Applying fuzzy techniques to cash flow analysis

Construction managers are interested in the direction of movement of cash flow at valuation periods rather than its forecast value, and fuzzy set theory applied to decision making might help in this process. Fuzzy models are particularly suited to making decisions involving new technologies where uncertainties inherent in the situation are complex. The problem of healthy cash flow at valuation periods relates to the proper estimation of cash in and out flows and project progress. The paper presents an alternative approach to cash flow analysis for construction projects. This project is based on the assumption that cash flow at particular valuation stages of a project is ambiguous. The paper discusses the weaknesses of existing methods for cash flow and establishes the need for an alternative approach. Using an example of 30 cash flow curves, the advantage of fuzzy cash flow analysis is demonstrated. Results of the analysis are presented and discussed. The model can be used to analyse the cash flow curve of projects at any progress period to make sure it is reasonable.     

Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines

The ability of project managers to make reliable cash flow predictions enhances project cost flow control and management. Reliable cash flow prediction over the course of a construction project puts the project manager in a better position to identify potential problems and develop appropriate strategies to mitigate the negative effects of such on overall project success. Therefore, managers should monitor project progress using cash flow data, which has unique characteristics, as time series data. However, the complex, mutable nature of construction projects currently requires significant reliance on experience and expert opinions to predict cash flow on an ongoing basis. Recent studies have indicated good potential for using artificial intelligence to reduce reliance on human input in cash flow prediction processes. The Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIM_T), an artificial intelligence hybrid system focusing on the management of time series data characteristics which fuses fuzzy logic (FL), weighted support vector machines (weighted SVMs) and a fast messy genetic algorithm (fmGA), represents a promising alternative approach to predicting cash flow. Simulations performed on historical cash flow data demonstrate the EFSIM_T is an effective tool for predicting cash flow.     

Modelling financial decisions in construction firms

Some contractors predict their corporate cash flow on the basis of individual contracts without considering the relationships between the overall before-tax profit, risks, other crucial qualitative factors, or the allocation of resources within the company. Moreover, some contractors, in predicting their cash flow, focus only on the early-start progress in the project and their predictions of progress are too pessimistic, or result in the overuse of resource in order to make up for delays. In the present research a decision model is established for a contracting firm. It provides a methodical system for construction financial decision-making, and a way of solving a financial decision problem under qualitative and fuzzy circumstances. The model can be applied to the management of corporate cash flow, thereby facilitating the minimal use of resources. The information provided by the model allows the planner to eliminate excess use or idleness of resources during the scheduling of a project. Financial forecasting may also suggest the best time to invest in a new project. Four projects for a medium size construction firm in Hong Kong were employed as case studies in order to evaluate the mathematical model. The cases involve two objectives: maximize profit margin and minimize construction risk (consider in a qualitative factor). The model leads to a compromise optimal schedule that provides the contracting firm with the optimal schedule for achieving optimal profit and construction risk by making optimal use of the contractor's resources.     

A construction project cash flow model—an idiographic approach

The paper introduces and supports the contention that an idiographic methodology is appropriate to the post- hoc study and interpretation of individual construction project cash flows. A cash flow model based on the logit transformation is proposed to be consistent with this methodology. The model is based on historical data, and yields two parameters to describe each individual project. The model is tested using two samples totalling 72 projects. Goodness of fit for the model, using a measure of standard deviation from 1.0% to 4.6%, with a median of 2.5%, is found for individual projects.

The experimental hypothesis (that there is substantial variation between projects) is supported by the graphical and statistical evidence of deviation, which is argued to be the result of the individual ontology of each project - systematic error - rather than random error from an ideal. The paper concludes that forecasts of individual cash flows are invalid when derived from analysis of grouped data.     

An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making

Most real world decision-making combines quantitative and qualitative (linguistic) variables. Conventional mathematics that combines qualitative and quantitative concepts exhibits difficulty in modelling actual problems. The research presented in this paper illustrates a mathematical approach to the solution of decision-making problems that combine qualitative and quantitative objectives. A methodical system for construction project management decision-making was developed using a combination of fuzzy multiple-objective decision-making theory and the fuzzy reasoning technique. The mathematical model can be applied to construction project management problems by suggesting an optimal path of corporate cash flow that results in the minimum use of resources. The information provided by the mathematical model allows the planner to eliminate excess use, or idleness, of resources during the construction of a project. Such information is indispensable for decision-makers in analysing the best time to invest in a new project. A case study is demonstrated to illustrate the application to a management decision problem.     

