Turkey Industry Research Articles

Fig industry in Turkey: production and marketing

Fig industry in Turkey: production and marketing

Evaluation of absenteeism and presenteeism status of factory employees.

While absenteeism refers to not attending work, presenteeism is defined as not being present at work. These two conditions, which negatively affect employee health, can be indicators of work efficiency, work peace, work safety and work engagement. Several factors were evaluated in this study concerning absenteeism and presenteeism among factory workers. This cross-sectional study was conducted among workers in a factory operating in a heavy industry in southern Turkey in 2021. A survey consisting of 57 questions was applied to 152 factory workers by face-to-face interview method. The participants' behaviour over the last month was evaluated regarding absenteeism and presenteeism. It was reported that 24 (15.8%) of all employees were absent from work in the last month (absenteeism), excluding holidays and sick leave, and that 20 (13.2%) employees engaged in presenteeism, working when they should not have been at the workplace. There was a high absenteeism rate among employees who had an acute illness and were dissatisfied with their jobs. Several factors have been associated with presenteeism, including poor economic conditions, family health problems, previous unemployment, working overtime, job dissatisfaction, poor relationships with colleagues and difficulty finding a replacement, acute illness, sleep problems and fatigue. To increase the productivity and commitment of employees, it may be useful to identify the reasons for absenteeism and present behaviours, to facilitate effective interpersonal communication skills by examining the organisational climate of employees, and to regulate workload based on a comprehensive assessment of the health status of employees.

Techno economic approach of biogas in Konya Türkiye

In determining biogas potential, the current number of materials that can be used in biogas production is as important as the availability and usability of the materials. Potential studies that were conducted without knowing the structures of the systems in which these materials could be evaluated give us only the theoretical upper limit that can be reached. The ‘theoretical’, ‘technical’ and ‘economic’ potential distinctions made to prevent the error from occurring unfortunately only take into account the availability of the materials, and the process sizes and properties are either very limited or not at all. This causes the facilities established by considering existing potential research to fail in implementation or operate at low capacity. In this study, the importance of the international production chain of the apparel industry in Turkey’s economy is examined. The study is based on Konya, which has a total of 1,079,644 cattle animals, 737,395 of which are dairy cattle. In the study, the number of dairy cattle with high waste collectability rates was particularly taken into consideration. Thus, the seasonal number of changes that may occur was tried to be avoided. In this study, the number of animals per farm and the distances between businesses were taken into consideration, and the biogas systems that could be installed were evaluated within this framework. Series of analysis were made under 3 different scenarios. In the first scenario, it was assumed that biogas systems would be installed by waste-generating enterprises. The installation of village-type common biogas systems was examined when possible within the scope of the second scenario. In the third scenario, the installation of district-based biogas systems was considered. Considering the difficulties in collecting the waste of enterprises with less than 50 cattle (due to the lack of regular collection systems), it is assumed that the waste of these enterprises cannot be used in village or central systems. According to the data obtained, Scenario 1 stands out in terms of creating employment and reducing environmental problems. The 3rd Scenario was the most profitable in macroeconomic terms. However, it was emphasized that the 1st and 2nd Scenarios stand out in terms of the sustainability of the systems.

The Impact of Green Business Ethics and Green Financing on Sustainable Business Performance of Industries in Turkey: The Mediating Role of Corporate Social Responsibility

The purpose of this research is to understand the relationship between green business ethics, green finance, and sustainable business performance, and to evaluate the role of corporate social responsibility (CSR) in this relationship. The impact of the damage inflicted on nature’s functioning order is being felt much more strongly today. In light of these realities, companies must emphasize sustainability principles not just out of financial concerns but as a result of corporate social responsibility. In this context, focusing on the role of corporate social responsibility in sustainable business performance is the main goal of this research. Quantitative research methods, specifically the cross-sectional survey method, were employed for data collection and analysis. For this purpose, a convenience sampling method was used to select 427 white-collar employees working in industries operating in Türkiye as the sample for this study. The data collected through surveys were analyzed using the AMOS 24 statistical program. The findings underscore that green business ethics and green finance have a significant impact on corporate social responsibility and sustainable business performance. Additionally, it was determined that corporate social responsibility plays an intermediary role in shaping sustainable business performance. These findings are expected to provide an important foundation that can guide both employees and managers in developing awareness about green policies and sustainability, emphasizing the importance of green policies in working life.

