Integrating Process Mining and Success Factors in Information

Systems Projects: A Decision Support System Approach

Joana Pedrosa

, Luís Gonzaga Magalhães

and Ricardo Martinho

ALGORITMI Center, University of Minho, Guimarães, Portugal

ALGORITMI Center / LASI, University of Minho, Guimarães, Portugal

INESCC - DL, ESTG, Polytechnic University of Leiria, Leiria, Portugal

Keywords: Decision, Information System, Project Management, Process Mining, Success Factors, Decision Support

System.

Abstract: The management of Information Systems (IS) projects involves addressing complex challenges such as

communication issues, resource allocation, time constraints, customer interaction and evolving requirements.

A project manager faces, therefore, a significant number of decisions on the progress of these projects, based

on the most important Success Factors (SF) that each project encloses. As far as we are aware, there is

currently no automated solution capable of effectively tackling these challenges, forcing managers to depend

on conventional approaches that often prove insufficient to provide the necessary support. This paper proposes

an architecture for a Decision Support System (DSS) designed to enhance project success by providing project

managers with recommendations. The DSS integrates Process Mining techniques with SF to suggest valuable

insights for decision-making. The system proposed aims to optimize project decisional outcomes and can

combine algorithms from Process Mining, Data Mining, and Predictive Mining to enhance its

recommendations.

1 INTRODUCTION

The development of Information Systems (IS) has

become more complex, requiring advanced planning,

scheduling and control processes, as they justify high

costs. Project management is fundamental to ensuring

that these systems are delivered on time, within

budget and to expectations (Avison & Torkzadeh,

2009).

In these projects, organizations also face

difficulties in terms of communication, resources or

even processes, such as changing requirements during

the project, ineffective communication between the

team, or problems allocating resources, which makes

managing IS projects particularly challenging. The

failure of these projects can have a significant impact,

given the strategic relevance they represent and the

often high costs involved (Pereira et al., 2021).

Evaluating project success is therefore essential, for

success management to become a systematic process

https://orcid.org/0000-0002-9514-268X

https://orcid.org/0000-0002-4426-0002

https://orcid.org/0000-0003-1157-7510

(Varajão, 2018). Freeman and Beale (1992) define

project success based on stakeholder perspectives

(customers, programmers, team, end users). Shenhar

et al. (1997) identify four success dimensions:

efficiency, customer impact, business success, and

preparation for the future.

Thus, the question emerges: how can IS project

managers maximize project success, considering the

multiplicity of critical decisions they must make

throughout project development? To our knowledge,

there is no automatic mechanism capable of meeting

this challenge, leaving managers dependent on

conventional practices that often lack adequate

support. We have already proposed in Pedrosa et al.

(2021) a general technological framework for this

matter, including the overall approach to tackle the

problem stated.

In this scenario, the implementation of Decision

Support Systems (DSS) appears to be a promising

solution. DSS are tools designed to help managers

Pedrosa, J., Magalhães, L. G. and Martinho, R.

Integrating Process Mining and Success Factors in Information Systems Projects: A Decision Support System Approach.

DOI: 10.5220/0013433500003928

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineer ing (ENASE 2025), pages 723-730

ISBN: 978-989-758-742-9; ISSN: 2184-4895

723

make complex decisions, by integrating historical

data, advanced algorithms and analytical techniques

to provide informed recommendations.

Process Mining techniques can, therefore, become

essential components for analysing and optimizing IS

project management processes. This approach uses

data generated by event logs, often dispersed in

project management tools such as Jira, Trello or

version control systems, offering a detailed view of

organizational processes (Gupta, 2014). By applying

Process Mining, it is possible to map actual

workflows, identify bottlenecks and promote

continuous improvements, providing valuable

information for more informed decision-making.

In this paper, we expand our previous work in

Pedrosa et al. (2021), taking it a step forward in

defining decision types that a project manager can

benefit from by using a DSS, as well as details on

such a system. Particularly, the objective is to present

the architectural components of the DSS to analyse

event logs from project histories, correlate the results

obtained through Process Mining with predefined SF,

and finally provide feedback to the project manager

on the recommended decisions to make.

