sustainability

Article
Eliminating Non-Value-Added Activities and Optimizing
Manufacturing Processes Using Process Mining: A Stock of
Challenges for Family SMEs
Abderrazak Laghouag 1, * , Faiz bin Zafrah 1 , Mohamed Rafik Noor Mohamed Qureshi 2 and Alhussain Ali Sahli 1

1 Department of Business Administration, College of Business, King Khalid University,

Abha 62552, Saudi Arabia; fzafrah@kku.edu.sa (F.b.Z.); alhussainsahli07@gmail.com (A.A.S.)
2 Department of Industrial Engineering, College of Engineering, King Khalid University,
Abha 62552, Saudi Arabia; mrnoor@kku.edu.sa
* Correspondence: alaghouag@kku.edu.sa

Abstract: Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding
leadership type, human resource management practices, innovation orientation, change management,
information and communication technology deployment, process maturity, and resource availability.
These differences present challenges when leading any change. Process mining (PM) tools can
optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”.
The present study investigates how a PM project is implemented in an FSME operating in the agri-
food sector, focusing on challenges faced in every project phase to extract the most appropriate
process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle
methodology combined with quality and lean management tools such as the fishbone diagram, Pareto
diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing
process for an FSME operating in the agri-food sector. To achieve theoretical production capacity
(TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM
toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational
Citation: Laghouag, A.; Zafrah, F.b.;
and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the
Qureshi, M.R.N.M.; Sahli, A.A.
studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle
Eliminating Non-Value-Added
methodology allowed for the identification of five critical causes. An action plan to eliminate causes
Activities and Optimizing
Manufacturing Processes Using
was proposed to the FSME managers.
Process Mining: A Stock of
Challenges for Family SMEs. Keywords: process mining (PM) project; family SME; Disco; ProM; event-logs; efficiency
Sustainability 2024, 16, 1694.
https://doi.org/10.3390/su16041694

Academic Editors: Nita Yodo and

1. Introduction
Arup Dey
As a rule of thumb, the ubiquity of small and medium enterprises (SMEs) in a given
Received: 23 January 2024 economy reflects that it is healthy since they increase economic diversity, contribute posi-
Revised: 13 February 2024 tively to a country’s gross domestic produce (GDP), offer employment opportunities, anchor
Accepted: 17 February 2024
global supply chain operations, and strengthen a country’s resilience during international
Published: 19 February 2024
crisis. In the same context, SMEs help build a country’s identity and foster community
service practices. The Kingdom of Saudi Arabia, through the ambitious “Vision-2030”, is
paying particular attention to this type of firm. The Small & Medium Enterprises General
Copyright: © 2024 by the authors.
Authority (Monsha’at) in August 2023 reported incredible governmental support through
Licensee MDPI, Basel, Switzerland.
investing in capital ventures and encouraging the private sector to pump investments into
This article is an open access article
SMEs. The statistics show that the actual number of SMEs operating in the Saudi market is
distributed under the terms and 1.27 million [1].
conditions of the Creative Commons Considering the increasing competition in global markets and the evolving require-
Attribution (CC BY) license (https:// ments of customers in terms of quality and delivery times, firms, nowadays, are striving to
creativecommons.org/licenses/by/ search for new and modern methods and approaches to lead work perfectly and achieve
4.0/).

Sustainability 2024, 16, 1694. https://doi.org/10.3390/su16041694 https://www.mdpi.com/journal/sustainability

Sustainability 2024, 16, 1694 2 of 20

their goals in this complex and uncertain environment. Previous empirical research ac-
knowledged the importance of lean management tools for SMEs to sustain their competitive
advantage [2,3]. Lean management tools provide continuous improvement initiatives that
concentrate on non-added-value operations along inter and intra-organizational processes.
Lean tools are strongly linked with eliminating waste, variation, and bottleneck; reducing
time cycles; achieving economies of scale; improving the flexibility of production; and
increasing profitability [4]. Lean management englobes many tools such as Value Stream
Mapping (VSM) to eliminate non-added-value activities, Just in Time (JIT) to optimize
inventory levels, Quick Response Manufacturing (QRM) to enhance operations flexibility,
Single Minute Exchange of Die (SMED) to reduce changing times, Event Flow Production
(EFP) to improve operations flow, TPM (Total Preventative Maintenance), 5S, the Total
Quality Management (TQM) approach, and the Six Sigma method to eliminate causes of
non-quality continuously and systematically [5]. In the same context, PM is a strategic
tool to design and build new efficient processes. PM is regarded as a diagnostic method
to optimize processes’ value by discovering the optimal way of processing operations. It
begins with identifying, monitoring, and improving real processes through the use of event
log data. This approach is a relatively recent area of research that connects data science and
process science [6]. The goal of PM is to identify, track, and enhance actual processes—that
is, processes that are not presumptive—by deriving insights from event logs that are easily
accessible in most modern systems [7].
Previous research highlighted the multitude of uses of PM in SMEs to enable effective
multi-product scheduling in manufacturing SMEs. Choueiri & Portela Santos [8] presented
an algorithmic framework that extracts the underlying industrial process using process
mining techniques. Lorenz et al. [9] proposed and empirically validated a procedure
to improve productivity in make-to-stock manufacturing. The research shows that PM
can take advantage of previously unrecognized opportunities to increase productivity.
Agostinelli et al. [10] highlighted how PM techniques might be applied to get over the
obstacles and difficulties related to structuring Big Data pipeline discovery tasks to be able
to give value to collected and unused data (Dark Data). Our research consists of analyzing
the manufacturing process of FSMEs operating in the agri-food sector.
Previous studies have identified several pre-requisite and key factors that determine
the success of applying PM tools within SMEs (family and non-family SMEs). These chal-
lenges could be technical, such as incompatible systems, impracticable event-logs with
mistakes, a lack of IS availability and data collection operations, process unaware IS, and
incorrect PM algorithm configuration, or organizational, such as a lack of stakeholder
support and involvement, lack of managers and staff technical experience and expertise,
managers’ lack of awareness in processing notion and quality tools, poor change manage-
ment, poor project management, lack of training, scarcity of resources, and immaturity
of processes [6,11–16].
The issue under examination can be summarized as follows: What benefits can process
mining tools offer in enhancing process value and efficiency, and what obstacles might arise
when applying them in a family small and medium enterprise (FSME)? To clarify how this
method is applied and answer the previous question, this study is structured as follows:
First, a theoretical framework is developed through a presentation of the PM approach as a
technique that applies data mining principles in business process management. Then, the
stages of conducting a PM project are discussed. Afterwards, a project of optimizing value
along the production process in an industrial firm operating in the dairy sector using the
PM approach is demonstrated.
The originality in studying the implementation of PM in family firms lies in the unique
dynamics and characteristics of these organizations. Unlike larger corporations, family
firms often have distinct organizational cultures, decision-making processes, and resource
constraints. Therefore, investigating how PM is adopted and utilized within the context of
family firms can provide valuable insights into the challenges, opportunities, and outcomes
associated with its implementation in such settings.
Sustainability 2024, 16, 1694 3 of 20

