Management Systems in Production Engineering Feed

Managing Improvement Projects Under Continuous Improvement: Practices, Portfolios, and Failure Factors – Evidence from Polish Industry

Thu, 06 Aug 2026 00:00:00 GMT

The study investigated the role and effectiveness of improvement projects (IPs) which have been implemented within the Continuous Improvement (CI) framework in Polish manufacturing companies. Although CI practices are widely recognized worldwide, little is known about how such projects are organized and managed in Poland. A survey has been conducted using computer-aided telephone interviewing (CATI) among 70 managers and quality specialists from manufacturing firms to address this gap. The research examined the types and frequency of IPs, project management methodologies (PMMs), the presence of project portfolios, and the impact of critical failure factors (CFFs) on project outcomes. The results show that projects focused on quality, efficiency, and waste reduction are the most common, whereas Six Sigma and 5S initiatives are less frequently applied. Organizations with higher CI maturity tend to engage more actively in improvement projects and use PMMs more consistently. In practice, tool-based approaches, such as project schedules or charters, are preferred over more formalized methodologies, like PMI or PRINCE2. The findings also reveal that operational barriers – especially insufficient engagement of project leaders – undermine the success of CI initiatives. This research offers the first empirical evidence on CI-related projects in Polish manufacturing, highlighting both enablers and barriers, and provides practical recommendations for managers seeking to strengthen the strategic impact of their improvement efforts.

Application of Data Analysis in Assessing Fuel Consumption Efficiency in a Logistics Enterprise

Thu, 06 Aug 2026 00:00:00 GMT

Fuel consumption efficiency is one of the key areas for evaluating the operational performance of vehicle fleets in transport enterprises. Rising operating costs and the need for rational resource management are making the analysis of data recorded during real-world vehicle use increasingly important. This article aims to comprehensively assess the operational efficiency of a vehicle fleet based on a case study of a selected transport enterprise. The analysis also includes the identification of vehicles characterised by unfavourable fuel consumption parameters. As part of the study, the consistency of input data on fuel consumption obtained from the CAN bus, invoices and fuel level sensor readings was assessed against reference data from the telematics system. On this basis, the data source demonstrating the highest level of consistency was identified and used in further analysis. Efficiency was assessed based on total and unit fuel consumption, CO2 emissions and the costs of excess fuel consumption. The applied criteria made it possible to identify vehicles with elevated fuel demand and suboptimal operational parameters. In addition, a scenario analysis was conducted to estimate potential savings and CO2 emission reductions achievable by improving the efficiency of the vehicle identified as requiring further inspection. This approach supports fleet management by facilitating the assessment of potential reductions in operating costs and CO2 emissions. The results indicate that targeted efficiency improvements may reduce fuel consumption and environmental impact without the need for immediate vehicle replacement.

A Micromechanically Grounded Methodology for Evaluating the Abrasivity of Particles in Accelerated Tribological Tests

Thu, 06 Aug 2026 00:00:00 GMT

This work proposes an improved methodology for evaluating the abrasivity of particles in accelerated tribological tests, extending a previously developed approach to materials testing under friction against loose abrasive. Instead of a single scalar sharpness measure, a micromechanically interpretable triplet of geometric descriptors {hmax, βmax, ρmin} is proposed–the protrusion height, the tangent rotation angle across the peak, and the minimum radius of curvature along the contour, computed from the analytical curvature of the elliptic Fourier reconstruction. This triplet is combined into a single dimensionless index κ* (effective cutting curvature index) that preserves the micromechanical interpretation of its components. On a sample of n = 200 silicon carbide 64C particles (50 particles at each of four test stages: 0, 250, 500, and 825 min in the steel 40/SiC 64C system), hmax is shown to be the dominant component of the triplet with respect to the test stage (Spearman ρ = −0.51, p < 10-13); the combined index κ* yields ρ = −0.46 (p < 10-10); the canonical SPLm parameter, implemented via elliptic Fourier reconstruction with N = 50 harmonics and piecewise-linear approximation of the peaks, yields ρ = −0.48 (p < 10-12) on the same sample. The advantage of the triplet and the combined κ* over SPLm lies in the transparent micromechanical interpretation of their components. A population balance model is constructed for the evolution of the log-normal moments of the shape parameters and coupled with the kinetics of wear intensity I(τ) as an a posteriori description of the two-scale structure of the kinetics. A direct fit of the kinetic data (33 intervals of 25 min duration) yielded a characteristic relaxation time τc = 24 min and, accordingly, an optimal duration of accelerated tests τ* = τc ln20 ≈ 72 min under the 5% criterion of |dI/dτ|0. The conclusions are directly substantiated for the investigated steel 40/SiC 64C system; verification on other counterbody materials and abrasives is the subject of further work. The obtained result increases the productivity of laboratory screening of materials and coatings and has direct value for production engineering systems.

