Adnan Aktepe

Bio: Adnan Aktepe is an academic researcher from Kırıkkale University. The author has contributed to research in topics: Service quality & Fuzzy logic. The author has an hindex of 6, co-authored 25 publications receiving 177 citations.

A meta analysis of factors affecting perceived usefulness and perceived ease of use in the adoption of e-learning systems

Abstract: The full potential of e-learning, a trend that is of growing importance lately, will not be reaped unless the users fully utilize the system, triggering extensive research to be conducted in order to provide valuable insight on a myriad of variables influencing user acceptance in e-learning systems. The main purpose of the study is to determine the factors that affect the intention of users to use e-learning and to get results which can guide system developers and researchers. In accordance with this purpose, 203 studies investigating the e-learning acceptance of the users through the Technology Acceptance Model (TAM) were found in the literature. In those studies, variables which are suggested to determine Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) and results of related hypotheses are analyzed. Finally, a model is proposed. In this model, the most widely accepted hypotheses, affecting PU and PEOU according to the literature are included in the original TAM. As a result; it determines Self Efficacy-PEOU, Subjective Norm-PU, Self Efficacy-PU, Interaction-PU, Enjoyment-PEOU, Anxiety-PEOU, Enjoyment-PU, Compatibility-PU, Subjective Norm-PEOU and Interaction-PEOU as variables that have statistical significance in users’ PU and PEOU, respectively. Moreover, the study examines the relationship between the User Satisfaction and original TAM variables, and proposes the Acceptance and Satisfaction Model for E-Learning (ASME) as a model to best explain the dependent variables described above.

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

Abstract: The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production control especially in dynamic production environments. This study proposes a decision support system (DSS) designed to increase the performance of dispatching rules in dynamic scheduling using real time data, hence an increase in the overall performance of the job-shop. The DSS can work with all dispatching rules. To analyze its effects, it is run with popular dispatching rules selected from the literature on a simulation model created in Arena®. When the number of jobs waiting in the queue of any workstation in the job-shop falls to a critical value, the DSS can change the order of schedules in its preceding workstations to feed the workstation as soon as possible. For this purpose, it first determines the jobs in the preceding workstations to be sent to the current workstation, then finds the job with the highest priority number according to the active dispatching rule, and lastly puts this job in the first position in its queue. The DSS is tested under low, normal, and high demand rate scenarios with respect to six performance criteria. It is observed that the DSS improves the system performance by increasing workstation utilization and decreasing both the number of tardy jobs and the amount of waiting time regardless of the employed dispatching rule.

A fuzzy analytic hierarchy process model for supplier selection and a case study

Abstract: In today’s competitive manufacturing and service industries decision making is a critical process. Supply chain management is a network of businesses and in this network there are several critical decision making problems. One of them is supplier selection decision. Supplier selection is a multi-criteria decision making problem and a fuzzy decision making model is proposed to this problem area in supply chain management. The extent analysis method and integral value calculation is used in the study for computing the priority weights of criteria and alternatives. In addition, a case study is added to the study.

Demand forecasting application with regression and artificial intelligence methods in a construction machinery company

Abstract: Demand forecasts are used as input to planning activities and play an important role in the management of fundamental operations. Accurate demand forecasting is an important information for many organizations. It provides information for each stage of inventory management. In this study, multiple linear regression analysis, multiple nonlinear regression analysis, artificial neural networks and support vector regression were applied in a production facility that produces spare parts of construction machinery. The aim of the study is to forecast the number of spare parts requested in the future period by the customer as close as possible. As the input variables in the developed models, the sales amounts of the past years belonging to the manifold product group, which is one of the important spare parts of the construction machinery, number of construction machines sold in the world, USD exchange rate and monthly impact rate are used as input variables. The inputs of the model are designed according to construction machinery sector. In the model, monthly impact rate enables us to create more robust model. In addition, the estimation results have high accuracy by systematic parameter design of artificial intelligence methods. The data of the 9 years (from 2010 to 2018) were used in the application. Demand forecasts were conducted for 2018 to compare actual values. In forecasts, artificial neural network and support vector regression produced better results than regression methods. In addition, it was found that support vector regression forecasting produced better results in comparison to artificial neural network. __________________________________________________________________________________________