2. PM Definition, Applications, and Project Execution

2.1. PM Definition: A Multidisciplinary Concept
A process refers to a field of research that involves the application of data analysis and
computational intelligence techniques to extract insights from event logs of information
systems. Its objective is to discover, monitor, and enhance processes [15,17]. PM is a
technique that can help improve business process management. By analyzing data recorded
by information systems, it is possible to gain a better understanding of how processes
work. This can help identify any deviations from expected behavior and improve the
accuracy of process models. Ultimately, this can lead to better performance and efficiency
in business operations [7]. PM methods can help identify compliance issues, pinpoint
bottlenecks, compare different versions of a process, and offer recommendations for process
improvements [18].
Drawing upon the definitions above, two main perspectives are identified: organiza-
tional and technical dimensions. (1) From an organizational perspective, PM is a valuable
tool for identifying inefficiencies and bottlenecks in business processes. Analyzing event
logs and process data can provide insights into how work is being done, as opposed to how
it is supposed to be done. This can help managers make informed decisions about where to
focus improvement efforts and lead to increased process efficiency, cost savings, and better
customer satisfaction. Additionally, PM can help organizations comply with regulations
and standards, such as ISO 9001 [19], by providing evidence of process performance and
identifying areas for improvement. Overall, PM offers a powerful way to gain visibility
into business processes and drive process improvement initiatives. (2) From a technical
perspective, PM involves using algorithms and advanced analytics to extract insights from
event logs and other process data. It typically involves several steps, including data extrac-
tion, data cleaning, process discovery, conformance checking, and process enhancement.
PM tools use a variety of techniques, such as process flow analysis, statistical analysis, and
machine learning, to identify patterns, anomalies, and inefficiencies in business processes.
These tools can help organizations visualize how processes are being executed, identify
root causes of process variations, and optimize processes to improve efficiency and reduce
costs. Additionally, PM can be integrated with other technologies, such as robotic process
automation (RPA) and artificial intelligence (AI), to automate process improvement tasks
and make them more efficient [20,21]. The manual processes, if replicated using RPA and
AI, will eliminate process errors and help in handling large amounts of data for better
managing the process of enhancing production volumes. Overall, PM offers a powerful way
to gain insights into business processes from a technical perspective and drive continuous
improvement initiatives that can lead to positive business outcomes.
PM provides an effective set of tools and techniques to generate appropriate knowl-
edge from business processes based on historical data collected from different information
systems databases. This approach consists of analyzing the entire business process (from
end to end) to reengineer it and optimize its value [22]. For Graafmans et al. [23], PM
offers a valuable set of tools for creating knowledge and insights based on historical data
stemming from event logs. Their literature review recognizes the potential benefits of
deploying PM techniques in lean management tools such as Six Sigma to improve process
activities’ value. Another lean management tool that is crucial for PM to succeed is VSM;
this method consists of mapping the actual state of a series of processes that are required
to produce a product or provide a service. PM could support VSM in analyzing business
processes and sequences using event logs to come up with the optimal process [24]. PM is
a relatively recent research system that exists between (1) artificial intelligence and data
mining on the one hand and (2) process modeling and analysis on the other hand [6,25].
PM aims to extract practical knowledge from event logs that may arise from all types of
information systems in an organization. Event logs usually contain information about
the completion of the steps and activities of a particular process and link these tasks or
processes with other metadata (resources). Process is a broad and very complex term both
from an applied and a technical point of view [26]. PM success depends on the reliability
 the reliability and validity of event logs. To enable PM in diverse situations and environ-
ments, the data collected and classified based on all IS databases in an organization need
to be translated into event logs. Diba et al. [22] show that the diversity of methods and
techniques is suitable for creating a reliable event log with relevant data resources that are
Sustainability 2024, 16, 1694 highly data correlated. As a consequence, it is noted that PM is a combination of data
4 of 20
mining and business process management (BPM), as shown in Figure 1.
The idea behind PM is to either (1) discover the optimal path (process), i.e., extract a
model, namely, the best way of doing things; (2) check and monitor the conformance of
and validity of event logs. To enable PM in diverse situations and environments, the data
the adopted model (process) with reality; or (3) enhance the actual processes or the previ-
collected and classified based on all IS databases in an organization need to be translated
ous event
into modellogs.
[11,13].
DibaPM could
et al. [22] be used
show inthe
that many situations,
diversity and, whatever
of methods the purpose,
and techniques de-
is suitable
ploying this method should be conducted by extracting knowledge from previously
for creating a reliable event log with relevant data resources that are highly data correlated. rec-
orded
As and available
a consequence, events
it is notedinthat
the PM
current IS database. This
is a combination idea
of data is welland
mining illustrated
businessinprocess
Figure
1.
management (BPM), as shown in Figure 1.

Figure 1.
Figure 1. Process
Process mining
mining types,
types, modified
modified from
from [27].
[27].

It is idea
The important
behind toPM
noteisthat to collect
to either various information,
(1) discover the optimal path an organization
(process), i.e.,should
extract uti-
a
model, namely,
lize different the bestand
activities wayresources.
of doing things;
This can (2)provide
check and monitor the
knowledge conformance
about the process, ofand
the
adopted model (process)
the organization’s with reality;
information systemsorcan(3) support
enhanceand the monitor
actual processes
this phase.or the previous
An event log
model
is created[11,13]. PM could
through be used in systems,
the information many situations, and, whatever
and the process model is the purpose, deploying
subsequently created
this
and method
analyzed should
basedbe onconducted by extracting
the available knowledge from
data and information usingpreviously
three types recorded and
of PM (dis-
available events in the current IS database. This idea is well illustrated
covery, conformance, and enhancement). The types of PM can be defined as follows: (1) in Figure 1.
It is important
Discovery: to note
This technique that to using
involves collect various
the event log information,
to producean theorganization
process model should
with-
utilize
out anydifferent activities
pre-existing and (2)
models. resources. This can
Conformance: If provide
a previous knowledge aboutthe
model exists, theprevious
process,
and the organization’s
process information
model can be compared systems
with can support
the event log of the and monitor
same this This
process. phase. Ancan
type event
be
log
used is to
created through
see if the realitythe information
matches systems,
the model and viceandversa.
the process model is subsequently
(3) Enhancement: This type is
created
used to and analyzed
improve based on
the existing the available
process model by data andinformation
using informationaboutusing the
three types
actual of PM
process.
(discovery, conformance, and enhancement). The types of PM can
It can detect errors and bottlenecks and allows for checking the alignment between the be defined as follows:
(1) Discovery:
model This[27].
and reality technique involves using the event log to produce the process model
without any pre-existing models. (2) Conformance: If a previous model exists, the previous
process model can be compared with the event log of the same process. This type can be
used to see if the reality matches the model and vice versa. (3) Enhancement: This type is
used to improve the existing process model by using information about the actual process.
It can detect errors and bottlenecks and allows for checking the alignment between the
model and reality [27].

2.2. Applications of PM in FSMEs: A Literature Review

To develop an insightful literature review about the different challenges facing FSMEs
when implementing PM projects, this study built on the research procedures provided
Sustainability 2024, 16, 1694 5 of 20

