Managing uncertainty in ERP-controlled manufacturing environments in SMEs

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Int. J. Production Economics 101 (2006) 109–127

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Managing uncertainty in ERP-controlled manufacturingenvironments in SMEs

S.C. Lenny Koha,�, Sameh M. Saadb

aManagement School, University of Sheffield, 9 Mappin Street, Sheffield S1 4DT, UKbSchool of Engineering, Sheffield Hallam University, City Campus, Sheffield S1 1WB, UK

Available online 1 July 2005

Abstract

This research examines how and to what extent uncertainty disturbs the SMEs in the manufacturing sector, which

plan and schedule their production using MRP, MRPII or ERP system, and proposes to apply a business model to

manage uncertainty. This environment is termed the ERP-controlled manufacturing environments. A comprehensive

literature review found that various buffering and dampening techniques have been used to tackle uncertainty. The

results of which show consistent late delivery performance, reinforcing that there is a clear shortage of knowledge and

guidance on how to tackle uncertainty, particularly in these SMEs. In this study, a business model that enables

diagnosis of underlying causes of uncertainty is applied through a questionnaire survey in order to identify the types of

underlying causes that are more likely to result in late delivery. The survey results provide the SMEs with a reference on

the underlying causes of uncertainty that must be tackled with higher priority. Simulation modelling and experimental

study of the underlying causes of uncertainty on late delivery based on a real case study verify and validate this

suggestion.

r 2005 Elsevier B.V. All rights reserved.

Keywords: Uncertainty; SMEs; MRP/MRPII/ERP

1. Introduction

Traditionally, Material Requirements Planning(MRP) and Manufacturing Resource Planning

e front matter r 2005 Elsevier B.V. All rights reserve

e.2005.05.011

ng author. Tel.: +441144 222 3395;

2 3348.

esses: [email protected] (S.C.L. Koh),

.uk (S.M. Saad).

(MRPII) systems are used by large enterprises asa production planning and scheduling tool. Overthe last 10 years, a new system has evolved fromthese systems, namely the Enterprise ResourcePlanning (ERP) system. These types of systemsare now seen as an enterprise wide integratedinformation system. In an enterprise context,their main aim is to provide information to/fromfinance, accounting, sales, marketing, planning,

d.

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S.C.L. Koh, S.M. Saad / Int. J. Production Economics 101 (2006) 109–127110

production, purchasing, human resource, logisticand distribution so that the entire organisationprocess from receiving a customer order, tomanufacturing, and final delivery is structurallyand systematically handled by the system. In aproduction context, their main aim is to produce areliable Planned Order Release (POR) schedule inorder to meet the delivery due date.

Whatever system is chosen, it must be capable ofperforming within an uncertain environment.Uncertainty, in this context, is defined as anyunpredictable event that disturbs the productionprocess in a manufacturing system that is plannedby MRP, MRPII or ERP system (Koh and Saad,2003a). Some researchers referred to uncertaintyas a form of disturbance (Lindau and Lumsden,1995; Frizelle et al., 1998; Saad and Gindy, 1998).Regardless of the terms that it is being referred to,overall, these researches examined a variety ofbuffering or dampening techniques to minimise theeffect of uncertainty.

The big ERP system vendors include SAP,BaaN, ORACLE, JDEDWARDS, and People-Soft. Boston-based Advanced Manufacturing Re-search predicted that the ERP market would reachUSD69 billion by 2003, at an estimated compoundannual growth rate of 32% (Angerosa, 1999).Customer expectations for shorter delivery lead-times, greater agility, improved quality andreduced costs have made the application of anappropriate MRP, MRPII or ERP system asignificant determinant of survival for manymanufacturing enterprises. It must be noted thatthe production planning logic in MRPII and ERPis based on the MRP release logic in an MRPsystem (Enns, 2001; Miltenburg, 2001; Koh andSaad, 2003b). Therefore, the POR schedulegenerated from either of these systems is indiffer-ent from one to the others.

One of the main drivers of the emerging trend ofmanufacturers in Small and Medium-Sized En-terprises (SMEs) implementing such system is theneed to compete in the Business-To-Business(B2B) and the Business-To-Consumer (B2C)markets. To compete in these markets, MRP,MRPII or ERP system plays a significant role inproduction planning and scheduling. They are theback office applications to process customer order

and to plan and ensure materials and resources areavailable to meet these orders so that they could bedelivered to the customers on time, at the rightquantity and at acceptable quality. Many largeenterprises have already utilised such system fortheir B2B and B2C business activities. However,the adoption of these systems for productionplanning and scheduling in SMEs is still at itsinfancy stage (Muscatello et al., 2003) where manyadvanced features, e.g. material allocation used inconjunction with production planning, in ERPhave not been explored and SMEs use ERP mainlyfor its finance/accounting functions.We used the definition from the Department of

Trade and Industry (DTI) UK to define ourSMEs’ sample, which includes SMEs that haveless than 250 employees and excludes micro SMEsthat have less than 10 employees. The supplychain competitiveness between supplier and custo-mer relies on how effective and efficient the orderand information is being handled between theparties in the supply chain. In the B2B markets,many SMEs are now have to be able to provide alevel of service that is compatible with theircorporate customers. Those SMEs that couldprovide such a level of service would have acompetitive advantage in winning the supplycontract.The implementation cost of such system is very

high, and thus it is difficult to justify to SMEs thecosts and benefits of the systems. To cater for theneed of these SMEs, many midrange and lesscomplex systems have been developed, e.g. Alli-ance/MFG—Exact Software, MFG/PRO—QAD,WinMan—TTW and All-in-One—SAP. In con-junction of using such system as a planning andscheduling tool, many SMEs combine this withother production planning and control concept,such as Just-In-Time (JIT), Optimised ProductionTechnology (OPT) and finite capacity schedulingto control the flow of materials and manageutilisation of resources. This combinatorial tech-nique shows that MRP, MRPII or ERP might be agood planning system, but they might not be agood control system. Managing uncertainty effec-tively and efficiently requires a well balancedplanning and control because one must under-stand which uncertainty to tackle and how to

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S.C.L. Koh, S.M. Saad / Int. J. Production Economics 101 (2006) 109–127 111

tackle it in order to get the maximum improve-ment to the system.

2. Literature review

Extensive research has been carried out infinding suitable techniques to tackle uncertaintyin manufacturing systems that are planned byMRP, MRPII or ERP system. Comprehensiveliterature review can be found in Guide andSrivastava (2000) and Koh et al. (2002). Theyidentify that safety stock and safety lead-time areamongst the most robust techniques used by manyresearchers and practitioners to tackle uncertainty.Overtime and multi-skilling labour are also foundto be the most used techniques by practitioners,but their use has yielded contradicting deliveryperformance (Koh et al., 2000). SMEs usuallyapply fire-fighting techniques to deal with uncer-tainty (Koh et al., 2000). This implies that they donot manage uncertainty systematically and hencedo not prepare themselves for the future if thesame uncertainty recurs.

