Lojistik merkezi yer seçimi için aralık değerli sezgisel bulanık sayılara dayalı genişletilmiş VIKOR yöntemi

Identifying the appropriate location for logistics centres (LC) is key to gaining and maintaining a competitive advantage and increasing the efficiency of supply chain activities. Increasing customer expectations, efforts to reduce logistics costs and the intensity of competition in the logistics sector have led to the establishment of many new LMs in recent years. These centers contribute significantly to increasing efficiency in freight transportation, optimizing logistics services and reducing the traffic. The increasing importance of LCs and the significant impact of their location on logistics activities have made the choice of installation site a strategic consideration. However, evaluating LC location alternatives is a complex process that must take many factors into account. The aim of the present study is to propose an extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) approach based on interval-valued intuitionistic fuzzy numbers (IVIFN) and test its feasibility. Applying IVIFN contributes to coping with uncertainty in human thought and decision processes. On the other hand, VIKOR is a decision-making technique that facilitates ranking criteria that are contradictory and represented by different units, and it offers a compromise solution. The feasibility of the extended VIKOR approach through IVIFN proposed in this study was tested in a numerical example in which LC location alternatives were evaluated. Three experts were consulted to determine the criterion weights and to rank the alternatives. Decision makers serve as logistics and planning specialist, logistics operations manager and supply chain chief engineer. In practice, alternatives were evaluated by considering six criteria. As a result, criteria are listed in the form of intermodal connection (0.255), infrastructure (0.194), security/safety (0.169), proximity to customers (0.158), proximity to suppliers (0.131), and labour supply (0.093), according to their weighted importance. It is expected that the findings obtained in the study will contribute to researchers and sector managers.Lojistik merkezler (LM) için uygun yerin belirlenmesi, rekabet avantajı elde etmenin, sürdürmenin ve tedarik zinciri faaliyetlerinin verimliliğini artırmanın anahtarıdır. Artan müşteri beklentileri, lojistik maliyetlerinin azaltılmasına yönelik çabalar ve lojistik sektöründe yaşanan rekabet yoğunluğu son yıllarda birçok yeni LM kurulmasına neden olmuştur. Bu merkezler yük taşımacılığında verimliliğin artmasına, lojistik hizmetlerin optimize edilmesine ve bulundukları kentteki trafiğin azaltılmasında önemli ölçüde katkı sağlamaktadır. LM'lerin artan önemi ve konumlarının lojistik faaliyetler üzerindeki önemli etkisi, kurulum yeri seçimini stratejik bir değerlendirme haline getirmiştir. Ancak, LM konum alternatiflerini değerlendirmek, birçok faktörün hesaba katılması gereken karmaşık bir süreçtir. Bu çalışmanın amacı, aralık değerli sezgisel bulanık sayılara (ADSBS) dayalı genişletilmiş bir VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) yaklaşımı önermek ve uygulanabilirliğini test etmektir. ADSBS'ın uygulanması, insan düşünce ve karar süreçlerindeki belirsizlikle başa çıkmaya katkıda bulunur. Öte yandan VIKOR birbiriyle çelişen ve farklı birimler tarafından temsil edilen kriterleri sıralamayı kolaylaştıran ve uzlaşmacı bir çözüm sunan bir karar verme tekniğidir. Bu çalışmada önerilen ADSBS aracılığıyla genişletilmiş VIKOR yaklaşımının uygulanabilirliği, LM konum alternatiflerinin değerlendirildiği sayısal bir örnekte test edilmiştir. Kriter ağırlıklarının belirlenmesi ve alternatiflerin sıralanması için üç uzman karar vericiye danışılmıştır. Karar vericiler, lojistik ve planlama uzmanı, lojistik operasyon yöneticisi ve tedarik zinciri başmühendisi olarak görev yapmaktadır. Uygulamada alternatifler altı kriter (altyapı, müşteriye yakınlık, tedarikçilere yakınlık, intermodal bağlantı, işgücü arzı ve güvenlik/güvenirlilik) dikkate alınarak değerlendirilmiştir. Sonuç olarak kriterler ağırlıklarına göre intermodal bağlantı (0,255), altyapı (0,194), güvenlik/güvenlik (0,169), müşterilere yakınlık (0,158), tedarikçilere yakınlık (0,131) ve iş gücü arzı (0,093) biçiminde sıralanmıştır. Çalışmada elde edilen bulguların araştırmacılara ve sektör yöneticilerine katkı sağlaması beklenmektedir