by Paul et al. [28] and Paul & Criado [29]. The procedures are as follows: (1) Attentively
select the research topic. For this, a topic was identified carefully and addressed “the aim
of applying PM project by FSMEs and what are the challenges standing behind its success”.
(2) Select journals involving several criteria aimed at ensuring the relevance and quality
of the research. Firstly, the focus should be on journals that specialize in topics related
to family business, process optimization, and data analytics. Furthermore, prioritizing
journals with rigorous peer-reviews introduced in well-known databases such as Scopus,
Web of Sciences, Elsevier, Emerald, Springer, Tylor & Francis, Wiley, etc., ensures that the
included studies meet scholarly standards. Finally, the accessibility and availability of the
journals are of high priority to use the journal. (3) Collect relevant papers focusing on PM in
family businesses, using keywords such as “Process Mining” or “Data mining”, “Business
Process Management”, “SMEs”, “Family SMEs”, “Family firms”, and “Family Business”.
(4) Search papers. The search here was limited to papers published as of 2015. Given the
rapid evolution of the topic, our attention was directed towards recent papers to ensure
relevance. (5) Structure the papers and organize them according to their importance and
relationship to the research aim. For this, the analysis started by describing the reasons for
and difficulties of implementing the PM method in SMEs—in general and then in family
firms. (6) Summarize the studies’ contributions, in tabular form, to easily identify the
different challenges for SMEs and then show the specific characteristics in family SMEs
regarding the success of PM projects.
Indeed, it might be difficult to use PM, especially for SMEs (family and non-family
ones) due to their limited resources and lower process maturity [12]. The increasing capa-
bility to generate and store data and the growing fusion of the physical and digital worlds
are fostering the use of PM. An organization can benefit from PM to remain competitive
and gain further competitive advantages through reductions in throughput times, cutting
costs, or increasing satisfaction among customers. Compared to large organizations, SMEs
have fewer resources [30] and less mature procedures [31]. Investigation into the skills
and knowledge needed to scale up and successfully apply PM is easily conducted by large
companies, but SMEs have yet to address this issue [13]. From an organizational standpoint,
SMEs are known to have immature processes, scarce resources, low formalization levels,
limited assets, embedded cultures, short communication channels, a lack of managerial
skills, and short-term-based planning [32].
Previous studies investigated the issue of applying the PM method in SMEs from
different points of view. The research of Burattin [11] is angled towards understanding
the problems that may occur when deploying a PM project. The research analyzed four
types of companies that were categorized based on two main criteria, namely, company
process-aware (unaware) and information system process-aware (unaware). For all sce-
narios, problems that hinder the success of the PM project can be divided into three types:
(1) Problems occurring before the PM project (when preparing data). In this phase, the
interoperability of systems (company IS and PM systems) seems to be crucial. This refers
to the ability to exploit the data extracted by a company’s information system by the PM
tools. The second problem relates to the lack of awareness of IS for the process notion. In
this situation, it becomes difficult to generate knowledge based on unaware IS. Moreover,
in many cases, even though the IS is process-oriented, the data collected in the event logs
lack accuracy and relevance, which means that the event logs provide data that mismatch
with the reality of executed activities. (2) Problems occurring during the PM project (when
actual PM is executed). In this phase, the entropy (noise) is associated with data processed
by PM tools. The second problem occurs when the organization lacks specialized staff
that can manipulate PM tools, which makes the configuration of PM algorithms difficult.
(3) Problems occurring after the PM project, when the mined process is interpreted and
evaluated and a judgment is made on its quality. The third problem in this phase relates to
the ability to design a clean and simplified process model without complicated activities,
tasks, or resources. The present study can take some insights from the study proposed
by Almeida & Bernardino [33] since it is angled at the obstacles to SME data mining [33].
Sustainability 2024, 16, 1694 6 of 20

This research finds that the vast volume of data present in SME contexts is stronger than
the actual data processing technologies and their applications. Also, the use of specific
open-source data mining tools such as KEEL (Knowledge Extraction based on Evolutionary
Learning), KNIME (Konstanz Information Miner), RapidMiner, and other open-source tools
can bring a lot of advantages to SMEs in terms of generating knowledge and optimizing
processes. Zeisler et al. [6] identified seven requirements for a PM to succeed in SMEs,
namely, (1) organizational requirements, (2) process-related requirements, (3) IT-related
requirements, (4) data-related requirements, (5) employee-related requirements, (6) legal
requirements, and (7) means and resources. Nebiaj [34] exposed the technical challenge
of developing a Workflow Management System for an ERP system by integrating process
discovery and design to optimize business processes in SMEs. Stertz et al. [16] focused on
the implementation phases of PM and how experts can exploit it in small and medium
manufacturing companies. Drawing upon a focus group study, the researchers compared
what is expected and experienced by staff at different levels during the implementation of
a PM project. The study shows that transparency, error avoidance, and decreased effort in
documenting (digitalization) are the most perceived benefits of PM projects, while appropri-
ate infrastructure and data collection operations present the main challenges. Vom Brocke
et al. [35] highlight that non-technical factors are also essential for the implementation
and administration of PM, in addition to the creation and enhancement of algorithms.
Drakoulogkonas & Apostolou [17] gave an overview of various software used as PM tool
for its selection. Drawing upon a multi-criteria framework, three techniques were used,
namely, ontology, decision trees, and AHP, to list and explain the parameters that can help
compare instruments to choose which software product best meets a company’s needs
according to the challenges that organizations meet. Eggert & Dyong [12] recognized the
challenging characteristics of applying PM for SMEs due to the scarcity of resources and
immaturity of processes. This study looked into the use of PM and clarified the difficulties
faced by an IT SME. The findings identified 13 PM challenges for SMEs and provided seven
recommendations for resolving them. Mamudu et al. [15] showed that studies on critical
success factors in PM are scarce. Furthermore, these studies only listed the variables; they
did not include crucial information on the success elements and how they interact. Using a
hybrid methodology, the study qualitatively examined 62 case studies on PM from several
angles. Nine important success determinants for PM were identified, their link to the PM
setting was explained, and their interrelationships concerning PM success were analyzed.
These factors are, respectively, support and involvement of stakeholders, accessibility of
information, technical proficiency, capabilities and features of PM tools, organized PM
approaches, data and event log quality, skills in project management, training in PM project
execution, and skills in change management. Kokkeler [14] bridged the gap related to PM
in SMEs and proposed a methodology called PROcess MIning for SmEs (PROMISE) to
successfully transform insights into actions. The research systematically analyzed 21 pa-
pers based on different criteria such as validation techniques, empirical evidence, the
methodology adopted, types of algorithms, types of PM tools, examined processes, and
types of results (i.e., analysis, implementation, framework, etc.). Based on Data Science
Methodology (DSM), the methodology PROMISE was refined and validated through two
case studies. The study highlighted some challenges when applying PM projects in SMEs
such as the immaturity of processes, lack of managerial and internal skills that can help
evolve the processes, high levels of informatization, low quality of documentation, lack of
change management, and high levels of changing the workforce. Table 1 below summarizes
all the challenges mentioned above.
Sustainability 2024, 16, 1694 7 of 20

Table 1. Application of PM method in family SMEs and associated challenges.

References Challenges Reported by Family SMEs

The main problems of applying PM projects successfully are (1) lack of interoperability of systems,
(2) irrelevant events log (noise), (3) configuration of PM algorithms, (4) evaluation of the mined
[11]
process, and (5) the need for the company and IS to be process-aware. For SMEs, three components
should be modeled using the appropriate tools: artifacts, control flow, and actors.
The main challenges are (1) immature processes, (2) scarce resources, (3) low formalization levels,
[32] (4) limited assets, (5) embedded cultures, (6) short communication channels, (7) lack of managerial
skills, and (8) short-term-based planning.
The main obstacles are that (1) the vast volume of data present in SME contexts is stronger than the
[33] actual data processing technologies and their applications and (2) open-source data mining tools such
as KEEL, KNIME, and RapidMiner can effectively benefit SMEs.
The main factors affecting PM project success are organizational, employees, legality, means and
[6] resources, processes, information technology, and data. (2) Processes, information technology, and
data are the most important challenges.
[34] The primary obstacle is the technical aspects of developing a Workflow Management System.
Transparency, error avoidance, and decreased effort in documenting (digitalization) are the most
[16] perceived benefits of PM projects, while appropriate infrastructure and data collection operations
present the main challenges.
[17] The primary difficulty is the alignment of software characteristics with an organization’s needs.
[12] The main barriers are (1) limited resources and (2) lower process maturity.
The main challenges are (1) support and involvement of stakeholders, (2) accessibility of information,
(3) technical proficiency, (4) capabilities and features of PM tools, (5) organized PM approaches,
[15]
(6) data and event log quality, (7) skills in project management, (8) training in PM project execution,
and (9) skills in change management.
The study recognizes the same challenges as [12]: (1) the immaturity of processes, (2) lack of
managerial and internal skills that can help evolve the processes, (3) high levels of informatization,
[14]
(4) low quality of documentation, (5) lack of change management, and (6) high levels of changing
the workforce.
The critical factors are that non-technical factors are also essential for the implementation and
[35]
administration of PM and the creation and enhancement of algorithms.
[30] The primary issue revolves around limited resources.
[31] The primary challenge lies in less developed procedures.
The most important factor is the lack of skills and knowledge needed to scale up and successfully
[13]
apply PM.

