FUNCTIONAL REQUIREMENT-BASED

FORECASTING ESTIMATING THE COST AND DURATION OF EARLY-STAGE

PROJECTS

Word count: 20,029

Arne Van Belleghem Student number : 000130582309

Supervisor: Prof. Dr. Mario Vanhoucke

Commissioner: Jeroen Burgelman

Master’s Dissertation submitted to obtain the degree of:

Master in Business Engineering: Operations Management

Academic year: 2018-2019

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Declaration

I declare that the content of this Master’s Dissertation may be consulted and/or reproduced, provided that the

source is referenced.

Arne Van Belleghem

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Acknowledgements

Almost 6 years have passed since I attended my first lecture at the University of Ghent and now my life as a

student has come to an end. Writing this dissertation was a challenge. I faced a lot of obstacles and difficulties

and I could not have overcome them all on my own. Therefore, I want to thank the people who inspired, guided

and supported me along the way.

First of all, I want to thank my supervisor Prof. Dr. Mario Vanhoucke, who inspired me to perform this research

with his enthusiasm for project management. During my school career, only 2 people were able to really

influence my educational choices and he is without a doubt the one who persuaded me to study Operations

Management.

Second, I want to acknowledge the guidance of commissioner Jeroen Burgelman through this research. His

knowledge, insights and feedback were invaluable in discussing and defining the path of this dissertation.

Furthermore, he was always available for me when I needed help, and for that I am very grateful.

Finally, I want to thank my family and friends who encouraged and supported me during the progress of this

research. I’m deeply indebted to my parents, who gave me the opportunity and resources to study Business

Engineering at the University of Ghent. Special thanks goes to my brother and friends, who were always there

to lift my spirit during the sometimes hard period writing this dissertation.

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Abstract

Cost and duration forecasts are often inaccurate due to the overoptimistic behaviour of project managers.

Functional Requirement-based Forecasting (FRF) is a forecasting technique, which is solely based on the

execution of projects in the past. Consequently, FRF avoids the biases of human judgement. The proposed

technique in this dissertation is applied to 41 real-life projects for which data was retrieved from the database

available at the Operations Research and Scheduling (OR&S) group of Ghent University. These projects can be

divided in 8 groups of similar projects and for each group the results are analysed. Furthermore, the effect of

different factors on the forecast accuracy of FRF is tested and a general evaluation of FRF is made. This

dissertation aims to introduce the use of a new technique to estimate the cost and duration of a project and test

its potential regarding forecast accuracy.

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Contents

List of acronyms ..................................................................................................................................................... vi

List of tables ......................................................................................................................................................... viii

List of figures .......................................................................................................................................................... ix

I Introduction .......................................................................................................................................................... 1

II Literature ............................................................................................................................................................. 4

III Research Hypothesis ........................................................................................................................................ 13

IV Methodology .................................................................................................................................................... 15

IV.I Project Data................................................................................................................................................. 15

IV.II Determining Functional Requirements ...................................................................................................... 17

IV.III Project Size ................................................................................................................................................ 20

IV.IV Calculations ............................................................................................................................................... 24

V Results ................................................................................................................................................................ 26

V.I Results per project group ............................................................................................................................. 27

V.II General Results ........................................................................................................................................... 38

V.III Regression Analysis .................................................................................................................................... 39

V.IV Sensitivity Analysis size-factor ................................................................................................................... 41

VI Conclusion ........................................................................................................................................................ 43

References ............................................................................................................................................................ 45

Appendix ............................................................................................................................................................... 47

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List of acronyms

AB = Apartment Building

ABFW = Apartment Building Finishing Works

ABSW = Apartment Building Structural Work

ADT = Axiomatic Design Theory

AoN = Activity-on-the-Node

APE = Absolute Percentage Error

BBRI = Belgian Building Research Institute

CN = Customer need

DP = Design Parameter

DSM = Design Structure Matrix

EVM = Earned Value Management

ES = Earned Schedule

FE = Absolute Forecast Error

FP = Function Point

FR = Functional Requirement

FRF = Functional Requirement-based Forecasting

LOC = Lines Of Code

MAPE = Mean Absolute Percentage Error

OFW = Office Finishing Works

OR&S = Operations Research and Scheduling

PLC = Project Life Cycle

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PM = Project Management

PMI = Project Management Institute

PV = Process Variable

RB = Railway Bridge

RH = Residential House

RHFW = Residential House Finishing Works

RCF = Reference Class Forecasting

ROT = Rule of Thumb

RQ = Research Question

RQa,b,c = Research sub-Questions

SAY = Social Apartments Ypres

SME = Small and Medium-sized Enterprise

WBDG = Whole Building Design Guide

WBS = Work Breakdown Structure

WTCB = Wetenschappelijk en Technisch Centrum voor het Bouwbedrijf

XSM = eXponential Smoothing-based Method

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List of tables

Chapter II

Table 2. 1 The DSM of the network ......................................................................................................................... 8

Chapter IV

Table 4. 1 Overview Project Groups ...................................................................................................................... 16

Table 4. 2 Examples of Activities and their Functional Requirements .................................................................. 19

Table 4. 3 Project sizing matrix. Source: Burgan & Burgan (2014) ........................................................................ 20

Chapter V

Table 5. 1 Residential House Finishing Works: Cost forecast ................................................................................ 27

Table 5. 2 Residential House Finishing Works: Duration forecast ........................................................................ 28

Table 5. 3 Residential House: Cost forecast .......................................................................................................... 29

Table 5. 4 Residential House: Duration forecast ................................................................................................... 29

Table 5. 5 Apartment Building Finishing Works: Cost forecast ............................................................................. 30

Table 5. 6 Apartment Building Finishing Works: Duration forecast ...................................................................... 30

Table 5. 7 Apartment Building Structural Work (1): cost & duration MAPE ......................................................... 31

Table 5. 8 Apartment Building Structural Work (2) & (3): Cost forecast ............................................................... 32

Table 5. 9 Apartment Building Structural Work (2) & (3): Duration forecast........................................................ 32

Table 5. 10 Railway Bridge: cost & duration MAPE ............................................................................................... 33

Table 5. 11 Apartment Building: cost & duration MAPE ....................................................................................... 34

Table 5. 12 Apartment Building: Absolute cost FE ................................................................................................ 34

Table 5. 13 Office Finishing Works: cost & duration MAPE .................................................................................. 35

Table 5. 14 Office Finishing Works: Absolute cost FE............................................................................................ 35

Table 5. 15 Social Apartments Ypres: cost & duration MAPE ............................................................................... 36

Table 5. 16 Social Apartments Ypres (b): cost & duration MAPE .......................................................................... 36

Appendix

Table A. 1 Cost forecast accuracy .......................................................................................................................... 52

Table A. 2 Duration forecast accuracy ................................................................................................................... 54

Table A. 3 Effect different values of a on the cost forecast accuracy ................................................................... 59

Table A. 4 Effect different values of a on duration forecast accuracy .................................................................. 61

Table A. 5 Forecast results with size factor = 1 ..................................................................................................... 63

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List of figures

Chapter II

Figure 2. 1 Literature overview ............................................................................................................................... 4

Figure 2. 2 Dynamic Project Scheduling. Source: Vanhoucke (2012) ...................................................................... 5

Figure 2. 3 Project Life Cycle (PLC). Source: Vanhoucke (2012) .............................................................................. 6

Figure 2. 4 Four levels of a WBS. Source: Vanhoucke (2012) .................................................................................. 7

Figure 2. 5 An example of a network ...................................................................................................................... 8

Figure 2. 6 Zigzagging. Source: Rebaiaia and Viera (2013) ...................................................................................... 9

Figure 2. 7 The design process. Source: Rebaiaia and Viera (2013) ...................................................................... 10

Figure 2. 8 Reverse Zigzagging (visualization) ....................................................................................................... 11

Chapter III

Figure 3. 1 Roadmap FRF ....................................................................................................................................... 13

Chapter IV

Figure 4. 1 Overview Methodology ....................................................................................................................... 15

Figure 4. 2 Methodology information searching process ..................................................................................... 17

Figure 4. 3 Correlation Actual Cost Actual Duration ............................................................................................ 22

Chapter V

Figure 5. 1 Overview Result Analysis ..................................................................................................................... 26

Figure 5. 2 Sensitivity analysis a: cost MAPE ......................................................................................................... 41

Figure 5. 3 Sensitivity analysis a: duration MAPE .................................................................................................. 42

Appendix

Figure A. 1 Effect average number of observations on cost MAPE ....................................................................... 55

Figure A. 2 Effect average number of observations on duration MAPE ............................................................... 55

Figure A. 3 Effect difference in size factor on cost MAPE ..................................................................................... 56

Figure A. 4 Effect difference in size factor on duration MAPE .............................................................................. 56

Figure A. 5 Effect difference in FRs on cost MAPE ................................................................................................ 57

Figure A. 6 Effect difference in FRs on duration MAPE ......................................................................................... 57

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Chapter I

Introduction

How often does a project-manager exactly know how long the execution of a project will take before it has

started? And how often does a project-manager sit down with investors and tells them the exact budget he

or she will need to perform a project? The estimated cost and duration of a project are vital pieces of

information for an organisation, because it could determine whether a project is dropped or continued.

Despite the importance of a good cost forecast, Flyvbjerg (2014) states that cost overruns are a problem in

both private and public sector projects and that things are not improving. “Overruns have stayed high and

constant for the 70-year period for which comparable data exist.” As a matter of fact rail and road projects

have average cost overruns of 44.7% and 20.4% respectively. A well-known example is the railway tunnel

that connects England and France or the Channel tunnel that had a construction cost overrun of 80%

(estimated £2600, actual cost £4650 million). Further there are numerous other examples like the cost

overrun of the new US international airport in Denver that was close to 200%, the Humber bridge in the UK

that had a 175% overrun and the Paris Nord TGV in France that had a 25% overrun (Flyvbjerg et al., 2003).

Another study of 52 civilian projects ranging in cost from $500 million to over $10 billion (in 1984) concludes

that the average cost overrun was 84%. The total cost overrun was over $30 billion and only 4 projects were

estimated on budget (Merrow et al., 1988). Other past cost overruns were even more extreme: Boston’s

Big Dig had an overrun of 220%, The UK National Health Service IT system between 400% and 700%, and

The Sydney Opera House even had an overrun of 1400% (Flyvbjerg, 2014). This phenomena does not only

hold for big projects, Aljohani et al. (2017) state that nine out of ten construction projects normally

experience cost overrun.

Moreover, estimating the duration of a project accurately is a worldwide problem as well. A field survey in

Saudi Arabia concluded that 70% of construction projects experienced time overrun, with an average

slipping period between 10% and 30% of the original duration (Assaf & Al-HHejji, 2006). Abdallah &

Battaineh (2002) evaluated progress reports of 164 building and 28 highway projects constructed during

the period 1996-1999 in Jordan. They also noticed extensive project delays: the average ratio of actual

completion time to the planned contract duration equals 120.3% for building projects and for road projects

even 160.5%. Additionally, in Europe a lot of projects faced severe delays as well. A famous example is the

Wembley Stadium project in the UK which started in 2002 and was expected to be finished in 2006, but had

a delay of 1 year (or a delay of 25%) and furthermore a cost overrun of 32%. Another example is the

Olkiluoto 3 Nuclear Power Plant in Finland. This project had a planned duration of 5 years, but the actual

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duration took 13 years! This came down to a delay of 160%, not to mention the cost overrun of 166% (Wilks,

2015).

Cost and duration overruns are not solely problems for construction projects, a study published in the

Harvard Business Review (Flyvbjerg & Budzier, 2011), which analysed 1,471 IT projects, found that all but

one in six projects had a cost overrun of 200% on average and a schedule overrun of almost 70%.

An explanation for these inaccuracies is given by Flyvbjerg (2006), who states that psychological and

political biases are the causes instead of imperfect data or bad forecast models. These biases induce project

managers to be overoptimistic about the cost and duration of a project, without reviewing similar projects

in the past. During the execution of most projects, there are simply too many factors that can affect both

cost and duration. In the field of project management a lot of research has already been done and there

are already a range of techniques who try to deal with these unpredictable factors during the execution of

the project. For example, Earned Value Management (EVM) originated in the 1960s, when the US

Department of Defense proposed a standard method to measure a project’s performance (Vanhoucke,

2012). Later, Earned Schedule (ES) was proposed as an alternative technique to measure a project’s time

performance by Lipke (2003). A more recent study proposes a technique called XSM (Batselier &

Vanhoucke, 2017), which is an acronym for eXponential Smoothing-based Method, integrates the EVM

technique with Holt's double exponential smoothing method (Holt, 1957, 2004) and Reference Class

Forecasting (RCF) (Flyvbjerg, 2006).

The proposed technique in this dissertation, Functional Requirement-based Forecasting (FRF), tries to limit

the overoptimistic behaviour of project managers by estimating the cost and duration of a project based

on the actual cost and duration of similar projects in the past. In contrast to the techniques described above,

which aim to forecast an ongoing project's actual duration and cost, FRF tries to predict the duration and

cost of the activities in an early stage of the project. In this dissertation this early stage can be defined as

the time period when the duration and cost of the activities are estimated before the execution of the

project has started.

In order to apply this technique, the functional requirements (FRs) of the project’s activities need to be

determined. Based on the past results of activities with the same FRs in similar projects, a prediction of

their cost and duration can be made. Before reading any further, it is important to understand what a FR

really is. A functional requirement emphasizes that what the system must do (Farid & Suh, 2016), or in other

words it defines the function that a system must fulfill. Based on their function, the duration and cost of

activities are estimated.

The proposed technique in this dissertation was applied to 41 real life projects retrieved from the dataset

available at the Operations Research and Scheduling (OR&S) group of Ghent University (Batselier &

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Vanhoucke, 2015). These projects can be divided in 8 groups of similar projects, with differences in project

group size and differences in similarity between projects of the same group.

This brings us to the main goal of this dissertation: testing the potential of Functional Requirement-based

Forecasting in terms of forecast accuracy for cost and duration. Consequently the research question (RQ)

of this dissertation is:

RQ: What is the potential of Functional Requirement-based Forecasting regarding forecast accuracy of

early-stage cost and duration estimates of a project?

Furthermore, it is important to know in which situations it is useful to apply this technique. In other words,

it is key to figure out which the drivers of a good estimation in FRF are. In order to find an answer to this

question, 3 sub-questions (RQa, RQb and RQc) are defined:

𝐑𝐐𝐚: What is the relation between the forecast accuracy and the number of observations on which

a forecast is based on?

𝐑𝐐𝐛: What is the relation between the forecast accuracy and the difference in project size between

similar projects in a project group?

𝐑𝐐𝐜: What is the relation between the forecast accuracy and the differences in the functional

requirements of activities in similar projects?

