2026· International Journal Of Engineering And Computer Science· Vol 15, pp. 28761-28768· 0 citations
Abstract
The reliability of critical financial infrastructure has become a strategic imperative as digital payment platforms, central bank digital currencies (CBDCs), and real-time settlement systems expand globally. This article investigates the theoretical foundations and practical application of a multi-level Service Level Indicator (SLI) and Service Level Objective (SLO) model designed specifically for critical financial infrastructure environments. The study examines how technical observability metrics can be systematically mapped to business scenarios, regulatory compliance requirements, and operational continuity objectives. Using a comparative analytical framework and case-study evidence drawn from CBDC integration practice at a systemically important bank, the research demonstrates that hierarchically structured SLI/SLO contracts, combined with AI-driven observability, reduce mean time to recovery (MTTR) by an order of magnitude and enable proactive error budget management. The findings reveal that a four-layer SLI/SLO hierarchy covering business outcomes, service contracts, component metrics, and infrastructure signals is necessary and sufficient for aligning engineering reliability work with financial regulatory mandates. The article will be of interest to financial technology architects, reliability engineers, banking regulators, and researchers working at the intersection of distributed systems engineering and financial services compliance.
- Enterprise resource planning (ERP) projects are often evaluated at acquisition, configuration or go-live, although the economic and operational value of an integrated system depends on whether the organisation redesigns processes, governs data, embeds controls, develops user capability and measures realised benefits. This study develops an acquisition-to-capability framework and tests its operational measurement layer using a public 70,000-record analytical extract of the DataCo Smart Supply Chain dataset. The extract contains 44,026 unique orders dated from 2015 to 2018. Analysis was conducted at order and order-line levels using descriptive statistics, cross-segment heat maps, robust logistic regression, holdout prediction, calibration assessment, bootstrap confidence intervals and sensitivity testing of a six-component ERP Operational Capability Index (EOCI). The order-level late-delivery rate was 54.7%, severe delay affected 23.6%, negative-profit exposure affected 19.6%, and hard workflow exceptions affected 10.8%. Shipping configuration was the dominant observable delivery factor: compared with Standard Class, adjusted late-delivery odds were 34.22 times higher for First Class, 5.42 times higher for Second Class and 1.40 times higher for Same Day. A leakage-controlled holdout model achieved an area under the receiver operating characteristic curve of 0.731, a Brier score of 0.197, a calibration intercept of 0.008 and a calibration slope of 0.967. Observed on-time delivery rose from 11.2% in the lowest readiness quartile to 62.9% in the highest, a 51.6-percentage-point difference. EOCI rankings were stable under alternative weights (Spearman rho=0.998). The shipping-mode odds ratios reflect this dataset's own service-level design and are reported as a demonstration of what transaction-level analytics can reveal, not as a general finding about ERP-enabled delivery performance. The findings show that transaction visibility can diagnose capability gaps, but software records alone cannot establish implementation causality. The paper therefore specifies a matched difference-in-differences field protocol, KPI dictionary, governance gates and implementation playbook for SMEs and small government vendors. The central conclusion is that ERP value is realised through a governed operating system, not through software possession
Nkosana Mkandla, M. Munjoma, Melody Sydney et al.· Iconic research and engineer...· 0 citations
Rapid changes in the global energy landscape and Nigeria's upstream sector require technical teams to realign strategies and capabilities to remain competitive and future ready. This paper presents a comprehensive strategic transformation framework for Team Quanta, a core technical unit within a major oil and gas corporation, aimed at improving performance, agility, and business alignment. The transformation applied an integrated toolkit comprising a Plan-Do-Check-Adjust (PDCA)-based performance framework, and several tools e.g., SWOT analysis, root cause diagnostics using the 5 WHYs, scenario planning, and stakeholder mapping. These tools enabled a systematic evaluation of historical performance, organizational culture, and operational inefficiencies. Diagnostics revealed fluctuating team delivery metrics, low staff morale, demographic imbalances, and underutilized legacy datasets. Through future scenario modelling, three strategic transformation options were compared: "Status Quo", "Blue Ocean", and "Radical Change". The "Blue Ocean" strategy – which proposed a gentle introduction of potentially disruptive change, emerged as the most balanced approach. The proposed FIRAM (Foster, Invest, Review, Adopt, Measure) roadmap addresses culture, skills, systems, and performance metrics through phased implementation. The framework offers a replicable model for aligning technical functions with evolving business imperatives. Recommendations include multi-skilling, cloud-based data management strategy, targeted recruitment, and outsourcing of non-core activities. This work contributes to the discourse on organizational transformation in energy operations and provides actionable insights for building high-performing, future-ready technical teams.