Integrated Quality Control 4.0 Model for Ready Meals and Canned Meat Production

Thu, 06 Aug 2026 00:00:00 GMT

The objective of this study was to develop an integrated Quality Control 4.0 model for ready meals and canned meat production. The research was conducted as an industrial case study based on a 2024 supplier quality dataset, an inventory of control devices used in the production system, and a structured literature review on Food Quality 4.0 and ready-meal safety. The dataset included an aggregated total of 3,939 deliveries, 209 non-conforming raw materials and 209 complaints. Visible supplier-level records included 58 coded suppliers, 3,572 deliveries and 188 non-conforming raw materials. Descriptive statistics, Pareto analysis, supplier non-conformity rates and technology-function mapping were used. The results showed that the overall non-conformity rate in the aggregated dataset was 5.31%. The dominant defect descriptors were appearance non-conformity (110 cases), fat within the +2% tolerance zone (41 cases), exceeded fat content (38 cases), and odour/colour non-conformity (31 cases). Supplier risk was concentrated: the five suppliers with the highest number of non-conforming materials accounted for 59.0% of visible supplier-level non-conformities. The proposed model integrates raw material inspection, composition standardisation, thermal-process validation, hygiene control, packaging inspection and KPI-based decision support. The model provides a practical framework for moving from reactive quality control towards data-driven quality assurance and improvement in meat-based convenience food manufacturing.

Temporal Hawkes Process for Modeling Short-Term Clustering of Crime Events in Ostrava

Thu, 06 Aug 2026 00:00:00 GMT

This paper applies a temporal Hawkes process to the analysis of short-term clustering in crime event data. The study uses publicly available records from the Crime Map of the Police of the Czech Republic and focuses on events of general crime in the Ostrava region during the period 2016-2025. A homogeneous Poisson process is used as a baseline model, while the Hawkes process with an exponential kernel is used to capture possible self-exciting behavior in the event sequence. The parameters are estimated by maximum likelihood and the models are compared using log-likelihood, AIC, BIC and residual diagnostics based on the time-rescaling principle. The results show that the temporal Hawkes model provides a substantially better description of the data than the homogeneous Poisson model. The estimated branching ratio is approximately 0.143, which indicates the presence of a measurable but not dominant self-exciting component. The estimated excitation half-life is about 1.11 hours, suggesting that the detected temporal dependence is mainly short-term. The results demonstrate that even a simple one-dimensional Hawkes model can provide an interpretable description of temporal clustering in crime data. At the same time, the analysis highlights the limitations of a stationary temporal model and motivates future extensions including non-stationary background intensity and spatial information.

Structural Optimization and Weight Reduction of a Belt Conveyor Supporting Frame Using Analytical and Finite Element Methods

Thu, 06 Aug 2026 00:00:00 GMT

This paper deals with the structural design of a belt conveyor equipped with a handling mechanism. The research was carried out within the framework of the KEGA 004TU Z-4/2024 and VEGA1/0302/26 projects and is focused on the design and analysis of the supporting structure in terms of its strength and operational reliability. Particular attention is paid to the methodological procedure for determining the distribution of individual loads arising at critical points of the structure during the operation of the conveyor and the handling mechanism. The paper also identifies the decisive loading conditions and their influence on the dimensioning of the supporting elements of the device. The obtained results contribute to the optimization of the structural design and to increasing the operational safety of the proposed system. The structure was found to be significantly overdesigned with a safety factor of 41.8. A subsequent optimization reduced the weight, while maintaining a safe stress level.

Operational Decision Support in Manufacturing and Technological Information Processing: A Systematic Literature Review

Thu, 06 Aug 2026 00:00:00 GMT

The article is based on a systematic literature review on operational decision support in production and technological information processing. The content analysis aimed to examine how modern information processing systems are integrated into decision-making models in the context of production operations. The literature review follows the PRISMA method, designed to ensure transparent reporting of systematic reviews and meta-analyses. The article covers the following stages: literature search, thematic selection, and quantitative and qualitative analysis of the collected literature. Based on the bibliometric analysis, the literature can be divided into three interrelated thematic areas. The first thematic area concerns operational decision support in production planning, scheduling, capacity allocation, and production control. The second thematic area refers to technological information processing, including the representation of process knowledge through CAD model integration and the interpretation of technical documentation. The third thematic area focuses on tools that have gained increasing importance in recent years in the broadly understood industrial sector: machine learning, digital twins, and Industry 4.0 infrastructure. The literature analysis indicates that a major research gap lies not in the absence of individual decision-support methods, but in the limited integration of operational and technological information within coherent models supporting decision-making under real manufacturing conditions.