Towards sustainable mining in an emerging economy: Assessment of sustainability challenges

The minerals industry in Turkey is critical to the nation's financial system and has a significant influence on its economic growth and well-being. The goal of this investigation is to undertake a complete review of the minerals sector's sustainability concerns, utilizing fuzzy decision-making to generate enlightening answers. Therefore, the major purpose of this research is to recognize and highlight sustainability concerns in the Turkish mining or minerals sector. This study employs expert opinions and a review of the literature to develop a complete set of criteria that will serve as the basis for the Fuzzy Best-Worst Method (FBWM), which is utilized for evaluating observed challenges. According to the findings of the study, “safety” is the most significant challenge that must be addressed immediately to safeguard the health and safety of mining people as well as the long-term sustainability of operations. This study contributes to an existing body of literature by examining industrial sustainability challenges using the FBWM technique. Using FBWM to acquire a deeper knowledge of the difficulties, researchers, stakeholders in the industry, and policymakers can get valuable insights into the connections between them.

Artificial neural network-based risk assessment for occupational accidents in the shipbuilding industry in Turkey

Artificial neural network-based risk assessment for occupational accidents in the shipbuilding industry in Turkey

Resilience and Innovation Strategies: How Businesses Adapt to Supply Chain Disruptions in the Post-Pandemic Era

The study explores strategies for dealing with supply chain disruptions in various industries in Turkey after the pandemic. The industries studied include manufacturing, pharmaceuticals, automotive, consumer electronics, retail, and food and beverage. The analysis involved using both qualitative data from interviews with six industry experts and quantitative analysis using NVivo and Python. NVivo and Python were utilized for thematic and sentiment analysis to summarize the findings. Common strategies identified include diversifying supply sources, integrating technology, and improving risk management. The study also revealed a consistent shift towards building resilient and agile supply chain systems. Additionally, the findings highlighted that while common strategies apply across different sectors, sector-specific differences also need to be considered when developing tailored strategies to address sector-specific challenges. This study not only addresses a significant gap in empirical insights into post-pandemic supply chain management but also provides managerial and policy recommendations for building supply chain resilience and fostering innovation in these sectors.

Understanding the Effects of Organizational Climate and Creative Work Involvement on Readiness for Organizational Change: A Research on Shipbuilding Industry in Turkey

The main objective of this article is to conduct an empirical and exploratory study on the effects of organizational climate and creative work involvement on readiness for organizational change to shed light on the readiness of the board members, professional managers, and other employees of the Turkish shipbuilding sector for the trend of organizational change, the frequency of which is increasing rapidly on both a global and national basis, in order to achieve sustainable success within the framework of sustainable development. To investigate the impact of organizational climate and creative work involvement on readiness for organizational change, Turkish shipbuilding companies and leading ship design service providers were surveyed. The results showed that organizational climate and creative work involvement positively and significantly affect readiness for organizational change, where organizational climate and creative work involvement reflected the most significant positive effects, respectively, on the management support and change efficacy subdimensions of readiness for organizational change. The contribution to the literature lies in the unique approach to this relation, which has not been explored in previous studies. This research opens up a new direction for empirical research on readiness for organizational change by combining these concepts.

Fabric defects identification for textile industry with a deep learning approach

The textile and clothing industries are crucial sectors that make significant contributions to the economic growth and advancement of countries. In addition to low labor costs, the sector’s development performance is influenced by flexible production structure and defect-free products manufacturing. However, in the textile companies, the manufacture, processing, and weaving of fabric can lead to undesirable defects, which ultimately leads to a decline in both quality and cost. This study investigates the effectiveness of different deep learning frameworks to accurately classify fabric defects commonly encountered in the textile industry in Turkey. For this purpose, an innovative data set is generated as comprising fabric defects such as lines, wrinkle marks, machine oil leaks, holes, and bleaching. Furthermore, the effectiveness of Adam and Ranger optimization functions in defect identification has been evaluated using various models in connection with explainable artificial intelligence. The findings indicate that the ResNet18 + Adam model, which is very simple and shallow, obtained a notable level of success with an accuracy of 99.30%. On the other hand, the more complex EfficientNetv2m + Adam model emerged by achieving an accuracy of 99.42%. The research results suggest that deep learning algorithms can effectively replace human manual control in the task of detecting fabric defects.

