project duration extensions, and second to analyze the effect of scope changes and ... engineering service environment, and uses means of structural equation ...
The Effect of Scope Changes on Project Duration Extensions Extended Abstract of a PhD Dissertation1 Moshe Ayal Faculty of Management, Tel Aviv University, Tel Aviv 69978
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. Duration 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. Duration Extensions 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
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL
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) Additional Materials, which refers to inventory orders, and (2) Additional Labor, both resulting from scope changes, and marked inside 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 Model
Technological Uncertainty
(H6)
(H7)
Additional Materials Additional Labor
Project Priority
H1
Waiting in Line
Duration Extension
H5 H4
Unforeseen Stoppages
H2
H3
Preemptive Processing Stoppage Period
Table 1 summarizes the proposed model variables and the rationale for their selection.
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EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL
Table 1. Proposed Model Variables Variable
Description
Rationale
Technological Uncertainty
Level of technological uncertainty associated with a certain work package
Technological uncertainty is correlated with duration (Shenhar, 2001)
Project Priority
Priorities assigned to projects by top management team
May decrease duration extensions by reducing waiting periods
Unforeseen Stoppages
Number of unforeseen operational stoppages caused by internal or external sources
A greater number of operational stoppages may increase in-process duration
Stoppage Period
Total time periods of operational stoppages caused by internal or external sources
Longer periods of operational stoppages may increase in-process duration
Additional Materials
Number of material orders resulting from scope changes
Waiting until materials arrival, if filing for an external supply, may increase the duration
Additional Labor
Additional labor resulting from scope changes
Scope changes could affect duration (Chick, 1999)
Waiting in Line
Time from the arrival of a work package to the beginning of its processing
Waiting periods may extend delivery times (Levy and Globerson, 1997)
Preemptive Processing
Number of breaks during processing a work package
Task splitting increases in-process duration (Goldratt, 1997)
Duration Extension
Work package in-process duration extension relative to planned duration
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
Additional Labor
+
Duration Extension
H2
Additional Materials
+
Duration Extension
In-process duration extensions may result from having to wait until materials arrive
H3
Preemptive Processing
+
Duration Extension
Goldratt's (1997) claim that splitting a task results in extending its inprocess duration
H4
Additional Labor
+
Preemptive Processing
Work package manager needs to wait for available resources and/or materials to arrive
H5
Waiting in Line
-
Preemptive Processing
The negative effect of waiting periods can be decreased by working intensively and continuously
External Scope Changes
++
Internal Scope Changes
+
Additional Materials
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
Technological Uncertainty
+
Project Priority
Allocating priority to a project may help in rapidly mitigating the uncertainties in its work packages
H6
H7
Additional labor calls for resources, which in many cases are not currently available
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EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL
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
1
2
1
Tech. Uncertainty
0.29
0.46
0
1
2
Project Priority
0.37
0.48
0
1
3
Unforeseen Stoppages
0.04
0.10
0
0.5
-.08*
-.02
4
Stoppage Period
0.10
0.36
0
6.5
-.05
-.05
3
4
5
6
7
8
.22** .53**
5
Additional Materials
0.02
0.06
0
0.5
.18**
.04
.01
-.01
6
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**
-.05
9 Duration Extension 0.31 Note . n=714 (work packages) * p < .05 ** p < .01
0.68
-0.66
7
.11**
-.08*
.31**
.60**
.41**
.48**
.28**
4
.25**
EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL
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 Duration Extensions Model
Hyp.
From
H1 Additional Labor
Standardized coefficients .34
To Duration Extension
t-values 13.49
H2 Additional Materials
Duration Extension
.21
8.80
Waiting in Line
Duration Extension
.23
10.92
Duration Extension
.10
4.81
Stoppage Period
Duration Extension
.61
29.83
Project Priority
Duration Extension
-.03
-1.62(*)
Technological Uncertainty Additional Labor
.20
6.38
Additional Materials
.51
16.36
.18
4.82
-.21
-5.74
H3 Preemptive Processing
Additional Labor
Technological Uncertainty Additional Materials Project Priority
Waiting in Line
Unforeseen Stoppages
Preemptive Processing
.23
8.44
Preemptive Processing
.32
7.76
Preemptive Processing
-.06
-1.46(*)
Preemptive Processing
-.08
-2.31
.53
16.62
H4 Additional Labor Additional Materials H5 Waiting in Line Unforeseen Stoppages
Stoppage Period
Note. n=714; NFI=0.98; NNFI=0.98; CFI=0.99; RMSEA=0.034. (*) path not significant
Duration 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 (R2 =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.
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EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL
Table 5. Predictors of Work Package Duration Extentions
(1) Best Fitting
Models: Predictor
Estimate
(2) without Scope Changes
St. Error
Estimate
St. Error
Project Priority
-0.039
0.039
Technological Uncertainty
0.218
0.041
Unforeseen Stoppages
-0.470*
0.214
1.188**
0.059
Stoppage Period
1.159**
0.039
Additional Materials
2.466**
0.282
Additional Labor
0.363**
0.027
Waiting in Line
0.552**
0.049
0.686**
0.063
Preemptive Processing
0.519**
0.107
1.133**
0.139
Intercept
-0.171**
0.026
-0.212**
0.038
R^2
0.707
0.513
Adj. R^2
0.705
0.509
Note . n=714. Partial F test for Model(2) = 328, p
