Semantic Representation of Key Performance Indicators Categories for

Prioritization

Tarique Khan, Alex Mircoli, Domenico Potena and Claudia Diamantini

Department of Information Engineering, Universit

a Politecnica delle Marche, Italy

Keywords:

Business Process Management, Integrated Indicator Framework, Key Performance Indicators, Ontology, Set

Theory.

Abstract:

Key Performance Indicators (KPIs) are crucial tools that are remarkably used to evaluate business perfor-

mance. Recently, the management of KPIs has fascinated the focus of both academic and business profession-

als, and that lead to the development of research on various methods dealing with issues such as modeling,

maintenance, and expressiveness of KPIs. As a need for organizations and processes to adapt to continuously

changing demands, the KPIs used to measure their effectiveness evolve too. In order to make KPI management

easier, this research aims to deﬁne the best sequence of KPIs evaluation based on semantic relations. After

an extensive analysis of the literature on KPIs ontologies, it proposes the idea of KPIs prioritization on the

basis of relations among different categories of kpis established by a KPIs ontology. Our approach can be used

independently from the particular KPI’s management strategy being employed.

1 INTRODUCTION

Due to the modern data society, businesses and in-

stitutions can now access a great deal of informa-

tion in many different forms. The appropriate use of

the available data can result in changes to an organi-

zation’s processes, systems, and procedures because

continuous business improvement is needed. Dif-

ferent factors, such as adjustments to the company’s

business strategy, the ﬁxing of identiﬁed problems,

changes to the law, or technical advancements, might

bring about changes (Cognini et al., 2018). Compa-

nies realized that they needed to be agile, ﬂexible, and

focused on their business strategy in order to succeed

in a dynamic environment with competitors, shorter

product lifecycles, and intense price pressures when

third-party countries are bringing costs down (Fer-

reira et al., 2017; Cortes et al., 2016; Sahno et al.,

2015; Haponava and Al-Jibouri, 2012).

In recent years, businesses have suffered from fail-

ing to retain customers and a lack of funding. The

company’s performance must be high to accomplish

those goals, and goods or services must be produced

or delivered at the right places, at the proper time,

in the appropriate amount, and for the suitable clien-

tele, (Konsta and Plomaritou, 2012; Azapagic, 2003).

The use of key performance indicators (KPIs), a con-

temporary technique, helps to sustain high levels of

manufacturing performance (Tsai and Cheng, 2012).

Following and monitoring the proper metrics in real-

time, organizations are able to identify and compre-

hend constraints, assess worker and machine efﬁ-

ciency, set higher targets, and accomplish them by

moving forward. The ability to measure performance

enables one to identify performance concerns, deter-

mine how well one is progressing toward one’s ob-

jectives, and provide precise instructions for resolv-

ing issues (Horv

athov

a et al., 2015). provide a novel

semantic framework for describing performance in-

dicators that helps create and maintain a concise and

reliable lexicon(Diamantini et al., 2016).

To identify the areas that need to be addressed, it

is essential to evaluate how well business operations

are performing. In every business sector, Key Perfor-

mance Indicators (KPIs) have been widely used and

deﬁned inside information systems. It makes sense

that there would be substantial and in-depth research

on this subject given how important KPIs are. De-

spite this, there is no established framework for the

deﬁnition and application of KPIs. In (Dom

ınguez

et al., 2019) the author discussed this problem and

suggested a solution.

In this study we provide a semantic approach to

the problem of KPI advancement management. Key

Performance Indicator (KPI) advancement manage-

ment is a crucial aspect of business success, as it helps

142

Khan, T., Mircoli, A., Potena, D. and Diamantini, C.

Semantic Representation of Key Performance Indicators Categories for Prioritization.

