause-and-e ect relationship of uncertainty exists Validation of the business modelwas carried out via an extensive experimental programme by modelling uncertaintywithin a simulation model developed using SIMAN V that truly represents a multi-level dependent demand system with multi products and which is controlled by PORbased on planned lead times

Simulation studies were carried out amongst uncertainties namely late deliveryfrom suppliers insecure stores planned set-up or changeover times exceededmachine breakdowns waiting for labours waiting for tooling unexpected or urgentchanges to schedule a ecting machines and customer design changes identireged from


753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high

Significantuncertainties

low


high

Scenario

FPDL

PDL

Figure 9 FPDL and PDL plot under four scenarios of uncertainties levels settings

the pilot experiments Dual performance measures were examined and it was identiregedthat FPDL is less sensitive for measuring the e ect of uncertainty while PDL is moresensitive and hence was applied

The fractional factorial design of experiments was executed whereby the simula-tion results were analysed in ANOVA There was signiregcant evidence identireged thatlate delivery from suppliers machine breakdowns unexpected or urgent changes to

schedule a ecting machines and customer design changes a ect PDL In additionthere was also signiregcant evidence identireged that interactions between late deliveryfrom suppliers and machine breakdowns machine breakdowns and unexpected or

urgent changes to schedule a ecting machines late delivery from suppliers plannedset-up or changeover times exceed and waiting for labours insecure stores machinebreakdowns and waiting for labours insecure stores planned set-up or changeover

times exceed and unexpected or urgent changes to schedule a ecting machines andwaiting for labours waiting for tooling and unexpected or urgent changes to sche-

dule a ecting machines result in additional e ects to PDLEach uncertainty produces knock-on and compound e ects within which its

occurrences are dependent upon whether the uncertainty directly a ects a batch in

a BOM chain or a ects resource availability that could be expandable across prod-ucts

Validation of the business model was proven from the simulation results in two

stages the regrst being that all uncertainties modelled induce late delivery and thesecond being that the higher the level of uncertainties the worse the delivery per-formances

For the regrst time a validated business model for diagnosing uncertainty in ERPenvironments was developed hence enabling the underlying causes of uncertaintythat signiregcantly a ect delivery performance to be tackled systematically

AcknowledgementsThe research was partially funded by the ORS Award from the Council of Vice

Chancellors and Principals (CVCP) The authors wish to acknowledge the helpful

suggestions received from the reviewers of this paper and the editor of this journal

References

Angerosa A M 1999 The future looks bright for ERP APICS The PerformanceAdvantage October 4plusmn6

Brennan L and Gupta S M 1993 A structured analysis of material requirements plan-ning systems under combined demand and supply uncertainty International Journal ofProduction Research 31 1689plusmn1707

Byrne M D and Mapfaira H 1998 An investigation of the performance of MRP plan-ning in an uncertain manufacturing environment Proceedings of the 14th NationalConference on Manufacturing Research Derby UK pp 211plusmn216

Cox J F and Blackstone J H 1998 APICS Dictionary 9th edn (APICSETHThe educa-tional society for resource management VA USA)

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Finance Special Report Forrester Research SeptemberGrasso E T and Taylor B W 1984 A simulation based experimental investigation of

supplytiming uncertainty in MRP systems International Journal of ProductionResearch 22 485plusmn497


Ho C J and Carter P L 1996 An investigation of alternative dampening procedures tocope with MRP system nervousness International Journal of Production Research 34137plusmn156

Ho C J and Ireland T C 1998 Correlating MRP system nervousness with forecasterrors International Journal of Production Research 36 2285plusmn2299

Ho C J Law W K and Rampal R 1995 Uncertainty dampening methods for reducingMRP system nervousness International Journal of Production Research 33 483plusmn496

Koh S C and Jones M H 1999 Manufacturing uncertainty and consequent dilemmascauses and e ects Proceedings of the 15th International Conference on ProductionResearch Limerick Ireland 1 pp 855plusmn858

Koh S C Jones M H and Saad S M 2001 Holistic modelling of uncertainty in MRPERP environments Proceedings of the 16th International Conference on ProductionResearch Prague Czech Republic

Koh S C Jones M H Saad S M Arunachalam S and Gunasekaran A 2000aMeasuring uncertainties in MRP environments Journal of Logistics InformationManagement 13 177plusmn183

Koh S C Jones M H and Saad S M 2000b Identifying and measuring underlyingcauses and e ects of uncertainty in Material Requirements Planning (MRP) environ-ments Proceedings of the Special International Conference on Production ResearchBangkok Thailand

Koh S C L Saad S M and Jones M H 2002 Uncertainty under MRP-planned manu-facture review and categorisation accepted for publication in the International Journalof Production Research

Krupp J A G 1997 Safety stock management Production and Inventory ManagementJournal 3rd quarter 11plusmn18

