In this innovative approach to the practice of social science, Charles Ragin explores the use of fuzzy sets to bridge the divide between quantitative and qualitative methods. Paradoxically, the fuzzy set is a powerful tool because it replaces an unwieldy, "fuzzy" instrument—the variable, which establishes only the positions of cases relative to each other, with a precise one—degree of membership in a well-defined set. Ragin argues that fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible. They let quantitative researchers abandon "homogenizing assumptions" about cases and causes, they extend diversity-oriented research strategies, and they provide a powerful connection between theory and data analysis. Most important, fuzzy sets can be carefully tailored to fit evolving theoretical concepts, sharpening quantitative tools with in-depth knowledge gained through qualitative, case-oriented inquiry. This book will revolutionize research methods not only in sociology, political science, and anthropology but in any field of inquiry dealing with complex patterns of causation.

Fuzzy-Set Social Science

https://findresearcher.sdu.dk:8443/ws/files/116118561/reverse_logistics.pdf

Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future

THE NEED FOR QUALITY The first thing that we need to consider, in any organization, is that quality is the most important thing. The quality of your work defines you.  Whoever you are,  Whatever you do,  I can find the same products and services cheaper somewhere else. But your quality is your signature. Developing and delivering high quality products and services means that you are doing things correctly from the beginning. As a consequence, you are reducing the need for additional services, from verification to warranty.

Total quality management.

Recent trends in modeling of deteriorating inventory

A comparative literature analysis of definitions for green and sustainable supply chain management

Big data analytics in supply chain management between 2010 and 2016: Insights to industries

Optimal inventory model for items with imperfect quality and shortage backordering

In this study, we develop an optimal inventory model for items with imperfect quality and shortage backordering. Such implicit assumption is reasonable in view of the fact that poor-quality items do exist during production. They are usually picked up during the screening process and are withdrawn from stock instantaneously. It is assumed that all customers are willing to wait for new supply when there is a shortage. The analysis shows that our model is a generalization of the models in current literatures. An algorithm and numerical analysis developed show that our model always results in better performance.

Hui-Ming Wee

Papers

Big data analytics in supply chain management between 2010 and 2016: Insights to industries

Optimal inventory model for items with imperfect quality and shortage backordering

Optimal inventory model for items with imperfect quality and shortage backordering

A deterministic lot-size inventory model for deteriorating items with shortages and a declining market

Economic production lot size model for deteriorating items with partial back-ordering