This paper is organized as follows: the next

section outlines the main motivation for this work,

highlighting challenges in IS project management and

most related work. Section 3 introduces the research

methodology applied. Section 4 focuses on the

architecture of the DSS, and finally, Section 5

presents conclusions and future work.

2 MOTIVATION AND RELATED

WORK

Information system project management presents

significant challenges, especially given the

multifactorial and multidimensional nature of the

decisions that need to be made. Decision-making is a

central element in any organization and is intrinsic to

its management and success. As Ada & Ghaffarzadeh

(2015) note, the success, growth, and even failure of

an organization are directly linked to the quality of

decisions made over time. However, decision-making

presents significant challenges, including uncertainty

about the outcomes that a particular choice may

produce.

Decision-making in IS project management is

crucial, involving choices on technology, resources,

strategy, and stakeholders. Given its complexity,

managers cannot rely solely on experience or

intuition. Decisions must consider multiple factors,

such as the SF that directly influence the outcome of

projects, as well as historical data and performance

standards for teams and processes.

Decisions also involve multiple dimensions - such

as human, financial and technological resources - and

depend on distributed sources of information, which

increases the level of complexity. As Eierman et al.

(1995) point out, the complexity and importance of

this task highlight the need for additional support for

decision-makers, particularly through the aid of

science and technology. A DSS is therefore

considered to play a decisive role for organisations

and project managers.

IS project management includes, by its nature, a

significant amount of decision-making, considering

not only the amount of project management processes

involved but also the variety of SF that each project

encloses. It is widely recognised that IS project

management is challenging, and many studies have

identified its problems, as illustrated in Table 1. For

Salmani et al. (2022) poor communication and lack of

domain knowledge are major challenges and often

cause other problems to emerge, such as a high

amount of rework and delays in project development.

The existence of bottlenecks is one of the most

frequently mentioned problems in the literature

(Gupta, 2014; Marques et al., 2018; Rubin et al.,

2014).

Urrea-Contreras et al. (2024) highlight several

software development project management issues,

affecting product quality, notably event flow

inconsistency and the lack of a formal document on

the software development process. In the Process

category, some authors point to problems related to

the process, such as the constant change in

requirements, their volatility (Salmani et al., 2022a,

2022b; Santos et al., 2015), difficulty in defining

priorities or even prioritising user stories (Mendes et

al., 2018; Vavpotič et al., 2022). Promising

approaches dealing with this changeability include

the work of Ferreira et al. (2014) and Mejri et al.

(2015), which comprise the notion of process

invariants and process flexibility, as ways to model

(software) processes with expected exceptions and

requiring more agile or flexible execution.

Nevertheless, Marques et al. (2018) mention the

failure to implement agile software processes, in this

case, the Scrum framework, as a possible problem.

Sometimes during the development of a software

project, the team does not follow the guidelines

established in Scrum and skips tasks or phases or

bypasses the basic meetings defined in the

framework.

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Table 1: List of problems, their SF and decisions.

