Business process analysis of university admissions: Combining TM Forum’s eTOM framework, discrete-event simulation, and queuing theory

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1. Introduction The organization of university admissions represents a complex and challenging task that requires the integration of various administrative, academic, and regulatory processes [1]. This process involves several stages, including document verification, entrance exams, applicant ranking, and final enrollment, all governed by strict deadlines and compliance requirements. With the advent of digital platforms like Public Services Portal of the Russian Federation, universities face increasing pressure to align their internal processes with these government platforms [2]. This creates a dual imperative for both efficiency and transparency in the admissions process. However, due to the lack of standardized methods for modeling and optimizing workflows, different institutions often adopt fragmented approaches that fail to scale during high-volume admission periods. The need to modernize admissions processes is emphasized by the increasing competition among universities for students [3]. For instance, at RUDN University, delays in document verification during the 2023 admissions cycle resulted in a 22% dropout rate among applicants, directly affecting institutional revenue and reputation. Globally, similar issues persist, with manual processes and isolated departmental operations contributing to inefficiencies. A 2022 UNESCO report found that 65% of universities in emerging economies lack digitalized admission systems, leading to extended processing times and dissatisfied applicants [4]. Addressing these challenges is crucial not only for institutional competitiveness but also for integrating higher education into broader digital transformation initiatives [5]. Current approaches to admissions optimization, such as BPMN (Business Process Model and Notation) for workflow visualization or retrospective statistical analyses, offer limited predictive power for complex, high-volume scenarios. While industry frameworks like ITIL (Information Technology Infrastructure Library) are effective in IT service management, they lack domain-specific adaptations for educational institutions [6]. Crucially, these methods do not provide quantitative tools for identifying real-time bottlenecks or allocating resources during peak loads. Moreover, the lack of standardized reference processes hinders cross-institutional benchmarking and perpetuates inefficiencies. To address these gaps, our study combines three complementary methodologies: the TM Forum’s Business Process Framework (also known as eTOM) [7], a hierarchical process map designed for telecommunications operations [8-10]; discrete-event simulation (DES), a computational technique that models process flows as sequences of events; and queuing network analysis, a mathematical framework that quantifies service delays, resource utilization, and bottlenecks [11, 12]. By adapting eTOM for university admissions, we aim to bridge the gap between educational process engineering and advanced computational modeling [13, 14]. This study aims to establish a unified reference framework and provide actionable insights for optimizing admissions processes based on data from RUDN University [15-17]. The main contributions of our study are as follows: o An adaptation of the eTOM framework for university admissions, which maps activities of the university admissions process to level 2 and 3 eTOM processes. o A DES model that allows for scenario testing under different load conditions, such as applicant surges. o A queuing network model that can be used for analytical analysis of the university admissions process, particularly during peak periods. The rest of the paper is organized as follows. Section 2 deconstructs RUDN University’s current admissions process and identifies potential pain points. Section 3 aligns these processes with eTOM, providing a framework for best practices. Section 4 describes the simulation design and analysis. Section 5 presents the queuing model. Section 6 concludes with a discussion of policy implications, limitations, and future directions for research. 2. Business process of university admissions The organization of university admissions is a critical and complex process that requires the seamless integration of various administrative, academic, and regulatory processes. The goal of this process is to evaluate and admit applicants while ensuring compliance with strict deadlines, standards, and institutional priorities. Drawing on the example of RUDN University, this section will outline the Terentyev, K. M. et al. Business process analysis of university admissions: Combining TM Forum’s … 159 image general structure of the admissions process. This structure can be applied to higher education institutions at various levels (bachelor’s, master’s, and doctoral programs) and with different funding models (public - state-funded or private - contract-based). The admissions process begins with document submission and verification. Applicants provide essential materials, such as academic records, identification documents, and test scores. For Russian institutions, this includes the Unified State Exam (USE) or internal entrance exams specific to the university. Admission officers carefully review these documents to verify their authenticity, completeness, and compliance with program requirements. Any discrepancies or missing information trigger notifications to the applicant, requiring revisions or additional documentation. This stage can be time-consuming, as manual verification of physical documents is still common, particularly in institutions without fully digital processes. Delays at this step can impact subsequent stages, creating bottlenecks in the admissions timeline. After document verification, applicants may be required to take additional assessments, such as university-specific entrance exams, for competitive programs like medicine, engineering, or the creative arts. For programs that rely on the USE, results are automatically retrieved from federal databases. Internal exams are administered and graded by departmental faculty, and the outcomes are compiled into ranked lists that determine eligibility for state-funded or contract-based enrollment. A critical challenge at this stage is synchronizing data from different sources, including federal systems for USE scores and internal databases for exam results. Manual updates to spreadsheets with applicant information can also cause discrepancies and delays in data integration, leading to inaccuracies in rankings and disadvantages for applicants. These issues can strain institutional credibility. The final stage, eligibility evaluation and enrollment, involves formalizing admission decisions and executing administrative procedures. Successful candidates are notified of their placement and asked to submit original documents in order to confirm their enrollment. For candidates funded by the state, this process is often straightforward, depending on meeting the published score thresholds. However, contract-based candidates need to go through additional steps, such as negotiating and signing tuition agreements, processing payments, and issuing enrollment orders. These orders are then published on institutional platforms and sent to federal education systems to finalize the applicant’s status as a student. Despite its clear process, this phase can be hindered by last-minute withdrawals, delayed submissions of documents, and resource constraints, especially during peak enrollment times. While the admissions process is well-structured, its implementation reveals several systemic vulnerabilities that are common across higher education institutions. Manual workflows: A heavy reliance on paper-based document checks and manual data entry lengthens processing times and increases error rates. Disconnected IT systems: Exam results, applicant profiles, and financial agreements are stored in separate systems, making it difficult to track the end-to-end process. Annual applicant surges: Exceeding 40,000 applicants at RUDN University overwhelm existing staff and infrastructure, leading to delays and dissatisfaction among applicants. Regulatory requirements: Evolving privacy laws and reporting standards demand continuous adjustments to the admissions process, but often without corresponding resource allocations for compliance. These challenges highlight the need for a comprehensive re-evaluation of admission processes, balancing efficiency and transparency. The following sections of this paper address these issues through a structured approach, combining process standardization, computational, and mathematical modeling. By breaking down the process into its individual stages, this work aims to provide a repeatable framework for universities to navigate the complexities of contemporary student recruitment and enrollment. 