In order to find an answer to these questions, this dissertation has the following structure. Chapter II

contains a literature review in order to give some context about the subject. In Chapter III the research

questions are put forward and transformed into research hypothesises. The used methodology of applying

this technique is described in Chapter IV. In Chapter V the obtained results are analysed and thoroughly

discussed. Finally, the conclusions and limitations of this dissertation are described in Chapter VI.

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Chapter II

Literature

This dissertation aims to introduce the use of a new technique to estimate the cost and duration of a

project. To the best of our knowledge only related research can be found on the subject. The goal of the

first part of this literature review is to provide the reader with a clear view on the context of the subject.

Subsequently, the theory from which the idea of Functional Requirement-based Forecasting is inspired from

and their link will be explained. Finally, the motivation why the proposed technique could be useful for

project managers is presented.

Figure 2.1 provides a scheme of the literature review.

The proposed technique Functional Requirement-based Forecasting is located in the field of project

management. Therefore to understand the concept of this dissertation, it is key to understand what project

management really is.

Project management (PM) can be defined as the discipline of planning, organizing and managing resources

to bring about the successful completion of specific project goals and objectives (Vanhoucke, 2012).

Figure 2. 1 Literature overview

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According to PMBOK (2013), PM is the application of knowledge, skills, tools, and techniques to project

activities to meet the project requirements. PMI (Project Management Institute) states that every project

is unique and temporary. It is not a routine operation, but a specific set of operations designed to

accomplish a singular goal. Besides, every project has a defined beginning and end in time, and therefore

defined scope and resources. A good project manager needs to be comfortable with change and complexity

in dynamic environments, because projects can be very complex and executed in unpredictable

circumstances. That is why scheduling a project is a dynamic process. During the life time of the project,

changes to the schedule happen all the time, therefore decisions have to be made until the end of the

project. To deal with this, dynamic project scheduling has three closely connected components: Baseline

Scheduling, Risk Analysis and Project Control (Vanhoucke, 2012).

Figure 2. 2 Dynamic Project Scheduling. Source: Vanhoucke (2012)

Baseline Scheduling: In this phase a timetable is constructed to provide a start and end date for

each project activity. It is key to take the activity relations, resource constraints and other project

characteristics into account while trying to reach a schedule objective.

Risk Analysis: What are the effects on our schedule objective when certain activities are delayed

or certain resources are unavailable? When dealing with uncertainty, the weaknesses of the

schedule need to be determined. In that way the schedule can be adapted or buffered to be more

robust to changes.

Project Control: The progress of the project is measured and evaluated using the information

obtained during the scheduling and risk analysis steps. In this way corrective actions can be taken

in case of problems to reach the objective.

FRF provides a technique to predict the cost and duration of a project and can be used during the baseline

scheduling phase. In dynamic project scheduling a project’s baseline schedule serves as a point of reference

in the project life cycle and is therefore only a prediction of the execution of the project. However, this

point-of-reference becomes very important when it is embedded in a wider dynamic scheduling

perspective. It acts as a tool for resource efficiency calculations, time and cost risk analyses, project control,

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performance measurements and much more and is therefore the starting point in any dynamic scheduling

analysis. That is why it is very important to have a good estimation of the costs and duration of the activities

in order to reach the predetermined objective (Vanhoucke, 2013).

Before and after a project is scheduled, it has to go through different phases. Together these phases form

the project life cycle (PLC). PMBOK (2013) defines the project life cycle as the series of phases that a

project passes through from its initiation to its closure. Although PMBOK represents the PLC slightly

different, the one described below is proposed by Vanhoucke (2012) and gives a clear overview of the

different phases.

Figure 2. 3 Project Life Cycle (PLC). Source: Vanhoucke (2012)

Conceptual phase: First, the need for a project is identified. Usually an organisation receives a

request from a customer or a recent market research indicates an opportunity.

Definition phase: In this phase the projects objectives, specifications and requirements are

defined. Furthermore, the organisation that will be involved is assigned to the project. It is key that

the project objectives are translated into a list of activities, a set of technological precedence

relations and the resource requirements, taking into account their availabilities.

Scheduling phase: Next, a timetable is constructed where each activity is assigned to a start and

finish time. The schedule takes into account the information received by the previous step, in order

to be precedence and/or resource feasible.

Execution and control phases: During the execution, the progress of the project is tracked to check

if it deviates from the schedule constructed in the previous phase. When needed, corrective actions

can be taken.

Termination phase: During this last phase the project is completed and evaluated. This evaluation

can be very important, because the obtained data can be used during the project life cycle of future,

similar projects. The proposed forecasting technique in this dissertation is based on this

information.

The idea behind FRF is inspired by the Axiomatic Design Theory (ADT) developed by Dr. Suh (1990). ADT is

a technique to generate a project network and can be situated in the definition phase.

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During this definition phase the activities are identified that are essential to reach the project goal. A well-

known concept to do this is the Work Breakdown Structure (WBS). WBS is the process of subdividing project

deliverables and project work into smaller, more manageable components (PMBOK, 2013). Starting from

the project objective, the project is decomposed until the project activities can be defined.

The WBS has multiple levels of detail and is visualised below.

Figure 2. 4 Four levels of a WBS. Source: Vanhoucke (2012)

This obtained information needs to be transformed into a network diagram that represents the project

activities and their technological links. These technological links define the logical sequence in which the

activities are executed. For example, when you are building a house, you cannot start with the brickwork if

the foundations are not yet finished. More detailed information about the different precedence

relationships can be found at Vanhoucke (2012).

Constructing a project network is not always an easy task. Projects can involve the completion of hundreds

or even thousands of parts or activities. Such projects are not simply coordinated or structurally well-linked,

they can be very complex and especially dynamic. In the project scheduling literature 2 methodologies have

been developed to generate activity-on-the-node (AoN) networks for such projects: the Design Structure

Matrix (Steward, 1981) and Axiomatic Design Theory (Suh, 1990).

Design Structure Matrix

The design structure matrix (DSM) or the dependency structure matrix and its manipulating algorithms was

originally developed by Steward (1981). DSM is a general method for representing and analysing system

models to better plan complex projects involving interdependencies, facilitating modularity, sequencing to

minimize costs and schedule risk in a variety of application areas (Rebaiaia and Viera, 2013). This method

uses the information obtained of the WBS and constructs a binary matrix that indicates the precedence

relationships between the activities.

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According to Rebaiaia and Viera (2013), a DSM associated with a graph is a binary square matrix with m

rows and m columns, with m equal to the number of activities, and the number of marked cells corresponds

to the links between nodes which are admitted equal to n. The rows of the activities represent their inputs,

their columns represent their outputs. The DSM can be defined as follows:

The DSM is a (m x m) Boolean matrix 𝐴 = [𝑎𝑖𝑗] composed of elements such as each element is defined

according to:

1 if (𝑎𝑗 → 𝑎𝑖)

𝑎𝑖𝑗 = ∀ 𝑖, 𝑗 𝜖 {1, … , 𝑚}

0 otherwise

where link (𝑎𝑗 → 𝑎𝑖) between 𝑎𝑗 and 𝑎𝑖 denotes that component 𝑎𝑗 transfers information to component

𝑎𝑖 so that the execution of 𝑎𝑖 cannot be proceeded if 𝑎𝑗 did not complete its execution. When 𝑎𝑖𝑗 equals 0,

the cells are empty and the diagonal cells are blackened. An empty row represents a source node and an

empty column represents a terminal node.

For example, for a given network:

Figure 2. 5 An example of a network

Table 2. 1 The DSM of the network

This matrix serves as the starting point of 2 algorithms: partitioning and tearing. Partitioning means

changing the order of the activities and is useful to show which activities or group of activities can be

A B C D E F G H I

A 1

B 1 1

C 1 1

D

E 1 1

F 1 1 1

G 1

H 1 1 1

I 1

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performed serial or simultaneously and which activities are coupled. Tearing is performed when so called

activity-blocks or coupled activities are identified and these activities have to be sequenced within the

blocks. The sequence of these activities depend on the judgement of the manager and will be different from

one person to another. For the specific algorithms and illustrative examples of the technique I refer to

Rebaiaia and Viera (2013).

DSM is a useful tool to decompose a system into subsystems and schedule tasks in an optimal sequence for

a complex project and is most useful when activities are listed in the order of their execution in the project.

DSM provides a simple way to visualise the structure of an activity network. It is a great technique to

capture, understand and manage the interactions occurring in a project. However this technique is limited

in predicting system interactions before detailed designing is carried out. In other words, the project must

be well defined in terms of activities and their precedence relationships before DSM can be applied.

Axiomatic Design Theory

The axiomatic design theory (ADT) (Suh, 1990) is a useful technique when the project design is still in an

early phase. When you design a new product or service you look for something customers want. By giving

form to your product or service you translate these customer needs (CNs) into functional requirements

(FRs). This technique looks to satisfy these functional requirements by adjusting their correlating design

parameters (DPs). The design parameters are determined through a process called zigzagging (Rebaiaia and

Viera, 2013). Zigzagging is an iterative process that decomposes the overall functional requirement and

determines its corresponding DP at the same hierarchical level. This process is visualised underneath.

Figure 2. 6 Zigzagging. Source: Rebaiaia and Viera (2013)

These design parameters are used to design the process and are being transformed into process variables

(PVs). This results in the whole design process and its 4 domains:

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Figure 2. 7 The design process. Source: Rebaiaia and Viera (2013)

Similar to the WBS method, zigzagging applies multiple hierarchical levels to decompose a project into

smaller, more manageable components. Although instead of focussing on the project objective and the

work that needs to be done to satisfy this goal, ADT focusses on the customer needs and translates the

functional requirements of a project into activities. This point of view that activities should satisfy the

functional requirements of a project is the basic idea behind Functional Requirement-based Forecasting!

For the specific method and the application of ADT to real projects we refer to Rebaiaia & Viera (2013) and

Farid & Suh (2016).

ADT can be used early in the design process and while DSM captures the As-Is model, ADT captures the

underlying structure of the design problem because the design model is driven by the functional

requirements. ADT provides insight on which Design Parameters the Functional Requirements depend.

Relying on the ADT, the design parameters can be manipulated in order to satisfy the functional

requirements. The biggest disadvantage is that, in contrast to DSM, ADT does not capture the system

interactions. Another disadvantage is that although on product or process level the uncoupled design is

always better than the coupled one, on business level this is not the case. An uncoupled design is easier to

use, control and service, but at the same time easier to imitate (Nakao, 2016).

It is clear that both techniques can be very useful in different situations and they both have their advantages

and disadvantages. Dong and Whitney (2001) believe these techniques can be very complementary to each

other and proposed a technique to combine those methods. Since we do not seek to research the AoN

network generation and do not want to lose ourselves into details, this falls out of the scope of this

dissertation.

Link Design Structure Matrix and Functional Requirement-based Forecasting

DSM is very useful to schedule the activities of a project in an optimal sequence. At an early stage, the

optimal sequence is based on the estimation of the cost and/or duration of an activity, depending on the

project objective(s). For example, if the goal is to minimize the total duration of a project, it is important to

have an accurate forecast of the duration of the activities. Consequently, FRF and DSM can be

complementary techniques to generate these optimal sequences.

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Furthermore, the data used in this dissertation from the database of Batselier and Vanhoucke (2015) is

generated in a software tool called Protrack. The scheduling process in Protrack is based on the traditional

critical path based forward (to create an earliest start schedule) and/or backward (to create a latest start

schedule) project scheduling calculations aiming to construct a project schedule with a minimal project lead

time, taking the predefined precedence relations and the various predefined activity constraints in account

(Vanhoucke, 2009).

Link Axiomatic Design Theory and Functional Requirement-based Forecasting

The technique proposed in this dissertation starts from the activities obtained in the WBS and determines

the functional requirement(s) each activity tries to satisfy. This is the exact opposite of the zigzagging

technique in ADT, which starts from the functional requirements of a project and determines its activities

based on them. Furthermore, ADT uses a top-down technique to determine its activities. Whereas FRF is

using a bottom-up approach: it starts from its activities to estimate the cost and duration of a project.

Consequently, the determination of the FRs in FRF can be seen as some sort of “reverse zigzagging” because

it does the exact opposite of what ADT does.

Figure 2. 8 Reverse Zigzagging (visualization)

Note that this “reverse zigzagging” is only necessary to determine the FRs for the activities that are already

determined. If FRF would be considered when the project activities are not determined yet, the project

manager could also start from the FRs and use the initial zigzagging technique to determine the activities.

Either way it could be interesting for a project manager to think about the function of an activity before

adding it to a project.

When all the FRs are determined, the duration and cost of each activity can be estimated by the actual

values of activities of similar past projects with the same FR(s). This will be discussed thoroughly in the next

chapters.

Why FRF?

Next, it is important to understand why the proposed technique in this dissertation can be useful. FRF aims

to provide accurate cost and duration estimates in an early-stage of a project. In the introduction it was

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already mentioned that this can be of vital importance to a company, because these estimates could

determine whether a project is dropped or continued. Pasco & Aibinu (2008) state that an accurate estimate

is an important piece of information needed for decision-making at the pre-tender stage. Nevertheless,

there is still a lot of room for improvement in terms of early stage forecast accuracies.

An explanation for these poor estimates is given by Flyvbjerg (2006), who states that psychological and

political biases are the cause instead of imperfect data or bad forecast models. He argues that substantial

resources have been spent to improve data and forecasting over the last decades and that this has had no

effect on the accuracy. Instead, he puts the optimism bias forward as the psychological explanation: “Most

people judge future events in a more positive light than is warranted by actual experience”. Likewise a

political bias in the form of strategic misinterpretation has its effect on the inaccuracy. In order to gain

approval or funding for a project, forecasters and managers tend to overestimate benefits and

underestimate costs. This is explained very well by De Smyter and Vandoorne (2018), who indicate that

most project managers particularly focus on the specifics of the project and its objective, without reviewing

similar completed actions. This “inside view” causes inaccurate forecasts as explained above.

Instead Functional Requirement-based Forecasting is based solely on similar projects executed in the past.

In this way this technique avoids these optimism and political biases and aims to be a useful tool in the

decision process in an early stage of a new project.

13

Chapter III

Research Hypothesis

It is already mentioned above that FRF is based on the cost and duration of similar projects. Applying this

technique is done in 4 steps. First, data of similar projects, which were executed in the past, is collected.

Together with the forecasted project, these similar projects form a project group. Second, the FR(s) of each

activity in the project group are determined. Subsequently, the size of the project is determined and

compared with the similar projects in its project group. This is of vital importance for the calculations,

because the actual values need to be compensated for the difference in size between projects. The last step

is to calculate the forecasted cost and duration of each activity of the project based on the actual cost and

duration of the activities in similar projects with the same FR.

In Figure 3.1 the roadmap of FRF is visualised.

Figure 3. 1 Roadmap FRF

To the best of our knowledge no results are available for a similar technique, consequently it will be

interesting to see if FRF can be useful for project managers in the early stage of a project. In order to provide

the reader a clear view on the topic of this dissertation, we repeat the research question and sub-questions

below:

RQ: What is the potential of Functional Requirement-based Forecasting regarding forecast accuracy of

early-stage cost and duration estimates of a project?

