# Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study

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## Abstract

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## 1. Introduction

- To explore the underlying behaviour of the cost flow at both the project (i.e., macro) and at the individual level of the cost component and work package (i.e., micro); and
- To develop a heuristic cost flow characterization zoning-based chart to enable a common platform for exchanging and comparing cost-flow data.

## 2. Conceptual Background

#### 2.1. Related Work

#### 2.1.1. Cash Flow Forecasting Using Mathematical Techniques

#### 2.1.2. Cash Flow Forecasting Using Artificial Intelligence (AI)

#### 2.1.3. Cash Flow Forecasting Using Building Information Modelling (BIM)

#### 2.2. Research Gap and Motivation

## 3. Research Methodology

#### 3.1. Research Strategy

#### 3.2. Data Collection

#### 3.3. Data Analysis

#### 3.3.1. Visual Examination of the Cost Flow Curves

#### 3.3.2. Modelling the Cost Curves Using the Logit Transformation Technique

## 4. Results and Discussion

#### 4.1. The Underlying Behaviours of Cost Flows

#### 4.1.1. The Aggregated Cumulative Cost Flow Behaviour at the Project Level

#### 4.1.2. The Periodic Cost Flow Behaviour of Individual Cost Components at the Project Level

#### 4.1.3. The Periodic Cost Flow Behaviour at the Work Package (Network) Level

#### 4.2. An Illustrative Heuristic Graph Characterizing the Cost Flow of Infrastructure Projects

#### 4.3. The Development of the Cash Flow Forecasting Model

#### 4.3.1. Fitting Parameters (Alpha and Beta) Using the Logit Transformation Technique

#### 4.3.2. The Cost Flow Forecast

#### 4.3.3. The Net Cash Flow Forecast

#### 4.4. Validation of the Proposed Heuristic CFF Model

## 5. Conclusions

#### 5.1. Practical Implications

#### 5.2. Theoretical Contribution

#### 5.3. Research Limitations

#### 5.4. Conclusion and Future Research

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Reference | Focus of Study | Methods | Limitations |
---|---|---|---|

[18] | Forecasting expenditure cash flow for transportation design-build projects from clients’ perspective. | Case-based reasoning framework based on data mining of completed projects. | Limited to clients and mathematically complex. |

[17] | Forecasting cumulative and monthly cost flows for building projects from contractors’ perspective. | Regression modelling and logit transformation techniques were employed. | Limited to building projects and required numerous inputs. |

[24] | Forecasting cash flow (inflow) for building projects from contractors’ perspective. | Integration of meta- heuristic with artificial neural network. | Limited to building projects, neglected the outflow, and complicated implementation. |

[6] | Generating cash flow curves for projects procured through integrated project delivery (IPD) approach. | Integration of 4D BIM with 5D BIM. | Limited to IPD building projects. |

Serial Number | Work Package (Network) | Brief Description of the Scope of Work | Unit of Measurement | Quantification (Project 1, Case Study) | Quantification (Project 2, Validation Project) |
---|---|---|---|---|---|

1 | Internal Roads | Cut/fill, pavement, kerbstone, and interlock tiles. | linear metre | 9400.00 | 9900.00 |

2 | Sewerage | Pipelines and manholes. | linear metre | 6250.00 | 8500.00 |

3 | Strom Water | Pipelines and manholes. | linear metre | 12,400.00 | 9000.00 |

4 | Electrical | Electrical cables and substations. | linear metre | 25,000.00 | 37,000.00 |

5 | Street Lighting | Electrical cables and poles. | number | 210.00 | 240.00 |

6 | Potable Water | Pipelines and valves. | linear metre | 9750.00 | 9300.00 |

7 | Telecom | Telecom ducts with joint boxes. | linear metre | 25,000.00 | 18,000.00 |

Cost Component | Project 1 (Case Study) Data | Project 2 (Validation Project) Data | Data Ranges Reported by [22] |
---|---|---|---|

Direct Equipment | 24.98% | 21.91% | 10% to 25% |

Direct Manpower | 13.19% | 11.00% | 5% to 15% |

Direct Materials | 47.22% | 55.26% | 25% to 35% |

Indirect (i.e., preliminaries) | 14.61% | 11.83% | 5% to 15% |

Total | 100.00% | 100.00% | N/A |

Serial Number | Cost Component | The Case Study Project Data | Parameters Reported by [27] | Parameters Reported by [5] | ||||||
---|---|---|---|---|---|---|---|---|---|---|

α | β | SDY | α | β | SDY | α | β | SDY | ||

1 | Materials | −2.3184 | 1.6642 | 2.41 | Not available | Not available | Not available | Not available | Not available | Not available |

2 | Manpower | −2.0457 | 1.7559 | 5.29 | ||||||

3 | Machinery | −0.7290 | 1.4703 | 2.34 | ||||||

5 | Total direct | −1.6707 | 1.5523 | 2.72 | ||||||

4 | Indirect | 0.2199 | 1.0026 | 3.54 | ||||||

6 | Total cost (direct + indirect) | −1.0982 | 1.1720 | 1.05 | −1.3620 | 1.5869 | 1.91 | −0.8200 | 1.1300 | 2.18 |

Serial Number | Cost Component | SDY (Between the Forecast and Actual) |
---|---|---|

1 | Materials | 6.16 |

2 | Manpower | 7.17 |

3 | Equipment | 3.01 |

4 | Total direct | 3.29 |

5 | Indirect | 3.50 |

6 | Total cost (direct + indirect) | 2.59 |

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**MDPI and ACS Style**

Msawil, M.; Elghaish, F.; Seneviratne, K.; McIlwaine, S. Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study. *Sustainability* **2021**, *13*, 11305.
https://doi.org/10.3390/su132011305

**AMA Style**

Msawil M, Elghaish F, Seneviratne K, McIlwaine S. Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study. *Sustainability*. 2021; 13(20):11305.
https://doi.org/10.3390/su132011305

**Chicago/Turabian Style**

Msawil, Mahir, Faris Elghaish, Krisanthi Seneviratne, and Stephen McIlwaine. 2021. "Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study" *Sustainability* 13, no. 20: 11305.
https://doi.org/10.3390/su132011305