Strategies for systems analysis and problem solving

Traditionally, systems analysis is associated with application development projects, that is, projects that produce information systems and their associated computer applications. Your first experiences with systems analysis will likely fall into this category. But systems analysis methods can be applied to projects with different goals and scope. In addition to single information systems and computer applications, systems analysis techniques can be applied to strategic information

systems planning and to the redesign of business processes.

There are also many strategies or techniques for performing systems analysis. They include modern structured analysis, information engineering, prototyping, and object-oriented analysis. These strategies are often viewed as competing alternatives. In reality, certain combinations complement one another. Let's briefly examine these strategies and the scope or goals of the projects to which they are suited. The intent is to develop a high-level understanding only.

MODERN STRUCTURED ANALYSIS

Structured analysis was one of the first formal strategies developed for systems analysis of information systems and computer applications. Modern structured analysis is still one of the most widely practiced techniques.

Modern structured analysis is a process-centered technique that is used to model business requirements for a system. The models are structured pictures that illustrate the processes, inputs, outputs, and files required to respond to business events (such as ORDERS). "By process-centered, we mean the initial emphasis in this technique is on the process building blocks in our information system framework. The technique has evolved to also include the DATA building blocks as a secondary emphasis.

Structured analysis was not only the first popular systems analysis strategy; it also introduced an overall strategy that has been adopted by many of the other techniques-model-driven development. A model is a representation of reality. Just as "a picture is worth a thousand words," most models use pictures to represent reality. "Model-driven development techniques emphasize the drawing of models to define business requirements and information system designs. The model becomes the design blueprint for constructing the final system.

Modern structured analysis is simple in concept. Systems and business analysts draw a series of process models called data flow diagrams that depict the essential processes of a system along with inputs, outputs, and files. Because these pictures represent the logical business requirements of the system independent of any physical technical solution, the models are said to be a logical design for the system.

2. Make a written translation of the text below.

METHODOLOGY OF HIGH-FREQUENCY

FORECASTING

Other approaches to forecasting of national economies at high frequency

have been used, but the general procedure used in this volume follows a

specific pattern. The generation of regular quarterly NIPA (National Income and Public Accounts) statistics for the USA and other countries studied in the succeeding chapters is based on one principle that is closely tied to the availability of informative data about entries in time periods shorter than one calendar quarter and published in real time, or at daily, weekly, or monthly intervals. It was noted above that comprehensive international trade statistics and corporate profit statistics are noteworthy laggards for reporting purposes, but many statistics that are closely related to corresponding entries in NIPA become available early – prior to NIPA publication.1 It is well known that the statistician–economists in the national accounting division of the US government examine the statistics of retail trade in order to estimate components of consumer expenditures in NIPA. The monthly sales of motor cars and light trucks become available within days or hours at the end of each month, and this information is directly relevant for predicting consumer outlays for such vehicles. Retail sales of major department stores and chains are available at about the same time as total retail sales. Real-estate companies publish figures for monthly building of new homes and sale turnover of existing homes. Early monthly statistics are available for industrial production, construction, stocks of key energy products, and many services. All these early indicators form the basis for the first of three regular monthly releases of quarterly NIPA (advance preliminary and final). Statistics on employment, unemployment, wage rates and hours worked, all based on sample surveys, are made available monthly, approximately one month after the surveys take place. Other indicators are prices, government outlays, construction activity, orders for industrial goods, agricultural production, and many other economic activities. Most major entries in quarterly NIPA have access to some earlier monthly, weekly or daily statistics that bear a close relationship with a desired NIPA entry. This is the point at which the technical advances in information delivery play an important role in making possible high frequency estimates of NIPA components.

For every NIPA entry, a simple linear or log-linear relationship is

maintained in ready-to-use form. This allows us to estimate NIPA entries on the basis of their ongoing relationships to each subdivision needed for forecasting each element of the NIPA (both income and expenditure sides). We do this by using sample regression of NIPA entries on the indicators that are most closely tied to any NIPA variable being estimated, with appropriate time lags.

What has been discussed in some detail is a high-frequency economic

table for the (final) demand side that captures the results of those equations in the model that are available for computing each week’s NIPA items in a complete system of accounts, including a similar layout for the supply side. The double-entry accounting system is followed in order to make sure that all identities from the dual-entry nature of the system of accounts aggregate to the final set of forecast values. This also involves attention to the income, or supply, side, as well as to the demand side.

The relationships used to compute NIPA accounting values are called

‘bridge’ equations. The ‘bridge’ exists between the accounting entries,

properly added or summed, that appear in the estimated ‘bridge’ relations, while the high-frequency indicators are found in each week’s reported data collected day by day. The indicators provide only approximate information, i.e. retail sales indicate aspects of consumption outlays in the macro accounts, but they do so imperfectly. Also, more than one single high-frequency indicator may be needed in order to provide relevant information.

 

 

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