Strategic planning development

CPhA operates according to its strategic plan developed in 1999 and revised in 2001. The plan has five key result areas ... 

Strategic Planning Development Refers to the planning steps required to develop the supply chain strategy. 

The interpretation step is for a reassessment of the inventory analysis, and the impact assessment is to assure that the goals of the study were met. The LCA has applications in process and product development and improvement, strategic planning, envirorunental benchmarking, and product marketing. 

One can identify two major categories of uncertainty in EIA data (scientific) uncertainty inherited in input data (e.g., incomplete or irrelevant baseline information, project characteristics, the misidentification of sources of impacts, as well as secondary, and cumulative impacts) and in impact prediction based on these data (lack of scientific evidence on the nature of affected objects and impacts, the misidentification of source-pathway-receptor relationships, model errors, misuse of proxy data from the analogous contexts) and decision (societal) uncertainty resulting from, e.g., inadequate scoping of impacts, imperfection of impact evaluation (e.g., insufficient provisions for public participation), human factor in formal decision-making (e.g., subjectivity, bias, any kind of pressure on a decision-maker), lack of strategic plans and policies and possible implications of nearby developments (Demidova, 2002). 

The ISO 9000 series encompasses the product development sequence from strategic planning to customer service. Currently, it is a series of five quality system standards, with two of the standards focusing on guidance and three that are contractual standards. 

The flexibility in the petrochemical industry production and the availability of many process technologies require adequate strategic planning and a comprehensive analysis of all possible production alternatives. Therefore, a model is needed to provide the development plan of the petrochemical industry. The model should account for market demand variability, raw material and product price fluctuations, process yield inconsistencies, and adequate incorporation of robustness measures. 

The above discussion shows the importance of petrochemical network planning in process system engineering studies. In this chapter we develop a deterministic strategic planning model of a network of petrochemical processes. The problem is formulated as a mixed-integer linear programming model with the objective of maximizing the added value of the overall petrochemical network. 

Cranfield Resilience Centre Cranfield University Royal Military College of Science Shrivenham, Swindon Wiltshire SN68LA UK Ph +44.0.1793.782551 www.rmcs.cranfield.ac.uk Development of mine action training programs for UN and assistance with nation s strategic planning for mine action. 

Interest in organic photochemistry has also developed from a different direction, as synthetic organic chemists have incorporated into their strategic planning of syntheses, reaction types that can best be accomplished photochemicafly, or in some instances can be carried out only by a photochemical method. A great many of these organic photochemical reactions have been discovered relatively recently. 

Under the NASA Headquarters Office of Life and Microgravity Sciences and Application, the Microgravity Research Program supports NASA s strategic plan in the Human F.xplnralion and Development of Space Enterprise. 

In practice decision makers typically are risk averse and the expected value approach does not take into account the variability of the solutions obtained under the probability distributions or scenarios considered for the uncertain parameters. Rosenhead et al. (1972) introduced the aspect of robustness as a criterion for strategic planning to address this issue. Building on the notion of robustness, Mulvey et al. (1995) developed the concept of robust optimization distinguishing between two different types of robust models. A model is solution robust if the solution obtained remains close to optimality for any realization of the uncertain parameters. The model itself is robust if it remains (almost) feasible for any realization of the uncertain parameters (model robust).36 Here, only solution robustness is of interest as the most important elements of uncertainty in production network design, namely demand volumes, costs, prices and exchange rates, should not lead to infeasibility problems under different scenarios considered. 

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