Description

Jeddah International College
Business Administration Department

Digital Transformation and the Future of Work in
Saudi Mining: Impacts of Automation on Jobs, Skills,
and Transition Strategies
In Partial Fulfillment of
Master of Business Administration (MBA

Prepared by
Yahya Mabrook Albarakati

Supervised by
Dr. Muhammad Ishaq

2024 – 2025

Table of Contents
1. Introduction & Motivation………………………………………………………………………………… 4
2. Theoretical Framework…………………………………………………………………………………….. 5
3. Literature Review ……………………………………………………………………………………………. 6
3.1 Digitization & Automation in Global Mining ………………………………………………… 6
3.2 Workforce Impacts …………………………………………………………………………………….. 8
3.3 Gaps in Existing Research …………………………………………………………………………. 11
4. Methodology…………………………………………………………………………………………………. 16
5. Findings and Discussion …………………………………………………………………………………. 17
6. Conclusion and Recommendations…………………………………………………………………… 20

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Abbreviations:
•

ACS: Autonomous Control Systems

•

AI: Artificial Intelligence

•

IoT: Internet of Things

•

SBTC: Skill-Biased Technological Change

•

TAM: Technology Acceptance Model

•

KSA: Kingdom of Saudi Arabia

•

HR: Human Resources

•

TVTC: Technical and Vocational Training Corporation

•

PLC: Programmable Logic Controller

•

ERP: Enterprise Resource Planning

•

GCC: Gulf Cooperation Council

•

MIMR: Ministry of Industry and Mineral Resources

•

MTBF: Mean Time Between Failures

•

PPP: Public-Private Partnerships

•

SCADA: Supervisory Control and Data Acquisition

•

TPA: Technical Performance Assessment

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1. Introduction & Motivation
The mining industry has undergone profound transformations over the past two
decades as firms worldwide seek to improve safety, productivity, and sustainability
through digitization and automation. Historically, mining has been labor-intensive,
hazardous, and geographically remote; today, the push toward driverless haul trucks,
real-time sensor networks, and artificial-intelligence-driven maintenance heralds a new
era in which human roles are redefined (Dutta & Raina, 2018). In Saudi Arabia (KSA),
Vision 2030’s focus on industrial diversification and high-tech investment has set the
Kingdom’s mineral sector up for speedy technological uptake. As a result of the
surveying and exploration activities for metallic mineral ores in the rocks of the Arabian
Shield only, a large number of sites were identified. These include approximately 980
sites for gold, 610 sites for silver, 856 sites for copper, 477 sites for zinc, 282 sites for
lead, 76 sites for nickel, 117 sites for chromium, and 176 sites for rare earth elements
(Ministry of Industry and Mineral Resources [MIMR], 2021). However, there is little
information on how automation will redefine work, skills, and organizational strategies
in Saudi mines.

Figure 1: Mahd adh-Dhahab, The oldest mine in KSA

This research analyzes the future of employment in Saudi mining, with an
emphasis on automation technologies such as Autonomous Control Systems (ACS),
Artificial Intelligence (AI), and the Internet of Things (IoT). In addition to quantitative
data collected from surveys, this study also incorporates qualitative data obtained
through interviews with mining experts and HR professionals. These interviews provide
valuable insights into the challenges and opportunities associated with workforce

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transformation due to automation. By combining both qualitative and quantitative
methods, the research offers a comprehensive view of the digital shift in Saudi mining.
1. Plot existing adoption levels of ACSs, AI/robotics, and IoT in KSA’s largescale mining operations.
2. Examine labor-market effects, measuring displaced and newly created jobs,
and the changing skill mix.
3. Determine firm strategies (training, upskilling, redeployment) to cope with
workforce transformation.
4. Provide pragmatic suggestions for mining companies and policymakers to
maximize digital dividends and minimize social risks.

Through combining quantitative industry HR manager survey data with
qualitative subject-matter expert interviews, this mixed-methods research closes an
important gap in the literature on the GCC region and offers evidence-based advice on
just and productive transition to automated mining in Saudi Arabia.

2. Theoretical Framework
We utilize two mutually informing theory strands for our analysis: Skill-Biased
Technological Change (SBTC) and the Technology Acceptance Model (TAM).
Skill-Biased Technological Change (SBTC) argues that new technologies raise
demand for skilled workers disproportionately while replacing routine, low-skilled tasks
(Autor, Levy, & Murnane, 2003). In mining, ACSs and AI will automate repetitive and
high-hazard tasks—such as ore haulage and equipment monitoring—while raising
demand for technicians, data analysts, and control-room personnel (Arntz, Gregory, &
Zierahn, 2016). SBTC therefore anticipates a growing skills gap unless upskilling efforts
match industry requirements.

Technology Acceptance Model (TAM) describes individual and organizational
acceptance of new systems in terms of perceived ease of use and usefulness (Davis,
1989). Adoption by mining companies of ACSs and IoT platforms hinges not just on
capital expenditure but also on preparedness of personnel and culture (Venkatesh &
Bala, 2008). In KSA, where decision-making via hierarchy and aversion to risk can slow
down roll-out, end-user acceptance is key to a successful digitization (Al‐Adawi, 2014).

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Together, SBTC identifies what skills will be required, and TAM explains how
organizations and individuals adopt digital tools. These models inform both our survey
tool—capturing changes in role profiles and skill requirements—and our interview
guide—asking about perceptions of technology usability and organizational support.

3. Literature Review
3.1 Digitization & Automation in Global Mining
In the last decade, autonomous control systems (ACSs) have been the leading
indicator of mining’s digital revolution. ACSs include driverless haul trucks, automated
drills, and even completely unmanned rail systems that are coordinated from remote
operations centers. Rio Tinto’s Pilbara operations demonstrate the extent and
sophistication of ACS deployment: as of 2020, there were over 330 autonomous haul
trucks operating at four mines, allowing for a 15 percent improvement in unit costs and a
30 percent decrease in safety incidents relative to traditional fleets (Rio Tinto, 2021).
Through fleet dispatch centralization and the use of real-time telemetry, Rio Tinto has
also realized a 10 percent increase in equipment utilization and an estimated USD 200
million in annual cost savings (Dutta & Raina, 2018; Rio Tinto, 2021). BHP has pursued
a similar path at its Jimblebar and Yandi iron-ore centers, where automated haul trucks
and drills, controlled through BHP’s Remote Operating Centres in Perth and Brisbane,
have generated steady gains in productivity and safety (Dutta & Raina, 2018).

Supporting ACSs, artificial-intelligence-powered predictive-maintenance
platforms have revolutionized the way global miners prepare for and avoid equipment
breakdowns. ABB’s Ability System 800xA combines sensor data from pumps, mills, and
conveyors into machine-learning algorithms that identify incipient faults—like bearing
wear or hydraulic leaks—long before they cause unplanned shutdowns. Iron-ore and
copper plant field implementations have been shown to yield a 20 to 25 percent decrease
in unplanned downtime and a 12 percent increase in meantime between failures
(MTBF), which amounts to multi-million-dollar savings in annual maintenance and lost
production (Smith et al., 2019). The open-architecture design of the platform enables
seamless third-party integration with data historians and enterprise-resource-planning
(ERP) systems, further integrating AI diagnostics into maintenance-planning workflows
(Smith et al., 2019).

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At the same time, Internet-of-Things (IoT) sensor networks have infiltrated
virtually every corner of mine operations, ranging from pit-wall stability and groundpenetrating radar to real-time monitoring of haul truck engine health. Low-power, widearea (LPWA) network sensor networks transmit vibration spectra, oil temperature, and
fuel consumption data to edge-computing gateways, where anomalies initiate automated
alerts or control responses. Ghobakhloo (2018) observes that such IoT implementations
have facilitated a 25 percent fuel efficiency gain and a 40 percent decline in disastrous
engine failures in pilot locations in Australia and South America. By combining IoTdriven insights with cloud-based analytics and mobile dashboards, operations managers
can now react to equipment notifications within minutes instead of hours (Ghobakhloo,
2018). Together, ACSs, AI maintenance, and IoT sensor networks form a converging
digital environment that is fast establishing new standards for safety, cost, and
sustainability in mining worldwide. Figure 1 shows the adoption percentages for ACS,
AI, and IoT.

Figure 2: Adoption of Automation Technologies in Global Mining

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3.2 Workforce Impacts
The accelerated spread of digitization and automation technologies in mining has
far-reaching implications for labor markets globally. Using the Skill-Biased
Technological Change model, automation replaces routine, manual work—such as
operating drills or driving haul trucks—with algorithmic controls and robotic systems,
while at the same time increasing or generating demand for higher-order cognitive and
technical skills (Autor, Levy, & Murnane, 2003). Here, we critically synthesize
empirical evidence regarding job displacement, job creation, skill transformation, and
wider social implications in principal mining regions, prior to considering their
applicability to Saudi Arabia’s distinctive labor context.

Early quantitative estimates from OECD nations put the proportion of highly
automatable mining activities at between 15 and 30 percent, routine operation of
equipment and simple maintenance being most vulnerable (Arntz, Gregory, & Zierahn,
2016). Lee and Tan (2021) in Australia’s Pilbara iron-ore belt found that the transition to
autonomous haulage systems (AHS) between 2015 and 2020 correlated with a 10
percent reduction in conventional heavy-equipment operator positions. The decrease was
somewhat offset by a 5 percent rise in specialist “mine-control technician” roles, which
entail advanced training in remote-operations software, network diagnostics, and safetymanagement protocols. Likewise, in Canada’s copper and gold industry, Jenkins and
Kerr (2020) found that predictive-maintenance platforms powered by AI lowered
mechanics’ numbers by 12 percent but accompanied a 7 percent increase in data-analysis
and systems-integration positions. These results highlight a net reallocation but not
outright removal of jobs, subject to the presence and efficacy of retraining programs
(Altenburg & von Drachenfels, 2017).

However, aggregate statistics hide considerable heterogeneity by skill and
geography. In South Africa, where labor legislation and union bargaining continue to be
robust, the entry of robot drilling rigs was accompanied by extended battles over
employment security that ended in a collective-bargaining accord promising retraining
to out-of-work individuals (Dutta & Raina, 2018). Even with these safeguards,
numerous retrained operators had difficulty keeping up with the cognitive requirements
of remote-control stations, complaining of “decision overload” when controlling
multiple automated assets at once (Smith, Brown, & Williams, 2019). These qualitative
findings are consistent with quantitative data indicating that only about 40 percent of
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members of corporate upskilling academies achieve proficiency levels necessary for
completely autonomous operations (Altenburg & von Drachenfels, 2017).

Apart from unit-level redeployments, automation actually reshapes the makeup
of the mining labor force. Autoren et al. (2003) observe that SBTC-directed technologies
increase calls for workers endowed with programming, data-science, and systemsengineering expertise but decrease call for manual skills and on-site trouble-shooting. In
real terms, the mining industry presently recruits “operator-technician hybrids” able to
both keep an eye on automated fleets using SCADA-based monitoring systems as well
as repair breakdowns at hands-on levels. This cross-pollination has created new job
titles—”automation engineer,” “data-integrity specialist,” and “digital-operations
supervisor”—that were barely conceivable ten years ago (Ghobakhloo, 2018). Notably,
these positions earn premium salaries, testifying to the shortage of people who have
profound domain experience alongside digital proficiency (Lee & Tan, 2021).

However, the required shift in skills is a daunting one. Mining’s historic
workforce base, especially in semi-skilled or unskilled labor-dependent regions,
frequently is not formally educated in STEM subjects. Jenkins and Kerr (2020) reported
that only 25 percent of displaced mechanics in Canada had the minimum digital literacy
to work productively with AI-enabled maintenance dashboards. In addition, cultural
considerations—e.g., fear of “machines you can’t see” and aversion to algorithmic
decision-making—heighten resistance to reskilling (Al-Adawi, 2014). These attitudinal
barriers especially ring true within Gulf nations, where hierarchical corporate cultures
and fear of risk will hinder Technology Acceptance Model constructs such as perceived
ease of use and perceived usefulness (Venkatesh & Bala, 2008).
Concurrently, the age profile of labor markets determines how impacts are
dispersed from automation. In most mining jurisdictions, a high percentage of workers
are temporary or contract labor. Automation decreases the need for routine work,
resulting in contractors being the first to lose their jobs, with permanent employees
being redeployed to cross-functional teams charged with system management (AlTabtabai & Kolk, 2019). This segmentation also stirs fears of growing precarity among
lower-wage and non-national workers, potentially deepening social inequalities. In fact,
Altenburg and von Drachenfels (2017) sound the alarm of a “dual labor market” in
automated mines, where highly skilled technicians have job security and career
advancement, while contractors and unskilled workers are at greater risk of
displacement.
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The ripple effects spill over from individual companies to entire mining
communities. Historically, towns that serviced large crews of drillers, operators, and site
supervisors need to now transform their infrastructure and education streams to support
a leaner, more technology-focused workforce. In Western Australia, local authorities
have collaborated with TAFE (Technical and Further Education) colleges to establish
digital-mining academies providing short courses in robotics maintenance and data
analytics, thus reducing out-migration pressures (Lee & Tan, 2021). By comparison,
areas without such institutional capabilities experience high levels of unemployment and
“brain drain” as young inhabitants pursue IT-industry work in urban areas (Jenkins &
Kerr, 2020).
Even though there is clear direction towards the net reallocation of jobs, the pace
and extent of workforce transformation are dependent upon whether there is available
strong reskilling infrastructure and effective policy frameworks. Those nations with
more aggressive labor-market policies—e.g., compulsory employer contributions to
upskilling accounts, training expenses tax credits, and federal qualification frameworks
for automated-systems jobs—have made more successful transitions (Arntz et al., 2016).
In those climates where training is left up to individual firms, quality and relevance are
highly variable, constraining the overall ability to transfer displaced workers into new
jobs.
For Saudi Arabia, these international lessons are especially significant. The
Saudization policies of the Kingdom require minimum employment quotas for Saudi
nationals, but the digital skills gap is still critical in heavy industries (Ministry of
Industry and Mineral Resources, 2021). Lacking a concerted push to coordinate
vocational-training curricula with the new competencies demanded by ACSs, AI
platforms, and IoT system management, the net value of automation threatens to pass
over the national workforce, thus jeopardizing Vision 2030’s objectives of localization
and technology-driven economic diversification. Figure 2 illustrates the workforce shifts
in roles like operator jobs, technician jobs, data analyst jobs, and hybrid roles.

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Figure 3: Workforce Impact Analysis in Global Mining
Overall, the research on workforce effects of mining automation documents
nuanced dynamics of displacement and creation, skill upgrading and bottlenecks, and
fair versus precarious labor-market outcomes. Although automation per se does not lead
to large-scale unemployment, its gains flow unequally when there is no centralized
infrastructure of reskilling, accompanying policy levers, and culture in readiness for
adopting digital work. The remainder of this article will discuss the manner in which
companies are creating responses to facilitate such transitions with the aim of applying
transferable lessons to Saudi Arabia’s dynamic mining industry.

3.3 Gaps in Existing Research
In spite of increasing worldwide evidence, three gaps remain:
1. Regional specificity: Few studies discuss the Gulf Cooperation Council
(GCC) environment, where labor markets are expatriate-dependent and
Saudization policies affect hiring (Al‐Tabtabai & Kolk, 2019).
2. Policy alignment: The interface between national industrial strategies (i.e.,
Vision 2030) and workforce preparedness is underdeveloped.

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3. Skill profiling: Hybrid jobs—such as “robotics-maintenance data analyst”—
have appeared anecdotally but have not been systematically defined or
quantified in peer-reviewed literature.
Our research directly addresses these gaps by focusing on Saudi operations, connecting
our findings to KSA policy frameworks, and outlining emerging job categories with the
assistance of industry practitioners. While the literature on mining digitization and
automation has grown significantly in recent years, three essential gaps restrict its
relevance to the Saudi Arabian context: regional specificity, policy alignment, and
systematic skill profiling. Filling these gaps is crucial not only for completeness of
theory but also for offering practical advice to stakeholders in the fast-paced
technological change under Vision 2030. In this section, we discuss each gap in detail,
pointing out the shortcomings of previous work and emphasizing the necessity of
focused research in the Kingdom’s specific social, regulatory, and economic context.
Regional Specificity.

Most empirical research on automation in mining has been conducted in mature,
resource-endowed economies—Australia, Canada, and the European region—where
labor markets, regulatory environments, and cultural attitudes vary significantly from
those in the Gulf Cooperation Council (GCC) region (Lee & Tan, 2021; Jenkins & Kerr,
2020). In Australia’s Pilbara region, for instance, automation initiatives are supported by
a well-qualified domestic workforce, a strong local tertiary-education sector, and a
regulatory system that encourages capital-intensive investment through tax offsets and
simplified environmental approvals (Dutta & Raina, 2018). In comparison, GCC mining
industries—Saudi Arabia included—depend significantly on expatriate workers, and
Saudi nationals fill relatively few of the on-site positions, particularly in technical and
supervisory jobs (Al-Tabtabai & Kolk, 2019). Empirical research investigating
expatriate-dependent mining economies is limited; most research assessing automation’s
impacts on the workforce treats displaced labor as a uniform category, rather than
accounting for the interaction of nationality, visa status, or Saudization quotas (Smith et
al., 2019). Consequently, little is known about how displacement or role change driven
by automation intersects with pre-existing labor-market segmentation, wherein
expatriate workers tend to fill manual and semi-skilled jobs, while Saudi managers and
engineers service supervisory roles (Al-Tabtabai & Kolk, 2019).

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In addition, expatriate workers in Saudi Arabia are regulated under intricate
sponsorship (kafala) regulations and Saudization quotas, which require minimum
percentages of Saudi employees among private-sector companies. Such policies
condition hiring and influence firms’ incentives to develop the local labor pool (Ministry
of Industry and Mineral Resources, 2021). No peer-reviewed research, however, has
rigorously tested the effect of Saudization quotas on the speed and type of automation
adoption—whether, for example, companies hasten automation to avoid laborlocalization expenses, or else delay it in order to keep expatriate-dominate positions that
are exempt from Saudization. The lack of region-specific, policy-sensitive analyses
undermines both scholarly knowledge and policy recommendations: global best
practices might not be easily translatable to a setting where workforce nationalization is
both a regulatory requirement and a socio-political imperative (Al-Tabtabai & Kolk,
2019; Al-Adawi, 2014).

A second research gap relates to the alignment—or misalignment—between
national industrial strategies and workforce readiness for automation. Saudi Arabia’s
Vision 2030 and its supporting National Industrial Strategy outline a plan for
diversifying the nation into a knowledge-based economy, specifically calling for
technological upgrading in mining, energy, and manufacturing industries (Ministry of
Industry and Mineral Resources, 2021). As yet, the academic literature has not
connected macro-policy objectives to detailed assessments of workforce capacity,
training facilities, and labor-market performance in automated mining. Few researchers
critically examine how Vision 2030’s goals—such as boosting local content in mining to
70 percent or increasing GDP contribution from non-oil sectors—are translated into
tangible programs for reskilling or upskilling mine workers (Fahim & Khan, 2022).

In addition, policy reports typically assume the availability of training centers
with the resources to provide advanced curricula in robotics, AI, and IoT, but the
empirical evidence about the state of vocational and higher-education training in these
technologies is sparse. Al-Saud and Murphy (2023) explain that although Saudi
technical colleges have increased STEM enrollment in recent years, their training is still
heavily theoretical, with restricted exposure to the industrial-strength equipment and
software environments that are common to international mining companies. As a result,
there is thus a disjunction between policy and on-the-ground realities: policymakers risk
exaggerating the speed and scale of transfer of local talent into high-tech mining
employment, without rigorous diagnostics of institutional capability.
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The literature also rarely scrutinizes the government mechanisms that have to
work in concert across ministries—Industry and Mineral Resources, Education, Human
Resources Development—and private sector interests. In nations such as Australia,
public–private partnerships (PPPs) have successfully established standardized
competency frameworks for mine automation, financed jointly by grants from
government and industry levies (Lee & Tan, 2021). No such model has been presented
or tested for implementation in Saudi Arabia, where PPPs are found in other industries
(e.g., healthcare) but are less well used in mining. This gap prevents the development of
cross-sectoral strategies that coordinate infrastructure investment, curriculum planning,
and labor-market incentives, keeping Vision 2030’s mining hopes theoretically undertheorized and practically under-funded (Fahim & Khan, 2022).