A new approach for project scheduling using fuzzy dependency structure matrix

As an alternative solution, the Dependency Structure Matrix (DSM) is a useful tool in project scheduling when approaching information dependency issues between activities. However, the current DSM approach faces the dilemma that the overlap of activities cannot be precisely estimated in the planning stage of a project, and the solution calls for a robust methodology for managing schedules within uncertain conditions of information dependency. The aim of our research is to propose an approach that utilizes fuzzy set theory to solve the problem within an uncertain environment. As an extension of traditional DSM-based scheduling, we describe the overlap and duration of activities as fuzzy numbers and put forth a systematic algorithm to calculate the time variables of activities and project duration thereof. An example is also provided to demonstrate the effectiveness of the algorithm.     

Artificial intelligence approaches to achieve strategic control over project cash flows

The ability over the course of a construction project to make reliable predictions regarding cash flows enhances project cost management. This paper uses artificial intelligence (AI) approaches to predict cash flow trends for such projects in order to develop appropriate strategies that apply factors such as float, process execution time, construction rate and resource demand to project cash flow control. AI approaches involved in this paper include K-means clustering, genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). K-means clustering is employed to categorize similar projects, while the other approaches are used to develop the Evolutionary Fuzzy Neural Inference Model (EFNIM), a knowledge learning model. FL and NN are employed in the EFNIM to develop a neural-fuzzy model that can deal with uncertainties and knowledge mapping. GA is used to optimize the membership functions of FL and NN parameters globally. The major target of this AI learning is to address sequential cash flow trends. This trained result is furthermore applied to a strategic project cash flow control. This cash flow control affects project performance within the banana envelope of the S-curve for project management.     

A Decision Support Model for Construction Cash Flow Management

Abstract: The excessive level of construction business failures and their association with financial difficulties has placed financial management in the forefront of many business imperatives. This has highlighted the importance of cash flow forecasting and management that has given rise to the development of several forecasting models. The traditional approach to the use of project financial models has been largely a project‐oriented perspective. However, the dominating role of “project economics” in shaping “corporate economics” tends to place the corporate strategy at the mercy of the projects. This article approaches the concept of cash flow forecasting and management from a fresh perspective. Here, the use of forecasting models is extended beyond their traditional role as a guideline for monitoring and control of progress. They are regarded as tools for driving the project in the direction of corporate goals. The work is based on the premise that the main parties could negotiate the terms and attempt to complement their priorities. As part of this approach, a model is proposed for forecasting and management of project cash flow. The mathematical component of the model integrates three modules: an exponential and two fourth‐degree polynomials. The model generates a forecast by potentially combining the outcome of data analysis with the experience and knowledge of the forecaster/organization. In light of corporate objectives, the generated forecast is then manipulated and replaced by a range of favorable but realistic cash flow profiles. Finally, through a negotiation with other parties, a compromised favorable cash flow is achieved. This article will describe the novel way the model is used as a decision support tool. Although the structure of the model and its mathematical components are described in detail, the data processing and analysis parts are briefly described and referenced accordingly. The viability of the model and the approach are demonstrated by means of a scenario.     

Impact of contractor's optimized financing cost on project bid price

The financing cost depends on the incoming and outgoing cash flow throughout the project, and can differ greatly from project to project. This study proposes a model that calculates the expected financing cost based on the cash flow forecast. This approach is more realistic than assuming an approximate percentage of the total cost. The proposed model calculates the bid price using an optimized financing cost that is obtained by selecting an optimum combination of available financing alternatives offered by different lenders. The proposed model minimizes financing cost, reduces the bid price, enhances the competitiveness of the bidder, increases the contractor`s negotiating power with a lender by providing an optimum financing schedule, and eliminates the risk of financing surprises during construction. This study investigates the impact of different financing considerations on bid price in three cases to prove the effectiveness of the proposed model.     

A Real Option based Six Sigma project evaluation and selection model

Identification and selection of Six Sigma projects are one of the most frequently discussed issues in the Six Sigma literatures today. In this paper a two-stage methodology has been proposed based on (i) Real Option Analysis for evaluating the value of the project to improve the managerial flexibility (ii) a zero–one integer linear programming model for selecting and scheduling an optimal project portfolio, based on the organization's objectives and constraints. The methodology is illustrated through a case study from petrochemical industry carried out during 2007. The study contributes to managerial practices by identifying a new way of valuing the Six Sigma projects through Real Option Analysis by considering various kinds of risks. Resource-constrained environment has been chosen to test the proposed approach of selection of project portfolio and the model is validated with a detailed discussion.     

