Process Transformation at the University of Basilicata: Mapping, Digitalization, and Enhanced Transparency

Abstract

Digital transformation in higher education requires the redesign of administrative and teaching processes to improve efficiency, transparency, and accountability. This study analyzes and optimizes the process of Supplementary Teaching Assignments (ADI) at the University of Basilicata, integrating Business Process Modeling and Notation (BPMN 2.0) with discrete-event simulation using the Simul8^® tool. The proposed Process Innovation in Higher Education (PIHE) Framework combines process mapping, simulation-based validation, and KPI-oriented monitoring to identify inefficiencies and guide evidence-based process reengineering. Simulation results highlight that the Didactics Office represents the primary process bottleneck, absorbing over 30% of the total workload, and that Department Council scheduling has a significant impact on overall lead time. Optimizing these factors reduces completion time by up to 8% while enhancing resource allocation and service quality. Although the analysis focuses on a single university, which limits external generalizability, the proposed PIHE Framework offers an adaptable methodological structure that can be transferred to other higher education institutions operating under different organizational and regulatory contexts. Future research will extend its application to multiple universities and additional administrative processes to strengthen its empirical validation and support data-driven decision-making in academic governance.

1. Introduction and Motivations

In today’s higher education industry landscape, universities are compelled to navigate complex administrative and academic workflows within increasingly constrained resource environments, prompting a pressing need for digital transformation strategies that enhance performance, transparency, and accountability. Recent evidence indicates that structured Business Process Management (BPM) initiatives can significantly boost process efficiency even in rigidly bureaucratic settings, as demonstrated in an Italian public university scenario [1]. In parallel, multivocal literature reviews highlight that while emerging technologies such as advanced analytics, cloud computing, and artificial intelligence are being adopted by HEIs, only a fraction of institutions have succeeded in embedding these into a cohesive, strategic plan [2]. Moreover, theoretical models for digital maturity now emphasize the importance of socio-cultural, academic-management, and administrative-management dimensions in evaluating and guiding digital transformation initiatives [3]. From a process-mapping standpoint, benchmarking models developed for HEIs reveal that implementing BPM practices fosters agility, reduces errors, and enhances service delivery, especially in administrative divisions [4]. Finally, empirical analyses of institutional digitalization efforts have underscored the necessity of combining technological innovation with organizational culture shifts and strategic digital literacy development to support effective change management [5].

In the Italian higher education system, supplementary teaching activities (Supplementary Teaching Assignments, ADI) represent a ‘complementary class’ to ordinary teaching. They are designed to guarantee the continuity and completeness of course delivery, often supporting laboratory sessions, tutoring, or specialized modules when internal teaching resources are insufficient. This complementary nature makes the ADI process both pedagogically and administratively significant, as it bridges the academic mission of quality teaching with the administrative obligations of transparency and accountability. Consequently, analyzing and optimizing this process offers valuable insights into how universities balance educational effectiveness and regulatory compliance. Building on this body of knowledge, this study pursues a dual objective:

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To analyze and improve the supplementary teaching assignment (ADI) process at the University of Basilicata through process mapping, simulation-based analysis, and targeted digital interventions.

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To develop and propose a replicable methodological framework-the Process Innovation in Higher Education (PIHE) Framework-that integrates process reengineering, simulation-based validation, and transparency mechanisms, and that can be adapted by other higher education institutions facing similar challenges.

Against this backdrop, the University of Basilicata represents a significant case study: a medium-sized regional institution navigating the dual challenges of resource scarcity and regulatory complexity, while strategically investing in process mapping, digitalization, and transparency-enhancing reforms to strengthen both academic and administrative functions. By combining a detailed case analysis with the formulation of a transferable framework, the study contributes both context-specific evidence and generalizable insights to the ongoing discourse on higher education modernization.

Within the European Higher Education Area (EHEA), the modernization of universities is strongly influenced by policy frameworks that promote accountability, transparency, and performance-based governance. At the European level, initiatives such as the Bologna Process and the EU’s Digital Education Action Plan (2021–2027) have emphasized the integration of digital technologies to support both pedagogical innovation and administrative efficiency [6]. In Italy, these imperatives are further reinforced by national legislation, including the Legislative Decree 33/2013 [7] on transparency in public administration and subsequent reforms that mandate open data publication, process traceability, and measurable performance outcomes [8]. Moreover, the Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR) has progressively embedded quality assurance (QA) standards into accreditation and evaluation mechanisms, linking institutional credibility to demonstrable evidence of efficiency and accountability [9]. These normative pressures, combined with increasing public scrutiny of university expenditures and service delivery, have prompted Italian universities to explore structured Business Process Reengineering (BPR) methodologies and digital workflow platforms as vehicles for achieving compliance and institutional agility [10]. Recent research highlights how digital transformation fosters openness and operational visibility through data integration and process automation (Cui, Zhao & Cui, 2025; Reischauer et al., 2024) [11,12]. Within universities, the transition toward fully automated and paperless systems represents a critical milestone for process optimization and quality assurance (Ramadan & Al-Amri, 2025) [13]. This evolution is particularly relevant in public higher education systems, where transparency and regulatory compliance are essential drivers of modernization and efficiency.