DOI: 10.5220/0011848700003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 2, pages 142-151

ISBN: 978-989-758-648-4; ISSN: 2184-4992

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

organizations to track and measure the metrics that are

most critical to their performance. By regularly mon-

itoring these KPIs, businesses can gain valuable in-

sights into their operations and make data-driven de-

cisions to drive improvement. The ﬁrst step in KPI ad-

vancement management is to identify the right KPIs

for the organization. This will vary depending on the

speciﬁc business and its goals, but could include met-

rics such as customer satisfaction, employee engage-

ment, and ﬁnancial performance. Once the KPIs have

been identiﬁed, it is important to establish goals and

targets for each one, to provide a clear understand-

ing of what success looks like. Next, organizations

need to implement a system for tracking and measur-

ing their KPIs on a regular basis. KPI advancement

management speciﬁcally suggests adopting the idea

of a KPI prioritization as the best KPIs evaluation or-

der to consider for a given class of KPIs. The prior-

itization order is then derived by semantic relations

among different categories of KPIs as modeled in an

ontology. The primary goal of the long-term research

project is to provide a universal framework for man-

aging KPIs advancement.

The rest of the paper is organized al follows: Sec-

tion 2 is devoted to a throughout analysis of related

work about ontologies of KPIs; in Section 3 the pro-

posed methodology is discussed. Finally, Section 4

draws some conclusions and future work.

2 LITERATURE STUDY

2.1 Towards a Framework for KPI

Evolution

Towards a Framework for KPI Evolution (Domınguez

et al., 2020), provides the emphasis on the con-

cepts of a conceptual framework, including a pattern-

driven KPI evolution speciﬁcation and a KPI evo-

lution meta-model composed of two interconnected

views. Firstly, the structural view of the meta-model

provides the basis to design meta-information for the

KPI’s evolution, and secondly, the execution view

concerns the applications built on the given struc-

tural views. Moreover, the approach presented in a

study (Domınguez et al., 2020) is broad enough to

be used independently of the particular KPIs man-

agement strategy being adopted. Several areas re-

quiring additional research must be addressed to de-

velop this general framework. The integration of the

proposal within other KPIs management approaches

must also be investigated to ascertain how proposed

approach are properly incorporated into these ap-

proaches. Some of the KPI strategies are brieﬂy dis-

cussed below, and Table 1 provides some ontology-

based techniques.

2.2 A Lightweight Version of National

Performance Indicator Ontology

(NPIonto)

Oarabile Sebubi et al. (Sebubi et al., 2019) pro-

posed a model that was created to meet the domain

needs for Botswana’s development agenda, which

calls for a consolidated indicator framework with dis-

tinct connection mappings and data deﬁnitions, as

well as data disaggregation and consideration of PIs’

multi-dimensional features. The model was con-

ceptualized using the ofﬁcial development agenda

documents as a foundation and developed using the

Knowledge Model Development (KMD) methodol-

ogy. Dom

ınguez et al. (Dom

ınguez et al., 2019) pri-

marily concentrate on this KPIs management element

aims to deliver outstanding advantages like improv-

ing KPIs management knowledge or assisting users

in selecting the best solution for their needs.

2.3 Implementation of Key

Performance Indicators Selection

Model as Part of the Enterprise

Analysis Model

Kaganski et al. (Kaganski et al., 2017) proposed the

ﬁndings of adopting the KPI selection model as part

of the Enterprise Analysis Model (EAM). The model

was put to the test by a private ﬁrm. The collection of

KPIs that management should use was created. The

suggested approach allows for time and resource sav-

ings during evaluation and metric selection.

2.4 Towards a KPI-Based Ontology for

Condition Monitoring of

Automation Systems

Pasic et al. (Pasic et al., 2019) presented a conceptual

paradigm and condition monitoring ontology for au-

tomation systems. This ontology combines ISO stan-

dards for key performance indicators and condition

monitoring (KPIs). Based on the proposed ontology

in an industrial project, the author created a condition

monitoring knowledge-based system for a centrifu-

gal separator and reported the criteria to evaluate this

work. This project aims to use semantic web query

languages to link various knowledge-based systems

engineering (MBSE) tools.

Semantic Representation of Key Performance Indicators Categories for Prioritization

143

Table 1: Common Ontologies from the Literature.

