Factors Affecting Choice of Distribution Channel- Part 3

Dec 28th, 2010
by Rashmi.
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3. Product Factor

a)      Sizes & weight  of the product – If the size, weight & price of the product is very large, then direct supply should be there as it will lead to convenience & low  transportation cost & there will be less chances of damage during transportation. For eg. Big industrial products like boilers, grinders etc.

On the other hand, if size & weight of product is not so big, a long chain can be as in case of Fast Moving Consumers Goods (FMCG)

b)      Unit Value – If the per unit value of product is less, say for eg. salt, sugar, wheat, rice etc. then the distribution channel may be large as consumption of it is comparatively more. But, if the unit price is very high, for eg. gold, silver, then a smaller distribution channel is required.

c)       Stability of the product – If the product is of perishable in nature, i.e. it becomes useless after a specific period of time, like milk, butter, cheese, fish, etc. then  a small distribution channel is required to ensure prompt delivery, but if the product is stable in nature like soaps, shampoo etc, then the distribution channel can be long.

d)      Standard v/s Specific products – Some distributors only want to sell standard & famous products, so if the product is standard in nature, manufacturer has to use these types of distributors or middlemen.

But, if the product is specific one, say : engineering & medical books, which are not kept by all book-sellers, so in that case, these specific dealers on middlemen have to be chosen.

e)      Technical nature of product – If the product is of technical nature then an effective after sales service is also to be provided. So, in this case, either direct marketing or marketing through authorized dealer should be used, on only then company can use the services of its services- engineers more effectively. For eg., in case of electronic item TV’s and Refrigerators, outlet is authorized dealers, so if after sales service is required customers may contact the dealer, which passes it to the company for final service.

f)       Expert of product line – The manufacturer has to decide that he should take the services of a wholesaler or retailers or both and then accordingly decide to increase or decrease the product line. For eg., if the manufacturer is manufacturing soaps, then he can increase the product line by incorporating shampoos also, as the distribution channel will be the same.

Components of information processing systems

Aug 12th, 2010
by Rashmi.
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Software Safety and Hazard Analysis

Feb 25th, 2010
by Rashmi.
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Software safety and hazard analysis are SQA activities that focus on the identification and assessment of potential hazards that may impact software negatively and cause entire system to fail. If hazards can be identified early in the software engineering process software design features can be specified that will either eliminate or control potential hazards.

A modeling and analysis process is conducted as part of safety. Initially hazards are identified and categorized by critically and risk.

Once hazards are identified and analyzed, safety related requirements could be specified for the softwares the specifications can contain a list of undesirable events  and desired system responses to these events. The roll of software in managing undesirable events is then indicated.

Although software reliability and software safety are closely related to one another, it is important to understand the subtle difference between them. Software reliability uses statistical analysis to determine the likelihood that a software failure will occur however, the occurrence of a failure \does not necessarily result in a hazard or mishap. Software safety examines the ways in which failure result in condition that can be lead to mishap. That is, failures are not considered in a vaccum. But are evaluated in the context of an entire computer based system.

Cost of Quality

Feb 25th, 2010
by Rashmi.
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Cost of Quality includes all codes incurred in the pursuit of quality or in performing quality related activities. Cost of quality studies are conducted to provide a base line for the current cost of quality, to identify opportunities for reducing the cost of quality, and to provide a normalized basis of comparison. The basis of normalization is usually money. Once we have normalized quality costs on a money basis, we  have the necessary data to evaluate where the opportunities lie to improve our process further. Moreover, we can evaluate the effect of changes in money based terms.

QC may be divided cost associated with

1. Prevention

2. Appraisal

3. Failure

Prevention costs include:

1. Quality Planning

2. Formal Technical Review on systems

3. Testing Equipments used for running the software

4. Training imparted for persons involved in development

Appraisal costs includes to gain into product condition the “First time through” each process.

Examples for appraisal costs includes:

1. In process and inter process inspection

2. Equipment calibration and maintenance

3. Testing

Failure Costs are costs that would disappear if no defects appeared before shipping a product to customer’s failure costs may be subdivided into internal and external failure costs.

 Internal failure costs are costs incurred when we direct an error in our product prior to shipment.

