Project Management Information System Data Model Development and

Explanation

Filippo Maria Ottaviani

, Massimo Rebuglio

and Alberto De Marco

Department of Management and Production Engineering, Politecnico di Torino, Turin, 10129, Italy

ﬁ

Keywords:

Project Management, Information System, Data Model.

Abstract:

The Project Management (PM) discipline is evolving towards the adoption of digital technologies, which are

to be integrated into a project management information system (PMIS). Despite the fact that many PMIS solu-

tions are already available, there is no standard data model for PMIS development, nor is the logic underlying

PMIS. Therefore, organizations struggle to integrate other business applications into the PMIS and cannot

leverage the data collected to improve both project management and execution. To address these issues, this

paper aims to provide a standard PMIS model as a foundation for database design and software development.

The PM objects are ﬁrst identiﬁed and then represented in a data model that outlines their attributes and meth-

ods and the relationships between the classes. All classes are structured to accommodate in their interface the

core PM processes, such as task and resource management, project scheduling, risk management, and progress

control. The study evaluates the impact and beneﬁts of implementing this standard model while acknowledg-

ing its limitations and providing recommendations for practical implementation.

1 INTRODUCTION

Project Management (PM) involves the use of tech-

niques and tools to plan and execute a unique set of

tasks, performed by resources, to attain speciﬁc ob-

jectives (BSI, 2012). PM has been increasingly digi-

tized in recent years thanks to technological advance-

ments and the spread of artiﬁcial intelligence mod-

els and frameworks. Different PM software for task

management, status tracking, resource allocation, and

online collaboration is now available to help stream-

line and automate PM processes. Therefore, it is now

imperative for organizations to make use of informa-

tion systems (ISs) for managing a complex project or,

more generally, to improve any project’s chances of

success.

An information system (IS) is used to collect,

store, process, and disseminate information to sup-

port decision-making and manage the ﬂow of infor-

mation within an organization. In the PM context, a

Project Management Information System (PMIS) is

designed to support various project processes. Its es-

sential functions are represented by project cost and

https://orcid.org/0000-0002-1150-9211

https://orcid.org/0000-0002-2636-4537

https://orcid.org/0000-0002-4145-2287

schedule planning and control. Furthermore, an IS

would allow for improved risk management and sim-

ulations or the training of machine learning models

for performance analyses (Ottaviani et al., 2022). Al-

though, for a PMIS to be effective, it must be built

upon a solid data model and be used in conjunction

with other PM tools and techniques.

Developing an IS entails a number of essential

steps, including data modeling and the deﬁnition of

procedures. The former involves the creation of a

visual representation of the data and relationships

within a system, providing a comprehensive under-

standing of the system’s structure and organization.

This allows for more efﬁcient decision-making and

problem-solving, as the organization’s components

and interactions are more clearly understood. By de-

picting the ﬂow of processes within the system, it is

possible to identify and understand the relationships

between different steps in a process by identifying and

understanding the relationships between them. This

can assist in ensuring that the process is as efﬁcient

as possible by identifying bottlenecks and areas for

improvement. A further element of attention is the in-

terface, i.e., how the software interacts with the user

or other software.

Although the number of PMIS offerings has in-

creased signiﬁcantly, their data storage logic remains

210

Ottaviani, F., Rebuglio, M. and De Marco, A.

Project Management Information System Data Model Development and Explanation.

DOI: 10.5220/0012052200003546

In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023), pages 210-217

ISBN: 978-989-758-668-2; ISSN: 2184-2841

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

unclear: no standard interfaces exist for extending

software functionality or integrating with other busi-

ness applications. In this regard, this study aims to

develop a standardized data model and interfaces that

can serve as a reference to develop a PMIS as it pro-

vides a generic and highly comprehensible model for

PM. The data model can be used directly by the user

or as a basic infrastructure for computational opti-

mizations that can be interchanged between existing

software programs. The study describes the PM ob-

jects, their attributes and relationships, and the proce-

dures by which these objects are analyzed and mod-

iﬁed. Particular emphasis is placed on the interfaces

that facilitate data analysis, enhanced scheduling, risk

optimization, and the underlying data and procedures

that enable their functionality.