Approach Domain

Ontology Lan-

guage Used

SUPER and SemBiz

(Hoang et al., 2010)

Ontologies stack WSMO

Jenz’s BPM (Raba-

hAzzam and Zhou,

2012)

Core business ontology; industry-speciﬁc ontology;

organization-speciﬁc ontology

OWL

M3po Project (Thapar

and Sharma, 2022)

M3po ontology WSMO

Genesis (Shanthi Bala

and Aghila, 2019)

Business OWL OWL

CNO (Rajsiri et al.,

2008)

Collaborative network ontology; Collaborative process on-

tology

OWL/SWRL

FlowMake (An-

daloussi et al., 2020)

Graph-based Ontology OWL

YAWL (Van Der Aalst

and Ter Hofstede,

2005)

Speciﬁcation of workﬂow and data perspective of business

processes

WFMS

E-C-A Based (Ndadji

et al., 2020)

Rule based process modeling to provide an integration

layer between process modeling languages

SDL

ADEPT (Dadam and

Reichert, 2009)

Rule-based ontology; designing and implementing multi-

agent systems for workﬂows

SDL

2.5 Generalized KPI Models

A generalized KPI model proposed to improve busi-

ness performance looked into the connection be-

tween institutional ownership and Malaysian pub-

licly traded ﬁrms’ ﬁnancial performance (Ahmad and

Jusoh, 2014). The Six Sigma method is a data-

driven approach to process improvement that focuses

on reducing defects and improving quality (Albliwi

et al., 2015). Some other generalized rules-based ap-

proaches developed such as the McKinsey 7S frame-

work tools used for analyzing and improving the ef-

fectiveness of organizations (Trompenaars and Co-

ebergh, 2014).

2.6 Toward an Ontology-Based Model

of Key Performance Indicators for

Business Process Improvement

Ammar et al. (Amor and Ghannouchi, 2017) pro-

posed a novel ontology to establish semantic linkages

between all terms based on an actual business pro-

cess. The author relied on the data mining technique

to extract the most crucial information from data mea-

surements. The application of the suggested contribu-

tion is demonstrated, and a case study in the ﬁeld of

healthcare is used to validate it. This study reveals

that understanding KPIs from patient experience in

the ED and interactions with other indicators are sig-

niﬁcant qualities for improving blood pressure.

2.7 Towards Measuring Key

Performance Indicators of Semantic

Business Processes

Wetzstein et al. (2008) present a key performance

indicator-based approach to semantic business pro-

cess performance management. The advantage of this

method over previous work is that business analysts

may completely specify KPIs during the process mod-

eling phase since the necessary data, such as business

objects and changes in their state, are available as se-

mantic annotations of activities.

Table I shows the most common ontologies used

in the literature for business organizations and their

respective tasks. It can be observed that graph-based

ontologies are mostly formed using OWL or similar

tools. Also, OWL is a tool that can be used in various

types of ontologies while many of the tools are

domain speciﬁc. For instance, Service Description

Language (SDL) can only be used in rule-based

ontologies and web service modeling language

(WSMO) can be applied in ontologies related to

business structures.

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3 METHODOLOGY

As businesses develop and grow, the management of

quality and performance becomes crucial. To achieve

success in businesses and enterprises, the considera-

tion of factors that affect business has great impor-

tance in Business Process Management (BMP). That

can be analyzed through dashboards, reports (Par-

menter, 2015) supporting decision-makers in identi-

fying opportunities for re-engineering the desired pro-

cess and improvements. There are many processes

involved in BPM to understand and measure the busi-

ness indicators. In our proposed methodology for

simpliﬁcation and generalization, we comprise the

whole BPM into three important parts (i.e., manage-

ment, methodological approach, and technology). As

concluded from the literature, there is a lot of research

available for BPM to calculate the Key Performance

Indicators (KPIs) in qualitative, quantitative, and se-

mantic ways. But, proposal lack the ability to iden-

tify KPIs serialization based on priority in a semantic

way. KPIs pertaining to a business process can be in-

terrelated to each other, either directly or indirectly.

For example, the sell price is directly related to the

product cost. Consequently, this research aims to ﬁnd

the best sequence of KPIs evaluation based on seman-

tic relations. The methodology has been graphically

viewed in Figure 1.

3.1 Business Process Management

(BPM)

Businesses are managed by high-level staff (man-

agers) for the smooth ﬂow of services and success.

However, business process management involves sev-

eral important steps and resources such as the cre-

ation, implementation, monitoring, and analysis of

operational business processes, including people, or-

ganizations, software, documents, and other sources

of information (Park et al., 2012). To simplify BPM in

a generalized way, the BMP is summarized into three

categories: management, methodological approach,

and technology.