𝐑𝐐𝐚: What is the relation between the forecast accuracy and the number of observations on which

a forecast is based on?

𝐑𝐐𝐛: What is the relation between the forecast accuracy and the difference in project size between

similar projects in a project group?

𝐑𝐐𝐜: What is the relation between the forecast accuracy and the differences in the functional

requirements of activities in similar projects?

These questions will be researched in the next chapters, but first the expectations of the relations, which

are to be investigated are discussed. In order to test these predictions, they are transformed into 3

hypothesises below.

14

Regarding the first sub-question, the expectation is that a forecast based on more observations will be

more accurate. The more similar projects a forecast is based on, the less sensitive it will be to an extreme

value in one of the projects. The goal is to search for an indication of how many similar projects we need

to obtain a relative good estimation using FRF. Note that some activities in similar projects can be

different. This can result in different FRs and consequently less observations on which the cost and

duration of an activity is based.

Second, it could be interesting to look if the difference in project sizes between similar projects in a project

group affects the forecast accuracy. Since the forecasted values are dependent on the differences in size

between the projects, the activities for which the cost and duration are independent of this size difference

will have a lower forecast accuracy. Consequently, we expect that the bigger the difference in size between

the similar projects in a project group is, the lower the forecast accuracy will be.

The goal of the last sub-question is to determine the effect of differences in the FRs of activities between

similar projects on the forecast accuracy. The expectation is that the more differences in terms of FRs

between similar projects exist, the lower the forecast accuracy will be. Similar to the difference in project

size, this can be an important factor to decide whether or not to add a similar project to the project group

on which a forecast is based. Maybe a trade-off has to be made between the raise in accuracy of an extra

observation and the loss in accuracy due to the differences between projects.

This results in the following research hypothesises:

Hypothesis 1: a forecast based on more observations will be more accurate.

Hypothesis 2: the bigger the difference in size between the similar projects in a project group, the

lower the forecast accuracy will be.

Hypothesis 3: the more differences in the functional requirements of activities between the similar

projects in a project group, the lower the forecast accuracy will be.

In the following chapters these hypothesises are investigated, taking into account the limitations of this

research.

15

Chapter IV

Methodology

The methodology used to apply FRF in this dissertation includes 4 steps. First, real-life project data was

collected to conduct the forecasting. Second, the FRs needed to be determined for each activity. Next, the

size of each project in the project groups were determined. Finally, the cost and duration forecasts were

calculated and compared with the actual cost and duration.

Figure 4. 1 Overview Methodology

IV.I Project Data

In order to test the proposed method in this dissertation, 41 real-life projects from the data available at the

OR&S group of Ghent University were used. These projects can be divided in 8 groups of similar projects. A

project belongs to a project group if it has the same subject and consequently the same project name in

the database. An overview of the project groups and the number of projects per group is given below (Table

4.1).

16

Project Group # Projects

Apartment Building (AB) 6

Apartment Building Finishing Works (ABFW) 4

Apartment Building Structural Work (ABSW) 3

Office Finishing Works (OFW) 5

Railway Bridge (RB) 5

Residential House (RH) 4

Residential House Finishing Works (RHFW) 11

Social Apartments Ypres (SAY) 3

Table 4. 1 Overview Project Groups

These projects are all located in the construction sector and have actual durations between 79 and 611 days

and their actual costs vary between €53,000 and €3,600,000. In 53.66% of the projects the actual duration

was longer than the planned duration and 70.73% of the projects had a budget overrun. Here must be noted

that the 11 ‘Residential House Finishing Works’ projects performed perfectly as planned in terms of

duration. It is highly unlikely that this has happened for 11 similar real-life projects, so one must be careful

by drawing conclusions based on these observations.

If the data of the Railway Bridge projects is compared with the statement of Flyvbjerg (2014) that rail

projects have an average cost overrun of 44.7%, one sees that only 2 of the 5 projects are performing worse

than the planned cost with respectively a 1.06% and a 4.81% cost overrun. This can be an indication that

the data used represents rather good projects in terms of cost and duration performance. This is confirmed

by the fact that the maximum time overrun is only 34.32% and that the maximum cost overrun is even

lower: 19.48%. On the other hand, 35 projects or 85.37% experience a cost or duration overrun, which

seems like a realistic figure. Still, since FRF is only applied to 41 projects, the results must be evaluated with

caution and further research will certainly be necessary to validate this technique.

The projects used are all construction projects and their number is limited to 41. The reason behind this

decision is that the other projects in the database do not have (sufficient) similar projects to be compared

with. Consequently, without enough similar projects to compare with, it is difficult to apply the FRF

technique on these other projects. The database can be consulted at the site of the OR&S group of the

University of Ghent. More information about the database can be found in Batselier & Vanhoucke (2015)

and Vanhoucke et al. (2016).

17

IV.II Determining Functional Requirements

Provided with the project data, the next step was to determine the functional requirement(s) of each

activity. As stated in the introduction, a functional requirement emphasizes that what the system must do

(Farid & Suh, 2016). When assigning a FR to an activity in a project, it is key to understand which function

an activity fulfils in the project. Often this was not an easy task, because on some subjects a lot of

information can be found and for other activities websites of small and medium-sized enterprises (SMEs)

needed to be consulted to figure out which function a material or activity had. In order to be consistent we

worked with the same methodology to find information about every activity. Figure 4.1 gives an sequential

overview of the consulted sources. Since the functional requirements of 1193 activities needed to be

determined, we decided that when useful information is found at source 1 (the Belgian Building Research

Institute), it was unnecessary to consult the other sources. If not, source 2 is consulted for which the same

rule applies. Only when extra information was found during the Google Search that could help in the

searching process, the searching process started again at source 1.

Figure 4. 2 Methodology information searching process

Since all projects are construction projects, the website of WTCB (Het Wetenschappelijk en Technisch

Centrum voor het Bouwbedrijf) or BBRI (Belgian Building Research Institute) was my most valuable source

of information. The BBRI is a private research institute and has 3 main tasks: perform scientific and technical

research for the benefit of its members, supply technical information and assistance to its members and

contribute to the general innovation and development in the construction sector. Via this website it is easy

to consult magazines, reports, files and technical information about a lot of construction related subjects.

Most functional requirements are determined based on the information found at WTCB.

The next source of information was the website of the WBDG (the Whole Building Design Guide). The WBDG

presents itself as “the only web-based portal providing government and industry practitioners with one-

18

stop access to up-to-date information on a wide range of building-related guidance, criteria and technology

from a 'whole buildings' perspective”.

When no useful information about an activity could be found at the previous scientific sources, the next

step was to search Google for some information. Most of the times it were the SME websites that provided

the best information, but also Wikipedia was useful to learn more about activities or materials. Occasionally,

a different name for an activity or material was found during this stage and then WTCB and WDBG were

consulted again to see if more information could be found about the subject under this different name.

The last source of information were experts in the construction industry. However, nowadays most

information is available online and except for some clarification about the use of some materials, this source

was not used. The links to the websites of the different sources of information can be consulted in Appendix

A.I.

Understanding what an activity does is one thing, but then it was key to give an accurate and good

description of its functional requirement. Sometimes two or even more different tasks were combined in

one activity, which made its FR description rather long. Furthermore, the level of detail in the description

of the activity differs a lot from project to project. In a lot of activity descriptions there is a lack of

information to give a detailed description of its FR, for example in a lot of activities the materials used are

not mentioned. This resulted in rather general descriptions of the FRs.

During the process of defining the FRs, some Rules of Thumb (ROT) were used to be consistent in their

description:

ROT 1: In the description of its FR, the emphasis lies on the main function of the activity and an

abstraction was made of its secondary functions.

ROT 2: The information obtained from the scientific sources WTCB and WDBG we considered as

reliable. If relevant information was obtained via SME-websites, Wikipedia or other sources during

the Google search, at least one other source had to confirm this information to be considered

reliable. In the situation where 2 sources do not agree about the function, extensive research was

done and multiple sources were consulted in order to be able to decide which information is

reliable.

ROT 3: If no or only little information could be found about an activity or material, related search

terms were searched and used to obtain the information needed. In the rare situation that we

could not determine what an activity does, because its description was unclear, we did not define

a FR.

19

Table 4.2 shows some examples of activities and their defined FR.

Activity Functional Requirement

Electricity Enable the use of electrical devices and providing light

Ventilation Ensure air quality

Kitchen Hygienic and safe cooking possibility

Pile Foundation Stability: transfer tensile and compressive forces to the underlying layers

Concrete Reinforcement Strength: resistance against tensile forces

Office Automation Efficient and qualitative automating the information processing and communication tasks

Solar Panels Efficient solar energy conversion to electricity

Gutter Rainwater drain

Roof Insulation Limit heat loss through the roof

Plastering the walls Flat finishing of the walls

Table 4. 2 Examples of Activities and their Functional Requirements

As stated before, the descriptions of the FRs are sometimes limited due to a lack of information in the

activity description. In the future this lack of information can be dealt with when the functional

requirements are added to the project information. This can have the advantage that project managers will

think more about the function and the importance of an activity in a project.

The method is called Functional Requirement-based Forecasting, so the importance of the functional

requirements must not be underestimated. By working with the methodology above I tried to be consistent

and made sure that the same activities have the same FR(s) across the different projects.

20

IV.III Project Size

The determination of the size of the projects was the next important step. The project size is a key factor in

the calculations of the estimated cost and duration of a project. Since these forecasts are based on the

actual cost and duration of past similar projects, the difference in project size between the projects needs

to be taken into account. For example, it would not be logical to estimate the cost and duration of

constructing an apartment building with 2 floors based on the actual values of an apartment building with

5 floors.

In the IT sector the project size can be determined by lines of code (LOC) and function point (FP), but how

could one determine the project size of a construction project to estimate the cost and duration? Can they

be based solely on the dimensions of the building? No, the project size includes factors as cost, duration,

staffing requirements, degree of complexity, level of risk and the strategic value to the organisation. This

size is used to quantify the importance and consequently the management effort an organisation will put

in a project. A tool that allows an organisation to compare their projects in terms of their relative magnitude

is the project sizing matrix. The factors included in this tool are dependent on the relevant project attributes

and an example of such a project sizing matrix is given below (Figure 4.2). In many cases projects are

categorised in small, medium and large projects. The purpose of this project categorisation is that small,

simple and low-risk projects need a different approach then large, complex and high-risk projects (Burgan

& Burgan, 2014).

Small Project Medium Project Large Project

• Project duration less than six

months

• Project duration between six

months and 12 months

• Project duration greater

than 12 months

• Project budget less than

$100,000

• Project budget between

$100,000 and $500,000

• Project budget greater than

$500,000

• Project team fewer than five

people

• Project team between five

and 20 people

• Project team greater than 20

people

• Minimal integration with

other business units

• Moderate integration with

other business units

• Significant integration with

other business units

• Impacts fewer than 25 end

users

• Impacts 25 to 250 end users • Impacts more than 250 end

users

Table 4. 3 Project sizing matrix. Source: Burgan & Burgan (2014)

Looking at the project data, one can see that there is again a lack of information regarding the project team,

degree of complexity, dimensions of the building, etc. The only relevant project attributes we have are the

cost and the duration of the projects. The big advantage here is that the actual cost and duration of the

21

projects are available, which would not be the case for an early stage project. Since the estimates in FRF

are based on similar projects, it is possible to make some assumptions.

Assumption 1: The complexity of the projects in a project group is similar and does not imply a

difference in size.

Assumption 2: The number of people working on the project is equal for similar projects. If this is

not the case and one project has a higher number of staff working on the project this will result in

higher costs and a lower duration of the activities.

Assumption 3: The dimensions of the construction project reflect in the cost and duration of the

project. The bigger the project in terms of height, width and length, the higher the cost and the

longer the duration of the project. Consequently, the project size based on these higher values will

be bigger.

Assumption 4: The importance and impact of the similar projects in a project group are assumed

equal.

These assumptions limit the comparison of the project sizes to similar projects, because they can only be

made for projects who are in the same project group. Consequently, the inputs of the forecasts are limited

to the data of similar projects. In contrast to the project sizing matrix, we did not divide the projects into

different categories (small, medium and large), but instead evaluated them separately based on their actual

cost and duration.

Visualisation of the project groups

If the project sizes in a group are equal to each other there must be a correlation between the cost and

duration of the projects. Assuming equal project sizes, the projects with a higher cost should have a shorter

project duration (and vice versa) and form an efficiency frontier. When we look at Figure 4.3 we see that

there is no clear correlation between cost and duration in any project group, so we assume that the project

sizes are different for each project. Note that some projects groups have more observations then their

number of projects. This is because some projects are divided in subprojects in Chapter V.

22

Figure 4. 3 Correlation Actual Cost Actual Duration

Quantifying the project size

The literature describes the division between small, medium and large projects, but does not include an

unambiguous formula to quantify these project sizes. Taking into account the assumptions and since we

have the actual cost and duration of all projects, the project sizes can be determined based on these data.

The cost of the projects is measured in Euro and their duration is measured in time-units, so they cannot

be added up to quantify the size of a project. Instead, what we can do is comparing the sizes of two different

projects within a project group by dividing the actual cost of the first project by the actual cost of the second

project and do the same for their actual durations. If we add these two values together, we get a

dimensionless value for their difference in project size. This dimensionless value will be referred to as the

‘size-factor’ in the following parts of this dissertation and is calculated by using the following formula:

23

𝑆𝑖𝑧𝑒𝑖,𝑥 = 𝑎 ∗𝐴𝑐𝑡𝑢𝑎𝑙 𝐶𝑜𝑠𝑡𝑥

𝐴𝑐𝑡𝑢𝑎𝑙 𝐶𝑜𝑠𝑡𝑖+ (1 − 𝑎) ∗

𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑥

𝐴𝑐𝑡𝑢𝑎𝑙 𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖 (1)

𝑆𝑖𝑧𝑒𝑖,𝑥 is the relative size of project 𝑥 compared to project 𝑖. As mentioned above, this is a key factor in the

calculations of the forecasted cost and duration of activities in project 𝑥. Because these forecasts are based

on actual values of project 𝑖, these actual values need to be adjusted to the size of project 𝑥 with this size-

factor.

Coefficient 𝑎 ∈ [0,1] indicates the relative importance of the actual cost in the determination of the relative

project size. Consequently, (1 − 𝑎) indicates the relative importance of the actual duration in the

determination of the size-factor. So, in order to determine the size-factors, a last assumption is needed.

Assumption 5: Cost and duration are equally important factors in the calculation of the project

size-factors.

Assuming that cost and duration are equally important factors in determining the project size, coefficient

𝑎 in Equation (1) is initially set equal to 0.50. In Chapter V a sensitivity analysis is performed on this

assumption.