The paper is structured as follows. Section 1 in-

troduces the PMIS and the different steps for its de-

velopment and implementation. In Section 2, stud-

ies pertaining to PMIS are ﬁrst grouped into differ-

ent categories and then analyzed one by one. Sec-

tion 3 consists of a detailed description of the pro-

posed model and its content. Lastly, Section 4 con-

cludes by addressing the limitations and presenting

the conclusions for future research.

2 LITERATURE REVIEW

Scientiﬁc studies on Project Management Informa-

tion Systems (PMIS) offer a comprehensive and var-

ied range of information that organizations can utilize

to enhance the design and implementation of PMIS

and determine the most suitable PMIS for their spe-

ciﬁc requirements. These studies are crucial for orga-

nizations seeking to enhance their PM processes and

attain successful project execution. Speciﬁcally, re-

search on PMIS can be classiﬁed into three categories:

modeling, evaluation, and comparison.

2.1 Modeling Studies

The ﬁrst category of PMIS studies centres on pro-

viding different PMIS architectures. These studies

utilize various modeling methods, such as data ﬂow

diagrams, entity-relationship diagrams, and object-

oriented modeling, to craft a standard PMIS architec-

ture that can be adapted by organizations of varying

sizes and across multiple industries. Moreover, these

studies aim to optimize the design and deployment

of PMIS by determining the most efﬁcient methods

for structuring and organizing the data. In addition,

they offer a standardized framework for PMIS devel-

opment.

Studies by (Raimond, 1987) and (Bj

ork, 1992)

evaluated the viability of using conceptual data mod-

eling to depict the information system within the PM

system. This involved modeling the structure of the

information that describes a project’s products, pro-

cesses, resources, and other elements. The two stud-

ies contrasted conceptual modeling with traditional

methods and emphasized the potential for integrating

PMIS with other ISs within the organization. On the

other hand, (Froese, 1992) introduced the idea of stan-

dard models, consisting of a data model to represent

the information, a domain model to express project

concepts, and a project model that stores the project

data and domain models.

Other studies have discussed the importance of in-

tegrating the PMIS with other applications used by

different business functions. (Schoultz von et al.,

1996) introduced the concept of integrated PMIS that

provides support to a set of PM processes and en-

sures uniform data access. Likewise, (Jaafari and

Manivong, 1998) emphasized the requirement for a

centralized approach to information management. In

contrast, (Garcia et al., 2016) proposed a meta-model

for managing a single project that is based on a con-

ceptual architecture that can be extended to the enter-

prise level.

Several studies have aimed at developing refer-

ence models for PMIS that cater to the key functions

of PM, such as cost, time, scope, and quality manage-

ment. Both (Karim and Adeli, 1999) and (Fadillah

and Fitriana, 2019) utilized object-oriented program-

ming to provide a PM information model that relates

to different PM classes. On the other hand, (Yeganegi

and Safaeian, 2012) stressed the importance of map-

ping the inﬂuence of stakeholders in the PMIS. Mean-

while, (Bashashin et al., 2016) documented the infor-

mation related to the project monitoring and control

process.

A number of studies have been carried out in

developing ISs for project management, albeit with

slight variations in their objectives. (Ahlemann,

2009) introduced a reference information model to

expedite the setup of project ISs. (Li et al.,

2015) developed a portfolio management IS based

on complexity-based management methods to decom-

pose information processing complexity. In (Teixeira

et al., 2016), the PMIS was depicted using the UML

class diagram graphical technique. Conversely, (Wa-

heed et al., 2019) created a meta-model to map project

integration management, with an emphasis on data

automation.

Project Management Information System Data Model Development and Explanation

211

2.2 Evaluation Studies

The second category of PMIS research is centred on

implementing a PMIS and its impact on project man-

agement and execution.

These studies analyze the beneﬁts of PMISs, such

as improved communication, increased efﬁciency,

and enhanced collaboration. For example, (Amami

et al., 1993) stressed the importance of communica-

tion between stakeholders in determining a project’s

success and how PM ﬁts into the organization’s strat-

egy. (Jalal Karim, 2011) proposed a PMIS model

and assessed how PMISs facilitate decision-making

in each phase of the project life-cycle.