When it comes to family businesses, previous research has found that family SMEs
tend to be relatively different from non-family SMEs regarding many aspects such as human
resource management, innovation, leadership, and information systems. This difference
is due to the embeddedness of the family dimension in the firm’s management [36]. The
owner-manager of family SMEs has a significant influence on the management style and
organizational culture [37]. According to the findings provided by Chahal & Sharma [38],
family-owned businesses do not demonstrate a significant performance advantage over
non-family enterprises. According to Darby et al.’s results [39], family business character-
istics are reflected in operations and supply chain management decision-making. Llach
& Nordqvist [40] observed differences in the role of human, social, and marketing capital
for innovation between family and non-family firms. Heinicke [41] recognized the influ-
ence of the family dimension and exchange of knowledge in developing a sophisticated
control system that englobes key indicators of functions, operations, activities, and process
performances. Giacosa et al. [42] identified cultural and cognitive aspects, values, and
abilities that affect the company behavior of small and medium family firms in terms of
BPM. Jacobs [43] justified the limited ability of family SMEs through their lack of BPM skills.
Sustainability 2024, 16, 1694 8 of 20

In the same context, while some research indicates that family firms are willing to execute
innovation in business processes like production and distribution operations [44,45], other
studies show that family firms invest less in research and development and that introduc-
ing change in processes and products is due to their flexibility [46]. Regarding the use of
information technology and data organization in family SMEs, Dutot et al. [47] showed
that FSMEs do not have mature, adequate management and use of ICT (information and
communication technology). The reality highlights that FSMEs do not fully utilize the po-
tential and advantages of information technologies in their operations and transactions [48].
Many researchers pointed out that FSMEs do not have BI (business intelligence) awareness
for decision-making. Accordingly, FSMEs are not mature in terms of analyzing data and
generating knowledge [49]. Management practices in family-owned businesses are less
formal than in other types of firms. In family SMEs [50], roles are not clear and staff
may have different and multiple activities to accomplish [51]. According to research by
R. S. Reid & Adams [52], family-owned businesses employ HRM in a different way than
their non-family competitors. They are “special cases” requiring particular instruction
and growth.
As for strategic long-term vision, FSMEs’ managers place less weight on employee
training initiatives and strategic planning as they do on competitive advantages [53].
Learning and change processes in FSMEs are significantly impacted by the degree of
family embeddedness, the size of the business, and the absence of formal processes and
systems [54]. Additionally, certain familial traits like nepotism combined with the standard
of corporate governance (structure, leadership style, and compensation) influence change
processes. Furthermore, it is challenging to maintain the use of contemporary tools if
management takes on an authoritarian and paternalistic management style [55]. Table 2
summarizes the specific characteristics of FSMEs compared to non-family SMEs.

Table 2. The main characteristics of family SMEs.

References Specific Characteristics of Family SMEs

[36,55] The embeddedness of the family dimension in the firm’s management.
The owner has a significant influence on organizational
[37,42]
cultural and cognitive aspects, values, and abilities affect the company’s behavior.
[38] Low performance levels compared to non-family businesses.
[39] The reflection of family characteristics on operations and supply chain management decision-making.
[40] The role of human, social, and marketing capital is weak.
The influence of the family dimension and exchange of knowledge in developing a sophisticated
[41]
control system.
[43] The lack of BPM skills.
[44–46] Less research and development and change in processes and products.
Inadequate management and use of ICT, lack of BI, immature capabilities of analyzing data and
[47–49]
generating knowledge.
[50–52] Roles are not clear, staff may have different and multiple activities to accomplish.
[53] Managers place less weight on employee training initiatives and strategic planning.
[54] The absence of formal processes and systems.

2.3. PM Project
An operating methodology is necessary for a PM project to provide proper orientation.
Several methodologies exist to apply PM tools in SMEs. This research provides an overview
without intending to evaluate them. Zuidema-Tempel et al. [56] identified and critically
reviewed four main PM methodologies as follows: (a) PM project methodology (PM2),
proposed by Van Der Heijden [57]. This methodology consists of six phases as follows:
(1) planning, (2) extraction, (3) data processing, (4) mining and analysis, (5) evaluation,
Sustainability 2024, 16, x FOR PEER REVIEW 9 of 20

Sustainability 2024, 16, 1694 9 of 20

follows: (1) planning, (2) extraction, (3) data processing, (4) mining and analysis, (5) eval-
uation, and,
and,finally,
finally, (6)
(6) process improvement and
process improvement and support.
support. (b) (b) PM
PMproject
projectmethodology
methodology(PMPM),
(PMPM),proposed
proposedby byVan VanEck
Ecketetal.
al.[58].
[58].This
Thismethodology
methodology also also consists
consists of
of six
six phases
phases asas follows:
follows: (1)
(1) scoping,
scoping, (2)(2)data
dataunderstanding,
understanding,(3) (3)event
eventloglogcreation,
creation,(4) process mining, (5)(5)
(4) process mining, evaluation,
evaluation,andand (6) deployment.
(6) deployment. (c) PM
(c) PM project
project proposal
proposal (PMPP),
(PMPP), proposed
proposed by Aguirre
by Aguirre et al.et[59]. This
al. [59]. This approach
approach includes
includes fourfour phases
phases asasfollows:
follows:(1)(1) project
project definition,
definition,(2)(2)data
dataprep-
preparation,
aration, (3)
(3) process
process analysis, and (4) process redesign. redesign. (d)(d) L*L* lifecycle
lifecycle model
model from
fromvanvander
derAalst [7].
Aalst [7].This
Thismethodology
methodology has has been adopted for the present research for many reasons.
adopted for the present research for many reasons. This
This methodology
methodology firstfirst
outlines the the
outlines PMPM activities andand
activities thethe
tools thatthat
tools areare
used
usedto to
support
support them,
them, thethe outcomes,
outcomes, andandthe
thereason
reasonbehind
behindselecting
selectingthe theappropriate
appropriatetooltoolfor
for each
each task.
task. Second,
Second, compared
compared to to previous
previousmethods,
methods,this thismethodology
methodologyoutlines outlinesconsiderably
considerably explicit
explicit steps.
steps. Finally,
Finally,both
bothlarge and
large medium-sized
and medium-sized businesses
businesses could useuse
could thisthis
methodology.
methodology. Fig- Figure 2
ure 2 summarizes
summarizes all all
thethe
stages forfor
stages successful PMPM
successful use.use.
TheThedescription andand
description details of each
details of each stage
stage willwill
be be presented
presented with
with thethe
casecase study.
study.