24

IV.IV Calculations

The last step in the methodology is to calculate the cost and duration forecasts and compare them with the

actual values of the project. The forecast formula is applied for every activity in a project that has at least

one similar project in its project group with an activity that has the same functional requirement(s). The

same formula (Equation (2)) is used to determine both the forecasted cost and duration. The forecasted

values represent the average actual values of activities with the same FR in similar projects, taking into

account the difference in project sizes. The forecast value of the activity with functional requirement 𝑦 in

project 𝑥 is denoted as 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑥,𝑦.

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑥,𝑦 =

∑ 𝑆𝑖𝑧𝑒𝑖,𝑥 ∗ 𝐴𝑐𝑡𝑢𝑎𝑙𝑉𝑎𝑙𝑢𝑒𝑖,𝑦𝑛𝑖=1

𝑛 (2)

𝐴𝑐𝑡𝑢𝑎𝑙𝑉𝑎𝑙𝑢𝑒𝑖,𝑦 denotes the actual cost or actual duration of the activity with functional requirement 𝑦 in

project 𝑖 dependent on whether the cost or duration is forecasted. As already explained in the previous

part, 𝑆𝑖𝑧𝑒𝑖,𝑥 is the relative size of project 𝑥 compared to (similar) project 𝑖. The number of similar projects

that have an activity with the same functional requirement 𝑦 is denoted by 𝑛.

After the forecasted values for the activities are computed, they are compared to their actual costs or

durations. To avoid biased results caused by extreme forecast errors, the forecast error of an activity is

limited to 100%. The forecast accuracy is measured for every activity with functional requirement 𝑦 of

project 𝑥 using formulas (3) and (4).

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑥,𝑦 = max

𝑥,𝑦(1,

|𝐴𝑐𝑡𝑢𝑎𝑙𝑥,𝑦 − 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑥,𝑦|

𝐴𝑐𝑡𝑢𝑎𝑙𝑥,𝑦 ) (3)

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦𝑥,𝑦 = 1 − 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑥,𝑦 (4)

Now the forecasts for the individual activities are obtained, the forecast accuracy of a project can be

evaluated in two ways. The first way is to use these forecasts as input to calculate the total forecasted cost

and duration of a project. The forecasted cost of an activity can be easily obtained by taking the sum of the

forecasted costs of its activities. However, the forecasted duration of a project depends on its critical path.

Because the critical path of a project can change when the durations of the activities change, the forecasted

duration cannot be obtained by taking the sum of the forecasted duration of the critical activities in the

existing baseline schedule. Fortunately, the dataset of Batselier & Vanhoucke (2015) available at the OR&S

group of Ghent University contains a Protrack file of each project. By changing the initial planned duration

of the activities to the forecasted duration of the activities in Protrack, the program automatically

25

recalculates the critical path and the total forecasted duration of the project. For practical purposes the

forecasted duration is rounded to the hour in Protrack, given that there are 8 (working) hours in a day.

To evaluate these total project cost and duration forecasts, the Absolute Percentage Error (APE) is used and

we will call this the ‘Absolute Forecast Error’ (FE) of a project for the remaining of this dissertation. Likewise,

the forecast accuracy of these forecasts will be referred to as the ‘Absolute Forecast Accuracy’ of project 𝑥.

𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑥 =

|𝐴𝑐𝑡𝑢𝑎𝑙𝑥 − 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑥|

𝐴𝑐𝑡𝑢𝑎𝑙𝑥 (5)

𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦𝑥 = 1 − 𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑥 (6)

Despite the fact that this is a realistic first assessment of Functional Requirement-based Forecasting, the

Absolute Forecast Accuracy cannot be calculated for every project. When only 1 activity in a project doesn’t

have a similar activity with the same functional requirement in its project group, the cost and duration of

that activity cannot be forecasted with FRF. Furthermore, by taking the sum of the activity forecasts,

forecast errors may cancel each other out. To avoid these two problems the Mean Absolute Percentage

Error (MAPE) of the activities is used to evaluate a project. In this way the activities without forecast can be

left out and every forecast error will be included in the assessment of the project forecast.

The formulas for the MAPE and the Forecast Accuracy of project 𝑥 are given below.

𝑀𝐴𝑃𝐸𝑥 =

1

𝑁∗ ∑ 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑥,𝑦

𝑁

𝑦=1

(7)

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦𝑥 = 1 − 𝑀𝐴𝑃𝐸𝑥 (8)

𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑥,𝑦 refers to Equation (3). It was already mentioned that the forecast error of an activity is

limited to 100% to avoid biased results caused by extreme forecast errors. 𝑁 denotes the number of

activities that can be forecasted with FRF in project 𝑥.

Next, the results of this methodology will be presented and analysed. The goal is to find the strengths and

weaknesses of Functional Requirement-based Forecasting and to find an answer on the research questions

of this dissertation.

26

Chapter V

Results

In this chapter the results of the FRF technique are analysed and thoroughly discussed. First, the forecasts

for the different project groups are evaluated. For every group some project specific information is given

and the forecast accuracy of each group is examined. The second part of this chapter consists of a general

assessment of the forecast accuracy of FRF for the data used in this dissertation. Subsequently, we test the

hypothesises made in this dissertation by performing a regression analysis of the different factors on both

the cost and duration MAPE. Finally, a sensitivity analysis is performed on the assumption that cost and

duration are equally important factors in the calculation of the project size-factors.

In figure 5.1 the structure of this chapter is visualised.

Figure 5. 1 Overview Result Analysis

27

V.I Results per project group

Residential House Finishing Works (RHFW)

This project group consists of 11 Residential House Finishing Works (RHFW) projects and is the group with

the largest number of projects. Looking at the activities, one sees that all projects are divided into 4 similar

phases. Moreover, except for RHFW (3), (4) and (5), who have the extra activity “floor heating”, the activities

of the projects are exactly the same! Consequently, the FRs of these activities are the same and their

forecasted cost and duration are based on the highest possible number of (actual) observations in this

project group.

Regarding the original planned costs, one sees that all projects are executed over budget, with an average

cost overrun of 6.90% and a maximum cost overrun of 19% in RHFW (3). The results of applying FRF to

forecast the costs of these projects are presented in Table 5.1. The average Absolute Forecast Error is

decreased to 5.35% and in only 4 projects the forecasted costs are lower than the average cost. Also the

MAPE of the cost of the activities are relatively low and the average MAPE of the projects is 13.73% and

consequently an average cost forecast accuracy of 86.27%.

Project Cost Forecast Actual Cost Absolute FE

MAPE

Residential House Finishing Works (1) 58,729.27 € 64,526.76 € 8.98% 11.79% Residential House Finishing Works (2) 57,419.47 € 64,580.17 € 11.09% 14.09% Residential House Finishing Works (3) 59,495.33 € 60,829.52 € 2.19% 12.59% Residential House Finishing Works (4) 56,058.21 € 53,351.38 € 5.07% 16.88% Residential House Finishing Works (5) 57,955.79 € 53,783.28 € 7.76% 17.20% Residential House Finishing Works (6) 58,154.16 € 54,996.22 € 5.74% 11.89% Residential House Finishing Works (7) 59,412.22 € 57,822.40 € 2.75% 10.60% Residential House Finishing Works (8) 58,888.42 € 56,645.71 € 3.96% 11.01% Residential House Finishing Works (9) 57,344.27 € 53,176.83 € 7.84% 13.33%

Residential House Finishing Works (10) 55,600.15 € 56,748.33 € 2.02% 16.84% Residential House Finishing Works (11) 54,073.71 € 53,319.24 € 1.42% 14.80%

Table 5. 1 Residential House Finishing Works: Cost forecast

In the previous chapter it was already mentioned that the actual duration and the initial planned duration

for all the projects in the RHFW-group are the same. As a result it is impossible to improve the duration

forecast accuracy with the proposed technique. Nevertheless, the Absolute Forecast Error is very low with

an average of 6.36% and 6 projects have an actual duration that is longer than the duration forecast.

Besides, the average MAPE of the activities regarding duration is 14.22%, which is similar to MAPE of the

cost forecasts. Furthermore, Table 5.2 indicates no extreme forecast errors.

28

Project Duration Forecast

Actual Duration Absolute FE

MAPE

Residential House Finishing Works (1) 97d 3h 90d 8.19% 20.90% Residential House Finishing Works (2) 95d 5h 86d 11.19% 17.09% Residential House Finishing Works (3) 100d 7h 91d 10.85% 15.28% Residential House Finishing Works (4) 93d 4h 91d 2.75% 14.49% Residential House Finishing Works (5) 98d 4h 91d 8.24% 18.03% Residential House Finishing Works (6) 94d 101d 6.93% 11.24% Residential House Finishing Works (7) 96d 3h 101d 4.58% 12.62% Residential House Finishing Works (8) 96d 1h 101d 4.83% 12.04% Residential House Finishing Works (9) 92d 101d 8.91% 10.35%

Residential House Finishing Works (10) 90d 6h 91d 0.27% 11.54% Residential House Finishing Works (11) 88d 1h 91d 3.16% 12.86%

Table 5. 2 Residential House Finishing Works: Duration forecast

This is without a doubt the project group with the most accurate forecasts. Furthermore, the fact that there

are only relatively small differences in actual cost and duration between the projects, results in size-factors

close to 1. Still, one must be careful on drawing conclusions about FRF based on the results of this project

group. Since these 11 projects are all performed exactly on their time schedule, the results may be biased.

Looking at Table 5.2 it is highly unlikely that these actual durations are the real actual durations of these

projects. The actual durations are very similar and this affects the size factor which is closer to 1 and as a

consequence less influential in the calculations of the forecasts.

Residential House (RH)

The project group Residential House (RH) consists of 4 projects with more or less the same structure. The

activities are subdivided into similar categories across the projects, but within these categories the activities

differ across the projects. Before looking at the result, it must be mentioned that for each project in this

group at least 1 activity cannot be forecasted, due to the lack of a similar functional requirement in the

other projects. Since these activities account only for a small amount of the total cost and duration, we

decided to take over their initial planned values in the calculations of the absolute FE for both cost and

duration. Note that the calculations of the MAPE are solely based on the estimates of the FRF technique.

The cost forecasts in Table 5.3 show that project RH (3) has the worst forecast with an absolute forecast

error of 22.11% and a MAPE of 53.52%. Furthermore, this is the only project that underestimates the actual

costs. The values of the MAPE of these projects lie very close to each other with an average of 51.96%. This

bad result is particularity caused by high forecast errors in low cost activities, which is reflected in the much

lower absolute forecast error values.

29

Project Cost Forecast Actual Cost Absolute FE

MAPE

Residential House (1) 191,352.33 € 163,189.00 € 17.26% 52.30% Residential House (2) 253,948.00 € 226,285.00 € 12.22% 50.05% Residential House (3) 295,435.92 € 379,300.00 € 22.11% 53.52% Residential House (4) 238,494.76 € 222,021.78 € 7.42% 51.97%

Table 5. 3 Residential House: Cost forecast

For the duration forecasts similar results can be noticed (Table 5.4). RH (3) performs even worse in terms

of its absolute duration forecast accuracy with an absolute duration forecast error of 39.92%. The other

projects have a more accurate absolute forecast with absolute forecast errors around 10%. Furthermore,

the duration MAPE values lie very close to each other as well, but it is remarkable that the average duration

MAPE for the RH project group is about 5 % lower than for its costs.

Project Duration Forecast

Actual Duration Absolute FE

MAPE

Residential House (1) 225d 3h 254d 11.27% 43.69% Residential House (2) 317d 7h 291d 9.24% 43.61% Residential House (3) 461d 6h 330d 39.92% 48.75% Residential House (4) 279d 7h 320d 12.54% 48.88%

Table 5. 4 Residential House: Duration forecast

Despite that the MAPE for both cost and duration are very high, the total forecasted values for RH (1), (2)

and (4) are acceptable, given the low number of projects in this project group. The fact that the forecasts

for RH (3) underestimate the cost and overestimate the duration of the execution, can be due to a

difference in project team size between these projects. More resources cost more money, but can shorten

the duration of the project significantly. Since these projects lack this information, it is impossible to know

what the cause of this bad forecast is. Still we must treat these absolute project results with caution due to

the fact that for some small activities the initial planned values are used. Looking at the MAPE results, we

can conclude that for the individual activities in this project group the forecasts of the FRF technique are

inadequate.

Apartment Building Finishing Works (ABFW)

The Apartment Building Finishing Works (ABFW) group is composed of 4 projects. ABFW (1) and (2) execute

the finishing works of apartment block A and ABFW (3) and (4) those of block B. Each project includes the

finishing works of 2 floors. Since most activities emerge in the execution of each floor, we chose to handle

the data as if it were 8 different projects. Note that one activity could not be forecasted using FRF: the (last)

activity “cleaning up” with an actual cost of € 901 and 5 days duration in project (4). Since this is only a small

part of the total cost and duration, we decided to use the initial planned cost (€ 920) and duration (5 days)

in the calculations of the absolute forecast error here as well. The MAPE is based on the FRF forecasts only.

30

The cost forecasts of each project in Table 5.5 equal the sum of the costs of its 2 floors. When taking the

sum of the actual costs and cost forecasts of each project, we notice that the total absolute forecast error

of the project group (or the 2 apartment blocks) is only 1.10%. This figure is the result of the fact that the

underestimating of the costs of project (2) and the overestimating of the costs of project (3) cancel each

other out. It is clear that the MAPE of the individual activities is higher, with an average of 16.19%. Since

the execution of each floor consists of only a dozen activities, the MAPE is sensitive to a bad forecast in one

activity. Still, the forecasts for most subprojects are relatively accurate.

Project Cost Forecast Actual Cost Absolute FE

MAPE

Apartment Building Finishing Works (1) 502,435.59 € 498,473.00 € 0.79% 16.88% Block A ground floor 264,031.24 € 267,887.00 € 1.44% 19.43%

Block A first floor 238,404.35 € 230,586.00 € 3.39% 14.09% Apartment Building Finishing Works (2) 443,640.83 € 496,991.00 € 10.73% 13.59%

Block A second floor 240,497.03 € 245,743.00 € 2.13% 8.99% Block A third floor 203,143.80 € 251,248.00 € 19.15% 18.20%

Apartment Building Finishing Works (3) 455,987.63 € 394,829.00 € 15.49% 19.74% Block B ground floor 245,590.94 € 204,250.00 € 20.24% 24.17%

Block B first floor 210,396.69 € 190,579.00 € 10.40% 14.91% Apartment Building Finishing Works (4) 391,626.31 € 383,871.00 € 2.02% 14.53%

Block B second floor 209,001.47 € 187,211.00 € 11.64% 16.38% Block B third floor 182,624.84 € 196,660.00 € 7.14% 12.67%

Table 5. 5 Apartment Building Finishing Works: Cost forecast

The results of the duration forecast are comparable with the results of the cost forecast. The same

reasoning can be applied here: the absolute forecast errors are relatively low, but the average MAPE is

18.17% due to its sensitivity to a bad forecast value of one activity. Nevertheless, Table 5.6 shows no

extremely high duration MAPE values.