Cate

gory

SF Problems Decision

Communication

Communication / Cooperation Poor communication

Improving communication

between all stakeholders

Multidisciplinary Work Teams

Ineffective communication between the

multidisciplinary team

Relationship Management

Balanced Team/Healthy

Environment

Team

Define Clear Responsibilities

Lack of mutual understanding between

the team members

Define clear roles and

responsibilities/ Meet

regularly for alignment

Efficient Project Management

/ Pro

ect Plannin

High rework rates Reduce rework

Human Resources

Mana

ement

Team rotation Evaluate team rotation

Resource

Sufficient and Appropriate

Resources

Influence of resource allocation on the

control-flow of the process

Adjusting resources

Define Clear Responsibilities

Unsatisfactory performance by specific

actors

Clearly define the role of

each acto

Human Resources

Mana

ement

Inefficiencies in resource allocation

Allocate resources according

to each of your roles

Define Clear Responsibilities

Balanced Team/Healthy

Environment

Sufficient and Appropriate

Resources

Knowledge Management

Lack of domain knowledge

Share information about the

domain

Knowledge Sharing

Process

Use Appropriate Business

Methodologies

Deficiencies in the software

development process

Adopt agile methodologies

Complete Requirements /

Avoid Customisation

Occasionally changing requirements

Avoid changing

requirements during the

roject

Formal Documentation

Inconsistency in the flow of events /

Inconsistencies in the system’s

documentation

Improve the sequence of

events/activities / Map and

document the workflow

Lack of documentation related to

processes/Lack of explicit process

modellin

Document the

process/system correctly

Complete Requirements

Gaps in process activity

Assign people responsible

for key activities/ Redefine

the

rocess flow

Project Planning

Define Clear Responsibilities /

Human Resources

Management

A lot of issues were closed by the same

assignee

Automate the assignment of

tasks

Monitoring/Control / Project

Plannin

Presence of loops Remove loops

Use Appropriate Business

Methodolo

ies

Failures in the implementation of the

Scrum methodology

Correctly implement/follow

the methodology

Use of Methodologies /

Processes (Gantt Chart, CPM,

WBS, amon

others

)

Avoid Customisation Deviations in process execution

Clearly prioritise each task.

Eliminate deviations

Project Planning Redundant activities Define clear responsibilities

Integrating Process Mining and Success Factors in Information Systems Projects: A Decision Support System Approach

725

Table 1: List of problems, their SF and decisions (cont.).

The annual Chaos Report by the Standish Group

evaluates software project outcomes, classifying

them as successful, challenged, or failed. Since 1994,

it has highlighted a high failure rate in IT/IS projects.

For each problem, there are one or more decisions

that the project manager must make, which the DSS

must suggest to help solve the problem. Table 1

shows a sample of the possible decisions for some of

the problems mentioned in the ‘Problems’ column.

Analysing the table, it can be seen, for example, that

for the problem “High rework rates”, the decision

involves reducing rework. Similarly, for

https://standishgroup.myshopify.com/

“Inconsistency in the flow of events”, the

corresponding decision could be improving the

sequence of events/activities.

The application of Process Mining techniques to

this theme can be highly advantageous, as it allows

the project manager to obtain a clear vision based on

the historical analysis of the organization's projects,

or even by types of projects, customers, teams or

other specific perspective. Process Mining stands out

as a non-intrusive area of research focused on

extracting knowledge from the records generated by

IS about the control-flow, data and resources

Cate

gory

SF Problems Decision

Use of Methodologies/Processes

(Gantt Chart, CPM, WBS, among

others)

Skipping the analysis task

Don't skip the task of analysing

the

ect

Deviations in the flow of activities

Avoid deviations. Follow the

defined flow of activities

Monitoring/Control / Risk

Mana

ement

Identification of the main bottlenecks

in the workflow

Monitor workflows in real time

Complete Requirements

Volatility in software requirements Define clear responsibilities

Project Planning

Clarif

Business Ob

ectives

Clarifying Business Objectives

Loops showing undesired repetition

of activities

Analysing historical data /

Review and optimise processes

Use of Methodologies/Processes

(Gantt Chart, CPM, WBS, among

others

)

Difficulty in defining and changing

priorities

Adopt agile methodologies

Complete Requirements

Omission of crucial phases in the

process

Describe all the phases of the

process clearly

Project Planning

Clarifying Business Objectives

Well-defined and Quality

Information/Services

Formal Documentation

Developers often deviate from the

defined

rocess

Document the process/system

correctl

Realistic Estimates

Often exceeded estimated sprint tasks'

time limits

Review initial estimates /

Divide tasks into smaller

arts

Priority of user stories Prioritise user stories correctly

Establishing Output Requirements Inefficiencies in project outcomes

Perform regular retrospectives.

Ado

t monitorin

tools

Performance

Efficient Project Management /

Pro

ect Plannin

Incorrect sequencing of performance

etween activities

Select the best sequencing

erformance between activities

Performance Management /

Systems Testing

Performance bottlenecks

Carry out performance

analyses.

