The Effect of Scope Changes on Project Duration ExtensionsExtended Abstract of a PhD Dissertation1Moshe AyalFaculty of Management, Tel Aviv University, Tel Aviv 69978AbstractThe objective of this research is twofold: first to construct and validate an explanatory model forproject duration extensions, and second to analyze the effect of scope changes and other relateddrivers on project delivery times. The research is based on a field study, which draws data from anengineering service environment, and uses means of structural equation modeling for analysis. Themodel contributes to a better understanding of the effect of various scope changes on projectduration, and enables the construction of a practical tool for estimating project duration.IntroductionProjects frequently finish late and over budget, thus causing organizations heavy penalties anddamage their prestige. Moreover, as projects are hardly ever completed without introducing changesto their original baseline plan, a major challenge is to accurately estimate the project delivery time,while understanding the effects of other factors that create the discrepancy between estimated andactual project completion times. Thus, the intention in this work is to quantify the factors affectingduration extensions, an issue that has barely been addressed in the literature. One way to quantifythese factors is by generating a descriptive empirical model that includes the major behavioral andquantitative measures of performance.LiteratureThe section reviews the following themes: (1) duration estimation, the basis of duration extensionmeasures, (2) possible generators of duration extensions, and (3) scope changes, and their effect onproject performance.Duration Estimation. The tools most commonly used are based upon mathematical models inwhich task duration is explained by technical parameters of the task and the experience of theexecuting entity. Well known examples of these tools are SLIM (Putnam, 1978), and COCOMOSoftwares (Boehm, 1981; Boehm et al., 2000). Burt and Kemp (1991) proposed predicting taskduration from knowledge about durations of categories of tasks. However, here a potential biasexists, known in the psychology literature as the planning fallacy, due to the tendency of individualsto underestimate the amount of time needed to complete a given project. In the words of Buehler,Griffin and Ross (1994), they tend to focus on the future, ignoring past experience.Duration Extensions Generators. Several generators are discussed in the literature. Levy andGloberson (1997) implemented concepts from queuing theory for reducing the impact of waitingperiods of critical work packages on the delivery times of projects executed in parallel. Goldratt(1997) claimed that task splitting, whether planned or results from preemptive processing mightlead to severe duration extensions. Shenhar (2001) classified technological uncertainty into fourlevels, correlating them with overall project duration. Shenhar et al. (2002) also claimed that1 Thesis Supervisor: Prof. Shlomo Globerson
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL2projects high in uncertainty must be managed differently, employing means to reduce theuncertainty. Low experience with technology often results in what we term low structured projects(Applegate, Austin, and McFarlan, 2003) where risk mitigation is of great need. Based on surveys,Chan and Kumaraswamy (2002) mentioned: (1) impractical design, (2) labor shortages, (3) poorperformance, (5) unforeseen conditions, and (6) poor communication. Still, literature lacksempirical quantifications of the effects of the above-mentioned generators on project duration.Scope Changes. Modification to the agreed upon scope (PMBOK, 2000) are considered asinherent in the nature of projects because of their complexity and the inevitable appearance ofunforeseen problems (Ertel, 2000). The evidence shows that scope changes have a significantimpact on the cost of projects. Chick (1999) showed that the later a change occurs in a project themore effect it will have on the project’s cost, and also mentioned a possible effect on projectschedule. Kauffmann et al. (2002) used the earned value method in quantifying scope change‘magnitude’ for cost adjustments. Barry et al. (2002) showed a correlation between software projectduration and effort. However, a thorough investigation of the effect of scope changes on projectduration has not yet been conducted.Research DesignFigure 1 illustrates the hypothesized work package duration extensions model. The modelincludes three exogenous variables: (1) Technological Uncertainty, (2) Project Priority, and (3)Unforeseen Stoppages. It also has six endogenous variables: (1) Additional Materials, which refersto inventory orders, and (2) Additional Labor, both resulting from scope changes, and markedinside a dashed box; (3) Waiting in Line; (4) Preemptive Processing; (5) Stoppage Period, and (6)the main dependent variable: Duration Extension, which refers to a work package.Figure 1. Hypothesized Work Package Duration Extensions ModelUnforeseenStoppagesProjectPriorityPreemptiveProcessingWaitingin LineDurationExtensionStoppagePeriodTechnologicalUncertaintyH1 H2H3(H7)(H6)H5H4AdditionalMaterialsAdditionalLaborTable 1 summarizes the proposed model variables and the rationale for their selection.