Project Duration Forecast

Actual Duration Absolute FE

MAPE

Apartment Building Finishing Works (1) 107d 6h 117d 7.91% 17.61% Block A ground floor 94d 6h 92d 2.99% 16.28%

Block A first floor 91d 2h 100d 8.75% 19.07% Apartment Building Finishing Works (2) 112d 4h 97d 15.98% 19.87%

Block A second floor 92d 7h 97d 4.25% 12.38% Block A third floor 85d 4h 70d 22.14% 27.36%

Apartment Building Finishing Works (3) 121d 2h 129d 6.01% 12.42% Block B ground floor 96d 6h 97d 0.26% 14.24%

Block B first floor 82d 4h 92d 10.33% 10.44% Apartment Building Finishing Works (4) 92d 2h 92d 0.27% 22.76%

Block B second floor 82d 2h 92d 10.60% 10.65% Block B third floor 83d 7h 70d 19.82% 34.87%

Table 5. 6 Apartment Building Finishing Works: Duration forecast

31

The forecast accuracy of the activities in this project group is on average above 80% for both cost and

duration. The fact that the ‘Finishing Works’ activities per floor are almost equal and that most forecasts

are based on 7 actual observations indicates why FRF is relatively accurate for this group. But since there

are not a lot activities per floor, the results are sensitive to a bad result of one activity. Still, FRF is a decent

forecast technique for the ABFW group.

Apartment Building Structural Work (ABSW)

The next group, Apartment Building Structural Work (ABSW), consists of only 3 projects. The first thing to

notice when looking at these projects is that ABSW (1) is very different from ABSW (2) and (3). ABSW (1)

executes the construction of the foundations, the sewerage and the cellar floor. ABSW (2) and (3) construct

the structure of respectively block A and block B of the apartment building with each 4 floors. Similar to the

previous project group, ABFW, we chose to handle the execution of every floor of ABSW (2) and (3) as if

they were 8 different projects. Since the FRs of the activities in ABSW (1) are different, FRF could not be

used to forecast the cost and duration based on the actual values of the other projects. Instead the

execution of most of the activities in this project is done in 2 phases, between marks Aa-Ab and Ab-Hb.

Before discussing the results of ABSW (1), it must be noted that the calculations of the size-factor of

subprojects Aa-Ab and Ab-Hb are done slightly different. Since the activities of the different subprojects are

not divided into different phases, in contrast to the other projects which are divided into subprojects, they

can be executed at the same time. Instead of taking the sum of the duration of the activities in the critical

path, the actual duration of these subproject are determined by the sum of the actual durations of all their

activities.

Table 5.7 depicts the MAPE results for both cost and duration. In contrast to previous projects, the MAPEs

of the duration forecasts are a lot bigger than those of the cost forecast. This is due to the fact that the

difference in actual duration between these subprojects are a lot bigger than their difference in actual cost.

Since these forecasted values are based on only one actual observation, FRF is very sensitive to these

differences for this project. Note that no total cost or duration is calculated because each subproject had

one activity with a different FR.

Project MAPE Cost MAPE Duration

Apartment Building Structural Work (1) 7.21% 35.80% Aa-Ab 7.24% 27.94% Ab-Hb 7.18% 43.66%

Table 5. 7 Apartment Building Structural Work (1): cost & duration MAPE

Looking at the cost forecasts of ABSW (2) and (3), we see that the absolute forecast errors of the different

subprojects cancel each other out and result in a high absolute forecast accuracy (Table 5.8). Although the

subprojects only consists of maximum 6 activities, the higher MAPE is not due to one bad forecast, but the

32

forecast errors are relatively consistent throughout the activities. The explicit difference in forecast

accuracy between both projects is due to the fact that three subprojects of ABSW (3) have one extra activity

with a big forecast error. This is reflected in higher absolute forecast errors and higher MAPEs for these

subprojects.

Project Cost Forecast Actual Cost Absolute FE

MAPE

Apartment Building Structural Work (2) 1,888,527.96 € 1,860,330.62 € 1.52% 14.48% Block A ground floor 399,834.08 € 465,147.27 € 14.04% 15.11%

Block A first floor 443,019.01 € 380,451.99 € 16.45% 17.69% Block A second floor 473,670.85 € 407,093.49 € 16.35% 15.94%

Block A third floor 572,004.01 € 607,637.86 € 5.86% 9.30% Apartment Building Structural Work (3) 1,534,206.75 € 1,353,361.29 € 13.36% 33.41%

Block B ground floor 419,210.89 € 348,020.95 € 20.46% 22.10% Block B first floor 421,123.50 € 308,583.20 € 36.47% 41.91%

Block B second floor 430,008.29 € 328,262.20 € 31.00% 38.06% Block B third floor 263,864.07 € 368,494.94 € 28.39% 29.69%

Table 5. 8 Apartment Building Structural Work (2) & (3): Cost forecast

While the subprojects of ABSW (2) have a better cost forecast, their duration forecasts are (a lot) worse

than those of ABSW (3). The eye-catcher in Table 5.9 is the execution of the third floor of ABSW (2) with a

MAPE of no less than 71.82%. Despite a MAPE of 40.49%, the absolute forecast error is only 6.08% for ABSW

(2). Again, one can see that the absolute forecast errors in the different subprojects cancel each other out.

Project Duration Forecast

Actual Duration Absolute FE

MAPE

Apartment Building Structural Work (2) 139d 148d 6.08% 40.49% Block A ground floor 21d 3h 46d 53.53% 40.39%

Block A first floor 30d 29d 3.45% 32.89% Block A second floor 32d 4h 31d 4.84% 16.85%

Block A third floor 45d 1h 32d 41.02% 71.82% Apartment Building Structural Work (3) 100d 6h 96d 4.95% 18.16%

Block B ground floor 29d 1h 28d 4.02% 17.04% Block B first floor 27d 5h 29d 4.74% 13.71%

Block B second floor 28d 5h 29d 1.29% 14.12% Block B third floor 15d 3h 10d 53.75% 27.60% Table 5. 9 Apartment Building Structural Work (2) & (3): Duration forecast

When focussing on the absolute forecast errors of the projects, FRF seems like a good technique to estimate

the cost and duration of these projects. Although when looking at the MAPE of the activities, the results of

FRF are contradictory. The fact that the subprojects were very small in terms of number of activities, could

be the reason of the varying forecast accuracies. On the other hand, the forecast of a project with many

activities based on only 1 observation is also very sensitive to variations.

33

Railway Bridge (RB)

This project group concerns 5 Railway Bridge (RB) projects. The execution of these projects is divided into

different phases, but in contrast to previous projects, these phases differ across the different projects. For

example, the construction of the bridge in RB (3) is done in two phases, while in RB (1), (2) and (4) this is

done in three phases. In order to apply FRF to all the projects, we decided to reform the projects by

collecting the activities with the same or similar FRs and categorize them under one activity. So instead of

forecasting the cost and duration of, for example, the activity ‘concrete’ for the different phases, we

preferred to forecast the total cost and duration of concrete for the different railway bridges.

Note that we have no absolute forecasts, because in every project at least one activity could not be

forecasted using FRF. Furthermore, we cannot know which part of the forecasted duration of the newly

compound activities belongs to which initial activity. In this way it is impossible to forecast the total duration

of the project without extra assumptions, because the duration of a project depends on the duration of the

activities on the critical path. Table 5.10 depicts big differences in the MAPE results for both cost and

duration. Especially the cost MAPE of 54.71% in RB (3) and the duration MAPE of 48.55% in RB (5)

demonstrate that FRF falls short to give a reliable estimate for this project group.

Project MAPE Cost MAPE Duration

Railway Bridge (1) 34.65% 26.01% Railway Bridge (2) 34.08% 41.22% Railway Bridge (3) 54.71% 35.69% Railway Bridge (4) 22.42% 38.82% Railway Bridge (5) 26.81% 48.55%

Table 5. 10 Railway Bridge: cost & duration MAPE

Looking at the individual activity forecasts it is remarkable that, although there are 5 projects in this group,

a lot of forecasts are only based on 1 or 2 observations. With an average cost MAPE of 34.53% and an even

higher average duration MAPE of 38.06% FRF proves to be unreliable in obtaining accurate forecasts for the

activities in the RB projects.

Apartment Building (AB)

The ‘Apartment Building’ project group is composed of 6 projects, but the structure of their activities differs

a lot. AB (1), (2) and (3) are not divided into different phases and can be considered pretty similar. Instead

the activities in AB (4) are divided in 3 groups: general activities, the activities done in the different floors

and the activities involving the construction of the ground floor. AB (5) and (6) are not explicitly divided into

phases, but consist of a lot of recurring activities. These recurring activities all have a general activity

description like “electricity”, so it is difficult to determine what the activity entails exactly. Moreover, every

activity in AB (5) and (6) has an initial planned duration of 1 week or 5 (working) days or a multiple of this

value. Similar to the previous project group RB, we decided to reform the 6 projects by grouping the

activities with the same or similar FRs under one activity.

34

Table 5.11 depicts the MAPE of the cost and duration forecast of every AB project. It is clear that for the

individual activities in this project group both the cost and duration forecasts of the FRF technique are

inadequate. The MAPE results of the cost forecasts are more or less similar across the different projects

with an average of no less than 61.36%. The results of the duration forecasts vary, but still have an average

of 61.35%.

Project MAPE Cost MAPE Duration

Apartment Building (1) 66.72% 46.03% Apartment Building (2) 68.83% 81.24% Apartment Building (3) 63.41% 40.79% Apartment Building (4) 58.61% 78.80% Apartment Building (5) 57.43% 57.61% Apartment Building (6) 54.64% 62.73%

Table 5. 11 Apartment Building: cost & duration MAPE

As already explained in the previous project we cannot forecast the total duration of the reformed projects

without extra assumptions. The total costs, on the other hand, can be forecasted if every activity has at

least one activity with the same FR in another project. For AB (3), (5) and (6) the total costs can be calculated

and its absolute forecast errors confirm that FRF falls short in the estimation of the costs for this project

group (Table 5.12).

Project Cost Forecast Actual Cost Absolute FE

Apartment Building (1) - - - Apartment Building (2) - - - Apartment Building (3) 2,514,661.91 € 1,289,696.78 € 94.98% Apartment Building (4) - - - Apartment Building (5) 1,881,783.00 € 2,590,796.73 € 27.37% Apartment Building (6) 862,110.84 € 2,563,675.86 € 66.37%

Table 5. 12 Apartment Building: Absolute cost FE

The big differences in the calculated project sizes of the AB projects might be the reason of these inaccurate

forecasts. Since the cost and/or duration of some activities totally depend on the size of the project and

other activities depend less on the size of the project, the forecast accuracies of these last category

decreases when the differences in project sizes are bigger.

Office Finishing Works (OFW)

The Next project group is Office Finishing Works (OFW). This project group consists of 5 projects and similar

to AB (5) and (6), the OFW projects are not explicitly divided into phases, but have some recurring activities.

Since their activity description is also very general and the number of times a recurring activity returns

differs from project to project, we decided to reform the projects in the same way we did in the two

previous project groups RB and AB. Furthermore it is remarkable that the projects differ a lot in number of

35

activities, and by reforming the projects, we only make this worse. For example, while OFW (4) only consists

of 4 activities, OFW (5) has 20 activities.

Looking at the results of the forecasts of the individual activities in Table 5.13, one can only conclude that

they are disastrous! With an average MAPE of 73.62% for the cost forecasts and an average MAPE of 77.51%

for the duration forecasts, the most striking result is the relative good cost MAPE of OFW (4). In OFW (2)

the MAPE results of the FRF technique reach an absolute low point with an cost MAPE of 89.71% and an

even higher duration MAPE of no less than 94.38%.

Project MAPE Cost MAPE Duration

Office Finishing Works (1) 78.32% 72.02% Office Finishing Works (2) 89.71% 94.38% Office Finishing Works (3) 76.46% 72.82% Office Finishing Works (4) 37.28% 77.80% Office Finishing Works (5) 86.32% 70.51%

Table 5. 13 Office Finishing Works: cost & duration MAPE

Similar to the previous project group AB, we could determine some total cost forecast for some OFW

projects (Table 5.14). When comparing the absolute forecast errors of the projects with the cost MAPE of

the individual activities, we see that the absolute FE is much lower. OFW (3) even has an absolute forecast

accuracy of 86.89%. Still the absolute forecast error of 41.53% in OFW (2) proves that this might be a lucky

result due to the fact that the forecast errors of the activities cancelled each other out.

Project Cost Forecast Actual Cost Absolute FE

Office Finishing Works (1) - - - Office Finishing Works (2) 106,808.80 € 75,468.30 € 41.53% Office Finishing Works (3) 348,759.86 € 308,343.78 € 13.11% Office Finishing Works (4) 151,279.69 € 198,567.00 € 23.81% Office Finishing Works (5) - - -

Table 5. 14 Office Finishing Works: Absolute cost FE

Using FRF for this project group definitely results in the worst MAPE values in the used data. Furthermore,

it is remarkable that there is a big difference in project size between the projects in this project group. This

difference is mainly caused by the biggest project, OFW (1), who’s actual cost is higher than 3 times the

actual cost of the second biggest (OFW (3)). Furthermore the actual duration of OFW (1) is almost twice the

actual duration of OFW (3). Second, the execution of the projects differ too much. Looking at, for example

the grouped activities “moveable walls” or “Office Automation”, it is strange how different their importance

is in terms of actual cost and duration compared with the total actual cost and duration of the project.

36

Social Apartments Ypres (SAY)

The last project group Social Apartments Ypres (SAY) concerns 3 projects. When looking at the execution of

these projects, their difference in terms of activities is the first thing to notice. Furthermore, there is a

difference in level of detail in the activity descriptions between these projects. In order to be able to use

FRF for these activities, we reformed the projects in a similar way as the previous projects by grouping the

activities with similar functional requirements into one activity. Still a lot of activities cannot be forecasted

with the proposed technique due to the lack of activities with the same FRs in the other projects.

In contrast to the previous 2 project groups, no absolute cost forecast accuracies could be calculated for

the projects in this group. Moreover, in SAY (1) and (3) more than half of the activities in the project do not

have an activity in a similar project with the same FR! Since the FRF forecasts are determined by the size

factor of the full project, the results in table 5.15 indicate that this might be the reason why the MAPEs are

very high.

Project MAPE Cost MAPE Duration

Social Apartments Ypres (1) 72.15% 41.67% Social Apartments Ypres (2) 47.42% 38.68% Social Apartments Ypres (3) 56.65% 73.55%

Table 5. 15 Social Apartments Ypres: cost & duration MAPE

In order to check if the forecasts of the SAY projects are biased by their size-factor, we decided to

experiment and forecast the cost and duration of their sub-projects. These sub-projects consists only of

activities which could be forecasted using FRF. Their size factor is determined by the actual cost, which is

equal to the sum of the cost the activities in the subproject, and the actual duration, which is equal to the

sum of the duration of the activities in the subproject instead of the duration of the critical path.

