Industrial Engineering in the Age of Business Intelligence

Evaluation based on Distance from Average Solution (EDAS) is an efficient multi-criteria decision making (MCDM) method, which determines the desirability of alternatives based on the total distance of alternatives from their corresponding averages for each criterion. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, use the idea of Pythagorean and Neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. On the other hand, interval-valued spherical fuzzy sets provide an increased area of fuzziness modeling capacity than the first single-valued type. This paper proposes a new interval-valued spherical fuzzy EDAS method and provides extra space for catching the vagueness in the nature of decision-making problems. The feasibility and practicality of the proposed model are illustrated with an application for evaluating the information technology (IT) auditor selection problem. Sensitivity analyses for criterion and decision-maker weights and a comparative analysis are also presented in the study.

The economic situation in Lebanon is abominable, vast supply chain materials are being cut, and businesses are closing. A huge bulk of products in Lebanon are imported from overseas. In this paper, two forecasting techniques are used to forecast future demand potentially imported from countries around the world to Lebanon. The first suggested technique is simple exponential smoothing, and the second technique is Winter's method.

Many studies have been conducted in the area of transaction costs. However, these studies have generally focused on testing which decisions companies take in case of a shock or transformation and how the transaction cost affects this. However, the literature study showed that when companies need to undergo a certain transformation and strategically create a product/project, there is a lack of decision-making technique in the literature that reveals whether they should do this job with internal or external resources according to the conditions. With our digitalizing age, technology companies need this decision-making technique. The main purpose of this paper is to create a decision-making method in which technology companies will decide with which source to do this work at the stage of product or project development. In line with the study, the cost of infrastructure and development and the cost of uncertainty and time emerged as the most important cost items to determine the decision. The importance levels of all cost items were also determined with this study. In this way, companies will be able to make more effective decisions.

Understanding the behaviors and attitudes of different generations is crucial not only for individuals but also for the organizations. Utility Theory is a well-known and well-accepted behavioral economics theory that explains the human decision-making process, yet it has some limitations. Prospect Theory has improved Utility Theory by eliminating the contradictions in its fundamental assumptions. Prospect theory can be very beneficial for understanding the difference between generations. In this study, we use Prospect Theory’s certainty and reflection effects for understanding the reactions of different generations (i.e., Gen X, Gen Y, Gen Z) to the same conditions. We also analyze the risk attitudes of generations by using Cumulative Prospect Theory’s questions. A questionnaire including 28 questions with 145 participants is used for collecting data. The results show that the predicted certainty effect is valid for different generations; however, Gen X and Gen Z do not show the predicted reflection effect. The results also show that different generations reacted differently in some of the gain and loss gambles when compared to the Cumulative Prospect Theory’s findings.

Repetitive motions, lifting tasks, and work pace may cause ergonomic risks for the workers since they constitute a basis for the growth of work-related musculoskeletal disorders (WMSD) that adversely affect the efficiency of manufacturing. With the development of technology, several ways to prevent these risks are possible. Recently, robots that collaborate with humans assist workers by undertaking heavy and repetitive tasks. However, in order to achieve this benefit, many issues such as design, integration, control, task assignment, scheduling of robots must carefully be considered. Therefore, new research topics in this area, along with the need for new methods and tools, have arisen. This study contributes to the Human-Robot Collaboration (HRC) literature by evaluating the studies that deal with ergonomics in design and planning together. High-quality articles from 2009 to the beginning of 2021 are included in this literature review. The main intent of this paper is to highlight gaps and contradictions of previous studies.

In recent years, many companies have aimed to increase the motivation and performance of their employees by using gamification to reach their business goals. This study investigates the factors affecting the sales employees’ adoption of gamification by extending the Technology Acceptance Model (TAM). A research model is constructed by including subjective norms from Planned Behavior Theory (PDT), perceived competence from Self-determination Theory (SDT), gamification, and job relevance constructs to TAM. A survey was conducted on the demo of the gamified sales system, and 277 questionnaires were collected. The data were analyzed with the Structural Equation Modeling (SEM) technique. According to the results, perceived usefulness, subjective norms, and perceived ease of use affect the intention to use gamification. Furthermore, job relevance and perceived competence have positive effects on the users’ usefulness perception of gamification in sales. Furthermore, gamification has a positive direct effect on perceived ease of use, which in turn indirectly affects intention to use gamification in sales.