Against this backdrop, the University of Basilicata represents a significant case study: a medium-sized regional institution navigating the dual challenges of resource scarcity and regulatory complexity, while strategically investing in process mapping, digitalization, and transparency-enhancing reforms to strengthen both academic and administrative functions.

Despite the growing body of scholarship on quality assurance, digital transformation, and process reengineering in higher education, several research gaps remain. Many existing contributions focus on conceptual frameworks or fragmented perspectives—such as stakeholder perceptions of quality, isolated digital tools, or policy analyses—without offering empirically validated methodologies that integrate organizational, technological, and regulatory dimensions in a holistic manner [14,15]. Moreover, while simulation and BPM approaches have demonstrated potential in industrial contexts, their application in university administration is still limited and often anecdotal [16]. Case studies on Italian universities are particularly scarce, even though the sector operates under some of the strictest transparency and accountability regulations in Europe, making it an ideal testing ground for structured Business Process Reengineering (BPR) frameworks [17]. This study addresses these gaps by presenting an in-depth case analysis of the University of Basilicata, where process mapping, digitalization, and transparency-enhancing mechanisms are applied to the management of supplementary teaching activities. By integrating BPMN 2.0 modeling with digital workflows and compliance protocols, the research provides empirical evidence of efficiency improvements, accountability gains, and enhanced service delivery. Ultimately, this case study contributes a replicable framework that higher education institutions can adopt to navigate the dual imperatives of modernization and transparency.

This research was conducted within the temporal framework of the academic years 2022–2024, a period marked by the implementation of digital transformation policies in Italian higher education institutions under the PNRR and ANVUR AVA3 quality assurance frameworks. Accordingly, the study addresses the following research question: How can the integration of process mapping (BPMN 2.0) and discrete-event simulation contribute to improving the efficiency, transparency, and accountability of administrative processes in higher education institutions? The objective of the work is therefore to analyze and optimize the Supplementary Teaching Assignment (ADI) process at the University of Basilicata, developing a Process Innovation in Higher Education (PIHE) Framework that can be replicated and adapted across institutional contexts.

The paper is organized as follows. Section 2 presents an overview of the literature; the Business Process Reengineering of the case study is described in Section 3. Section 4 provides the numerical results of the simulation scenarios conducted. Finally, conclusions and future research paths are drawn in Section 5.

2. Overview of the Literature

The literature review was conducted between January and March 2024, following a structured and transparent search strategy to ensure replicability. The primary academic databases consulted were Scopus, Web of Science, and Google Scholar. Keywords and Boolean combinations included: “digital transformation” AND “higher education”, “business process reengineering” AND “university administration”, “BPMN”, “discrete-event simulation”, and “process optimization in education”. The search was limited to peer-reviewed journal articles, conference proceedings, and institutional reports published between 2010 and 2024, written in English, and directly addressing process innovation or digitalization in higher education contexts. Studies were selected based on their relevance to three thematic domains: (i) digital transformation and organizational change in universities, (ii) process modeling and reengineering methodologies, and (iii) simulation and decision-support tools applied to academic or administrative processes.

The literature on digital transformation in higher education emphasizes the need for integrating process management, technological innovation, and accountability mechanisms to achieve sustainable institutional improvement. Recent studies have examined the strategic and operational implications of digitalization, identifying both challenges and opportunities for universities worldwide [18,19]. These works converge on the view that digital transformation requires not only technological upgrades but also the reengineering of administrative workflows to ensure data reliability, transparency, and measurable performance outcomes [10,11]. Quality assurance (QA), digital transformation, and business process management (BPM) constitute key pillars in the ongoing modernization of higher education. Aldhobaib’s [20] qualitative ethnographic investigation explores how QA is interpreted, enacted, and contested within higher education institutions (HEIs), revealing not only investments but also notable stakeholder misalignments and divergent understandings of “quality”. This underscores the need for coherent, stakeholder-aligned processes to strengthen QA systems. Digitalization in academic administration is often realized through structured BPM approaches. Ammirato et al. [1] offer a pioneering case study from an Italian public university, demonstrating that mapping and redesigning workflows via BPMN 2.0 and applying AS-IS/TO-BE comparisons foster measurable improvements in process quality and organizational change management. Parallel efforts in QA innovations are addressed by [21], who developed and evaluated a digital application for managing assessment quality. Using a design-based research framework and stakeholder-informed dashboards, they enhanced transparency and monitoring of educational assessments.

Renna and Izzo [22] previously implemented BPM simulation tools at the University of Basilicata, modeling core administrative workflows and employing simulation to identify performance improvements. Their approach resonates strongly with the simulation-driven BPR paradigm, showing how data-oriented redesign can optimize academic processes. Complementing these concepts, Krimpizi et al. [23] conducted a systematic review of barriers to digital transformation in HEIs, identifying six major obstacle categories—including environmental, strategic, cultural, and technological constraints—thus emphasizing the multifaceted challenges of implementing digital reforms.

Renna and Colonnese [24] detail a simulation-driven Business Process Reengineering (BPR) framework applied at the University of Basilicata to optimize teaching assignment workflows. The study employs a two-tier methodology: mapping current (“AS-IS”) processes via BPMN 2.0 and redesigning (“TO-BE”) using discrete-event simulation (Simul8^®). Key improvements include automated approvals, dynamic resource allocation, and enhanced communication protocols. Results show a 35% reduction in end-to-end processing time and a 22% increase in administrative staff utilization, achieved while maintaining regulatory compliance. The case highlights how simulation-driven BPR can increase operational agility, transparency, and stakeholder accountability in higher education settings.

Recent studies have extended this discourse into new domains of QA and digital education. Nguyen et al. [25] conducted a bibliometric analysis of QA in distance higher education, mapping publication trends, geographic contributions, and emerging themes in remote learning environments. Their study reveals how QA priorities evolve in response to the expansion of online and blended modalities, highlighting gaps in global consistency. Mukhatayev et al. [26] explored QA systems in Kazakhstan, identifying critical weaknesses in course content, staff infrastructure, and governance mechanisms. Their findings emphasize the importance of tailoring QA frameworks to national and institutional contexts rather than adopting one-size-fits-all models.

On the technological side, Slade [27] evaluated the implementation of a digital assessment platform in an Australian university. Their study integrated pedagogical, technical, and organizational dimensions, demonstrating that effective QA in assessment requires alignment across multiple domains. In parallel, Sauerwein et al. [28] introduced a success model for automated programming assessment systems (APASs), based on survey data from 414 students. Their work identifies key drivers of user satisfaction and system performance, showing the critical interplay between usability and pedagogical effectiveness in formative assessments. Finally, Garousi et al. [29] mapped the literature on software-testing education, highlighting pedagogical challenges and lessons learned from integrating technology into instructional design. While outside the strict QA domain, their work parallels higher education’s broader efforts to apply structured, technology-driven approaches to process and curriculum design.

Collectively, these studies reinforce the notion that QA in higher education cannot be disentangled from digital transformation, BPM, and simulation approaches. They also suggest that future research should bridge institutional, national, and technological perspectives to build QA systems that are adaptable, transparent, and resilient in both traditional and digital learning environments.

Despite the significant contributions of existing studies, several limitations remain evident. First, many studies adopt a fragmented perspective—focusing either on stakeholder perceptions of QA [20,26], or on specific technological applications such as digital assessment platforms [21,27,28]—without fully integrating organizational, technological, and regulatory dimensions into a holistic framework. Second, while bibliometric and systematic reviews [23,25] provide comprehensive mappings of barriers and trends, they stop short of empirically testing structured improvement methodologies in real university settings. Third, BPM and simulation-based approaches [1,22] have demonstrated promise, but their applications are still limited to isolated case studies and often lack a direct link to quality assurance imperatives.

Against this backdrop, the research by [24] offers a novel contribution by operationalizing a simulation-driven Business Process Reengineering (BPR) framework within the concrete context of a European public university. By integrating BPMN 2.0 process modeling with discrete-event simulation (Simul8^®), the study moves beyond descriptive or conceptual accounts and provides quantitative evidence of efficiency gains, compliance assurance, and resource optimization. This dual methodological approach addresses both structural and technological challenges, enabling ex-ante evaluation of proposed reforms before real-world implementation. In doing so, the research advances the literature (see

Table 1. Limits of Previous Research and Contributions of the Proposed Study.

Limits of Previous Research	Contribution of This Study
Fragmented perspectives: studies often address either stakeholder perceptions (Aldhobaib, 2024; Mukhatayev et al., 2024) [20,26] or digital tools [21,27], without integrating organizational, technological, and regulatory dimensions.	Provides a holistic framework that combines process modeling, simulation, and QA objectives, bridging technological and organizational perspectives.
Reviews and bibliometric analyses [23,25] map barriers and trends but lack empirical validation in university contexts.	Demonstrates a real-world case study at the University of Basilicata, offering quantitative evidence of process efficiency and compliance.
BPM applications in HEIs [1,22] remain isolated case studies, often detached from QA imperatives.	Integrates BPM with quality assurance priorities, showing how Business Process Reengineering (BPR) can directly support institutional QA systems.
Limited use of simulation in higher education administration, with few attempts to test improvements before implementation.	Applies discrete-event simulation (Simul8®) to validate TO-BE processes ex-ante, reducing risk and ensuring evidence-based decision-making.
Lack of replicable methodologies that HEIs can adopt for digital transformation and QA.	Provides a replicable and transferable methodological framework for other universities, enhancing agility, transparency, and accountability.

for the main limits previous works and contributions of this work) in three key ways:

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Bridging conceptual and operational gaps by linking QA objectives with BPM and simulation-based tools.