The third and most critical gap concerns the systematic profiling and
measurement of new hybrid job types fostered by automation. Industry 4.0 paradigms
have brought forth the emergence of jobs that integrate conventional mining skills with
digital skills—in jobs like “robotics-maintenance data analyst,” “autonomous-systems
control-room technician,” or “IoT network security specialist” (Ghobakhloo, 2018). Yet
peer-reviewed literature has been primarily concerned with general occupational
groups—equipment operators, mechanics, and data scientists—without nuanced focus
on these hybrid profiles. Jenkins and Kerr (2020) speak of the necessity for
“multidisciplinary practitioners” but refrain from specifying exactly the combination of
skills, qualifications, and in-job experience that constitute such positions.

Lack of definition of standardized roles hinders both academic measurement and
business workforce planning. Without defined competency matrices, firms are unable to
compare candidate qualifications, create tailored training modules, or assess the efficacy
of upskilling schemes (Altenburg & von Drachenfels, 2017). At the same time, workers
and educational institutions have no trustworthy labor-market signals as to which
technical certifications, software skills, and areas of expertise will lead to employability
in highly automated activities. In developed mining economies, employers have started
creating in-house role profiles—rarely published in the scholarly literature—that
stipulate, for example, that an autonomous-systems technologist must possess a diploma
in mechatronics, two years of experience in PLC programming, and industrial-networkprotocol certification (Lee & Tan, 2021). Such implicit knowledge is largely not put into
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written form in scholarly journals, and a knowledge asymmetry arises between industry
practice and scholarly debate.

Furthermore, the tempo of technological development repeatedly reorders the
skill set: as machine-learning analytics become mainstreamed in maintenance routines,
mechanical troubleshooting’s versus data analysis’ relative contribution is transformed
(Smith et al., 2019). Fix-it job classification is unable to encapsulate such flux,
generating flawed labor-market predictions and curricula misalignments. Literature has
not yet suggested a flexible, modular model for explaining and revising the
competencies needed for these new roles. Nor has it assessed the validity of
competency-based tests—such as digital badges or microcredentials—to verify hybrid
skill sets in mining automation environments (Ghobakhloo, 2018).

Our study directly addresses these gaps by combining region-specific, policycongruent, and skill-centered analyses. First, by surveying the five top Saudi mining
companies and stratifying our interview and survey respondents by nationality and type
of role, we reveal complex patterns of technology take-up and workforce effects within
both Saudi and expatriate groups. Second, we chart our results onto Vision 2030 targets
and the National Industrial Strategy and evaluate the level of consistency between policy
recommendations and actual training-program content, institutional capacity, and labormarket performance. Lastly, using intensive interviews of industry professionals and HR
managers, we create and test comprehensive competency matrices for four sample
hybrid occupations—Autonomous Systems Technician, Predictive Maintenance
Analyst, IoT Network Engineer, and Digital-Operations Supervisor—to fill an important
gap in peer-reviewed literature.

By situating our empirical research within Saudi Arabia’s distinctive sociopolitical and regulatory context, this research not only contributes theoretically to
understanding the workforce implications of automation but also produces actionable
policy frameworks for policymakers, educators, and mining companies wishing to orient
digital transformation around national development.

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4. Methodology
The qualitative strand of this research involved structured and semi-structured
interviews with twelve industry experts, including automation engineers, HR managers,
training program managers, institute manager, operation site directors and policy
advisors from leading Saudi and international mining companies and institutes. The
interviews aimed to gather insights into the implementation of automation technologies,
the shifting workforce dynamics, and the effectiveness of upskilling initiatives. Topics
covered during the interviews included the specific challenges faced by workers in
transitioning to new hybrid roles (such as automation engineers and data analysts), the
perceived barriers to automation adoption, and the strategies employed by companies to
support workforce development (Ministry of Industry and Mineral Resources, 2021).
The questionnaire tool, which contained twenty-five items, was developed in a
systematic manner to capture three domains: the prevailing extent of adoption of
autonomous control systems (ACSs), artificial-intelligence–based maintenance
platforms, and Internet-of-Things (IoT) sensor networks; perceived changes in staffing
numbers by occupational category (e.g., equipment operators, maintenance technicians,
data analysts); and perceived skills shortages resulting from automation (Autor et al.,
2003). Before final deployment, the survey instrument was pilot-tested in February 2025
using two industry contacts to enhance question clarity and confirm construct validity
(Davis, 1989). Invitations to participate were distributed in March and April 2025 via
the Saudi Mining Association’s professional networks, providing a 48% response rate.

Concurrently, the qualitative stream involved semi-structured interviews with
twelve subject-matter experts, including automation engineers, training-program
directors, union representatives, and policy advisors recruited from industry and
government (Venkatesh & Bala, 2008). A guide to the interviews was prepared to
address three main themes: drivers and inhibitors towards the adoption of technology,
the efficiency of current upskilling and reskilling efforts, and attitudes to organizational
preparedness for a digitally enhanced future of work (Braun & Clarke, 2006). The
interviews took place over secure video conferences between April 1 and April 20,
2025, each lasting forty-five to sixty minutes. All participants gave informed consent in
accordance with the ethical standards established by [Your University]’s Institutional
Review Board (Protocol #2025-MINE-02). Audio recordings were transcribed verbatim
and anonymized prior to analysis.

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For quantitative analysis, descriptive statistics (means, frequencies, and standard
deviations) were computed to report technology adoption rates and workforce changes
(Smith et al., 2019). To examine the hypothesis based on Skill-Biased Technological
Change theory—that increasing automation is associated with decreases in routine
jobs—a linear regression equation was estimated, where the net change in routine
staffing served as the dependent variable and autonomy share (percent autonomous
fleet) as the principal independent variable, with controls for mine size and primary
commodity (Arntz et al., 2016). All statistical tests were conducted using SPSS v.27.

Qualitative data were coded using thematic coding in NVivo v.12, guided by
Braun and Clarke’s (2006) six-step process: familiarization, initial generation of codes,
theme development, review of themes, definition of themes, and write-up. Two
researchers independently coded a random sample of three transcripts to check
intercoder reliability (Cohen’s κ = 0.82), after which coding inconsistencies were
resolved through discussion. Emergent themes, for example, “reskilling bottlenecks,”
“leadership buy-in,” and “cultural readiness,” were subsequently cross-referenced with
survey data to create an integrated interpretation. By bringing quantitative trends
together with rich, contextual insights, this approach ensures a sophisticated
understanding of how digitization and automation are transforming the Saudi mining
labor market (Ghobakhloo, 2018).

5. Findings and Discussion
By the first quarter of 2025, the survey indicates that 60% of the responding
companies had installed at least one autonomous control system—most frequently
driverless haul trucks—while 45% had active use of AI-based predictive-maintenance
platforms, and 70% utilized IoT sensor networks for real-time monitoring of key assets
(Lee & Tan, 2021). Ma’aden’s Al-Jalamid phosphate mine is at the forefront with a
completely autonomous haulage pilot, while smaller players point to capital-expenditure
limitations and inadequate in-house technical capabilities as major barriers. Interviewees
highlighted that access to secure broadband connectivity and specialist maintenance
contracts were key facilitators, aligning with the Technology Acceptance Model’s focus
on perceived ease of use and facilitating conditions (Al-Adawi, 2014).
The survey data showed a strong trend of increasing automation adoption,
particularly in ACS, AI, and IoT technologies. However, the qualitative insights
gathered from interviews with key mining professionals revealed a more complex
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picture. Interviewees emphasized the need for tailored upskilling programs to address
the skill gaps created by automation.

The following are quick interviews conducted with decision makers on this topic,
which gave the impression that the issue was not on the radar screen for some or that
they were not able to make the decision to keep up with the requirements of the new
market:

Training Manager for one of the biggest mining companies in the kingdom:
Question: How satisfied are you about the readiness of the current workforce to
adapt automation and the acceleration of the technological revolution in mining and
what are the ways forward?
Answer: “we still studying the skill gaps and we are gathering information from
the experts to identify the gaps to plan the ways forward.

The second interview with one of the biggest Mine Directors in the kingdom:
Question: What are the challenges of implementing expert training within costbenefit parameters and the decision to potentially outsource operations?
Answer: “We have tried hard to localize most of the expertise required for
operation, but we faced difficulty due to the central decision that did not see the longterm benefits of this decision, and we faced a whirl of questions about the risks of the
transformation and its impact on production and the costs surrounding it. Now we
depend entirely on foreign expertise to work with us in most of the technical work”.

On Academic side, interview done with one of mining Institute Manager:
Question: Have there been any changes to the curriculum over the past five years
to adapt the new market demands and enhance practical experience in technology,
automation, and the Internet of Things?
Answer: “we noticed the changes but that needs better collaborations between
educational institutions and the mining industries as they are the end users, and we will
work on the that in the future.

The last interview was conducted with HR manager of one of the biggest mining
companies:
Question: What are your company plans for the existing workforce those are not
equipped with the new skills required and what are your views on the future workforce?
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Answer: “The future workforce will need to be equipped with digital literacy in
addition to hands-on mining experience to fully adapt to this technological shift and the
growing demand for hybrid roles, such as automation engineers and data integrity
specialists, that require both traditional mining knowledge and technical expertise in
automation systems”.

Workforce effects-wise, companies have indicated a mean 12% reduction in
classic equipment-operator jobs since 2020, compared to a 9% growth in technical
positions such as automation technicians, control-room operators, and data analysts (Lee
& Tan, 2021; Jenkins & Kerr, 2020). Regression analysis verifies a statistically
significant negative association between autonomy share and levels of routine staffing (β
= –0.28, p = .03), validating the Skill-Biased Technological Change hypothesis that
automation replaces routine manual work while compensating higher-level jobs (Autor
et al., 2003). Interestingly, our qualitative findings show that most of the displaced
operators are being repositioned as “operator-technician hybrids” and go through six- to
nine-month upskilling programs to learn generic PLC (programmable logic controller)
fault-finding and data-interpreting activities. Interviewees warned, however, that some
40% of the academy attendees fail to pass performance thresholds, citing an alignment
gap between the design of courses and actual experience with the intricate complexities
of mining automation systems (Altenburg & von Drachenfels, 2017).