The fact that sports federations become autonomous with the laws enacted on the financial issues and that the federations represent the countries on international platforms makes the evaluation of the federations’ performances important. Data Envelopment Analysis (DEA) has been a commonly used method to determine the effectiveness of sports federations since DEA enables experts from different fields to easily interpret the results and ensure that multiple inputs/outputs are evaluated together without relying on assumptions. This study aims to determine the efficiency changes of the 25 sports federations in Turkey between 2013 and 2017 and the causes of these changes. Different from the inputs used in the studies in the literature related to efficiency analysis of sports federations, educational and financial aspects of the efficiency changes are considered by taking the number of coaches per athlete and the total income per athlete as input, respectively. Since this study is the first one to analyze the efficiency of Turkish Sports Federations, it makes a significant contribution to the literature. The analysis showed that the total productivity index of the federations included in the study indicates a 50% improvement in efficiency and that a significant part of this improvement stems from technological progress.

A guard is defined as an entity capable of observing the terrain or sensing an event on the terrain. By this definition, relay stations, sensors, watchtowers, military units, and similar entities are considered as guards. Terrain Guarding Problem (TGP) is about locating a minimum number of guards on terrain such that points on the terrain are guarded by at least one of the guards. Terrains are generally represented as triangulated irregular networks (TIN), and TINs are also referred to as 2.5 dimensional (2.5D) terrains. TGP on 2.5D terrains is known as 2.5D TGP. 1.5D terrain is a profile of a 2.5D terrain, and the guarding problem on a 1.5D terrain is referred to as 1.5D TGP. This paper presents an example that illustrates that the set of vertices in TIN does not necessarily contain an optimal solution, which implies that an optimal solution is yet to be found for 2.5D TGP. We show that a finite dominating set (FDS) found earlier for 1.5D TGP is optimal in the sense that no other FDS has a smaller cardinality.

Traditional machine learning algorithms may not produce satisfactory results on high-dimensional and imbalanced datasets. Therefore, the popularity of the concept of ensemble learning has increased, especially in recent years. Standard machine learning algorithms try to learn a single hypothesis from the training dataset, while ensemble-learning algorithms create a set of hypotheses and try to combine them. In this way, they can produce better results than the common machine learning algorithms. The fuzzy logic concept is also used in machine learning problems, especially in clustering problems in recent years. The fuzzy logic approach, by its nature, is used to solve problems that do not have a definite result, such as real-life problems, brings this approach to the fore, especially in machine learning problems. In this paper, we survey the latest status of ensemble learning and fuzzy clustering methods. Also, we proposed a new approach that combines fuzzy clustering and ensemble learning. This approach is applied in a case study, and results are compared with existing ensemble learning algorithms in the methodology section.

Public transportation is a backbone for cities. During Covid-19, public transportation became one of the sectors that did not stop due to the transport of goods and people. As a result, it is a critical issue for sustainable mobility. Due to improving the service quality of public transportation, passenger demands need to be investigated. This study presents sentiment analysis on public transportation as an exploratory study to take into account public demands. R programming is used to extract and analyze tweets about public transportation. Data and network analysis are conducted along with the sentiment analysis. The results show that there is a positive perception of public transportation despite the pandemic. In addition, wearing a mask, the Covid-19 test, and vaccination are seen as important issues. The study shed light on public opinions and perceptions about public transportation. Also, this work presents several significant results which might be critical to consider by decision-makers and practitioners.

Car rental is one of the most important industries worldwide in terms of tourism and professional travel. However, due to high initial costs and inadequate planning brings causes capacity problems which lead to potential revenue loss. Considering the high volatility in tourism and the high costs of facilitating a car rental location, forecast models and decision support systems are gaining importance. In this study, we aim to design a forecast model using different algorithms to estimate the upcoming car rental demands. To this end, two approaches are used, first holistic demand data is used to make a forecast, and in the second approach, customers are divided into segments, and segmented demand is used in the study. The results show that ARIMA and Holtwinters techniques provide the best results for the study. The results of the study show that a short-term forecast can be beneficial in the industry for making car supply decisions.