160 Computer science DCM&ACS. 2025, 33 (2), 157-171 image 3. Applying the eTOM framework In this section, we formalize the university admissions process using the TM Forum’s eTOM, a hierarchical framework for standardizing business operations. By mapping RUDN University’s workflows to the eTOM’s level 2-3 processes, we have established a reference model for benchmarking and optimizing cross-institutional operations. 1. Process with detailed activities The admissions process consists of 14 steps, each with a unique identifier to align with the eTOM hierarchical structure (see Figure 1). These steps are numbered and described below, along with concise titles that are used in Table 1. § Step 1: Online Admission Portal. Prospective students access RUDN University’s online admission portal to create a personal account, submit an application, and track the progress. This digital platform provides guidelines, application deadlines, and automatic validation for required documents such as diplomas and identification. § Step 2: Student Guidance & Career Counselling. Specialized advisors assist students in choosing programs that align with their academic profile and career goals. They help clarify admission criteria, scholarship opportunities, and pathways after graduation. § Step 3: Application Submission & Data Verification. Students upload personal information and relevant documents through the platform. Admissions staff verify the completeness of the data and initiate automatic background checks (such as plagiarism detection for admission essays). § Step 4a: Document Verification and Notification (Manual). After completing all steps, students receive a notification about the status of their application. If everything is in order, they are invited to the next stage of the admission process. Officers manually verify physical or scanned documents (such as diplomas and exam certificates) to ensure their authenticity. Applicants are notified automatically about acceptance or rejection via email or SMS. § Step 4b: Entrance Exam Scheduling. If a program requires internal exams (for example, medicine), the system creates personalized schedules based on applicant preferences and faculty availability. § Step 5a: Exam Administration. Exams are proctored either on-site or remotely. Results are digitized and stored in the RUDN University database. Applicants are notified about the results within 48 hours through automated alerts. § Step 5b: Result Integration. The USE results are retrieved from federal systems and combined with internal scores to create a unified applicant profile. § Step 6a: Eligibility Evaluation. Applicants are ranked based on their composite scores using algorithms. Their scores are compared to program-specific admission thresholds for budget and contracts. § Step 6b: Document Request. Candidates qualifying for budget seats are required to submit original diplomas within 7 days. If they fail to comply, they will be shifted to contract-based pools. § Step 7: Contract Offer for Non-Qualified Applicants. Applicants who do not meet the budget threshold will receive formal offers for enrollment based on a contract, detailing tuition fees, payment plans, and academic requirements. § Step 8: Contract Review & Signature. Legal teams will draft contracts, which applicants will review and electronically sign via a secure platform. Digital signatures will be timestamped and archived. Terentyev, K. M. et al. Business process analysis of university admissions: Combining TM Forum’s … 161 image image Figure 1. University admissions detailed activities on the eTOM framework 162 Computer science DCM&ACS. 2025, 33 (2), 157-171 image Mapping of university admissions detailed activities to eTOM level 2 processes Table 1 No. step eTOM level 2 process University admissions detailed activities 1 Customer Interaction Management Online Admission Portal 2 Selling Student Guidance & Career Counselling 3 Customer Relationship Management Application Submission & Data Verification 4a Customer Interaction Management Document Verification and Notification (Manual) 4b Customer Interaction Management Entrance Exam Scheduling 5a Resource Order Management Exam Administration 5b Resource Order Management Result Integration 6a Customer Order Processing Eligibility Evaluation 6b Customer Order Processing Document Request 7 Customer Relationship Management Contract Offer for Non-Qualified Applicants 8 Selling Contract Review & Signature 9 Customer Bill Management Invoice Creation & Payment 10 Customer Receivables Management Admitted Student List Preparation 11 Customer Order Processing Enrollment Preparation 12 Customer Information Management Enrollment Finalization 13 Customer Interaction Management Enrollment Publication 14 Resource Data Management Enrollment Archiving § Step 9: Invoice Creation & Payment. Systems will automatically generate invoices reflecting the contractual terms, which will be processed through integrated banking gateways with real-time updates on payment status. § Step 10: Admitted Student List Preparation. Staff will compile final lists of enrolled students, generating unique enrollment orders for federal reporting purposes. § Steps 11-14: Publication & Archiving of Enrollment Records. Enrollment records will be published and archived according to the established process. Orders are published on the RUDN University website, emailed to applicants, and stored in accordance with data retention policies. Terentyev, K. M. et al. Business process analysis of university admissions: Combining TM Forum’s … 163 image 2. Mapping to eTOM framework Figure 1 shows the end-to-end alignment of RUDN University’s admissions process with the eTOM’s level 2 processes. Table 1 provides a detailed mapping, showing how each university activity (Steps 1- 14) corresponds to eTOM’s standardized categories (for example, Customer Interaction Management and Resource Order Management). Note that Steps 4a-5b (Document Verification to Exam Result Integration) correspond to eTOM’s Resource Order Management, highlighting the importance of resource coordination. Steps 7-9 (Contract Proposal to Invoice Generation) reflect the focus on Customer Relationship Management, which is centered around the applicants. Parallel processes such as budget/contract enrollment are unified under the hierarchical levels of eTOM, allowing for scalable optimization. 4. Discrete-event simulation model In this section, we describe the development and results of a DES model that was designed to analyze the efficiency of the admissions process at RUDN University. By translating the 14-step process of the institution into a computationally manageable model, the simulation identified bottlenecks, quantified delays, and proposed data-driven optimization strategies. 0. Process with aggregated activities To strike a balance between granularity and computational feasibility, the original eTOM-aligned process (see Section 3) was simplified into seven aggregated stages (Table 2). This simplification prioritized tasks that depend on staff, excluding automated steps or those that are driven by applicants (e.g., document uploads and email notifications). For instance, Stages 1-4a of the original process, which involved document verification and notifications to applicants, were combined into Stage A (Document Processing) to reflect the shared responsibility of admission officers. The normalized BPMN model (Figure 2) was implemented using the BIMP platform [18], selected for its ability to simulate stochastic events and scale to 10,000 concurrent users, which represents 25% of RUDN University’s 2023 applicant volume due to free-tier constraints. Input parameters were derived from historical data. Activity durations followed normal or exponential distributions, calibrated to 2023 operational timestamps (Table 3). Branching probabilities (Table 4) governed decision points, such as document verification success (92.61%) or budget eligibility (25.6%). Resource limits mirrored actual staffing levels, with 15 admissions officers and 8 commercial team members. 1. Numerical results The simulation replicated a scaled-down version of RUDN University’s 2023 admission process, revealing critical inefficiencies. The average admission process took 14.2 days, and Stage A (Document Processing) accounted for 68% of the delays (Figure 3). Manual verification of physical documents, modeled as