Mather H 1977 Reschedule the reschedules you just rescheduledETHWay of life for MRPProduction and Inventory Management Journal 18 60plusmn79

Murthy D N P and Ma L 1996 Material planning with uncertain product qualityInternational Journal of Production Planning and Control 7 566plusmn576

New C and Mapes J 1984 MRP with high uncertainty yield losses Journal of OperationsManagement 4 315plusmn330

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Pegden D Shannon R E and Sadowski R P 1995 Introduction to Simulation usingSIMANC 2nd edn (McGraw-Hill)

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PDL Parts Delivered LatePOR Planned Order Release

PSTE Planned Set-up or change over Times ExceededUCSM Unexpected or urgent Changes to Schedule a ecting Machines

WFL Waiting For LabourWFT Waiting For Tooling

1 IntroductionModern manufacturing industry is facing increasing pressure to improve its

responsiveness to market dynamics Central to this are the issues addressed byproduction planning and control systems and philosophies Customer expectationsfor shorter delivery lead-times greater agility improved quality and reduced costshave made the e ective application of an appropriate system a signiregcant deter-minant of survival for many manufacturing enterprises

Within batch manufacture a wide variety of systems each with its own philo-sophy may be adopted These systems include Material Requirements Planning(MRP) Manufacturing Resource Planning (MRPII) Enterprise ResourcePlanning (ERP) Customer Relationship Management (CRM) Just-In-TimeOptimized Production Technology and Advanced Production Scheduling eitherused discretely or in combination Over the last 30 years many millions of poundsand considerable man-years of e ort have been invested in improving the perform-ance of MRP-based systems These systems include MRPII ERP and CRMBoston-based Advanced Manufacturing Research predicts the ERP market willreach $666 billion by 2003 at an estimated compound annual growth rate of 32(Angerosa 1999) However a contemporary study by Forrester Research identiregedthat 40 of enterprises believe ERP will not provide any optimization capabilitieswhile 46 expect them to play a partial role at best (Gormley 1998)

Whatever system is chosen it must be capable of performing within an uncertainworld As the growth of ERP is increasing even while the systems only operate bestin deterministic and predictable manufacturing environments (Dilworth 1996) thisresearch started from the basis that causes and e ects of uncertainty were not gen-erally well understood or managed within batch manufacture ERP environmentsUltimately underperformance of ERP was reported as the assessment was based onstochastic and unpredictable manufacturing environments

Cox and Blackstone (1998) deregned uncertainty as unknown future events thatcannot be predicted quantitatively within useful limits Thus the occurrence ofuncertainty is unpredictable and its e ect is di cult to quantify This implies thatuncertainty by nature is stochastic An example of this uncertainty is late delivery ofa purchased part The e ect of this would be a delay in the manufacture of the parentassembly that is di cult to quantify because the loading on the shop macroor when thepart ultimately arrives may be very di erent to that planned when it was due toarrive This may result in other planned work being displaced to make way for thedelayed assembly or the delay may be increased if a suitable production space cannotbe found

Without the existence of uncertainty the POR regle generated from the MRP runwould be a deregnitive workable plan that would guarantee time delivery Howevermodern manufacturing environments are subjected to continuous changes bothinternally and externally which require the ERP environments to be adaptive touncertainty in order to satisfy the customers





























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itin

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POR

Is part aParent



queue

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No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

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AD

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rent

(Child

ltgt

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epende

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ure

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Sim

ula

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mo

del

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A

Pare

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pe

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ns

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S =

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q d

ete

rmin

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the r

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pe

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tatio

ns

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du

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ies

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end

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1A

ll P

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end

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du

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end

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0

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tion

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ype

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es

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ce a

ll ch

ildre

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ave

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tity

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ase

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r pro

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n

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fore

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rent

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tity

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tion S

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its

rele

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t S

eq

uen

ce N

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ber

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itia

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ie ch

ang

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fro

m H

old

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to P

art

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e

C

SE

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end

ent

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for

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rent

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y

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MO

VE

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ren

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ntit

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om

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en

den

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ue

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AR

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ep

en

den

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et(

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mp

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ag =

= P

art

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g)

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CS

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CH

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ck g

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he r

esp

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pen

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pa

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of

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h in

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te)

and

go

es

toLa

be

l CS

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(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

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Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References






















































Ma

teri

al s

ho

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ge

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ab

ou

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ort

ag

es

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chin

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ap

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ap

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wo

rkF

inis

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d p

rod

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ple

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-no

td

eliv

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exp

ecte

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rgen

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ange

s to

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edu

le

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of

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ck r

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App

lica

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of

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ct s

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l ru

les

Poo

r su

pplie

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eliv

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perf

orm

anc

e

Un

exp

ecte

d d

em

and

patt

ern

cha

nge

s

Dem

and

usa

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nal

ysis

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sed

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driv

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es

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Cu

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Cu

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Abs

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ala

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kin

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el 0

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rfor

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el 1

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el 2

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el 3

un

derl

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ca

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ed P

rod

uct

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eliv

ered

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e

Fig

ure

2S

tru

cture

of

the

busi

nes

sm

od

el










tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

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LO

CK

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hild

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Child

= 0

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tSys

tem

AS

SIG

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ted

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Sim

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del

logic

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nt

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Ro

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AS

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pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References














































