Popular Nonprofit Management Research Sources on CD-ROM

Nonprofit World 1983–1996. Madison, Wis.: Learning Institute for Nonprofit Organizations, 1997. $50 CD-ROM. FC Search: The Foundation Center's Database on CD-ROM, Version 2.0. New York: Foundation Center, 1997. $1,995 CD-ROM personal version.     

Food Well-Being in Older Adults: Effects of a Universal Non-contributory Pension in Mexico

Social Indicators Research - One of the few universal programs in Latin America is the Food Pension for Adults Older than 68 (PAAM) of Mexico City, which aims to reduce food insecurity while...     

Application of value stream mapping (VSM) for lean and cycle time reduction in complex production environments: a case study

The application of VSM is a proven approach for lean-based improvements. Typically, this becomes challenging, when applied for complex production environments. The purpose of this research is to demonstrate, how with some approximations and simplifications in VSM application, lean can be successfully achieved in these environments. The research follows case study method and systematically guides about the segregation and treatment of non-value-adding(NVA) and value-adding (VA) activities of heavy-duty industrial power transformer making process. The focal company operates in engineer to order (ETO) and high-mix low-volume (HMLV) environments. With a research team using gemba walks and systematic questioning technique, relevant data were collected for mapping. Taguchi method was also applied for one of the critical steps, which was influencing cycle time and energy requirements. The generalisable findings endorse the strengths of approach for lean by addressing wastes and cycle time reduction. The study establishes that VSM application-based lean messages remain same for both simple and complex environments. It also exposes that non-compliance of ‘VSM assumptions’ and ‘micro-concepts’ are the root causes for application complexities. The study also offers useful insights and practical guidelines to facilitate lean in ETO, construction and HMLV production environments.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.