Internal failure costs includes

1. Rework

2. Repair

3. Failure Mode Analyses

External failure costs are the cost associated with defects found after the product has been shipped to the customer.

 Examples of external failure costs are

1. Complaint Resolution

2. Product return and replacement

3. Helpline support

4. Warranty work

QUALITY CONTROL (QC)

Feb 25th, 2010
by Rashmi.
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QC is the series of inspections,reviews, and tests used throughout the development life cycle to ensure that each work product meets the requirements placed upon it. QC includes a feedback loop to  the process that created the work product. The combination of measurement and feedback allows us to tune part of the manufacturing process QC activities may be fully automated, manual or a combination of automated tools and human interaction. An essential concept of QC is that all work products have defined and measurable specification to which we may compare the outputs of each process the feedback loop is essential periodically to improve the software quality. They review the code, standards,process followed at every stages.

QUALITY OF CONFORMANCE

Feb 25th, 2010
by Rashmi.
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Quality of conformance is the degree to which the design specifications are followed during manufacturing. Again, the greater the degree of conformance, the higher the level of quality of conformance.

In software development, quality of design encompasses requirements,specifications and design of the system. The requirements must be gathered formally and there should not be any ambiguity and must be complete in all respects. Specifications must be elaborated and defined formally. Design must follow the design and the resulting system meets its requirements and performance goals,conformance quality high.

QUALITY CONCEPTS

Feb 25th, 2010
by Rashmi.
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Its Quality concepts

1. Quality

2. Quality control

3. Quality assurance

4. Cost of quality

The American heritage dictionary defines quality as “a characteristic or attribute of something”. As an attribute of an item quality refers to measurable characteristic-things, which we are able to compare to known standards such as length,color,electrical properties, and malleability and so on. However,software is largely an intellectual entity more challenging to characterize than physical object.

Neverthless,measures of a programs characteristic do exist. These properties include

1. Cyclomatic complexity

2. Cohesion

3.Number of function points

4. Lines of code

When we examine an item based on its measurable characteristics, two kinds of quality may be encountered:

1. Quality of design

2. Quality of conformance

DATA MINING FOR FINANCIAL DATA ANALYSIS

Feb 25th, 2010
by Rashmi.
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Most banks and financial institutions offer a wide variety of banking services (for example saving balance checking, individual transactions), credit (such as loans, mortgage) and investment services (mutual funds). Some also offer insurance services and stock investment services.

Financial data collected in the banking and financial i9ndustry are often relatively complete,reliable and of high quality,which facilities systematic data analysis and data    analysis and data mining. The various issues are.

1. Design and construction of data warehouses for multidimensional data analysis and data mining

 Data warehouses need to be constructed for banking and financial data. Multidimensional data analysis methods should be used to analyze the general properties of such data. Data warehouses,datacubes, multifeature and discovery=driven data cubes, characteristic and comparative analyses and outlier analyses all play important roles in financial data analysis and mining.

2. Loan payment prediction and customer credit policy analysis

Loan payment prediction and customer credit analysis are critical to the business of a bank. Many factors can strongly or weakly influence loan payment performance and customer credit rating. Data mining methods, such as feature selection and attribute relevance ranking may help identify important factors and eliminate irrelevant ones. In some cases, analysis of the customer payment history may find that say, payment-to-income ratio is dominant factor,while education level and debt ratio are not. The bank may then decide to adjust its loan-granting policy so as to grant loans to those whose application was previously denied but whose profile shows relatively low risks according to the critical factor analysis.

3. Classification and clustering of customers for targeted marketing

Classification and clustering methods can be used for customer group identification and targeted marketing. Effective clustering and collaborative filtering methods can help identify customer groups,associate a new customer with an appropriate customer group and facilitate targeted marketing.

4. Detection of money laundering and other financial crimes

To detect money laundering and other financial crimes, it is important to integrate information from multiple databases, as long as they are potentially related to the study. Multiple data analysis tools can then be used to detect unusual patterns,such as large amounts of cash flow at certain periods, by certain group of people and so on. Linkage analysis tools that are used to identify links among different people and activities,classification tools that is used to group different cases,outlier analysis tools which is used to detect unusual amounts of fund transfer or other activities and sequential pattern analysis tools which is used to characterize unusual access sequence.