Ma

teri

al s

ho

rta

ge

sL

ab

ou

r sh

ort

ag

es

Ma

chin

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tysh

ort

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ap

Re

wo

rkF

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d p

rod

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ple

ted

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td

eliv

ere

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exp

ecte

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rgen

tch

ange

s to

pro

duct

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edu

le

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of

sto

ck r

eco

rds

App

lica

tion

of

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rre

ct s

tock

con

tro

l ru

les

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r su

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rd

eliv

ery

perf

orm

anc

e

Un

exp

ecte

d d

em

and

patt

ern

cha

nge

s

Dem

and

usa

ge a

nal

ysis

not u

sed

to

driv

e s

tock

con

trol

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es

Ina

ccur

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fore

cast

Cu

sto

me

r ch

ange

sde

live

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ad

tim

es

Cus

tom

er c

ha

nges

ord

ere

d q

uan

tity

Cu

sto

me

r ch

an

ges

pro

duct

ord

ere

d

Cu

sto

me

r ch

ang

essp

ecifi

ed

leve

l of

qual

ity

Cu

sto

me

r de

sig

nch

ang

es d

uri

ng

afte

r p

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g

Inte

rnal

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urin

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Abs

ente

eis

m

Sch

edul

ew

ork

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t fo

llow

ed

Lac

k of

ski

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La

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Ma

tern

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Sic

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Hol

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ed

ule

w

ork-

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st n

otco

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olle

d

Sch

edu

lew

ork

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rod

uced

bu

tno

t ava

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our

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pla

nne

dm

achi

ne

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ntim

e

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chin

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verl

oad

Idle

mac

hin

ew

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ng

for

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s

Item

s m

issi

ngin

BO

M

Inse

cure

stor

es

Poo

r tr

ansa

ctio

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ere

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of

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bou

r

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xpe

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ma

nd fo

rp

artic

ula

r sk

ill

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P p

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che

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ma

chin

e)

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exp

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du

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s to

sche

dule

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ach

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ign

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t)

Re

ject

ed

by q

ual

ity

Del

iver

ed w

ithsh

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s

Lat

e d

eliv

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Inco

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Un

acce

pta

ble

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duc

t qua

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En

gin

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cha

nge

s du

ring

af

ter

pro

duct

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Pla

nne

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ain

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air

time

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sch

ed

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ent

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aitin

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ycl

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Cu

sto

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rnal

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n

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itin

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om

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er w

ork

ce

ntre

d

epar

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iteW

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r m

ate

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ern

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in

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end

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Un

avai

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of t

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ala

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kin

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ish

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eliv

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Fig

ure

1

Ish

ikaw

ad

iagra

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fca

use

sand

eec

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gto

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ate

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o c

ust

om

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Ma

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ere

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or

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ach

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with

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te d

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ep

tab

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rod

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gin

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gd

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ign

cha

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du

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ga

fte

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nn

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ep

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d

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Bre

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n

La

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or

De

fect

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ra

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l

Ma

chin

e e

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sch

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exp

ect

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me

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Aw

aiti

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Aw

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Cu

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ign

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pro

duc

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rna

l de

sig

nch

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r p

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n

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g f

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mla

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ech

an

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ticd

evi

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Wa

itin

g f

or

lab

ou

r

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itin

g f

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too

ling

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fo

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ate

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tern

ally

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er

wo

rk c

en

tre

d

ep

art

me

nt

site

Wai

ting

fo

rm

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l ext

ern

ally

su

ppl

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de

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nd

en

tsu

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su

bc

on

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cto

rs

Ite

ms

on

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ld(F

ina

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al)

