The balance between customer expectations and company's commitment to customer service is important for business success. Service industries increasingly encounter many intangible factors alongside the tangible objects. Intangible factors such as innovative ideas, new service attributes, learning principles and self-service technologies have great impacts on business success and customer satisfaction. However, these attributes are versatile, associated with service personnel action, customer relation, service practices, self-service technologies, service information and relationship with trade partners. Therefore, evaluation of these attributes to prioritize service effort and resource allocation to improve the service performance is believed to be a Multi Criteria Decision Making problem which has grown significantly in past decades. Researchers have proposed several effective ways to quantify service quality attributes assigning relative weights to important quality attributes that the service industry offers to retail customers, trade partners and business outlet. In the present study the Analytic Hierarchy Process is adopted to establish a local and global hierarchical priorities among different categories of service quality attributes. Ranking the attributes drives a service quality index between and within two hierarchical levels. An empirical illustration provides various managerial and resource allocation implications to improve service quality responsive action.

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In this paper, we investigate the material procurement and delivery policy in a production system where raw materials enter into the assembly line from two different flow channels. The system encompasses batch production process in which the finished product demand is approximately constant for an infinite planning horizon. Two distinct types of raw materials are passed through the assembly line before to convert them into the finished product. Of the two types of raw materials, one type requires preprocessing inside the facility before the assembly operation and other group is fed straightway in the assembly line. The conversion factors are assigned to raw materials to quantify the raw material batch size required. To analyze such a system, we formulate a nonlinear cost function to aggregate all the costs of the inventories, ordering, shipping and deliveries. An algorithm using the branch and bound concept is provided to find the best integer values of the optimal solutions. The result shows that the optimal procurement and delivery policy minimizes the expected total cost of the model. Using a test problem, the inventory requirements at each stage of production and their corresponding costs are calculated. From the analysis, it is shown that the rate and direction change of total cost is turned to positive when delivery rates per batch reaches close to the optimal value and the minimum cost is achieved at the optimal delivery rate. Also, it is shown that total incremental cost is monotonically increasing, if the finished product batch size is increased, and if, inventory cost rates are increased. We examine a set of numerical examples that reveal the insights into the procurementdelivery policy and the performance of such an assembly type inventory model.

Freight transportation and logistics decisions such as modal choice decisions are strategically important for effective supply chain operation and economic benefits. The freight selection logistic is a multi-criteria multi-objective (MCMO) process, crucial for smooth sourcing of materials, cost-effective delivery of products to customers in the right time, at the right quantity. The study discusses the major transport logistics attributes and the order preference by similarity ideal solution (TOPSIS) algorithm as the preferred MCMO model to support comparative ranking among the alternative freights. The entropy weight coefficient method minimizes the subjectivity in the selection of weight of the attribute. This study integrates the entropy technique on TOPSIS platform to improve the freight selection decision. A numerical example illustrates the procedure of the proposed algorithm and ranks the choices among truck, rail, and several intermodal transport combinations (rail/truck and air/truck) in a transportation selection model.

This study examines the characteristics of a prediction model for businesses in the online marketplace by considering the market trend, prior sales and decision maker's preference on potential demand estimate. With the rapid growth of the electronic market, the main challenge for online sellers is the ability to analyze customer expectation, market data, and sales information to make the accurate procurement decision. The proposed model integrates a mathematical structure for a target season sale comprising upcoming demand projection by seller's internal team, data from past sales and the overall trend of seller's e-brand to determine the online customer demand. The study proposed a newsvendor model as a tool for sellers to make the instantaneous decision of ordering merchandise from the supplier when the quick response to the customer order is a priority for electronic market. Results of the study provide insights into the procurement dynamics and implications of the e-commerce inventory plan.