In examining the software quality, the informa-

tion output quality, as well as the inﬂuence of the

PMIS user on project success, (Kahura, 2013) and

(Taniguchi and Onosato, 2018) concluded that the

use of PMIS enabled the project to be successful

while respecting the project constraints and meeting

the project objectives at the same time. Similarly,

(Nguyo, 2014), (Park et al., 2018), and (Nyandongo

and Lubisi, 2019) demonstrated a strong and posi-

tive correlation between project success and PMIS

quality, information quality, system user, and system.

(Bor

stnar and Pucihar, 2014) demonstrated that PMIS

implementation could be successful if the organiza-

tion is aligned from human resources, ﬁnancial man-

agement, and collaboration perspectives.

Other studies also explored the critical success

factors of PMIS implementation, including user train-

ing, change management, and data security. (Cani

els

and Bakens, 2012) and (Rahman et al., 2018) exam-

ined how the use of a PMIS is advantageous to project

managers, while no adverse effects were observed due

to project and information overload. (Braglia and

Frosolini, 2014) remarked that any PMIS has key req-

uisites that relate to the project scope, resource alloca-

tion (Corrigan et al., 2019), time management, deliv-

erables, assignments, risk management, project mon-

itoring (Fachrizal et al., 2020), and quality (Bielova

et al., 2019).

These studies help organizations to understand the

potential impact of PMIS on their project manage-

ment processes and to identify the best practices for

successful PMIS implementation. For instance, (Ray-

mond and Bergeron, 2008) examined the impact of

PMIS on both project management and project execu-

tion performance, which conﬁrmed that PMIS adop-

tion contributes to improving budget cost and time

control. (Mccarty and Skibniewski, 2014) proposed a

multi-dimensional framework for PMIS training ini-

tiatives and (Nguyen et al., 2016) proposed a success

model of PMIS for ERP projects validated through a

survey study with path analysis.

2.3 Comparison Studies

The third category of PMIS research focuses on com-

paring various PMIS solutions and determining the

criteria for selecting the most suitable PMIS for a spe-

ciﬁc organization. These studies assess PMISs based

on functionality, scalability, cost-effectiveness, and

other relevant factors. The goal of these studies is

to assist organizations in choosing the PMIS that best

meets their speciﬁc needs.

(Liu et al., 2008) presented a practical approach

to analyze PMIS requirements, taking into account

acquisition rules and the requirement analysis pro-

cess. (Berzi

sa and Grabis, 2011) highlighted the im-

portance of PM knowledge during a PMIS conﬁgura-

tion and detailed the knowledge acquisition and uti-

lization processes. Using a questionnaire, (Lee and

Yu, 2012) developed and validated the ASP-PMIS

success model; this study served as a foundation for

positioning and comparing PMIS success research.

Finally, studies by (Kostalova et al., 2015), (Bellah

et al., 2018), and (van Besouw and Bond-Barnard,

2021) compared free and licensed PMISs and con-

cluded that the choice depends on the project com-

plexity and requirements, as free PMIS lack advanced

features, while licensed PMISs have a steeper learn-

ing curve.

2.4 Summary

Analysis of the literature in the ﬁeld of PMIS has

identiﬁed a signiﬁcant gap regarding the development

of basic conceptual models. Speciﬁcally, there is a

lack of a standardized management model that can be

applied across different areas. This gap highlights the

need for a literature-based approach to PMIS devel-

opment that focuses on creating a universal standard

for project management.

Moreover, studies evaluating commercial PMIS

software have revealed another signiﬁcant gap in the

ﬁeld. These solutions often come with no concep-

tual data model, which must be reverse-engineered to

integrate them with other software within the orga-

nization. This lack of standardization makes it difﬁ-

cult for organizations to leverage the data collected by

PMIS for improved project management and execu-

tion. Therefore, it is crucial to develop a standardized

approach to PMIS development that includes a con-

ceptual data model to enable better integration with

other business applications. Such an approach would

enhance the efﬁciency of project management and ex-

ecution while also facilitating the analysis of project

data.