To tackle uncertainty at external demand andexternal supply, Ho et al. (1995) developed aframework to dampen the system nervousnesscaused by these uncertainties. The frameworksuggests the use of safety stock, safety capacity,safety lead-time and rescheduling to tackle theseuncertainties. Ho and Carter (1996) simulate staticdampening, automatic rescheduling and cost-based dampening techniques to tackle externaldemand uncertainty. The simulation results in-dicate that the performance of the techniquesdepends on the operating environment of themanufacturing system. The results also show thata reduction in the level of uncertainty (measuredby rescheduling frequency) does not necessarilylead to better system performance. They concludethat the system improvement is dependent on theappropriate use of the dampening techniques andthe lot-sizing rules.

To tackle the unpredictable increase in scraplevel, Murthy and Ma (1996) develop a mathema-tical model to calculate the optimal over planningfactor required to cater the increase as a result ofsupply and process failures. The optimal over

planning factor is then used as a dampeningallowance in the subsequent planning process. Todeal with forecast inaccuracy, Krupp (1997)proposes a statistical model that expresses devia-tions in units of time rather than quantity toprovide safety stock calculations that are respon-sive to trend and/or seasonality in future forecasts.A forecast tracking signal is used to dampenforecast inaccuracy by adjusting safety stockcalculations in cases where forecasts are consis-tently overoptimistic.Examining from the perspective of design for

manufacture, Yang and Pei (1999) model the effectof engineering changes on inventory level. AStandard for Transfer and Exchange Product(STEP) model database integration environmentis developed to link design tasks with MRPactivities. For each engineering change activity,Engineering Bill of Material (EBOM) data thatrelates to the change and stores in a ComputerAided Design (CAD) database are extractedand transformed to a Manufacturing Bill ofMaterial (MBOM) data and are stored in theMRP database. The modified MRP record isthen generated and compared with the originaldata. Based on this information, the designercan determine an appropriate design alternativesuch that the effect on inventory level can beminimised.Brennan and Gupta (1993) examine the effects

of uncertainty on enterprise’s performance, ratherthan finding suitable techniques to tackle uncer-tainty. They model uncertainty at external de-mand, delivery and process lead-times. Analysis ofVariance (ANOVA) is applied to the simulationresults and show that lead-time and demanduncertainties are individually and interactivelyaffecting the enterprise’s performance; the numberof parts at a given level in the product structureand its shape are significant when lead-time anddemand uncertainties are applied; the choice oflot-sizing rule has a significant effect on perfor-mance and the value of the ratio of set-up toholding cost has significant effect on performancewhen lead-time and demand uncertainties occur.Koh and Saad (2002, 2003a, c) also examineuncertainty using similar approach and attemptto identify uncertainties that are significant to

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delivery performance prior to suggesting suitabletechniques to tackle them.

This review shows that many researchers andpractitioners apply various techniques to tackleuncertainty, but with little knowledge on theirunderlying causes. The present approach will onlyprovide a temporary way to minimise the effect,but will not enable understanding of the causes ofsuch uncertainty. The more effort that we put inrectifying the effect, the more nervous the systemand environment might become. Although safetystock, safety lead-time, safety capacity, reschedul-ing, over-planning, overtime, multi-skilling labour,forecast tracking, and engineering database inte-gration, are found feasible for tackling uncertainty,a mixed of performance is identified and it does notprovide a clear direction within which theirapplications can give an improved performance incertain manufacturing environments and in SMEs.

Little study has been carried out to examine howand to what extent uncertainty disturbs the SMEmanufacturers, which plan and schedule theirproduction using MRP, MRPII or ERP system.Most of the past research focuses on medium tolarge enterprises, hence the uncertainty identifiedand the proposed techniques to tackle the uncer-tainty may not be valid and applicable for the SMEmanufacturers. SMEs usually have limited access toresources required and their relatively less complexmanufacturing environments cannot be overlooked.

To this end, a business model for diagnosing theunderlying causes of uncertainty in manufacturingenvironments that use MRP, MRPII or ERPsystems for production planning and scheduling isproposed (Koh and Saad, 2002). We refer to theseenvironments as ERP-controlled manufacturingenvironments due to the current systems’ develop-ment by their vendors, although they are based onthe same MRP release logic. This business model isapplied in this study, particularly in the context ofSMEs. The application of this business model inthis study aims to shed some lights to SMEmanufacturers on how uncertainty disturbs theirproduction process and what can be done toovercome this problem so that the underlyingcauses of uncertainty in their ERP-controlledmanufacturing environments can be managedeffectively and efficiently.

3. Business model development and application

results

The business model is conceptualised from theconstruction of an Ishikawa diagram, whichstructures causes and effects of uncertainty inmanufacturing environment that is planned byMRP, MRPII or ERP system (Koh and Saad,2002). Fig. 1 shows the structure of the businessmodel. The ultimate performance measure used isthe proportion of Finished Products DeliveredLate (FPDL), which is located at level zero in thebusiness model. The business model consists of fiveseparate strands, namely material shortages, la-bour shortages, machine capacity shortages, scrap/rework and finished products completed—notdelivered. The business model has four levels.The link between each uncertainty at each levelshows the cause-and-effect relationship. The un-derlying causes of uncertainty are located at levelthree in the business model. These are the potentialreasons causing FPDL. No further levels decom-position is considered necessary because thebusiness model is designed mainly to operatewithin a single tier manufacture.With such a business model, tackling the

significant underlying causes of uncertainty isenabled through diagnosis within each chain. Aquestionnaire survey is carried out to the SMEmanufacturers in the UK to collect data on thedegree of contributions of the causes to the effects.The degree of contributions is measured by therelative percentage of the structured causes result-ing in the effects. The goal of this data collection isto provide the SME manufacturers with a set ofreferences on which underlying causes of uncer-tainty are more likely to result in late delivery.The questionnaires are administered to directors

or managers, operations directors or managers,and planning managers of some 147 SME manu-facturers in the UK, which are pre-identified asusers of MRP, MRPII or ERP system from ourprevious studies and personal contact. An overallvalid response rate of 35% (51 enterprises) isachieved after telephone follow-up, which signifiesSMEs that use these systems for productionplanning and scheduling. We found that majorityof the respondents are unable to supply objective

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Fig. 1. Structure of the business model.


data, choosing instead to estimate the percentagecontribution of causes to specified effects based ontheir experiences. This is due to the fact that manyof these enterprises do not record or measureuncertainty. Although the data collected aremainly estimation, the findings will still be usefulfor the SME manufacturers because these types ofreferences do not exist.