Requirements for Clustering in Data Mining

Feb 25th, 2010
by Rashmi.
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Clustering is a challenging and interested field potential applications pose their own special requirements.

The following are typical requirements of clustering in data mining:

1. Scalability: Many clustering algorithms work well on small data sets containing fewer than 200 data objects. However, a large database may contain millions of objects. Clustering on a sample of a given large data set may lead to biased results. Highly scalable clustering algorithms are needed.

2. Ability to deal with different types of attributed: Many algorithms are designed to cluster interval-based (numerical)data. However, applications may require clustering other types of data, such as binary, categorical (nominal), and ordinal data, or mixtures of these data types.

3. Discovery of clusters with arbitrary shape: Many clustering algorithms determined clusters based on Euclidean or Manhattan distance measures. Algorithms based on such distance measures ‘end to find spherical clusters with similar size and density’. However, a cluster could be of any shape. It is important to develop algorithms that can detect clusters of arbitrary shape.

4. Minimal requirements for domain knowledge of determine input parameters:  Many clustering algorithms require users to input certain parameters in cluster analysis (such as the number of desired clusters). The clustering results can be quite sensitive to input parameters. Parameters are often hard to determine, especially for data sets containing high-dimensional objects. This not only burdens users, but also makes the quality of clustering difficult to control.

5. Ability to deal with noisy data: Most real-world databases contain outliners or missing, unknown, erroneous data. Some clustering algorithms are sensitive to such data and may lead to clusters of poor quality.

6. Insensitivity to the order of input records: Some clustering algorithms are sensitive to the order of input data; for example, may generated dramatically different clusters. It is important to develop algorithms that are insensitive to the order of input.

7. High dimensionality: A database or a data warehouse can contain several dimensions or attributes. Many clustering algorithms are good at handling low-dimensional data, involving only two to three dimensions. Human eyes are good at judging the quality of clustering for up to three dimensions. It is challenging to cluster data objects in high-dimensional space, especially considering that such data can be very sparse and highly skewed.

8. Constraint-based clustering: Real-world applications may need to perform clustering under various kinds of constraints. Suppose that your job is to choose the locations for a given number of new automatic cash-dispensing machines (ATMs) in a city. To decide upon this, we may cluster household while considering constraints such as the city’s rivers and highway networks and customer requirements per region. A  challenging task is to find groups of data with good clustering behavior that satisfy specified constraints.

9. Interpretability and usability: Users expect clustering results to be interpretable, comprehensible, and usable. That is, clustering may need to be tied up with specific semantic interpretations and applications. It is important to study how an applications goal may influence the selection of clustering methods.

CLUSTERING IN DATA MINING

Feb 25th, 2010
by Rashmi.
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Clustering is a division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Representing data by fewer clusters necessarily loses certain fine details (akin to lossy data compression), but achieves simplification. It represents many data objects by few clusters, and hence, it models data by its clusters.

Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical  analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. Therefore, clustering is unsupervised learning of a hidden data concept. Data mining deals  with large databases that impose on clustering analysis additional severe computational requirements.

Clustering techniques fall into a group of undirected data mining tools. The goal of undirected data mining is to discover structure in the data as a whole. There is no target variable to be variable to be predicted, thus no distinction is being made between independent and dependent variables.

Clustering techniques are used for combining observed examples into clusters (groups) which satisfy two main criteria:

1. each  group or cluster is homogenous; examples that belong to the same group are similar to each other.

2. each group or cluster should be different from other clusters, that is examples that belong to one cluster should be different from the examples of other clusters.

Depending on the clustering technique, clusters can be expressed in different ways:

1. identified clusters may be exclusive, so that any example belongs to only one cluster.

2. they may be overlapping : an example may belong to several clusters.

3. they may be probabilistic, whereby an example belongs to each cluster with a certain probability.

4. clusters might have hierarchical structure, having crude division of examples at highest level of hierarchy, which is then refined to sub-clusters at lower levels.

Dynamic Linking

Feb 24th, 2010
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Dynamic linking is the process by which an application in the Windows environment is able to link  to library functions at run time.

Ina standard C# program, various built-in functions, which are invoked by the program, are resolved to a library during compilation. The library contains code for these functions.

The compiler identifies the library required for the function and copies the code from the library to the program. This technique is called static linking.

