畢業(yè)設(shè)計(jì)外文翻譯—成本管理計(jì)劃支持系統(tǒng)——工程造價(jià)控制策劃和規(guī)劃的新范例

1、<p>　　COMPASS-NEW PARADIGM FOR PROJECT COST CONTROL STRATEGY AND PLANNING </p><p>　　By Makarand Hastak/ Associate Member, ASCE, Daniel W. Halpin,2 Member, ASCE, and Jorge Vanegas/ Associate Memb

2、er, ASCE</p><p>　　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-

3、on factors that might adversely. impact the cost of a project. </p><p>　　This paper describes a decision support system, COMPASS (Cost Management Planning Support System) for project cost control strategy a

4、nd 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 ap

5、proval, and error/rework, that might be the cause for project cost escalation. Furthermore, COMPASS assists management in formulating a cost co</p><p>　　INTRODUCTION </p><p>　　Project ost sc

6、alation 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

7、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 strategi

8、es. These strategies should allow them to</p><p>　　To date, various methodologies have been developed for project cost control such as earned value system management. exception reporting, and

9、 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, an

10、d error/rework on the project cost. Existing methods of cost control focus on identifying and controlling line items (co</p><p>　　What is required, however, is a paradigm shift. A new method is

11、 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 sh

12、ould 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</p&g

13、t;<p>　　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 attrib

14、utes on the project cost. This would require developing a project</p><p>　　cost control strategy to either eliminate or reduce the impact of identified attributes on the line items, ther

15、eby 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

16、strategy would identify and suggest control of a set of attributes to minimize the probable project cost escalation.</p><p>　　To analyze and control the impact of these attributes on the project cost, i

17、t 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 approp

18、riate analytical medium. A computerized decision support system (DSS) would therefore be advantageous to assist the user in developing a suitable project cost control strategy.</p><p>　　ATTRIBUTE

19、 VERSUS LINE ITEMS </p><p>　　The tenn "attribute" (as used in the present paper) does not refer to the conventional tenn "line items." However, it pertains to the fact

20、ors 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

21、researchers have addressed the issue of cost control by using techniques such as Monte Carlo simulation, mana</p><p>　　During the estimating process for a given project, we might assume a cer

22、tain 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

23、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 attribut</p>&l

24、t;p>　　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 estim

25、ating 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 st

26、atus might be assigned to the attribute, management, or project team</p><p>　　Attribute state is defined by using a binary mode, where state = 1 implies that the attribute was in active st

27、ate 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 th

28、e potential to trigger a domino effect. This effect could not only influence some other attributes but could also influence the project </p><p>　　THE ATTRIBUTES</p><p>　　For the

29、 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

30、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 attrib

31、utes. Attributes that have a cost value associated </p><p>　　FIG. 1. Example Influence Pattern</p><p>　　Refers to the percentage cost escalation over the estimated project cost. To satisfy

32、 these requirements, a DSS such as COMPASS would be most suitable. </p><p>　　MODELING ASSUMPTIONS</p><p>　　The interrelationships between attributes, the resulting influence pattern, and the imp

33、act of attributes on the project cost have been structured by defining the five following modeling assumptions:</p><p>　　Assumption 1</p><p>　　If an attribute, e.g., F (refer to Fig. 1) is influ

34、enced 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>&

35、lt;p>　　p[(F n C)I(F n D)] =p(F n C) (Ia)</p><p>　　:. p[(F n C) n (F n D)] -;- p(F n D) =p(F n C) (I b)</p><p>　　::::) p[(F n C) n (F n D)] = p(F n C) X p(F n D) (Ie)</p><p>　　Ass

36、umption 2</p><p>　　All 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 precedin

37、g 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).<

38、;/p><p>　　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,

39、 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 ot

40、her active attributes that would define the state of that quantifiable attribute</p><p>　　Assumption 3</p><p>　　Only the starting attributes, i.e., A and B (refer to Fig. 1), can be influenced b

41、y 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 inc

42、luded in the influence pattern</p><p>　　Assumption 4</p><p>　　There is a probability that, although an attribute is in the active state, the attributes influenced by it might not get into the ac