The data collected are analysed in ANOVAusing SPSS statistical package. To diagnose for thesignificant underlying causes of uncertainty, AN-OVA is considered to be an entirely appropriateanalysis because it establishes the existence orotherwise such cause-and-effect relationship be-tween uncertainties by hypothesis testing andprovides the significance level of effect. Due tothe nature of the responses: derivation fromestimation and the estimates themselves aretaken after application of buffering or dampening

techniques to tackle the uncertainty, a confidencelevel of 80% is set to diagnose for their signifi-cance. This can be interpreted as any infinitenumber of samples selected from this population;at least 80% of the computed intervals will containthe population mean or variance. This level isconsidered suitable in this study because the dataitself is subjected to uncertainty and henceallowance is given to reflect to the individualcharacteristics of companies’ data and estimation.This implies that any cause of uncertainty with app0:20 will be considered having significant effect.The steps taken in ANOVA are:

Step 1: Diagnose the significant causes of FPDLAssign dependent variable ¼ FPDL (effect)Assign independent variable ¼ {Material short-ages, labour shortages, machine capacityshortages, scrap/rework, finished product

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completed – not delivered} (causes)Model a full factorial ANOVARun ANOVA

Step 2: Diagnose the significant causes ofmaterial shortages

Assign dependent variable ¼Material short-ages (effect)Assign independent variable ¼ {Poor suppli-er delivery performance, inaccuracies ofstock records, application of incorrect stockcontrol rules, unexpected/urgent changes toproduction schedule} (causes)Model a full factorial ANOVARun ANOVA

Step 3: If pPoor supplier delivery performancep0.20,diagnose the significant causes

Assign dependent variable ¼ Poor supplierdelivery performance (effect)Assign independent variable ¼ {Rejected byquality, delivered with shortages, late deliv-ery, incorrect items supplied} (causes)Model a full factorial ANOVARun ANOVAEnd If

Table

ANOV

Source

Correc

Interce

Materi

Labou

Machi

Scrap/

Finishe

Labou

Materi

Error

Total

Correc

�pp0:2

Step 2 is recurred for all uncertainties at level 1
in the business model. A condition is not appliedin this case as in step 3 because our aim is todiagnose the significant underlying causes ofuncertainty to FPDL and the causes of FPDL
1

A results of FPDL causes diagnosis

Type III sum

of squares

ted model 37.103

pt 84.041

al shortages 2.999

r shortages 4.594

ne capacity shortages 2.159

rework 9.256E�02

d product completed—not delivered 6.950

r shortages�Machine capacity shortages 5.185

al shortages�Scrap/rework 8.772E�03

3.250

169.000

ted total 40.353

0.

must contribute to late delivery to a certain extent.Step 3 is only recurred when the condition ismet, i.e. p-values of the uncertainties at level 2 inthe business model are p0.20. A full factorialANOVA is modelled in order to investigate theexistence of interactions between the uncertaintiesor otherwise.Table 1 shows the ANOVA results of FPDL

causes diagnosis. All causes of FPDL, exceptscrap/rework, are found to be significant even toa minimum confidence level of 97%. This resultshows that material shortages, labour shortages,machine capacity shortages and finished productcompleted—not delivered are the most likelycauses of late delivery to customer in ERP-controlled manufacturing environments in SMEs.Although we found that scrap/rework does notgive a significant effect on FPDL, their effectcannot be ignored at this stage because furtherdiagnosis will reveal to what extent the causes ofsuch uncertainty affects the level of scrap/rework.It is also important to note that the uncertaintieswith p-values 40.20 do result in FPDL, but theirlikelihood of such effect is lower as compared tothose with pp0:20.The ANOVA results also show some two-way

interactions between the uncertainties, namelylabour shortages and machine capacity shortages;and material shortages and scrap/rework. Theinteraction between the former pair is found to be

df Mean square F Sig. (p)

35 1.060 4.893 0.001*

1 84.041 387.881 0.000*

4 0.750 3.460 0.034*

2 2.297 10.601 0.001*

2 1.079 4.981 0.022*

1 9.256E�02 0.427 0.523

2 3.475 16.038 0.000*

2 2.593 11.966 0.001*

1 8.772E�03 0.040 0.843

15 0.217

51

50

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significant. In these SMEs’ manufacturing envir-onments, it is found that when labour shortagesand machine capacity shortages simultaneouslyaffect the production process, an additional levelof FPDL will be resulted. Despite some additionallevels of FPDL as a result of the concurrentoccurrence of material shortages and scrap/re-work, their effect is found to be less severe becausescrap/rework has the minimum main effect onFPDL.

Table 2 shows the ANOVA results of materialshortages causes diagnosis. Poor supplier deliveryperformance is found to be the cause that mostlikely to result in material shortages. We alsofound some additional FPDL when poor supplierdelivery performance and unexpected/urgentchanges to production schedule occur simulta-neously. These results show that these SMEmanufacturers rely on external material supply.However, if changes to the production scheduleoccur, its effect on material shortages whenamalgamated with poor supplier delivery perfor-mance will significantly affect the delivery perfor-mance of these SMEs.

Table 2

ANOVA results of material shortages causes diagnosis

Source Type III sum

of squares

Corrected model 52.849

Intercept 174.463

Poor supplier delivery performance 17.976

Inaccuracies of stock records 1.412

Application of incorrect stock control rules 0.530

Unexpected/urgent changes to production

schedule

10.710

Poor supplier delivery

performance� Inaccuracies of stock records

2.066

Poor supplier delivery

performance�Unexpected/urgent changes

to production schedule

5.114

Inaccuracies of stock records�Unexpected/

urgent changes to production schedule

2.328

Application of incorrect stock control

rules�Unexpected/urgent changes to

production schedule

7.840E�02

Error 42.131

Total 548.000

Corrected total 94.980

�pp0:20.

