Browsing by Author "Kabak, K.E."

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Analysis of Inoculation Strategies During Covid-19 Pandemic With an Agent-Based Simulation Approach
(Elsevier Ltd, 2025) Kulaç, O.; Toy, A.Ö.; Kabak, K.E.
Background: The severity of recent Coronavirus (COVID-19) pandemics has revealed the importance of development of inoculation strategies in case of limited vaccine availability. Authorities have implemented inoculation strategies based on perceived risk factors such as age and existence of other chronic health conditions for survivability from the disease. However, various other factors can be considered for identifying the preferred inoculation strategies depending on the vaccine availability and disease spread levels. This study explores the effectiveness of inoculating different groups of population in case of various vaccine availabilities and disease spread levels by means of some performance metrics namely: Attack Rate (AR), Death Rate (DR) and Hospitalization Rate (HR). Method: In this study we have implemented a highly detailed Agent-Based Simulation (ABS) model that extends classical SEIR Model by including five more additional states: Asymptomatic (A), Quarantine (Q), Hospitalized (H), Dead (D) and Immune (M) which can be used as a decision support tool to prioritize the groups of the population inoculated. The approach employs the modelling of daily mobility of individuals, their interactions and transmission of virus among individuals. The population is heterogeneously clustered according to age, family size, work status, transportation and leisure preferences with 17 different groups in order to find the most appropriate one to inoculate. Three different Disease Spread Levels (DSL) (low, mid, high) are experimented with four different Vaccine Available Percentages (VAP) (25%, 50%, 75% and 85%) with a total of 84 scenarios. Results: As the benchmark, under the No Vaccine case Attack Rate, Hospitalization Rate, and Death Rate goes as high as 99.53%, 16.96%, and 1.38%, respectively. Corresponding highest performance metrics (rates) are 72.33%, 15.95%, and 1.35% for VAP = 25%; 50.25%, 9.55%, and 0.94% for VAP = 50%; 24.53%, 2.62%, and 0.25% for VAP = 75%; and 11.51%, 0.002%, and 0.08% for VAP = 85%. The results of our study shows that the common practice of inoculation based on the age of individual does not yield the best outcome in terms of performance metrics across all DSL and VAP values. The groups containing workers and students that represent highly interactive individuals, i.e. Group (9, 10), Group (9, 11, 10‾) and Group (9, 10, 11, 12‾) emerge as a commonly recommended choice for inoculation in the majority of cases. As expected, we observe that the higher is the VAP levels the more is the number of alternative inoculation groups. Conclusions: Findings of this study present that: (i) inoculation considerably decreases the number of infected individuals, the number of deaths and the number of hospitalized individuals due to the disease, (ii) the best inoculation group/groups with respect to performance metrics varies depending on the vaccine availability percentages and disease spread levels, (iii) simultaneous implementation of both inoculation and precautions like lock-down, social distances and quarantines, yields a stronger impact on disease spread and its consequences. © 2024 Elsevier Ltd
Shared Manufacturing and the Sharing Economy Ideal: Strategic Limits in a Fragmenting World
(Research and Development Academy, 2025) Yeralan, S.; Kabak, K.E.
This study offers a strategic critique of shared manufacturing (SharedMfg), a concept rooted in the broader sharing economy (SE) and promoted as a mechanism for optimizing industrial capacity through peer-to-peer coordination. While such frameworks emphasize digital platforms, scheduling efficiency, and resource pooling, they frequently neglect the deeper constraints that govern the real-world feasibility of manufacturing. In particular, SharedMfg models are often constructed atop idealized abstractions, treating manufacturing units as modular, cyber-physical assets within an Industry 4.0 ecosystem, while overlooking the material, energetic, and geopolitical foundations on which all manufacturing ultimately depends. Extending beyond critique, we explore the conceptual underpinnings of SharedMfg within its systemic context, a prelude to the layered pyramid model advanced in this study. This paper argues that manufacturing does not evolve autonomously, but rather reflects the socio-political order in which it is embedded. To address this oversight, we propose a layered conceptual framework – a manufacturing transformation pyramid – that begins not with coordination, but with the substrate: matter, energy, and institutional structure. We contend that genuine transformation in manufacturing systems must be grounded in these foundational realities, rather than in digital optimization alone. Absent this grounding, SharedMfg/SE risks becoming a transient theoretical exercise, bounded by the specific conditions of its historical moment and detached from the structural realities that shape industrial capacity. © The Author 2025.
Citation - WoS: 1
Using Ai Tools To Enhance the Risk Management Process in the Automotive Industry
(Springer Science and Business Media Deutschland GmbH, 2024) Dragomir, D.; Popișter, F.; Kabak, K.E.
The paper presents an exploratory investigation concerning the usage of AI tools in automotive companies in order to streamline their risk management processes. A risk identification procedure is performed at organizational and process levels, and a comparative analysis is undertaken between the classical approach for developing proper mitigation measures and the AI-supported manner of doing the same. Some of the most popular tools in this field are employed and studied, such as large language models, data analytics and knowledge representation. The differences and changes are analyzed from the point of view of their effectiveness, efficiency and adaptability within the existing manufacturing frameworks in the automotive industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.