Management: A company’s management team

often deﬁnes processes, categorizes metrics, monitors

performance and goals, and works to improve pro-

cesses in order to meet market demands and provide

valuable goods and services. Hereafter, the manage-

ment team has extensive involvement in the conclu-

sion of indicators.

Methodological Approaches (for BMP): Busi-

ness process improvement is a continuous cycle that

is a part of the BPM methodology. Using phases,

actions, and procedures, aids businesses in improv-

ing their business operations. For example dynamic

BPM, agile BMP, and social BMP. To design, select,

monitor, and alter the business indicator depend on

which methodological approach is adopted for busi-

ness.

Technology (used for BMP): BPM technology

is mostly software systems that can trace and record

business processes to enhance analytics and business

communication. This technology helps automate ac-

tivities and track business projects and performance.

In order to improve performance, quality, and efﬁ-

ciency, BPM technology essentially aids businesses

in having a clear grasp of numerous processes within

the organization. It helps in identifying the important

business indicators.

3.2 Indicators (for Business)

To successfully evaluate a business for its perfor-

mance, it is necessary to consider, analyze, and man-

age the factors/indicators that affect the quality, efﬁ-

ciency, and growth of the business. There are numer-

ous indicators for BPM to be considered. Most of the

indicators are generic and can be selected by any busi-

ness accordingly, while others are speciﬁc and vary

from business to business. Speciﬁc indicators are de-

signed and selected by each business as per their re-

quirements. In this study, indicators are divided into

categories such as short-term indicators, long-term,

quantitative, qualitative indicators see Table 2.

This research paper considers the generic indica-

tors that are most common in every business activ-

ity to streamline the research model in a general per-

spective for the generalization and extraction of KPIs.

In contrast, speciﬁcally selected KPIs for a business

could not be suitable for others’ consideration. The

next step, the generalization of KPIs, is discussed in

detail.

3.3 Key Performance Indicators (KPIs)

A KPI is a quantiﬁable statistic that shows how well

a business is accomplishing its important business

goals. To extract the generalized form of KPIs, which

are able to be considered by all types of businesses,

the generalized and reﬁned indicators are selected by

BPM. In order to select the most generalized KPIs,

we selected the common business indicator categories

that are included in almost every BPM. The common

categories include long-term, quantitative, dependent,

independent, cost, time, productivity, quality, high-

level, and low-level indicators. Whereas short-term,

qualitative, external, and internal indicators are spe-

ciﬁc to the business type, short-term indicators are

Semantic Representation of Key Performance Indicators Categories for Prioritization

145

Figure 1: Proposed Methodology.

Table 2: General Categories of Business Indicators Assessed in Process Management.

Abbreviation

Category of business indi-

cator

Description

STI Short term indicators

Those business indicators that are set for short-term goals e.g., production

target for tomorrow

LTI Long term indicators

Those business indicators that are selected for long-term goals e.g., prod-

uct quality

QI I Qualitative indicators Those indicators that belong to the relationship e.g., customer satisfaction

QI II Quantitative indicators

The business indicators that can be evaluated and assigned value e.g.,

delivery time

DI Dependent indicators

The indicators which depend on other indicators e.g., the delivery time

depends on the employ efﬁciency

II Independent indicators The indicators that can be evaluated independently e.g., investment

CI Cost indicators

these indicators allow you to evaluate the process involved in economic

resources, e.g.,

production cost per unit

TI Time indicators

Indicators that represent and evaluate the time of different processes e.g.,

minimum delivery time

PI Productivity indicators

Productivity the indicator allows you to evaluate the operational efﬁ-

ciency e.g., percentage downtime

QI III Quality indicators

These indicators measure the quality of production and effectiveness of

the process e.g.,

the number of errors occurred

HLI High level indicators

These indicators usually show the overall performance of the business

e.g., annual growth

LLI Low level indicators

Low-level indicators are selected to evaluate the team-wise performance

of the employee e.g.,

employee progress

EI External indicators

External indicators again target high-level performance e.g., collective

goal achievement

II Internal indicators

Internal indicators target low-level performance e.g., team-wise perfor-

mance

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Figure 2: KPIs selection based on the purpose and requirement of organization.

Table 3: KPIs and its Evaluation Parameters along with

Datatypes.

