Fair and Reasonable Markup (FaRM) Pricing Model

Farid, Foad; Boyer, LeRoy T.

doi:10.1061/(asce)0733-9364(1985)111:4(374)

Cited by 14 publications

(3 citation statements)

References 2 publications

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“…Nevertheless, mark-up has been extensively researched, and several approaches are adapted to provide the mark-up factor. These include • Artificial intelligence (Moselhi et al, 1991) • Number of bidders • Differences between contractors' average bids • Success rate sensitivity to changes in mark-up • Friedman's probabilistic model (Ioannou, 1988) • FARM (Fair and Reasonable Mark-up) model (Farid and Boyer, 1985) • Decision support system models There are several models for applying a mark-up factor and each company may also develop their own model. If the reader wishes to find out more about any of the models above, they can refer to the references for insight.…”

Section: Mark-upmentioning

confidence: 99%

A Decision Support System Specification for Cost Escalation in Heavy Engineering Industry

Dawood

Bates

2002

Computer aided Civil Eng

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The heavy civil engineering industry (railways, sewage-treatment, chemical and pharmaceutical facilities, oil and gas facilities, etc.) is one of the major contributors to the British economy and generally involves a high level of investment. Clients in this industry are demanding accurate cost estimates, proper analysis of out-turn cost and cost escalation, and a high quality risk analysis throughout the construction processes. Current practices in the industry have suggested that there is a lack of structured methodologies and systematic cost escalation approaches to achieve an appropriate cost analysis at the outset of projects and throughout the construction processes. In this context the prime objective of this research work is to develop a structured cost escalation methodology for improving estimating management and control in the heavy engineering industry construction processes. The methodology is composed of a forecasting model to predict cost indices of major items in industry and a risk knowledgebase model for identifying and quantifying causes of cost escalations. This paper reviews and discusses a knowledgebased model for applying a cost escalation factor. The cost escalation factor is made up of market variation, a risk element, and a component for bias. A knowledge elicitation strategy was employed to obtain the required knowledge for the model. The strategy included questionnaires, interviews, and workshops, and deliverables came in the form of influences and their effect on project cost escalation. From these deliverables, a decision support system and specifications for applying cost escalation to base estimates are presented.

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Section: Mark-upmentioning

confidence: 99%

A Decision Support System Specification for Cost Escalation in Heavy Engineering Industry

Dawood

Bates

2002

Computer aided Civil Eng

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“…Although project scheduling models with monetary objectives have received increasing attention, studies on the resource-constrained multi-project scheduling problem with discounted cash ows (RCMPSP-DCF) have rarely been conducted in the past. In addition, Elmaghraby and Herroelen (1990) and Faird and Boyer (1985) pointed out that project cash ows are closely related to project bidding contracts. However, the problem of incorporating the relevant contract contents into the project NPV model has seldom been investigated.…”

Section: Introductionmentioning

confidence: 99%

An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows

Chiu¹,

Tsai²

2002

Construction Management and Economics

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The main purposes of this study are to incorporate both the project delay penalty and early completion bonus into the objective function of the resource-constrained multi-project scheduling problem with discounted cash flows (RCMPSP-DCF) and to develop an efficient heuristic search scheduling rule. The effectiveness of the proposed heuristic rule is evaluated by comparing it with the optimal solution obtained by the optimal model for 42 small-scale problems. The result indicates that the solution obtained using the proposed heuristic rule is very close to the optimal solution, and that the proposed heuristic rule provides significant savings in computation time. Moreover, the proposed heuristic rule is also compared with four existing heuristic rules based on an experiment involving the single-project and multi-project scheduling problems. The results indicate that the proposed heuristic rule is superior to the four existing rules under the performance criteria of the average total project net present value and the average total project delay. In addition, the number of times that the proposed heuristic rule can obtain the best solution is also far more than that of any other rule considered in this paper under the two aforementioned performance criteria. Furthermore, this study has found that the performance of a heuristic scheduling rule is significantly affected by the number of projects. A rule that can lower the total project delay to the minimum is also able to maxi5 mize the total project net present value.Multi-PROJECT Scheduling, Project Management, Resource Constraints, Net Present Value, Heuristic Rules,

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“… Artificial intelligence; Number of bidders; Differences between contractors’ average bids; Success rates sensitivity to changes in mark‐up; Friedman's probabilistic model (Ioannou 1988); FARM (Fair and Reasonable Mark‐up) model (Farid & Boyer 1985); and Decision support system models. …”

Section: Introductionmentioning

confidence: 99%

A decision support system specification for out‐turn cost and cost escalation in the heavy engineering industry

Dawood

Bates

2000

Engineering, Construction and Architectural Management

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The heavy civil engineering industry (railways, sewage treatment, chemical and pharmaceutical facilities, oil and gas facilities, etc.) is one of the major contributors to the British economy and generally involves a high level of investment. Clients in this industry are demanding accurate cost estimate, proper analysis of out‐turn cost and cost escalation and a high quality risk analysis throughout the construction processes. Current practice in the industry has suggested that there is a lack of structured methodologies and systematic cost escalation approach to achieve an appropriate cost analysis at the outset of projects and throughout the construction processes. In this context the prime objective of this research work is to develop a structured cost escalation methodology for improving estimating management and control in the heavy engineering industry construction processes. The methodology is composed of a forecasting model to predict cost indices of major items in industry and a risk knowledge base model for identifying and quantifying causes of cost escalations. This paper, as part of the research, reviews and discusses a knowledge‐based model for applying a cost escalation factor. The cost escalation factor is made up of market variation, a risk element and a component for bias. A knowledge elicitation strategy was employed to obtain the required knowledge for the model. The strategy included questionnaires, interviews and workshops and deliverables came in the form of influences and their effect on project cost escalation. From these deliverables, the concepts of a decision support model and system specification for applying cost escalation to base estimates is proposed.

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Fair and Reasonable Markup (FaRM) Pricing Model

Cited by 14 publications

References 2 publications

A Decision Support System Specification for Cost Escalation in Heavy Engineering Industry

A Decision Support System Specification for Cost Escalation in Heavy Engineering Industry

An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows

A decision support system specification for out‐turn cost and cost escalation in the heavy engineering industry

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