We then diagnose the underlying causes ofpoor supplier delivery performance. No particularunderlying causes and interactions between thecauses are found to be significant owing to thedata variations between the enterprises onthe causes of poor supplier delivery performance.This does not mean that the underlying causes donot affect poor supplier delivery performance; itsimply implies that we need more data to conformthe effects. Nevertheless, the main finding on poorsupplier delivery performance being the mostsignificant cause of material shortages in theseERP-controlled manufacturing environments doesprovide a clear direction to the SMEs to improvetheir supply chain management.Table 3 shows the ANOVA results of labour

shortages causes diagnosis. Schedule/work-to-listnot followed is found to be the cause that mostlikely to result in labour shortages. No interactionsbetween the causes are found. Further diagnosisof the underlying causes of schedule/work-to-listnot followed is then carried out. Table 4 showsthe ANOVA results of schedule/work-to-list notfollowed causes diagnosis. The result shows that

df Mean square F Sig. (p)

29 1.822 0.908 0.601

1 174.463 86.960 0.000*

4 4.494 2.240 0.099*

3 0.471 0.235 0.871

1 0.530 0.264 0.613

4 2.677 1.335 0.290

1 2.066 1.030 0.322

1 5.114 2.549 0.125*

1 2.328 1.161 0.294

1 7.840E�02 0.039 0.845

21 2.006

51

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Table 4

ANOVA results of schedule/work-to-list not followed causes diagnosis

Source Type III sum

of squares

df Mean

square

F Sig. (p)

Corrected model 29.537 16 1.846 1.657 0.106*

Intercept 75.641 1 75.641 67.902 0.000*

Schedule/work-to-list not produced 0.375 3 0.125 0.112 0.952

Schedule/work-to-list not controlled 16.166 4 4.041 3.628 0.014*

Schedule/work-to-list produced but not available to labour 3.204 3 1.068 0.959 0.423

Error 37.875 34 1.114

Total 237.000 51

Corrected total 67.412 50

�pp0:20.

Table 3

ANOVA results of labour shortages causes diagnosis

Source Type III sum of squares df Mean square F Sig. (p)

Corrected model 50.555 29 1.743 1.713 0.102*

Intercept 161.362 1 161.362 158.593 0.000*

Absenteeism 4.847 4 1.212 1.191 0.344

Schedule/work-to-list not followed 11.915 4 2.979 2.928 0.045*

Lack of skill availability 0.190 2 9.524E�02 0.094 0.911

Labour overload 4.083 3 1.361 1.338 0.289

Error 21.367 21 1.017

Total 284.000 51


�pp0:20.


the most likely underlying cause of schedule/work-to-list not followed is when it is not controlled.A 99% confidence level is identified for thediagnosis of this cause. This finding shows thatthis type of SMEs need a structured and systematiccontrol for their production schedule or work-to-list in order to prevent the occurrence of scheduleor work-to-list not being followed, which poten-tially results in labour shortages and ultimatelylead to FPDL.

The ANOVA results of machine capacityshortages causes diagnosis show that no particularcauses and interactions between the causes arefound to be significant. This does not mean thatthe causes do not affect machine capacityshortages; it simply implies that we need moredata to conform the effects. Despite the findingthat machine capacity shortages result in FPDL ata confidence level of 98% (see Table 1), its effect is

less severe as compared to the effect from labourshortages. Amongst the resources in the forms ofmaterial, labour and machine capacity, it is foundfrom this diagnosis that labour shortages are morelikely to give a significant effect on FPDL in suchan ERP-controlled manufacturing environment inSMEs.Table 5 shows the ANOVA results of scrap/

rework causes diagnosis. Unacceptable productquality and engineering design changes during/after production are found to be the significantcauses of scrap/rework in these SMEs. Addition-ally, they also found to be creating additional levelof scrap/rework when they occur simultaneously.This interaction is found to be very likely due tothe incidents that engineering design changes areoften linked to unwanted or unacceptable productspecifications, which will normally create anunacceptable product quality. Although the FPDL

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Table 5

ANOVA results of scrap/rework causes diagnosis

Source Type III sum

of squares

df Mean

Square

F Sig. (p)

Corrected model 3.957 10 0.396 1.754 0.102*

Intercept 44.783 1 44.783 198.512 0.000*

Unacceptable product quality 1.416 4 0.354 1.569 0.201**

Engineering design changes during/after

production

1.601 4 0.400 1.774 0.153*

Unacceptable product

quality�Engineering design changes

during/after production

0.894 2 0.447 1.981 0.151*

Error 9.024 40 0.226

Total 101.000 51


�pp0:20, ��pp0:20 (approximation).


causes diagnosis (see Table 1) only shows anapproximately 50% confidence level of the maineffect from scrap/rework, their significant causesfound in this diagnosis show that scrap/reworkcannot be overlooked.

From this finding, we then diagnose the under-lying causes of unacceptable product quality andengineering design changes during/after produc-tion. Again, no particular underlying causes andinteractions between the causes are found to besignificant owing to the data variations betweenthe enterprises on the underlying causes ofunacceptable product quality and the underlyingcauses of engineering design changes during/afterproduction.

The ANOVA results of finished product com-pleted—not delivered causes diagnosis show thatnone of the causes are found to be significant inthe SMEs in ERP-controlled manufacturing en-vironments. These causes do not result in addi-tional level of finished product completed—notdelivered. Nevertheless, it has already been foundthat finished product completed—not delivereddid result in FPDL at a confidence level of 100%(see Table 1), thus its causes are very likely to besignificant. We need more data to conform theeffects.

Fig. 2 shows a summary of the significant causesof uncertainty and the significant interactionsbetween uncertainties in such SMEs’ manufactur-ing environment. The results can be used as a

reference to note which causes of uncertainty aremore likely to result in FPDL in ERP-controlledmanufacturing environments in SMEs and toidentify the underlying causes of each significantcause. It also provides the SMEs in such environ-ment with the knowledge of which uncertaintiesinteract and will result in additional effect on latedelivery.It must be noted that these results are derived

from estimation from practitioners and changes inthe performance of a particular SME manufac-turer may change the results on which causes areto be significant. Nonetheless, this provides ageneral guideline on the common problem areasthat SME manufacturers face. It also provides avalid form of diagnosis of the underlying causes ofuncertainty that lead to FPDL for a particularSME manufacturing environment. The resultsfrom the diagnosis are later used to investigatewhether FPDL can be reduced if the significantcauses of uncertainty and the significant interac-tions are tackled. This is carried out throughsimulation experiments, uncertainty modelling anda case study.

4. Simulation experimental design and uncertainty

modelling

Using data from a real case enterprise, asimulation study is carried out to investigate the

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FPDL

Materialshortages

Labour shortages

Machine capacityshortages

Scrap/rework

(p>0.20)

Finished productcompleted - not

delivered

Poor supplierdelivery

performance

Unexpected/urgentchanges to production

schedule (p>0.20)

Schedule/work-to-list notfollowed

Schedule/work-to-list notcontrolled

Unacceptableproduct quality

Engineeringdesign changes

during/afterproduction

Fig. 2. Significant causes of uncertainty and interactions in ERP-controlled manufacturing environments in SMEs.


effects of the significant causes of uncertainty andthe significant interactions identified from theANOVA results on FPDL. It is envisaged thatthe level of FPDL will be reduced when the level ofsignificant causes of uncertainty is low.