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212

3 MODEL DEVELOPMENT

3.1 Diagrams and Notations Used

UML class diagram is structural diagram that shows

the static structure of a system, its classes, attributes,

interfaces, and the relationships between them. A

class diagram is one of the most widely used UML

diagrams and is often used to design, document, and

communicate the structure of an object-oriented sys-

tem (Rumbaugh et al., 2004). Classes consist of

objects that share a common structure and behav-

ior. Attributes represent class properties. An inter-

face consists of a set of methods or operations to be

implemented by a class. The solid lines connecting

classes represent the associations between them, and

the numbers at their ends indicate their cardinality.

The solid lines ending with a diamond shape indicate

a composition, i.e., an association by which a child

class cannot exist without the parent class. Arrows

indicate an inheritance relationship between a generic

class and its specializations. Instead, the dotted ar-

rows indicate when an interface of one class makes

use of data from another class. Doing so helps en-

sure that the PMIS accurately reﬂects the business re-

quirements and facilitates effective PM by providing

a clear and comprehensive understanding of the data

being managed.

3.2 Classes and Interfaces

The PMIS data model, which represents the various

data entities and their relationships within the PMIS,

is illustrated using the UML diagram depicted in Fig-

ure 1.

The Organization class indicates the organiza-

tion that uses the PMIS to manage its project port-

folios (Portfolio class), programmes (Programme

class), and projects (Project class). A project port-

folio is a collection of programmes and individual

projects that are performed to achieve the organiza-

tion’s goals. In turn, a programme comprises mul-

tiple interdependent projects that are coordinated to

achieve a shared outcome through efﬁcient resource

management. Lastly, a project consists of multi-

ple tasks (Task class) carried out using the organi-

zation resources (Resource class). The aforemen-

tioned classes have a single attribute related to a pri-

mary identiﬁer (ID); each of them is connected with

mandatory one cardinality to the hierarchically su-

perior class, and with many optional cardinality to

the hierarchically inferior class.

The Project class presents two interfaces: the

cost() interface returns the project Budget at Com-

pletion (BAC), while the time() interface returns its

Planned Duration (PD).

The Risk class and its specializations,

OrganizationRisk, ResourceRisk, and TaskRisk,

represent risks associated with the different modeled

entities. The class is deﬁned by an ID, a validity

period (start and end attributes), the probability

of occurrence during that time, and the impact on

resource productivity or task requirements.

The resources available to the organization to

carry out the tasks provided by the projects are repre-

sented using the Resource class. The designation

attribute is used to distinguish between resources. The

type attribute indicates whether the resource is of the

work or material type. A work resource indicates

any resource whose cost scales with the time the

resource is used. Instead, the amount or units used

determines the material resource cost. The max at-

tribute deﬁnes the resource maximum work time per

day (Work) or units available (Material); while the

association with the ResourceRisk class identiﬁes

possible risks associated with the resource.

Resources are managed and coordinated at the

programme level through the ProgrammeAllocation

association class, where the max attribute indicates the

amount of resources.

The Task class presents the overhead costs at-

tribute (OH) to account for any indirect cost, ex-

pressed in monetary units per unit of time. A task

can have none or multiple predecessors or succes-

sors; class Task is therefore associated with itself

with optional many - optional many cardinality

(Precedes association). A task can also includes

one or more subtasks, and it can have no more than

one parent; class Task is therefore associated with

itself with optional many - optional one cardi-

nality (Includes association). Since a child task has

no meaning without its parent task, the Includes as-

sociation is a composition. The model allows for

tasks with no associated requirements in order to

represent tasks that have the sole purpose of group-

ing other tasks in a parent-child relationship. The

class Task is associated with the class Schedule with

optional one cardinality: each instance of Sched-

ule represents the time information of a task instance,

and it is possible that, transiently, a task may have

no Schedule instances connected. The class Task is

also associated with the class Resources by means of

the association class Requirement, which represents

the allocated resources with their respective quanti-

ties; and with the class TaskRisk, which describes

the risks. In both cases, cardinality is optional

many. Another association is with the Progress

class, whose cardinality is optional many: each

Project Management Information System Data Model Development and Explanation

213

Organization

+ ID

Project

+ ID

+ cost()

+ time()

Task

+ ID

+ OH

+ schedule()

+ cost()