KPIs Cat-

egories

Evaluation Datatypes

Long-

Term

Descriptive (string), Measured

(Double), Measured (integer)

Quantita-

tive

Measured (Double), Measured (inte-

ger)

Dependent N/A

Indepen-

dent

N/A

Cost Currency (string)

Time Time (integer)

Productiv-

ity

Percentage (double)

Quality Grading (string)

High-

Level

Describe (string)

Low-Level Grading (string)

bound to the goals on an hourly basis and do not meet

the generic nature of an indicator. Qualitative indi-

cators depend on qualitative data, which varies from

business to business. External and internal indicators

are mostly covered by high-level and low-level indi-

cators, which are consequently excluded from gener-

alized indicators. In the end, generalized KPIs are ob-

tained based on generalized categories of indicators

and the purpose of the selection.

3.4 KPIs Selection Purpose

KPIs are selected by the high authorities in BPM ac-

cording to the predeﬁned purpose. Additionally, the

selection process also depends on the available infor-

mation regarding involved activities, processes, goals,

and achievements of the business. However, KPIs are

mainly used for the purposes deﬁned in Figure 2.

3.5 Generalization of KPIs

The aim of our proposed methodology is to design

a generalized framework that helps all types of busi-

nesses in their BPM. It is important to design the cat-

egories of indicators for the extraction of the prede-

ﬁned groups of generic KPIs to achieve the aforemen-

tioned goal.

3.6 KPIs Evaluation

After the selection of KPIs categories and subcate-

gories, the second step is to select the correct evalu-

ation mechanism. Consequently, the implementation

of a speciﬁc KPI needs the evaluation parameters to

perform the exact analysis. Table 3 explains the as-

sessment evaluation and its data types for selected cat-

egories of KPIs.

3.6.1 Outcome Knowledge

KPI outcome knowledge is a critical component

of successful business management and decision-

making. By having a deep understanding of KPIs and

their outcomes, organizations can make informed de-

cisions, track progress, and continually improve their

performance.

3.6.2 Derived Actions

Derived KPIs actions are powerful tools for busi-

ness management and decision-making, providing a

more comprehensive understanding of a particular as-

pect of the organization. By effectively using de-

rived KPIs, organizations can make informed deci-

sions, track progress, and continually improve their

performance.

3.7 KPIs Prediction

KPI prediction involves analyzing past performance

data, identifying trends and patterns, and using sta-

tistical models to make predictions about future val-

ues. The accuracy of KPI predictions depends on the

quality and availability of historical data, the choice

of appropriate predictive models, and the ability to

accurately capture and account for relevant external

factors that may affect future performance.

Semantic Representation of Key Performance Indicators Categories for Prioritization

147

Figure 3: Main Ontology Graphical View.

3.7.1 Active Process Analysis

KPI active process analysis is an important tool for

improving business processes and achieving strategic

objectives. By regularly monitoring and evaluating

KPIs, organizations can identify areas for improve-

ment and make the changes necessary to optimize

processes and increase efﬁciency.

3.7.2 If Analysis Failed

If a Key Performance Indicator (KPI) analysis fails,

it means that the chosen KPIs were not effective in

measuring the performance of the business processes

they were designed to monitor. In such a scenario,

there are several options that organizations can con-

sider. Few are enumearated below:

Re-evaluate KPIs: The ﬁrst option is to re-evaluate

the KPIs that were used in the analysis. This may in-

volve changing the KPIs, redeﬁning their objectives,

or adjusting the way they are measured.

Use Alternative Methodologies: If the KPI analy-

sis fails, organizations can consider using alternative

methodologies, such as process mapping, root cause

analysis, or customer satisfaction surveys, to identify

areas of improvement.

Monitor Progress: Finally, organizations should

regularly monitor progress and evaluate the results

of any changes made. This will help to determine

whether the new approach is effective and whether

further adjustments are necessary.

3.8 Ontology (for Semantic

Representation)

This ontology offers all the data requirements for wise

decision-making. As a result, it will enable the acqui-

sition of some guidance regarding the selection and

application of the proper category of KPIs in accor-

dance with the requirements. To put it more precisely,

it ﬁrst aids the decision-making makers of the KPI

category to be used for business process performance

measurements. Second, the ontology operates at a de-

gree of detail that is sufﬁcient to give a thorough foun-

dation for evaluating the relationships between every

component of the business process. Figure 3 provides

a graphical view of the ontology built by WebVOWL

(Horridge et al., 2009).