Sustainable supply chains gained importance worldwide because of the increasing environmental concerns and new governmental legislations. Optimization of the reverse logistics networks takes an important place to achieve sustainability. Moreover, glass containers are one of the most environment-friendly packaging materials with almost 100% recyclable characteristic. In this study, we propose a goal programming model to optimize the designed reverse logistics network of glass containers. Financial loss and carbon dioxide emission minimization are the objectives of the model. The model concerns a single-product, multiple facilities, echelons, and periods and decides on the location of the glass-recycling facility, investments, waste transportation frequencies, and allocation of the resources. The model is solved with Augmented Epsilon Constraint Method (AUGMECON) and justified with a case study in Turkey, including real data. The results show that a remarkable amount of CO₂ emission is avoided to be exposed in the nature by building the recycling facility. Besides the financial and environmental aspects, the proposed model also creates social value by considering the employment generated throughout the network and completely achieves sustainability.

In recent years, the fourth industrial revolution started to transform business, processes, services, and products in many different manufacturing and service industries. Industry 4.0 describes the ongoing trend towards automation and data exchange and provides real-time interaction between things, machines, and human beings to develop digital and smart business systems. This digitalization is re-shaping the supply chains through the implementation and acceleration of Industry 4.0 technologies. Supply Chain 4.0 can be defined as the new generation digital supply chains that utilize Industry 4.0 technologies such as the internet of things, advanced robotics, and big data analytics to improve performance and customer satisfaction. Digitization in the supply chain provides companies various benefits, including improved productivity, profitability, agility, flexibility, and responsiveness. However, the transition from a traditional supply chain towards a digitalized supply chain comes up with some new risks, including lack of information, IT failures, computer security risks, and cyber-attack risks, etc. This paper aims to identify and categorize the Supply Chain 4.0 risks by providing a comprehensive and systematic literature review. For this purpose, 5-phases research methodology is defined, and related research questions are structured. Findings and discussions are evaluated in the frame of research questions.

Supply Chain Management (SCM) requires the alignment of the flow of material, information, and services in a network of suppliers, manufacturers, distributors, and customers. Timing, quantity, and quality should be streamlined by all the stakeholders. The efficiency of SCM operations is especially essential for the growing e-commerce businesses in an era where uncertainties and risks abound and expectations change dynamically. This study initially identifies and prioritizes SCM risk factors for a footwear retailer, which is in both the traditional and e-commerce market, using the Analytical Hierarchy Process (AHP). After the supplier selection risks are found to be the most important risks for the company, a detailed data analysis is conducted to compare the performances of three critical suppliers. Based on historical data, demand forecasting for the year 2021 was made using seasonal factors. Forecasts are then used as requirements in a supply simulation to identify the extent to which the demands will be met and whether there will be any delays in the procurement process. According to the data analysis, forecasting, and simulation results, recommendations for supplier selection and order timing are made.

With the recent increase in e-commerce, automated warehousing industries seek technology solutions providing high transaction rates with economic investment costs. In this context, the application of smart operational policies towards future smart factories’ concepts becomes a critical issue. With the help of recent IT and technological advancements towards Industry 4.0 developments, we study intelligent autonomous vehicle operation policies where vehicles can make decentralized decisions for their safe and flexible travels between multiple aisles in a warehouse. By that, instead of assigning vehicles within a dedicated zone, we allow vehicles to travel freely between multiple aisles. The advantage of such a travel policy might result in a reduced number of vehicle requirements in a warehouse compared to a dedicated path policy. However, the disadvantage of such a flexible travel policy might be the development of smart collision and deadlock control algorithms, and that travel of vehicles might result in increased travel time during their operation due to deadlock and collision cases. First, we develop a smart travel policy approach for the vehicles using an agent-based simulation modeling approach. Then, we apply a statistical method, analysis of variance (ANOVA), to identify which input design factors significantly affect the system performance. As a result, it is observed that the number of bays is the most significant factor affecting the performance of such a system.

This research aims to conduct a quantitative analysis of academic literature by assessing the publications related to a social network, Instagram. The data needed is collected via Web of Science. “Instagram” was selected as the keyword for conducting the research, and the criteria of topic and title were used for identifying the publications. In this study, publications between 2010 and 2018 were taken into consideration, and totally there were 1019 publications. The number of studies reached 267 in 2018. Among all the document types, there were ten document categories for research on Instagram. Articles were the most used document type, while poetry and correction were the least used document type among others. Again, among all publications, the most productive country was the United States, and English was the most commonly used in all other document types with 942 publications in English. Furthermore, Computers in Human Behavior appeared as the most used distribution channel when source titles are considered, while computer science, communication, and engineering were the trendiest research areas.