J. Osimobi, J. Uche, M. C. Chejieh et al.· SPE Nigeria Annual Internati...· 0 citations
The article develops an operational model of the strategic management system for industrial resilience (SMSIR) under circular economic transformation. The previously formed multilevel adaptive architecture establishes a structural framework but does not by itself guarantee effective functioning, since practical results depend on managerial interactions, the sequence of management functions, coordination mechanisms, and feedback between levels. To close this gap, the study formalises the operational logic of the system through a closed-loop model combining the classical cycle (goal-setting, planning, organisation, implementation, monitoring, evaluation, correction) with the multilevel architecture and an adaptive-corrective loop ensuring self-renewal and responsiveness to change. Functions of the six architectural levels – target, spatial, institutional, functional, information-analytical, and implementation-transformational – are systematised with the cycle stages they most influence and expected results, and an interaction matrix is formalised, representing linkages among levels. The study identifies subjects of strategic management and delineates responsibility boundaries across three spatial tiers – regional industrial zones, industrial-logistics axes, and the national level – distinguishing operational, coordination, and strategic centres. A methodical approach to organising interaction among subjects is proposed: identification of subjects, distribution of functional powers, formation of coordination linkages, integration of information flows, and adaptive adjustment. An organisational-functional model is constructed that integrates architectural levels, subjects, responsibility centres, and implementation mechanisms within a single adaptive loop. The findings confirm that system effectiveness depends not on isolated decisions but on coherence among governance levels, providing a methodological basis for implementing resilience mechanisms under polycrisis conditions and circular transition.
Unknown authors· State and regions Series Eco...· 0 citations
Manufacturing systems are major drivers of resource use and environmental impacts, and current trajectories indicate that efficiency and consistency strategies alone are unlikely to achieve the absolute reductions required to stay within planetary boundaries. Sufficiency, understood here as moderating absolute demand for material goods and intensifying the use of existing assets, is increasingly discussed as a third, complementary sustainability strategy alongside efficiency and consistency, but remains weakly operationalized at the business-model level, resulting in a low transparency on sufficiency. This article addresses the resulting measurement gap by developing and empirically validating a business-model-level scoring model for industrial sufficiency in manufacturing firms. Building on sufficiency literature and business-model frameworks, the study adopts a Design Science Research approach to derive three assessment dimensions: Value Proposition, Value Creation & Delivery, and Value Capture, operationalized through eight sufficiency-related parameters scored on a three-point ordinal scale. The model validated through five expert interviews with practitioners from manufacturing companies using a mixed-methods validation design that combines standardized ratings and qualitative comments. The results show that the model can be applied as a lean self-assessment tool, discriminates between different maturity levels across parameters, and, in the participating firms’ self-assessments, highlights comparatively low maturity levels in parameters such as sufficiency in supply chain and logistics, pricing strategies for promoting sufficiency, and sufficiency-oriented value capture. The study concludes that the scoring model fills a gap between largely qualitative sufficiency frameworks and outcome-oriented assessment tools such as LCA or ESG metrics. For practitioners, it offers a structured basis for self-assessment and strategic prioritization of sufficiency measures at business-model level; for researchers, it provides an operational instrument that can be further tested, calibrated and extended in future empirical work.
Raphael Peters, Paul Schmidhäuser· Frontiers in Sustainability· 0 citations
: Information security investment decisions increasingly require structured evaluation under uncertainty and organizational constraints. Organizations frequently struggle to efficiently allocate limited resources while ensuring alignment between security scope and business objectives. This study proposes a process-integrated decision-support framework that combines Design Science Research with Multi-Attribute Utility Theory (MAUT) to address this challenge. The framework embeds structured scope elicitation within project-management processes and links it to interval-based multi-criteria aggregation. It is evaluated through two analytically constructed scenarios representing low-and high-complexity environments. Results demonstrate that iterative scope construction combined with uncertainty-aware MAUT modelling enhances preference robustness, improves transparency of trade-offs, and reduces misalignment between investment alternatives and organizational requirements. The study contributes to the bridging of governance processes with formal decision analysis and by reframing information security investment as a dynamic scope-alignment problem rather than a static optimization task. Implications for research and practice are discussed.
Adam P. Filippidis, C. Hilas· International Conference on...· 0 citations
The Sustainable Development Goals (SDGs) form an indivisible agenda, yet policy design and implementation
remain dominated by sector-specific programmes that can generate unrecognized spillovers, delayed effects and burden
shifting. This study develops a dynamic systems-of-systems (SoS) decision-support framework that represents all 17 SDGs
as operationally distinct but interdependent constituent systems. The framework integrates a signed and directed interaction
matrix, causal-loop analysis, stock-flow system dynamics, seven cross-sector policy levers, constrained multi-objective
optimization and Monte Carlo uncertainty analysis. A transparent proof-of-concept application uses normalized goal-level
states and literature-informed interaction directions to compare business-as-usual, siloed growth, green-transition, humandevelopment and optimized SoS portfolios over 2025-2035. The optimized portfolio increases the mean normalized SDG
score from 0.601 under business-as-usual to 0.710 by 2030 and from 0.693 to 0.835 by 2035. It also raises the weakest-goal
score from 0.452 to 0.631 by 2030 and from 0.512 to 0.753 by 2035. In contrast, the siloed-growth scenario improves the
aggregate score but depresses the weakest-goal and environmental outcomes, demonstrating that aggregate progress can
conceal systemic deterioration. Network out-strength identifies SDGs 16, 4, 17, 13 and 5 as high-leverage goals. Across 1,000
uncertain parameter realizations, the optimized portfolio retains a narrow 2035 mean-score interval of 0.829-0.840. The
numerical results are illustrative rather than forecasts, but they demonstrate how systems engineering can convert
qualitative SDG interlinkage knowledge into an auditable, adaptive and optimization-ready policy architecture.
C. B. Uchechukwu, Ogbonnia Kingsley Chibuzo· International Journal of Inn...· 0 citations
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