A comparative study on photodegradation of twenty-three wood species after UV irradiation

The study investigates the effect of UV irradiation on different native and exotic wood species which is an open subject of research. Thirteen native wood species and ten exotic wood species, totally twenty-three wood species, all important for wood industry in Turkey, were subjected to UVA-340nm irradiation with a constant irradiance of 0.89 W/m2/nm and a temperature of 60 °C for 2, 4, 8, 16, 24, 48, 72, 96, 120, 144, 168, 252 and 336 h to determine the critical degradation time for the wood species. A short exposure time (from 2h to 48h) was especially studied since the previous studies reported that important and rapid changes occurred within the initial exposure periods. The effect of UV irradiation on wood surfaces was investigated by color, surface roughness, and FTIR-ATR measurements. The results proved that UV radiation caused a rapid color change for extractive-rich wood species at the initial period of exposure (16h–48h) which decreases upon prolonged exposure. The overall color change and surface roughness were higher for native wood species in a long exposure period than for exotic ones. Elm, oak, plane, walnut, acacia and linden as native wood species, iroko, tiama, acajou, okoume, garapa, sapelli, dibetou and teak as exotic wood species were found to be more resistant to color changes in prolonged irradiation periods, probably due to content of their components especially extractives and lignin. Similar to greater color change found within the beginning of the irradiation period (16h–48h), the first 8h–24h seemed to cause a greater increase in roughness for exotic species and 16h–24h for native wood species probably due to the changes in surface chemistry. Acacia, elm, cherry, beech, tiama, teak and okoume had the highest RI whilst S. pine, ash, poplar, plane, sapelli, limba and iroko had the lowest RI among the wood species. FTIR–ATR analysis demonstrated that UV light ended up with photodegradation of lignin and photooxidation of hemicelluloses which leads carbonyl-containing chromophores, even after short-time exposures (8h–24h).

High-resolution Serotyping Reveals Salmonella Surveillance Challenges in the Turkey Industry

Despite extensive Salmonella controls used at processing, 5.5% of salmonellosis cases are linked to turkey. This study had two objectives: (i) to summarize USDA-FSIS turkey Salmonella verification program data and (ii) to evaluate Salmonella through turkey production and processing of 22 flocks. In objective 1, USDA-FSIS data show the average Salmonella prevalence in ground turkey from 2016 to 2022 was 15.9%, and that the leading serovar changes frequently. For objective 2, bootsocks (n = 22) were collected on-farm right after load-out. At processing, prescald wingtips (n = 6 composites of 10/flock), prechill wingtips (n = 6 composites of 10/flock), mechanically separated turkey (MST; n = 6 bins/flock), and ground turkey (n = 6 bins/flock) were collected. Salmonella prevalence was determined by a commercial qPCR and culture confirmed. In 33.2% of PCR-positive samples, Salmonella was not confirmed by culture, highlighting a discrepancy between molecular and culture detection. On-farm, 8/22 flocks were Salmonella positive, compared to 21 flocks that were positive at one or more processing locations, including 18 flocks that were positive in at least one final product sample. A logistic regression showed higher Salmonella prevalence in prescald (53.8%) than in prechill (18.2%), MST (27.3%) or ground turkey (26.5%). CRISPR-SeroSeq analysis of 148 culture−positive samples detected 18 Salmonella serovars and showed 35.1% of samples contained multiple serovars. In 16 flocks, one or more serovars detected in final products were absent from any upstream samples. Two−thirds of final product samples containing serovar Typhimurium typed as a live-attenuated Typhimurium vaccine strain. Salmonella on-farm and at prescald did not reflect Salmonella observed in final product. These data underscore the complexity of serovar tracking in turkey production and highlight challenges to identify surveillance samples that accurately represent Salmonella in turkey products.

