畢業(yè)設(shè)計論文 外文文獻翻譯 中英文對照 成本管理計劃支持系統(tǒng)——工程造價控制策劃和規(guī)劃的新范例

COMPASS-NEW PARADIGM FOR PROJECT COST CONTROL STRATEGY AND PLANNING By Makarand Hastak/ Associate Member, ASCE, Daniel W. Halpin,2 Member, ASCE, and Jorge Vanegas/ Associate Member, ASCE ABSTRACT: The need to remain competitive while generating profit requires management to develop innovative. cost management strategies that will allow them to distinguish and control early-on factors that might adversely. impact the cost of a project. This paper describes a decision support system, COMPASS (Cost Management Planning Support System) for project cost control strategy and planning. Throughout the life cycle of a project, COMPASS methodology assists management in evaluating the potential degree of cost escalation. It also identifies attributes such as management errors, regulatory approval, and error/rework, that might be the cause for project cost escalation. Furthermore, COMPASS assists management in formulating a cost control strategy while utilizing their experience and past project performance data. The attributes identified by the cost control strategy, if controlled, would minimize the expected loss.INTRODUCTION Project ost scalation and cost management are clearly two of the most important management concernsin the intensely competitive environment of the construction industry. Consequently, it s very important for management to detect at an early stage of a project the actual or potential cost overruns. To remain competitive while generating profit, management needs to identify and adopt in novative cost management strategies. These strategies should allow them to identify and control early on factors that might adversely impact the cost of a project. To date, various methodologies have been developed for project cost control such as earned value system management. exception reporting, and cost trend analysis. However, none of these methods considers at a macro level the influence of many important factors (or attributes) such as waste, project management practices, change orders, and error/rework on the project cost. Existing methods of cost control focus on identifying and controlling line items (cost components) that have already experienced a cost escalation. In other words, existing methods of cost control relate to symptoms rather than the cause. What is required, however, is a paradigm shift. A new method is needed that, in addition to recognizing he symptoms, identifies and focuses our attention on the attributes that are a potential cause for escalation in the line items for a given project. The new paradigm should have the capability to analyze a given project while incorporating the past project performance data and the experience of the project team. This analysis should identify and suggest control of attributes such as management errors, regulatory approval, and error/rework, which have a potential to instigate cost escalation in the line item estimate. Moreover, it is of importance to identify and control these attributes before they influence the project cost.Identification of attributes that might be responsible for project cost escalation is not sufficient in itself. What is equally important is to control the influence of the identified attributes on the project cost. This would require developing a projectcost control strategy to either eliminate or reduce the impact of identified attributes on the line items, thereby minimizing the expected loss.Existing methods of cost control do not assist management in developing a cost control strategy to minimize the impact of all such attributes on the project cost. The optimum strategy would identify and suggest control of a set of attributes to minimize the probable project cost escalation.To analyze and control the impact of these attributes on the project cost, it is important to collate the past project performance data available with the user firm. Furthermore, these data should be analyzed with respect to the new project characteristics by using an appropriate analytical medium. A computerized decision support system (DSS) would therefore be advantageous to assist the user in developing a suitable project cost control strategy. ATTRIBUTE VERSUS LINE ITEMS The tenn "attribute" (as used in the present paper) does not refer to the conventional tenn "line items." However, it pertains to the factors that might be responsible for generating cost escalation in the line items of a project. The difference is emphasized to delineate the point of departure for this research. In recent years, many researchers have addressed the issue of cost control by using techniques such as Monte Carlo simulation, management exception reporting, and probabilistic estimating. Nonetheless, their research fothe variance in line items. However, from the cost management perspective, it would be more beneficial to identify the cause of variance in the line items, which, when controlled, would minimize the overall project cost escalation.During the estimating process for a given project, we might assume a certain state for attributes such as management errors, regulatory approval, error/rework, worker morale, and crew balance. The underlying concept of this research is that during the course of the project the assumed state of these attributes might change due to one reason or another. The change in state or loss of equilibrium of an attribute might not only influence certain other attributes but also might influence the line items that were estimated based on the assumed state of the attribute. This, in turn, might cause a percentage escalation in the estimated project cost.An attribute is considered to be in the active state if, over the course of the project, the cost or status of an attribute differs from what was assigned to it at the estimating stage. For example, the labor productivity obtained during the course of the project might differ from what was assumed at the es timating stage. Similarly, at the estimating stage, a nonactive status might be assigned to the attribute, management, or project team. However, there is a possibility that during the course of the project the management or project team might make a decision error, influencing many other attributes. This would change the nonactive status of the attribute, management, or