Modeling and Motion Control of a Mobile Wheeled Robot at Low Speeds

Thu, 06 Aug 2026 00:00:00 GMT

The article deals with the issue of modeling and motion control of a mobile wheeled robot at low speeds. Attention is focused on the basic methods of mobile robot control, namely synchronous steering, Ackermann steering, and differential drive control. The main emphasis is placed on differential drive control, which is suitable for small mobile robotic platforms due to its design, good maneuverability, and ability to rotate in place. The article describes the kinematic and dynamic model of a mobile robot, taking into account quantities such as position and orientation. The proposed solution also includes the design of a control loop that enables the control of the angular velocities of the wheels and the tracking of a desired trajectory. The proposed simulation model was created in the MATLAB/Simulink environment and divided into subsystems describing kinematics, dynamics, control, and velocity transformation. The practical part of the article is devoted to the structural design and implementation of an experimental prototype of a mobile robot. The result is a functional prototype suitable for verifying the principles of mobile robotics, regulation, and motion control under real conditions.

Diagnosis of Atypical Water Injection Mechanisms for Production System Integrity Management: A Case Study of X Field

Thu, 06 Aug 2026 00:00:00 GMT

In the absence of a reservoir pressure maintenance (RPM) system at Field X, a critical increase in water cut to 92.6% has been observed, with current production at 72.4% of the initial recoverable oil reserves. Such a high water cut at a stage that does not correspond to full production suggests the presence of atypical mechanisms. This study aimed to comprehensively analyze the causes of the abnormal water cut using modern diagnostic methods, including geophysical well logging (GWL), reservoir pressure monitoring, hydrogeochemical analysis of associated waters and detailed laboratory studies of core samples. The analysis revealed that the primary cause is a man-made breach in the production column of Well #4, resulting in the overflow of highly mineralized water from the overlying Albian-Cenomanian aquifer into the productive J-XI formation. Petrophysical studies revealed high reservoir heterogeneity. However, the oil displacement coefficient by water was 0.53 for highly permeable formations, ruling out low oil recovery as the main cause of water flooding. Key evidence of the human-made nature of the problem includes the abnormal increase in reservoir pressure, from 23.9 MPa in 2022 to 25.5 MPa in 2023, as well as the presence of sulphate ions in the production water, which is characteristic of the Albian-Cenomanian complex. A set of practical recommendations has been developed, including the urgent repair and insulation of Well #4, the introduction of a casing integrity monitoring system and the adaptation of the development strategy. The results emphasize the importance of monitoring the technical condition of the well fleet to ensure the effective late-stage development of similar fields. The study demonstrates that integrating this diagnostic methodology into a production management system enables proactive risk mitigation, supports decision-making for well intervention, and contributes to optimizing the overall production strategy in mature fields.

A House of Risk-Based Framework for Managing Risks in IoT-Enabled Social Manufacturing Systems

Thu, 06 Aug 2026 00:00:00 GMT

This study proposes a risk management framework for an IoT-based Social Manufacturing system using the House of Risk (HOR) method. In a social manufacturing system, all distributed and collaborative production activities are highly dependent on real-time internet connectivity. In this study, 27 risk events and 22 risk agents were identified through observation and interviews, as well as SCOR-based process mapping. The method used is HOR Phase 1 and HOR Phase 2 analysis. In HOR Phase 1, which calculates the Aggregate Risk Potential (ARP), it was found that 12 risk agents predominantly contributed 78.52% of the total system risk, with the highest ARP values related to production planning errors (1872), production delays (1813), and inaccurate customer order identification (1148). In HOR Phase 2, 21 preventive actions were evaluated using the Effectiveness-to-Difficulty Ratio (ETD), yielding a priority-based mitigation sequence. The most effective actions include more careful production planning, improved coordination with SMRs, early identification of production bottlenecks, comprehensive recording of customer requests, and selective SMR qualification. This research contributes to the risk management literature by applying HOR to Industry 4.0 and provides practical guidance for risk mitigation in IoT-based social manufacturing systems.