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Decision-making model for designing telecom products/services based on customer preferences and non-preferences

The design of the packages of products/services to be offered by a telecom company to its clients is a complex decision-making process that must consider different criteria to achieve both customer satisfaction and optimization of the company’s resources. In this process, Intuitionistic Fuzzy Sets (IFSs) can be used to manage uncertainty and better represent both preferences and non-preferences expressed by people who value each proposed alternative. We present a novel approach to design/develop new products/services that combines the Lean Six Sigma methodology with IFSs. Its main contribution comes from considering both preferences and nonpreferences expressed by real clients, whereas existing proposals only consider their preferences. By also considering their non-preferences, it provides an additional capacity to manage the high uncertainty in the selection of the commercial plan that best suits each client’s needs. Thus, client satisfaction is increased while improving the company’s corporate image, which will lead to customer loyalty and increased revenue. To validate the presented proposal, it has been applied to a real case study of the telecom sector, in which 2135 users have participated. The results obtained have been analysed and compared with those obtained with a model that does not consider the non-preferences expressed by users.Spanish Ministry of Science and Innovation (State Research Agency)Junta de Andalucia PID2019-103880RB-I00 PID2019-109644RB-I00 PY20_0067

Factors That Drive the Selection of Business Intelligence Tools in South African Financial Services Providers

Innovation and technology advancements in information systems (IS) result in multiple product offerings and business intelligence (BI) software tools in the market to implement business intelligence systems (BIS). As a result, a high proportion of organisations fail to employ appropriate and suitable software tools meeting organisational needs, resulting in a prime number of BI solution failures and abandoned projects are therefore recorded. Due to such project failures, benefits associated with BI are not realised hence organisations loose enormous investments on BI solutions and competitive advantage. The study aims at discovering and exploring critical factors influencing the selection of BI tools when embarking on the selection process. This is a quantitative research study and questionnaire surveyed data was collected from 92 participants working in South African financial services providers listed on the Johannesburg Stock Exchange (JSE) appearing in the top 100 based on market capitalization. The data was analysed quantitative by employing the use of SPSS and SmartPLS-3 software's to test the significance of influential factors using the proposed conceptual model that emerged from the literature. The findings showed that a combination of domain technical and non-technical factors is critical. Therefore, software tool technical factors (functionality, ease of use, compatibility, availability of an integrated hardware/software package, and availability of source code), vendor technical factors (availability of technical support, technical skills, quality of product, availability of user manual for important information, tutorial for learning and troubleshooting guide, and experience in using product developed by the same vendor), and opinion non-technical factors (end-users, subordinates, outside personnel acquaintances, and improvement in customer service) emerged as significant combination of influential factors to be considered. The study contributes to both academia and industry by providing influential determinants for software tool selection. It is hoped that the findings presented will contribute to a greater understanding of factors influencing the selection of BI tools to researchers and practitioners alike. Furthermore, organisations seeking to select and deliver appropriate BI tools will be better equipped to drive such endeavours

Cloud Technology Selection: A structured framework for decision making

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis study aims to get organizations to improve their decision making during the selection of cloud technology process. As the technology evolves alongside an ever-increasing abundance in market offer, it may be challenging to choose the desirable service that encompasses several business approaches. For the purpose of this study to be attained, the reader must first comprehend the definition of Cloud Technology: it is the delivery of IT resources over the Internet, being applications, software, storage, among other services. Furthermore, understanding the current main technologies/architectures and their capabilities/limitations will play an important role in designing and developing the prospected solution. A thoroughly research will be produced to better define the criteria used in the process. Despite the fact that technology is able to be tailored up to a certain level for the organization needs, a higher level of participation will encourage vendors and architecture designers to develop a better knowledge on the companies’ desires, thus delivering more appropriate features to their unique needs

How digital data are used in the domain of health: A short review of current knowledge

In the era of digitalization, digital data is available about every aspect of our daily lives, including our physical and mental health. Digital data has been applied in the domain of healthcare for the detection of an outbreak of infectious diseases, clinical decision support, personalized care, and genomics. This paper will serve as a review of the rapidly evolving field of digital health. More specifically, we will discuss (1) big data and physical health, (2) big data and mental health, (3) digital contact tracing during the COVID-19 pandemic, and finally, (4) ethical issues with using digital data for health-related purposes. With this review, we aim to stimulate a public debate on the appropriate usage of digital data in the health sector