The data used for the simulation model aregathered from a medium-sized commercial trans-former manufacturer who uses a proprietary ERPsystem for production planning and scheduling.Ten products are modelled, which consist of 3runners, 4 repeaters and 3 strangers, according to

the definition of (Parnaby, 1988). The products aredifferentiated in terms of their order intervals,mainly to create a mixed demand pattern in a SMEmanufacturing environment. The products consistof up to five BOM levels with 434 different parts.Sixty percent of purchased parts and 40% of made-in parts are modelled. A 2-year Master ProductionSchedule (MPS) is collected for these products,which is then run through an MRP model (Kohand Saad, 2003b). This has resulted in some 50,000purchase and work orders in a POR schedule.

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A multi-products, multi-level dependent de-mand simulation model, which is controlled byPOR schedule, has been developed using SIMANV in ARENA simulation software (Pegden et al.,1995) in our previous research (Koh and Saad,2003b). This simulation model is used as the basisfor experimentation in this study. The simulationparameters have been modified to suit the objec-tive of this simulation study. The simulation modellogic has not been changed. The causes ofuncertainty identified in this research are repre-sented in the simulation model.

In order to accurately represent the causes ofuncertainty in the simulation model, it is impor-tant to clearly define the causes of uncertainty.Unexpected/urgent changes to production sche-dule can be defined as variations in the MPS interms of changed order quantity and/or due date.The effect of these changes is that materials neededfor a particular order may not be availableparticularly when there is an increase in orderedquantity and shortened due date. Poor supplierdelivery performance can be referred to as sub-standard external supply. This can be in the formsof poor quality, shortages, lateness and wrongsupplies. The effect of such sub-standard ismaterial shortages on the supplies required.Schedule/work-to-list not controlled can be de-fined as an event within a manufacturing environ-ment, which has a lack of discipline in ensuringthat the work is carried out according to plan. Theeffect of this event is that the schedule/work-to-listwill not be followed. In such case, the labour(resource) that is supposed to carry out the workmay be working on some other order. Therefore,the effect of this will be seen as labour shortages onthe affected order.

Unacceptable product quality can be referred toas sub-standard output during production. Thiscause focuses on the quality issues of internalproduction process. The effect of this cause isscrap/rework on the affected product. Engineeringdesign changes during/after production can bedefined as late engineering changes that occurwhen the products are in production. The effect ofthis late changes is that scrap/rework will beresulted. Machine capacity shortages are a rela-tively broad issue that can be referred to as an

event, which indicates the situation when theprocess of an order is truncated due to under-performance at a resource (comprised of machin-ing facilities). Such underperformance can be inthe forms of breakdown, stoppage and overload.The effect of this event is directly linked to FPDL.Finished product completed—not delivered can bedefined as a post-production event, which occursoutside the control of the production process. Thetriggers for this event can be awaiting final qualityclearance or dispatch. The effect of this event isalso directly linked to FPDL.Seven factors (causes), each with two levels, are

modelled in this simulation experiment. A half-factorial design of experiments is used, thusrequire a total of 64 experiments (27–1). This is aresolution VII design. Such design ensures up totwo-way interactions are not confounded witheach other. Therefore, higher order interactionsare excluded from the results analysis. A resolutionIV design will be resulted if 27–2 experiments areused. Further factorial design is not advisablebecause a minimum of resolution V design isrequired (Law and Kelton, 2000) to yield a non-confounded result. Two levels are modelled torepresent the uncertainty at high and low levels.Table 6 shows the factors, the modelling methodsand the levels setting for the simulation experiments.Each factor (cause) is randomised with a specific

probability distribution. Some parameters arefixed and some are designed to vary to representthe levels setting. The modelling methods for eachfactor are decided based on how the uncertaintywill occur according to the earlier definitions. Thedata collected from the case enterprise areanalysed to give an accurate representation onthe nature of the probability distribution. InputAnalyser in ARENA is used to fit the correct typeof distribution to a specific data. The proportionof the orders affected in the POR schedule isdecided based on the results obtained in ourprevious research that these are the range of levelsexperienced in industry (Koh and Saad, 2003c).During the pilot study, five replications (Law

and Kelton, 2000) for each experiment are run.This equates to 320 replications for the simulationexperiments. However, some additional replica-tions are found to be necessary because the

ARTICLE IN PRESS

Table

6

Factors,modellingmethodsandlevelssettingforthesimulationexperim

ents

Factors

(causesof

uncertainty)

Modellingmethods

Fixed

parameterssetting

Variable

parameterssetting

Low

High

Unexpected/urgent

changes

toproduction

schedule

Arandom

proportionofthe

ordersin

thePOR

schedulewere

affectedwithincreasedin

customer

order

quantity

with

shorten

duedate

Batchsize�2

Discreteprobability

distributionof2%

ofthe

ordersin

thePOR

schedule

Discreteprobabilitydistribution

of5%

oftheordersin

thePOR

schedule

Planned

duedate—

one

day

Poorsupplier

delivery

perform

ance

Arandom

proportionofthe

deliveryofthepurchase

orders

inthePOR

schedule

were

affectedwiththeneedfor

replenishment/reordering

Purchasedlead-tim

e�2

Discreteprobability

distributionof2%

ofthe

purchase

ordersin

the

POR

schedule


of5%

ofthepurchasedordersin

thePOR

schedule

Schedule/w

ork-to-listnot

controlled

Afixed

proportionofthework

ordersin

thePOR

schedulewere

affectedwithchanges

inthe

queuingrules

Discreteprobability

distributionof2%

ofthe

work

ordersin

thePOR

schedule

Earliest

DueDate

(EDD)

atlabour-operated

resources’queues

Last

InFirst

Out(LIF

O)at

labour-operatedresources’