Figure 2. PM project
Figure 2. PMstages, modified
project from [7]. from [7].
stages, modified

3. PM Implementation
3. PM Implementation in aSME
in a Family Family SME
3.1. Firms’ Challenges and Manufacturing Process Description
3.1. Firms’ Challenges and Manufacturing Process Description
The company
The studied studied company has two
has two main main activities:
activities: First, the First, the import–export
import–export of food prod-
of food prod-
ucts that are generally oriented to be resold as is. The second activity is manufacturing
ucts that are generally oriented to be resold as is. The second activity is manufacturing
dairy products, which was the subject of analysis in this study. The first advantage of this
dairy products, which was the subject of analysis in this study. The first advantage of this
company is that the supply of raw products, mainly, milk powder, is ensured by the firm
company is that the supply of raw products, mainly, milk powder, is ensured by the firm
itself. Another advantage is the deeper understanding that the firm has of its business
itself. Another advantage is the deeper understanding that the firm has of its business
market, customers’ needs, and diversified network of suppliers. Moreover, one of the
market, customers’ needs, and diversified network of suppliers. Moreover, one of the con-
conventional practices in the studied firm is that most employees are from the same family.
ventional practices in the studied firm is that most employees are from the same family.
This may represent an advantage since it gives the firm a more cohesive and committed
This may represent an advantage since it gives the firm a more cohesive and committed
workforce, which can lead to a greater level of participation and engagement in improve-
workforce, which can lead to a greater level of participation and engagement in improve-
ment initiatives. The firm gives more importance to family ties and blood relationships
ment initiatives. The firm gives more importance to family ties and blood relationships
rather than people’s knowledge, skills, and competencies. Most family members lack
rather than people’s knowledge, skills, and competencies. Most family members lack
knowledge and skills in management and quality concepts, which makes their contribution
knowledge and skills
restricted in management
to simple and quality
initiatives rather concepts, which
than complicated makes
projects suchtheir
as PM contribu-
tools. The firm’s
tion restricted to simple initiatives rather than complicated projects such as PM tools.
staff cannot help ensure that the PM results are accurately interpreted and appropriately The
firm’s staff cannot help ensure that the PM results are accurately interpreted and
acted upon. Consequently, the studied firm has intimate knowledge of their workflows, appro-
priately roles,
acted and
upon. Consequently,
responsibilities thatthe
are studied firm has intimate
highly overlapped knowledge
among people, whichofcan their
impede PM
analysis. In this firm, the leadership spectrum varies between autocratic to laisser-faire style;
Sustainability 2024, 16, 1694 10 of 20

this latest style is usually applied with family members. On the other hand, an autocratic
style makes it difficult for subordinates to suggest and implement change initiatives, even
though the communication channel is short. Moreover, the studied firm is reluctant to
adopt new technologies and processes, particularly if the owners are unfamiliar with them.
The firm has a relatively reactive approach, namely, the managers are ready to adopt only
technologies that have already been implemented and tested in other companies. This
makes it challenging to successfully implement PM initiatives, which require significant
cultural and organizational change.
As for the manufacturing process, this involves several precise steps to ensure product
quality, hygiene, and efficiency. The packaging process includes filling aluminum pouches
with milk powder and subsequently placing the pouches into boxes and then into larger
cartons. To understand this, here is an overview of the manufacturing process: (1) Loading
the packaging machine, weighing, and dispensing. The milk powder is accurately weighed
and dispensed into the packaging line. This process ensures that each pouch receives the
correct amount of milk powder. (2) Setting machines for production. This step refers to
the process of configuring and adjusting machinery and equipment to ensure that they are
ready and optimized for use. This is a crucial step in the production process and it involves
fine-tuning various parameters and settings to achieve the desired output efficiently and
accurately. The goal is to meet quality standards, maintain consistency, and improve
overall productivity. The studied manufacturing unit has a semi-automatic packaging
system that consists of the following machines: (a) a hopper that plays a crucial role in
feeding the filling machine, (b) a milk powder filling machine, (c) conveyor systems, (d) a
boxing/cartoning machine, (e) a labeling and coding machine, (f) a tape maker. (3) Pouch
forming, filling, and sealing. The packaging material, in this case, aluminum foil, is fed
into the filling machine. The machine forms the material into pouches of the desired size
and shape, leaving an opening at the top for filling. Then, the milk powder is filled into the
pre-formed pouches. This process is often automated to ensure precision and minimize
the risk of contamination; then, the open end of the pouch is sealed to enclose the milk
powder securely. (4) Boxing and closing. The pouches are placed manually into boxes
according to the appropriate weight and then placed into the boxing machine to close them
securely. (5) Controlling and labeling. Each box is coded with essential information such
as manufacturing date, expiration date, batch number, and other relevant details. Labels
with nutritional information, ingredients, and branding are also applied to the box. Then,
the boxes undergo quality control checks to ensure that the packaging is intact, the correct
weight of milk powder is present, and the coding and labeling are accurate. (6) Cartoning
and palletizing. The cartons are typically designed to accommodate a specific number of
boxes. The boxes are put into the relevant cartons, which are made manually and then
closed using a tape machine. Then, the closed cartons are stacked manually on pallets and
transported directly to the warehouse to ensure easy shipment and distribution.
Throughout the entire process, the firm must follow stringent hygiene standards to
guarantee high quality from production to consumption. Unfortunately, many factors
have hindered the packaging line from improving efficiency and minimizing errors. These
factors will be discussed when demonstrating the PM stages.

3.2. Calculation of OEE

The studied firm operates in the sector of agri-food. As shown above, the main activities
within the manufacturing unit are (1) loading, (2) setting machines, (3) filling and sealing,
(4) boxing and closing, (5) controlling and labeling, and (6) cartoning and palletizing.
Data for the study were gathered through observation and interview techniques. To
gain a thorough understanding of the activities, interviews with the production manager
and sales manager were undertaken. Apart from conducting interviews, a thorough obser-
vation of several process activities was conducted. These activities included feeding the
manufacturing unit, loading machines, packaging, palletizing, and warehousing. By rely-
Sustainability 2024, 16, 1694 11 of 20

ing upon managers’ expertise, documentation and video recordings of the manufacturing
process were examined. Then, an event log was made using these data.
Based on a sample of 50 working days, the OEE was determined to identify the primary
dysfunctions in the unit and guarantee that the manufacturing process improvement project
was oriented correctly. The OEE components are compiled in Table 3.

Table 3. OEE within the manufacturing unit.

Availability = (Operation Time/Loading Time) × 100% 287/350 = 0.82

Performance = (Actual Product X Ideal Cycle
275.63/287 = 0.96
Time)/Operation Time × 100%
Quality = (Good Products − Total Defect)/Gross
257.11/275.63 = 0.93
Products × 100%
OEE 0.82 × 0.96 × 0.93 = 0.73

At the production facility, the overall equipment efficiency was 73.44%. Over 85% is
the minimum required. So, this was not a high rate. Improving the availability rate, which
was the weakest aspect, was the problem. By removing every cause contributing to these
flaws, the PM tool could address the manufacturing facility’s lack of efficacy and efficiency.
Finding the real business process was the first goal, followed by an attempt to improve it.
To clarify the problem occurring in the manufacturing unit, real production levels were
compared to average production and mean theoretical production capacity over 50 working
days. The results showed a significant deviation between real production levels and the
average (2520 units) and theoretical production capacity (3500 units/7 h as working time).
The unit was unable to provide the required level of production during this period and
struggled with meeting customer demand. Also, the results showed that the production
level exceeded theoretical production capacity two times, but the data demonstrated that
this was due to irregular double-shift work. Consequently, improving the manufacturing
process by eliminating the causes and optimizing time is imperative for this firm to survive
and continue in the market.