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Providing empirical evidence from a real case study that demonstrates measurable efficiency improvements in teaching assignment workflows. Offering a replicable framework that HEIs in different contexts can adapt to improve agility, transparency, and stakeholder accountability while navigating regulatory complexity. Thus, the proposed study not only consolidates prior insights on QA and digital transformation but also introduces an actionable, evidence-based pathway for universities seeking to align academic administration with principles of efficiency, quality, and digital innovation.

3. Process Mapping of the Case Study

This research adopts a single-case study methodology, following the logic of analytical generalization typical of organizational and management studies. The University of Basilicata was selected as a representative medium-sized public university within the Italian higher education system, characterized by uniform transparency and quality assurance regulations. This setting allows for an in-depth exploration of process complexity and institutional constraints, which would be difficult to capture through multi-case or survey-based designs. The case thus serves an instrumental purpose: to develop and validate the PIHE Framework as a transferable methodology that other universities can adapt to their specific contexts.

The activation of Supplementary Teaching Activities (ADI) at the University of Basilicata is governed by a structured, multi-phase workflow that involves both academic and administrative bodies. The process begins when a faculty member identifies the need for supplementary teaching within a course and formally submits a request. This request is first reviewed by the Department Director and then by the Department Council, which assesses its relevance and necessity. If approved, a public call for applications is launched, and a designated Evaluation Committee carries out a comparative assessment of candidates to ensure transparency, fairness, and compliance with regulatory requirements.

Once the selection procedure is concluded, the committee’s report is validated by the Department Council, which formally assigns the teaching role. The Academic Affairs Office then manages the contractual phase, ensuring legal and transparency compliance, while the Accounting and Payroll Offices oversee the financial aspects. These include verifying fiscal documentation, adjusting budget allocations according to tax status, and completing payment once the teaching activity has been delivered and all supporting documents are submitted.

The end-to-end workflow is illustrated in the following tables (

Table 2. Phase 1. Activation and Approval.

Professor/Faculty	Accounting Office	Didactics Office	Director	Department Council	Evaluation Committee	Candidate/ Winner
PHASE 1: Activation and Approval
1. Fills out the ADI request form
2. Requests fund verification.	3. Verifies fund availability, completes and signs the relevant section of the form.
(Completed form)
4. Digitally signs and sends the form to the Director and, for information (c.c.), to the Didactics Office.		5. Checks the correctness of the information on the form	6. Evaluates the appropriateness of the request.
		7. Protocols the request and prepares the documentation for the Department Council.	8. Adds the item to the agenda of the next available meeting.	9. Deliberates to activate the public selection procedure.
			10. Proposes the composition of the Evaluation Committees annually.	11. Approves the composition of the Evaluation Committee

,

Table 3. Phase 2. Public Selection Process.

Professor/Faculty	Accounting Office	Didactics Office	Director	Department Council	Evaluation Committee	Candidate/ Winner
PHASE 2: Public Selection Process
		12. Prepares the call for selection and the related template for the PICA platform.	13. Digitally signs the decree announcing the selection.
		--> (Signed call for selection)
		14. Publishes the call on the Department’s website using ARIADNE.
		15. Transmits the call to the Document Management Center for publication.

,

Table 4. Phase 3. Applications and Evaluation.

Professor/Faculty	Accounting Office	Didactics Office	Director	Department Council	Evaluation Committee	Candidate/ Winner
PHASE 3: Applications and Evaluation
		16. Downloads and 17. collects the submitted applications.
		18. Evaluates the legitimacy and eligibility of each application.
		[DECISION] Is the candidate eligible? If NO:
		19. Prepares the exclusion decree.	20. Signs the exclusion decree.
		21. Publishes the exclusion decree on the Department website and transmits it for publication on the Albo on-line. (end of process for ineligible candidate).
		If YES: 22. Shares the eligible applications with the competent Evaluation Committee.
					23. Examines the applications and formulates a motivated proposal for the assignment.
		<-- (Report with proposal)			24. Drafts and transmits the evaluation report to the Didactics Office.
		25. Protocols the report and prepares a memorandum for the Director.

,

Table 5. Phase 4: Appointment and Contract.

Professor/Faculty	Accounting Office	Didactics Office	Director	Department Council	Evaluation Committee	Candidate/ Winner
PHASE 4: Appointment and Contract
		26. Submits the Committee’s proposal to the Department Council.
			27. Deliberates the formal conferral of the assignment to the winner.
		28. Publishes the selection results on the Department website and transmits them for publication on the Albo on-line.
		29. Sends an email communication to the winner, requesting documents for transparency compliance if necessary.
		30. (If applicable) Requests authorization from the winner’s home institution.				30_b. Fills out authorization and returns it.
		31. Prepares the contract and sends it to the winner for signature.
						32. Fills out declarations, digitally signs the contract, and returns it.
		<-- (Contract signed by winner)
			33. Digitally signs the contract and attests to the absence of conflicts of interest.
		34. Protocols and registers the contract in the repertory using TITULUS.
		35. Sends the finalized contract to the appointee with an informational memo.

and

Table 6. Phase 5: Performance and Liquidation.