Cochlosoma anatis in Poultry and its Industry Impact.

Cochlosoma anatis is a flagellated protozoan parasite classified in the Trichomonadidae family and is the causative agent of cochlosomiasis, an enteric disease of turkeys, waterfowl, and other wild birds. Cochlosomiasis symptoms largely consist of watery diarrhea, lethargic birds, depressed weight gain, and widespread flock morbidity causing flock nonuniformity. The known distribution of C. anatis is centered around areas of turkey production farms in the southeast United States, e.g., North Carolina, Missouri and Arkansas, but has been reported in other states and some other countries. Diagnosis is confirmed through examination of enteric mucosal scrapings using light microscopy. Following the withdrawal of approval of effective antiprotozoal medications for use in commercial animal production, cochlosomiasis has become a greater concern for commercial turkey industry professionals. Transmission of C. anatis occurs via the fecal-oral route, but the organism is fragile outside the host, suggesting the implication of a vector in the introduction of disease to susceptible farms. Research regarding C. anatis pathogenicity, transmission, and environmental involvement has been limited, creating a gap in cochlosomiasis knowledge. Future research is needed to further explore ways to prevent and treat cochlosomiasis, with needs centered on disease pathogenesis, transmission patterns, and prophylaxis and treatment methods.

Location selection for a lumber drying facility via a hybrid Pythagorean fuzzy decision-making approach

The strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.

The Automotive Industry in Turkey

The Automotive Industry in Turkey

Genetic architecture of white striping in turkeys (Meleagris gallopavo).

White striping (WS) is a myopathy of growing concern to the turkey industry. It is rising in prevalence and has negative consequences for consumer acceptance and the functional properties of turkey meat. The objective of this studywas to conduct a genome-wide association study (GWAS) and functional analysis on WS severity. Phenotypic data consisted of white striping scored on turkey breast fillets (N = 8422) by trained observers on a 0-3 scale (none to severe). Of the phenotyped birds, 4667 genotypic records were available using a proprietary 65K single nucleotide polymorphism (SNP) chip. The SNP effects were estimated using a linear mixed model with a 30-SNP sliding window approach used to express the percentage genetic variance explained. Positional candidate genes were those located within 50kb of the top 1% of SNP windows explaining the most genetic variance. Of the 95 positional candidate genes, seven were further classified as functional candidate genes because of their association with both a significant gene ontology and molecular function term. The results of the GWAS emphasize the polygenic nature of the trait with no specific genomic region contributing a large portion to the overall genetic variance. Significant pathways relating to growth, muscle development, collagen formation, circulatory system development, cell response to stimulus, and cytokine production were identified. These results help to support published biological associations between WS and hypoxia and oxidative stress and provide information that may be useful for future-omics studies in understanding the biological associations with WS development in turkeys.

The Automotive Industry in Turkey

The Automotive Industry in Turkey

Concurrent Histomonas meleagridis and Hemorrhagic Enteritis Virus Infection in a Turkey Flock with Recurrent History of Blackhead Disease.

Intestinal health is one of the key factors required for the growth and production of turkeys. Histomoniasis (blackhead disease), caused by a protozoan parasite, Histomonas meleagridis, is a reemerging threat to the turkey industry. Increased incidences of histomoniasis have been reported in recent years due to withdrawal of antihistomonas treatments. H. meleagridis affects ceca and causes cecal inflammation and necrosis. H. meleagridis migrates from ceca to the liver and causes liver necrosis, resulting in high mortalities. Ironically, field outbreaks of histomoniasis are not always associated with high mortalities, while low mortalities have also been documented. There are several exacerbating factors associated with high mortality rates in histomoniasis outbreaks, with concurrent infection being one of them. Recurrent histomoniasis outbreaks in a newly constructed barn were documented, and concurrent infection of H. meleagridis and hemorrhagic enteritis virus was confirmed. Currently, neither commercial vaccines nor prophylactic or therapeutic solutions are available to combat histomoniasis. However, there are treatments, vaccines, and solutions to minimize or prevent concurrent infections in turkeys. In addition to implementing biosecurity measures, measures to prevent concurrent infections are critical steps that the turkey industry can follow to reduce mortality rates and minimize the production and economic losses associated with histomoniasis outbreaks.