Requirement

+ total

Schedule

+ ID

+ start

+ end

+ duration

Resource

+ ID

+ designation

+ type

+ max

+ cost

ResourceRisk

TaskRisk

Risk

+ ID

+ start

+ end

+ probability

+ impact

Precedes

Programme

+ ID

Portfolio

+ ID

Organization

Risk

Progress

+ date

+ tWP

+ cWP()

+ AC()

+ cWS()

+ tWS()

+ cSPI()

+ CPI()

+ tSPI()

CostProgress

+ cWPi

+ ACi

Programme

Allocation

+ max

0..*

0..* 0..*

0..*

0..1

Includes

Figure 1: UML diagram showing classes, attributes and main interfaces of the model.

instance of Progress represents a temporal snap-

shot of the actual progress of the task. The Task

class includes a cost() and a schedule() interfaces.

The cost() interface returns the task BAC based

on the resources allocated (Requirement class), the

planned schedule (Schedule class), and the task over-

head costs. The schedule() interface is used to

schedule the task start, respecting the precedence

and resource utilization constraints (max attribute of

ProgrammeAllocation class).

The Schedule class is conceptually an extension

of the Task class as it contains information about

when and how many resources inherited from the

Requirement class will be allocated to perform the

work required by the task. Each schedule corresponds

to exactly one task (mandatory one cardinality). The

attributes start, duration and end indicate the ex-

pected start time, expected duration and expected end

time of the work, respectively. The duration only rep-

resents the time required by the task in question, while

the end attribute may be moved forward to wait for

the end of a child task.

The Progress class provides snapshots of the

progress of a task at a speciﬁc date. The task

is always identiﬁable by virtue of the relationship

with mandatory one cardinality with the class Task,

while the date to which the snapshot refers is con-

tained in the date attribute. The task scheduled

progress is determined through the tWS() interface

and compared with the progress performed, tWP. In-

stead, the task scheduled payments are determined

through the cWS() interface and compared with the

actual payments, cWP(), and the actual costs incurred,

AC(). Interfaces are also provided to calculate perfor-

mance indices: cSPI(), for time analysis based on the

work done; cSPI(), for time analysis based on expen-

diture; CPI(), for cost analysis. Both cWP() and AC()

are determined through interfaces as they association

class CostProgress, which allows for a single input

for each resource. For the model to work, an instance

of Progress with progress indicators of zero must be

entered at the task start date.

The association class CostProgress contains

cost-based progress indicators, one for each resource

associated with the task. Its cWPi and ACi attributes

represent a disaggregated form of z and k, broken

down by resource.

3.3 Features

The proposed model, as designed, allows three core

PMIS functionalities to be implemented: scheduling,

simulation, and project performance analysis.

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214

Table 1: Aggregated logical view of interfaces and at-

tributes aimed at cost and time control. Underlined data are

attributes, non underlined data are interfaces. All attributes

and functions are of the class Progress or its aggregation

class CostProgress, except for cost, which is an interface

of the class Task.

Performed (detail) cWPi ACi

Performed tWP cWP AC

Total tWS cWS Task.cost

Performance index tSPI cSPI CPI

3.3.1 Scheduling

Project scheduling is the process of creating a detailed

plan that outlines the sequence of activities, timelines,

and resources required to complete a project. The pro-

posed PMIS is designed to identify the critical path

(CP) and estimate the project duration by establish-

ing the Precedence Diagram Matrix (PDM) using the

precedes association relationships between Tasks.

The PDM is used by the PMIS to determine the differ-

ent paths in the project, calculate their duration using

the schedule() method, and identify the path with

the longest duration as the CP. This capability is es-

sential for accurately estimating the project duration

during both the planning and execution phases. By

identifying the CP, the PMIS enables project man-

agers to focus their attention and resources on the

most critical tasks, helping to ensure that the project

is completed on time and within budget.

3.3.2 Simulation

The proposed PMIS data model can be used for sim-

ulation of project scheduling by incorporating risk

factors into the model. As detailed in the preced-

ing subsection, distinct project schedules are associ-

ated with differing expected risk values, given the de-

ﬁned start (Risk.start) and end (Risk.end) dates

of each risk and the probability (Risk.probability)

and impact (Risk.impact) values inﬂuenced by Task

timing. Simulation of project execution takes into ac-

count these risks and their impact on Tasks, yield-

ing varying schedules and potential modiﬁcations to

project time and budget. By running simulations fea-

turing diverse risk scenarios, the model generates a

spectrum of potential project completion times (PD)

and costs (BAC), thereby enabling a comprehensive

analysis of project time and cost estimates. This ap-

proach facilitates identiﬁcation of the most probable

time and cost range for project completion through a

joint analysis of project time and cost.