queues

Unacceptable

product

quality

Arandom

proportionofthe

work

ordersin

thePOR

schedule

weredelayed

dueto

rejectionfrom

inspectionor

quality

controlforrework

Planned

operationtime�2

Discreteprobability

distributionof2%

ofthe

work

ordersin

thePOR

schedule


of5%

ofthework

ordersin

the

POR

schedule

Engineeringdesign

changes

during/after

production

Arandom

proportionofthe

work

ordersin

thePOR

schedule

wereaffectedwith

additionaloperationsin

the

routings

Analternate

routingwith

additionaloperations

sequence

Discreteprobability

distributionof2%

ofthe

work

ordersin

thePOR

schedule

routedto

the

alternate

routing


of5%

ofthework

ordersin

the

POR

schedule

routedto

the

alternate

routing

Machinecapacity

shortages

Arandom

proportionofthe

work

ordersin

thePOR

schedule

wereaffectedwith

reductionin

machinecapacity

Discreteprobability

distributionof2%

ofthe

work

ordersin

thePOR

schedule

thatwould

be

processed

bytheaffected

machines

Allmachines’capacity

with(nX3)�

1

Allmachines’capacity

with

(nX3)�

2

Finished

product

completed—notdelivered

Arandom

proportionofthe

finished

product

ordersin

the

POR

schedule

thathasbeen

completedwasaffectedwith

delayforadditionaltimefor

post-productionactivities

Analternate

routingwith

additionaloperations

sequence

Discreteprobability

distributionof2%

ofthe

finished

product

ordersin

thePOR

schedule

routed

tothealternate

routing


of5%

ofthefinished

product

ordersin

thePOR

schedule

routedto

thealternate

routing


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distribution half-width (h) is greater than 0.05. Inthis study, we consider an acceptable level ofh-value to be less than 5% of the sample mean.The formula applied to calculate the h-value isshown below:

h ¼ t1�a=2;n�1SðxÞffiffiffi

np , (1)

where h is the distribution half-width, t1�a=2;n�1 thestandard deviate in t-distribution for a confidencelevel, S(x) the unbiased estimator of the standarddeviation, n the number of replications.

The confidence level is set in all cases to be 95%.To estimate the number of further replicationsrequired, the equation is used:

n� ¼ nh

h�

� �2

, (2)

where n* is the total replications required, n theinitial number of replications, h the initial calcu-lated half-width for n replications, h* the desireddistribution half-width.

This has resulted in a total of 485 replicationsfor the simulation experiments.

5. Simulation results, analysis and discussions

ANOVA in SPSS is used to analyse thesimulation results. The dependent variable set inANOVA is FPDL, whilst the independent vari-ables are the factors modelled. A minimum of 95%confidence level is used, which implies that allfactors with pp0:05 are significant. Therefore,the causes of uncertainty with such values showthat increased in the levels of such cause ofuncertainty will significantly increase the level ofFPDL. Table 7 shows the ANOVA results of themain effects and two-way interactions of thesimulation experiments.

The ANOVA results show some significantmain effects and two-way interactions up to 99%confidence level. All factors (causes of uncertainty)modelled are found to have significant main effectson FPDL, with the exception of finished productcompleted—not delivered. This may be due to thenature of the data of the case enterprise, whichcomprises only a small proportion of finished

product orders as compared to the overall ordersin the POR schedule. Hence, the effect of finishedproduct completed—not delivered is found to beless significant. The effects of the other factors arefound to be consistent with the earlier findings thatthese are the causes of uncertainty that result inFPDL in the SME manufacturing environments.Whenever any delay affects a part within a

BOM, the effect will be propagated up through theBOM chain to cause additional lateness unlessslack exists to recover the delay. The extent oflateness and the number of parts affected willdepend upon the BOM level at which theuncertainty applied. The term knock-on effectsare coined to explain this phenomenon. Where adelay occurs on a resource, it affects not only thepart being processed, but also every part held inthe queue for the resource. This queue can containparts from a number of different BOM thus thedelay can be propagated across products, whichwill then create consequent knock-on effects in theproducts concerned unless slack exists to recoverthe delay. The term compound effects are coinedto explain this phenomenon. The types of effectsexerted depend upon how the causes of uncer-tainty have taken place. Therefore, knock-oneffects will primarily affect some parts, whereascompound effects will primarily affect other parts.Since knock-on effects are easier to visualise(according to BOM structure) as compared tocompound effects, such effect tends to be easier tocontrol. Compound effects are fuzzy and predic-tions on parts that will be routed to the affectedresources are difficult, hence it is important toknow which causes of uncertainty yield such effectso that appropriate techniques can be proposed totackle the causes.Unexpected/urgent changes to production sche-

dule and poor supplier delivery performance aremainly exerting knock-on effects because theaffected parts can be identified from the where-used function in an MRP, MRPII or ERP system.Once the affected parts are identified, the relatedparent parts can be revealed and viable techniquescan then be applied to halt the effects on FPDL.Some systems have single-level pegging andsome have multi-level pegging. Multi-level peggingallows parent parts identification up to level zero

ARTICLE IN PRESS

Table

7

ANOVA

resultsofthemain

effectsandtw

o-w

ayinteractionsofthesimulationexperim

ents

Source

TypeIIIsum

ofsquares

df

Mean

square

FSig.

(p)

Correctedmodel

398966.815

28

14248.815

85.801

0.000**

Intercept

426478.049

1426478.049

2568.089

0.000**

Unexpected/urgentchanges

toproductionschedule

15652.795

115652.795

94.255

0.000**

Poorsupplier

deliveryperform

ance

8038.353

18038.353

48.404

0.000**

Schedule/w

ork-to-listnotcontrolled

3785.747

13785.747

22.796

0.000**

Unacceptable

product

quality

42874.397

142874.397

258.173

0.000**

Engineeringdesignchanges

during/after

production

673.543

1673.543

4.056

0.045*

Machinecapacity

shortages

1166.582

11166.582

7.025

0.008**

Finished

product


4.217

14.217

.025

0.873


toproductionschedule�Poorsupplier

deliveryperform

ance

163.142

1163.142

.982

0.322


toproductionschedule�Schedule/w


69.654

169.654

.419

0.518


toproductionschedule�Unacceptable

product

quality

11861.227

111861.227

71.424

0.000**


toproductionschedule�Engineeringdesignchanges

during/after

production

368.592

1368.592

2.220

0.137


toproductionschedule�Machinecapacity

shortages

159.294

1159.294

.959

0.328


toproductionschedule�Finished

product


595.740

1595.740

3.587

0.059

Poorsupplier

deliveryperform

ance�Schedule/w


206.677

1206.677

1.245

0.265

Poorsupplier

deliveryperform

ance�Unacceptable

product

quality

383.226

1383.226

2.308

0.129

Poorsupplier

deliveryperform

ance�Engineeringdesignchanges

during/after

production

5952.224

15952.224

35.842

0.000**

Poorsupplier

deliveryperform

ance�Machinecapacity

shortages

639.920

1639.920

3.853

0.050*

Poorsupplier

deliveryperform

ance�Finished

product


93.582

193.582

.564

0.453

Schedule/w

ork-to-listnotcontrolled�Unacceptable

product

quality

894.716

1894.716

5.388

0.021*

Schedule/w

ork-to-listnotcontrolled�Engineeringdesignchanges

during/after

production

14236.942

114236.942

85.729

0.000**

Schedule/w

ork-to-listnotcontrolled�Machinecapacity

shortages

7447.583

17447.583

44.847

0.000**

Schedule/w

ork-to-listnotcontrolled�Finished

product


1016.164

11016.164

6.119

0.014*

Unacceptable

product

quality�Engineeringdesignchanges

during/after

production

1164.198

11164.198

7.010

0.008**

Unacceptable

product

quality�Machinecapacity

shortages

4772.113

14772.113

28.736

0.000**

Unacceptable

product

quality�Finished

product


56.415

156.415

.340

0.560


during/after

production�Machinecapacity

shortages

249.171

1249.171

1.500

0.221


during/after

production�Finished

product


312.927

1312.927

1.884

0.171

Machinecapacity

shortages�Finished

product


4.241E�02

14.241E�02

.000

0.987

Error

75727.126

456

166.068

Total

2544583.791

485

Correctedtotal

474693.941

484

�pp0:05,��pp0:01.


