Applying Input-Output Analysis

In this chapter, we focus on recent and emerging technologies that either are or soon will be applied commercially. Older technologies are discussed to provide historic perspective. Brief discussions of potential future technologies are provided to indicate current development directions. The chapter substantially extends an earlier publication (Davis et al., 1996a) and is divided into seven main sections beyond the introduction Data Analysis, Input Analysis, Input-Output Analysis, Data Interpretation, Symbolic-Symbolic Interpretation, Managing Scale and Scope of Large-Scale Process Operations, and Comprehensive Examples. 

Input-output analysis can be applied to a wide variety of problems, but its major contribution is that it reflects changes in final demand to be measured quantitatively for a great many industrial sectors. For example, the effect on CPI output of an increase in automobile sales can be determined by using input-output analysis. The techniques of input-output analysis can also be used to determine the effect on the CPI of increasing the amount of plastics in cars and decreasing the amount of steel. The effect of changes in the GNP on the CPI and on any other sector also can be estimated with input-output analysis. Also the... 

A simple example will illustrate how the various relationships are applied and indicate some additional uses of input-output analysis. 

Input-output analysis has been described in this chapter and used to identify the role of the chemical processing industries in the U.S. economy. We have also shown how the industrial responses to changes in demand can be measured. Input-output analysis can also be applied in a variety of situations by individual corporations. A. D. Little, Inc. conducted a survey among more than 200 companies concerning the use of input-output analysis in some form of corporate planning at one time or another. About one-third of these major firms use input-output analysis on a regular basis. Input-output analysis has been used in the following corporate functions ... 

Lack of published case studies of how input-output analysis has been applied by individual companies. 

The regional input-output model for North Karelia is constructed from the relevant input-output statistics (Statistics Finland, 2006). An input-output model is an application of the neo-classical theory of general equilibrium to the empirical analysis of the interdependence between economic sectors, such as industries, consumption and exports, and compensations for households and imports. It was originally developed to analyze the connections between different industries within a national economy (Leontief, 1966 134), and is a useful tool for showing the structure of the economy in terms of the flows of goods and services and for analyzing the impacts of changes in flnal demand. The input-output model applied here takes as an initial assumption that industrial outputs are determined by the final demand as linear functions of inputs from other industries, labor, capital and imports. The model is written as... 

As discussed and illustrated in the introduction, data analysis can be conveniently viewed in terms of two categories of numeric-numeric manipulation, input and input-output, both of which transform numeric data into more valuable forms of numeric data. Input manipulations map from input data without knowledge of the output variables, generally to transform the input data to a more convenient representation that has unnecessary information removed while retaining the essential information. As presented in Section IV, input-output manipulations relate input variables to numeric output variables for the purpose of predictive modeling and may include an implicit or explicit input transformation step for reducing input dimensionality. When applied to data interpretation, the primary emphasis of input and input-output manipulation is on feature extraction, driving extracted features from the process data toward useful numeric information on plant behaviors. 

We give only a short description of the three supply chain configurations and their simulation models for details we refer to Persson and Olhager (2002). At the start of our sequential bifurcation, we have three simulation models programmed in the Taylor II simulation software for discrete event simulations see Incontrol (2003). We conduct our sequential bifurcation via Microsoft Excel, using the batch run mode in Taylor II. We store input-output data in Excel worksheets. This set-up facilitates the analysis of the simulation input-output data, but it constrains the setup of the experiment. For instance, we cannot control the pseudorandom numbers in the batch mode of Taylor II. Hence, we cannot apply common pseudorandom numbers nor can we guarantee absence of overlap in the pseudorandom numbers we conjecture that the probability of overlap is negligible in practice. 

Hierarchical Approach is a simple but powerful methodology for the synthesis of process flowsheets. It consists of a top-down analysis organised as a clearly defined sequence of tasks grouped in levels. Each level solves a fundamental problem as, number of plants, input/output structure, reactor design and recycle structure, separation system, energy integration, environmental analysis, safety and hazard analysis, and plantwide control. At each level, systematic methods can be applied for the synthesis of subsystems, as chemical reaction, separations, or heat exchangers network. 

The methodology presented hereafter regards a MIMO system that can be handled by a combination of multi SISO loops. It is an input/output controllability being based on linear analysis tools. It can be applied to a stand-alone complex unit, as a distillation column, or to a flowsheet. In this later case it has the character of a decentralised (integral) plantwide control problem. 

System analysis and synthesis have long been a well-established engineering approach that has been used in many different branches of engineering. This formal way of examining the inputs, outputs, and transfer functions applied to a health care system or portion thereof has provided some important... 

Another tricky question arises when facing life cycles of multi-output processes. Inputs, outputs and the related environmental impacts must then be allocated to products. This can be done according to physical properties of the product flows (mass or energy flows). If this is not possible or not justifiable the usual way is to allocate according to the economic value of the products (prices). In our study, both mass and price allocation have been applied to highlight the influence of this allocation procedure on the outcome of the analysis. 

In the case of flexible robots, several identification schemes have been studied. Some on-line identification schemes are based on input-output ARMA representations [15, 16, 17]. Another approach consists in elaborating a minimal identification model based on a knowledge model of the robot and in applying a least-squares method. There are two kinds of minimal identification model the first consists in applying the theorem of energy for the robot, the second comes from the dynamic model. More details on these two models applied in the case of one or two link-planar robots can be found in [18] and [19]. A set of standard parameters has been proposed. Its minimality has been demonstrated using a numerical rank analysis of the observation matrix which is constructed with a random sequence of points. 

The associated process for the Finite Element Method is slightly different and in consequence is referred to in the present document as a threads analysis. The process requires breaking down the analysis into a set of stages and for each stage applying an algorithm which identifies discrete steps together with inputs/outputs. Table 1 shows how the threads process applies to the external part of the analysis process in which the real world is converted into an idealised finite element model. 

The paucity literature reported on applications of ANNs in marine and offshore safety engineering reflects that the concept of ANNs is still an extremely raw technique to this area. Published research literature providing a step by step explanation of input data identification through network architecture design and output analysis is somewhat sparse. Buxton et al. (1997) applied the techniques of ANNs to statistics of losses of bulk carriers due to fire to determine whether it is of potential value as a predictor of overall risk. More recently, some initial findings based on a feasibility study of using ANN techniques in offshore and maritime safety-based decision support system has been reported (Sii et al. (2000), Wang et al. (2001)). 

---