Un

ava

ilab

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of

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nsp

ort

Aw

aitin

g b

ala

nce

of

ord

er

See

kin

gco

nce

ssio

n

Lev

el 0

pe

rfor

man

ce m

easu

re

Lev

el 1

st

rand

s

Lev

el 2

Lev

el 3

un

derl

ying

ca

uses

Fin

ish

ed P

rod

uct

s D

eliv

ered

Lat

e

Fig

ure

2S

tru

cture

of

the

busi

nes

sm

od

el










tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References







































Ma

teri

al s

ho

rta

ge

sL

ab

ou

r sh

ort

ag

es

Ma

chin

e c

ap

aci

tysh

ort

ag

es

Scr

ap

Re

wo

rkF

inis

he

d p

rod

uct

com

ple

ted

-no

td

eliv

ere

d

Un

exp

ecte

du

rgen

tch

ange

s to

pro

duct

ion

sch

edu

le

Ina

ccu

raci

es

of

sto

ck r

eco

rds

App

lica

tion

of

inco

rre

ct s

tock

con

tro

l ru

les

Poo

r su

pplie

rd

eliv

ery

perf

orm

anc

e

Un

exp

ecte

d d

em

and

patt

ern

cha

nge

s

Dem

and

usa

ge a

nal

ysis

not u

sed

to

driv

e s

tock

con

trol

rul

es

Ina

ccur

ate

fore

cast

Cu

sto

me

r ch

ange

sde

live

ry le

ad

tim

es

Cus

tom

er c

ha

nges

ord

ere

d q

uan

tity

Cu

sto

me

r ch

an

ges

pro

duct

ord

ere

d

Cu

sto

me

r ch

ang

essp

ecifi

ed

leve

l of

qual

ity

Cu

sto

me

r de

sig

nch

ang

es d

uri

ng

afte

r p

lan

nin

g

Inte

rnal

de

sig

nch

ang

es d

urin

g

afte

r p

lan

nin

g

Abs

ente

eis

m

Sch

edul

ew

ork

-to

-lis

t no

t fo

llow

ed

Lac

k of

ski

lla

vaila

bili

ty

La

bour

ove

rloa

d

Ma

tern

ity

Sic

kne

ss

Hol

iday

Sch

edul

e

wor

k-to

-list

not

pro

du

ced

Sch

ed

ule

w

ork-

to-li

st n

otco

ntr

olle

d

Sch

edu

lew

ork

-to-

list p

rod

uced

bu

tno

t ava

ilab

le to

lab

our

Un

pla

nne

dm

achi

ne

dow

ntim

e

Ma

chin

e o

verl

oad

Idle

mac

hin

ew

aiti

ng

for

reso

urce

s

Item

s m

issi

ngin

BO

M

Inse

cure

stor

es

Poo

r tr

ansa

ctio

nre

cord

ing

Inh

ere

ntsh

ort

age

of

skill

ed la

bou

r

Une

xpe

cte

dde

ma

nd fo

rp

artic

ula

r sk

ill

MR

P p

lan

ove

rlo

ad(I

nfin

ite s

che

dulin

gof

ma

chin

e)

Un

exp

ecte

du

rgen

tch

ange

s to

sche

dule

(M

ach

ine

ass

ign

men

t)

Re

ject

ed

by q

ual

ity

Del

iver

ed w

ithsh

ort

age

s

Lat

e d

eliv

ery

Inco

rre

ct it

ems

supp

lied

Un

acce

pta

ble

pro

duc

t qua

lity

En

gin

eeri

ng d

esi

gn

cha

nge

s du

ring

af

ter

pro

duct

ion

Pla

nne

dm

ain

ten

ance

rep

air

time

exc

eede

d

Pla

nne

d s

et-

up

cha

nge

ove

rtim

e e

xce

eded

Bre

akd

own

Lab

our

err

or

Def

ect

ive

ra

wm

ate

rial

Ma

chin

e e

rro

r

MR

P p

lan

ove

rlo

ad(I

nfin

ite s

ched

ulin

gof

lab

our

)

Une

xpe

cte

du

rge

ntch

ang

es to

sch

ed

ule

(La

bou

r a

ssig

nm

ent

)

Aw

aitin

g q

ualit

ycl

eara

nce

Aw

aitin

g d

espa

tch

Cu

sto

me

r d

esig

nch

ange

s d

urin

gaf

ter

pro

duct

ion

Inte

rnal

des

ign

cha

nge

s d

urin

ga

fter

pro

du

ctio

n

Wai

ting

for

insp

ect

ion

fro

mla

bou

r

Wai

ting

for

insp

ect

ion

from

me

cha

nica

lro

bot

ic d

evic

e

Wa

itin

g fo

rla

bour W

aiti

ng

for

tool

ing

Wai

ting

for

mat

eria

lin

tern

ally

su

pplie

d fr

om

oth

er w

ork

ce

ntre

d

epar

tme

nts

iteW

aitin

g fo

r m

ate

rial

ext

ern

ally

su

pplie

dfr

om

in

dep

end

ent

sup

plie

rs

subc

ont

ract

ors

Item

s on

-ho

ld(F

inan

cia

l)

