WHICH YOUTUBER SHOULD BE FOLLOWED? A COMPARISON BASED DELPHI-AHP-TOPSIS

With the development of technology, education activities also get their share from the rapid transformation. With social media platforms, it is now possible to access information from anywhere at any time. While the accuracy of every information in the virtual world is discussed, the information provided on platforms such as YouTube also has results related to the channel that transmits the information. Many videos are shared on YouTube in various fields (education, music, sports, comedy etc.). Education-based YouTube channels are also highly preferred among these channels. This study aims to identify and prioritize possible alternatives to identify how YouTube-based users decide on the YouTubers (Youtube channel owner) that they select and in order to be constantly improved. Comparison with a Delphi-AHP-TOPSIS based methodology is divided into 3 stages. The first stage is the Delphi Method, where basic performance factors and sub-factors are defined, synthesized and prioritized. The second stage is the use of AHP method to obtain the general performance index of the main factors and sub-factors. The third stage is the ranking of possible alternatives between TOPSIS technique and 10 YouTubers for continuous improvement of YouTube channels. As a result of the analyzes, the most important criteria in choosing YouTuber were tried to be determined and ideal solutions were presented in decision making

Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios

A railway system plays a significant role in countries with large territorial dimensions. The Brazilian rail cargo system (BRCS), however, is focused on solid bulk for export. This paper investigates the extreme performances of BRCS through a new hybrid model that combines TOPSIS with a genetic algorithm for estimating the weights in optimized scenarios. In a second stage, the significance of selected variables was assessed. The transport of any type of cargo, a centralized control of the operation, and sharing the railway track pushing competition, and the diversification of services are significant for high performance. Public strategies are discussed.Indisponível

An integrated logistics performance measurement system for logistics management

In the 1980's there was a revolution that changed the nature of traditional performance measurement systems. Since then there has been an explosion in the number of scholars and practitioners seeking new and better ways of measuring organizational performance. Performance measurement systems (PMS) specialized for logistics management caught attention much later when more enterprises began to focus on logistics to reduce operational cost and increase profits. Meanwhile, there are more demands on logistics performance measurement systems (LPMSs). The role of an LPMS is beyond monitoring logistics performance, but also to provide logistics improvement suggestions, resolve trade-offs between different logistics activities and so on. To design an LPMS, this thesis addresses the following four objectives: 1) review the evolution of performance measurement systems (PMS) for logistics since 2000; 2) determine the requirements for the design an ILPMS; 3) propose an ILPMS that satisfies these requirements; and 4) apply the ILPMS to a case study. The ILPMS consists of three components: 1) a hybrid performance measurement framework, combining a hierarchical and process-based structure, to facilitate developing logistics performance measures and metrics; 2) different strategies for developing logistics performance measures and logistics activity metrics; 3) a hybrid multi-criteria decision making methodology, analytic network process (ANP) and decision-making trial and evaluation laboratory (DEMATEL), to prioritize performance measures and metrics for managerial purposes. The ILPMS developed illustrates the procedures to establish a logistics performance measurement system for a manufacturing company. The results from the ILPMS provide effective feedback for performance management process and suggestions about performance improvement for managers. Keywords: integrated logistics performance measurement framework (ILPMS), performance measures/metrics, multi-criteria decision making methodology (MCDM

When risks need attention: adoption of green supply chain initiatives in the pharmaceutical industry

The pharmaceutical industry is very important in delivering life-saving products/services to society. There are many ways for materials/products/services concerned with pharmaceuticals to influence the environment; these include improper disposal of pills/tablets by patients, expired and unused medications, improper release of drugs by pharmacies or household sewage mixed with surplus drugs. In view of this, the present work seeks to integrate green supply chain (GSC) concepts in the pharmaceutical sector in a developing economy Indian context. In so doing, managers need to determine the potential risks in adopting GSC initiatives to achieve sustainability in operational perspectives. In this sense, this work seeks to distinguish the potential risks in adopting GSC initiatives within the pharmaceutical industry. This work uses a literature review and fuzzy Delphi approach in finalising the risks. This research also uses fuzzy Analytical Hierarchy Process (AHP) for prioritisation of the risks under vague and unclear surroundings. According to the findings, cold chain technology and supply risks categories are highly prioritised. This work can assist practising managers and government authorities in effectively developing and managing GSC initiatives in line with sustainable development goals in the context of the pharmaceutical industry. Finally, a sensitivity test is applied to evaluate the stability of ranking of risks.N/

Performance evaluation on aquatic product cold-chain logistics

Purpose: The requirements for high quality and diversification aquatic products are increasing with the improvement of Chinese living standard. However, the distribution between the place of production and the place of consumption are uneven, which results in large cold-chain logistics demand for aquatic products. At present, the low-level development of cold chain logistics has a bad impact on the circulation of aquatic products in China. So it is very urgent to develop cold-chain logistics in China. Design/methodology/approach: In order to do this, we apply performance evaluation, a well-known management tool, to study Chinese aquatic product cold-chain logistics. In this paper we first propose SISP (Subjects, Indexes, Standards, and Phases of performance evaluation) model and ACSSN model (Aquatic product, Customer, Supply Chain, Society, and Node enterprises of supply chain) for aquatic products cold-chain logistics performance evaluation. Then an ANP-Fuzzy method is proposed to evaluate the operational performance of Shandong Oriental Ocean Sci-Tech Co., Ltd. Furthermore, a system dynamic model is built to simulate the impact of temperature on the profits in aquatic products cold-chain sales section. Findings: We find out within a reasonable temperature range, lower temperature brings higher profit level. Also, performance improvement methods are proposed and the simulation of performance evaluation system is developed. Practical implications: Our findings can help to improve the level of aquatic product coldchain logistics in China. Originality/value: The paper proposes the SISP (Subjects, Indexes, Standards, and Phases of performance evaluation) model and ACSSN model (Aquatic product, Customer, Supply Chain, Society, and Node enterprises of supply chain) for aquatic products cold-chain logistics performance evaluation.Peer Reviewe

A process based approach software certification model for agile and secure environment

In today’s business environment, Agile and secure software processes are essential since they bring high quality and secured software to market faster and more cost effectively. Unfortunately, some software practitioners are not following the proper practices of both processes when developing software. There exist various studies which assess the quality of software process; nevertheless, their focus is on the conventional software process. Furthermore, they do not consider weight values in the assessment although each evaluation criterion might have different importance. Consequently, software certification is needed to give conformance on the quality of Agile and secure software processes. Therefore, the objective of this thesis is to propose Extended Software Process Assessment and Certification Model (ESPAC) which addresses both software processes and considers the weight values during the assessment. The study is conducted in four phases: 1) theoretical study to examine the factors and practices that influence the quality of Agile and secure software processes and weight value allocation techniques, 2) an exploratory study which was participated by 114 software practitioners to investigate their current practices, 3) development of an enhanced software process certification model which considers process, people, technology, project constraint and environment, provides certification guideline and utilizes the Analytic Hierarchy Process (AHP) for weight values allocation and 4) verification of Agile and secure software processes and AHP through expert reviews followed by validation on satisfaction and practicality of the proposed model through focus group discussion. The validation result shows that ESPAC Model gained software practitioners’ satisfaction and practical to be executed in the real environment. The contributions of this study straddle research perspectives of Software Process Assessment and Certification and Multiple Criteria Decision Making, and practical perspectives by providing software practitioners and assessors a mechanism to reveal the quality of software process and helps investors and customers in making investment decisions

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy