Identifying the Convergence Components of Risk Reduction and Quality Enhancement with a Meta-Synthesis Approach
Keywords:
Production Optimization, Meta-Integration, Quality Management, Risk ManagementAbstract
The aim of this study was to identify the convergence components of risk reduction and quality enhancement in production processes using a meta-synthesis approach. This applied qualitative research employed a library-based design using the meta-synthesis technique. A total of 208 studies published between 2000 and 2025 in Scopus, Web of Science, and Persian databases were screened through the CASP framework, and 41 articles were selected for in-depth analysis. Data were coded and analyzed using MAXQDA software. Through thematic analysis, 44 components were identified under 18 key indicators grouped into three main dimensions. The results revealed that the convergence framework consists of three major dimensions: (1) risk reduction (financial, operational, technological, environmental and legal, supply chain, organizational and managerial, cognitive and informational risks), (2) quality enhancement (product or service quality, process quality, human resource quality, information quality, customer-centric quality, innovation quality, sustainability and social responsibility), and (3) integrative convergence (uncertainty management, cost–quality optimization, decision-making management, and predictive and machine learning models). These interrelated components create a synergistic mechanism that simultaneously improves production performance, strengthens quality assurance, and mitigates potential risks across industrial systems. The study concludes that risk reduction and quality enhancement are complementary, convergent processes rather than isolated objectives. Organizations can achieve sustainable competitiveness by adopting an integrated, data-driven, and predictive strategy that combines intelligent risk management and systematic quality improvement. The results highlight the importance of incorporating advanced analytics, AI-based decision tools, and continuous process optimization in establishing resilient and high-quality production ecosystems.
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Abdelaoui, F. Z. E., Jabri, A., & Barkany, A. E. (2023). Optimization techniques for energy efficiency in machining processes-a review. Int J Adv Manuf Technol, 125, 2967-3001. https://doi.org/10.1007/s00170-023-10927-y
Aqil, S., & Allali, K. (2021). Two efficient nature inspired meta-heuristics solving blocking hybrid flow shop manufacturing problem. Eng. Appl. Artif. Intell., 100, 104196. https://doi.org/10.1016/j.engappai.2021.104196
Avadiappan, V., & Maravelias, C. T. (2021). State estimation in online batch production scheduling: concepts, definitions, algorithms and optimization models. Comput. Chem. Eng., 146, 107209. https://doi.org/10.1016/j.compchemeng.2020.107209
Azad, T., Rahman, H. F., Chakrabortty, R. K., & et al. (2022). Optimization of integrated production scheduling and vehicle routing problem with batch delivery to multiple customers in supply chain. Memetic Comp., 14, 355-376. https://doi.org/10.1007/s12293-022-00372-x
Chen, Y., Zhou, Y., Fang, S., & Li, M. (2022). Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty. Science of The Total Environment, 809, 151152. https://doi.org/10.1016/j.scitotenv.2021.151152
Fathi Vajargah, K., & Larak, A. (2024). Risk Management Optimization Using Quasi-Random Sequences Compared with Markov Chain Monte Carlo. Civil Engineering and Project Journal, 6(12). https://doi.org/10.22034/cpj.2024.466068.1308
Fooeik, A. M. L., Sadjadi, S. J., & Mohammadi, E. (2024). Stochastic Portfolio Optimization: A Regret-Based Approach on Volatility Risk Measures: An Empirical Evidence From the New York Stock Market. PLoS One, 19(4), e0299699. https://doi.org/10.1371/journal.pone.0299699
Gheisariha, E., Tavana, M., Jolai, F., & Rabiee, M. (2021). A simulation-optimization model for solving flexible flow shop scheduling problems with rework and transportation. Math. Comput. Simul., 180, 152-178. https://doi.org/10.1016/j.matcom.2020.08.019
Guan, X., Servranckx, T., & Vanhoucke, M. (2024). Risk response budget allocation based on fault tree analysis and optimization. Annals of Operations Research, 337(2), 523-564. https://doi.org/10.1007/s10479-022-05155-8ER -
Gupta, R. K., & Khan, D. (2022). Heuristic solutions for interval-valued games. Iranian Journal of Numerical Analysis and Optimization, 2, 187-200.