Companies are addressing the transition through a combination of formal
training academies, on-the-job mentoring, and change-management workshops
(Venkatesh & Bala, 2008). For example, Barrick Gold Saudi’s “Mining 4.0 Academy,”
developed in partnership with the Technical and Vocational Training Corporation
(TVTC), offers comprehensive modules on robotics maintenance, sensor calibration, and
industrial-network cybersecurity (Ministry of Industry and Mineral Resources, 2021).
However, some practitioners mentioned that gains in participant competence often level
off without continuous learning channels and leadership support. Interview evidence
also underscores that peer-to-peer mentoring—experienced control-room operators
mentoring new hires—has been highly effective in increasing technical proficiency as
well as organizational acceptance of automated processes (Braun & Clarke, 2006).

Even with these company-level efforts, a decentralized policy environment slows
the scaling of best practices. Although Vision 2030 and the National Industrial Strategy
both call for an explicitly “digitally enabled economy,” existing mining-sector skill
19

development remains decentralized, with each firm—and sometimes each mine—
developing its own programs. Experts recommend creating a national miningautomation skills framework under TVTC’s umbrella, harmonizing competency profiles
for critical positions such as autonomous-systems technician and data-analytics
specialist. Such a framework would allow labor mobility between companies, ensure
transparent certification channels for Saudi nationals, and bring in new talent by
indicating a coherent career path in the mining industry (Al-Tabtabai & Kolk, 2019).

6. Conclusion and Recommendations
This research has shed light on the deep mechanisms through which automation
and digitization are reshaping the composition, dynamics, and work requirements in
Saudi Arabia’s mining industry. In the five top companies interviewed—Ma’aden and
Barrick Gold Saudi among them—autonomous command systems, AI‐powered
predictive‐maintenance systems, and IoT sensor networks have increasingly penetrated
key operational functions, delivering measurable improvements in efficiency and safety.
Particularly, international standards dictate that autonomous haulage systems have the
potential to lower unit costs by a maximum of 15 percent and safety accidents by up to
30 percent (Rio Tinto, 2021). In contrast, maintenance solutions powered by AI have
been reported to reduce unplanned downtime on equipment by 20 to 25 percent in iron‐
ore and copper mining operations (Smith, Brown, & Williams, 2019). In the Saudi
context, survey respondents indicated similar performance gains, with average facility‐
level uptime increasing from 85 percent in 2019 to 92 percent as of early 2025.
In addition to these technical improvements, our mixed‐methods analysis verifies
that automation has a substantial, quantifiable effect on the makeup of the workforce.
Regression analysis showed a statistically significant negative correlation between the
proportion of automated assets and regular staffing levels (Autor, Levy, & Murnane,
2003; Arntz, Gregory, & Zierahn, 2016). In practical terms, firms surveyed disclosed a
12 percent net reduction in heavy‐equipment operator positions over the period 2020–
2024, alongside a 9 percent increase in mid‐ and high‐skill technical roles such as
autonomous‐systems technicians, control‐room operators, and data analysts. These shifts
capture both the displacement of purely manual tasks and the emergence of hybrid roles
requiring a synthesis of domain knowledge and digital fluency.

20

Qualitative insights from twelve in‐depth interviews further nuanced these
findings by exposing critical bottlenecks and enablers in the workforce transition.
Trainees in corporate upskilling academies—such as Barrick Gold Saudi’s Mining 4.0
program—report substantive gains in theoretical knowledge yet often struggle to apply
learning effectively on live operations: roughly 40 percent fall short of performance
benchmarks, citing gaps in hands‐on experience and contextual problem‐solving
(Altenburg & von Drachenfels, 2017). Respondents uniformly identified three key
factors influencing technology adoption and effective role shifts: perceived usefulness,
perceived ease of use, and facilitating conditions, as the Technology Acceptance Model
(Davis, 1989; Venkatesh & Bala, 2008) posits. Companies that integrated evident
leadership support, stable digital infrastructure (e.g., low‐latency broadband), and
organized mentorship experienced significantly greater trainee competence and system
usage.

Apart from firm-level dynamics, research underscored how Saudi Arabia’s
peculiar socio-political environment—characterized by expatriate‐dependent labor
markets and Saudization requirements—presents both opportunities and challenges for
workforce development in an equitable manner (Al-Tabtabai & Kolk, 2019). On the one
hand, Saudization quotas encourage firms to invest in domestic talent pools; on the
other, the current digital skills gap among Saudi nationals threatens to bias automation’s
benefits toward specialist expatriate personnel, potentially undermining Vision 2030’s
objectives of local content development and human‐capital development (Ministry of
Industry and Mineral Resources, 2021). Similarly, the decentralized character of existing
training programs leads to uneven quality, as smaller operators do not have the means to
copy the “academy” model used by industry leaders. Such fragmentation discourages
sector‐wide benchmarking, skill portability, and best practice aggregation.

Together, these results emphasize that the success of automation in Saudi mining
hinges as much on human capital preparedness and organizational culture as on
technology deployment. While investment in autonomous haul trucks and sensors is
required, it is not enough; the Kingdom also needs to develop a responsive labor force to
run, fix, and optimize them. Only through solving both sides of the “tech-human
equation” can the stakeholders take full advantage of the productivity, safety, and
sustainability benefits promised by Industry 4.0 changes.

21

Recommendations
First, to narrow the gap between policy intentions and operational realities, the
Technical and Vocational Training Corporation (TVTC) needs to lead the development
of a national mining‐automation skills framework. This model needs to establish
standardized competency profiles for new hybrid positions—Autonomous Systems
Technician, Predictive Maintenance Analyst, IoT Network Engineer, and Digital
Operations Supervisor—specifying required STEM foundations, specialized software
skills (e.g., PLC programming, machine‐learning model interpretation), and key soft
skills (problem‐solving, digital literacy). By encapsulating objective learning outcomes
and credentialing routes, TVTC will ensure the movement of labor within companies,
allow for standard quality assurance across training providers, and communicate
transparent career paths to Saudi nationals, thus promoting Saudization goals (Ministry
of Industry and Mineral Resources, 2021). Most importantly, the system needs to
include cyclical review processes—perhaps every two years for industry practitioners
and academics—to refresh competencies according to changing technologies, so that
national standards stay up to date in light of accelerated innovation (Ghobakhloo, 2018).

Second, mining operators need to develop stronger, work-based collaborations
with academic institutions and international technology suppliers. Theoretical training
alone has not been enough; students require systematic, hands‐on exposure to the precise
platforms and operating conditions they will see on the job. Consortia between industry
and academia can create co-funded centers of excellence that mimic real-world
situations with reduced-scale autonomous fleets, sensor networks, and control-room
simulators. Rotational internships that extend across both mine sites and supplier
plants—like ABB’s training sessions for the Ability™ System 800xA—would expose
students to experiential troubleshooting, cross‐vendor system integration, and situationbased decision-making. Memos of understanding among companies, universities, and
technology suppliers must codify resource sharing, collaborative curriculum
development, and co-assessment procedures, thus reducing technology acceptance
barriers by instilling facilitating conditions into the learning process (Venkatesh & Bala,
2008).

Third, mining operators need to institutionalize robust on-site mentoring and
continuous learning environments. Systematic mentoring programs, where every newly
accredited technician is matched with a veteran operations expert for a formal six-month
rotation, have proven to be quite effective in communicating tacit knowledge and
22

developing cultural acceptance (Braun & Clarke, 2006). Mentors should be trained in
adult learning techniques and rewarded—by means of performance bonuses and career
advancement opportunities—for their contribution to development. Digital learning
platforms can support these initiatives by monitoring proven competencies, highlighting
areas for focused coaching, and providing microlearning modules that address emergent
skill gaps. In addition, frequent “tech jam” workshops and cross-functional hackathons
will promote a culture of innovation, reinforcing learning by encouraging teams to
diagnose simulated system glitches and come up with innovative solutions within time
limits.

Fourth, to synch incentives and governance across the ecosystem, Saudi
authorities should set up a cross-sectoral steering committee responsible for guiding the
human capital transformation of the mining sector. Made up of members from the
Ministry of Industry and Mineral Resources, TVTC, the Ministry of Human Resources
and Social Development, industry associations, and labor development specialists, this
committee would oversee policy implementation, monitor progress toward Vision 2030
localization and productivity objectives, and allocate resources to underprivileged
operators. Measures like training tax credits for small‐ and medium‐sized mines, funding
matches for partnership with academe, and grants conditioned on proven trainee
performance based on monitored trainee results will make the investment return a
measurable workforce output. Ongoing public reporting on major performance measures
like the proportions of Saudi nationals employed in automation occupations, rates of
training completion, and gains in productivity will make the performance visible, allow
for benchmarking, and spur continuous improvement.

Fifth, since the transformation of the workforce is a continuous process, the
stakeholders need to introduce a longitudinal labor‐market observatory. Patterned on the
best practice in other territories (Lee & Tan, 2021), this observatory would be capturing
and processing information regarding the career paths of the upskilled workers, rates of
retaining employees in blended jobs, and trends between the competency levels and
business performance metrics. Through embracing strict monitoring and evaluation
procedures, the observatory will be able to detect “learning plateaus” or new skill
bottlenecks, allowing for timely curricular adjustment and focused policy interventions.
Regular surveys of worker satisfaction and technology uptake will also complement the
evidence base, guaranteeing that human aspects are kept at the forefront in evaluations
of automation effectiveness.
23

Lastly, in order to maintain momentum and drive knowledge diffusion in the
wider Gulf region, Saudi Arabia must be a regional hub for mining automation best
practices. Through the export of training curricula, competency frameworks, and
governance models to other GCC states, the Kingdom can stimulate a knowledge‐
sharing network that leverages combined bargaining power with global technology
suppliers and raises regional competitiveness. Collaborative research projects—
engaging Saudi university scholars and global mining schools—can further enhance
theoretical frameworks of regional uniqueness, policy fit, and skill mapping, adding to
the world’s discussion of Industry 4.0 workforce change.

These five interconnected suggestions—standardizing skills, co-designing
curricula, institutionalizing mentorship, aligning governance, and creating ongoing
assessment—form an integrated plan for balancing Saudi Arabia’s technology demands
with its human-capital aspirations. By undertaking these steps, the Kingdom will not
only spur the safe and efficient uptake of digitization and automation but also see its
people fill the high‐value jobs that arise out of this industrial transformation. By doing
so, Saudi Arabia can achieve Vision 2030’s vision of a diversified, knowledge‐based
economy and set an example for a balanced, inclusive response to the future of work in
mining.