3.3. Presentation of PM Project Stages

Following a more than two-month analysis of the manufacturing unit’s operations
and performance, it could be concluded that the PM approach was pertinent in helping
the manufacturing unit find solutions and enhance its business operations to reach the
necessary level of production. The various actions depicted in Figure 2 above were used in
the following manner:
Stage 0: Planning and justification
The PM project for the manufacturing unit was mainly oriented towards three main
axes: discovery, answering questions, and achieving goals.
• Discovery project: This refers to the exploration of all the activities and processes in-
volved in the production process and their nature to gain a better understanding of the
value flow in the process. Therefore, the project aimed to detect all bottlenecks that the
manufacturing unit experiences that result in a slowdown in the production process.
• Question-oriented project: By utilizing PM, a series of questions can be addressed.
The primary inquiry in this research was the reason behind the daily variations in
production volume. What factors contribute to this variability?
• Goal-oriented project: A number of objectives, including enhancing manufacturing
performance, particularly in terms of quality and timeliness, could be accomplished
through the PM project.
Stage 1: Data extraction
The company does not record the specifics of each activity; instead, it records the start
and end of each working day, the amount of production, and any issues that arise. As a
 • Goal-oriented project: A number of objectives, including enhancing manufacturing
performance, particularly in terms of quality and timeliness, could be accomplished
through the PM project.
Stage 1: Data extraction
Sustainability 2024, 16, 1694 The company does not record the specifics of each activity; instead, it records the 12 start
of 20
and end of each working day, the amount of production, and any issues that arise. As a
result, the focus at this point was on extracting details and information about the activities
result,
and the the focus at this
production point was
process. on extracting
Observing details and information
the manufacturing about
process, it was the activities
discovered that
and the production
the personnel did notprocess.
follow the Observing
procedures thewithmanufacturing process,
rigor or respect, which it resulted
was discovered
in many
that the personnel
manufacturing did not
errors. follow the most
Furthermore, procedures with rigor or stops
of the unscheduled respect,
thatwhich
caused resulted
the man- in
many manufacturing
ufacturing process toerrors.
stop wereFurthermore,
behind the most of the unscheduled
company’s inability tostops
meet that caused
client demand.the
manufacturing
Two primary reasonsprocess were
to stopresponsible
were behind forthe company’s
this outcome:inability
The firsttoreason
meet client
was demand.
a lack of
Two primary reasons were responsible for this outcome: The
effective supply planning, which frequently resulted in raw material shortages first reason was a lack
and, of in
effective supply
turn, delayed planning,
orders to thewhich
point frequently
where customersresultedwaited
in rawmore
material
than shortages and, in
a week. Frequent
turn,
power delayed
outagesorders
(powerto the
cuts)point
werewhere
anothercustomers
issue that waited morethe
hindered than a week.
ability Frequent
to respond to
power outages (power cuts) were another issue that hindered
client demand because they repeatedly forced production to halt, often for hours. the ability to respond to
clientThe
demand because they
aforementioned repeatedly
issues and theforced
type production to halt,necessitated
of data gathered often for hours.constant ob-
The aforementioned issues and the type of data gathered
servation and tracking to determine how they affected the manufacturing process. necessitated constantThis ob-
servation and tracking to determine how they affected the manufacturing
was the most crucial phase since the process can be examined and enhanced in light of the process. This
was
data.the
Themost crucial
main phase
issues thatsince the process canunit
the manufacturing be examined
faced are and enhanced
depicted in light
in Figure of the
3 below.
data. The main
The Ishikawa issues that
diagram the manufacturing
pinpoints several reasons unit
whyfaced are depictedwas
the performance in Figure
below 3par.below.
The
The Ishikawa diagram pinpoints several reasons why the performance
“Disco” program was used to analyze each of these issues in detail as well. was below par. The
“Disco” program was used to analyze each of these issues in detail as well.

Figure3.3.Cause
Figure Causeand
andeffect
effectdiagram
diagramat
atthe
themanufacturing
manufacturingunit.
unit.

Stage
Stage2:2: Creating
Creatingthe theflow
flowcontrol
controlmodel
modeland andmaking
makingaaconnection
connectionto tothe
theevent
eventlog
log
•• Event
Eventlog logcreation:
creation: The
The event
event log
log contained
contained information
information about
about each
each batch
batchproduced
produced
during
during the day, with two batches produced in the morning and evening. Eachbatch
the day, with two batches produced in the morning and evening. Each batch
was
was identified
identified with
with aa unique
unique IDIDcalled
calledaa“Case
“CaseID”.
ID”. The
The log
log also
also included
included details
details
about
aboutthe theproduction
productionprocess
processstages,
stages,referred
referredto toas
as“Activity”,
“Activity”,along
alongwith
withthe
theworker
worker
responsible for each stage, called “Resource”. Moreover, the volume
responsible for each stage, called “Resource”. Moreover, the volume of production of production
achieved
achieved and the problems
and the problemsfaced
facedby bythe
themanufacturing
manufacturing unit
unit in each
in each batchbatch
werewere
also
also recorded. The start and end times of each stage of production
recorded. The start and end times of each stage of production in each batch were in each batch
were mentioned
mentioned in theinlog.
the This
log. This information
information couldcould be analyzed
be analyzed through
through the “Disco”
the “Disco” pro-
program
gram to identify bottlenecks that occurred at any stage and determine the
to identify bottlenecks that occurred at any stage and determine the time
time
wasted in each stage of production. This analysis could help to improve the production
process and achieve the desired level of production.
• Extraction of the current business process: The “Disco” program generated a model
based on a 20-day sample. Each day contained batches, with 40 cases (batches) in
total. Each case had around six activities, resulting in a total of 238 “Events”. Figure 4
illustrates all the business process activities in the manufacturing unit.
 wasted in each stage of production. This analysis could help to improve the produc-
tion process and achieve the desired level of production.
• Extraction of the current business process: The “Disco” program generated a model
based on a 20-day sample. Each day contained batches, with 40 cases (batches) in
Sustainability 2024, 16, 1694 13 of 20
total. Each case had around six activities, resulting in a total of 238 “Events”. Figure
4 illustrates all the business process activities in the manufacturing unit.

Figure 4. The actual manufacturing process.

Figure 4. The actual manufacturing process.
Based on an analysis of the overall process activities, the current production model
was constructed. However, there were a few instances where the process slowed down
Based on an analysis of the overall process activities, the current production model
or halted due to certain activities. For example, if the stock of boxes, aluminum foil, or
was constructed. However, there were a few instances where the process slowed down or
cartons ran out, then the workers needed to go back to the previous activity to resupply.
halted due to certain activities. For example, if the stock of boxes, aluminum foil, or car-
On the other hand, if there was enough raw material available, the workers could directly
tons ran out, then the workers needed to go back to the previous activity to resupply. On
start the machine setting stage without going back to the previous activity. One of the main
the other hand, if there was enough raw material available, the workers could directly
factors that caused delays in the production process was machine failure, which may have
start the machine setting stage without going back to the previous activity. One of the
been due to frequent power cuts or mechanical issues in any of the packaging machines,
main factors that caused delays in the production process was machine failure, which may
including the aluminum packaging machines.
haveTo been due to
validate frequent
the order andpower cuts or mechanical
consistency issues
of the activities, in“ProM
the any of 6.12”
the packaging ma-
software was
chines, including the aluminum packaging machines.
utilized to create a “Dotted chart”, which is a popular tool in PM. This chart provides a
clear To validate the order
comprehension of the and consistency
sequence of theinactivities,
of activities the study’s theevent
“ProMlog,6.12” software
where was
each point
utilized atodifferent
denotes create aactivity
“Dottedandchart”, which
the lines is a popular
indicate tool in
cases. Figure PM. This chart
5 demonstrates theprovides
sequencea
clear comprehension
of activities. of the sequence of activities in the study’s event log, where each
pointItdenotes a different activity and the lines indicate cases. Figure 5 demonstrates
is evident from the diagram that there is consistency in the color sequence, except the
sequence of activities.
for a few instances where the sequence of points in the lines appears different. The
manufacturing process and sequence of activities are presented straightforwardly due to
the high degree of automation. The Disco 6.3.0 software provided a video that displays the
entire manufacturing process, allowing for a clear identification of the primary bottlenecks
in some cases that slow or halt production, resulting in a failure to achieve the required
production level. This particular stage of the PM project is concerned with developing
VSM, which focuses on identifying the primary sources of waste in the process to eliminate
them and attain the desired production level.
bility 2024,Sustainability
16, x FOR PEER2024, 16, 1694
REVIEW 14 of 20 14 of 20

Figure 5. “Dotted Chart” shows the homogeneous sequences of business process activities.
Figure 5. “Dotted Chart” shows the homogeneous sequences of business process activities.

