The Effect of Scope Changes on Project Duration Extensions

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1 The Effect of Scope Changes on Project s Extended Abstract of a PhD Dissertation 1 Moshe Ayal Faculty of Management, Tel Aviv University, Tel Aviv Abstract The objective of this research is twofold: first to construct and validate an explanatory model for project duration extensions, and second to analyze the effect of scope changes and other related drivers on project delivery times. The research is based on a field study, which draws data from an engineering service environment, and uses means of structural equation modeling for analysis. The model contributes to a better understanding of the effect of various scope changes on project duration, and enables the construction of a practical tool for estimating project duration. Introduction Projects frequently finish late and over budget, thus causing organizations heavy penalties and damage their prestige. Moreover, as projects are hardly ever completed without introducing changes to 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 and actual project completion times. Thus, the intention in this work is to quantify the factors affecting duration extensions, an issue that has barely been addressed in the literature. One way to quantify these factors is by generating a descriptive empirical model that includes the major behavioral and quantitative measures of performance. Literature The section reviews the following themes: (1) duration estimation, the basis of duration extension measures, (2) possible generators of duration extensions, and (3) scope changes, and their effect on project performance. Estimation. The tools most commonly used are based upon mathematical models in which task duration is explained by technical parameters of the task and the experience of the executing entity. Well known examples of these tools are SLIM (Putnam, 1978), and COCOMO Softwares (Boehm, 1981; Boehm et al., 2000). Burt and Kemp (1991) proposed predicting task duration from knowledge about durations of categories of tasks. However, here a potential bias exists, known in the psychology literature as the planning fallacy, due to the tendency of individuals to 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. s Generators. Several generators are discussed in the literature. Levy and Globerson (1997) implemented concepts from queuing theory for reducing the impact of waiting periods 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 might lead to severe duration extensions. Shenhar (2001) classified technological uncertainty into four levels, correlating them with overall project duration. Shenhar et al. (2002) also claimed that 1 Thesis Supervisor: Prof. Shlomo Globerson

2 projects high in uncertainty must be managed differently, employing means to reduce the uncertainty. 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) poor performance, (5) unforeseen conditions, and (6) poor communication. Still, literature lacks empirical quantifications of the effects of the above-mentioned generators on project duration. Scope Changes. Modification to the agreed upon scope (PMBOK, 2000) are considered as inherent in the nature of projects because of their complexity and the inevitable appearance of unforeseen problems (Ertel, 2000). The evidence shows that scope changes have a significant impact on the cost of projects. Chick (1999) showed that the later a change occurs in a project the more effect it will have on the project s cost, and also mentioned a possible effect on project schedule. 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 project duration and effort. However, a thorough investigation of the effect of scope changes on project duration has not yet been conducted. Research Design Figure 1 illustrates the hypothesized work package duration extensions model. The model includes three exogenous variables: (1) Technological Uncertainty, (2) Project Priority, and (3) Unforeseen Stoppages. It also has six endogenous variables: (1) Materials, which refers to inventory orders, and (2) Labor, both resulting from scope changes, and marked inside a dashed box; (3) Waiting in Line; (4) ; (5) Stoppage Period, and (6) the main dependent variable:, which refers to a work package. Figure 1. Hypothesized Work Package s Model Technological Uncertainty (H6) Materials (H7) Labor H1 H2 Project Priority H4 Waiting in Line H5 H3 Unforeseen Stoppages Stoppage Period Table 1 summarizes the proposed model variables and the rationale for their selection. 2