For international businesses to thrive, global managers must have global mindsets, which require cognitive complexity. Complex thinking should include the ability to see and analyze issues from all levels of analysis, from the micro, meso, and macro levels. However, no studies measure multilevel thinking in cognitive complexity. Because of the complexities involved in international business, cognitive complexity is particularly important when global managers deal with multifaceted international business management issues like the management of organizational behavior in terms of, for instance, communication, motivation, leadership, conflict, and negotiation. Although global managers deal with these issues daily, cognitive complexity is not yet studied in relation to the level of complexity with which managers think about these issues. This paper argues the need to revise the conceptualization and operationalization of cognitive complexity to cover the full complexity of managerial thinking. Therefore, measurement of managers’ cognitive complexity as it relates to a particular issue should include analysis of their thinking at the micro, meso, and macro levels. The paper also argues that such measurement requires measurement methods like cognitive mapping, which has several advantages over other methods. This study fills a gap in the research by proposing an assessment of multilevel thinking using cognitive maps to measure managers’ cognitive complexity in addressing issues like the management of international organizational behavior.

Ataturk University has renewed the university website within the scope of studies conducted to become a new generation university. In this context, the Usability Testing (UT) with Morae V.3.3.0 program was applied for the first time to the user group consisting of undergraduate students in order to determine the usability level of the Ataturk University New Website (AUNWS). According to UT results, the usability problems, usage needs, expectations from the website, and satisfaction levels of the undergraduate students were determined. As a result of the study, the problems that need to be solved primarily in the AUNWS are the complex design of the home page and the insufficiency of the search button. Moreover, the AUNWS and Ataturk University Old Website (AUOWS) was compared in terms of usability levels in the study, and the participants found the new website more usable. The tests conducted under COVID-19 pandemic conditions have caused the distance education students to concentrate on the tests more willingly and more carefully during the pandemic process. The test results were positively affected since students do all their processes through the university website due to distance education.

Abnormalities are samples in data that do not fit the normal patterns. Various reasons such as malware, fraud, cyber-attack, terrorist activities, faults, system behavior changes, instruments, and human error might generate abnormalities. Anomaly detection is a technique that provides unexpected situations or patterns to be found in the data. These unexpected situations or patterns are called anomalies, outliers, and unexpected cases in the literature that do not fit the expected behavior of the data. Diverse research and applications have been carried out for anomaly detection, which is critical for the industries. To predict the anomaly, there are numerous learning methods as supervised, semi-supervised and unsupervised. In this scope, this paper proposes a novel concept as building an automated anomaly detector system for a business operation platform at Token Financial Technologies company, a leader in the payment systems industry in Turkey, by using the Isolation Forest algorithm developed in Python. Thanks to this system, as Token Financial Technologies company, abnormal data in the system might be detected in real-time. In this study, to integrate the business operation platform, we have first examined the data of deleting banking applications on the EFT-POS devices and detected the anomaly. The detection helped Token Financial Technologies to save more than 55% of the banking applications on the devices from deletion by contacting banks and customers instantly in the last quarter of 2020.

Digital revolution is generally evaluated as the most important phenomenon that shapes the future in all the industries, including the construction and the real estate sectors. Each industry has different nature in adapting digitalization and implementing the digital transformation. While some of them, such as the manufacturing or automotive industries, has been transforming too fast, others, like the construction sector, may need a longer time to adopt the new technological and digital developments. Moreover, because of the industries’ different structures and traditions, the use of the digital transformation pillars and their impacts are also different. Adaptation of the digital tools and trends on project management systems are also essential to get an insight into the future management perspectives of the industries both at project and organization levels. Thus, this study investigates Turkish construction and real estate professionals’ perception of the future of construction project management from a digital transformation perspective. According to the survey results, the use of building information modeling (BIM), artificial intelligence (AI), and big data are found the most important factors that may have an impact on the digital future of construction project management. Based on the results of the research, some digital transformation-focused organizational change management steps are recommended for real estate and construction companies.

The objective of the current study is to investigate users’ continuance intention of using the most dominant player in the market of online social networking sites (SNS), Instagram, usage in Turkey. The Structural Equation Modeling (SEM) methodology is applied to detect the elements influencing the continuance intention to use Instagram. The proposed model is assessed with the help of data collected from 201 Instagram users by using SmartPLS software. It is found that people’s intention to use Instagram is determined by habit, attitude, and perceived usefulness. Moreover, satisfaction, entertainment, self-expression, and information seeking are indirect factors of intention to use. On the other hand, perceived usefulness is found to have an insignificant direct effect on continuance intention to use Instagram. The paper concludes with a discussion of the findings, theoretical and practical implications of the research, and recommendations for future research.