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus, tentatively named turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality and reduced marketability of commercial turkeys. To combat these issues, detecting and classifying the types of reoviruses in turkey populations is essential. This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types of turkey reoviruses and identify novel emerging variants. Additionally, it focuses on classifying variants of turkey reoviruses by leveraging various machine learning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, and deep learning algorithms, including convolutional neural networks (CNNs). The experiments use real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods. The results indicate that machine learning methods achieve an average accuracy of 92%, F1-Macro of 93% and F1-Weighted of 92% scores in classifying reovirus types. In contrast, the CNN model demonstrates an average accuracy of 85%, F1-Macro of 71% and F1-Weighted of 84% scores in the same classification task. The superior performance of the machine learning classifiers provides valuable insights into reovirus evolution and mutation, aiding in detecting emerging variants of pathogenic TARVs and THRVs.

Barriers to BIM Implementation in the HVAC Industry: An Exploratory Study

In recent times, the rise of urbanization, industrialization, population growth, food security, and the COVID-19 pandemic have led to an increased demand for indoor spaces with efficient air conditioning systems. As a result, there is a growing interest in creating more complex HVAC systems to improve indoor spaces. Building information modeling (BIM) offers numerous benefits to the HVAC industry, such as clash detection, budget and time reductions, and increased efficiency. However, its implementation is currently hindered by various challenges. This research aims to identify the major barriers to BIM implementation in the HVAC industry in Turkey, using a questionnaire survey of 224 domain experts working in 42 different companies across various fields of the HVAC industry. The study utilized several statistical analyses to categorize and prioritize the most critical barriers, including reliability tests, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), the Kaiser–Meyer–Olkin (KMO) test, Bartlett’s test, and ranking of factors (IRI). The results indicate that the “Deficiencies of Infrastructure and Lack of Qualified Personnel (DIP)” factor group constituted the most significant barrier, followed by “Lack of Documentation and Specifications (LDS)”, “Deficiencies of Case Studies and Project Drawings (DCP)”, and “Lack of Motivation and Resistance to BIM (LMR)”. Moreover, our research revealed that 60% of the participants’ companies allocate less than 40% of their budgets to technological infrastructure, which hinders the adoption of BIM. To promote BIM in the HVAC sector, we recommend enhancing personnel capacity building, improving skills and knowledge about BIM, promoting guidelines, and providing free access to documentation for practitioners.

Efficiency Assessment Based on Data Envelopment Analysis for Occupational Accidents and Diseases in Furniture Industry of Turkey

In this study, the provinces in which enterprises operate heavily in the Turkish furniture industrywere subjected to an efficiency assessment using the data envelopment analysis (DEA) method based on work accidentsand occupational diseases. The data related to the furniture industry was obtained from the Social Security Institution (SSI) and the Ministry of Labor and Social Security (MoLSS). The number of operating businesses and the number of employees insured in each province were evaluated as inputs for the analysis. The total number of workplace physicians and other health personnel in each province, the number of workplace physician and occupational safety centers and A, B, and C-class occupational health and safety specialists were used as outputs. The number of employees who experienced work accidents and occupational diseases, deaths resulting from work accidents and occupational diseases, total temporary disability periods, the number of beneficiaries, and the number of those receiving permanent disability income in each province were evaluated as undesirable outputs. The data was analyzed using the DEA method by modeling the undesired outputs in 6 different ways, and the efficiency of each province was determined. It was seen that Model 6 gave the most ideal results in DEA efficiency assessments. Aydın, Çanakkale, Diyarbakır, Eskişehir, Malatya, Muğla, Trabzon and Zonguldak were determined as the most effective provinces in terms of occupational health and safety. The results were evaluated along with the literature, and recommendations were presented.