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The combination of global warming and peak oil has made finding alternative sources of energy more important than ever. Written in an easy-to-read format, Oil Sands Production Processes provide the reader with an understandable overview of the chemistry, engineering, and technology of oil sands. The various chapters have been written to include the latest developments in the oil sands industry, including evolving and new processes as well as the various environmental regulations. Overview of the chemistry, engineering, and technology of oil sands. Updates on the evolving processes and new processes. Evolving and new environmental regulations regarding oil sands production processes

The Profit Impact of Marketing Strategy (PIMS) database was used to
test the relationship between production process type (small batch,
large batch/assembly, and continuous) and eight organizational policy
decisions (new products, new plant and equipment, finished goods
inventory, raw materials/work‐in‐process inventory, capacity
utilization, fixed capital assets, manufacturing costs, gross margin).
In addition, the effect of six broad industry types on the proposed
relationships was also investigated. Overall industries, raw
materials/work‐in‐progress, capacity utilization, manufacturing costs,
fixed assets, and gross margin varied with production process type while
new products, new plant and equipment, and finished goods inventory did
not vary. Within each industry, the findings showed less support for the
relationships between production process type and the eight
organizational policy decisions. Further analysis showed that most of
the industries are dominated by a production process type. Suggests a
movement away from the traditional differentiation of production process
technologies and a shift of research emphasis to the differing uses of a
particular production process technology within an industry.

Hydrogen is a very important industrial gas in chemical processes. It is very volatile; therefore, it can escape from the process units and its mass balance is not always correct. In many industrial processes where hydrogen is reacted, kinetics are often related to hydrogen pressure. The right thermodynamic properties of hydrogen can be found for a process simulation and optimization; they can be estimated by the Grayson-Streed model. In the case studied, a methanol plant with a capacity of 150,000 tons/year, the flow rate of hydrogen (H2) can be optimized using nonlinear programming, but good estimates of thermodynamic properties of hydrogen had to be found. In the case study, the model is in relatively good agreement with experimental measurements in the existing methanol production plant. An additional flow rate of hydrogen can increase the methanol production by 1.2%. Total potential profit increase of the additional methanol production was estimated to be 30 kEUR/a (or 103 euros/year).

The financial crisis of 2008 not only started the Great Recession, but also set off fundamental changes in production processes, government fiscal practices, and housing. Technological progress has enabled firms to outsource and offshore parts of the production process, leading to a fragmentation of global value chains. We briefly discuss this "second unbundling,"global versus regional fragmentation and some of the consequences that became visible during the Great Recession's trade collapse. We discuss the consequences for some local clusters, both from a theoretical and empirical perspective, and some consequences for government fiscal health and housing from an American perspective.

I study optimal incentive schemes in organizations where agents perform their tasks sequentially. I consider a model in which agents' effort decisions are mapped into the probability of the project's success. An optimal investment‐inducing mechanism allocates rewards to agents so as to induce all of them to exert effort in equilibrium at minimal cost to the principal. I characterize the unique optimal mechanism in several versions of my benchmark model. I also address the problem of allocating individuals with diverse qualifications to different slots of the production process as well as allocating tasks of different importance across different agents.

We develop a theory of a firm in an incomplete contracts environment which decides on its complexity, organization, and global scale. Specifically, the firm decides i) how thinly it wants to slice its production process by choosing the mass of symmetric intermediate inputs that are simultaneously combined to a final product, ii) if the supplier of each component is an external contractor or an integrated affiliate, and iii) if that component is offshored to a foreign country. We also consider the case of asymmetric inputs. Our model leads to a rich set of novel predictions about the structure of multinational firms that are consistent with stylized facts from the recent empirical literature.

The financial crisis of 2008 not only started the Great Recession, but also set off fundamental changes in production processes, government fiscal practices, and housing. Technological progress has enabled firms to outsource and offshore parts of the production process, leading to a fragmentation of global value chains. We briefly discuss this "second unbundling," global versus regional fragmentation and some of the consequences that became visible during the Great Recession's trade collapse. We discuss the consequences for some local clusters, both from a theoretical and empirical perspective, and some consequences for government fiscal health and housing from an American perspective.

We start with the definition of experimental data. First place we consider the controllable variables, whose levels are established by technical production experts with regard to a theoretic "model" defining the production components, their connections and working rules in sight of the envisaged achievements. Then, the responses or results of production activities, which typically show random variability. The controllable variables are used to regulate the means of their distributions and aspects of variability (Taguchi approach). An information system represents a data collection, which is directed to specific purposes like: financial control, accounting control, personnel control, business control, production control, framed around production programs, control of production lines and related maintenance, quality control, framed around control of nonconformities regarding some specification limits and ensuring product good quality over time. Qualitative guidelines for choosing the data for a quality control information system are that it must be as near as possible to a sufficient system which means that it is capable of explaining any nonconformity in a product characteristic, as well as being complete, that is, such that it is also capable of suggesting how to remove them. In a natural way quality control implies a dynamic approach based on a systematic improvement of production activities, starting from specific reference conditions. We consider two examples appropriated to explain the fundamentals of a methodology useful to cope with this complex subject. The first example refers to a real industrial application regarding the process control of polyvinyl chloride production, found in literature [9]. The second example concerns the distribution process of perishable goods, which is considered to be an innovation. In this case we suppose that there is a finite set of operating situations (points of sale) and that we can define an indicator Q=Q1 /c = Quality of a reference product/a corresponding economic quantity (cost or earning). In the case considered, by means of the ordering of the ratios total cost/total earning, we come to the most profitable situation.

We start with the definition of experimental data. First place we consider the controllable variables, whose levels are established by technical production experts with regard to a theoretic "model" defining the production components, their connections and working rules in sight of the envisaged achievements. Then, the responses or results of production activities, which typically show random variability. The controllable variables are used to regulate the means of their distributions and aspects of variability (Taguchi approach). An information system represents a data collection, which is directed to specific purposes like: financial control, accounting control, personnel control, business control, production control, framed around production programs, control of production lines and related maintenance, quality control, framed around control of nonconformities regarding some specification limits and ensuring product good quality over time. Qualitative guidelines for choosing the data for a quality control information system are that it must be as near as possible to a sufficient system which means that it is capable of explaining any nonconformity in a product characteristic, as well as being complete, that is, such that it is also capable of suggesting how to remove them. In a natural way quality control implies a dynamic approach based on a systematic improvement of production activities, starting from specific reference conditions. We consider two examples appropriated to explain the fundamentals of a methodology useful to cope with this complex subject. The first example refers to a real industrial application regarding the process control of polyvinyl chloride production, found in literature [9]. The second example concerns the distribution process of perishable goods, which is considered to be an innovation. In this case we suppose that there is a finite set of operating situations (points of sale) and that we can define an indicator Q=Q1 /c = Quality of a reference product/a corresponding economic quantity (cost or earning). In the case considered, by means of the ordering of the ratios total cost/total earning, we come to the most profitable situation.

ABSTRACTA descriptive life cycle model of process development and innovation, as previously reported, is used as a framework to analyze historical trends in the technological development of U.S. automotive engine plants. Using the plant as the unit of analysis, the author examines trends in product diversity, equipment development, backward integration, and productivity improvement. Results support the original research framework and help to clarify the important impact of process changes on the sources of productivity improvement, the capability for product innovation, and the cost of change.