Our ontology represents the generic categories of

all related KPIs and aims at facilitating all types of

business processes rather than focusing on a speciﬁc

use case. The purpose of this ontology is to ﬁnd

the semantic relationship between the categories of

generic KPIs which helps to ﬁnd the execution order

in case of sequential execution to avoid any bottle-

neck. More speciﬁcally, this ontology also provides

the assessment tool for the evaluation of KPIs and

their datatypes.

In order to take a more informed decision, it is im-

portant to get and represent more information. There-

fore, we created three main classes (categories) and

subclasses of the main KPIs class to relate the infor-

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Table 4: KPIs categories and its Abbreviation.

Abbreviations KPIs category Abbreviation KPIs category

ST-KPIs Short-term KPIs I-KPIs Independent KPIs

LT-KPIs Long-Term KPIs C-KPIs Cost KPIs

D-KPIs Dependent KPIs T-KPIs Time KPIs

DD-KPIs Directly Dependent KPIs P-KPIs Productivity KPIs

IDD-KPIs Indirectly Dependent KPIs Q-KPIs-II Quality KPIs

Q-KPIs-I Quantitative KPIs LL-KPIs Low-Level KPIs

HL-KPIs High-Level KPIs - -

mation. The subclasses of the main KPIs classes are

related according to their dependencies to linearize

sequential order logically to achieve the aforemen-

tioned objective of this research. To get the order of

execution after selecting a set of required categories

we use a set-based representation which is explained

in the following section.

3.9 Prioritization

For a BPM, it is important to select the potential KPIs

from the generic categories as per the business re-

quirements. But most importantly, the question arises

in which order these KPIs need to be considered to

achieve high performance. This research paper pro-

vides the solution to this question by establishing the

semantic relationship between the generic categories

of KPIs through our ontological representation. More

speciﬁcally, we provide the evaluation order of KPIs

as the results of the set-theoretic semantics of onto-

logical relations. KPI Prioritization refers to the pro-

cess of identify and prioritize the most important Key

Performance Indicators (KPIs) for a business. It is

a critical step in performance management as it helps

organizations to focus on the metrics that are most rel-

evant to their goals and objectives.

Set-theoretic semantics deﬁnes the interpretation

of concepts (resp. relations) in terms of sets of in-

dividuals (resp. set of pairs of individuals) of a do-

main. In the case of our problem, let us consider the

three parent classes Directly Dependent KPIs, Indi-

rectly Dependent KPIs, and Independent KPIs. The

full logic speciﬁcation of these categories prescribes

to set a non-disjointness property among them. It

means that in the corresponding Venn diagram the

three sets representing those classes are overlapping.

In order to fully specify the relationships among all

the KPIs classes we ﬁrst deﬁne the short forms of

generic KPIs categories in Table 4.

KPIs categories relations are depicted through

Venn Diagram as three-parent sets and their subsets

(synthesized by their name for simplicity). Figure 4

represents the concept as an intersection among par-

ent sets derived from the ontological representation

of semantic information. As the usage (or calcula-

tion) order of the KPIs generic categories depends on

their interdependency, therefore, the most dependent

categories need to be considered ﬁrst i.e., the regions

A ∩ B ∩ C. Whereas, the KPIs categories that lie in

A ∩ B or B ∩C or A ∩C need to be considered in the

second order. Lastly, the KPIs categories in the region

A or B or C will be considered in the end. The follow-

ing extracted queue in Figure 4 shows the exact order

of execution based on the generic KPIs categories. To

optimize any business process during BPM, the man-

agers need to select or update the potential KPIs from

the generic categories as mentioned according to the

business requirements. Secondly, the KPIs priority is

to be assigned based on the following queue shown in

Figure 5 to get high performance without any bottle-

neck.