When looking at the results in Table 5.16 we see that the MAPE of the different projects is more aligned,

but the FRF forecast accuracy is not increasing dramatically. While the average cost MAPE is slightly

decreasing from 58.74% to 57.24%, the average cost MAPE even increases from 51.30% to 54.82%.

Project MAPE Cost MAPE Duration

Social Apartments Ypres (1) (b) 59.36% 45.58% Social Apartments Ypres (2) (b) 53.91% 58.03% Social Apartments Ypres (3) (b) 58.44% 60.83%

Table 5. 16 Social Apartments Ypres (b): cost & duration MAPE

Neither the cost, nor the duration forecast accuracies of the projects are reliable. So we can conclude that

FRF fails in estimating these values accurately for this project group. Even when we pull the activities

without a forecast out of the project and determine a new size factor, we do not get better results. The

difference in execution between the execution of the social apartments can be the reason of these poor

forecasts. Furthermore this project group only consists of 2 projects, so the observations on which a

37

forecast is made are scarce. Also the grouping of activities can be another reason of the inaccuracy, but

similar as in other projects we had to do this in order to get comparable results and be able to use FRF.

38

V.II General Results

Now that we discussed the peculiarities and results of the different project groups, we can make a general

evaluation FRF for the data used in this research. Appendix A.2 includes an overview of the MAPE and

absolute FE of all projects for both cost (Table A.1) and duration forecasts (Table A.2). Note that we consider

the subprojects as full-fledged projects. So when appropriate, the average forecast accuracy is calculated

using the results of the subprojects. Looking at the results of the different project groups, some remarkable

patterns can be noticed.

First, we see that for almost every project group the absolute forecast values of the FRF technique are

better than the average MAPE of its activities. Note that the comparison between the total average MAPE

of the projects and their total average absolute FE does not make much sense, because we could not obtain

an absolute FE for every project. Consequently, the average absolute FE is based on fewer projects and

should not be compared with the average MAPE. Nevertheless, it is clear that the absolute FE is much lower

than the MAPE for most projects. This is due to the fact that the absolute forecast errors in the different

activities are sometimes able to cancel each other out and give a more accurate forecast for the total project

cost and duration. Therefore, while assessing the forecast accuracy of FRF, we will focus on the MAPE

results.

Second, the FRF forecast accuracy of the project groups that consist of projects with a similar structure is

remarkably lower than those of the projects we needed to reform to obtain a forecast. When we divided

the project groups in these 2 categories, we see that the mean MAPE of the similar structured projects is

20.97% for cost and 24.02% for duration forecasts. However the average MAPE of the reformed projects is

more than 30% higher for both cost (57.11%) and duration (57.79%). This indicates that the structure of the

project and specific description of the activities are important when using FRF. When projects are executed

in a different way, the results indicate that FRF falls short in predicting an accurate (summed) forecast for

a group of activities with a similar main FR.

Finally, we can conclude that an average cost forecast accuracy of 65.83% and an average duration forecast

accuracy of 63.64% is not the result we are trying to achieve. The large differences in forecast accuracy

between the different project groups indicate that FRF should not be applied in all situations. In the next

part we will test the Hypothesises made in this dissertation and try to determine which factors have an

important effect on the forecast accuracy of FRF.

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V.III Regression Analysis

In the following paragraphs, we analyse the hypothesises made in this dissertation in order to find the

answer to our sub-questions and determine the drivers of an accurate forecast using the FRF technique. In

order to do this we used the well-known statistical tool SPSS to perform a regression analysis on the cost

and duration forecasts. The results of the analysis are discussed for every hypothesis below. The output of

the regression analysis can be consulted in the Appendix (A.III).

Hypothesis 1: a forecast based on more observations will be more accurate.

Regarding the first hypothesis, we performed a simple linear regression of the effect of the average number

of observations on which the activity forecasts of a project are based on the cost and duration MAPE.

Looking at the results of the F-test, we see that the average number of observations is a statistical significant

predictor of both cost and duration MAPE (p<0.001, for both cost and duration). Furthermore, the average

number of observations explains a significant part of the variance in cost (𝑅2 = 0.492) and duration MAPE

(𝑅2 = 0.497). The predicted decrease in MAPE for cost and duration is respectively 5.2% and 5.1% per extra

observation. This means that if the average number of observations of the activities in a project are equal

to 10, the cost forecast accuracy is estimated to be 91%. In order to get a cost forecast accuracy above 80%

the average number of observations for the activities must be at least 7.88. For the duration forecast

accuracy the average number of observations must be at least 8.39 to get this forecast accuracy. So we can

conclude that the expected accuracy will be higher when it is based on more observations.

Hypothesis 2: the bigger the difference in size between the similar projects in a project group, the lower

the forecast accuracy will be.

In order to determine this difference for every project, we calculated the average difference between the

project sizes of the projects in a project group using Equation (9):

∆𝑆𝑖𝑧𝑒𝑦 = ∑ ∑|𝑆𝑖𝑧𝑒𝑖,𝑥 − 1|

𝑛 ∗ (𝑛 − 1)

𝑛

𝑥=1

𝑛

𝑖=1

∀ 𝑖 ≠ 𝑥 𝜖 𝑦 (9)

In this formula 𝑦 denotes the project group with size 𝑛 and 𝑖 and 𝑥 are projects that are part of this project

group. 𝑆𝑖𝑧𝑒𝑖,𝑥 is the relative size of project 𝑥 compared to (similar) project 𝑖 and is calculated using Equation

(1). The absolute value of the difference between the size factor and 1 reflects the difference in project size.

Note that every project in a project group gets the same value.

40

When we test the effect of this factor on the MAPE results of every project, we see that this is again a

significant predictor of both the cost and duration MAPE. Moreover, the difference in size between the

projects explains an even bigger part of the variance in cost (𝑅2 = 0.689) and duration MAPE (𝑅2 = 0.644)

than the average number of observations. Looking at the 𝛽-values of this simple linear regression, we see

that the expected duration forecast accuracy decreases from 83% to 27.4% when the difference in project

size increases from 0 to 1. The expected cost forecast accuracy decreases even more dramatically from

86.1% to 27.6%. Again we can statistically confirm our hypothesis based on the data used.

Hypothesis 3: the more differences in the functional requirements of activities between the similar projects

in a project group, the lower the forecast accuracy will be.

The difference in the FRs of the activities of a project and the similar projects in its project group is

calculated by the average number of observations on which the forecast of the activities in a project is

based on divided by the maximum number of observations a forecast can be based on. When this factor is

equal to 1 the FRs of the activities between the different projects of a project group are the same. The more

this factor decreases, the more differences in terms of FRs.

Also this last independent variable is a statistical significant predictor of both the cost and duration MAPE.

In contrast to the last two factors, there is a clear difference in 𝑅2 values between cost (𝑅2 = 0.644) and

duration (𝑅2 = 0.475). This difference is also noticeable in the 𝛽-values, when the difference in FRs increases

and consequently the independent variable in this simple linear regression decreases, it reflects more in

the expected cost forecast accuracy than in the expected duration forecast accuracy. For example when

this factor decreases from 1 to 0.50, the expected cost forecast decreases with 44.35%, while the expected

duration forecast accuracy decreases with (only) 29.9%. Nevertheless, based on this regression analysis, we

can conclude that all three hypothesises can be statistically proven for the data we used in this dissertation.

41

V.IV Sensitivity Analysis size-factor

As already mentioned in chapter IV, coefficient a ∈ [0,1] in Equation (1) indicates the relative importance

of the actual cost in the determination of the relative project sizes. Consequently, (1-a) indicates the relative

importance of the actual duration in the determination of the size-factors. Until now we made the

assumption that the project’s actual cost and duration are equally important factors in the calculation of

the project size-factors (assumption 5). This means that until now we have calculated the size-factors of the

projects with a = 0.5. In the last part of this chapter we perform a sensitivity analysis on this assumption.

An overview of the MAPE results for the different values of a is presented in Appendix A.IV.

Figure 5.2 visualizes the effect of changing the relative importance of the project’s actual cost in the

determination of the size-factors on the total average cost MAPE of the projects. It is clear that when a

increases, the cost MAPE decreases. Furthermore, we can see an almost perfect linear correlation between

the values of a and the average cost MAPE. Looking at the exact values in Table A.3, we see that this effect

is returning in almost every project group. The total average cost forecast accuracy even increases above

70% for a = 1. When we determine the relative project sizes solely based on their actual duration and

therefore set a = 0, we see that the mean total cost MAPE increases to 40.85%.

Figure 5. 2 Sensitivity analysis a: cost MAPE

In contrast to the cost MAPE, Figure 5.3 depicts much smaller differences in the total average duration

MAPE for the different values of a. Furthermore, in Table A.4 we see that when a = 0 and the size-factor is

solely determined by the actual duration ((1-a) = 1), the mean duration MAPE is even a little higher (36.74%)

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

0 0.25 0.33 0.4 0.5 0.6 0.67 0.75 1

Cost MAPE

42

than for its initial value (36.36%). We can conclude that the average duration MAPE is relative insensitive

to changes in coefficient a, except for a clear increase to 39.51% when we set a = 1.

Figure 5. 3 Sensitivity analysis a: duration MAPE

In the last part of this sensitivity analysis, we analyse the effect of changing our methodology and set the

size factors equal to 1. In this way the forecasts are based solely on the actual values of the other activities

with the same FR. Consequently, the activities that do not depend on the project size will be estimated

more accurately.

Looking at the results in Table A.5, we see that the total average cost MAPE increases to 36.03%, which is

still a lot better than the 39.51% for a = 1. Instead, the total average duration MAPE decreases to its lowest

level (35.18%). This decrease is not that big, but it indicates that estimating the duration is not dependent

on the size of the project for all activities.

34.00%

35.00%

36.00%

37.00%

38.00%

39.00%

40.00%

0 0.25 0.33 0.4 0.5 0.6 0.67 0.75 1

Duration MAPE

43

Chapter VI

Conclusion

The goal of this dissertation is to introduce the use of a new technique to estimate the cost and duration of

a project. However, the average forecast accuracy results of the 41 projects used in this research are not

the results we are trying to achieve. Nevertheless, the large differences in forecast accuracy between the

different project groups indicate that FRF has the potential to be provide accurate forecasts, but should not

be applied in all situations.

In order to determine in which situations FRF could be useful, we performed a regression analysis. In this

regression analysis, we tried to find an answer to our research question by testing the hypothesises made

in Chapter III. The results of the regression analysis statistically confirmed the hypothesises made. In other

words, the 3 factors which were tested are statistical significant predictors of both the cost and duration

forecast accuracy. Moreover, this analysis indicated that when the values of these variables change this has

a serious impact on the expected forecast accuracy, which confirmed that FRF should only be applied when

certain conditions are met. These conditions include that the forecasts should be based on sufficient

observations, the differences in project sizes in a project group are low and the FRs of the activities in the

projects of a project group are to a great extent the same.

Furthermore, we performed a sensitivity analysis on the assumption that the project’s actual cost and

duration are equally important factors in the calculation of the project size-factors. Looking at the results

we see that we obtain a lot better predictions for costs when the project size is determined solely by the

actual cost of the projects. Regarding the duration forecasts, we see that the best results are obtained when

we change our methodology and set the project size-factors equal to 1. This indicates that the cost and

duration forecasts should not be calculated by using the same methodology.

When assessing the potential of the proposed technique, we need to acknowledge the limitations of this

research. First of all, FRF is only applied to 41 projects, which are all located in the construction sector. In

order to validate this technique, it should be tested on more projects and for projects which are located in

different industries.

Second, the lack of information in the activity descriptions made it difficult to determine an accurate FR and

base the forecast on the activities with the exact same FR. Instead, in this dissertation more general FRs

were defined and this sometimes resulted in poor forecasts based on activities that are not that comparable

with the activity that needs to be forecasted.

44

The next limitation is the restriction that the duration and cost of the project activities can only be

forecasted based on the actual values of similar projects. Due to a lack of information in the project data,

we needed to make assumptions to determine the relative project sizes. These assumptions made it

possible to determine the relative project sizes of similar projects, but not between the projects of different

project groups. Furthermore, the size-factors are solely determined by the actual cost and actual durations

of the projects, while a lot of other factors play an important role in the project size as well.

Finally, not for every project an absolute forecast error could be calculated, since this requires that every

activity in a project has at least one activity with same FR in a similar project. Furthermore, the reforming

of some projects based on a (limited) activity description, might bias the results of these projects.

Consequently, a lot of future research could still be performed. Not only could this method be applied to

more projects in different sectors and with more information. It would also be interesting to see what the

result of FRF could be on a new project. Until now the technique is only applied to forecast projects that

have already been executed.

The sensitivity analysis of the size-factor has shown that the current size-factor is far from optimal in terms

of forecast accuracy. Moreover, considering using a different methodology to forecast the cost and another

one to forecast the duration, could be the starting point to redesign FRF. The final limitation is that FRF

lacks a method to compare the sizes of projects from different project groups. Therefore, the activities

cannot be based on the values of projects from a different group. Consequently, developing a method to

quantify the project size of projects could be a real future breakthrough for FRF.