Hamdy, S. M., Danial, A. W., Gad El-Rab, S. M. F., & et al. (2022). Production and optimization of bioplastic (Polyhydroxybutyrate) from Bacillus cereus strain SH-02 using response surface methodology. BMC Microbiol, 22, 183. https://doi.org/10.1186/s12866-022-02593-z
Hyun, H., Yoon, I., Lee, H. S., Park, M., & Lee, J. (2021). Multi-objective optimization for modular unit production lines focusing on crew allocation and production performance. Autom. Constr., 125, 103581. https://doi.org/10.1016/j.autcon.2021.103581
Kashyap, A. K., Tsomocos, D. P., & Vardoulakis, A. P. (2024). Optimal bank regulation in the presence of credit and run risk. Journal of Political Economy, 132(3), 772-823. https://doi.org/10.1086/726909
Khan, D., & Gupta, R. K. (2023). Production optimization with the maintenance of environmental sustainability based on multi-criteria decision analysis. Environ Dev Sustain. https://doi.org/10.1007/s10668-023-03316-8
Krein, J., & Pelton, M. (2023). Production Management. In Careers in Food Science: From Undergraduate to Professional. https://doi.org/10.1007/978-3-031-14353-3_15
Li, X. (2021). Research on optimization of setup time for product in the multi-product production system. Microprocessors Microsyst., 80, 103618. https://doi.org/10.1016/j.micpro.2020.103618
Mokhtari, H., & Shirani, H. (2021). Planning a Multi-Stage Production-Rework System Considering Energy Consumption. Industrial Management Studies, 62, 226-257.
Nadiri, M., Mehrjou, M., & Naderi, J. (2022). Dynamics of Value at Risk: An Optimized Copula-VAR Approach with a PSO Metaheuristic Algorithm. Financial Management Strategy, 37.
Pasha, N., Amoozad Mahdiraji, H., & Razavi Hajiagha, S. H. (2023). A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology. Oper Manag Res. https://doi.org/10.1007/s12063-023-00412-w
Romero, D., & et al. (2021). Advances in Production Management Systems: Issues, Trends, and Vision Towards 2030 Advancing Research in Information and Communication Technology (Vol. 600). Springer, Cham.
Safaeian, M., Moses, R., Ozguven, E. E., & Dulebenets, M. A. (2024). An optimization- based risk management framework with risk interdependence for effective disaster risk reduction. Progress in Disaster Science, 21, 100313. https://doi.org/10.1016/j.pdisas.2024.100313
Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. J. Manuf. Syst., 54, 138-151. https://doi.org/10.1016/j.jmsy.2019.11.004
Shakhsi Niaei, M., & Ghiour, F. (2021). Presenting a Multi-Product Aggregate Production Planning Model by Combining Multi-Criteria Decision Making for Production Preferences and Multi-Objective Optimization: Case Study of Industrial Gas Valve Production. Industrial Management Studies, 63, 164-192.
Skėrė, S., Žvironienė, A., Juzėnas, K., & Petraitienė, S. (2023). Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises". Sensors, 23(9), 4498. https://doi.org/10.3390/s23094498
Song, D. (2025). Machine Learning for Price Prediction and Risk -Adjusted Portfolio Optimization in Cryptocurrencies. https://doi.org/https://doi.org/10.4018/979 -8 -3693 -8186 -1.ch013
Urbaniak, M., Rogala, P., & Kafel, P. (2023). Expectations of manufacturing companies regarding future priorities of improvement actions taken by their suppliers. Oper Manag Res, 16, 296-310. https://doi.org/10.1007/s12063-022-00307-2
Verhaelen, B., Martin, M., Peukert, S., & et al. (2023). Practice-oriented methodology for increasing production ramp-up efficiency in global production networks of SME. Prod. Eng. Res. Devel., 17, 145-177. https://doi.org/10.1007/s11740-022-01154-7
Weerasuriya, A. U., Zhang, X., Wang, J., Lu, B., Tse, K. T., & Liu, C. H. (2021). Performance evaluation of population-based metaheuristic algorithms and decision-making for multi-objective optimization of building design. Build. Environ., 198, 107855. https://doi.org/10.1016/j.buildenv.2021.107855
Winkler, H., Franke, F., Franke, S., & Riedel, R. (2022). Optimization of Production Processes in SMEs: Practical Methodology for the Acquisition of Process Information. Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, https://doi.org/10.1007/978-3-031-16407-1_7
Xu, W., & Song, D. P. (2022). Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies. Oper Res Int J, 22, 2343-2371. https://doi.org/10.1007/s12351-020-00609-y
Zhang, Y., Tochev, E., Ratchev, S., & German, C. (2020). Production process optimization using feature selection methods. Procedia CIRP, 88, 554-559. https://doi.org/10.1016/j.procir.2020.05.096
Zuo, F., & Zio, E. (2024). Managing secondary risks with optimal risk response strategy and risk-related resource scheduling. Reliability Engineering & System Safety, 245IS - 1, 110028.
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Copyright (c) 2025 Mehdi Bazesh (Author); Hassan Mehrmanesh (Corresponding author); Seyyed Zabihollah Hashemi (Author)
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