Test in real scenarios

Paying Attention to User Needs /

User Participation

Systems Testing

Ignore real user and system runtime

ehaviou

Conduct tests based on real

scenarios

Time

Time Management

Delays in follow-up activities

Define the time for each

task/activit

High waiting time between activities

Eliminate unnecessary

dependencies

Response time Reduce response time

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involved in carrying out business processes (Van Der

Aalst et al., 2012). Using its three main techniques -

discovery, conformance checking and improvement –

Process Mining helps identify bottlenecks, reduce

rework, improve communication, and address other

IS project management problems (such as those listed

in Table 1).

Given this, and to the best of our knowledge and

research, no system covers all these criteria to support

the project manager's decision-making.

3 METHODOLOGY

To achieve the aim of this research, the application of

Design Science Research as a research method was

considered (Hevner & Chatterjee, 2010; Peffers et al.,

2007).

The first stage of the DSR process is identifying

the problem and the motivation to solve it. This study

addresses the need for a system to support IS project

managers in decision-making, given the complexity

of the challenges involved.

Once the problem is identified, the next step is to

define the solution's objective: to enhance managers'

decisions by providing information on project SF,

reducing risks and failures. The goal is to maximize

project success and support managers. Given the

range of issues, a technological and innovative

artifact is proposed to diagnose and predict the

success of IS projects, enabling more agile and

organization-specific decisions. Both these DSR

stages have already been addressed in our previous

work in Pedrosa et al. (2021).

DSR stage 3 - Design & Development as

suggested by Peffers et al. (2007) consists of

developing an artefact. Thus, in the context of this

work, the artefact is a DSS based on Process Mining

techniques to study the organisation's context in terms

of IS projects and corresponding SF.

Some of the activities for the development and

evaluation of this artefact are as follows: 1)

identification of IS project management data of event

logs, to feed Process Mining algorithms; 2) applying

multiple-perspective Process Mining algorithms to

discover and check the conformance of IS project

management processes, towards identifying project

success deviations; 3) identify heuristics for the

mapping of Process Mining results and SF, and

generate corresponding alarmistic and

recommendations.

Concerning DSR’s steps 4 (Demonstration) and 5

(Evaluation), we will be, in further research,

collecting data from real projects in real organisations

to validate the developed DSS.

4 DSS SOFTWARE

ARCHITECTURE

Given the many challenges in IS project management,

organizations and project managers must proactively

address and mitigate these issues to maximize project

success. In this way, the approach proposed by

Pedrosa et al. (2021) aims to help the organisation's

project manager make decisions considering the

history of previous projects and the SF. The approach

proposed and shown in Figure 1 includes two phases:

the diagnostic phase and the prognostic phase.

The approach comprises the use of business logic

to correlate historical data between project

management processes (discovered and measured

through Process Mining) and SF registered or

retrieved automatically from a knowledge base. It

also includes two main components:

Figure 1: A Process Mining approach for IS project success factors management (Pedrosa et al., 2021).

Integrating Process Mining and Success Factors in Information Systems Projects: A Decision Support System Approach

727

the ‘Process Discovery’ and the ‘Success Factors-

based Decision Support’, represented by the blue

dotted line. The DSS will take the event logs from the

project management IS as input data. The system

concludes in the prognosis phase, presenting the user,

in this case, the project manager, with a set of possible

decisions.

A DSS consists of three main modules: Interface

(user interaction), Data Management (integration and

processing), and Model Management (analytical and

simulation models for decision support)(Bâra &

Lungu, 2012). Thus, the approach illustrated in

Figure

2 is composed of these main modules, each one being

subdivided into more specific components.

The interface module serves as the user

interaction point and is divided into two sections. The

Pre-Selection Interface allows users to select the

parameters needed to run the DSS, such as the project

to optimize and the SF to evaluate. The SF will be in

a predefined list according to those available in the

knowledge base.