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL3Table 1. Proposed Model VariablesVariable Description RationaleTechnologicalUncertaintyLevel of technological uncertainty associatedwith a certain work packageTechnological uncertainty is correlated withduration (Shenhar, 2001)Project PriorityPriorities assigned to projects by topmanagement teamMay decrease duration extensions byreducing waiting periodsUnforeseenStoppagesNumber of unforeseen operational stoppagescaused by internal or external sourcesA greater number of operational stoppagesmay increase in-process durationStoppagePeriodTotal time periods of operational stoppagescaused by internal or external sourcesLonger periods of operational stoppages mayincrease in-process durationAdditionalMaterialsNumber of material orders resulting fromscope changesWaiting until materials arrival, if filing for anexternal supply, may increase the durationAdditionalLaborAdditional labor resulting from scope changesScope changes could affect duration (Chick,1999)Waiting in LineTime from the arrival of a work package to thebeginning of its processingWaiting periods may extend delivery times(Levy and Globerson, 1997)PreemptiveProcessingNumber of breaks during processing a workpackageTask splitting increases in-process duration(Goldratt, 1997)DurationExtensionWork package in-process duration extensionrelative to planned durationThe main dependent variable of the researchTable 2 provides five hypotheses that are derived directly from the duration extensions model,based on its flow. The sixth hypothesis involves two exogenous variables, not included in the modelfor reasons of parsimony: (1) Internal Scope Changes, and (2) External Scope Changes. The seventhhypothesis is indicated by a correlation in the duration extensions model, and is tested separatelywithin the projects’ framework.Table 2. HypothesesVariable Effect on Variable RationaleH1 Additional Labor +DurationExtensionAdditional labor calls for resources, which in many cases are notcurrently availableH2AdditionalMaterials+DurationExtensionIn-process duration extensions may result from having to wait untilmaterials arriveH3PreemptiveProcessing+DurationExtensionGoldratts (1997) claim that splitting a task results in extending its in-process durationH4 Additional Labor +PreemptiveProcessingWork package manager needs to wait for available resources and/ormaterials to arriveH5 Waiting in Line -PreemptiveProcessingThe negative effect of waiting periods can be decreased by workingintensively and continuouslyExternal ScopeChanges++Internal ScopeChanges+H7TechnologicalUncertainty+ Project PriorityAllocating priority to a project may help in rapidly mitigating theuncertainties in its work packagesWork package managers who introduce scope changes try to avoidmaterial orders and use in stock materials, so as not to wait for thematerials to arrive. Thus, external scope changes are expected tohave a greater effect on material orders than Internal scope changesAdditionalMaterialsH6
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL4Analysis. We analyze the model using means of structural equation modeling (SEM) in order toelicit the partial correlations amongst the variables, and to establish the causal relations. Two of thevariables are dummy ones: (1) Project Priority, which takes the value of ‘1’ for prioritized project,and ‘0’ otherwise, and (2) Technological Uncertainty, which takes the value of ‘1’ for workpackage with technological uncertainty, and ‘0’ otherwise. We conduct linear multiple regression inexamining Hypothesis 6, and use means of binomial regression to test Hypothesis 7, on the effect oftechnological uncertainty on project priority. We use the EQS software package (Byrne, 1994) forconducting the SEM analysis. In order to bring all data to the same reference point, the model’svariables, except for the dummies, are divided by the planned duration of the work package.Data collection. The study draws data from 714 work packages comprising the 56 systemsengineering projects being performed at the time by a leading engineering services corporation. Theprojects ranged in value from several thousand dollars to one hundred thousand dollars, while thework packages comprising these projects ranged in duration from several days to a month. Theprojects had a sequential PERT/CPM structure, thus above 90% of the work packages wherecritical. Top management team, department managers, project managers and professional sectionmanagers were involved in data gathering. They used an interactive data collection interface, whichwas part of the project control system of the corporation.ResultsTable 3 shows the means, standard deviations, variables ranges and bivariate correlations for thevariables of the proposed model. Note that in some cases total labor hours invested in a workpackage were decreased as a result of scope changes. However, in our data it was the rare case, asmost of the time scope changes resulted in additional labor, which sometimes accumulated to ashigh as several hundreds of percents of the allocated labor hours!Table 3. Descriptive Statistics and Pearson Correlation MatrixVariables Mean s.d. Min Max 1 2 3 4 5 6 7 81 Tech. Uncertainty 0.29 0.46 0 12 Project Priority 0.37 0.48 0 1 .22**3 Unforeseen Stoppages 0.04 0.10 0 0.5 -.08* -.024 Stoppage Period 0.10 0.36 0 6.5 -.05 -.05 .53**5 Additional Materials 0.02 0.06 0 0.5 .18** .04 .01 -.016 Additional Labor 0.23 0.63 -0.47 7 .29** .06 -.03 -.05 .54**7 Waiting in Line 0.30 0.29 0 1.5 -.08* -.21** -.003 .002 .03 .12**8 Preemptive Processing 0.15 0.14 0 1 .09* .01 .28** .08* .11** .27** -.059 Duration Extension 0.31 0.68 -0.66 7 .11** -.08* .31** .60** .41** .48** .28** .25**Note . n=714 (work packages)* p < .05** p < .01