3.3.3 Performance Analysis

Project performance analysis involves evaluating the

progress of project activities with regard to cost and

schedule. For each task in the project schedule, the

planned progress, payments, and budget spent are

calculated. These values are determined using the

tWS(), cWS(), and cWS() methods, respectively.

At each project review (denoted by i), actual time

advances (tWP()), payments (cWPi), and costs (ACi)

are provided as inputs for each task. This data al-

lows the PMIS to calculate performance indices for

scheduling (tSPI()), payments (cSPI()), and costs

(CPI()). These indices can then be interpreted to

evaluate performance, and can also be used to cal-

culate estimates at completion for project time, pay-

ments, and costs.

4 CONCLUSIONS

The purpose of this study is to establish a standardized

PMIS data model. To that end, a UML data model has

been devised that illustrates the interconnections be-

tween tasks and resources and, thereby, project sched-

ule and cost.

The paper begins with a description of the Project

Management Information Systems (PMIS), a type of

information system (IS) designed speciﬁcally to sup-

port project planning and cost and schedule control.

To be effective, a PMIS must be based on a solid data

model and must be used in conjunction with other

project management tools and techniques. Therefore,

the goal of the study is to provide a standardized data

model and interfaces.

A literature review on PMIS is conducted, iden-

tifying three main categories of studies: modeling

studies, which provide PMIS architectures, standard

models, and reference models for PMIS development;

evaluation studies, which assess the beneﬁts of PMIS,

including improved communication, efﬁciency, and

collaboration, and explore the critical success fac-

tors of PMIS implementation; and comparison stud-

ies, which examine how to select the best PMIS for

an organization.

A PMIS data model is developed, including inter-

faces for both task scheduling and control. These in-

terfaces are present at the project level, with the possi-

bility of expanding to the portfolio and program level

as well. The model incorporates attributes for mon-

itoring costs, overheads, risks and progress. Using

a UML diagram, a high-level representation of the

relationships between the different classes, their at-

tributes, and interfaces is provided. The model pro-

Project Management Information System Data Model Development and Explanation

215

vides a framework for cost control at several levels,

including monitoring of actual costs incurred, com-

parison with planned costs and identiﬁcation of devi-

ations from the plan.

The developed model serves as the foundation

for the database design and software development of

a PMIS system, providing a logical framework that

binds the various components of project management.

Additionally, its transparency and simplicity make

it compatible with integration into external manage-

ment applications.

The proposed PMIS data model was speciﬁcally

designed to facilitate the implementation of methods

that can perform three critical functions of project

management: scheduling, simulation, and perfor-

mance analysis. These methods are essential in meet-

ing time and cost targets, which are crucial aspects of

project quality management. Unlike inherent project

functionality and quality, which are often unique to

a speciﬁc project type or stakeholder requirements,

meeting time and cost targets are universal constraints

that can be standardized. Our goal in developing the

PMIS was to create a system that could effectively

manage these universal constraints and enable project

managers to achieve their time and cost targets across

a broad range of projects. By implementing methods

for scheduling, simulation, and performance analy-

sis, the PMIS enables project managers to plan and

manage projects more effectively, optimize resource

utilization, and identify potential issues before they

arise. The system’s ability to standardize these critical

functions also makes it easier to maintain consistency

and ensure that best practices are followed across all

projects.

The proposed model is immediately suitable to

fulﬁll the main functions of a PMIS, such as activ-

ity management, resource allocation, scheduling and

static risk management. However, for the implemen-

tation of advanced functions such as stakeholder man-

agement, dynamic risk management, prescriptive de-

cision support tools and so on, modiﬁcations and ex-

tensions to the model may be necessary. In the current

model’s design, we took the implementation perspec-

tive into account, but did not describe interfaces to

make the model talk to other software or an optimizer.

Future research directions will address the limita-

tions of the model and seek to enhance it by adding

methods and relationships to existing classes.

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