The Feedback Interface, presented at the end,

provides the project manager with alerts and

recommended decisions. Based on these selections,

the system automatically derives the relevant Process

Mining perspectives (time, case, organizational,

control-flow) for further analysis.

The Model Management module contains all the

business logic models needed for the DSS to function

correctly. This module can integrate data mining

techniques (Apriori, Decision Tree, Association

Rules), Process Mining methods (discovery,

conformance checking, improvement), and prediction

models (Linear Regression, Predictive Process

Mining).

Ideally, the three types of models should interact

with each other to maximize the chosen SF for a

particular project. In addition to the models

mentioned above, this module also includes a specific

component called Choreographer, whose main

function is to choreograph the possible interactions

between the previously mentioned models. That is, it

Figure 2: DSS software architecture.

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will coordinate the interaction between the different

models and the sequence in which each executes its

algorithms. For example, for a particular SF, the

component could choose to run a certain sequence of

business logic models, starting, for instance, with a

Process Mining algorithm, then injecting the results

into a specific data mining algorithm and finally

applying a predictive algorithm for

recommendations.

For better understanding, we will be specifying a

concrete example of our architectural approach in

Figure 2. Therefore, the starting point for using the

DSS is for the project manager to select the project

for which s/he wants to have decision-making support

(for instance, Project A), as well as the set of SF to

maximize. Within this set, the manager can choose,

for instance, “Time Management” as a specific SF.

Next, the DSS derives which IS project management

process perspectives it needs to analyse, considering

the pre-selected project and SF. For example, the

system would, in this case, consider the time and

control-flow perspectives

That said, based on the selection, the DSS can run

appropriate Process Mining techniques on the events

logs of the organization's projects stored in the Data

Warehouse (e.g., heuristics miner) to discover the

most common performance metrics for the project to

be analysed, including, for example, average

execution, service, wait and synchronisation times, as

well as accuracy, generalisation and simplicity - see,

for example, Van der Aalst (2016). Next, the

choreographer component would use the results of

Process Mining to correlate them with the chosen SF.

For instance, it could choose the Apriori algorithm to

discover which activity sequences from similar

projects had the most performant execution time.

Finally, the choreographer component would ask a

predictive model to calculate decision-support

recommendations, such as following a certain

sequence or imposing stricter deadlines for the

remaining project management activities.

In the end, the DSS would return a decision grid

to its interface module. In this way, the manager

would be able to identify where to act and how. In this

example, the manager should reduce execution time

and take task D after task C.

5 CONCLUSIONS

The paper begins with identifying and correlating the

most common SF, problems and improvement

decisions associated with IS project management.

Considering the number of variables involved in these

contexts, we recognize the need for a project manager

to be supported when defining such SF and

performing decisions along the management of an IS

project.

Nevertheless, and since each organization might

have its own SF and particular ways to optimize them,

we earlier proposed an approach which takes into

consideration the organizations historical data on IS

project management for better decision-making.

It includes a diagnosis phase, where Process

Mining algorithms are executed to identify the best-

performing projects based on a specific SF, and a

prognosis phase, where these results are correlated

and compared with the performance of the current

project to support decision-making.

This paper presents the software architecture of a

DSS to implement such an approach, composed of

three main modules: Model Management, Data

Management, and Interface Management. In the

latter, the project manager can select the project to

optimize, as well as the targeting SF (for instance, for

project A, optimize the time management SF). In the

Data Management module, the DSS stores previously

executed project management data and computed SF,

which can be retrieved by the Model Management

module as input to Process Mining, data mining

and/or predictive algorithms. These can, in turn,

discover the best performant projects regarding the

chosen SF, and predict/advise the project

management where to conduct the current project.

The approach points out that integrating Process

Mining techniques with SF can provide valuable

recommendations for informed decision-making. The

DSS architecture provides an additional

Choreographer component to allow a flexible and

customisable business logic sequencing and

interaction between Process Mining, data mining and

predictive models.

ACKNOWLEDGEMENTS

This work has been supported by FCT – Fundação

para a Ciência e Tecnologia within the R&D Unit

Project of ALGORITMI Centre

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