AHP-Based Framework for Optimizing, and Decision-Support in Enterprise Architecture Implementation

Sharifi, Hadi; Akhavan-Safar, Mostafa; Sadr, Mohammad Mohsen

doi:10.30473/coam.2025.74499.1308

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	AHP-Based Framework for Optimizing, and Decision-Support in Enterprise Architecture Implementation
Control and Optimization in Applied Mathematics
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 07 دی 1404 اصل مقاله (457.88 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2025.74499.1308
نویسندگان
Hadi Sharifi¹؛ Mostafa Akhavan-Safar^* ²؛ Mohammad Mohsen Sadr²
¹Faculty of Engineering, Department of Computer Engineering, Imam Reza International University, Mashhad, Iran
²Department of Computer and Information Technology Engineering, Payame Noor University (PNU), Iran
چکیده
Enterprise architecture (EA) offers an integrated framework for strategic planning and organizational governance. Implementing EA effectively requires prioritizing a concise set of criteria within a complex system, leveraging mathematical modeling and optimization to inform decisions under uncertainty. This study introduces a hierarchical decision-making approach using Analytic Hierarchy Process (AHP) to extract and weight the most impactful criteria from an extensive literature base and expert opinions, with a focus on control-theoretic and optimization perspectives. Using insights from 18 experts from various fields and the proposed approach, key criteria of successful enterprise architecture deployment were identified and quantified: commitment (0.1143), governance (0.1082), infrastructure (0.0751), organizational management (0.0589), and senior management support (0.0484). The methodology integrates weights with objective-function considerations, sensitivity analyses, and optimization-oriented interpretations to ensure robust prioritization under uncertainty. The resulting framework supports decision-makers in (i) controlling and steering EA initiatives, (ii) optimizing resource allocation and process efficiencies, and (iii) designing data-driven, scenario-based decision models for dynamic organizational environments. These findings offer actionable guidance for managers aiming to enhance performance, reduce costs, and secure competitive advantage through disciplined governance, rigorous modeling, and evidence-based decision support.
تازه های تحقیق
Proposes a structured, optimization-oriented decision framework for enterprise architecture (EA) implementation that integrates Analytic Hierarchy Process (AHP) with control-theoretic and objective-function considerations under uncertainty. Synthesizes evidence from an extensive literature review and expert judgment (18 domain specialists) to systematically extract, screen, and prioritize EA success criteria within a hierarchical decision model. Identifies and quantitatively ranks the most influential EA implementation criteria, with commitment, governance, infrastructure, organizational management, and senior management support emerging as dominant drivers of success. Demonstrates how weighted EA criteria can be interpreted within sensitivity analysis and optimization perspectives to support robust, data-driven, and scenario-based managerial decision-making. Provides actionable insights for decision-makers to improve governance quality, optimize resource allocation, and enhance organizational performance through disciplined, mathematically informed EA planning.
کلیدواژه‌ها
Enterprise architecture؛ Optimization؛ Analytic hierarchy process؛ Decision Support System؛ Strategic alignment

مراجع
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AHP-Based Framework for Optimizing, and Decision-Support in Enterprise Architecture Implementation