ARTICLE IN PRESS


in the BOM. In SMEs environments, these peggingare seldom used in practice for this purpose.Hence, it is important to understand the effectsexerted by the significant uncertainty and it ispossible that SMEs can use pegging to track downthe effects in the future.

The remaining causes of uncertainty are mainlyexerting compound effects because they affect theresources more than the parts. Schedule/work-to-list not controlled changes the queuing rules at thelabour-operated resources, thus it results in a levelof unpredictability on which parts that may beaffected by the change. Parts from a differentBOMmay be routed to the affected resources, thusthe delay is compounded to the subsequent partsin each BOM and also the subsequent parts in thequeue of the affected resources. This type of eventoccurs in the shop floor level and hence thevariations need to be acted upon and feedback tothe planning system at the time when the varia-tions are encountered if an accurate POR scheduleis to be achieved. Even if such feedback is used, itis still not practical to update the system whenevervariations are encountered because this mayinduce system nervousness, particularly in SMEsenvironments, which have limited resources andrequire quick response. Therefore, knowing thetypes of effects exerted by the significant uncer-tainty is essential to enable further prevention ofthose causes and effects. This uncertainty is mainlypeople problem. To deal with this uncertainty,SMEs can apply a discipline to planner andoperator involved by emphasising the importanceof schedule adherence, e.g. through a rewardscheme on timely and quality delivery.

Unacceptable product quality not only affectsthe delivery of the rejected parts, but also createsan additional time required at a resource to reworkthe parts. Such additional time spent at a resourcedelays the planned work to be released to theresource. Therefore, this effect can be seen as atype of compound effects due to the unpredict-ability of the parts that may be routed to theresource. To minimise such effect and to deal withthis uncertainty, SMEs can introduce a simplequality management technique, e.g. quality circles.Engineering design changes during/after produc-tion exert the same effects on FPDL owing to the

additional operations in the alternative routing,which requires seizing of the resources for a longerperiod of time. This uncertainty can be prevented.SMEs can introduce an engineering change note orprocedure during design or planning to preventsuch changes at the shop floor level. Machinecapacity shortages are also found to exert com-pound effects due to the random reduction ofcapacity level at the affected machines, which inturn result in accumulation of parts at the queue ofthe affected machines and hence delay the releaseof their parent parts. Parts that will be at the queueof the affected machines are unpredictable. There-fore, it is not known to what extent the delay willaffect the FPDL. To deal with this uncertainty,SMEs can outsource or subcontract some of thework to free up capacity. Finished product com-pleted—not delivered is also seen to be exertingcompound effects on FPDL due to the additionalcommitment of a certain type of resources, e.g.inspection device or inspector, to perform the post-production activities. SMEs can prevent thisuncertainty by building in quality control in theproduction process or planning in safety lead-timein the system to carry out post-production activities.Nine significant two-way interactions are found

from the simulation experiments. It signifies thatwhen the pair of causes of uncertainty occurssimultaneously, a significant level of FPDL will beresulted. Hence, it is important to understand notonly which causes interact with one another, butalso to be aware of the condition when they caninteract. The significant interactions (causes fromlevel 2 onwards in the business model) identifiedpreviously are firstly checked through this result tofind out whether there is any difference. It is asurprise to find out that the result on previouslysignificant interactions between unexpected/urgentchanges to production schedule and poor supplierdelivery performance cannot be repeated whenthey are modelled using the case enterprise. Thereason for this finding may be due to therandomised occurrence of the causes of uncer-tainty, which has resulted in both causes to occurat a rather different simulation time and henceaffecting different parts. Therefore, the parts arenot simultaneously affected by both causes, andhence additional level of FPDL cannot be found.

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The result supports the previous finding thatwhen unacceptable product quality and engineer-ing design changes during/after production occursimultaneously, an additional level of FPDL willbe resulted. These causes can affect the same partin any order to result in FPDL. The reason for itssignificance may be due to a consistently frequentoccurrence of such interaction in the simulation,which both causes are found to exert compoundeffects on FPDL.

The result also reveals some significant two-wayinteractions, which have not been found in theprevious study. These include interactions be-tween: unexpected/urgent changes to productionschedule and unacceptable product quality; poorsupplier delivery performance and engineeringdesign changes during/after production; poorsupplier delivery performance and machine capa-city shortages; schedule/work-to-list not controlledand unacceptable product quality; schedule/work-to-list not controlled and engineering designchanges during/after production; schedule/work-to-list not controlled and machine capacityshortages; schedule/work-to-list not controlledand finished product completed—not delivered;and unacceptable product quality and machinecapacity shortages.

Unexpected/urgent changes to production sche-dule must precede unacceptable product quality toenable an additional level of FPDL to occur.Unexpected/urgent changes to production sche-dule will occur at the point between planning andproduction, whilst unacceptable product qualitywill occur at the production stage in the shop floor.The same reason can be used to explain the orderof occurrence of poor supplier delivery perfor-mance and engineering design changes during/after production, within which poor externalsupply would happen before the production stage,which will then result in the latter cause ofuncertainty to affect the same part. The conditionon which interaction between poor supplierdelivery performance and machine capacityshortages can occur is when the former causeprecedes the latter cause. According to the MRPrelease logic, the parts at the lower level BOM(tend to be purchased parts) will be released beforethe parts at the upper level BOM. Parts at the

upper level BOM tend to be manufactured partsand hence machine capacity shortages are likely toaffect the parts at the later stage of the simulation,whilst these parts have already been delayed bypoor external supply.Schedule/work-to-list not controlled is found to