3 Table 1. Proposed Model Variables Variable Description Rationale Technological Uncertainty Project Priority Unforeseen Stoppages Stoppage Period Materials Labor Waiting in Line Level of technological uncertainty associated with a certain work package Priorities assigned to projects by top management team Number of unforeseen operational stoppages caused by internal or external sources Total time periods of operational stoppages caused by internal or external sources Number of material orders resulting from scope changes labor resulting from scope changes Time from the arrival of a work package to the beginning of its processing Number of breaks during processing a work package Work package in-process duration extension relative to planned duration Technological uncertainty is correlated with duration (Shenhar, 2001) May decrease duration extensions by reducing waiting periods A greater number of operational stoppages may increase in-process duration Longer periods of operational stoppages may increase in-process duration Waiting until materials arrival, if filing for an external supply, may increase the duration Scope changes could affect duration (Chick, 1999) Waiting periods may extend delivery times (Levy and Globerson, 1997) Task splitting increases in-process duration (Goldratt, 1997) The main dependent variable of the research Table 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 model for reasons of parsimony: (1) Internal Scope Changes, and (2) External Scope Changes. The seventh hypothesis is indicated by a correlation in the duration extensions model, and is tested separately within the projects framework. Table 2. Hypotheses Variable Effect on Variable Rationale H1 Labor + H2 H3 Materials H4 Labor + H5 Waiting in Line - H6 H7 External Scope Changes Internal Scope Changes Technological Uncertainty Materials + Project Priority labor calls for resources, which in many cases are not currently available In-process duration extensions may result from having to wait until materials arrive Goldratt's (1997) claim that splitting a task results in extending its inprocess duration Work package manager needs to wait for available resources and/or materials to arrive The negative effect of waiting periods can be decreased by working intensively and continuously Work package managers who introduce scope changes try to avoid material orders and use in stock materials, so as not to wait for the materials to arrive. Thus, external scope changes are expected to have a greater effect on material orders than Internal scope changes Allocating priority to a project may help in rapidly mitigating the uncertainties in its work packages 3

4 Analysis. We analyze the model using means of structural equation modeling (SEM) in order to elicit the partial correlations amongst the variables, and to establish the causal relations. Two of the variables 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 work package with technological uncertainty, and 0 otherwise. We conduct linear multiple regression in examining Hypothesis 6, and use means of binomial regression to test Hypothesis 7, on the effect of technological uncertainty on project priority. We use the EQS software package (Byrne, 1994) for conducting the SEM analysis. In order to bring all data to the same reference point, the model s variables, 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 systems engineering projects being performed at the time by a leading engineering services corporation. The projects ranged in value from several thousand dollars to one hundred thousand dollars, while the work packages comprising these projects ranged in duration from several days to a month. The projects had a sequential PERT/CPM structure, thus above 90% of the work packages where critical. Top management team, department managers, project managers and professional section managers were involved in data gathering. They used an interactive data collection interface, which was part of the project control system of the corporation. Results Table 3 shows the means, standard deviations, variables ranges and bivariate correlations for the variables of the proposed model. Note that in some cases total labor hours invested in a work package were decreased as a result of scope changes. However, in our data it was the rare case, as most of the time scope changes resulted in additional labor, which sometimes accumulated to as high as several hundreds of percents of the allocated labor hours! Table 3. Descriptive Statistics and Pearson Correlation Matrix Variables Mean s.d. Min Max Tech. Uncertainty Project Priority ** 3 Unforeseen Stoppages * Stoppage Period ** 5 Materials ** Labor ** ** 7 Waiting in Line * -.21** ** *.01.28**.08*.11**.27** ** -.08*.31**.60**.41**.48**.28**.25** Note. n=714 (work packages) * p <.05 ** p <.01 4

5 Model Fit. Using structural equation modeling means of analysis, the hypothesized model of work package duration extensions is found significant: χ 2 = 32. 6, 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 the model 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 to have statistical significance. Note that two of the hypothesized relations are proved not to be significant: (1) additional material orders affect preemptive processing, but only indirectly, via the resulted additional labor; and (2) project priority affects duration extension only indirectly, via shorter waiting in line periods. In addition, out of the three correlations tested, only the one between project priority and technological uncertainty is found significant (r =0.22). Table 4. Direct Relations in the Work Packages s Model Hyp. From To Standardized coefficients t-values H1 Labor H2 Materials Waiting in Line H Stoppage Period Project Priority (*) Technological Uncertainty Labor Materials Labor Technological Uncertainty Materials Project Priority Waiting in Line Unforeseen Stoppages H4 Labor Materials (*) H5 Waiting in Line Unforeseen Stoppages Stoppage Period Note. n=714; NFI=0.98; NNFI=0.98; CFI=0.99; RMSEA= (*) path not significant Prediction. Using means of linear multiple regression, we derive a mathematical model for the prediction of work package duration based on its predetermined variables, performance variables, and disruptions like forced stoppages and scope changes (R 2 =0.71). Table 5 shows the predictors of the best-fitting model. Note that if we ignore the two scope changes variables additional material orders, and additional labor - we explain only 51% of the variance in the duration extension. Conducting a partial F-test we again confirm the hypothesis that the two variables representing scope changes are significant in the model. 5

