Outsourcing is a management approach by which an organization delegates some noncore functions to specialized and ef®cient service providers. In the era of ªglobal marketº and ªe-economyº, outsourcing is one of the main pillars of the new way to conceive the relationships among companies. Despite outsourcing large diffusion, huge business cases and big deals of documentation available on network or press, there is no structured procedure able to support the govern of the evolution of a generic outsourcing process. In accordance with the principles of total quality management, this paper describes a proposal of a new approach for managing outsourcing processes. The model, which can be easily adapted to different application ®elds, has been conceived with the main aim of managing strategic decisions, economic factors and human resources. The approach is supported by different decision and analysis tools, such as benchmarking techniques, multiple criteria decision aiding (MCDA) methods, cost analysis, and other process-planning methodologies. An application of the method to a real case is also provided.
The paper presents a new method for statistical process control when ordinal variables are involved. This is the case of a quality characteristic evaluated by an ordinal scale. The method allows a statistical analysis without exploiting an arbitrary numerical conversion of scale levels and without using the traditional sample synthesis operators (sample mean and variance). It consists of a different approach based on the use of a new sample scale obtained by ordering the original variable sample space according to some specific 'dominance criteria' fixed on the basis of the monitored process characteristics. Samples are directly reported on the chart and no distributional shape is assumed for the population (universe) of evaluations. Finally, a practical application of the method in the health sector is provided.
Many practical problems of quality control involve the use of ordinal scales. Questionnaires planned to collect judgments on qualitative or linguistic scales, whose levels are terms such as ''good,'' ''bad,'' ''medium,'' etc., are extensively used both in evaluating service quality and in visual controls for manufacturing industry. In an ordinal environment, the concept of distance between two generic levels of the same scale is not defined. Therefore, a population (universe) of judgments cannot be described using ''traditional'' statistical distributions since they are based on the notion of distance. The concept of ''distribution shape'' cannot be defined as well. In this article, we introduce a new statistical entity, the so-called ordinal distribution, to describe a population of judgments expressed on an ordinal scale. We also discuss which of the traditional location and dispersion measures can be used in this context and we briefly analyze some of their properties. A new dispersion measure, the ordinal range, as an extension of the cardinal range to ordinal scales, is then proposed. A practical application in the field of quality is developed throughout the article.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.