The primary objective of the research paper is to provide some insights on the concepts of intellec-tual capital and its important dimensions alongside to investigate the possible association between intellectual capital and attainment of competitive advantage. This study focuses on Iraqi hotel industry which is an under-research area in the context of intellectual capital and its relationship with competitive advantage. An adapted questionnaire was utilized to collect the responses from top and middle level managers of four and five-star hotels in Iraq. The reliability and validity of data collec-tion instrument were measured through Cronbach’s alpha, Composite Reliability and Average Var-iance Extracted respectively. The competitive advantage was then regressed against Human, Rela-tional and Structural capital by application of Partial Least Square methodology. Results of the study showed a positive and strong connotation among intellectual capital and attainment of competitive advantage. The human capital had the highest contribution for competitive advantage in hotel sector of Iraq. The top management of hotels should take interest to develop, maintain and retain human capital to attain competitive advantage over competitors.

In the era of digital manufacturing and highly competitive environment, it is desirable to deliver the right item, right quantity at right time at minimal cost. Under this volatile market environment, the inventory should be readily available at the manufacturing level at the lowest possible cost. Many industries have been conventionally employing traditional ABC analyses based on a single criterion of annual consumption cost for classification of inventory items in spite of other criteria such as unit cost, consumption rate, average inventory cost that may be important in inventory classification. To address such problems, incorporation of Multi-criteria decision making (MCDM) methods is considered an advantage. The present article focuses on a new approach to categorize inventory items using Modified similarity-based method. The proposed method is applied to the inventory data of raw materials from a renowned conveyor belt manufacturing company of West Bengal, India. By using Modified similarity-based method, the items are classified in A, B and C categories. Results obtained from the said method using R program are compared with those of well recognized TOPSIS and AHP methodologies to validate the application of this method for inventory classification.

This paper deals with data mining applications for the supply chain inventory management. ABC characterization is typically utilized for stock items arrangement on the grounds that the quantity of stock items is large to the point that it is not computationally practical to set stock and admin-istration control rules for every individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could influence the sale of other items (cross selling effect). Consequently, within each cluster, the inventories should be classified. In this paper, a modified approach is proposed considering both cross-selling effect and clusters to rank stock items. A numerical case is utilized to clarify the new ap-proach. It is represented that by utilizing this modified approach; the ranking of items may get influenced bringing about higher profits.