45

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47

Appendix

A.I List of consulted websites to determine the Functional Requirements

Source 1: BBRI (=WTCB)

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20188.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2010_3_nr9.

pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact30&art=458&lang=nl

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact49&art=735

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2016_3_nr6.

pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact28&art=425

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact23&art=340&lang=nl

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=infofiches&pag=20&lang=nl

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20208.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20227.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=WTCB_Rapport_10.pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact39&art=598

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact43&art=650

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2013_4_nr1

2.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20220.pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact35&art=544

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=Meetstaat%202_3.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=WTCB_Tijdschrift_1990_4.6.

pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=WTCB_Tijdschrift_1994_3_p

17.pdf

48

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2018_3_nr1

2_scheidingswanden_uit_akoestisch_verbeterde_gipsplaten_en_profielen.pdf

http://www.confederatiebouw.be/portals/38/CDSchrijnwerk/data_tech_doc_schrijnwerk/docs/Houtcons

tructies/Dimensionering%20van%20houtconstructies.%20Deel%202.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2015_2_nr3

3.pdf

https://www.wtcb.be/homepage/download.cfm?dtype=publ&doc=WTCB_Rapport_19.pdf&lang=nl

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact22&art=332&lang=nl

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN_265.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2015_2_nr2

5.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20188.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20209.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2018_3_nr1

2_scheidingswanden_uit_akoestisch_verbeterde_gipsplaten_en_profielen.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20237.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=Meetstaat%202_27_1.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20232.pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact40&art=616

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=Meetstaat%202_27_3.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=WTCB_Digest_nr_14.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20233.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20214.pdf

https://www.wtcb.be/homepage/download.cfm?dtype=publ&doc=WTCB_Rapport_19.pdf&lang=nl

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact4&art=54&lang=nl

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-

contact&pag=Contact35&art=548

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=WTCB_Tijdschrift_1993_2_p

47.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20144.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20129.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20247.pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=infofiches&pag=13&lang=nl

49

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20147.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2006_3_nr1.

pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2018_1_nr9_ventilatie_van_woningen_hybride_systemen_en_toekomstige_tendensen.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2018_1_nr5_een_nieuwe_kijk_op_sanitaire_verdeelinstallaties.pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=infofiches&pag=48&art=2&lang=nl

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=Meetstaat%202_20_21.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20251.pdf

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=bbri-contact&pag=Contact56&art=848

https://www.wtcb.be/homepage/index.cfm?cat=publications&sub=infofiches&pag=69&art=4&lang=nl

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=WTCB_Digest_nr_2.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=TVN%20198.pdf

https://www.wtcb.be/homepage/download.cfm?lang=nl&dtype=publ&doc=wtcb_artonline_2015_2_nr28.pdf

Source 2: WBDG

http://www.wbdg.org/FFC/ARMYCOE/TECHNOTE/technote16.pdf

http://www.wbdg.org/FFC/DOD/UFGS/UFGS%2012%2035%2039.pdf

http://www.wbdg.org/FFC/DOL/OSHA/OSHA3755.pdf

http://www.wbdg.org/guides-specifications/building-envelope-design-guide/below-grade-systems/floor-slabs

Source 3: Google Search

https://www.amsadvocaten.nl/woordenboek/bouwrecht/regiebasis/

https://www.astrimex.be/index/metalen-plafonds

http://betonwerkenguessoum.be/werken/

http://www.expertisebetonenstaal.be/sites/default/files/atoms/files/H%202.6%20%20uitzetvoegen%20v

an%20bruggen_0.pdf

https://www.gidsduurzamegebouwen.brussels/nl/akoestiek-van-een-verplaatsbare-

wand.html?IDC=10164#

http://www.joostdevree.nl/bouwkunde2/jpgb/baksteen_16_technische%20eigenschappen_www_bakste

en_be.pdf

http://www.joostdevree.nl/shtmls/meerwerk.shtml

50

https://www.knauf.be/nl/node/8313

http://www.leendertjanse.nl/plafonds/vaste-plafonds/

https://resiplast.be/waterdichte-rok-op-bruggen/

https://www.techopedia.com/definition/4319/office-automation-oa

https://www.vdbbeton.be/nl/pages/grachtelement.html

https://nl.wikipedia.org/wiki/Schanskorf

https://nl.wikipedia.org/wiki/Boogbrug

https://nl.wikipedia.org/wiki/Paalfundering

https://nl.wikipedia.org/wiki/Pleister_(bouw)

https://www.youtube.com/watch?v=aVO7hVUzfBY

51

A.II Overview of the results

Cost forecast

Project MAPE Absolute FE

Residential House Finishing Works (1) 11.79% 8.98%

Residential House Finishing Works (2) 14.09% 11.09%

Residential House Finishing Works (3) 12.59% 2.19%

Residential House Finishing Works (4) 16.88% 5.07%

Residential House Finishing Works (5) 17.20% 7.76%

Residential House Finishing Works (6) 11.89% 5.74%

Residential House Finishing Works (7) 10.60% 2.75%

Residential House Finishing Works (8) 11.01% 3.96%

Residential House Finishing Works (9) 13.33% 7.84%

Residential House Finishing Works (10) 16.84% 2.02%

Residential House Finishing Works (11) 14.80% 1.42%

Residential House Finishing Works 13.73% 5.35%

Residential House (1) 52.30% 17.26%

Residential House (2) 50.05% 12.22%

Residential House (3) 53.52% 22.11%

Residential House (4) 51.97% 7.42%

Residential House 51.96% 14.75%

Apartment Building Finishing Works (1) 0A 19.43% 2.99%

Apartment Building Finishing Works (1) 1A 14.09% 8.75%

Apartment Building Finishing Works (2) 2A 8.99% 4.25%

Apartment Building Finishing Works (2) 3A 18.20% 22.14%

Apartment Building Finishing Works (3) 0B 24.17% 0.26%

Apartment Building Finishing Works (3) 1B 14.91% 10.33%

Apartment Building Finishing Works (4) 2B 16.38% 10.60%

Apartment Building Finishing Works (4) 3B 12.67% 19.82%

Apartment Building Finishing Works 16.11% 9.89%

Apartment Building Structural Work (1) Aa-Ab 7.24% -

Apartment Building Structural Work (1) Ab-Hb 7.18% -

Apartment Building Structural Work (1) 7.21% -

Apartment Building Structural Work (2) 0A 15.11% 14.04%

Apartment Building Structural Work (2) 1A 17.69% 16.45%

Apartment Building Structural Work (2) 2A 15.94% 16.35%

Apartment Building Structural Work (2) 3A 9.30% 5.86%

Apartment Building Structural Work (3) 0B 22.10% 20.46%

Apartment Building Structural Work (3) 1B 41.91% 36.47%

Apartment Building Structural Work (3) 2B 38.06% 31.00%

Apartment Building Structural Work (3) 3B 29.69% 28.39%

Apartment Building Structural Work (2) & (3) 23.72% 21.13%

Railway Bridge (1) 34.65% -

Railway Bridge (2) 34.08% -

Railway Bridge (3) 54.71% -

Railway Bridge (4) 22.42% -

Railway Bridge (5) 26.81% -

Railway Bridge 34.53% -

52

Apartment Building (1) 66.72% -

Apartment Building (2) 67.34% -

Apartment Building (3) 63.41% 94.98%

Apartment Building (4) 58.61% -

Apartment Building (5) 57.43% 27.37%

Apartment Building (6) 54.64% 66.37%

Apartment Building 61.36% 62.91%

Office Finishing Works (1) 78.32% -

Office Finishing Works (2) 89.71% 41.53%

Office Finishing Works (3) 76.46% 13.11%

Office Finishing Works (4) 37.28% 23.81%

Office Finishing Works (5) 86.32% -

Office Finishing Works 73.62% 26.15%

Social Apartments Ypres (1) 72.15% -

Social Apartments Ypres (2) 47.42% -

Social Apartments Ypres (3) 56.65% -

Social Apartments Ypres 58.74% -

Total AVG 34.17% 17.11% Table A. 1 Cost forecast accuracy

53

Duration forecast

Project MAPE Absolute FE

Residential House Finishing Works (1) 20.90% 8.19%

Residential House Finishing Works (2) 17.09% 11.19%

Residential House Finishing Works (3) 15.28% 10.85%

Residential House Finishing Works (4) 14.49% 2.75%

Residential House Finishing Works (5) 18.03% 8.24%

Residential House Finishing Works (6) 11.24% 6.93%

Residential House Finishing Works (7) 12.62% 4.58%

Residential House Finishing Works (8) 12.04% 4.83%

Residential House Finishing Works (9) 10.35% 8.91%

Residential House Finishing Works (10) 11.54% 0.27%

Residential House Finishing Works (11) 12.86% 3.16%

Residential House Finishing Works 14.22% 6.36%

Residential House (1) 43.69% 11.27%

Residential House (2) 43.61% 9.24%

Residential House (3) 48.75% 39.92%

Residential House (4) 48.88% 12.54%

Residential House 46.23% 18.24%

Apartment Building Finishing Works (1) 0A 16.28% 2.99%

Apartment Building Finishing Works (1) 1A 19.07% 8.75%

Apartment Building Finishing Works (2) 2A 12.38% 4.25%

Apartment Building Finishing Works (2) 3A 27.36% 22.14%

Apartment Building Finishing Works (3) 0B 14.24% 0.26%

Apartment Building Finishing Works (3) 1B 10.44% 10.33%

Apartment Building Finishing Works (4) 2B 10.65% 10.60%

Apartment Building Finishing Works (4) 3B 34.87% 19.82%

Apartment Building Finishing Works 18.16% 9.89%

Apartment Building Structural Work (1) Aa-Ab 27.94% -

Apartment Building Structural Work (1) Ab-Hb 43.66% -

Apartment Building Structural Work (1) 35.80% -

Apartment Building Structural Work (2) 0A 40.39% 53.53%

Apartment Building Structural Work (2) 1A 32.89% 3.45%

Apartment Building Structural Work (2) 2A 16.85% 4.84%

Apartment Building Structural Work (2) 3A 71.82% 41.02%

Apartment Building Structural Work (3) 0B 17.04% 4.02%

Apartment Building Structural Work (3) 1B 13.71% 4.74%

Apartment Building Structural Work (3) 2B 14.12% 1.29%

Apartment Building Structural Work (3) 3B 27.60% 53.75%

Apartment Building Structural Work (2) & (3) 29.30% 20.83%

Railway Bridge (1) 26.01% -

Railway Bridge (2) 41.22% -

Railway Bridge (3) 35.69% -

Railway Bridge (4) 38.82% -

Railway Bridge (5) 48.55% -

Railway Bridge 38.06% -

Apartment Building (1) 46.03% -

Apartment Building (2) 80.35% -

54

Apartment Building (3) 40.79% -

Apartment Building (4) 78.80% -

Apartment Building (5) 57.61% -

Apartment Building (6) 62.73% -

Apartment Building 61.05% -

Office Finishing Works (1) 72.02% -

Office Finishing Works (2) 94.38% -

Office Finishing Works (3) 72.82% -

Office Finishing Works (4) 77.80% -

Office Finishing Works (5) 70.51% -

Office Finishing Works 77.51% -

Social Apartments Ypres (1) 41.67% -

Social Apartments Ypres (2) 38.68% -

Social Apartments Ypres (3) 73.55% -

Social Apartments Ypres 51.30% -

Total AVG 36.36% 12.54% Table A. 2 Duration forecast accuracy

55

A.III Output regression analysis SPSS

Hypothesis 1

Figure A. 1 Effect average number of observations on cost MAPE

Figure A. 2 Effect average number of observations on duration MAPE

56

Hypothesis 2

Figure A. 3 Effect difference in size factor on cost MAPE

Figure A. 4 Effect difference in size factor on duration MAPE

57

Hypothesis 3

Figure A. 5 Effect difference in FRs on cost MAPE

Figure A. 6 Effect difference in FRs on duration MAPE

58

A.IV Sensitivity Analysis size-factor

Cost forecast

Project a=0 a=0.25 a=0.33 a=0.4 a=0.5 a=0.6 a=0.67 a=0.75 a=1

RHFW (1) 16.74% 13.27% 12.75% 12.30% 11.79% 11.45% 11.31% 11.36% 12.56%

RHFW (2) 21.77% 17.29% 15.86% 14.89% 14.09% 13.45% 13.16% 12.83% 13.76%

RHFW (3) 13.96% 13.01% 12.77% 12.68% 12.59% 12.63% 12.66% 12.80% 13.33%

RHFW (4) 17.80% 17.34% 17.19% 17.06% 16.88% 16.69% 16.56% 16.42% 16.04%

RHFW (5) 18.14% 17.66% 17.51% 17.38% 17.20% 17.06% 16.96% 16.87% 16.67%

RHFW (6) 17.29% 14.51% 13.66% 12.92% 11.89% 10.93% 10.27% 9.69% 8.69%

RHFW (7) 13.12% 11.84% 11.44% 11.08% 10.60% 10.14% 9.83% 9.47% 8.34%

RHFW (8) 14.21% 12.61% 12.09% 11.65% 11.01% 10.37% 9.92% 9.58% 8.77%

RHFW (9) 21.48% 17.41% 16.10% 14.96% 13.33% 11.95% 11.09% 10.35% 9.50%

RHFW (10) 16.98% 16.91% 16.89% 16.87% 16.84% 16.82% 16.80% 16.78% 16.71%

RHFW (11) 14.98% 14.81% 14.75% 14.70% 14.80% 14.93% 15.03% 15.13% 15.47%

RHFW 16.95% 15.15% 14.64% 14.23% 13.73% 13.31% 13.05% 12.84% 12.71%

RH (1) 55.16% 53.81% 53.34% 52.91% 52.30% 51.82% 51.53% 51.42% 51.19%

RH (2) 51.59% 50.88% 50.64% 50.40% 50.05% 49.70% 49.46% 49.19% 48.37%

RH (3) 48.71% 51.13% 51.88% 52.50% 53.52% 54.86% 55.58% 56.26% 59.60%

RH (4) 55.35% 53.79% 53.30% 52.80% 51.97% 51.11% 50.50% 49.79% 47.61%

RH 52.70% 52.40% 52.29% 52.16% 51.96% 51.87% 51.77% 51.66% 51.69%

ABFW (1) 0A 26.76% 23.01% 21.81% 20.76% 19.43% 18.25% 17.71% 17.26% 16.28%

ABFW (1) 1A 16.15% 15.06% 14.71% 14.41% 14.09% 13.86% 13.79% 13.76% 13.83%

ABFW (2) 2A 9.04% 9.01% 9.00% 9.00% 8.99% 8.98% 8.97% 8.96% 8.94%

ABFW (2) 3A 34.57% 26.38% 23.76% 21.47% 18.20% 15.51% 13.71% 12.80% 11.73%

ABFW (3) 0B 34.80% 29.48% 27.78% 26.30% 24.17% 22.15% 20.91% 19.50% 16.34%

ABFW (3) 1A 27.60% 20.97% 18.85% 17.12% 14.91% 13.02% 12.00% 11.12% 8.97%

ABFW (4) 2B 28.59% 22.48% 20.53% 18.82% 16.38% 13.94% 12.54% 11.30% 8.71%

ABFW (4) 3B 14.34% 13.06% 12.92% 12.82% 12.67% 12.52% 12.52% 12.57% 12.71%

ABFW 23.98% 19.93% 18.67% 17.59% 16.11% 14.78% 14.02% 13.41% 12.19%

ABSW (1) Aa-Ab

14.18% 9.62% 8.58% 7.81% 7.24% 6.91% 7.05% 7.24% 12.68%

ABSW (1) Ab-Hb

11.91% 8.20% 7.50% 7.36% 7.18% 7.71% 8.11% 9.33% 15.29%

ABSW (1) 13.04% 8.91% 8.04% 7.59% 7.21% 7.31% 7.58% 8.28% 13.99%

ABSW (2) 0A 5.88% 6.17% 8.03% 10.94% 15.11% 19.27% 22.18% 25.52% 35.92%

ABSW (2) 1A 29.43% 23.56% 21.68% 20.03% 17.69% 15.34% 13.69% 11.81% 6.05%

ABSW (2) 2A 27.47% 21.71% 19.86% 18.25% 15.94% 13.63% 12.02% 10.17% 5.83%

ABSW (2) 3A 14.46% 9.96% 9.56% 9.45% 9.30% 9.14% 9.03% 8.90% 10.86%

ABSW (3) 0B 37.92% 30.01% 27.48% 25.26% 22.10% 18.93% 16.72% 14.19% 7.36%

ABSW (3) 1B 72.55% 59.42% 53.82% 48.91% 41.91% 34.90% 30.00% 24.39% 7.52%

ABSW (3) 2B 63.49% 52.87% 48.13% 43.98% 38.06% 32.13% 27.98% 23.24% 8.88%

ABSW (3) 3B 64.39% 47.04% 41.49% 36.63% 29.69% 22.74% 17.88% 12.33% 6.50%

ABSW (2) & (3)