project team, to an active state. The probability and the resulting cost impact of these events cannot be neglected.Attribute state is defined by using a binary mode, where state = 1 implies that the attribute was in active state in that project, whereas state = 0 implies otherwise. The complex in terrelationship between the attributes suggests that even a minor change in the assumed equilibrium state of an attribute has the potential to trigger a domino effect. This effect could not only influence some other attributes but could also influence the project cost. Therefore, the binary mode of representation was considered to be most appropriate for this research, since any intennediate state between active and nonactive would not provide any additional infonnation.THE ATTRIBUTESFor the purpose of this research, attributes that have a potential to cause project cost escalation were identified . In the past, several authors have examined the impact of isolated attributes on project cost However, no project management tool is available to account for the collective impact of all possible attributes. The attributes were divided into two groups, quantifiable and nonquantifiable attributes. Attributes that have a cost value associated with them in the project estimate were defined as quantifiable attributes, e.g., total material cost, total labor cost, total equipment cost, project management cost, and total cost of the project at end of work. Attributes that do not have a cost value associated with them in the project estimate were defined as nonquantifiable attributes. The need to differentiate between quantifiable and nonquantifiable attributes is elaborated later under modeling assumptions. FIG. 1. Example Influence PatternRefers to the percentage cost escalation over the estimated project cost. To satisfy these requirements, a DSS such as COMPASS would be most suitable. MODELING ASSUMPTIONSThe interrelationships between attributes, the resulting influence pattern, and the impact of attributes on the project cost have been structured by defining the five following modeling assumptions:Assumption 1If an attribute, e.g., F (refer to Fig. 1) is influenced by a set of attributes, i.e., C and D, then the individual influence of the attributes in that set on F (i.e., the influence of C on F and the influence of D on F) is considered to be independent, i.e.p(F n C)I(F n D) =p(F n C) (Ia):. p(F n C) n (F n D) -;- p(F n D) =p(F n C) (I b):) p(F n C) n (F n D) = p(F n C) X p(F n D) (Ie)Assumption 2All nonquantifiable attributes are conditionally dependent on their preceding attributes, i.e., a nonquantifiable attribute can attain the active state only if at least one of its preceding attributes is in the active state; e.g., attribute F (refer to Fig. 1) can attain the active state (i.e., F = 1) only if at least one of its preceding attributes C or D is in the active state (i.e., C = 1 or D = 1).However, this constraint is not applicable for quantifiable attributes, i.e., X, Y, and Z (refer to Fig. I), because quantifiable attributes, apart from being influenced by their preceding attributes, are also directly related with certain line items (e.g., quantifiable attribute total material cost would be related with material cost associated with various other line items), some of which might be influenced by other active attributes that would define the state of that quantifiable attribute (e.g., total material cost) as activeAssumption 3Only the starting attributes, i.e., A and B (refer to Fig. 1), can be influenced by factors external to the system, whereas other attributes within the system can only be influenced by attributes preceding them in the influence pattern (refer to Fig. 1). The system represents all of the attributes included in the influence patternAssumption 4There is a probability that, although an attribute is in the active state, the attributes influenced by it might not get into the active state, i.e., C = 1 and D = 1 but F =0 (refer to Fig. 1) A corollary to assumption 4 would be that the active state probability of an attribute is a function of the independent influence of its preceding attributes, as defined in the influence pattern, e.g., p(F =1) =fp(C =1) n (F =1), p(D =1) n (F = I). It is important to note that the accuracy of the active state probability of attributes is contingent upon the interrelationships defined in the influence pattern by the user. For example, if attribute F were influenced by a third attribute (say, H) in addition to C and D (as defined in Fig. 1), then p(F = 1) =fp(C =1) n (F = 1), p(D =1) n (F =1), p(H =1) n (F = I). However, since only C and D have been defined as the attributes preceding F, p(F = 1) will only reflect the influence of C and D.Assumption 5If an attribute gets into the active state, it has an independent capacity to cause a certain percentage cost escalation (% CE) in the estimated project cost, i.e., if an attribute gets into the active state, it might influence the attributes following it, and also independently cause a % CE by influencing certain line items that were estimated based on an assumed state of the attribute. All the assumptions have been carefully considered to provide an ease in computation and modeling of the complex nature of the problem.The first assumption is necessary to create a situation that would provide ease in computing the active state probability of attributes and in modeling the interrelationship between the attributes.It might be argued that in the construction context, all the attributes are interrelated under one situation or another and are thus dependent. However, it is computationally tedious and unproductive to consider the labyrinth of relationships existing between the attributes. Thus, it is imperative to define a structured and computationally manageable approach, as defined in the assumption.The second and third assumptions are derived from (1) the definition of the system (defined earlier; refer to Fig. 1); (2) the interrelationships between attributes established in the influence pattern; and (3) the need to create a structured environment for computing the influence of attributes on each other and also on the project cost. The fourth assumption has been included to establish the fact that, although the attributes preceding a particular attribute