It is evident from
Therethewas diagram that therebetween
no correlation is consistency in theof
the amount color
goods sequence,
produced except
and the time spent
for a few instances where them
producing the sequence of points
(R = 0.106). in the linesthere
Additionally, appears wasdifferent. The man-
inconsistency in the volume of
ufacturing process and sequence of activities are presented straightforwardly
production. For example, the production volume could range from 20 boxes per hour to due to the
high degree oflessautomation.
than that over The Disco
a longer 6.3.0 software
period. provided that
This indicated a video
therethatweredisplays
many ways the of conducting
entire manufacturing process, allowing for a clear identification of the primary
the activities, as well as a problem with the availability rate of 82%. The correlation bottlenecks
in some cases that slow or
coefficient halt production,
indicated a problemresulting
with the in a failure to achieve
manufacturing the requiredrate of 96%, but
unit’s performance
production level.thisThis
rate particular stage of of
was not reflective thereality
PM project
as there is were
concerned
many with weakdeveloping
performances in January.
VSM, which focuses
There on were identifying
also casesthe primary
where sources of waste
the production process in slowed
the processdown to or
elimi-
stopped, with the
nate them and Disco
attain program
the desired production
showing the timelevel.spent on each case, from least to most time-consuming
There waspart
no correlation
of the workflow between the amount of goods produced and the time spent
process.
producing them (R All = 0.106).
cases Additionally,
were organized there
based was oninconsistency
the time spent inontheeach
volume
case.of It pro-
was found that the
average the
duction. For example, time for all cases
production was 72.1
volume min.range
could The analysis
from 20 identified
boxes per the hour cases that took the least
to less
than that over amount
a longerof time under
period. normal circumstances
This indicated that there were and those
many thatoftook
ways the longest amount of
conducting
time
the activities, as due
well as to issues during
a problem with the
the process.
availabilityTheserateproblematic
of 82%. The cases were focused
correlation coef- on by filtering
and keeping only the cases that were identified
ficient indicated a problem with the manufacturing unit’s performance rate of 96%, but as causing a slowdown in the production
this rate was not reflective of reality as there were many weak performances in January. out of a total
process. Next, bottlenecks were identified. The analysis showed that 7 cases
There were also of cases
40 caseswhere (equivalent to 18% of
the production eventsslowed
process or 44 events)
down or took at leastwith
stopped, 1 h andthe 38 min, which
Disco programamounts
showingtothe 17%timeof spent
all cases. The process
on each case, fromfor these
least tocases
most was reexamined and analyzed to
time-consuming
identify
part of the workflow the bottlenecks and the main reasons causing them. To determine the bottleneck
process.
causes, various
All cases were organized based times taken
on theintimethe spent
sevenon cases
each extracted fromfound
case. It was the process
that thewere presented.
average time for all cases was 72.1 min. The analysis identified the cases that took thethat
The time taken in the production process did not necessarily reflect leastthe process was
amount of time under normal circumstances and those that took the longest amount of showed that
slow until compared with the quantities produced in that case. The results
some
time due to issues cases the
during thatprocess.
took theThese
least amount
problematic of time
caseshadwere
goodfocused
productionon byvolumes
filter- compared to
ing and keeping only the cases that were identified as causing a slowdown in the produc- This indicates
cases that took the longest amount of time but had low production volumes.
that in
tion process. Next, addition to
bottlenecks the identified.
were problem encountered
The analysis byshowed
the manufacturing
that 7 casesunit out inof that
a batch, there
total of 40 cases (equivalent to 18% of events or 44 events) took at least 1 h and 38 min, details of the
was a problem with the pace of work, which was weak. Table 4 shows the
which amountsvarious
to 17%extracted
of all cases.cases.
The process for these cases was reexamined and ana-
The discrepancy
lyzed to identify the bottlenecks and inthe
the main
time taken
reasons in the production
causing them. process
To determinecompared the to the amount
of production achieved is very clear, and it is important
bottleneck causes, various times taken in the seven cases extracted from the process were to explain the causes that led to
the differences in the quantities produced. Figure 6 shows an analysis of the workflow for
presented. The time taken in the production process did not necessarily reflect that the
the few cases in which bottlenecks were found, indicating the waiting time spent (average
process was slow until compared with the quantities produced in that case. The results
waiting time) between activities. The most time spent between activities is indicated by the
showed that some cases that took the least amount of time had good production volumes
prominent arrows in red in an attempt to focus on, reduce, and optimize this wasted time.
compared to cases that took the longest amount of time but had low production volumes.
This indicates that in addition to the problem encountered by the manufacturing unit in
that batch, there was a problem with the pace of work, which was weak. Table 4 shows
the details of the various extracted cases.
 Table 4. Bottlenecks cases list.

Case Batch Date Time Wasted Production Main Cause

5 1st 03-01 2 h 20 m 96 Delay in setting machines
13
Sustainability 1st16, 1694
2024, 09-01 3 h 42 m 61 Shortage of aluminum foil 15 of 20
14 2nd 09-01 1 h 50 m 121 Malfunction in the packaging machine
19 1st 12-01 2h 87 The slow pace of work
21 1st 15-01 Table 4.1Bottlenecks
h 45 m cases list.128 Delay in setting machines
31
Case
1st Batch 23-01 Date 4 h 30 mTime Wasted 171 Production
Late-coming workersMain
and slow performance
Cause
34 2nd 24-01 2 h 13 m 64 Late-coming workers
5 1st 03-01 2 h 20 m 96 Delay in setting machines
13 1st 09-01 3 h 42in
The discrepancy mthe time taken
61 in the productionShortage
processof aluminumtofoil
compared the amount
14 2nd of 09-01
production achieved
1 h 50 mis very clear,
121and it is important to explain
Malfunction the causesmachine
in the packaging that led to
19 1st the12-01
differences in the
2 hquantities produced.
87 Figure 6 showsThean analysis
slow paceofofthe workflow for
work
the few cases in which bottlenecks were found, indicating the waiting time spent (average
21 1st 15-01 1 h 45 m 128 Delay in setting machines
waiting time) between activities. The most time spent between activities is indicated by
31 1st
the23-01 4 h 30 m
prominent arrows 171
in red in an attempt Late-coming workers and slow performance
to focus on, reduce, and optimize this wasted
34 2nd 24-01
time. 2 h 13 m 64 Late-coming workers

Figure
Figure6.
6.Average
Averagetime
timespent
spentin
inbottleneck
bottlenecksituations.
situations.

4.Results
4. ResultsAnalysis
Analysisand andDiscussion
Discussion
Based on
Based on the
the information
information presented,
presented,the
the focus
focus of
of the
the PM
PM project
project was
was on
on extracting
extracting
knowledge from recorded information and designing an optimal business
knowledge from recorded information and designing an optimal business model. model. However,
How-
after analyzing the manufacturing unit’s process, it was evident that the current
ever, after analyzing the manufacturing unit’s process, it was evident that the current process
pro-
is the
cess is most reliable
the most pathpath
reliable to follow. Therefore,
to follow. designing
Therefore, an operations
designing model
an operations in this
model in case
this
was not
case wasnecessary. Instead,
not necessary. it wasitbetter
Instead, to maintain
was better the same
to maintain theprocess and make
same process andvarious
make
improvements to eliminate the causes of production failure and achieve the theoretical
production capacity of 350 boxes/h.
The primary focus, therefore, is on improving the business process at the manufactur-
ing unit. This will be achieved by providing operational support, which is the last stage of
the PM project. By analyzing the results and providing the necessary recommendations,
the performance of the firm can be improved in the future. The various causes behind the
 Table 5. Main causes ranking.