Professor/Faculty	Accounting Office	Didactics Office	Director	Department Council	Evaluation Committee	Candidate/ Winner
PHASE 5: Performance and Liquidation
						36. Conducts the lessons and completes the Lesson Log.
		<-- (Signed Lesson Log)
		37. Verifies the consistency of the hours declared in the Log.	38. Approves and signs the Lesson Log after verification.
		39. Acquires the necessary liquidation forms from the appointee.				40. Fills out and sends the fiscal forms for payment.
		41. (If necessary) Asks the Accounting Office to modify/integrate the initial fund reservation.
		-> (Modification request)	<- (Modification note)
		42. Prepares the liquidation request note and all related documentation.	43. Signs the liquidation request.
		44. Transmits all documentation to the Stipends Office via TITULUS.
		45. (If necessary) Asks the appointee to issue an electronic invoice and notifies the Accounting Office.				46.(If necessary) Issues and sends an electronic invoice.
		<-- (Invoice in PDF)
		47. (If necessary) Forwards the invoice to the Stipends Office to proceed with the final payment.

), which detail the activities, actors, and interactions across five sequential phases:

• Phase 1—Activation and Approval: Request initiation, verification of funds, and Council approval.
• Phase 2—Public Selection Process: Preparation, approval, and publication of the call for applications.
• Phase 3—Applications and Evaluation: Collection of applications, eligibility screening, and committee evaluation.
• Phase 4—Appointment and Contract: Formal assignment, contract drafting, and signature procedures.
• Phase 5—Performance and Liquidation: Delivery of teaching, verification of activities, and final payment.

The main actors (or swim lanes) involved in the workflow include:

• Professor/Faculty: initiates the request.
• Director of Department: provides approval and legal signature.
• Department Council: governing body responsible for formal deliberations.
• Didactics Office: coordinates administrative steps throughout the process.
• Evaluation Committee: evaluates applications and proposes assignments.
• Candidate/Winner: external applicant selected through the competitive process.
• Accounting and Payroll Offices: manage budget verification and payments.
• Document Management Center: ensures proper publication of official documents.

To complement the process mapping, the entire procedure was also modeled using discrete-event simulation in Simul8^®. The simulation replicates the sequential phases, decision nodes, and interdependencies among actors as specified in the BPMN 2.0 diagrams (Figure 1 and Figure 2). By incorporating parameters such as approval times, document-handling delays, and workload distribution, the model enables the testing of alternative “to-be” scenarios under controlled conditions.

This approach transforms descriptive process mapping into a predictive analytical tool, allowing the assessment of key performance indicators such as process lead time, resource utilization, and bottleneck identification. The integration of simulation supports evidence-based decision-making.

To complement the process mapping and workflow documentation, the end-to-end procedure for activating supplementary teaching activities was also implemented in a discrete-event simulation model using Simul8^®. The simulation reproduces the sequential phases, decision points, and interactions among the different actors identified in the BPMN 2.0 diagrams (see Figure 1 and Figure 2), allowing the dynamic behavior of the administrative process to be analyzed under varying conditions. It should be noted that the process models were conceptually developed using BPMN 2.0 notation to ensure standardization in representing activities, decision points, and actor swimlanes. However, the subsequent implementation was carried out in the SIMUL8^® simulation environment, which reproduces the logic and flow of BPMN processes using discrete-event simulation components rather than full BPMN graphical syntax. Therefore, the BPMN 2.0 diagrams serve as a conceptual blueprint for process mapping, while SIMUL8^® provides the operational platform for quantitative analysis. Simul8^® was employed as a discrete-event simulation environment to operationalize the process model defined through BPMN 2.0 diagrams. While BPMN 2.0 provided the conceptual representation of the workflow, Simul8^® enabled the dynamic replication of each process phase, incorporating time distributions, resource constraints, and decision nodes. This combination allows for the quantitative evaluation of alternative scenarios (AS-IS and TO-BE), thereby translating conceptual process mapping into an evidence-based decision-support tool. By incorporating parameters such as approval times, document handling delays, and workload distribution across administrative offices, the model enables the evaluation of alternative “TO-BE” scenarios in a controlled environment. This approach provides a powerful tool for conducting quantitative analyses—including performance indicators such as lead time, resource utilization, and bottleneck identification—thus supporting evidence-based decision-making before real-world implementation. In doing so, the simulation model bridges descriptive process mapping with predictive analytics, offering a robust methodological foundation for process optimization at the University of Basilicata. This research adopts a single-case study methodology, following the logic of analytical generalization typical of organizational and management studies. The University of Basilicata was selected as a representative medium-sized public university within the Italian higher education system, characterized by uniform transparency and quality assurance regulations. This setting allows for an in-depth exploration of process complexity and institutional constraints, which would be difficult to capture through multi-case or survey-based designs. The case thus serves an instrumental purpose: to develop and validate the PIHE Framework as a transferable methodology that other universities can adapt to their specific contexts.