Un

avai

lab

ility

of t

ran

spor

t

Aw

aitin

gb

ala

nce

of

ord

er

See

kin

gco

nce

ssio

n

Fin

ish

edP

rod

uct

sD

eliv

ered

Lat

e

Fin

ish

edP

rod

uct

sD

eliv

ered

Lat

e

Fig

ure

1

Ish

ikaw

ad

iagra

mo

fca

use

sand

eec

tso

fu

nce

rtai

nty

leadin

gto

FP

DL


L

ate

de

live

ry t

o c

ust

om

er

Ma

teria

l sh

ort

ag

es

La

bo

ur

sho

rta

ge

sM

ach

ine

ca

pa

city

sho

rta

ge

sS

cra

pR

ew

ork

Fin

ish

ed

pro

du

ct c

om

ple

ted

-no

td

eliv

ere

d

Po

or

sup

plie

rd

eliv

ery

pe

rfo

rma

nce

Ina

ccu

raci

es

of

sto

ck r

eco

rds

Ap

plic

ati

on

of

inco

rre

ct s

tock

co

ntr

ol r

ule

s

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exp

ect

ed

u

rge

nt

ch

an

ge

s to

pro

du

ctio

nsc

he

du

le

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exp

ec

ted

de

ma

nd

pat

tern

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ng

es

De

ma

nd

usa

ge

an

aly

sis

no

t u

sed

to

dri

ve s

tock

con

tro

l ru

les

Ina

ccu

rate

fo

reca

st

Cu

sto

me

r ch

an

ge

sd

eliv

ery

lea

d t

ime

s

Cu

sto

me

r ch

an

ge

so

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red

qu

an

tity

Cu

sto

me

r ch

an

ge

spr

od

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ord

ere

d

Cu

sto

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r ch

an

ge

ssp

eci

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leve

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qu

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ust

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er

de

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nch

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s d

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a

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r p

lan

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g

Inte

rna

l de

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ng

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du

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er

pla

nn

ing

Ab

sen

tee

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ed

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ork

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t n

ot

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d

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ck o

f sk

illa

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ty

La

bo

ur

ove

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ad

Ma

tern

ity

Sic

kne

ss

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Sch

ed

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t p

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ed

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du

ce

d b

ut

no

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vaila

ble

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ou

r

Unp

lann

ed

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chin

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ow

ntim

e

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chin

eo

ve

rlo

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Idle

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ine

wa

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g f

or

reso

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ms

mis

sin

g in

BO

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Inse

cure

sto

res

Po

or

tra

nsa

ctio

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cord

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Inh

ere

nt

sh

ort

ag

eo

f sk

ille

d la

bo

ur

Un

exp

ect

ed

de

ma

nd

fo

rp

art

icu

lar

skill

MR

P p

lan

ove

rlo

ad

(In

finit

e s

che

du

ling

of

ma

chin

e)

Un

exp

ec

ted

urg

en

tch

an

ge

s to

sch

ed

ule

(M

ach

ine

ass

ign

me

nt)

Re

ject

ed

by

qu

alit

y

De

live

red

with

sho

rta

ge

s

La

te d

eliv

ery

Inco

rre

ct it

em

ss

up

plie

d

Un

acc

ep

tab

lep

rod

uct

qu

alit

y

En

gin

ee

rin

gd

es

ign

cha

ng

es

du

rin

ga

fte

rp

rod

uct

ion

Pla

nn

ed

ma

inte

na

nc

er

ep

air

time

exc

ee

de

d

Pla

nn

ed

se

t-u

p

cha

ng

eo

ver

time

exce

ed

ed

Bre

akd

ow

n

La

bo

ur

err

or

De

fect

ive

ra

wm

ate

ria

l

Ma

chin

e e

rro

r

MR

P p

lan

ov

erl

oa

d(I

nfi

nite

sch

ed

ulin

go

f la

bo

ur)

Un

exp

ect

ed

urg

en

tch

an

ge

s to

sch

ed

ule

(L

abo

ur

ass

ign

me

nt)

Aw

aiti

ng

qu

alit

ycl

ea

ran

ce

Aw

aiti

ng

de

spa

tch

Cu

sto

me

r d

es

ign

cha

ng

es

du

rin

g

aft

er

pro

duc

tion

Inte

rna

l de

sig

nch

an

ge

s d

uri

ng

a

fte

r p

rod

uctio

n

Wa

itin

g f

or

insp

ec

tion

fro

mla

bo

ur

Wa

itin

g f

or

insp

ec

tion

fro

mm

ech

an

ica

lro

bo

ticd

evi

ce

Wa

itin

g f

or

lab

ou

r

Wa

itin

g f

or

too

ling

Wai

ting

fo

rm

ate

ria

l in

tern

ally

sup

plie

d f

rom

oth

er

wo

rk c

en

tre

d

ep

art

me

nt

site

Wai

ting

fo

rm

ate

ria

l ext

ern

ally

su

ppl

ied

fro

min

de

pe

nd

en

tsu

pp

liers

su

bc

on

tra

cto

rs

Ite

ms

on

-ho

ld(F

ina

nci

al)