About the authors

Konstantin M. Terentyev

RUDN University

Email: terentyev-km@rudn.ru
ORCID iD: 0009-0005-4379-3017

Ph.D. student of the Department of Probability Theory and Cyber Security; Director of the Department of Digitalization and Digital Transformation

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Leyla D. Abuzyarova

RUDN University

Email: d.abuzyarova@yandex.ru
ORCID iD: 0009-0007-0084-0201

M.Sc. student of the Department of Probability Theory and Cyber Security

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Irina A. Kochetkova

RUDN University; Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Email: kochetkova-ia@rudn.ru
ORCID iD: 0000-0002-1594-427X
Scopus Author ID: 35332169400
ResearcherId: E-3806-2014

Candidate of Physical and Mathematical Sciences, Assosiate Professor of the Department of Probability Theory and Cyber Security, RUDN University; Senior Researcher, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation; 44-2 Vavilova St, Moscow, 119133, Russian Federation

Konstantin E. Samouylov

RUDN University; Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Author for correspondence.
Email: samuylov-ke@rudn.ru
ORCID iD: 0000-0002-6368-9680
Scopus Author ID: 14009785000
ResearcherId: E-9966-2014

Professor, Doctor of Technical Sciences, Head of the Department of Probability Theory and Cyber Security, RUDN University; Senior Researcher, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation; 44-2 Vavilova St, Moscow, 119133, Russian Federation

References

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