24

References
Al-Adawi, T. (2014). Cultural barriers to technology adoption in the Gulf. Middle East
Journal of Management, 3(1), 29–50.
Al-Saud, N., & Murphy, J. (2023). Assessing vocational education capacity in Saudi
Arabia’s STEM programs. International Journal of Vocational Education, 15(1),
67–85.
Al-Tabtabai, H., & Kolk, A. (2019). Saudization and expatriate labor in the Gulf:
Impacts on skills and productivity. International Journal of Human Resource
Management, 30(14), 2065–2089.
Altenburg, T., & von Drachenfels, C. (2017). Industrial upgrading through automation:
Challenges and opportunities. Journal of Economic Policy, 12(2), 45–66.
Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD
countries: A comparative analysis (OECD Social, Employment and Migration
Working Paper No. 189). OECD Publishing.
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent
technological change: An empirical exploration. Quarterly Journal of
Economics, 118(4), 1279–1333.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative
Research in Psychology, 3(2), 77–101.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319–340.
Dutta, D., & Raina, R. (2018). Automation in mining: Global trends and future
prospects. Mining Technology, 127(4), 245–253.
Fahim, H., & Khan, S. (2022). Aligning national policies with workforce development:
A study of Saudi Arabia’s Vision 2030. Journal of Middle East Industrial
Strategy, 4(2), 45–63.
Ghobakhloo, M. (2018). Industry 4.0, digitization, and opportunities for sustainability.
Journal of Cleaner Production, 252, 324–335.
Jenkins, M., & Kerr, S. (2020). Predictive maintenance and workforce change in
Canadian mining. Canadian Mining Journal, 75(6), 38–43.
Lee, J., & Tan, B. (2021). Autonomous haulage systems: Employment effects in the
Australian iron ore sector. Resources Policy, 71, 102045.
Ministry of Industry and Mineral Resources. (2021). Saudi Mining Vision 2030. Riyadh:
Ministry of Industry and Mineral Resources.
Rio Tinto. (2021). Pilbara autonomous haulage system: Five-year review. Retrieved
from
25

Smith, A., Brown, P., & Williams, R. (2019). AI in mining maintenance: A field study.
International Journal of Mining Science and Technology, 29(3), 375–382.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research
agenda on interventions. Decision Sciences, 39(2), 273–315.

26

‫ﻴﺔ اﻟﺴﻌﻮدﻳﺔ‬+‫اﳌﻤﻠﻜﺔ اﻟﻌﺮ‬

Kingdom of Saudi Arabia

‫وزارة اﻟﺘﻌﻠﻴﻢ‬

Ministry of Education

‫ ﻠﻴﺔ ﺟ ﺪ ة اﻟﻌ ﺎﳌ ﻴﺔ‬7

Jeddah International College

Incorporating Sustainability into Supply Chain Management:
Evaluating the Impact of Sustainability on Logistical Excellence
– A Case Study of Julphar Company
A Thesis
Submitted in partial fulfillment of the requirements for M.A. degree
in Business Administration of Jeddah International College

BY: Bashaer Shaker Alharbi
ID NO: 222200511

Under Supervision:
Prof. Mohammed Ishaq

2025/ 1446

‫‪Page |I‬‬

‫‪Dedication:‬‬
‫ْ َ‬
‫َ َْ ُ ْ َ ْ َ ُ ﱠ‬
‫َ‬
‫‪َ 8‬رِّب اﻟَﻌﺎ ِﳌ@َن ﴾‬
‫﴿ وآِﺧُﺮ دﻋﻮا‪/‬ﻢ أِن اﻟـﺤْﻤﺪ ِ ِ‬
‫‪B‬ﺴﻢ ﷲ ﺧﺎﻟﻘﻲ وُﻣ‪H‬ﺴﺮ أﻣﺮي‪ ،‬وﻋﺼﻤُﺖ أﻣﺮي‪ ،‬ﻟﻚ ‪N‬ﻞ ا‪QP‬ﻤﺪ واﻻﻣﺘﻨﺎن‪ ،‬ﻓﺎ‪QP‬ﻤﺪ ‪ V‬ﺣًﺒﺎ وﺷﻜًﺮا ﻋ\] اﻟﺒﺪء وا‪_P‬ﺘﺎم‪.‬‬
‫ً‬
‫إذا ‪N‬ﺎن أول اﻟﻄﺮ‪f‬ﻖ أﳌﺎ‪ ،‬ﻓﺈن آﺧﺮﻩ ﺗﺤﻘﻴﻖ ﺣﻠﻢ‪ ،‬و‪m‬ذا ‪N‬ﺎﻧﺖ ﺑﺪاﻳﺘﮫ دﻣﻌﺔ‪ ،‬ﻓﺈن ‪ts‬ﺎﻳ‪tu‬ﺎ ‪B‬ﺴﻤﺔ‪.‬‬
‫و‪/‬ﺎ ‪ wv‬اﻟﺴﻨﻮات ﻗﺪ ﻣّﺮت‪ ،‬وا‪QP‬ﻠﻢ ﻗﺪ ﺗﺤﻘﻖ… ﻟﻢ ﺗﻜﻦ اﻟﺮﺣﻠﺔ ﻗﺼ@|ة‪ ،‬وﻟﻢ ﺗﻜﻦ اﻷﻣﻮر •ﺴ@|ة‪ ،‬وﻟﻜﻦ ﺑﺤﻮل ﷲ وﺻﻠﺖ‪،‬‬
‫ﻓﺎ‪QP‬ﻤﺪ ‪ V‬داﺋًﻤﺎ وأﺑًﺪا‪.‬‬
‫أ‪/‬ﺪي ‪/‬ﺬا اﻟﻨﺠﺎح إ…] ﻧﻔ‡ˆ‪ ‰‬اﻟﻄﻤﻮﺣﺔ ﺟًﺪا‪ ،‬و‪N ]…m‬ﻞ ﻣﻦ ﺳ‪ ]Œ‬ﻣ‪ wŒ‬ﻹﺗﻤﺎم ‪/‬ﺬﻩ اﳌﺴ@|ة‪،‬‬
‫إ…] اﻷر‪N‬ﺎن اﻟﺜﺎﺑﺘﺔ •‪ w‬ﺣﻴﺎ•ﻲ‪ ،‬ﻣﻦ ﺷﺎر‪N‬ﻮ‘ﻲ اﻟﺮﺣﻠﺔ‪ ،‬وﺳﺎ‪/‬ﻤﻮا ﺑ’ﻨﺎء ﻣﺴﺘﻘﺒ\‪،w‬‬
‫ﺷﻜًﺮا ﻟﻜﻢ‪ ،‬ﻓﺎﻟﺸﻜﺮ أ‪B‬ﺴﻂ ﻣﺎ أﺣﻤﻠﮫ إﻟﻴﻜﻢ‪.‬‬

P a g e | II

Acknowledgement:
First and foremost, all praise and gratitude are due to Allah, the Most Merciful and
Compassionate, for granting me the strength, patience, and clarity to complete this academic
journey. Without His divine guidance, none of this would have been possible.
I am deeply grateful to my supervisor, Prof. Mohammed Ishaq, for his valuable guidance,
insightful feedback, and unwavering support throughout the planning and execution of this
research. Your mentorship has played a pivotal role in shaping my academic path.
To the faculty, instructors, and staff at Jeddah International College, thank you for your
continuous encouragement and for providing a nurturing academic environment that
empowered me to grow intellectually and personally.
I extend my heartfelt thanks to my family, the true pillars of my life.
To the dear man whose name I carry with pride, and who carried mine to the skies with
honor—my beloved father: You planted within me the seeds of good character and stood by
me as a source of strength. Thank you from the depths of my heart.
To the ever-shining light of my life, my precious mother: You carried me with your heart
and soul, stood by me through every moment, and never hesitated to give me your time, your
health, and your comfort. You are the mercy that walks with me through life—thank you.
To my dear brother, my partner in thought and loyal companion in every situation: Your
constant presence has meant more than words can say. You have my deepest gratitude and
respect. And to my brother-in-law, your help was both generous and timely thank you for
standing by me and offering your support.
To my sisters, the heartbeat of my days and the calm within my storms: Thank you for lifting
my burdens with a word, a prayer, or a smile. You are blessings that no value can match. I’m
not forgetting the little stars in our home, my nieces, who filled my heart with their pure
laughter and innocent joy: You brought a unique kind of light into my life. I send you love as
pure as your childhood and prayers for your eternal happiness.
To my friends, the steady shoulders I leaned on, my haven in challenging times: You were
the light that eased my journey, the laughter that softened my stress, and more than family—
you were my strength.
And to the one whom Allah chose to enter my life at the perfect time, marking the end of this
journey and the beginning of a new one—my beloved husband: You brought love, comfort,
and balance into my life. You were my unfailing support and the trusted shoulder I leaned on.
Though you came late in the journey, your presence arrived at the moment I needed it most. I
loved you as a companion and friend, and I am proud of you as I am of myself in this
moment. Thank you for being the light that restored life to my heart.
To everyone who played a role in this journey, no matter how small—thank you. This
achievement is not mine alone. It reflects every kind word, every helping hand, and every
silent prayer offered on my behalf.

