Cloud computing in supply chain management: Exploring the relationship

This research study addresses the advantages and difficulties of Cloud Computing (CC) in Supply Chain Management (SCM). An overview of the current state of SCM and the difficulties businesses in this sector confront is presented at the beginning of the article. It then explores how cloud-based solutions can address these challenges, such as through the use of real-time data analytics, collaborative platforms, and intelligent automation. Additionally, the paper investigates the potential risks and challenges associated with cloud-based SCM, including data security and privacy concerns, vendor lock-in, and the need for robust disaster recovery plans. To provide a comprehensive understanding of the topic, the paper includes a case study that illustrates how a company successfully implemented cloud-based SCM solutions to improve their operations. The paper concludes by highlighting the key takeaways and insights from the research, and by identifying potential future directions for research in this field. Overall, this study delivers insightful information about the function of CC in SCM and offers useful suggestions for companies looking to use this technology to enhance their supply chain operations

Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS‐ARAS and COPRAS‐ARAS

Traditional Multi‐Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision‐making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS‐ARAS and COPRAS‐ARAS were applied to solve a real‐time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate th

Outranking Methods: Promethee I and Promethee II

This article highlights the application of the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) I and II in selecting the best laptop model among six different available models in the market. Seven important criteria, that is, processor, hard disk capacity, operating system, RAM, screen size, brand, and color, are selected, based on which the selection process have been made. Analytic hierarchy process (AHP) is adopted for calculating the weightages of the seven criteria and PROMETHEE is applied to select the best alternative. PROMETHEE I provides the partial ranking and preferences of one model over another, whereas PROMETHEE II provides the complete ranking of the alternatives. From this analysis, Model 4 is coming out to be the best laptop model occupying the first position and Model 1 occupies the last position, thus indicating it as the worst model among the group. The objectives of this article are to select the best laptop model among six available alternatives and to understood the steps of both multiple criteria decision-making (MCDM) methodologies, that is, PROMETHEE and AHP, in details

Application of Simple Average Weighting Optimization Method in the Selection of Best Desktop Computer Model

Multi-Criteria Decision Making (MCDM) is one of the most emerging concepts in today’s world which enables a decision maker to select the best strategies among different available alternatives. MCDM technique helps to remove the biasness and confusion while selecting a product or process. In recent few years different MCDM methodologies finds wide area of applications in industries as well as in our daily life. In this paper, such one type of application is broadly described. One example is taken from our daily life, which is generally faced by most of the students while purchasing a desktop computer. The main objective of this paper is to select the best desktop computer models among five different models actual available in the market having different configurations. For this analysis, 100 computer users have been surveyed to know their relative preferences and choices, which of the computer specifications is most important to them. For this present analysis few numbers of criteria have been considered and also there are number of sub-criteria within each criterion (for example, the processor may be different for different models like I3, I5, I7 etc.). The MCDM methodology which is adopted for this selection process is known as Simple Average Weighting (SAW) method

Вибір ідеального повітряного провідника на основі IoT для оптимізації продуктивності проекту малої гідроелектростанції

Вибір проекту малої гідроенергетики та його критеріїв для інвестування є критичним завданням, що включає різні аспекти та плани. Таке прийняття рішень також можна розглядати як питання багатокритеріального перегляду з кореляцією критеріїв та альтернатив. Ця роль повинна брати до уваги низку конкуруючих аспектів через зростаючу складність технічних і екологічних факторів. Багатокритеріальні підходи мають дедалі більше універсальних інструментів. Метою цієї статті є оцінка застосовності методу прийняття рішень за багатьма критеріями (MCDM) на основі значення індексу близькості (PIV) і комбінованого компромісного рішення (CoCoSo) під час планування та розробки проектів малої гідроенергетики. Застосування цього нового підходу PIV до організації проекту малої гідроелектростанції та сценарію розширення відсутні в літературі з відновлюваної енергетики через складність його оцінки.The selection of a suitable small hydropower project and its criteria for investment is a crucial task involving various aspects and plans. This decision-making can also be seen as a multi-criteria review issue with the correlation of criteria and alternatives. This role should take into account a number of competing aspects due to the growing complexities of social, economic, technical and environmental factors. Traditional decision-making methods cannot address the complexities of such systems. Multi-criteria approaches have more and more versatile tools. The goal of this paper is to assess the applicability of Multiple Criteria Decision-Making (MCDM) based Proximity Index Value (PIV) and Combined Compromise Solution (CoCoSo) technique during the planning and development of small hydropower projects. The application of this PIV novel approach to a small hydropower project organization and expansion scenario is lacking in renewable energy literature due to the difficulty of its evaluation