43、tive 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,

44、as defined in the influence pattern, e.g., p(F =1) =f{p[(C =1) n (F =1)], p[(D =1) n (F = I)]}. It is important to note that the accuracy of the act</p><p>　　Assumption 5</p><p>　　If an attribut

45、e 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 at

46、tributes following it, and also independently cause a % CE by influencing certain line items that were estimated based on an assumed state of the attribute. </p><p>　　All the assumptions have been carefully

47、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

48、attributes and in modeling the interrelationship between the attributes.</p><p>　　It might be argued that in the construction context, all the attributes are interrelated under one situation or another and a

49、re 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 man

50、ageable approach, as defined in the assumption.</p><p>　　The second and third assumptions are derived from</p><p>　　(1) the definition of the system (defined earlier; refer to Fig. 1);</p>

51、<p>　　(2) the interrelationships between attributes established in the influence pattern; </p><p>　　and (3) the need to create a structured environment for computing the influence of attributes on eac

52、h other and also on the project cost. </p><p>　　The fourth assumption has been included to establish the fact that, although the attributes preceding a particular attribute might have attained the active sta

53、te, 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).</p><p>　　The fifth assumption was derived from the definition of the in

54、fluence 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

55、that there has been a change in the status or value of the attribute from what was assumed at the estimating stage. </p><p>　　This change in state of an attribute would thus directly influence the cost of ce

56、rtain 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

57、 and to make the DSS more responsive to the user.</p><p>　　THE DSS COMPASS</p><p>　　A DSS is defined as a computer-based system for decision support, with an ability to improve the effectiveness

58、 and productivity of the decision maker by utilizing the built-in analytical, situation modeling, and database management facilities (Ghiaseddin 1987).</p><p>　　Accordingly, COMPASS was developed in three mo

59、dules (refer to Fig. 2):</p><p>　　(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; <

60、;/p><p>　　(2) module 2-to determine the probable cost influence of attributes in a new project; </p><p>　　and (3) module 3to develop a project cost control strategy to minimize the expected loss.&l

61、t;/p><p>　　FRAMEWORK OF COMPASS</p><p>　　The 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 pa

62、st 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 aspe

63、ct and to isolate the necessary information from the available past project performance data. </p><p>　　DPM However, since every construction project is unique, the historical data cannot be used in analyzin

64、g a new project without giving proper consideration to the new project characteristics. </p><p>　　The GDM was developed to take into account this important aspect and to calibrate the past project performanc

65、e 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

66、 project (refer to Fig. 2).</p><p>　　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

67、 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 influen

68、ce of attributes in that project.The output of the PWPCE model (i.e., the individual cost influence of attribu</p><p>　　The computerization of the COMPASS methodology has eliminated the need for the user to

69、follow the flow of information within the modules. </p><p>　　To apply the COMPASS methodology, the user interaction with the system is limited to the decision making points, while the data analysis and compu

70、tations are performed by the system. </p><p>　　The user interaction with the computerized system is required at the following instances:</p><p>　　(1) relevant data extraction from the past proje

71、ct performance data (to be used in the DPM); </p><p>　　(2) team member input for group decision (in the GDM); </p><p>　　and (3) user input to establish threshold PWPCE value to isolate potential

72、 risk attributes by using the DAM and for developing a project cost control strategy.</p><p>　　Several logical checks have been provided throughout the system to assist the user with data entry and analysis.