Un

ava

ilab

ility

of

tra

nsp

ort

Aw

aitin

g b

ala

nce

of

ord

er

See

kin

gco

nce

ssio

n

Lev

el 0

pe

rfor

man

ce m

easu

re

Lev

el 1

st

rand

s

Lev

el 2

Lev

el 3

un

derl

ying

ca

uses

Fin

ish

ed P

rod

uct

s D

eliv

ered

Lat

e

Fig

ure

2S

tru

cture

of

the

busi

nes

sm

od

el










tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




























Ma

teri

al s

ho

rta

ge

sL

ab

ou

r sh

ort

ag

es

Ma

chin

e c

ap

aci

tysh

ort

ag

es

Scr

ap

Re

wo

rkF

inis

he

d p

rod

uct

com

ple

ted

-no

td

eliv

ere

d

Un

exp

ecte

du

rgen

tch

ange

s to

pro

duct

ion

sch

edu

le

Ina

ccu

raci

es

of

sto

ck r

eco

rds

App

lica

tion

of

inco

rre

ct s

tock

con

tro

l ru

les

Poo

r su

pplie

rd

eliv

ery

perf

orm

anc

e

Un

exp

ecte

d d

em

and

patt

ern

cha

nge

s

Dem

and

usa

ge a

nal

ysis

not u

sed

to

driv

e s

tock

con

trol

rul

es

Ina

ccur

ate

fore

cast

Cu

sto

me

r ch

ange

sde

live

ry le

ad

tim

es

Cus

tom

er c

ha

nges

ord

ere

d q

uan

tity

Cu

sto

me

r ch

an

ges

pro

duct

ord

ere

d

Cu

sto

me

r ch

ang

essp

ecifi

ed

leve

l of

qual

ity

Cu

sto

me

r de

sig

nch

ang

es d

uri

ng

afte

r p

lan

nin

g

Inte

rnal

de

sig

nch

ang

es d

urin

g

afte

r p

lan

nin

g

Abs

ente

eis

m

Sch

edul

ew

ork

-to

-lis

t no

t fo

llow

ed

Lac

k of

ski

lla

vaila

bili

ty

La

bour

ove

rloa

d

Ma

tern

ity

Sic

kne

ss

Hol

iday

Sch

edul

e

wor

k-to

-list

not

pro

du

ced

Sch

ed

ule

w

ork-

to-li

st n

otco

ntr

olle

d

Sch

edu

lew

ork

-to-

list p

rod

uced

bu

tno

t ava

ilab

le to

lab

our

Un

pla

nne

dm

achi

ne

dow

ntim

e

Ma

chin

e o

verl

oad

Idle

mac

hin

ew

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s m

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es

Poo

r tr

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ctio

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ere

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r

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ma

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rp

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r sk

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P p

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che

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ma

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e)

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ecte

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ach

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t)

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ject

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ithsh

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s

Lat

e d

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om

in

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eliv

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e

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ered

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e

Fig

ure

1

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ikaw

ad

iagra

mo

fca

use

sand

eec

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nce

rtai

nty

leadin

gto

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L

ate

de

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o c

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er

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teria

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ma

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nd

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che

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ma

chin

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exp

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ach

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ject

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by

qu

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with

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ge

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te d

eliv

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d

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acc

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tab

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qu

alit

y

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gin

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rin

gd

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ign

cha

ng

es

du

rin

ga

fte

rp

rod

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Pla

nn

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ma

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ep

air

time

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d

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se

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time

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ed

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La

bo

ur

err

or

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fect

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ra

wm

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ria

l

Ma

chin

e e

rro

r

MR

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ov

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d(I

nfi

nite

sch

ed

ulin

go

f la

bo

ur)

