Global climate change continues to have a negative impact on many sectors, particularly supply chains and logistics. Companies are progressively incorporating environmentally friendly practices into their strategies to tackle this global problem. Accordingly, the increase in environmental, social, and governance (ESG) risks has increased the importance of sustainable corporate governance. As a result, companies tend to place more emphasis on ESG initiatives. The study aims to analyze the importance levels of ESG criteria and to select the most ideal environmentally sensitive competitive strategy for multinational logistics companies. For this purpose, a set of criteria and alternatives for the businesses in the context of the related problem was defined, and a decision model was proposed to handle problems, which include uncertain information. In this context, q‐rung orthopair fuzzy (q‐ROF) weighted average technique was used for weighting the criteria and q‐ROF‐evaluation based on distance from average solution (q‐ROF‐EDAS) method was utilized for prioritizing the strategies. As a result, “greenhouse gas emissions” was identified as the most important criterion, while “eco‐efficiency” was selected as the best option for environmentally conscious competitive strategies.

Purpose: Knowledge management (KM) significantly affects supply chain management (SCM) and its performance in today's highly competitive corporate climate. It is crucial to consider this relationship to achieve optimal supply chain performance (SCP). This study aims to assess this impact by defining and examining the multi-dimensional relationships between KM Process Elements (KMPEs) and SCP Evaluation Criteria (SCPEC) within a comprehensive theoretical framework.Design/methodology/approach: Integrating KMPEs and SCPEC becomes an uncertain decision-making problem due to data deficiency and the vagueness of decision-makers’ judgments. To address uncertainties, this study uses interval-valued neutrosophic (IVN) sets and proposes an IVN model consisting of SWARA, which is one of the effective multi-criteria decision-making (MCDM) approaches, and house of quality (HOQ) methods. IVN-SWARA is used to weight the SCPEC while IVN-HOQ establishes relationships and prioritizes the KMPEs and SCPEC.Findings: The results show that reliability is the most significant SCP evaluation criterion. Among the KMPEs, capitalization, sharing, and transfer exhibit stronger associations with the SCPEC compared to the other elements. Capitalization as one of the KMPEs was found to be the most critical one, and efficiency is the criterion most affected by all elements of the KM process.Originality/value: This study uses innovative methodologies to evaluate the adoption of KM processes on SCP under uncertain environments and involving multi-decision-makers. The proposed integrated model demonstrates flexibility and practicality in combining KM and SCM, leading to improved SCP. Notably, this study presents the development of IVN-SWARA and the use of the integrated IVN-SWARA - IVN-HOQ decision tool, which are novel contributions to the existing literature.

Occupational accidents in the construction industry occur frequently when compared with other industries. Construction accidents still cannot be prevented at the desired level. These accidents have been explored for prediction purposes using traditional statistical and machine-learning techniques. However, some challenges regarding imbalanced and multicollinearity problems present in the dataset are not considered while analyzing large and categorical variable-rich data. For this reason, this study aims to predict the severity of non-fatal construction accidents considering these challenges to obtain accurate results. In this study, standard binary logistic regression with Firth, Ridge, Lasso, and Elastic Net regularized models were utilized for the prediction of lost workdays in the construction industry. The data used were classified into five groups: victim, workplace, accident time, accident and sequence of events, and post-accident state-related variables. The results showed that Firth’s logistic model is the best-performing model and age, education, vocational education, size of the workplace, project type, working environment, accident month and year, general and specific activities, material agent of deviation, type of injury, and part of body injured are the most significant variables. This study by providing interpretable machine learning tools is the first attempt to use proposed models in the construction safety field.

In the quest to reduce occupational accidents and diseases, the ergonomic performance levels of industries remain pivotal. Within this context, the metal industry in Turkiye, notorious for ergonomic challenges, was scrutinised regarding its occupational health and safety (OHS) indicators. Five pivotal criteria were employed to delineate the industry's performance: the incidence of occupational accidents, the occurrence of fatal occupational accidents, the reporting rate of occupational diseases, the cumulative days of temporary incapacity, and the overall count of insured individuals obtaining permanent incapacity benefits. A decadal period, spanning 2013-2022, served as the temporal backdrop for this examination. Utilising the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, an esteemed Multiple-Criteria Decision-Making (MCDM) technique, an assessment was conducted to ascertain the years marred by sub-optimal ergonomic performance. Notably, 2014, 2013, and 2020 were identified as the least problematic years, whereas 2022 emerged as the most critical year. This investigation underscores the imperative for strategic planning to augment ergonomic conditions in professional settings in light of OHS, particularly in recent times.

İşyerinde uygun olmayan vücut çalışma duruşları, gürültü, sıcaklık, vb. ergonomik riskler sonucunda ortaya çıkan işe bağlı kas-iskelet sistemi rahatsızlıkları (İKİSR), sanayileşmiş ülkelerde karşılaşılan büyük bir sağlık problemidir. İşyerinde alınacak tedbirlerle İKİSR büyük ölçüde önlenebilmektedir. Hızlı Tüm Vücut Değerlendirmesi (Rapid Entire Body Assessment-REBA) yöntemi, İKİSR için çalışanın tüm vücudunu göz önüne alması dolayısıyla, çalışma duruş analizlerini yapmak ve yapılan işlerin risk düzeylerini belirlemek amacıyla yaygın olarak kullanılan gözlemsel araçlardan biridir. REBA yönteminde risklerin değerlendirilmesi esnasında uzmanların görüşlerinin bir sınıflandırma ile tarif edilmesi ve sözel değerlerin kullanılması nedeniyle belirsiz durumlar ortaya çıkmaktadır. Bu belirsizlikler bulanık sayılar kullanılarak analiz edilebilir. Bu çalışmada, bir mobilya fabrikasında yapılan işlerin İKİSR açısından ne kadar riskli olduğunun ve bu risklerin önlenmesi için yapılacak müdahalelerin belirlenmesi amaçlanmıştır. Çalışma duruşundan kaynaklanan riski değerlendirmede Bulanık REBA yöntemi kullanılmıştır. Bulanık REBA yöntemi iki farklı hesaplama metodolojisini ele alarak uygulanmıştır. Bu çalışmada, incelenmeye alınan farklı istasyonlara ait altmış üç adet duruş fotoğrafından ayrıntılı olarak iki çalışma duruşunun risk değerlendirilmesi anlatılmıştır. Ayrıca, iki iş istasyonunda iş yükünü dolaylı olarak etkileyen gürültü, toz, sıcaklık, nem ve aydınlatma düzeyleri ölçülmüştür. Ardından, analiz sonuçlarına göre ortaya çıkan riskleri azaltmak için yapılabilecek ergonomik iyileştirmeler ele alınmıştır.