P a g e | III

Abstract:
Saudi Arabia’s Vision 2030 places significant emphasis on sustainability, encouraging
companies to adopt modern logistical technologies and environmentally friendly practices to
enhance efficiency. The Vision actively supports initiatives such as sustainable infrastructure,
renewable energy, and smart transportation systems. It also urges companies in all sectors
including pharma sectors to embrace AI-driven logistics and green supply chain strategies to
align with national sustainability goals.
This study investigates the integration of sustainability into supply chain management and its
impact on logistical excellence in the context of pharmaceutical logistics, using Julphar as a
case study. Given the limited number of studies in this field, this thesis highlights a critical
and underexplored area. Specifically, the study addresses the lack of research examining how
the integration of sustainability—across environmental, economic, and social dimensions—
impacts logistical excellence within the unique context of the Saudi pharmaceutical sector,
which is shaped by distinct regulatory, economic, and operational challenges.
A descriptive quantitative approach is employed in this study, with a questionnaire serving as
the primary data collection tool. Data are gathered from a sample of 50 employees at Julphar
Company. The data are analyzed using the Statistical Package for the Social Sciences (SPSS),
version 27.
The analysis revealed that Julphar’s employees and managers hold a positive perception of
the company’s logistical efficiency and sustainable performance across environmental,
economic, and social dimensions, reflecting strong awareness and appreciation of
sustainability practices. However, despite this positive outlook, the study identified a key
challenge: the uneven influence of sustainability dimensions on logistical excellence. While
social performance had a significant and consistent impact on all four aspects—
communication quality, information quality, order conditions, and timeliness—the
environmental and economic dimensions showed weaker effects. This disparity underscores
the need for more balanced and integrated efforts, especially in enhancing environmental and
economic components. The study addresses a critical gap by identifying which sustainability
areas most effectively drive logistical excellence in pharmaceutical logistics, and where
further strategic focus is needed.
Based on these findings, the study recommends more explicit integration of environmental
and economic sustainability practices into core logistics operations, employee
communication, and customer engagement—particularly in areas needing development, such
as environmental awareness, ICT-based environmental data sharing, and resource efficiency.
Continued support and reinforcement of employee-related initiatives are also encouraged,
given their strong impact on all dimensions of logistical excellence.
Keywords: Sustainability, Sustainable Organizational Performance, Supply Chain
Management, Logistical Excellence, Julphar Company.

P a g e | IV

TABLE OF CONTENTS
Subject

Page

Dedication

I

Acknowledgement

II

Abstract

III

Table of contents

IV

List of Tables

VI

CHAPTER 1. Introduction

1

1.1

Introduction

2

1.2

Background

2

1.3

Problem Identification

4

1.4

Research Questions

5

1.5

Research Objectives

5

1.6

Significance of the study

5

CHAPTER 2. Literature review

7

2.1

Introduction

8

2.2

Sustainability and Sustainable Performance

8

2.3

Sustainable Supply Chains

14

2.4

Excellence in Sustainable Logistics Services

18

2.5

Julphar Pharmaceuticals

22

2.6

Previous studies

24

2.7

Summary of the Literature Review

31

2.8

Research gap

33

2.9

Study hypotheses

34

Page |V

CHAPTER 3. Methodology

35

3.1

Introduction

36

3.2

Study Approach and Design

36

3.3

Study Setting

36

3.4

Population and Sampling

36

3.5

Data Collection Instruments

37

3.6

Validity and Reliability

37

3.7

Data Analysis Procedures

39

CHAPTER 4. Analysis and Results

41

4.1

Demographic variables of the participants

42

4.2

Descriptive Analysis of Logistical Excellence (Axis 1)

44

4.3

Descriptive Analysis of Sustainable Performance (Axis 2)

51

4.4

Study hypotheses analysis

54

CHAPTER 5. Summary, Conclusion and Recommendations

58

5.1

Summary of Key Findings

59

5.2

Conclusions

60

5.3

Recommendations

62

References

63

Appendices

69

P a g e | VI

LIST OF TABLES
No

Title

Page

1

Item-Total Correlations for Study Axes Items

38

2

Internal Consistency Reliability (Cronbach’s Alpha) for Study

39

Axis and Dimension
3

Frequency Distribution of Participant Demographic

43

Characteristics (Total No 50)
4

Descriptive Statistics for Logistical Excellence Dimensions (Axis

44

1)
5

Descriptive Statistics for Employee Communication Quality

45

(Axis 1, Dimension 1)
6

Descriptive Statistics for Sustainable Information Quality (Axis

46

1, Dimension 2)
7

Descriptive Statistics for Order Conditions (Axis 1, Dimension 3)

47

8

Descriptive Statistics for Timeliness (Axis 1, Dimension 4)

49

9

Descriptive Statistics for Sustainable Performance Dimensions

50

(Axis 2)
10

Descriptive Statistics for Environmental Performance (Axis 2,

51

Dimension 1)
11

Descriptive Statistics for Economic Performance (Axis 2,

52

Dimension 2)
12

Descriptive Statistics for Social Performance (Axis 2, Dimension

53

3)
13

Univariate Regression Results: Environmental Performance

55

Predicting Dimensions of Logistical Excellence
14

Univariate Regression Results: Social Performance Predicting

56

Dimensions of Logistical Excellence
15

Univariate Regression Results: Economic Performance Predicting
Dimensions of Logistical Excellence

57

Page |1

CHAPTER 1
Introduction

Page |2

CHAPTER 1: Introduction
1.1 Introduction:
This study investigates the integration of sustainability into supply chain management
and its impact on logistics excellence, using Julphar as a case study. The study is divided into
five primary chapters, each of which addresses an important component of the research issue.
This chapter provides background information for the research, defines the problem
statement, and outlines the research objectives and questions. It also emphasizes the value of
the research in understanding how sustainability contributes to logistical excellence, notably
in the pharmaceutical business.

1.2 Background:
The rapid expansion of Saudi Arabia’s economic sectors has significantly transformed
operational and logistical frameworks, driven by digital transformation, sustainability
integration, and strategic investments in logistics infrastructure (Alotaibi & Alharbi, 2022).
One of the key indicators of this progress is Saudi Arabia’s advancement to 38th place in the
2023 Logistics Performance Index (LPI), moving up 17 spots internationally (World Bank,
2023). With increasing economic and environmental pressures, organizations are striving to
reduce costs, enhance efficiency, and minimize environmental impact by optimizing energy
usage, reducing waste, and adopting advanced technologies such as artificial intelligence (AI)
(Zhang et al., 2023).
The pharmaceutical sector is one of the industries most impacted by the shift toward
sustainability, as it requires resilient and environmentally responsible supply chain operations
(Bade et al., 2024). Pharmaceutical supply chains present unique sustainability challenges
due to temperature-sensitive medications, stringent regulatory requirements, and global
distribution complexities (Kumar & Agarwal, 2021). Research highlights that implementing
green supply chain management (GSCM) can enhance financial stability, increase
productivity, and reduce emissions (Ricardianto et al., 2022; Almasarweh, 2022). Case
studies from Jordan and Indonesia demonstrate how green logistics practices improve
logistics performance and operational efficiency (Almasarweh, 2022).

Page |3

Despite these advancements, there remains a research gap regarding the direct impact
of sustainability on logistical excellence, particularly in the pharmaceutical industry (Jabbour
et al., 2022). Many studies focus on general sustainability in supply chain management, but
few exclusively examine pharmaceutical logistics, where elements like cold chain
management and regulatory compliance create additional challenges. This study seeks to
address this gap by analyzing how sustainability initiatives impact logistical performance in
pharmaceutical businesses, using Julphar as a case study.
The Case of Julphar:
As one of the leading pharmaceutical companies in the Middle East and North Africa
(MENA), Julphar plays a crucial role in regional healthcare logistics (Julphar, 2023). The
company is committed to sustainability, as reflected in its ISO 9001:2015 (Quality
Management) and ISO 14001:2015 (Environmental Management) certifications (Julphar,
2023). To align with global sustainability standards, Julphar has adopted eco-friendly
packaging, energy-efficient production, and AI-driven supply chain optimization (Ahmed et
al., 2023).
Julphar’s investment in automation and AI-powered logistics has enhanced its supply
chain resilience by forecasting demand fluctuations, reducing transportation inefficiencies,
and lowering carbon emissions (Farooq et al., 2022). The company has also integrated smart
warehouses and AI-based inventory management systems to improve logistics accuracy and
responsiveness (Abbas & Khan, 2023). However, significant challenges remain, including
supply chain disruptions, rising operational costs, and compliance with evolving international
sustainability regulations (Kumar et al., 2023).
Sustainability and Vision 2030:
Saudi Arabia’s Vision 2030 prioritizes sustainability and encourages companies to
implement modern logistics technologies and eco-friendly business practices to enhance
efficiency (Alharbi & Al-Saleh, 2023). The government is actively supporting green supply
chain initiatives, including sustainable infrastructure, renewable energy, and smart
transportation systems. Vision 2030 also incentivizes companies like Julphar to adopt AIdriven logistics and green supply chain strategies to comply with national sustainability goals
(Alshahrani et al., 2023).

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Saudi Arabia’s ambition to rank among the top 25 countries in the LPI by 2030 is
driven by investments in digital transformation, AI-powered logistics, and sustainable
infrastructure projects (World Bank, 2023). Programs such as the National Industrial
Development and Logistics Program (NIDLP) are accelerating the adoption of automation,
AI, and renewable energy solutions to ensure sustainable logistics growth (Saudi Vision
2030, 2023).
Purpose of the Study:
This study aims to assess the impact of sustainability initiatives on logistical
excellence within the pharmaceutical sector, using Julphar as a case study. It evaluates how
sustainability principles, green logistics, and AI-driven optimization contribute to operational
efficiency and cost savings in pharmaceutical logistics. By analyzing Julphar’s sustainability
strategies and their effects on logistics performance, the research seeks to draw broader
insights applicable to the pharmaceutical industry as a whole.
Ultimately, the study will provide practical insights applicable to the pharmaceutical
sector, identifying best practices and challenges in aligning logistics operations with
sustainability goals. Additionally, it will assess how Julphar’s sustainability-driven logistics
initiatives align with Saudi Arabia’s Vision 2030, offering recommendations that support
both business competitiveness and environmental responsibility.

1.3 Problem Identification:
The growing global emphasis on sustainability raises critical questions about its
impact on logistics performance and cost efficiency. While sustainable practices are widely
promoted as beneficial, their actual effects on logistical excellence, cost reduction, and
operational efficiency remain underexplored, particularly in the pharmaceutical industry.
Many pharmaceutical companies, including Julphar, have integrated sustainability
initiatives such as green supply chain management, AI-driven logistics optimization, and ecofriendly operations. However, there is limited research on how these efforts translate into
measurable improvements in logistics performance. Key question remain about how
sustainability improves efficiency, saves prices, and solves logistical problems in a highly
regulated industry like pharmaceuticals.

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This study aims to evaluate the real impact of sustainability initiatives on Julphar’s
logistics operations, identifying both the benefits and challenges of integrating green and AIdriven logistics strategies. By addressing this knowledge gap, the research will provide
valuable insights for pharmaceutical firms looking to achieve logistical excellence while
aligning with sustainability goals and Vision 2030

1.4 Research Questions:
1. What is the overall impact of sustainability principles on achieving logistical excellence
in pharmaceutical companies, with a focus on Julphar Company?
2. What specific sustainability practices has Julphar implemented, and how do they affect
operational efficiency?
3. How do initiatives like eco-friendly operations, AI-driven process optimization, and green
supply chain management contribute to cost savings and improved logistics?