Un

exp

ect

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du

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g

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nch

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n

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itin

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itin

g f

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ech

an

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itin

g f

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r

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g f

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ting

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ria

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tern

ally

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rom

oth

er

wo

rk c

en

tre

d

ep

art

me

nt

site

Wai

ting

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rm

ate

ria

l ext

ern

ally

su

ppl

ied

fro

min

de

pe

nd

en

tsu

pp

liers

su

bc

on

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cto

rs

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ms

on

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ina

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ava

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of

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nsp

ort

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aitin

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ala

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kin

gco

nce

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n

Lev

el 0

pe

rfor

man

ce m

easu

re

Lev

el 1

st

rand

s

Lev

el 2

Lev

el 3

un

derl

ying

ca

uses

Fin

ish

ed P

rod

uct

s D

eliv

ered

Lat

e

Fig

ure

2S

tru

cture

of

the

busi

nes

sm

od

el










tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




























L

ate

de

live

ry t

o c

ust

om

er

Ma

teria

l sh

ort

ag

es

La

bo

ur

sho

rta

ge

sM

ach

ine

ca

pa

city

sho

rta

ge

sS

cra

pR

ew

ork

Fin

ish

ed

pro

du

ct c

om

ple

ted

-no

td

eliv

ere

d

Po

or

sup

plie

rd

eliv

ery

pe

rfo

rma

nce

Ina

ccu

raci

es

of

sto

ck r

eco

rds

Ap

plic

ati

on

of

inco

rre

ct s

tock

co

ntr

ol r

ule

s

Un

exp

ect

ed

u

rge

nt

ch

an

ge

s to

pro

du

ctio

nsc

he

du

le

Un

exp

ec

ted

de

ma

nd

pat

tern

cha

ng

es

De

ma

nd

usa

ge

an

aly

sis

no

t u

sed

to

dri

ve s

tock

con

tro

l ru

les

Ina

ccu

rate

fo

reca

st

Cu

sto

me

r ch

an

ge

sd

eliv

ery

lea

d t

ime

s

Cu

sto

me

r ch

an

ge

so

rde

red

qu

an

tity

Cu

sto

me

r ch

an

ge

spr

od

uct

ord

ere

d

Cu

sto

me

r ch

an

ge

ssp

eci

fied

leve

l of

qu

alit

yC

ust

om

er

de

sig

nch

an

ge

s d

uri

ng

a

fte

r p

lan

nin

g

Inte

rna

l de

sign

cha

ng

es

du

rin

g

aft

er

pla

nn

ing

Ab

sen

tee

ism

Sch

ed

ule

w

ork

-to

-lis

t n

ot

follo

we

d

La

ck o

f sk

illa

vaila

bili

ty

La

bo

ur

ove

rlo

ad

Ma

tern

ity

Sic

kne

ss

Ho

liday

Sch

ed

ule

wo

rk-t

o-

list

no

t p

rod

uce

d

Sch

ed

ule

wo

rk-t

o-

list

no

t co

ntr

olle

d

Sch

ed

ule

wo

rk-t

o-

list

pro

du

ce

d b

ut

no

t a

vaila

ble

to

lab

ou

r

Unp

lann

ed

ma

chin

ed

ow

ntim

e

Ma

chin

eo

ve

rlo

ad

Idle

ma

ch

ine

wa

itin

g f

or

reso

urc

es

Ite

ms

mis

sin

g in

BO

M

Inse

cure

sto

res

Po

or

tra

nsa

ctio

nre

cord

ing

Inh

ere

nt

sh

ort

ag

eo

f sk

ille

d la

bo

ur

Un

exp

ect

ed

de

ma

nd

fo

rp

art

icu

lar

skill

MR

P p

lan

ove

rlo

ad

(In

finit

e s

che

du

ling

of

ma

chin

e)

Un

exp

ec

ted

urg

en

tch

an

ge

s to

sch

ed

ule

(M

ach

ine

ass

ign

me

nt)

Re

ject

ed

by

qu

alit

y

De

live

red

with

sho

rta

ge

s

La

te d

eliv

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Inco

rre

ct it

em

ss

up

plie

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Un

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y

En

gin

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gd

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ign

cha

ng

es

du

rin

ga

fte

rp

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Pla

nn

ed

ma

inte

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nc

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ep

air

time

exc

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Pla

nn

ed

se

t-u

p

cha

ng

eo

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time

exce

ed

ed

Bre

akd

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n

La

bo

ur

err

or

De

fect

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ra

wm

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l

Ma

chin

e e

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P p

lan

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d(I

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nite

sch

ed

ulin

go

f la

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Un

exp

ect

ed

urg

en

tch

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s to

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(L

abo

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ign

me

nt)

Aw

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qu

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ycl

ea

ran

ce

Aw

aiti

ng

de

spa

tch

Cu

sto

me

r d

es

ign

cha

ng

es

du

rin

g

aft

er

pro

duc

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Inte

rna

l de

sig

nch

an

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s d

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a

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r p

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n

Wa

itin

g f

or

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Wa

itin

g f

or

insp

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mm

ech

an

ica

lro

bo

ticd

evi

ce

Wa

itin

g f

or

lab

ou

r

Wa

itin

g f

or

too

ling

Wai

ting

fo

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ate

ria

l in

tern

ally

sup

plie

d f

rom

oth

er

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rk c

en

tre

d

ep

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nt

site

Wai

ting

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l ext

ern

ally

su

ppl

ied

fro

min

de

pe

nd

en

tsu

pp

liers

su

bc

on

tra

cto

rs

Ite

ms

on

-ho

ld(F

ina

nci

al)

Un

ava

ilab

ility

of

tra

nsp

ort

Aw

aitin

g b

ala

nce

of

ord

er

See

kin

gco

nce

ssio

n

Lev

el 0

pe

rfor

man

ce m

easu

re

Lev

el 1

st

rand

s

Lev

el 2

Lev

el 3

un

derl

ying

ca

uses

Fin

ish

ed P

rod

uct

s D

eliv

ered

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e

Fig

ure

2S

tru

cture

of

the

busi

nes

sm

od

el










tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




































tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References





























tion model













POR

Is part aParent



queue

Yes

No



Route tomanufacture



No

No

Yes Yes

Deliver tocustomer



ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




























ST

AR

T

RE

AD

PO

R

Pa

rent

(Child

ltgt

0)