73、</p><p><b>　　MODULE 1</b></p><p>　　The objective of module 1 is to extract information regarding the conditional relationship attributes and their relative cost influence. </p&g

74、t;<p>　　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.</p><p>　　Module 1 is compr

75、ised of two models (refer to Fig. 2), the DPM and the GDM.</p><p>　　Data Processing Model (DPM)</p><p>　　The DPM has two stages (refer to Fig. 3). </p><p>　　The objective of stage 1

76、 of the DPM is to analyze the past project performance data provided by the user. </p><p>　　This analysis establishes the conditional probability of an attribute attaining active state, given that the attrib

77、utes preceding it in the influence pattern have attained the active state. </p><p>　　The conditional probabilities calculated in this model are calibrated in the GDM. </p><p>　　The calibrated co

78、nditional probabilities are later used by the PWPCE model in module 2 to compute the active state probability of attributes in a new project. </p><p>　　The individual cost influence of attributes in each his

79、torical 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). </p><p>　　Two criteria are recommended f

80、or selecting past projects; </p><p>　　they should have similar scope of work; </p><p>　　and (2) they should have faced a cost escalation. </p><p>　　For each historical project, the

81、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). </p><p>　　This information about the state of attrib

82、utes in historical projects is processed by the DPM to determine</p><p>　　The 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)]</p>

83、;<p>　　The individual cost influence of attributes (refer to part C of Fig. 3)</p><p>　　The conditional probabilities are further calibrated in the GDM. </p><p>　　This calibration is done

84、 with respect to the new project characteristics. </p><p>　　The calibrated conditional probabilities and the individual cost influence of attributes are used as an input for the PWPCE model in module 2 to an

85、alyze a new project.</p><p>　　p(C =llA =1) =p[(C =1) n (A =1)] -i- p(A =1) (2)</p><p>　　p(C =llA =1) =2: [(C =1) and (A =1)]/ -i- 2: (A =1)/</p><p>　　where j = 1 ... n (n = number o

86、f past projects selected) (3)</p><p>　　In the second stage of the DPM, a significant level of escalation is defined for each historical project (Le., a level of escalation that was accounted for in the conti

87、ngency fund for the project). </p><p>　　The critical line items and their associated escalation values are identified by considering the significant level of escalation thus defined (refer to part C of Fig.

88、3). Critical line items are those line items that had faced a cost escalation greater than the significant level of escalation defined for that project. Each critical line item is associated with a quantifiable attribute

89、. </p><p>　　For example, (refer to part C of Fig. 3), critical line item number 1 is associated with quantifiable attribute X (say, total labor cost) and critical line item number 2 is associatedwith quantif

90、iable attribute Y (say, total material cost).</p><p>　　After establishing the association between the critical line items and the quantifiable attributes, the user analyzes each critical line item with respe

91、ct to the list of attribute relationships.This analysis is conducted to subjectively identify attributes (or attribute relationships) that could have influenced that particular line item, thus making it critical. Again a

92、 binary mode (1 =yes, and 0 =no) is used to define the subjective relationships. </p><p>　　For instance, (refer to part C of Fig. 3), critical line item number 1 could have been influenced by attribute A and

93、 attribute relationship D IA (i.e., the active state of attribute D due to the influence of its preceding attribute A). </p><p>　　As an example, consider the influence of a remote site location (attribute A)

94、 on availability of resources (attribute D) and their collective impact on the labor cost (attribute X) of a critical line item j (say, structural steel erection).</p><p>　　Similar analysis is done for each

95、of the n historical projects by the user or the person(s) knowledgeable about the selected projects. </p><p>　　The DPM computes the individual cost influence of attributes per historical project. It utilizes

96、 the analysis done by the user and the data with respect to the state of attributes obtained in stage 1 (refer to part B of Fig. 3). </p><p>　　This information is later used by the PWPCE model of module 2 to

97、 determine the relative cost influence of attributes and their cost influence in a new project. </p><p>　　The individual cost influence of attributes in a particular project is computed as shown for line ite

98、m number 1 in (4)-(11) (refer to part C of Fig. 3).</p><p>　　Line Item Number 1 Influenced by Attributes A and DIA (Refer to Part C of Fig. 3)</p><p>　　p(X = 11A = 1) = p[(X = 1) n (A = 1)] -;-

99、p(A = 1) (4)</p><p>　　p(X = 11A = 1, D = 1) = p[(X = 1) n (A = 1) n (D = 1)]</p><p>　　-;- p[(A =1) n (D =1)] (5)</p><p>　　cost influence (CI) of attribute A on line itemj = CI(A)j (