Recent studies have reported that companies’ efforts to keep their current customers are considerably less costly than acquiring new ones. Therefore, it is critical for companies to analyze and understand the loss of customers and avoid the mistakes made throughout the customers’ journey. Both in theory and practice, churn analysis is widely used in industries such as telecommunications, insurance, and financial services since customers might switch to competitors easily. Limited research has been conducted on churn analysis and prediction in e-commerce. In this study, the factors, directly and indirectly, affecting the loss of customers in e-commerce are discussed, and an accurate and effective churn prediction model is suggested. The proposed probabilistic graphical model based on Bayesian networks allows (i) representing the relationships among factors in a convenient graphical representation (ii) both deductive and abductive reasoning under uncertainty, (iii) examining several different what-if scenarios, (iv) predicting consequences of possible interventions, and (v) using expert views and if available historical data. The applicability and effectiveness of the model are presented through an application. Analysis with actual data demonstrated that the proposed model achieves high prediction accuracy.

Understanding burnout and its effects on employees’ performance and wellbeing has been studied since the 1980s. Monitoring burnout syndrome among health care professionals, such as physicians and nurses, is a critical task. On the one hand, medical staff has to be good at controlling their own emotions in confronting the health care challenges; on the other hand, they share similar emotional exhaustion, frustration, and feelings with their own patients. This can lead to medical diagnosis errors, weak performance, and lower quality of treatments. The main purpose of this study is to provide a concise literature review of research works on burnout syndrome among medical staff, including research methods, assessments, solutions, and best practices. No matter their field of expertise and responsibilities, burnout is a considerable common psychological syndrome among medical staff, ranging from 40 to 66%. The Maslach Burnout Inventory (MBI) has been the most widely used questionnaire for data collection in the literature. Factors such as age, gender, marital status, and working conditions are identified as the most contributive parameters in the severity of burnout. Furthermore, studies have found that the degree of burnout can be reduced by under-taking self-improvement techniques such as meditation, development of communication skills, peer coaching, and doing art therapy. A considerable number of medical personnel are vulnerable to burnout syndrome in various degrees and forms. This common phenomenon threatens patients’ health and lives due to a reduction in medical care quality. This review covers the best practices for protecting and overcoming the hardnesses and challenges ahead of many health care professionals in their careers.

Response time of ambulances or waiting times in hospitals are examples of operational targets in healthcare. Healthcare policy-makers generally attempt to make these times equitable by imposing the same operational target for a healthcare service in a country. However, Felder and Brinkmann (Reg Sci Urban Econ 32:27–45, 2002 [5]) showed that equal operational targets in emergency medical services (EMS) result in different valuations of patients’ lives. We extend their work by incorporating operational targets in a mathematical model with queuing and survival approximations that aim to optimize the distribution of healthcare resources. We also present a case study for a local EMS with empirical analysis.

Appointment scheduling is one of the most important aspects of healthcare management with specific challenges. In order to minimize the patient average waiting time and doctor average idle time, overtime and cost, it is significant to determine how to schedule the number of patient appointments. This paper aims to perform a scientometric study that evaluates academic publications on appointment scheduling under the topic of healthcare. In this scientometric study, all publications with the keywords of “appointment scheduling” and “healthcare” were searched in the database of Scopus throughout 1970–2021. Overall, 401 publications were examined, and all found publications that meet these criteria are categorized under publication year, subject area, document type, source title, affiliation, country, source type, and language headings. Results of the study give an insight into the appointment scheduling to researches of the healthcare field.

The spread of COVID-19 worldwide in a short time has ensured to take precautions at the government level to mitigate the transmission of coronavirus. Implementing social distancing interventions and aiming to reduce mobility are the main methods to prevent the spread of COVID-19. In this study, the case of Turkey is discussed to explore the degree of relationships among daily death, daily cases, social distancing regulations, and mobility. To accomplish this goal, data from the Ministry of Health of Turkey, Coronavirus Government Response Tracker by Blavatnik School of Management and Oxford University, and Google’s Covid-19 Community Mobility Reports between March 12 and July 29, 2020, was used. A proposed model was tested with integrated data via Partial Least Squares (PLS) Structural Equation Modelling (SEM) methodology. Our results show that daily deaths are the main driver while developing social distancing regulations. Also, social distancing interventions have a negative impact on mobility in groceries & pharmacies, transit stations, retail and recreational areas, parks, and workplaces, but a positive one on residential mobility.