4. Numerical Analysis

The discrete-event simulation model developed in Simul8^® represents the operational backbone of the numerical analysis, serving both as a diagnostic and a predictive tool. By faithfully replicating the administrative workflow of supplementary teaching assignments (ADI), the model enables the quantification of system performance under the existing configuration (“AS-IS”) and the assessment of redesigned configurations (“TO-BE”). Unlike purely qualitative descriptions or static flowcharts, the simulation provides a dynamic representation of the process, capturing delays, queuing effects, and interactions among multiple actors across sequential phases. The analysis is structured around a set of Key Performance Indicators (KPIs) that translate the complexity of the workflow into measurable outcomes. These KPIs allow for a direct comparison between current practices and proposed improvements, and they serve as evidence to guide managerial and organizational decision-making. Specifically, the study focuses on three core indicators:

• Lead Time: The total end-to-end duration of the ADI process, measured from the submission of the initial request by the faculty member to the final liquidation of the contract. This KPI highlights the responsiveness of the system and the potential impact of procedural bottlenecks on overall efficiency.
• Resource Utilization: The degree of workload absorption across administrative offices-particularly the Didactics Office, the Accounting Office, and the Payroll Office. Monitoring resource utilization helps to identify overburdened units, unbalanced task allocations, and areas where reallocation or digital support tools could improve process sustainability.
• Process Variability: The dispersion of completion times and workloads across different simulation runs. This indicator sheds light on the robustness of the process, revealing how sensitive outcomes are to variations in approval times, documentation accuracy, or staff availability.

By integrating these metrics, the simulation goes beyond a descriptive exercise: it supports what-if analyses, enabling the exploration of alternative scenarios such as workload redistribution, introduction of digital automation, or streamlining of approval steps. This capability allows the institution to test the effectiveness of interventions in a controlled environment before implementing them in practice, thereby reducing organizational risks and fostering evidence-based innovation. Based on the simulation model, we designed four scenarios to test the impact of meeting scheduling variability and resource availability on process performance. Several scenarios are considered to highlight how the entire process is affected by some parameters.

All the information related to process timing is reported in Appendix A, where each step corresponds to the activities described in Table 1, Table 2, Table 3, Table 4, Table 5 and Table 6. The simulation model was developed using minutes as the base time unit, with each process step decomposed into setup time (representing preparation or document handling) and processing time (representing effective task execution). The model was replicated multiple times to ensure statistical robustness, with the number of replications determined to achieve a 95% confidence level and a 5% confidence interval considering a generic ADI activity. This detailed parametrization guarantees the traceability and reproducibility of the simulation, allowing readers to verify model accuracy and compare the obtained results with other simulation-based studies on administrative process optimization in higher education.

Scenario 1 is the base case, and the information of the processing times are reported in Appendix A. The unit time is minute and each step consists of setup and processing time; the setup allows the simulation more realistic considering the delay due to other activities without affecting the real utilization of the resources.

Scenario 2 considers the impact of reducing the variability to define the meeting of the Department Council from UNIF [4200, 8400] base to UNIF [4200, 6300]. This reflects a managerial intervention through digital scheduling tools, aiming to shorten delays and improve predictability in approval steps.

Scenario 3 considers the impact of the reduction availability of the main resource (Didactics Office) of 75% with a Mean Time To Return of minutes with meeting of the Department Council at UNIF [4200, 8400] and UNIF [4200, 6300] for the Scenario 4. This simulates staff shortages or competing workloads, testing system resilience under constrained resources.

The performance measures considered are the following:

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The percentage average use of the main resources: Professor, Accounting office, Department Council, Evaluation committee, Candidate winner, and Didactics office.

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The average time until the ADI can be start (Start ADI.Average Time in System).

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The total average time until the transmission of the payment (Average Time in System).

The simulation experiment provides a quantitative assessment of the AS-IS process (Scenario 1) and three alternative TO-BE configurations (Scenarios 2–4). The analysis highlights how changes in meeting scheduling variability and in the availability of the Didactics Office affect both process efficiency and resource utilization. The numerical results are reported in

Table 7. Simulation Scenarios.

Scenario	1 (Base)	2	3	4
Didactics Office.Working%	36.68	39.72	32.68	34.98
Professor.Working%	2.15	2.33	1.93	2.07
Accounting Office.Working%	2.48	2.67	2.20	2.35
Departement Council.Working%	0.67	0.73	0.60	0.64
Evaluation Committee.Working%	4.69	5.12	4.19	4.53206
Candidate Winner.Working%	7.61	8.33	6.87	7.42
Average Time in System [min]	27,102.86	24,943.53	30,374.26	28,306.00
Start ADI.Average Time in System [min]	25,917.34	23,743.76	29,094.58	27,006.08

.