Sr. No. Causes Stop Time (m) Percentage % Cumulative

1 Power Cut 786 53.396 53.396
2
Sustainability 2024, 16, 1694 Late Workers 270 18.342 71.739 16 of 20
3 Loading and Sourcing Problems 208 14.1304 85.869
4 Machine Breakdowns 178 12.092 97.961
5 Quality Problems 30 2.038 100 as they are sources
production failure are shown in Figure 7, and they need to be addressed
of wasted time.
Total 1472 100 100

Pareto diagram
1000 100 100
800 97.961 80
600 71.739 85.869 60
400 53.396 40
200 20
0 0
Power Cut Late Workers Loading and Machine Quality
Sourcing Breakdowns Problems
Problems

Stop Time (m) % Cumulative

Figure 7. Pareto diagram.

Figure 7. Pareto diagram.
The graph above displays the percentage breakdown of causes that impacted pro-
In Figurethat
duction. It can be observed 7, the frequency
power of each
cuts alone cause is displayed.
accounted for 53.39%The chart
of the works on the principle
problems
during the firstofmonth.
Pareto, After
which brainstorming
involves identifying
with the 20%firm’sof causes that it
managers, arewas
responsible
recom- for 80% of the
problems. However, using this chart to determine the proportion
mended that the company take quick action to resolve this issue to prevent production of each problem based
from stopping. This problem worsened in the following months. Additionally, the firm that the effect
on frequency alone may not always be accurate. This is because it assumes
of each
needed to address causeof
the issue is employees
constant and, therefore,
arriving theimplementing
late by repetition of causes has the same effect. To
strict measures
accurately
to ensure that they determine
arrive on time andthe extent
begin of the effect
preparing of the
for the causes illustrated
production in the figure, it is better
process without
to use
delay. To resolve thethe weighting
problem of causesraw
of sourcing method, whereeffective
materials, the duration of the stoppage
programming is re-is calculated for
each cause. Table 5 provides information on the bottleneck cases.
quired at two levels: (1) supply the unit with the necessary raw materials from the com-
pany’s warehouse to avoid production halts and (2) plan and schedule the supply of raw
materials from Table 5. Main causes ranking.
the suppliers.
Sr. No. Causes Stop Time (m) Percentage % Cumulative
1 Power Cut 786 53.396 53.396
2 Late Workers 270 18.342 71.739
3 Loading and Sourcing Problems 208 14.1304 85.869
4 Machine Breakdowns 178 12.092 97.961
5 Quality Problems 30 2.038 100
Total 1472 100 100

The graph above displays the percentage breakdown of causes that impacted produc-
tion. It can be observed that power cuts alone accounted for 53.39% of the problems during
the first month. After brainstorming with the firm’s managers, it was recommended that
the company take quick action to resolve this issue to prevent production from stopping.
This problem worsened in the following months. Additionally, the firm needed to address
the issue of employees arriving late by implementing strict measures to ensure that they
arrive on time and begin preparing for the production process without delay. To resolve the
problem of sourcing raw materials, effective programming is required at two levels: (1) sup-
ply the unit with the necessary raw materials from the company’s warehouse to avoid
production halts and (2) plan and schedule the supply of raw materials from the suppliers.

5. Conclusions
The paper discussed the PM approach, which helps to optimize process value by
extracting knowledge from historical data on current activities. This knowledge can then
be used to eliminate waste and improve the final product to meet customer requirements.
This paper covered several concepts related to lean management, including event logs, bot-
tlenecks, data mining, business process management, value-added activities, value stream
Sustainability 2024, 16, 1694 17 of 20

mapping, process discovery, conformance, and enhancement. Actually, PM helps optimize

process value in different ways by (1) improving efficiency through eliminating redundant
steps, delays, or bottlenecks; (2) rationalizing resource allocation through the effective us-
age of equipment and materials, labor management, etc.; (3) improving quality to eliminate
defects, rework, etc.; and (4) automating processes to focus on value-added activities.
Furthermore, this paper provided an overview of the different stages involved in
implementing a PM project, from planning and justification to final operational support.
Various software programs were also mentioned, such as Disco for visually discovering
the current process and associated bottlenecks and Prom 6 for testing the homogeneity of
activity sequences and drawing value stream maps.
Based on the results obtained, it is important to review the various steps taken to
complete the case study. Firstly, the OEE rate was calculated at the manufacturing unit
level to gain a better understanding of equipment usage and specifically to identify any
failure points, such as availability rate, performance rate, or quality. The results indicated
that availability presented a challenge and was a priority area for improving the effective-
ness and efficiency of the manufacturing unit. The OEE outputs provided a convincing
justification for initiating a preventative maintenance project.
The second step in this study involved completing the stages of the PM project. The
project’s purpose was clarified at the outset and then various data associated with activity
completion were extracted to understand the different causes underlying the low OEE
rate. To this end, a cause-and-effect diagram (Ishikawa) was established to categorize the
main and sub-causes. The second stage involved understanding how these causes affected
the overall manufacturing process. To accomplish this, an event log was created, which
statistically described the running of all activities. The current manufacturing process
was then established using “Disco”. This study relied on the most frequent sequence of
activities using “Prom 6” to build a model process and improve it. The results showed a
homogeneous sequence of activities, and all bottlenecks were identified along with their
associated causes. For the studied unit, five main causes were identified. To address the
most critical ones, a Pareto diagram was developed. The results indicated that power cuts
and late-coming employees had a significant negative impact. Finally, several recommenda-
tions were made to optimize the manufacturing process value at the studied unit as follows:
(1) Invest in a new electricity generator to eliminate the problem of frequent power cuts.
(2) Invest in new machines to support the manufacturing process and address the issue
of machine breakdowns. (3) Enhance employees’ commitment by implementing a strict
system that prevents tardiness and raises their awareness about the importance of meeting
customer demand and achieving the required production level. Any actions that may
slow down the unit’s performance should also be avoided. (4) Improve sourcing activities
planning to ensure the timely supply of raw materials to the firm or the manufacturing unit.
(5) Provide necessary training to employees for taking timely corrective actions related
to machines’ maintenance without relying on external services. (6) Adopt an advanced
information system that records all data related to the firm’s activities and promotes the
continuous improvement of operations.
The application of PM in family SMEs dealing in manufacturing holds significant
promise for the future development of manufacturing companies. By leveraging PM
tools, family SMEs can gain deep insights into their manufacturing processes, identifying
inefficiencies, bottlenecks, and opportunities for improvement. This data-driven approach
enables them to streamline operations, reduce waste, and enhance overall productivity.
Moreover, the implementation of PM fosters a culture of continuous improvement within
the organization, empowering employees to proactively identify and address process
inefficiencies. As a result, family manufacturing companies can achieve higher levels of
efficiency, quality, and competitiveness, positioning themselves for sustainable growth and
success in the rapidly evolving manufacturing landscape.

Author Contributions: Conceptualization, A.L.; methodology, A.L.; software, A.L.; validation, A.L.,
F.b.Z., M.R.N.M.Q. and A.A.S.; formal analysis, A.L.; investigation, A.L. and M.R.N.M.Q. resources,
Sustainability 2024, 16, 1694 18 of 20

F.b.Z.; data curation, A.A.S.; writing—original draft preparation, A.L.; writing—review and editing,
M.R.N.M.Q.; visualization, M.R.N.M.Q.; supervision, F.b.Z.; project administration, F.b.Z.; funding
acquisition, A.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Deanship of Scientific Research at King Khalid University
for funding this work through the Large Groups Project under grant number RGP. 2/214/44.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is contained within the article.
Acknowledgments: The authors extend their appreciation to the Deanship of Scientific Research at
King Khalid University for funding this work through the Large Groups Project under grant number
RGP. 2/214/44.
Conflicts of Interest: The authors declare no conflicts of interest.

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