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL5Model Fit. Using structural equation modeling means of analysis, the hypothesized model ofwork package duration extensions is found significant: 6.322=χ , with 18 degrees of freedom,which rejects the null model hypothesis. In addition, the goodness of fit indices: NFI=0.98;NNFI=0.98; CFI=0.99; and RMSEA=0.034 (for n=714 work packages), indicating a good fit of themodel to the data (Bagozzi & Yi, 1988; Bagozzi & Yi, 1989).Hypotheses 1 to 5. From the partial correlations given in Table 4, Hypotheses 1 to 5 are seen tohave statistical significance. Note that two of the hypothesized relations are proved not to besignificant: (1) additional material orders affect preemptive processing, but only indirectly, via theresulted additional labor; and (2) project priority affects duration extension only indirectly, viashorter waiting in line periods. In addition, out of the three correlations tested, only the one betweenproject priority and technological uncertainty is found significant (r =0.22).Table 4. Direct Relations in the Work Packages Duration Extensions ModelHyp. From ToStandardizedcoefficientst-valuesH1 Additional Labor Duration Extension .34 13.49H2 Additional Materials Duration Extension .21 8.80Waiting in Line Duration Extension .23 10.92H3 Preemptive Processing Duration Extension .10 4.81Stoppage Period Duration Extension .61 29.83Project Priority Duration Extension -.03 -1.62(*)Technological Uncertainty Additional Labor .20 6.38Additional Materials Additional Labor .51 16.36Technological Uncertainty Additional Materials .18 4.82Project Priority Waiting in Line -.21 -5.74Unforeseen Stoppages Preemptive Processing .23 8.44H4 Additional Labor Preemptive Processing .32 7.76Additional Materials Preemptive Processing -.06 -1.46(*)H5 Waiting in Line Preemptive Processing -.08 -2.31Unforeseen Stoppages Stoppage Period .53 16.62Note. n=714; NFI=0.98; NNFI=0.98; CFI=0.99; RMSEA=0.034.(*) path not significantDuration Prediction. Using means of linear multiple regression, we derive a mathematicalmodel for the prediction of work package duration based on its predetermined variables,performance variables, and disruptions like forced stoppages and scope changes (R2=0.71). Table 5shows the predictors of the best-fitting model. Note that if we ignore the two scope changesvariables – additional material orders, and additional labor - we explain only 51% of the variance inthe duration extension. Conducting a partial F-test we again confirm the hypothesis that the twovariables representing scope changes are significant in the model.
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL6Table 5. Predictors of Work Package Duration ExtentionsModels:Predictor Estimate St. Error Estimate St. ErrorProject Priority -0.039 0.039Technological Uncertainty 0.218 0.041Unforeseen Stoppages -0.470* 0.214Stoppage Period 1.159** 0.039 1.188** 0.059Additional Materials 2.466** 0.282Additional Labor 0.363** 0.027Waiting in Line 0.552** 0.049 0.686** 0.063Preemptive Processing 0.519** 0.107 1.133** 0.139Intercept -0.171** 0.026 -0.212** 0.038R^2 0.707 0.513Adj. R^2 0.705 0.509Note . n=714. Partial F test for Model(2) = 328, p<0.001* p < .05** p < .01(1) Best Fitting (2) without Scope ChangesHypothesis 6. To test Hypothesis 6, on the effects of internal and external scope changes onadditional material orders, we regressed the additional material orders against the number ofexternal and internal scope changes, dividing both by the planned duration, as with all the variables.The results showed in Table 6 indicate that external scope changes result in a significantly higheramount of material orders than do internal scope changes. Note, however, that the number ofinternal and external scope changes is almost the same.Table 6. Additional Material Orders by Number of External and Internal Scope ChangesPredictor Number Estimate St. ErrorInternal Scope Changes 113 0.065** 0.02External Scope Changes 120 0.267** 0.021Intercept 0.003 0.002R^2 0.19Adj. R^2 0.187Note . n=714 (work packages)** p < .01Hypothesis 7. Figure 2 shows the number of prioritized and non-prioritized projects by thenumber of work packages with technological uncertainty. The data shows that the majority of theprojects of five or more work packages with technological uncertainty are prioritized. To formallycorroborate Hypothesis 7, we regressed project priority against the number of work packages withtechnological uncertainty in the project, using means of binomial regression.