Note: The functions, which are provided by the language , are called built-in functions.

In the Windows environment , when a program is compiled, the compiler does not copy the code from the library to the program. Instead, the compiler inserts a reference, consisting of the same of the library and function, in the program. The reference is looked up at run time. This is called dynamic linking.

Event-Driven Programming

Feb 24th, 2010
by Rashmi.
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Windows enables you to execute programs by using a mouse. When you click on a control, an event is generated. An event is an action performed by the user.

Windows generates messages in response to each event performed by the user. These messages are sent to the application and, depending on the messages received, the application performs preprogrammed actions.

The Windows programming interface allows you to create event-driven programs. These events are generated in programs by user interaction.

 Take an example of the game MineSweeper.When a player clicks a cell, the game displays the number of mines around it. Clicking the cell is an event. During the game, if a player clicks on a mine, program uncovers all the mines and changes the smiley icon to a sad-face icon. All these activities, such as uncovering a number, uncovering the mine, and changing the smiley to a sad-face, are responses to events.

As in Minesweeper, the games that you create by using Windows based applications will be event driven. When any event is generated, the Windows operating system will send a message to the application. As s programmer, you must decide how your program should respond to the generated events.

Types of Dialog Boxes in Windows

Feb 24th, 2010
by Rashmi.
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Windows provides the following types of dialog boxes :

  • Modal
  • System modal
  • Modeless

 

Modal Dialog Box

A modal dialog box does not allow you to switch focus to another area of the application which has invoked the dialog box. However, you can switch to other windows applications while the modal dialog box is being displayed on the screen.

For example, the Save As dialog box of Microsoft Word is a modal dialog box. If you are trying to save a Word file by using the Save As dialog box, you cannot make any changes in the word document until is saved. The following shows the Save As dialog box.

System Modal Dialog Box

The system modal dialog box takes control of the entire Windows environment. For example, the Windows Log On dialog box is a system modal dialog box.

Sometimes , error messages are displayed by using a system modal dialog box. When such messages appear on the screen, the user is not allowed to switch to, or interact with, any other windows application until the system modal dialog box is closed.

Modeless Dialog Box

The modeless dialog box stays n the screen and is available for use at any time. For example, the Find and Replace dialog box of Microsoft Word is a modeless dialog box. This modeless dialog box allows you to switch  to another area of the application, which has invoked the dialog box, or to another Windows applications.

Subqueries

Feb 24th, 2010
by Rashmi.
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A subquery  places one query inside another one. The second query resides somewhere within the WHERE clause of a SELECT statement. One or more values returned by the subquery are used by the main query to return the results to the user

Subquery: A query that is embedded in a main, or parent, query and used to assist in filtering the result set from a query.

The types of operators allowed in the WHERE clause depend on whether the subquery returns one row or more than one row. If only a single row is returned from a query, the comparison operators =, !=, <, >, ?, ?, and so forth are valid. If more than one row is returned from a subquery, operators such as IN, NOT IN, ANY, and ALL are valid.

Objectives of Financial Reporting

Feb 24th, 2010
by Rashmi.
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Financial reporting  refers to external financial reporting by business enterprises.  Financial reporting includes financial statements & other means of communicating information that relates  to the information provided by the accounting system, information about an enterprise’s resources, obligations, earnings etc. Management personnel may be required to communicate information to those outside the enterprise by means of financial reporting other than formal financial statements. Information communicated by means of financial reporting other than financial reporting other than financial statements may take various shapes or forms and relate to various matters. Corporate annual reports are common examples of reports that include financial statements and other financial information.

Note: Financial reporting is not an end in itself but is intended to provide information that is useful in making business and economic decisions.

The objectives of financial reporting are as follows:

1. To provide information useful for investment decisions

2. To provide information useful in assessing cash flow prospects.

3. To provide information about economic resources, obligations and owners’ equity to identify the enterprise’s financial strength and weakness.

4. Liquidity, solvency and funds flow; Financial reporting should provide information about how an enterprise obtains and spends cash, about its burrowing and repayment of burrowing, about  its capital transactions, including cash dividends and other distributions of enterprise resources to owners and about other factors that may affect an enterprise’s liquidity or solvency.

5.   To provide information about performance to the Management.

6. To include Management explanations and interpretations.

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