might have attained the active state, there exists a probability that the attribute in question may not attain the active state, i.e., 1-p(CIA) 2: 0 (refer to Fig. 1).The fifth assumption was derived from the definition of the influence pattern and the active state of attributes; i.e., the influence pattern is a "shadow" network of attributes and these attributes are significant only when they attain the active state. This would imply that there has been a change in the status or value of the attribute from what was assumed at the estimating stage. This change in state of an attribute would thus directly influence the cost of certain line items that were estimated based on an assumed status or value of the attribute. These assumptions collectively provide a structured environment for modeling the complex interrelationship between the attributes and to make the DSS more responsive to the user.THE DSS COMPASSA DSS is defined as a computer-based system for decision support, with an ability to improve the effectiveness and productivity of the decision maker by utilizing the built-in analytical, situation modeling, and database management facilities (Ghiaseddin 1987).Accordingly, COMPASS was developed in three modules (refer to Fig. 2): (1) module I-to isolate pertinent information from past project performance data and to calibrate the data for a new project with respect to the project characteristics; (2) module 2-to determine the probable cost influence of attributes in a new project; and (3) module 3to develop a project cost control strategy to minimize the expected loss.FRAMEWORK OF COMPASSThe accuracy of a system depends to a large extent on the validity of the input data provided by the user. Therefore, it is important to properly analyze past project performance data before the data are used in identifying the potential risk attributes and in developing a project cost control strategy for a new project. The DPM was developed to assist the user in this aspect and to isolate the necessary information from the available past project performance data. DPM However, since every construction project is unique, the historical data cannot be used in analyzing a new project without giving proper consideration to the new project characteristics. The GDM was developed to take into account this important aspect and to calibrate the past project performance data (as analyzed in the DPM) before the data are used in analyzing a new project. The calibration is performed by soliciting subjective input from the team members with respect to the unique characteristics of the new project (refer to Fig. 2).The PWPCE model assists the user in calculating the probability of an attribute influencing the cost of a project and also the percentage cost escalation (with respect to the estimated project cost) due to that influence. This model utilizes the input provided by the DPM and the GDM to calculate the expected percentage cost escalation in a new project and also the individual cost influence of attributes in that project.The output of the PWPCE model (i.e., the individual cost influence of attributes and their probability of influencing the project cost) is then utilized by the DAM to formulate a cost control strategy for the new project.The computerization of the COMPASS methodology has eliminated the need for the user to follow the flow of information within the modules. To apply the COMPASS methodology, the user interaction with the system is limited to the decision making points, while the data analysis and computations are performed by the system. The user interaction with the computerized system is required at the following instances: (1) relevant data extraction from the past project performance data (to be used in the DPM); (2) team member input for group decision (in the GDM); and (3) user input to establish threshold PWPCE value to isolate potential risk attributes by using the DAM and for developing a project cost control strategy.Several logical checks have been provided throughout the system to assist the user with data entry and analysis.MODULE 1The objective of module 1 is to extract information regarding the conditional relationship attributes and their relative cost influence. This information is calibrated for use in a new project, with respect to the subjective input provided by the team members regarding the new project characteristics.Module 1 is comprised of two models (refer to Fig. 2), the DPM and the GDM.Data Processing Model (DPM)The DPM has two stages (refer to Fig. 3). The objective of stage 1 of the DPM is to analyze the past project performance data provided by the user. This analysis establishes the conditional probability of an attribute attaining active state, given that the attributes preceding it in the influence pattern have attained the active state. The conditional probabilities calculated in this model are calibrated in the GDM. The calibrated conditional probabilities are later used by the PWPCE model in module 2 to compute the active state probability of attributes in a new project. The individual cost influence of attributes in each historical project is computed in stage 2 of the DPM (refer to Fig. 3).The process starts by isolating the necessary information from a number of past projects (say, n). Two criteria are recommended for selecting past projects; they should have similar scope of work; and (2) they should have faced a cost escalation. For each historical project, the user subjectively identifies the state of attributes by using a binary mode, as explained earlier under the modeling concepts (refer to part B of Fig. 3). This information about the state of attributes in historical projects is processed by the DPM to determineThe conditional probabilities, e.g., p(C =llA =1), p(E = 11 C = 1), and so forth refer to part A of Fig. 3 and (2) and (3)The individual cost influence of attributes (refer to part C of Fig. 3)The conditional probabilities are further calibrated in the GDM. This calibration is done with respect to the new project characteristics. The calibrated conditional probabilities and the individual cost influence of attributes are used as an input for the PWPCE model in module 2 to analyze a new project.p(C =llA =1) =p(C =1) n (A =1) -i- p(A =1) (2)p(C =llA =1) =2: (C =1) and (A =1)/ -i- 2: (A =1)/w