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL7Figure 2. Prioritized and Non-Prioritized Projects by Work Packages with Technological Uncertainty051015200-1 2-3 4 5 6-11Work Packages with Technological UncertaintyProjectsRegular ProjectsPrioritized ProjectsTable 7 presents the results of the binomial regression, which indicate that a project consists offive or more work packages with technological uncertainties is likely to get priority by topmanagement. This might shed some new light on the way top management reduces technologicaluncertainties in projects.Table 7. Project Priority by Number of Work Packages with Technological UncertaintiesPredictor Estimate St. Error SignificanceTechnological Uncertainty 0.88 0.26 0.001Intercept -4.19 1.13 0.001-2LL 45.88Note . n=56 (projects).Implications and ConclusionsFunctional Level. Functional managers usually do not have a picture of the entire projects.Rather they see various work packages arriving from different project managers that must beprocessed according to certain priority rules. The model developed here can help them to betterassess the effects of the various disruptions that occur while the work packages are being processed.They can also use the model to compensate for duration extensions, thus enabling betterperformance of overall workload.
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL8Project / Multi-Project Level. At the project level, we should distinguish between critical workpackages that need to be tightly controlled, and non-critical work packages. At the multi-projectlevel, top management has to differentiate between prioritized and non-prioritized projects, givingtop priority to critical work packages belonging to prioritized projects. This will assure that theprojects with the highest priority will be delivered on time. The proposed model assesses the effectof unforeseen disruptions on critical work packages of high-priority projects, and provides bettertools for estimating final duration and compensating for delays.SummaryThe study contributes to an improved understanding of duration extensions and their causes, byquantifying scope changes, differentiating amongst the various types of scope changes, andconstructing an integrated model of work package duration extensions. The study pointed to thegreater effect of external scope changes, compared with internal ones, on material orders, and thuson the total duration extensions. Generally, the model can be implemented for forecasting workpackage duration extensions, and estimating the effect on the project’s duration. Finally, severalimplications at the functional, the project, and the multi-project levels are suggested.AcknowledgmentsThis work is based on my PhD dissertation, supervised by Prof S. Globerson. I wish to expressesmy gratitude to him, and to all whose insights have contributed to this thesis.ReferencesApplegate, L.M., Austin, R.D., & McFarlan, F.W. 2003 Corporate Information Strategy andManagement: Text and Cases. NY: McGraw-Hill Irwin.Bagozzi, R.P., & Yi, Y. 1988. On the evaluation of structural equation models. Journal of Academyof Marketing Science, 16(1): 74–94.Bagozzi, R.P., & Yi, Y. 1989. On the use of structural equation models in experimental designs.Journal of Marketing Research, 26(3): 271–284.Barry, E.J., Mukhopadhyay, T., & Slaughter. S.A. 2002. Software Project Duration and Effort: AnEmpirical Study. Information Technology and Management, 3(1-2): 113-136.Boehm, B.W. 1981. Software engineering economics. Advances in computing science andtechnology. NJ: Prentice-Hall.Boehm, B.W., Horowitz, E., Madachy, R., Reifer, D., Clark, B.K., Steece, B., Brown, A.W.,Chulani, S., & Abts, C. 2000. Software Cost Estimation with COCOMO II. NJ: Prentice Hall.Buehler, R., Griffin, D., & Ross, M. 1994. Exploring the "planning fallacy": Why peopleunderestimate their task completion times. Journal of Personality and Social Psychology, 67:366-381.Burt, C.D.B., & Kemp, S. 1991. Retrospective duration estimation of public events. Memory &Cognition, 19: 252-262.
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