3.9.1 Case Study

A case study on the semantic representation of Key

Performance Indicator (KPI) categories for prioriti-

zation was conducted in a ﬁnancial organization. The

goal of the study was to develop a more efﬁcient and

effective method for prioritizing KPIs and aligning

them with business goals. The ﬁrst step of the study

was to conduct a comprehensive review of existing

KPIs and categorize them based on their relevance to

the organization’s goals and objectives. This process

involved identifying all of the KPIs that were cur-

rently being used by the organization and grouping

them into categories based on their relevance to

speciﬁc business goals and objectives. Once the KPIs

had been categorized, the next step was to semanti-

cally represent them using a graph-based approach.

This involved creating a visual representation of the

relationships between different KPIs and categories,

with the goal of providing a clearer understanding

of the relationships between different KPIs and the

impact they had on the overall performance of the or-

ganization. The next step was to align them with the

organization’s goals and objectives which ensure that

Semantic Representation of Key Performance Indicators Categories for Prioritization

149

Figure 4: Venn diagram (KPIs categories relational representation).

Figure 5: KPIs Execution Order.

the KPIs were aligned with the speciﬁc goals and ob-

jectives.

The case study demonstrated the value of using a

semantic representation of KPIs for prioritization in

a ﬁnancial organization. This resulted in signiﬁcant

improvements in their ability to monitor and improve

performance and has laid the foundation for further

progress in the future.

4 CONCLUSIONS

In order to facilitate KPIs advancement management,

this study has given a semantic approach and built

methods based on set theory. In speciﬁc, we proposed

using a notion of a KPI advancement pattern and a

KPI advancement meta-model, made up of two in-

terrelated views, to facilitate KPIs development. The

long-term research project’s main objective is to build

a generic framework for handling KPIs advancement.

The main highlights of this paper are: (i) Used a se-

mantic ontology to express KPIs. (ii) KPI levels were

chosen using the set theory concept. (iii) KPI Priori-

tization. In the future study, we will create a semantic

Business Process (SBP) monitor and adapt an exist-

ing SBP modeling tool to incorporate the suggested

technique as well.

REFERENCES

Ahmad, A. C. and Jusoh, M. A. (2014). Institutional own-

ership and market-based performance indicators: Uti-

lizing generalized least square estimation technique.

Procedia-Social and Behavioral Sciences, 164:477–

485.

Albliwi, S. A., Antony, J., and halim Lim, S. A. (2015). A

systematic review of lean six sigma for the manufac-

turing industry. Business Process Management Jour-

nal, 21(3):665–691.

Amor, E. A. E. H. and Ghannouchi, S. A. (2017). Toward an

ontology-based model of key performance indicators

for business process improvement. In 2017 IEEE/ACS

14th International Conference on Computer Systems

and Applications (AICCSA), pages 148–153. IEEE.

Andaloussi, A. A., Burattin, A., Slaats, T., Kindler, E., and

Weber, B. (2020). On the declarative paradigm in hy-

brid business process representations: A conceptual

framework and a systematic literature study. Informa-

tion Systems, 91:101505.

Azapagic, A. (2003). Systems approach to corporate sus-

tainability: a general management framework. Pro-

cess Safety and Environmental Protection, 81(5):303–

316.

Cognini, R., Corradini, F., Gnesi, S., Polini, A., and Re,

B. (2018). Business process ﬂexibility-a systematic

literature review with a software systems perspective.

Information Systems Frontiers, 20(2):343–371.

Cortes, H., Daaboul, J., Le Duigou, J., and Eynard, B.

(2016). Strategic lean management: Integration of

operational performance indicators for strategic lean

management. IFAC-PapersOnLine, 49(12):65–70.

Dadam, P. and Reichert, M. (2009). The adept project: a

decade of research and development for robust and

ﬂexible process support. Computer Science-Research

and Development, 23(2):81–97.

Diamantini, C., Potena, D., and Storti, E. (2016). Sempi:

A semantic framework for the collaborative construc-

tion and maintenance of a shared dictionary of perfor-

mance indicators. Future Generation Computer Sys-

tems, 54:352–365.

Dom

ınguez, E., P

erez, B., Rubio, A. L., and Zapata, M. A.

(2019). A taxonomy for key performance indica-

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

150

tors management. Computer Standards & Interfaces,

64:24–40.

Domınguez, E., P

erez, B., Rubio, A. L., and Zapata, M. A.

(2020). Towards a framework for kpi evolution.