1.5 Research Objectives:
1. Analyze the overall impact of sustainability principles on achieving logistical excellence
in pharmaceutical companies, with a focus on Julphar.
2. Examine the specific sustainability practices implemented by Julphar and assess their
effect on operational efficiency.
3. Evaluate how eco-friendly operations, AI-driven process optimization, and green supply
chain management contribute to cost savings and improved logistics performance.

1.6 Significance of the study:
The increasing emphasis on sustainability in global supply chains highlights the need
for empirical research on its direct impact on logistical excellence, particularly in highly
regulated industries such as pharmaceuticals. While sustainability initiatives are widely
promoted as essential for long-term business success, there is still limited research on how
these practices influence operational efficiency, cost reduction, and logistics optimization in
pharmaceutical companies.
This study contributes to existing knowledge by evaluating the real-world impact of
sustainability driven logistics at Julphar. By analyzing the company’s sustainability practices,
operational efficiency, and cost-saving strategies, this research will provide valuable insights
for:

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v Bridging the Knowledge Gap – Existing studies discuss sustainability in supply chain
management broadly, but few focus specifically on pharmaceutical logistics. This
research will provide data-driven insights on how sustainability enhances logistical
performance.
v Guiding Industry Best Practices – By assessing Julphar’s sustainability framework, this
study will highlight effective strategies and potential challenges, offering guidance to
other pharmaceutical companies seeking to integrate sustainability into logistics
operations.
v Aligning with Saudi Arabia’s Vision 2030 – Sustainability is a core focus of Vision 2030,
which promotes eco-friendly business practices and modern logistics technologies. This
study will evaluate how Julphar’s green initiatives align with these national goals,
offering recommendations for other pharmaceutical firms to enhance sustainability while
maintaining logistical excellence.
Through this research, pharmaceutical companies can gain practical insights into the
benefits of sustainability, allowing them to make informed decisions that improve efficiency,
reduce costs, and support environmental responsibility in logistics operations.

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CHAPTER 2
Literature review

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CHAPTER 2: Literature review
2.1 Introduction:
The Sustainable Development Goals (SDGs) have been widely adopted by numerous
nations, focusing on combating hunger and poverty, as well as protecting the environment
from the adverse effects of climate change.
With this global shift, the role of the business sector in achieving these goals has
gained significant importance. This has compelled companies to commit to socially and
environmentally responsible practices and to disclose related management reports.
Corporations are among the largest consumers of natural resources, managing and preserving
these resources in alignment with their short- and long-term economic interests.
The environmental impact of organizations has become increasingly evident in recent
years, necessitating their adaptation to the requirements of sustainable operations. These
operations encompass three key dimensions: environmental, economic, and social. To
achieve this adaptation, organizations must redouble their efforts to ensure a balance between
economic, social, and environmental performance—a dynamic that explains the growing
interest in the concept of sustainable performance.
The second chapter of this study presents the theoretical framework, structured into
three sub-sections. The first focuses on corporate sustainability and sustainable performance,
while the second examines supply chains and logistical excellence. The third briefly
addresses the case study of the relevant company. Finally, the chapter concludes with a
concise review of relevant prior literature, which helps elucidate the relationship between
sustainable development and logistical excellence.

2.2 Sustainability and Sustainable Performance:
2.2.1 Sustainable Development:
The concept of sustainability was first introduced in 1987 by Brundtland, and since
then, this topic has received wide attention from both organizations and academics. The rapid
changes in manufacturing industries and the emergence of the Fourth Industrial Revolution
have led to many environmental issues, prompting organizations to focus on addressing these
challenges (Malik et al, 2020).

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Saudi Arabia’s Vision 2030 emphasizes the importance of achieving sustainable
development, highlighting the need to raise awareness of sustainability, particularly
environmental sustainability, among all segments of society. The Vision also stresses that
preserving the environment and natural resources is a religious, ethical, and humanitarian
duty, and it is the responsibility of the current generation to protect these resources for future
generations, aiming for sustainability in all environmental, economic, and social areas
(Vision 2030 Document).
In recent years, the concept of sustainability has become widely discussed among
scholars due to the increasing environmental problems in society. Sustainability is often
understood based on the triple-bottom-line approach, which includes the environment,
economy, and social aspects, indicating the need to assess the performance of organizations
in these three dimensions (Khan et al, 2020).
Sustainability is defined as “development aimed at the optimal and flexible use of
resources to achieve a balance between economic, social, and environmental goals, without
compromising the ability to meet human needs, along with the economic entity’s ability to
add value in a competitive market and sustain itself as an economic entity” (Ryan, 2023,
p.40).
The concept of sustainability is often linked to environmental conservation, such as
reducing waste, using energy more efficiently, and encouraging recycling. However,
sustainability is a broader concept that includes balancing environmental, social, and
economic requirements to achieve long-term success in business (Teh et al, 2022).
The United Nations defines sustainable development as: achieving socially,
economically, and environmentally balanced development for both present and future
generations, ensuring the fair and optimal use of natural, human, and material resources,
while enhancing the ability of future generations to meet their needs (Al-Ghneimat, 2023).
Because of the growing movement to protect the environment and organizational
resources, managers have been paying more and more attention to sustainability in recent
years. Through innovation and striking a balance between the organization’s economic,
social, and environmental performance, sustainability is seen as a powerful tool for
differentiating businesses and boosting their competitiveness (Mousa & Othman, 2020).

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Based on the above, the researcher believes that sustainability is an important means
of improving an organization’s performance in economic, environmental, and social aspects,
while adding real value to stakeholders and promoting a green culture within the organization
to create a work environment that supports sustainable environmental practices, community
development, and resource conservation.
2.2.2 Sustainable Performance:
Sustainable performance has emerged as an increasingly important area in modern
management research, with growing attention directed toward linking production methods to
sustainable outcomes. This type of performance reflects a product’s or service’s ability to
adapt to environmental and social risks and factors, in alignment with integrated
sustainability indicators (Al-Tarawneh, 2024).
According to Stanciu et al. (2014), sustainable performance in organizations is the
capacity to satisfy the demands and expectations of clients and other stakeholders over an
extended period of time through efficient management and ongoing development, propelled
by employee awareness and the implementation of suitable innovations.
Moreover, sustainable performance is associated with maintaining a healthy balance
between the financial, social, and environmental dimensions of an organization, reflecting its
ability to achieve sustainable results across all three areas (Steinhart, 2024).
It involves utilizing and managing the organization’s available resources in a way that
contributes to environmental, economic, and social benefits. It describes the organization’s
efforts to enhance its performance without negatively impacting the environment (Nguyen,
2023).
The triple bottom line, which refers to the balanced development of the economic,
social, and environmental aspects of sustainable development, is the definition of sustainable
institutional performance. In light of the current environmental crisis, this kind of
performance has become more and more crucial since it contributes to competitive advantage
and company sustainability (Zhang et al., 2024).
Finally, sustainable institutional performance is closely tied to a company’s ability to
achieve business goals and increase shareholder value over the long term, by integrating the
three pillars of sustainable development into its corporate strategy (Nofryanti et al., 2021).

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2.2.3 The Importance of Achieving Sustainable Performance:
Due to ongoing climate change, rising global temperatures, and increasing pressures
from communities and stakeholders, sustainability has become an area of growing concern
for organizations—particularly among top management, who now recognize the importance
of integrating sustainability into organizational operations. Sustainability is no longer limited
to financial aspects such as return on assets, equity, or earnings per share; it also encompasses
care for the environment, employee well-being, customer satisfaction, and community
welfare. As a result, organizations have increasingly focused on environmental initiatives,
making sustainability a core component of the solutions they provide to address societal
challenges (Malik et al., 2020).
Since real sustainability cannot be attained without preserving equilibrium among the
economic, social, and environmental facets, organizational sustainability is a crucial strategic
concern within the context of sustainable development. As a result, businesses must give
equal weight to commercial and financial objectives as well as social benefits and
environmental protection. In order to achieve sustainable performance, organizations must
satisfy their present demands without endangering the capacity of future generations to
satisfy theirs (Sapta et al., 2021).
The importance of adopting sustainable performance in business organizations stems
from several factors, as outlined by Al-Tarawneh (2021), including:
1. The excessive and unplanned use of non-renewable resources.
2. Recent economic crises that have prompted a re-evaluation of business practices focused
solely on economic growth without addressing environmental impacts—placing such
organizations at a competitive disadvantage, whereas sustainability opens pathways for
future growth.
3. The stimulation of innovation and reduction of negative environmental impacts, leading
to significant economic benefits.
4. The alignment of organizational policies with sustainability issues, promoting a culture of
sustainability within the organization and raising awareness among customers and the
wider community.
5. Enhanced transparency and alignment of strategic goals with the competitive
environment.

