Empirical research has identified the phenomenon of destination bonding as a result of summated physical and emotional values associated with the destination. Physical values, namely natural landscape & other physical settings and emotional values, namely the enculturation processes, have a significant role to play in portraying visitors’ cognitive framework for destination preference. The physical values seemed to be the stimulator for bonding that embodies action or behavior tendencies in imagery. The emotional values were the conditions that lead to affective bonding and are reflected in attitudes for a place which were evident in text narratives. Social networking on virtual platforms offers the scope for hybrid cognitive expression using imagery and text to the visitors. Instagram has emerged as an application-window to capture these hybrid cognitions of visitors. This study focuses on assessing the relationship between hybrid cognition of visitors expressed via Instagram and their bond with the destination. Further to this, the study attempts to examine the impact of hybrid cognition of visitors on the behavioral pattern of prospective visitors to the destination. The study revealed that sharing of visual imageries and related text by the visitors is an expression of the physico-emotional bonding with the destination. It was further established that hybrid cognition strongly asserts destination bonding and has been also found to have moderating impact on the link between destination bonding and electronic-word-of-mouth.

Maintenance Qualitn Function Deployment (MQFD) is a methodology for improving the quality and effectiveness of maintenance services in a manufacturing organization. One major part of it is House of Quality (HoQ). HoQ translates the experts’ voice into technical requirements for the improvement of maintenance quality. These data are generally vague in nature. Fuzzy numbers are generally used to represent vague data in HoQ. Since some parameters are predefined in fuzzy approach, the experts’ opinion may not be truly reflected in the HoQ analysis. In this work, a rough set - fuzzy approach, is proposed for MQFD to overcome this drawback.The objective of this model is to prioritize the technical requirements effectively with the proper reflection of customers/experts’ perceptions in the output. An illustrative example is presented to explain this approach.

Multiple attribute decision making (MADM) methods are very useful in choosing the best alternative among the available finite but conflicting alternatives. TOPSIS is one of the MADM methods, which is simple in its methodology and logic. In TOPSIS, Euclidean distances of each alternative from the positive and negative ideal solutions are utilized to find the best alternative. In literature, apart from Euclidean distances, the city block distances have also been tried to find the separations measures. In general, the attribute data are distributed with unequal ranges and also possess moderate to high correlations. Hence, in the present paper, use of statistical distances is proposed in place of Euclidean distances. Procedures to find the best alternatives are developed using statistical and weighted statistical distances respectively. The proposed methods are illustrated with some industrial problems taken from literature. Results show that the proposed methods can be used as new alternatives in MADM for choosing the best solutions.

This work is focused on the influence of different friction stir welding (FSW) parameters on AA6082 T-6 and AA5083-O alloys welding quality, by using Taguchi, Grey Relational and Weight Method. Four welding parameters were investigated, namely tool rotation speed (TRS), welding speed (WS), tool pin profile (TPP) and shoulder diameter (SD). The optimized setting of these input parameters was investigated so that weld parts quality could be optimized. Analysis of variance (ANOVA) was used to investigate the effects of these welding process parameters on response variables, viz. elongation (EL) and ultimate tensile strength (UTS). Single response optimization was carried using Taguchi Technique while grey relational analysis (GRA) was used for simultaneous optimization of two responses. Once the optimal settings of control factors were identified, confirmation experiments were performed for the validation of results. In the multi-response optimization, TRS was found to have the maximum effect (57.9%), followed by WS, SD and TPP. Weight method was applied for providing the priority to the response (i.e. EL and UTS). The response with higher priority presented a weight equal to 0.7, while the lower priority given corresponds to a weight of 0.3.

In today’s competition inherited business world, managing inventory of goods is a major challenge in all the sectors of economy. The demand of an item plays a significant role while managing the stock of goods, as it may depend on several factors viz., inflation, selling price, advertisement, etc. Among these, selling price of an item is a decisive factor for the organization; because in this competitive world of business one is constantly on the lookout for the ways to beat the competition. It is a well-known accepted fact that keeping a reasonable price helps in attracting more customers, which in turn increases the aggregate demand. Thus in order to improve efficiency of business performance organization needs to stock a higher inventory, which needs an additional storage space. Moreover, in today’s unstable global economy there is consequent decline in the real value of money, because the general level of prices of goods and services is rising (i.e., inflation). And since inventories represent a considerable investment for every organization, it is inevitable to consider the effects of inflation and time value of money while determining the optimal inventory policy. With this motivation, this paper is aimed at developing a two-warehouse inventory model for deteriorating items where the demand rate is a decreasing function of the selling price under inflationary conditions. In addition, shortages are allowed and partially backlogged, and the backlogging rate has been considered as an exponentially decreasing function of the waiting time. The model jointly optimizes the initial inventory and the price for the product, so as to maximize the total average profit. Finally, the model is analysed and validated with the help of numerical examples, and a comprehensive sensitivity analysis has been performed which provides some important managerial implications.