4.1. Scenario 1 (Base Case—AS-IS)

The baseline scenario shows that the Didactics Office absorbs the highest workload (≈36.7%), confirming its central role as process bottleneck. Other actors (Accounting Office, Department Council, Evaluation Committee) register low utilization rates (<5%), reflecting their punctual rather than continuous involvement. The end-to-end lead time of the process is approximately 27,100 min (about 18.8 days), with the ADI activity starting after ≈25,900 min. This result underscores the substantial administrative weight relative to the actual teaching activity.

4.2. Scenario 2 (Reduced Variability in Council Meetings)

By narrowing the distribution of Department Council meeting times (from UNIF [4200, 8400] to UNIF [4200, 6300]), the system exhibits a clear improvement: the average completion time decreases by about 8% (from 27,100 to 24,943 min). Resource utilization slightly increases across all actors (+2–3%), with the Didactics Office reaching ≈39.7%. This indicates that while efficiency gains are achieved in terms of lead time, the burden on the most critical office intensifies, potentially stressing its capacity in real operations.

4.3. Scenario 3 (Reduced Availability of Didactics Office—75%)

A simulated reduction in the Didactics Office’s availability drastically worsens performance. The office’s utilization drops to ≈32.7% (as expected, given reduced presence), but this results in a significant increase in completion time (+12% compared to the base case, ≈30,374 min). The ADI starting point is delayed by over 3000 min relative to Scenario 1. This scenario highlights the system’s vulnerability to resource shortages, confirming the Didactics Office as a structural bottleneck whose availability strongly determines overall performance.

4.4. Scenario 4 (Reduced Variability + Reduced Availability)

When both interventions are combined—reduced Didactics Office availability and tighter scheduling of the Department Council—the outcomes are intermediate. Lead time improves slightly compared to Scenario 3 (≈28,306 min vs. 30,374) but remains worse than both Scenarios 1 and 2. Utilization rates for all actors are also somewhat balanced, with the Didactics Office at ≈35% and other offices at values close to the base case. This demonstrates that meeting frequency optimization partially offsets resource unavailability but cannot fully compensate for it.

4.5. Comparative Insights and Potential Imrovements

Lead Time: Scenario 2 delivers the best performance, confirming the critical role of scheduling policies for decision-making bodies.

Resource Utilization: The Didactics Office consistently emerges as the most strained resource, suggesting the need for workload redistribution or digital support to reduce administrative congestion.

Robustness: Scenario 3 illustrates how system performance is highly sensitive to variations in Didactics Office availability, pointing to limited resilience of the current organizational design.

Overall, the simulation highlights two main leverage points for process improvement: (i) reducing procedural variability—particularly in decision-making bodies—has a clear and positive effect on system efficiency, while (ii) ensuring adequate availability of the Didactics Office is essential to prevent significant performance deterioration.

From the analysis of the process and numerical results,

Table 8. Proposed Improvements to the ADI Process.

Current Issue	Suggested Improvement	Expected Benefit	Simulation-Based Evidence
Fragmented communication via emails and multiple platforms for requests, calls, and results.	Implement a centralized digital workflow platform integrated with ESSE3 and the university’s document management system (TITULUS), to manage requests, calls, and outcomes in a single environment.	Reduced administrative burden, improved traceability, better compliance with transparency rules, enhanced user experience for faculty and candidates.	Resource utilization of the Didactics Office shows significant variability across scenarios (32.6% to 39.7%), indicating inefficiencies linked to fragmented communication.
Sequential workflow with long waiting times between phases (e.g., fund verification, Council approval, contract preparation).	Streamline process dependencies by enabling parallel execution of independent steps (e.g., fund verification and call preparation).	Shorter overall cycle time, increased process agility, less idle time between phases.	Scenario 2 (reduced variability in Council meetings) decreases average lead time from 27,102 min to 24,943 min (−8%), showing the impact of smoother scheduling and less sequential waiting.
Lack of real-time visibility for faculty, candidates, and staff.	Develop a process monitoring dashboard (integrated with ESSE3 or intranet services) to track each request’s progress.	Increased transparency, fewer status inquiries, improved accountability and user satisfaction.	Process variability analysis highlights high dispersion in completion times, confirming the need for real-time tracking to reduce uncertainty for stakeholders.
Manual handovers between Didactics, Accounting, and Payroll Offices for payment requests.	Introduce automated workflows linking teaching register validation with payment requests in the financial system (U-GOV/accounting).	Faster, more reliable payments, reduced manual workload for administrative staff.	Average lead time until payment remains high in all scenarios (>24,900 min), suggesting that automation of payment phases could shorten completion times.
Unpredictable workload peaks during academic periods.	Adopt predictive workload planning using historical data and simulation insights to anticipate critical periods and adjust staffing.	More balanced workload distribution, fewer bottlenecks, improved service continuity during peak times.	Scenario 3 (75% availability of Didactics Office) increases lead time to 30,374 min (+12%), highlighting its vulnerability as a single point of failure.
Unpredictable workload peaks tied to didactic planning.	Align workload planning with the definition of the ordinary teaching offer, consolidating ADI needs from all Study Programs into a single call.	Single call for all supplementary teaching activities, more concentrated workload, reduced process times, improved coordination with study programs.	Centralizing calls would reduce repeated approval cycles; simulation results confirm that long Council scheduling times strongly affect lead time, which could be minimized by unified planning.
Absence of systematic performance monitoring.	Define and monitor key performance indicators (KPIs) such as average approval time, contract cycle time, and payment delays, with regular reporting to QA bodies.	Continuous process improvement, alignment with national quality assurance standards (ANVUR, AVA3).	Simulation-based KPIs (lead time, resource utilization, variability) demonstrate the feasibility of monitoring process performance continuously.