SD

epende

nt

SE

TS

Pare

nts

Queue

WA

IT B

LO

CK

Child

= C

hild

- 1

Child

= 0

Sta

tion S

ET

S

Exi

tSys

tem

AS

SIG

NC

om

ple

ted

Tag

=P

are

ntT

ag

SE

AR

CH

SD

epend

ent

SE

TS

for

Pare

nt

Entit

y

DIS

PO

SE

EN

D

Ye

s

No

Route

Pare

nts

to

resp

ect

ive

SD

epende

nt

Sta

tion

s

RE

MO

VE

Pare

nt E

ntit

y fr

om

SD

epend

ent

Queue

Ye

s

No

AB

C

Fig

ure

4

Sim

ula

tion

mo

del

logic

inS

IMA

NV


A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




























A

Pare

nt

(Child

ltgt

0)

Ro

ute

Pa

ren

ts t

ore

spe

ctiv

eS

De

pe

nd

en

tS

tatio

ns

AS

SIG

NN

S =

Ho

ldS

eq

AS

SIG

NN

S =

Hold

Seq

Hold

Se

q d

ete

rmin

es

the r

esp

ectiv

eS

De

pe

nde

nt S

tatio

ns

for

each

pa

rent

----

----

----

----

----

All

Pro

du

ct 1

1 e

ntit

ies

go

es

to S

Dep

end

ent1

1A

ll P

rodu

ct 1

2 e

ntit

ies

go

es

to S

Dep

end

ent1

2

All

Pro

du

ct 2

0 e

ntit

ies

go

es

to S

Dep

end

ent2

0

B

Sta

tion

SE

TS

AS

SIG

NN

S =

Pa

rtT

ype

AS

SIG

NN

S =

Pa

rtT

ype

Child

= 0Y

es

On

ce a

ll ch

ildre

n h

ave

be

en

mad

e p

are

nt

en

tity

is r

ele

ase

d fo

r pro

du

ctio

n

Be

fore

rele

asi

ng

pa

rent

en

tity

into

Sta

tion S

ET

its

rele

van

t S

eq

uen

ce N

um

ber

is r

ein

itia

ted

ie ch

ang

ing

NS

fro

m H

old

Seq

to P

art

Typ

e

C

SE

AR

CH

SD

ep

end

ent

SE

TS

for

Pa

rent

Entit

y

RE

MO

VE

Pa

ren

tE

ntit

y fr

om

SD

ep

en

den

tQ

ue

ue

SE

AR

CH

D

ep

en

den

tQS

et(

Ho

ldS

eq

)

(Co

mp

lete

dT

ag =

= P

art

Ta

g)

----

----

----

----

----

--R

EM

OV

E J

D

ep

end

entQ

Set(

Hold

Se

q)

CS

OP

--

----

----

----

----

----

SE

AR

CH

blo

ck g

oe

s th

rou

gh t

he r

esp

ectiv

eS

De

pen

den

t Q

ue

ue (

de

term

ine

d b

y ch

ildH

old

Se

q a

ttrib

ute

) O

nce c

on

diti

on is

sa

tisfie

d(c

hild

ha

s fo

und

its

pa

ren

t) q

ue

ue in

dex

of

the

pa

ren

t is

ass

ign

ed

to

se

arc

h in

dex

J

RE

MO

VE

blo

ck r

em

ove

s th

e J

ind

exe

d p

are

nt

from

its

respe

ctiv

e S

Dep

end

ent

Que

ue

(de

term

ine

d b

y H

old

Se

q a

ttri

bu

te)

and

go

es

toLa

be

l CS

OP

(C

he

ck S

tatu

s o

f P

are

nt)

Fig

ure

5

Sim

ula

tion

sub

-mo

del

slo

gic

inS

IMA

NV
































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References


























































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References











































Code Uncertainty




Level No0

1

2

3

Key

10006 Wire kit (2) 10007 Tape kit (2)





FPDL

PDL












randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References





































randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




























randomly subjected














code 1 2






















Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References































Shellipxdaggern

p hellip1dagger

where




n para nh

h

sup3 acute2

hellip2dagger

where













Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References


































Low

Hig

h

PS

TE WFL W

FT CD

C

INS

T

UC

SM

LDFS M

BD

012345

6

7

89

10

Levels

Uncertainties


Low

Hig

h

INS

T

PSTE W

FL WFT CD

C

UC

SM

LDFS M

BD

0

10

20

30

40

50

60

Levels

Uncertainties










Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References



































Corrected model 57 225029 92 622011 98463 0000

Intercept 149609592 1 149 609592 23 682779 0000

Error 9 103130 1441 6317

Total 256965068 1534



Source SS df MS F p


















Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References









































Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References
































Source SS df MS F p





Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References




























Source SS df MS F p



















753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References










































753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References

































753117

9168

1846 1605

221

9107

1848

0

10

20

30

40

50

60

70

80

90

100

Alluncertainties

low

Alluncertainties

high


low


high

Scenario

FPDL

PDL
















References









































References













































Documents

Development of a business model for diagnosing uncertainty in ERP