Simulation-Based Production Process Improvement Considering the Quality-Cost Trade-Off

Thu, 06 Aug 2026 00:00:00 GMT

Production systems are increasingly required to maintain high quality standards while simultaneously reducing operational costs and ensuring production efficiency. This study presents a simulation-based approach to production process improvement considering the quality–cost trade-off. The main objective of the research was to develop a decision-support framework enabling the analysis of how processing time and repair policies influence production system performance under varying operational conditions. A discrete-event simulation model was developed in the FlexSim environment to represent selected stages of a manufacturing process, including processing operations, quality inspection, and rework activities for nonconforming products. The proposed approach incorporates the relationship between processing time and product quality, enabling the evaluation of multiple production scenarios characterized by different operational strategies and repair limits. The study applies a multi-criteria analysis focused on balancing quality-related outcomes and operational profitability. Simulation results were evaluated using Pareto-based analysis to identify efficient production configurations characterized by different levels of product quality, production cost, and economic performance. The obtained results indicate that extending processing time may improve product conformity; however, it can simultaneously reduce operational efficiency and increase production costs. Moreover, excessive rework intensity does not necessarily generate proportional economic benefits. The proposed approach may support managerial decision-making in manufacturing systems by enabling the identification of production strategies adapted to different quality and cost priorities.

Green Procurement Adoption in Malaysia Versus Emerging Economies: A Post-Covid Systematic Literature Review

Thu, 06 Aug 2026 00:00:00 GMT

Green procurement has become increasingly important in the post-COVID-19 era as governments and organisations strive to strengthen supply chain resilience while advancing sustainability goals. The purpose of the review was to examine post-COVID procurement trends, describe their drivers and obstacles, analyse their theoretical applicability, and identify sector-specific insights. This study conducted a PRISMA 2020 systematic literature review to examine the post-pandemic adoption of green procurement in Malaysia, benchmarked against India, Indonesia, Brazil, and South Africa, covering the period from 2019 to August 2025. Following rigorous inclusion, screening, and quality assessment procedures, 20 peer-reviewed empirical studies were retained for analysis. The findings show that 55% of the reviewed studies (11/20) directly addressed post-COVID procurement issues, and only 40% (8/20) used theoretical concepts such as Institutional Theory, the Resource-Based View, the Technology Acceptance Model, or ISO 20400. The literature was dominated by sectoral public procurement, education, and manufacturing, and healthcare and logistics have not been well explored. Malaysia is performing moderately, driven by the private sector’s dominance in digitalisation and the use of ISO-based practices; however, it is worse off than India and Brazil in applying these in the public sector, in policy consistency, and in institutional cohesion. The thematic analysis identifies six reorganised themes: institutional preparedness and governance capacity, policy and regulatory drivers, technological and digital facilitation, sectoral execution practices, adoption barriers, and post-COVID adaptive competence. This review addresses the main gaps in providing a comparative, post-COVID, theory-informed synthesis of green procurement in the Global South. It also identifies gaps in theoretical synthesis, sectoral balance, and resilience-focused analysis. The research offers practical implications for policy-makers and further develops the academic literature by making the case for more robust behavioural, institutional, and systems-based frameworks in future procurement studies.

Inflation, Scarcity, and B2B Purchasing Behaviour: A Conceptual Framework from the Cee Dry Construction Distribution Sector

Thu, 06 Aug 2026 00:00:00 GMT

This paper is conceptual and exploratory; it does not provide direct empirical measurement of firm-level B2B purchasing behaviour. It develops a sector-specific framework explaining the paradoxical resilience of construction activity in the dry construction distribution channel across selected Central and Eastern European (CEE) countries – Slovakia, the Czech Republic, Poland and Hungary – during the inflationary period 2021-2024. Drawing on dynamic capabilities theory, the resource-based view and the supply chain disruption literature, the framework theorises that when an inflationary shock and supply chain disruption occur simultaneously, the primary B2B supplier-selection criterion in the dry construction distribution channel temporarily shifts from price to product availability, granting distributors with strong inventory positions a temporary bargaining and competitive advantage. The empirical component is restricted to a descriptive analysis of Eurostat macroeconomic indicators (HICP, construction production index, construction PPI) for 16 country-year observations. These aggregated national-level indicators serve only as a macroeconomic backdrop and do not measure dry construction distribution behaviour directly; no inferential statistical model is reported. Four propositions are derived and presented as premises for future empirical research using surveys, case studies and longitudinal designs. The framework offers conceptual guidance for distributors formulating inventory strategies under shortage conditions and for manufacturers designing distribution-partnership mechanisms.