32.25% 25.22% 23.11% 21.48% 19.22% 17.09% 15.66% 14.13% 11.90%

RB (1) 42.73% 37.29% 35.84% 34.83% 34.65% 34.80% 35.16% 35.58% 38.22%

RB (2) 33.84% 33.31% 33.55% 33.77% 34.08% 34.39% 34.60% 34.85% 35.90%

RB (3) 53.97% 54.34% 54.46% 54.56% 54.71% 54.86% 54.96% 55.08% 55.51%

59

RB (4) 23.82% 23.08% 22.86% 22.68% 22.42% 22.15% 21.97% 21.75% 21.09%

RB (5) 30.89% 27.52% 27.11% 26.99% 26.81% 26.64% 26.56% 26.62% 27.78%

RB 37.05% 35.11% 34.76% 34.57% 34.53% 34.57% 34.65% 34.78% 35.70%

AB (1) 82.20% 75.92% 73.27% 70.96% 66.72% 62.29% 58.73% 55.49% 52.07%

AB (2) 69.26% 67.84% 67.29% 67.39% 67.34% 65.91% 64.68% 63.62% 62.37%

AB (3) 67.31% 65.89% 65.14% 64.48% 63.41% 62.04% 60.74% 58.70% 49.26%

AB (4) 66.61% 62.61% 61.33% 60.21% 58.61% 57.01% 56.22% 55.78% 57.19%

AB (5) 56.56% 57.00% 57.13% 57.26% 57.43% 57.60% 57.63% 57.34% 56.41%

AB (6) 73.15% 63.89% 60.93% 58.34% 54.64% 50.94% 49.04% 47.56% 42.95%

AB 69.18% 65.52% 64.18% 63.11% 61.36% 59.30% 57.84% 56.41% 53.37%

OFW (1) 74.59% 73.81% 75.25% 76.52% 78.32% 80.13% 81.40% 82.84% 82.92%

OFW (2) 96.52% 97.15% 97.36% 95.54% 89.71% 82.23% 77.00% 68.27% 52.05%

OFW (3) 78.10% 77.65% 77.50% 77.38% 76.46% 75.49% 74.80% 74.03% 74.88%

OFW (4) 36.25% 36.77% 36.93% 37.08% 37.28% 37.49% 37.63% 37.80% 38.32%

OFW (5) 88.17% 87.75% 87.61% 87.14% 86.32% 85.50% 84.93% 84.28% 80.27%

OFW 74.73% 74.62% 74.93% 74.73% 73.62% 72.17% 71.15% 69.44% 65.69%

SAY (1) 89.02% 82.96% 79.33% 76.34% 72.15% 67.20% 63.06% 57.30% 39.29%

SAY (2) 61.23% 53.67% 51.21% 49.65% 47.42% 45.19% 43.63% 42.38% 42.27%

SAY (3) 69.27% 58.06% 55.93% 55.25% 56.65% 58.19% 58.97% 59.05% 59.98%

SAY 73.17% 64.90% 62.16% 60.41% 58.74% 56.86% 55.22% 52.91% 47.18%

Total AVG 40.85% 37.32% 36.26% 35.40% 34.17% 32.94% 32.10% 31.17% 29.42% Table A. 3 Effect different values of a on the cost forecast accuracy

60

Duration forecast

Project a=0 a=0.25 a=0.33 a=0.4 a=0.5 a=0.6 a=0.67 a=0.75 a=1

RHFW (1) 17.00% 15.99% 17.56% 18.93% 20.90% 22.71% 23.97% 25.41% 29.92%

RHFW (2) 16.47% 14.93% 14.44% 14.53% 17.09% 19.63% 21.29% 23.18% 29.09%

RHFW (3) 13.62% 13.11% 13.29% 14.11% 15.28% 16.44% 17.26% 18.20% 21.12%

RHFW (4) 13.62% 14.05% 14.19% 14.31% 14.49% 14.66% 14.79% 14.93% 15.36%

RHFW (5) 17.62% 17.82% 17.89% 17.94% 18.03% 18.11% 18.16% 18.23% 18.43%

RHFW (6) 14.74% 12.99% 12.43% 11.94% 11.24% 10.54% 10.05% 10.49% 12.83%

RHFW (7) 14.74% 13.68% 13.34% 13.04% 12.62% 12.19% 11.90% 11.56% 10.49%

RHFW (8) 14.74% 13.39% 12.96% 12.58% 12.04% 11.50% 11.13% 10.70% 10.19%

RHFW (9) 14.74% 12.55% 11.85% 11.23% 10.35% 10.83% 11.69% 12.67% 15.74%

RHFW (10) 11.88% 11.71% 11.65% 11.61% 11.54% 11.47% 11.42% 11.36% 11.19%

RHFW (11) 11.88% 12.37% 12.53% 12.67% 12.86% 13.06% 13.19% 13.35% 13.84%

RHFW 14.64% 13.87% 13.83% 13.90% 14.22% 14.65% 14.99% 15.46% 17.11%

RH (1) 44.99% 44.10% 43.97% 43.86% 43.69% 43.69% 43.74% 43.74% 43.69%

RH (2) 44.60% 44.13% 43.96% 43.81% 43.61% 43.40% 43.26% 43.11% 42.62%

RH (3) 41.84% 44.46% 45.78% 47.11% 48.75% 50.77% 52.56% 54.48% 60.49%

RH (4) 52.13% 50.46% 49.93% 49.48% 48.88% 48.35% 48.10% 47.80% 47.00%

RH 45.89% 45.79% 45.91% 46.07% 46.23% 46.55% 46.92% 47.28% 48.45%

ABFW (1) 0A 11.47% 12.76% 13.89% 14.87% 16.28% 17.78% 19.12% 20.80% 26.06%

ABFW (1) 1A 22.12% 20.59% 20.10% 19.68% 19.07% 18.46% 18.03% 17.54% 16.34%

ABFW (2) 2A 12.68% 12.53% 12.48% 12.44% 12.38% 12.32% 12.28% 12.23% 12.08%

ABFW (2) 3A 31.55% 29.66% 28.55% 28.06% 27.36% 27.88% 29.39% 31.12% 38.04%

ABFW (3) 0B 17.86% 15.34% 14.99% 14.68% 14.24% 13.79% 13.97% 14.96% 18.05%

ABFW (3) 1A 10.64% 8.57% 8.36% 9.10% 10.44% 11.92% 12.96% 14.14% 18.71%

ABFW (4) 2B 10.13% 7.93% 8.20% 9.02% 10.65% 12.27% 13.40% 14.83% 19.98%

ABFW (4) 3B 32.49% 33.68% 34.06% 34.39% 34.87% 35.21% 35.38% 35.57% 36.18%

ABFW 18.62% 17.63% 17.58% 17.78% 18.16% 18.70% 19.32% 20.15% 23.18%

ABSW (1) Aa-Ab

25.97% 26.95% 27.27% 27.54% 27.94% 28.33% 28.60% 28.92% 29.90%

ABSW (1) Ab-Hb

33.15% 38.93% 40.78% 42.13% 43.66% 45.19% 45.81% 46.45% 48.44%

ABSW (1) 29.56% 32.94% 34.02% 34.83% 35.80% 36.76% 37.21% 37.68% 39.17%

ABSW (2) 0A 28.51% 34.45% 36.35% 38.01% 40.39% 42.77% 44.43% 46.33% 52.27%

ABSW (2) 1A 33.09% 32.99% 32.96% 32.93% 32.89% 32.85% 32.83% 32.79% 32.70%

ABSW (2) 2A 19.46% 17.81% 17.50% 17.23% 16.85% 16.47% 16.20% 15.89% 14.94%

ABSW (2) 3A 56.86% 64.34% 66.74% 68.83% 71.82% 74.81% 76.91% 79.30% 86.78%

ABSW (3) 0B 23.01% 19.37% 18.56% 17.88% 17.04% 16.83% 16.68% 16.52% 16.00%

ABSW (3) 1B 28.07% 18.65% 16.57% 15.02% 13.71% 13.26% 13.22% 13.67% 20.20%

ABSW (3) 2B 28.07% 19.86% 17.51% 15.96% 14.12% 12.90% 12.38% 12.23% 16.22%

ABSW (3) 3B 43.49% 31.70% 30.92% 28.31% 27.60% 33.09% 37.40% 43.56% 65.91%

ABSW (2) & (3)

31.75% 30.73% 30.83% 30.79% 31.07% 32.11% 32.88% 33.94% 38.41%

RB (1) 39.28% 31.24% 28.83% 27.23% 26.01% 25.70% 25.83% 26.05% 28.56%

RB (2) 37.50% 38.71% 39.51% 40.22% 41.22% 42.22% 42.93% 43.73% 46.24%

RB (3) 34.58% 35.06% 35.26% 35.44% 35.69% 36.16% 36.61% 37.13% 39.14%

RB (4) 39.80% 39.09% 38.88% 38.86% 38.82% 38.78% 38.76% 38.73% 38.64%

RB (5) 50.41% 49.30% 48.84% 48.70% 48.55% 48.39% 48.28% 48.16% 47.44%

61

RB 40.31% 38.68% 38.26% 38.09% 38.06% 38.25% 38.48% 38.76% 40.00%

AB (1) 52.33% 48.03% 47.24% 46.52% 46.03% 44.87% 44.84% 43.90% 46.94%

AB (2) 79.06% 80.43% 80.58% 80.48% 80.35% 80.21% 79.97% 79.26% 74.30%

AB (3) 52.25% 48.75% 46.21% 43.98% 40.79% 37.61% 35.65% 33.56% 36.10%

AB (4) 75.27% 78.37% 78.75% 78.78% 78.80% 78.81% 78.68% 78.70% 77.83%

AB (5) 61.20% 58.34% 57.61% 57.14% 57.61% 58.93% 60.49% 62.26% 67.80%

AB (6) 78.88% 66.47% 65.17% 64.17% 62.73% 61.30% 60.30% 59.15% 52.40%

AB 66.50% 63.40% 62.59% 61.85% 61.05% 60.29% 59.99% 59.47% 59.23%

OFW (1) 64.42% 74.04% 75.51% 74.31% 72.02% 69.74% 68.14% 66.31% 60.69%

OFW (2) 91.77% 93.30% 93.79% 94.21% 94.38% 90.24% 85.26% 79.57% 63.80%

OFW (3) 69.28% 72.37% 72.51% 72.64% 72.82% 73.00% 73.13% 73.27% 72.61%

OFW (4) 75.23% 76.52% 76.93% 77.29% 77.80% 78.32% 78.68% 79.09% 79.42%

OFW (5) 70.92% 70.71% 70.65% 70.59% 70.51% 70.42% 71.13% 72.16% 75.17%

OFW 74.33% 77.39% 77.88% 77.81% 77.51% 76.34% 75.27% 74.08% 70.34%

SAY (1) 43.56% 41.18% 41.03% 41.77% 41.67% 41.06% 41.51% 42.38% 54.88%

SAY (2) 35.15% 35.52% 36.53% 37.41% 38.68% 40.26% 41.41% 42.97% 50.73%

SAY (3) 46.60% 58.50% 64.21% 67.71% 73.55% 79.96% 82.84% 84.83% 91.06%

SAY 41.77% 45.07% 47.25% 48.97% 51.30% 53.76% 55.25% 56.73% 65.56%

Total AVG 36.74% 36.11% 36.15% 36.17% 36.36% 36.72% 37.04% 37.45% 39.51% Table A. 4 Effect different values of a on duration forecast accuracy

62

Size factor = 1

Project MAPE Cost MAPE dur

RHFW (1) 13.40% 16.04%

RHFW (2) 14.94% 14.38%

RHFW (3) 12.94% 13.20%

RHFW (4) 19.32% 13.20%

RHFW (5) 19.86% 17.20%

RHFW (6) 10.45% 10.00%

RHFW (7) 8.32% 10.00%

RHFW (8) 9.31% 10.00%

RHFW (9) 12.97% 10.00%

RHFW (10) 17.06% 11.04%

RHFW (11) 15.24% 11.04%

RHFW 13.98% 12.37%

RH (1) 62.90% 53.15%

RH (2) 53.12% 46.84%

RH (3) 49.34% 42.36%

RH (4) 53.34% 49.59%

RH 54.68% 47.99%

ABFW (1) 0A 29.79% 8.73%

ABFW (1) 1A 15.25% 13.25%

ABFW (2) 2A 14.19% 8.27%

ABFW (2) 3A 16.01% 27.53%

ABFW (3) 0B 24.85% 12.16%

ABFW (3) 1A 20.03% 7.47%

ABFW (4) 2B 22.44% 6.97%

ABFW (4) 3B 22.87% 36.84%

ABFW 20.68% 15.15%

ABSW (1) Aa-Ab 7.24% 27.94%

ABSW (1) Ab-Hb 7.18% 43.66%

ABSW (1) 7.21% 35.80%

ABSW (2) 0A 45.97% 61.66%

ABSW (2) 1A 13.34% 35.84%

ABSW (2) 2A 6.01% 13.57%

ABSW (2) 3A 33.92% 41.63%

ABSW (3) 0B 25.70% 22.05%

ABSW (3) 1B 40.77% 16.33%

ABSW (3) 2B 32.71% 16.33%

ABSW (3) 3B 18.37% 68.21%

ABSW (2) & (3) 21.67% 34.82%

RB (1) 34.80% 23.93%

RB (2) 33.76% 35.88%

RB (3) 54.14% 33.15%

RB (4) 22.06% 38.11%

RB (5) 24.88% 47.76%

RB 33.93% 35.76%

AB (1) 94.35% 67.08%

AB (2) 58.27% 53.32%

AB (3) 65.15% 37.38%

63

AB (4) 58.59% 72.80%

AB (5) 50.60% 56.81%

AB (6) 45.98% 57.69%

AB 62.16% 57.51%

OFW (1) 68.04% 60.06%

OFW (2) 93.92% 84.55%

OFW (3) 74.73% 70.63%

OFW (4) 49.05% 75.18%

OFW (5) 85.84% 75.14%

OFW 74.31% 73.11%

SAY (1) 95.52% 57.52%

SAY (2) 57.37% 38.81%

SAY (3) 66.40% 46.49%

SAY 73.10% 47.61%

Total AVG 36.03% 35.18%

Table A. 5 Forecast results with size factor = 1