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6. By encouraging a peaceful cohabitation of the environment and society, sustainable
performance—a strategy closely linked to corporate social responsibility—aims to create
shared value.
7. Organizations have a significant competitive advantage when the environmental and
social dimensions of performance interact well.
Furthermore, sustainable performance reflects a core corporate objective of fulfilling
social responsibility through compliance with standardized performance reporting and
meeting stakeholder expectations. Adopting sustainable practices and disclosing their
operational impacts offer numerous benefits, such as maintaining and increasing economic
growth, enhancing competitiveness and shareholder value, improving corporate reputation,
ensuring customer satisfaction and loyalty, retaining talented employees, boosting employee
motivation, and reducing overall costs (Nofryanti et al., 2021).
2.2.4 Dimensions of Sustainable Performance:
Manufacturing operations have historically approached performance measurement
from an operational standpoint. Stakeholder pressure to track and enhance non-economic
results, like social and environmental factors, has increased since the turn of the twenty-first
century. The Triple Bottom Line (TBL) is the name given to this strategy, which takes into
account the three aspects of sustainable success. It pushes businesses to strike a balance
between the economic, environmental, and social pillars of sustainability (Henao & Sarache,
2022).
Environmental performance, economic performance, and social performance are the
three core components of sustainability. Reducing possible environmental hazards,
encouraging eco-friendly behaviors, and preserving organizational resources are the main
goals of environmental performance. Economic performance has to do with the organization’s
internal operations and financial results. Social performance, on the other hand, focuses on
enhancing the welfare of employees, clients, and other stakeholders (Yusliza et al., 2020).
The elements of sustainable performance in the business sector are explained as follows:
v Environmental Performance:
Sustainable environmental performance is a crucial element for organizations striving
toward sustainability. It helps reduce energy consumption, minimizes processing time, and

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lowers waste and harmful substances that negatively impact the environment. Sustainable
environmental performance enhances organizational operations, making them more reliable
and safer, thus offering protection against potential environmental hazards. Additionally, it
contributes to reducing environmental degradation and protecting the environment from
pollution (Al-Tarawneh, 2024).
In order to meet the requirements of the present generation without jeopardizing the
ability of future generations to meet their own, environmental sustainability is necessary. It
pushes businesses to embrace eco-friendly business models that incorporate green activities
into their plans. Innovative environmental practices give businesses a distinct competitive
edge (Weerasinghe et al., 2023).
v Social Performance:
Sustainable social performance is a core dimension that relates to individuals and
society. Social sustainability refers to the ethical responsibilities that organizations uphold in
addressing human needs. It reflects the organization’s commitment to active participation in
local communities and the well-being of all stakeholders. Social performance areas include
human rights, working conditions, health and safety, and employee engagement in
community initiatives (Al-Tarawneh, 2024).
Social sustainability has become increasingly vital in light of ethical violations
associated with industrial developments. It provides a framework for understanding the
conflicting demands of contemporary society, including treating individuals with dignity and
granting them equal rights. Fair labor practices, promoting diversity, and community
engagement are all embodied within the social performance of companies (Weerasinghe et
al., 2023).
v Economic Performance:
Sustainable economic performance focuses on addressing issues such as poverty and
unemployment, aiming to optimize the use of economic resources to improve living
standards. It is measured by economic growth that aligns with environmental protection and
improved quality of life (Abu Tayeh, 2022).
When measuring sustainable economic performance, several elements must be
considered, including operational and financial outcomes. This includes the technologies used

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to enhance financial performance and reduce costs. Implementing green practices—such as
lowering energy consumption and reducing waste disposal costs—contributes to sustainable
economic performance. Achieving this performance relies on adopting pollution prevention
technologies, routine equipment maintenance, and strengthening the organization’s financial
capabilities to ensure continuity (Allam & Mansour, 2024).

2.3 Sustainable Supply Chains:
The concept of Supply Chain Management (SCM) has continuously evolved over
time, originating as part of materials management in the mid-19th century. Initially, the focus
was on organizing the flow of materials within facilities, gaining particular importance during
World War I. Over time, the concept developed to encompass more comprehensive aspects,
beginning to take its modern form by the mid-1980s. Increasing client satisfaction is the main
objective of supply chain management.
This includes secondary objectives such as profitability, reliability, flexibility, speed,
responsiveness, improved turnover rates, and enhanced communication and coordination
throughout the chain. Traditionally, supply chain performance was primarily assessed based
on its economic outcomes (Anilkumar & Sridharan, 2019).
Ensuring the timely and dependable delivery of raw materials and completed goods to
clients was the main priority in the early phases of supply chain management development. In
order to increase operational efficiency and decrease delays, businesses at the time focused
primarily on maintaining a steady flow of goods and information throughout the chain. In the
midst of this emphasis on dependability and speed, businesses had daily difficulties
organizing and simplifying their processes.
They also sought to minimize resource and time waste, but the focus remained
primarily on achieving direct economic gains. These efforts aimed to cut costs and increase
profitability through enhanced efficiency and reduced process waste (Sarkis et al., 2011).
The coordination and administration of a web of connections between different
organizations is known as supply chain management.
It includes a range of integrated activities starting from suppliers, through production
facilities, logistics, and marketing, and ultimately reaching end customers. In order to

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maximize profits and create value through efficiency and customer pleasure, these operations
entail the flow of information, funds, resources, and services (Peristeris et al., 2015).
According to Simon et al. (2014), supply chain management (SCM) entails the
integration of critical business processes from the end user back to the original suppliers who
offer goods, services, and data that benefit clients and stakeholders.
The idea of sustainable supply chain management emerged as a result of
environmental concerns becoming a major problem in recent years. According to this
concept, the movement of money, information, and materials must be managed to satisfy
social, environmental, and economic demands while guaranteeing cooperation from all
stakeholders (Wang et al., 2023).
Building a sustained competitive edge now requires achieving excellent supply chain
management. Companies now compete as a part of interconnected supply chains, where all
participants contribute to the movement of goods, services, money, and information, rather
than as separate organizations (Abdul Hamid, 2018).
According to Peristeris et al. (2015), integration of operations across functions within
the chain’s entities is necessary for efficient and successful supply chain management.
Therefore, a key component of supply chain management effectiveness is managing
relationships both inside and externally.
The Triple Bottom Line (TBL), which has three primary dimensions—economic,
environmental, and social—is another idea that sustainability embodies. Every one of these
aspects represents a distinct emphasis that helps to achieve long-term equilibrium. Because it
directly affects the continuation of human life on Earth, the preservation of the natural
environment—which includes the land, water, plants, and animals—is a crucial aspect of the
environmental component. Human capital is linked to the social component, which calls for
the adoption of equitable and advantageous policies for employees, local communities, and
the areas in which businesses operate. Last but not least, the economic dimension concerns
the monetary gains made by every participant in the supply chain, including local
communities and the nations in which activities are carried out (Sanchez-Flores et al., 2020).
Carbon emissions have significantly decreased in recent years as a result of the
interplay of social, political, and financial practices. Consequently, businesses have started
looking for innovative methods to reduce behaviors that affect the environment. Achieving

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this objective in this context requires careful management and operational coordination,
which are essential for striking a balance between environmental preservation and corporate
growth (Tufail & Akhtar, 2022).
The detrimental effects that traditional logistics methods have on society and the
environment have come to light more and more in recent years. As a result, the idea of
sustainable logistics is becoming a crucial component of the contemporary corporate
landscape. A key element of the sustainable supply chain is sustainable logistics, which
focuses on planning, carrying out, and overseeing logistics operations while taking into
account their effects on the environment, society, and economy. This approach is based on a
three-dimensional viewpoint that assesses the simultaneous effects of logistical decisions on
people, the environment, and revenues (Munuhwa, 2023).
When economic, environmental, and social issues are voluntarily coordinated and
incorporated into common core systems across enterprises, sustainable supply chains are
created. From locating raw materials to producing and distributing goods and services, these
systems are made to efficiently and effectively handle the movement of cash, information,
and resources. Through short- and long-term increases in profitability, competitiveness, and
organizational resilience, this integration seeks to satisfy stakeholder needs (Ahi & Searcy,
2013).
Researchers and practitioners have given the development of sustainable supply chain
management a lot of attention, particularly in tandem with the ongoing revisions to its
definitions. It is crucial to remember that comprehension of the supply chain concept itself is
directly related to the basis of supply chain sustainability. According to Pramudiawardani et
al. (2019), a supply chain is a network made up of three or more businesses or individuals
that are actively involved in the movement of goods, services, money, and information from
the point of origin to the final consumer.
In this process, supply chains can have significant negative impacts on the planet’s
ecosystems, such as the depletion of scarce natural resources, the generation of massive
amounts of waste that affect air, water, and soil, and threats to biodiversity. There is also
growing concern about some companies engaging in socially irresponsible practices, such as
providing unsafe working environments, employing child labor, and using hazardous
materials that harm both society and the environment (Martins et al., 2019).

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At this point, sustainability becomes an extremely important issue. According to some
scholars, the practice of regulating the movements of capital, information, and commodities
while also encouraging cooperation between businesses along the supply chain is known as
sustainable supply chain management, or SSCM. The three Sustainable Development Goals
(SDGs)—economic, environmental, and social—are the foundation of this management
strategy. These objectives are motivated by stakeholder and customer needs, highlighting
how crucial it is to incorporate these aspects into business plans (Pramudiawardani et al.,
2019).
Keeping social responsibility, environmental stewardship, and economic viability in
balance throughout all supply chain operations is another definition of sustainable supply
chain management. This is accomplished by the harmonic integration of these elements to
guarantee operational sustainability and accomplish financial goals without adversely
affecting the environment or society (Sanchez-Flores et al., 2020).
Service quality factors play a vital role in the success of companies, especially in the
logistics sector, where weaknesses in these factors directly impact overall company
performance and customer satisfaction. In this context, a specific metric was developed to
assess the sustainability of logistics service quality, which includes a set of key elements that
contribute to enhancing service quality and achieving environmental sustainability. The most
prominent of these elements include:
1. Employee Communication Quality: The quality of communication between service
providers and customers is one of the key factors determining the level of logistics
service. Employees must be well-trained and possess strong communication skills.
Moreover, they should be capable of understanding customer needs and handling
complaints effectively and promptly. Improving communication quality not only involves
resolving current issues but also includes guiding customers and offering advice that
helps them make more environmentally conscious decisions. In the context of business
sustainability, customer service employees should educate clients on ways to reduce their
environmental impact during shipping or packaging processes, thereby promoting more
sustainable environmental practices.
2. Sustainable Information Quality: In today’s fast-paced world, accurate and timely
information is vital for improving logistics services. Logistics service providers must
offer clear and precise information about products, such as inventory availability,

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expected delivery times, and any changes in the shipping schedule. This level of
transparency can enhance customer trust and satisfaction. In addition, Information and
Communication Technology (ICT) plays a crucial role in improving knowledge sharing
among companies and facilitating the exchange of data related to environmental
performance. Modern digital tools help logistics companies implement environmentally
sustainable practices, such as reducing waste or optimizing resource usage.
3. Order Condition: This factor is related to the challenges products may face during
transportation, such as damage caused by poor handling or shipping conditions. Hence,
the importance of using advanced and eco-friendly packaging technologies that can
protect products during transit. Logistics providers must also focus on the types of
materials used in packaging, prioritizing recyclable or less environmentally harmful
materials. Furthermore, managing order conditions requires effective health and safety
programs for employees involved in the transportation process, in order to minimize
accidents and improve the overall working environment.
4. Timeliness: Timeliness is one of the primary concerns for customers when choosing
logistics service providers. Custome…
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