Ferreira, W. d. P., Silva, A. M. d., Zampini, E. d. F., and

Pires, C. (2017). Applicability of the lean thinking in

bakeries. Espacios, 38(2).

Haponava, T. and Al-Jibouri, S. (2012). Proposed sys-

tem for measuring project performance using process-

based key performance indicators. Journal of man-

agement in engineering, 28(2):140–149.

Hoang, H. H., Tran, P.-C. T., and Le, T. M. (2010). State of

the art of semantic business process management: An

investigation on approaches for business-to-business

integration. In Asian Conference on Intelligent In-

formation and Database Systems, pages 154–165.

Springer.

Horridge, M., Jupp, S., Moulton, G., Rector, A., Stevens,

R., and Wroe, C. (2009). A practical guide to build-

ing owl ontologies using prot

e 4 and co-ode tools

edition1. 2. The university of Manchester, 107.

Horv

athov

a, J., Mokri

sov

a, M., Suh

anyiov

a, A., and

Suh

anyi, L. (2015). Selection of key performance

indicators of chosen industry and their application

in formation of creditworthy model. Procedia Eco-

nomics and Finance, 34:360–367.

Kaganski, S., Majak, J., Karjust, K., and Toompalu, S.

(2017). Implementation of key performance indica-

tors selection model as part of the enterprise analysis

model. Procedia Cirp, 63:283–288.

Konsta, K. and Plomaritou, E. (2012). Key performance in-

dicators (kpis) and shipping companies performance

evaluation: The case of greek tanker shipping compa-

nies. International Journal of Business and Manage-

ment, 7(10):142.

Ndadji, M. M. Z., Tchendji, M. T., Djamegni, C. T., and

Parigot, D. (2020). A language and methodology

based on scenarios, grammars and views, for admin-

istrative business processes modelling. arXiv preprint

arXiv:2010.13347.

Park, J. H. J., Kim, J., Zou, D., and Lee, Y. S. (2012). In-

formation Technology Convergence, Secure and Trust

Computing, and Data Management: ITCS 2012 &

STA 2012, volume 180. Springer Science & Business

Media.

Parmenter, D. (2015). Key performance indicators: devel-

oping, implementing, and using winning KPIs. John

Wiley & Sons.

Pasic, F., Wohlers, B., and Becker, M. (2019). Towards

a kpi-based ontology for condition monitoring of au-

tomation systems. In 2019 24th IEEE International

Conference on Emerging Technologies and Factory

Automation (ETFA), pages 1282–1285. IEEE.

RabahAzzam, S. and Zhou, S. (2012). Semantic web ap-

proach for organizations management.

Rajsiri, V., Lorr

e, J.-P., B

enaben, F., and Pingaud, H.

(2008). Collaborative process deﬁnition using an

ontology-based approach. In Working Conference on

Virtual Enterprises, pages 205–212. Springer.

Sahno, J., Shevtshenko, E., Karaulova, T., and Tahera,

K. (2015). Framework for continuous improvement

of production processes. Engineering Economics,

26(2):169–180.

Sebubi, O., Zlotnikova, I., and Hlomani, H. (2019). A

lightweight version of national performance indicator

ontology (npionto). In 2019 Conference on Next Gen-

eration Computing Applications (NextComp), pages

1–6. IEEE.

Shanthi Bala, P. and Aghila, G. (2019). Q-genesis: Question

generation system based on semantic relationships. In

Advances in Big Data and Cloud Computing, pages

509–517. Springer.

Thapar, P. and Sharma, L. S. (2022). Implementing sparql-

based preﬁltering on jena fuseki tdb store to reduce the

semantic web services search space. In Evolutionary

Computing and Mobile Sustainable Networks, pages

319–333. Springer.

Trompenaars, F. and Coebergh, P. (2014). 100+ man-

agement models: How to understand and apply the

world’s most powerful business tools. Inﬁnite Ideas

Limited.

Tsai, Y.-C. and Cheng, Y.-T. (2012). Analyzing key perfor-

mance indicators (kpis) for e-commerce and internet

marketing of elderly products: A review. Archives of

gerontology and geriatrics, 55(1):126–132.

Van Der Aalst, W. M. and Ter Hofstede, A. H. (2005). Yawl:

yet another workﬂow language. Information systems,

30(4):245–275.

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