Open Practices in the Innovation Ecological Network and Sustainable Business Model Innovation: The Moderating Role of Absorptive Capacity

Thu, 06 Aug 2026 00:00:00 GMT

This study investigates the influence of the innovation ecological network (EN) on inbound open innovation (IOI), outbound open innovation (OOI), and sustainable business model innovation (SBMI). Drawing on absorptive capacity theory, the study further examines the moderating role of absorptive capacity (AC) in the relationships between open innovation (IOI, OOI) and SBMI. Data were collected through a Google Forms survey, producing 506 valid responses from middle‑ and senior‑level managers in manufacturing enterprises in Vietnam. Partial Least Squares Structural Equation Modeling (PLS‑SEM) was employed to assess both the measurement and structural models. The findings show that EN positively influences IOI, OOI, and SBMI. Both IOI and OOI exert significant positive effects on SBMI. Moreover, AC significantly moderates the relationship between OOI and SBMI, whereas its moderating effect on the IOI–SBMI relationship is not supported. These results extend the theoretical foundation of SBMI by demonstrating that SBMI is shaped by the innovation ecological network, inbound and outbound open innovation, and firms’ absorptive capacity. The study concludes with recommendations and highlights limitations that provide avenues for future research.

AI-Supported Process Management Tools for Handling Disturbances in Industry 4.0 Production Systems

Thu, 06 Aug 2026 00:00:00 GMT

Production systems operating in Industry 4.0 environments are increasingly exposed to disturbances that affect process stability, performance, and managerial decision-making. The growing complexity and digital integration of production processes require structured approaches that go beyond reactive disturbance handling and support process-oriented management. In this context, artificial intelligence (AI) is increasingly used as a decision-support mechanism to enhance data interpretation and managerial responses. This article aims to examine how AI-supported process management tools can be used to handle disturbances in Industry 4.0 production systems. A structured qualitative coding methodology is proposed and applied to systematically identify disturbances, corresponding management actions, and disturbance-related process signals. Disturbances are classified using D-codes, management responses using A-codes, and observable indicators using signal codes, forming an integrated analytical framework for process-level analysis. Empirical data collected from real production systems were analyzed using the proposed coding approach. AI-supported data processing was used to assist pattern recognition, signal interpretation, and the linking of disturbances with management actions, without replacing human judgment in decision-making. The results provide an empirically grounded classification of disturbances and process management tools relevant to digitally integrated production environments. The study contributes to production engineering and management literature by offering a transferable methodological framework for analyzing disturbance handling in Industry 4.0 production systems. From a practical perspective, the findings demonstrate how basic process management tools, supported by AI-based decision-support, can enhance managers’ ability to identify, interpret, and respond to disturbances in complex production processes.

A Hybrid Algorithm for Modeling and Optimizing the AISI 304L Stainless Steel Electrical Discharge Machining Parameters

Thu, 30 Apr 2026 00:00:00 GMT

Electric Discharge Machining (EDM) is known for its ability to machine hard and brittle conductive materials, as it can melt any electrically conductive substance irrespective of its hardness and geometric intricacy. Machining technologies have progressively advanced from a basic tool and die manufacturing process. The purpose of this paper is to investigate the performance in achieving the maximum material removal rate (MRR) while minimizing surface roughness (Ra) and tool wear rate (TWR) together and to optimize the parameters’ operation utilizing ANOVA, RSM, and the hybrid BPNN-PSO. The AISI 304 L stainless steel is machined under different parameters, such as peak current (IP), pulse-on time (Ton), and pulse-off time (Toff). The ANOVA was used to identify significant components in single-objective optimization, whereas the Response Surface Methodology (RSM) was used for multiple-objective optimization of the parameters. The hybrid BPNN-PSO algorithm was used in single and multiple-objective modes to improve the parameters that were worked on in two scenarios. The results showed that the electrical current had a 75% impact on the output. From the analysis, the hybrid BPNN-PSO result achieved significant improvement over both the ANOVA and the RSM algorithms. More specifically, IP was optimized by 1.25 times on the quantity of MRS technique, whereas Ton was 58.18 μs in the second scenario. Based on these data, the second scenario is capable of achieving even better results in terms of optimization.