Finance‐based CPM/LOB scheduling of projects with repetitive non‐serial activities

Projects of repetitive non‐serial activities constitute a major category of construction projects which can be scheduled more conveniently using the line of balance (LOB) technique. Generally, scheduling activities such that the expenditures are always in balance with the available cash is a must to devise financially feasible schedules. The objective is to integrate a CPM/LOB model for a project of repetitive non‐serial activities with a cash flow model and utilize the integrated model to devise financially feasible schedules. The genetic algorithms (GAs) technique is employed to maximize the profit at the end of the project under the constraints of available cash. The optimization of the integrated models was demonstrated using an example project of 15 activities carried out at five units. The CPM/LOB model was validated against the results of a dynamic programming model in the literature and further by conducting a sensitivity analysis of the results of the integrated model. Finally, the model offers an effective financial planning tool for projects of repetitive non‐serial activities.     

Fuzzy critical chain method for project scheduling under resource constraints and uncertainty

This paper develops a fuzzy critical chain method for project scheduling under resource constraints and uncertainty. The method consists of developing a desirable deterministic schedule under resource constraints, and adding a project buffer (PB) to the end of the schedule to deal with uncertainty. The size of the project buffer is determined by computations with fuzzy numbers. During project execution, the proposed method focuses on the penetration level in the project buffer, and dynamically updates the schedule to provide a more accurate schedule for actual progress. The use of a project buffer makes the method akin to critical chain project management (CCPM), although no feeding buffers are used. The proposed method is useful for both project planning and execution.     

Separating project risk from the time value of money: A step toward integration of risk management and valuation of infrastructure investments

The rationale for using heuristics to establish a risk premium that is added to the risk-free rate to obtain the value of an investment is questioned and an alternative method, termed decoupled net present value (DNPV), is proposed. Rather than using utility theory concepts to decrease the value of uncertain cash flows, the risks associated with project cash flows are discretely quantified using insurance and contingent claim valuation concepts. Synthetic insurance premiums are designed to “protect” the value of expected cash flows which are treated as additional project costs. Because identified project risks are quantified in financial terms and treated as a real cost to the project, DNPV allows business executives to evaluate the effect on the value of the project of different risks and select management techniques that are deemed more effective. Hence, DNPV is both a valuation methodology and a risk management tool.     

An empirical examination of project contractors' supply-chain cash flow performance and owners' payment patterns

Supply-chain (SC) cash flow performance is an essential component of SC performance management. Despite the panoply of approaches to SC cash flow modeling, relatively few published studies assess the effect of SC cash flow performance on a project contractor's financial performance. Little research thus explores the behavioral patterns in the project owner-contractor dyad in the context of payment-term negotiation for improving the SC cash flow performance of a project contractor. Using data from 42 Taiwanese construction project contracting corporations, this paper systematically quantifies the effects of SC cash flow performance on the financial performance of construction project contractors. Further analysis using data from 118 returned, usable surveys reveals important behavioral patterns of project owners regarding payment terms with project contractors during the contracting phase of construction projects. These behavioral patterns provide project contractors with a base for supporting implementation efforts for improving SC cash flow performance.     

Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction

The time–cost–quality tradeoff (TCQT) problem is to decide an optimal combination of construction methods with the objective of minimizing cost and time while maximizing quality. Searching for such an optimal combination of construction methods needs to evaluate the total cost, time and quality of the project. These performances, especially the quality, may be collected and recorded in terms of imprecise or vague data rather than precise numbers. This paper presents a fuzzy-multi-objective particle swarm optimization to solve the fuzzy TCQT problem. The time, cost and quality are described by fuzzy numbers and a fuzzy multi-attribute utility methodology incorporated with constrained fuzzy arithmetic operations is adopted to evaluate the selected construction methods. The particle swarm optimization is applied to search for the TCQT solutions by incorporating the fuzzy multi-attribute utility methodology. The proposed methodology is implemented and justified through computational analyses. The study is expected to provide an alternative methodology for solving the time–cost–quality tradeoff problem.     

Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems

The resource-constrained problem seeks to find the optimal sequence that minimizes project duration under current precedence constraints and resource limitations. This study integrates the fuzzy c-means clustering technique and the chaotic technique into the Differential Evolution (DE) algorithm to develop the Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) algorithm, an innovative approach to solving complex optimization problems. Within the FCDE, the chaotic technique prevents the optimization algorithm from premature convergence and the fuzzy c-means clustering technique acts as several multi-parent crossover operators in order to utilize population information efficiently and enhance convergence efficiency. Further, this study applies a serial method to reflect individual-user priorities into the active schedule and the project duration calculations. The FCDE and serial method are then integrated into a novel optimization model called the Fuzzy Clustering Chaotic-based Differential Evolution for Solving Resource Constrained Project Scheduling Problem (FCDE-RCPSP). Experiments run indicate that the proposed FCDE-RCPSP obtains optimal results more reliably and efficiently than the benchmark algorithms considered. The FCDE-RCPSP is a promising alternative approach to handling resource-constrained project scheduling problems.     

Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects

The undertaking of construction projects in metropolitan areas is a risky, competitive, and dynamic proposition requiring a reliable risk assessment model for adequate planning. This study employs a fuzzy multiple criteria decision making (FMCDM) approach to systematically assess risk for a metropolitan construction project. Consistent fuzzy preference relations (CFPR) are used to measure and investigate the relative impact on project performance of twenty identified risk factors included in four risk dimensions. The fuzzy multiple attributes direct rating (FMADR) approach is employed to analyze the occurrence probability of multiple risk factors. Furthermore, the level of risk for the overall project caused by individual risk factor is evaluated with the synthesized analysis of the relative impacts and probability of occurrence. The implementation of FMCDM makes the proposed risk assessment approach more reliable and practical than the traditional statistical approach. The proposed approach can be employed to effectively evaluate the overall project risk, and can be benefited to efficiently identify significant risks of a metropolitan construction project.     

Systems analysis of project cash flow management strategies

Cash flow management is one of the most important determinants of the success of construction project management. Overdraft, retainage, financing, payment and billing policies constitute the most significant financial issues that contractors must plan, control and manage for the successful completion of construction jobs. Particularly, in an attempt to reduce project costs, contractors must balance cost savings of material discounts due to early payments and extra interest expenses because of additional overdraft. Through identifying feedback loops in project cash flows, a system dynamics model is developed for project cash flow management. The model is flexible to incorporate typical front‐end and back‐end loading cash flow management strategies and provides an interactive predication of project cash flows. A warehouse project is discussed to demonstrate how various cash flow strategies improve overdraft financing requirements and profitability. Especially, the analysis shows an 11% reduction on overdraft requirements while using an overbilling strategy, and 30% reduction if the trade credit strategy is implemented.     

Modeling and evaluating construction project competencies and their relationship to project performance

In contemporary construction environments, construction organizations measure their performance against a set of predefined performance measures. These performance measures are governed by the ability of the organization to maintain necessary sets of “competencies” that assist in the successful execution of its construction projects. Competencies are often difficult to define and measure due to the multidimensional and subjective nature of their assessment. This paper identifies 41 project competencies with a total of 248 criteria for evaluating the different project competencies. This paper also identifies seven performance categories with 46 project key performance indicators. A systematic framework and methodology are presented in this paper to measure project competencies and project key performance indicators. A new modeling approach considering prioritized fuzzy aggregation, factor analysis, and fuzzy neural networks is presented to identify the relationship between project competencies and project key performance indicators. Data collected from seven construction projects are first aggregated using prioritized fuzzy aggregation to measure the different construction project competencies. The different project competencies are then analyzed using factor analysis. The factor analysis results are used with the prioritized fuzzy aggregation results to calculate inputs for the fuzzy neural networks. The fuzzy neural networks are then trained and tested using the data collected from the seven construction projects to identify and quantify the relationship between the different project competencies and project key performance indicators. This paper contributes to the current body of knowledge in project competencies and performance by establishing a standardized framework and methodology for evaluating the impact of construction project competencies on project key performance indicators. Furthermore, this paper incorporates advanced modeling techniques through the application of fuzzy set theory and neural networks to identify the relationship between the different project competencies and project key performance indicators. Identifying the relationship between construction project competencies and project key performance indicators allows construction organizations to improve their overall construction project performance by enhancing their projects competencies.     

Fuzzy-based MOGA approach to stochastic time–cost trade-off problem

In construction projects, time and cost are manageable objectives with significant interdependencies for which sets of trade-offs may exist. This study presents a new approach for the solution of time–cost trade off problems in an uncertain environment. Fuzzy numbers are used to address the uncertainties in the activities execution times and costs. Fuzzy sets theory is then explicitly embedded into the optimization procedure. A multi-objective genetic algorithm is specially tailored to solve the discontinuous and multi-objective fuzzy time- cost model with relatively large search space. The proposed approach identifies the best set of implementation options defined by the sets of non-dominated solutions Accepted risk level and optimism of the decision maker are addressed using α-cut approach and optimism index (β) respectively. To illustrate the application and performance of the model, two case examples are presented, for which separate Pareto fronts are developed. The fuzzy presentation of the non-dominated solution helps the project manager to apply his own level of risk acceptance and degree of optimism in decision making process. Different risk acceptance level and/or optimism leads to different scheduling and sets of Pareto solutions from which the project manager may select his preferred options.