be frequently affecting the same parts simulta-neously with all other causes of uncertainty, withthe exception of unexpected/urgent changes toproduction schedule and poor supplier deliveryperformance. It is noted that these significantinteractions comprise of all compound effects ofindividual causes and multiple causes. Therefore,their effects on FPDL are found to be significant.The interactions of schedule/work-to-list not con-trolled with unacceptable product quality andengineering design changes during/after produc-tion will not depend on the order of occurrence ofthe causes. However, schedule/work-to-list notcontrolled must precede finished product com-pleted—not delivered to enable an additional levelof FPDL to occur. The reason for this order is thatfinished product completed—not delivered tendsto affect parts at the very end of each productioncycle and hence those parts that are affectedsimultaneously by the causes must have alreadybeen delayed by schedule/work-to-list not con-trolled. For a significant interaction betweenschedule/work-to-list not controlled and machinecapacity shortages to occur, the machines will havebeen simultaneously affected by both causes andhence an additional level of FPDL is resulted fromthe accumulation of the number of parts that arecompounded from the two causes. The interactionbetween unacceptable product quality and ma-chine capacity shortages also will not depend theorder of occurrence of the causes.The main findings from the simulation experi-

ments when compared with the survey resultsare that:

�
The significant causes of uncertainty that areidentified in the survey can be used as areference for the SME manufacturers fordirecting their efforts in reducing late delivery.This finding is supported by the simulationexperiments and consistent results have beenobtained.

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�
The significant interactions between uncertain-ties that are identified in the survey will notprovide a clear guidance for the SME manu-facturers for directing their efforts in reducinglate delivery because a large amount of sig-nificant interactions that have not been identi-fied in the survey, are then found from thesimulation experiments. It shows that there areno common significant interactions betweenuncertainties that can be developed to guidethe SME manufacturers in tackling the interac-tions due to the parameters difference betweeneach production system in a particular enter-prise. In this case, individual cases of the SMEshave to be studied and the outcome cannot begeneralised. � The simulation results also show that the causes
of uncertainty produce knock-on and com-pound effects on late delivery. Compoundeffects are found to be more difficult to controlas compared to knock-on effects. These effectshave not been identified in the survey. Thehighlights of the types of effects found exertedby the causes can provide the SME manufac-turers with some knowledge (proactive) inadvance so that more appropriate buffering ordampening techniques can be applied to controlthe causes, particularly for those causes thatexert compound effects.

6. Conclusions and implications

A business model for diagnosing the underlyingcauses of uncertainty in manufacturing enterprisesthat use MRP, MRPII or ERP for productionplanning and scheduling was applied to study theunderlying causes of uncertainty in ERP-con-trolled manufacturing environments in SMEs. Aquestionnaire survey was carried out to identifythe causes that are more likely to result in latedelivery in those SMEs. ANOVA results showedthat poor supplier delivery performance, schedule/work-to-list not controlled, machine capacityshortages, finished product completed—not deliv-ered, unacceptable product quality and engineer-ing design changes during/after production have

significant effect on late delivery. The interactionsbetween unacceptable/urgent changes to produc-tion schedule and poor supplier delivery perfor-mance; and unacceptable product quality andengineering design changes during/after produc-tion yielded additional level of late delivery.To test whether these results could be used as a

reference for the SME manufacturers in tacklingthe significant causes of uncertainty, simulationmodelling and experimental study using a real casestudy were carried out. The significant causes ofuncertainty and the significant interactions be-tween uncertainties found in the survey weremodelled. A half-factorial design of experimentswas performed and 485 replications of the experi-ments were run. The data used in the simulationexperiments was derived from a medium-sizedtransformer manufacturer. The size of the para-meters in the simulation model could be generallycharacterised by: two-year MPS data, 434 differentparts and 50,000 orders in the POR schedule.The simulation results were analysed using

ANOVA. The results showed that the significantcauses found in the survey were proved to beconsistently significant in yielding late delivery.Although finished product completed—not deliv-ered was found to have less effect on late delivery,this might be due to the nature of the data of thecase enterprise, which comprises only a smallproportion of finished product orders as comparedto the overall orders in the POR schedule. Theeffects of the other causes were found to be, inmost cases, more significant than that from theearlier findings. Hence, it can be concluded thatthese are the common causes of uncertainty thatresult in late delivery in ERP-controlled manufac-turing environments in SMEs, and they could beused as a reference for the SME manufacturers forprioritising their efforts in reducing late delivery.It was clearly shown that there were no common

significant interactions between uncertainties thatcould be developed to guide the SME manufac-turers in tackling the interactions due to theparameters difference between each productionsystem in a particular enterprise. Therefore,individual cases of the SMEs have to be studiedand the outcome cannot be generalised. Thesimulation results also showed that the causes of

ARTICLE IN PRESS


uncertainty produce knock-on and compoundeffects on late delivery. Compound effects werefound to be more difficult to control as comparedto knock-on effects. These effects were notidentifiable in the survey. Therefore, the highlightsof the types of effects found exerted by the causescould provide the SME manufacturers with someknowledge (proactive) in advance so that moreappropriate buffering or dampening techniquescan be applied to control the causes.

It was suggested that SMEs could use pegging inERP more often to deal with knock-on effectsfrom unexpected/urgent changes to productionschedule and poor supplier delivery performance.It was also suggested that the findings of whichsignificant uncertainty exerts compound effectsprovide the first step to SMEs to deal with thecauses and hence the compound effects. To dealwith this schedule/work-to-list not controlled,SMEs could apply a discipline to planner andoperator involved by emphasising the importanceof schedule adherence, e.g. through a rewardscheme on timely and quality delivery. To dealwith unacceptable product quality, SMEs couldintroduce a simple quality management technique,e.g. quality circles. To prevent engineering designchanges during/after production, SMEs couldintroduce an engineering change note or procedureduring design or planning to prevent such changesat the shop floor level. To deal with machinecapacity shortages, SMEs could outsource orsubcontract some of the work to free up capacity.To prevent finished product completed—notdelivered, SMEs could build in quality control inthe production process or plan in safety lead-timein the system to carry out post-productionactivities.

The findings of the significant causes of un-certainty and the types of effects that would beexerted by the causes could now be exploited to theadvantage of the SME manufacturers whenprioritising their effort in tackling these causes.This research showed that the business model fordiagnosing underlying causes of uncertainty couldbe applied in SMEs that operate in ERP-con-trolled manufacturing environments for reducinglate delivery. It was demonstrated in this studythat such diagnosis enables causes of uncer-

tainty to be dealt with effectively and efficiently.This would also enable higher agility and respon-siveness in managing uncertainty via learningfrom the collected knowledge. Future SMEs insuch environment will be better equipped inmanaging uncertainty through this knowledgeand model.

Acknowledgement

We are indebted to Ian Turner who provided uswith the data for the real case study.

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