Titlebook: Enterprise Risk Management Models; David L. Olson,Desheng Dash Wu Textbook Jan 20172nd edition Springer-Verlag GmbH Germany 2017 Disaster

揭穿真相

https://doi.org/10.1007/978-1-4613-4268-7ision making models have been compared and extended. When the data is presented with uncertainty, stochastic DEA provides a good tool to perform efficiency analysis by handling both inefficiency and stochastic error.

憤世嫉俗者

側(cè)面左右

使虛弱

Enterprise Risk Management in Supply Chains,hose responsible for decision making are identified, and a process outlined where risks, responses, and measures of outcomes are included..A process of risk identification, assessment, strategy development and selection, implementation and monitoring is reviewed. Representative mitigation strategies were extracted from published sources.

municipality

Simulation of Supply Chain Risk,Arena (or many others) are very appropriate for that type of modeling. The other type is supportable by spreadsheet tools such as Crystal Ball, demonstrated in this chapter. Spreadsheet simulation is highly appropriate for inventory modeling as in push/pull models. Spreadsheet models also are very useful for system dynamic simulations.

Common-Migraine

一條卷發(fā)

Data Mining Models and Enterprise Risk Management,n industry and has all of the benefits of open source software (many eyes are monitoring it, leading to fewer bugs; it is free; it is scalable). Further, the R system enables widespread data manipulation and management.

Calibrate

Natural Disaster Risk Management,ems have made the price very high, and we have not figured out how to acceptably dispose of the waste. While natural disasters come as surprises, we can be prepared. This chapter addresses natural domain risks in the form of disaster management.

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Crumple

Gilbert Newton Lewis,Merle Randallrices provide valuable initial tools when high levels of uncertainty are present. Quantitative risk assessment in the form of indices as demonstrated would be preferred if data to support it is available.