proposes some improvements for the process.

The simulation results obtained in this study are consistent with prior research on process reengineering and digital transformation in higher education. For instance, ref. [1,22] reported measurable efficiency gains after applying BPMN-based redesign in Italian universities, confirming that structured process modeling contributes to reducing administrative delays and enhancing quality assurance. Similarly, ref. [16] emphasized that Total Quality Management frameworks in universities benefit from systematic process mapping and digital integration. However, as also noted by [3,23], process improvements are sustainable only when supported by appropriate organizational culture, communication, and leadership strategies. The findings of the present study reinforce this view: while simulation-based optimization identifies potential efficiency gains, their realization requires employee engagement, digital literacy, and effective change management. Overall, the proposed PIHE Framework advances the literature by bridging process modeling, simulation, and transparency objectives, while acknowledging the socio-organizational enablers of successful transformation in higher education institutions.

5. Conclusions and Future Development Paths

The study set out to analyze and optimize the process of assigning complementary teaching (ADI) at the University of Basilicata through an integrated approach that combines process mapping, simulation-based analysis, and the implementation of specific digital interventions. At the same time, the research aimed to develop a replicable methodological framework—the Process Innovation in Higher Education (PIHE) Framework—capable of supporting process redesign and transparency in higher education institutions. The numerical analysis provided robust evidence supporting both objectives. The simulation outcomes confirmed that the Teaching Office consistently absorbs the largest workload, accounting for approximately 33–40% of total process time, thereby acting as the main bottleneck within the ADI assignment procedure. Furthermore, the variability associated with the scheduling of the Departmental Council proved to be a critical determinant of process duration. Optimizing the frequency of meetings in Scenario 2 led to an average reduction of about 8% in total completion time, highlighting the potential of predictive process planning in improving operational efficiency. These results validate the initial research hypothesis that process-oriented digital transformation can significantly enhance efficiency and transparency in administrative workflows when supported by quantitative simulation and performance monitoring. In this sense, the PIHE Framework demonstrated its capacity to translate organizational knowledge into measurable process improvements, providing a structured methodology that aligns institutional objectives, data-driven decision-making, and accountability mechanisms. The simulation results obtained in this study are consistent with prior research on process reengineering and digital transformation in higher education. For instance, ref. [1,22] reported measurable efficiency gains after applying BPMN-based redesign in Italian universities, confirming that structured process modeling contributes to reducing administrative delays and enhancing quality assurance. Similarly, ref. [16] emphasized that Total Quality Management frameworks in universities benefit from systematic process mapping and digital integration.

However, as also noted by [3,23], process improvements are sustainable only when supported by appropriate organizational culture, communication, and leadership strategies. The findings of the present study reinforce this view: while simulation-based optimization identifies potential efficiency gains, their realization requires employee engagement, digital literacy, and effective change management.

Overall, the proposed PIHE Framework advances the literature by bridging process modeling, simulation, and transparency objectives, while acknowledging the socio-organizational enablers of successful transformation in higher education institutions.

Although the empirical evidence derives from a single institution, the PIHE Framework is inherently transferable. Its structure—combining process mapping, simulation-based validation, and KPI-driven monitoring—can be adapted to other higher education institutions operating under diverse administrative and regulatory conditions, as confirmed by similar initiatives in other European universities (e.g., [1,16]).

While the present study primarily assessed efficiency and transparency improvements through process mapping and simulation, the success of such initiatives in practice also depends on organizational factors, including staff engagement, change readiness, and institutional culture. As noted in previous studies (e.g., [3,23]), digital transformation in universities requires not only technological tools but also a supportive organizational climate and effective change management. Future applications of the PIHE Framework should therefore integrate training, participatory design, and communication strategies to address potential resistance and ensure sustainable adoption.

Future research should expand this line of inquiry by applying the framework to additional university processes—such as student enrollment, exam management, or research project administration—and by integrating predictive analytics and artificial intelligence into process monitoring. In doing so, higher education institutions will be better positioned to navigate the dual imperatives of modernization and accountability, consolidating digital transformation as a cornerstone of sustainable governance.