Evaluating E-Learning Quality in Higher Education: A Mixed-Methods Study of Student Experience

Thu, 30 Apr 2026 00:00:00 GMT

The rapid development of digital technologies has significantly reshaped the way higher education institutions deliver their services. E-learning, as a form of digital service (e-service), has become an essential component of academic teaching, especially following the global shift toward remote learning. However, the effectiveness and quality of these services remain a subject of increasing interest and scrutiny. This study evaluated the quality of e-learning in higher education from the student-experience perspective using a mixed-methods design. An online questionnaire was administered to university students, and the findings were triangulated with empathy mapping and a structured usability audit of the learning platform. The analysis examined five dimensions: content accessibility, clarity of communication, technical usability, student-instructor interaction, and emotional engagement. Quantitative indicators of service quality were integrated with qualitative evidence capturing students’ thoughts, feelings, and expectations regarding online learning. Conducted in courses at a Polish university, the study offers evidence-informed recommendations for the design, delivery, and evaluation of online courses to improve student satisfaction and learning outcomes.

Determination of Production Start Time for Inventory Storage Space Minimization

Thu, 30 Apr 2026 00:00:00 GMT

The research explores a production scheduling problem in a system where multiple items are manufactured on a single production line on a rotating basis. The objective is to identify the proper production start time for each item within a predetermined production sequence by strategically inserting idle time between the production of consecutive items to minimize the maximum required storage space. A mathematical model and the Idle Time Insertion for Storage Space minimization (ITISS) constructive algorithm are presented. The performance of the ITISS algorithm is evaluated on 300 randomly generated problems across five different problem sizes (10, 20, 30, 40, and 50 items) and two slack time levels (0.01 and 0.03 years). The ITISS algorithm provides optimal solutions for all 300 experimental problems with more than a 99% reduction in computational time compared to solving the linear programming model. In comparison to consecutive production schedules, production schedules with optimized start times can reduce total inventory storage space by 13.81%. This reduction is more emphasized in the production system with larger slack time.

The Concept of Electromobility: The Views of University Students in Poland

Thu, 30 Apr 2026 00:00:00 GMT

The phenomenon of electromobility is the subject of discussion among many diverse groups. Although its implementation in individual countries poses many challenges that must be addressed, among others, by electric car manufacturers and sellers (for example, in the form of developing technological solutions to increase the safety of electric cars or adapting recycling processes to specific technological solutions), the benefits it brings are measurable. Therefore, the main objective of the research conducted in this manuscript is to analyse the advantages and disadvantages of the concept of electromobility based on the opinions of university students in Poland. A total of 859 respondents were selected to participate in the study, including 488 men (56.81%), 353 women (41.09%), and 18 people who identified their gender as other (2.10%). The statistical calculations used included the Mann-Whitney U test, Spearman rank correlation, multidimensional cluster analysis, and Profit analysis. University students are sceptical about the concept of electromobility. They attach greater importance to the disadvantages of this concept (related to the high purchase cost of electric cars and their repairs). Among the advantages of electromobility, the most important (in the students’ opinion) are technological progress, no need to purchase increasingly expensive fuel, and the low failure rate of electric cars.

Artificial Intelligence in Business Continuity Management and the Resilience of Production Systems to Safety Disruptions

Thu, 30 Apr 2026 00:00:00 GMT

This article investigates the transformative role of artificial intelligence in business continuity management and the resilience of production systems exposed to safety disruptions. The study conceptualizes AI not merely as an optimization technology but as a structural component of organizational security architectures embedded within resilience engineering and continuity governance. A mixed-method, model-driven research design was employed, integrating quantitative system modelling, simulation-based scenario analysis, and qualitative organizational assessment across high-risk manufacturing environments. The findings demonstrate that AI-enabled continuity systems significantly enhance early disruption detection, reduce cascading failure propagation, and accelerate recovery dynamics compared to traditional continuity frameworks. Predictive analytics and adaptive recovery coordination substantially increase system shock absorption capacity, shorten mean time to recovery, and improve procedural compliance. At the same time, the results reveal that the effectiveness of AI-driven continuity governance is contingent upon data integrity, cybersecurity robustness, and human – AI collaboration quality. The study advances continuity management and resilience engineering theory by reconceptualizing resilience as an emergent, algorithmically governed system property rather than a static infrastructural attribute. From a practical perspective, the results provide evidence-based guidance for organizations seeking to design intelligent, self-regulating safety architectures capable of sustaining operational continuity under complex, multi-dimensional safety disruptions.