6 Table 5. Predictors of Work Package Extentions Models: (1) Best Fitting (2) without Scope Changes Predictor Estimate St. Error Estimate St. Error Project Priority Technological Uncertainty Unforeseen Stoppages * Stoppage Period 1.159** ** Materials 2.466** Labor 0.363** Waiting in Line 0.552** ** ** ** Intercept ** ** R^ Adj. R^ Note. n=714. Partial F test for Model(2) = 328, p<0.001 * p <.05 ** p <.01 Hypothesis 6. To test Hypothesis 6, on the effects of internal and external scope changes on additional material orders, we regressed the additional material orders against the number of external 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 higher amount of material orders than do internal scope changes. Note, however, that the number of internal and external scope changes is almost the same. Table 6. Material Orders by Number of External and Internal Scope Changes Predictor Number Estimate St. Error Internal Scope Changes ** 0.02 External Scope Changes ** Intercept R^ Adj. R^ Note. n=714 (work packages) ** p <.01 Hypothesis 7. Figure 2 shows the number of prioritized and non-prioritized projects by the number of work packages with technological uncertainty. The data shows that the majority of the projects of five or more work packages with technological uncertainty are prioritized. To formally corroborate Hypothesis 7, we regressed project priority against the number of work packages with technological uncertainty in the project, using means of binomial regression. 6

7 Figure 2. Prioritized and Non-Prioritized Projects by Work Packages with Technological Uncertainty Regular Projects Prioritized Projects Projects Work Packages with Technological Uncertainty Table 7 presents the results of the binomial regression, which indicate that a project consists of five or more work packages with technological uncertainties is likely to get priority by top management. This might shed some new light on the way top management reduces technological uncertainties in projects. Table 7. Project Priority by Number of Work Packages with Technological Uncertainties Predictor Estimate St. Error Significance Technological Uncertainty Intercept LL Note. n=56 (projects). Implications and Conclusions Functional 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 be processed according to certain priority rules. The model developed here can help them to better assess 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 better performance of overall workload. 7

8 Project / Multi-Project Level. At the project level, we should distinguish between critical work packages that need to be tightly controlled, and non-critical work packages. At the multi-project level, top management has to differentiate between prioritized and non-prioritized projects, giving top priority to critical work packages belonging to prioritized projects. This will assure that the projects with the highest priority will be delivered on time. The proposed model assesses the effect of unforeseen disruptions on critical work packages of high-priority projects, and provides better tools for estimating final duration and compensating for delays. Summary The study contributes to an improved understanding of duration extensions and their causes, by quantifying scope changes, differentiating amongst the various types of scope changes, and constructing an integrated model of work package duration extensions. The study pointed to the greater effect of external scope changes, compared with internal ones, on material orders, and thus on the total duration extensions. Generally, the model can be implemented for forecasting work package duration extensions, and estimating the effect on the project s duration. Finally, several implications at the functional, the project, and the multi-project levels are suggested. Acknowledgments This work is based on my PhD dissertation, supervised by Prof S. Globerson. I wish to expresses my gratitude to him, and to all whose insights have contributed to this thesis. References Applegate, L.M., Austin, R.D., & McFarlan, F.W Corporate Information Strategy and Management: Text and Cases. NY: McGraw-Hill Irwin. Bagozzi, R.P., & Yi, Y On the evaluation of structural equation models. Journal of Academy of Marketing Science, 16(1): Bagozzi, R.P., & Yi, Y On the use of structural equation models in experimental designs. Journal of Marketing Research, 26(3): Barry, E.J., Mukhopadhyay, T., & Slaughter. S.A Software Project and Effort: An Empirical Study. Information Technology and Management, 3(1-2): Boehm, B.W Software engineering economics. Advances in computing science and technology. NJ: Prentice-Hall. Boehm, B.W., Horowitz, E., Madachy, R., Reifer, D., Clark, B.K., Steece, B., Brown, A.W., Chulani, S., & Abts, C Software Cost Estimation with COCOMO II. NJ: Prentice Hall. Buehler, R., Griffin, D., & Ross, M Exploring the "planning fallacy": Why people underestimate their task completion times. Journal of Personality and Social Psychology, 67: Burt, C.D.B., & Kemp, S Retrospective duration estimation of public events. Memory & Cognition, 19:

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