In this paper, an imperfect production inventory system with multi-production run for single vendor and single buyer is studied where the defective items are reworked after several production processes and there is no break down during the rework process. The model is considered for deteriorating items under two delivery policies in order to determine the delivery policy leading to the minimum holding cost of system. The objective of this model is to determine the optimal production setup, number of deliveries and lot size by minimizing the total relevant cost of the proposed vendor-buyer integrated system. To minimize the manufacturer’s and buyer’s holding cost, the supply of finished products are made in small quantities. The total relevant cost of the vendor and the buyer is derived and a numerical example is presented to demonstrate the utility of the model.

Weather forecasting is essential and demanding scientific task of meteorological services across the world. It is a complex procedure that includes many specific technological field of study. The prediction is intricate process in meteorology because all decisions are made within a facet of uncertainty associated with weather systems. This research finding introduces a novel rough fuzzy computing approach for a short term rainfall forecasts. The model consists of rough set based optimal weather parameter selection module and fuzzy rule based classification module. The proposed fuzzy decision support model is compared with benchmarked classification approaches. The fuzzy classification model used in fuzzy decision support system is trained and tested using the reduct sets generated using proposed maximum frequency weighted feature reduction technique. The optimal reduct set constituting the weather parameters; minimum temperature, relative humidity and solar radiation achieved better prediction accuracy than complete feature set and the reducts. Most of the classification models have shown better accuracy when trained using the selected subsets of the target input. Thorough evaluation of the proposed model has revealed that coupling fuzzy decision support system and rough based pre-processing techniques was a better approach than traditional techniques. The experimental results revealed the proposed rough fuzzy model as a better rainfall prediction approach for modeling short range rainfall forecast.

The purpose of this study is to explore the role of implementing e-supply chain management (E-SCM) on the competitive position of companies and whether implementing customer relationship management (CRM) can affect the relationship between E-SCM and competitive advantage. To achieve this objective, a quantitative approach was utilized. A total of 300 questionnaires were distributed where 243 questionnaires were returned, with 17 incomplete questionnaires being excluded, leaving 226 usable questionnaires. PLS-SEM software was used to analyze the data. The results of this study demonstrate the imperative role of implementing E-SCM and CRM on creating a competitive advantage for firms. It also shows that CRM mediates the relationship between E-SCM and competitive advantage, suggesting that utilizing different technologies can help firms better communicate with their customers and thus better serve them which in turn will enhance customers’ satisfaction and thus boost the competitive position of the firm.

The controlling of bug fixing process during the system testing phase of software development life cycle is very important for fixing all the detected bugs within the scheduled time. The presence of open bugs often delays the release of the software or result in releasing the software with compromised functionalities. These can lead to customer dissatisfaction, cost overrun and eventually the loss of market share. In this paper, the authors propose a methodology to quantitatively manage the bug fixing process during system testing. The proposed methodology identifies the critical milestones in the system testing phase which differentiates the successful projects from the unsuccessful ones using Mahalanobis Taguchi system. Then a model is developed to predict whether a project is successful or not with the bug fix progress at critical milestones as control factors. Finally the model is used to control the bug fixing process. It is found that the performance of the proposed methodology using Mahalanobis Taguchi system is superior to the models developed using other multi-dimensional pattern recognition techniques. The proposed methodology also reduces the number of control points providing the managers with more options and flexibility to utilize the bug fixing resources across system testing phase. Moreover the methodology allows the mangers to carry out mid- course corrections to bring the bug fixing process back on track so that all the detected bugs can be fixed on time. The methodology is validated with eight new projects and the results are very encouraging.

In the present scenario of high unemployment; Multilevel Marketing (MLM) generates employment for people who have no permanent source of earning. MLM system has emerged as one of the prime alternatives in the current marketing system. India has become a very popular destination of doing MLM business with high potential of growth. MLM system provides lucrative compensation that works as motivation for people to join this business. Motivation for executives of any firm plays a major role in its success. It also leads commitment of employees towards work and responsibilities. An attempt has been made to identify the motivational variables that have the highest level of contribution for joining the MLM system. Most of the MLMcompanies focus on compensation plan or reward system but apart from that a number of variables have been found which motivate the distributors to engage in MLM business. Further, the distributors play a vital role in the growth of the business. In this study, we also propose a motivational model to help MLM companies formulate better strategies in making a large network of people for growth of business.