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- MakaleYayıncıda açık erişim2026Scopus SJRQ4A data-driven decision-support framework for prioritizing retention interventions among high attrition-risk employee segments
Şimşek, Tuğçe; Şimşek, Aybala
Predicting employee attrition risk provides a useful signal for human resource decision-making. However, this signal alone does not determine which employee groups should be targeted first for retention interventions. This study develops a data-driven decision-support framework for prioritizing retention interventions among employees at risk of attrition. The framework uses the IBM HR Analytics Employee Attrition and Performance dataset and integrates calibrated machine learning, SHAP-based explanatory analysis, Entropy-CRITIC weighting, and PROMETHEE II ranking. Machine-learning models were first used to estimate employee-level attrition probabilities. These probabilities were then aggregated into segments defined by combinations of Department, JobRole, and OverTime, from which the high-risk segment pool was identified for multi-criteria analysis. Eight high-risk segments were detected. The segment ranked first by PROMETHEE II was not the segment with the highest attrition risk, indicating that intervention priority and attrition risk represent distinct decision outputs. Sensitivity analyses further showed that the segment-ranking structure remained stable under alternative weight-shift and threshold scenarios. The proposed framework is therefore not intended to identify individual employees at risk; rather, it supports transparent, auditable, and ethically compliant segment-level retention decisions.
- MakaleAçık Erişim2023Scopus SJRQ4A decIsIon-makIng approach for open InnovatIon model selectIon In the TurkIsh automotIve Industry
DemIr, Ayse; Peker, Iskender; Gök, A. Cansu
The concept of open InnovatIon (OI) has become crucIal In the sustaInable competItIveness of IndustrIes. The selectIon and applIcatIon of approprIate OI models for busInesses also compose an Important research questIon. ThIs study aIms to decIde the most suItable OI model In the TurkIsh automotIve Industry through a three-step multI-crIterIa decIsIon-makIng (MCDM) model. FIrstly, the crIterIa that may affect the evaluatIon of the OI models are stated and weIghted by analytIc hIerarchy process (AHP). Afterward, VlseKrIterIjumska OptImIzacIja I KompromIsno Resenje (VIKOR) Is used to rank the OI models that can be Implemented In the automotIve Industry. In the last phase, the Impacts of dIfferentIatIon In crIterIa weIghts are tested wIth one-dImensIonal sensItIvIty analysIs. AccordIng to the results, the coupled open InnovatIon model Is ranked as the best alternatIve and the dIfferentIatIon In crIterIon weIghts does not affect thIs rankIng. ThIs study Is the fIrst attempt that attaIns the most suItable OI model In the automotIve Industry by a hybrId MCDM approach.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2023Scopus SJRQ3Exploring managerial insights through multi criteria decision making techniques in pharmacy inventory classification problem
Şimşek, Ahmet Bahadır; Göktekin, Zekiye; Geliç, Büşra
The current research addresses the inventory classification problem of community pharmacies, which have a dual role as both a vital component of the pharmaceutical supply chain and a typical retail store. Despite the existing literature indicating that pharmacists may lack knowledge on inventory management, it seems that the MCIC literature is weak in explaining how pharmacists can benefit from MCDA techniques in all aspects. To bridge this gap, the study aims to demonstrate that pharmacists can utilize MCDA techniques to gain deeper insights beyond mere classification in the context of inventory management. Real-world data from a community pharmacy in Turkey was classified using the EDAS method. Sensitivity analysis was performed for MCDA inputs, about which pharmacists may lack information. Scenario findings based on criterion weights and threshold values offer important managerial implications for pharmacists. This study provides a critical contribution to the literature on inventory management in community pharmacies by highlighting the potential of MCDA techniques to support decision-making beyond mere classification. The sensitivity analysis also sheds light on areas where pharmacists may lack knowledge and suggests ways to address these gaps. Overall, the study underscores the need for pharmacists to have a deeper understanding of inventory management and highlights the potential benefits of MCDA techniques in addressing this challenge.
AtıfScopus4WoS1OpenAlex6İncele