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A Framework for Evaluating Information Transparency in Supply Chains
Get PDFPrivate, public, profit, and non-profit organizations and society as a whole currently face a significant reliable information necessity problem. Especially supply chains need trustworthy information to perform their activities successfully. This study aims to propose a framework and identify how reliability of information can be evaluated and measured through the concept of transparency. In this context, dimensions such as comprehensiveness, regularity, timeliness, content, scope, and user-friendliness are the pillars of the proposed framework. Selected criteria have been used as inputs to develop the information transparency level. The fuzzy analytic network process (ANP) is used to obtain weights of these inputs, and data envelopment analysis (DEA) is used for the determination of the efficiency ranking for transparency. Results demonstrated that content, scope, and comprehensiveness dimensions have 75% impact on the transparency of data. The remaining 25% is affected by timeliness, regularity, and user-friendliness
Problematic Mukbang Watching and Its Relationship to Disordered Eating and Internet Addiction: A Pilot Study Among Emerging Adult Mukbang Watchers
No full textInternet technology has facilitated the use of a wide variety of different activities and applications in online contexts. One such activity is watching mukbang (i.e., watching videos of eating broadcasts where someone eats a large amount of food while inPsychology, Clinical; Substance Abuse; PsychiatryPsychology; Substance Abuse; Psychiatr
Stock market prediction in brics countries using linear regression and artificial neural network hybrid models
No full textThe BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)-Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets
How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders
Get PDFObjectives: Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care, they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore, understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. Methods: Month and week of the year, day of the week, and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models, obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. Results: Day of the week, and number of patients with ICD-10 codes of ‘A00-B99’, ‘I00-I99’, ‘J00-J99’, ‘M00-M99’ and ‘R00-R99’ were significant in both test types. In addition to these, although daily patient frequencies with ‘H60-H95’, ‘N00-N99’ and ‘O00-O9A’ were significant for laboratory services, ‘L00-L99’, ‘S00-T88’ and ‘Z00-Z99’ were significant for imaging services. Although prediction accuracies of regression models were, respectively, as 93.658% and 95.028% for laboratory and imaging services modelling, they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. Conclusion: The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays, thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making
Sağlık sektörü birinci basamak özelinde kardiyovasküler risk tahminlemesi için yapay öğrenme teknikleri kullanılarak klinik karar destek sistemi tasarlanması
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A multiphase acceptance sampling model by attributes to investigate the production interruptions in batch production within tobacco industry
Get PDFPurpose: This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production. Design/methodology/approach: Based on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models. Findings: A three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP. Practical implications: The current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure. Originality/value: Acceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated