Quiz held for MBA Sem. III Groups A & B on 19th July, 2008
1.Who is the Steel Minister of India ? Ram Vilas Paswan
2.Whom did India beat to enter 6th Junior Asia Cup Hockey Finals? Pakistan
3.What is the current inflation rate of India ? 11.91 %
4.Which newspaper group publishes The Economic Times ? Bennet ,Coleman & Co Ltd.
5.Who is the CEO and MD of Ranbaxy ? Malvinder Singh
6.Who is the Chairman of Reliance Industries Ltd. (RIL) ? Mukesh Ambani
7.After which politician Lucknow airport has been recently renamed ? Charan Singh
8.Who is the Democrat nominee of US presidential elections ? Barack Obama
9.Which group is the owner of Big Bazar Chain ? Pantaloon
10. Bihar was formerly a part of which state ? The undivided Bengal
11.What does AMC stands for in the name HDFC AMC Ltd? Asset Management Company.
12.What is the expansion of the abbreviation IAEA ? International Atomic Energy Agency
13.Which Institute is being converted into eighth IIT ? IT BHU
14.What is the expansion of the abbreviation CNG ? Compressed Natural Gas
15.Which only non christen has become the prime minister of England till date ? Benjamin Desraili
16. Who is the author of the novel “ The Call of the Wild ? “ Jack London
17. Who was the first captain of Indian Cricket Team ? C.K. Naidu
18.What is capital of Goa? Panajim
19. Who is the chairman of UB group ? Vijay Malaya
20.What is the expansion of the abbreviation SIM? Subscriber Identification Module
21.Who is the creator of fictitious character Sherlock Holmes ? Sir Arthur Conan Doyle
22. Who is the prime minister of Bhutan ? Lyonchen Jigmi Y Thinley
23 Who is the Governor of RBI ? Y.V.Reddy
24.Who is the CEO and MD of Tata Consultancy Services (TCS)? S.Ramadorai
25.What is the expansion of the abbreviation HDFC ? Housing Development Finance Corporation
Tuesday, July 22, 2008
Monday, July 21, 2008
Database_Normalization
Database normalization, sometimes referred to as canonical synthesis, is a technique for designing relational database tables to minimize duplication of information and, in so doing, to safeguard the database against certain types of logical or structural problems, namely data anomalies. For example, when multiple instances of a given piece of information occur in a table, the possibility exists that these instances will not be kept consistent when the data within the table is updated, leading to a loss of data integrity. A table that is sufficiently normalized is less vulnerable to problems of this kind, because its structure reflects the basic assumptions for when multiple instances of the same information should be represented by a single instance only.
Higher degrees of normalization typically involve more tables and create the need for a larger number of joins, which can reduce performance. Accordingly, more highly normalized tables are typically used in database applications involving many isolated transactions (e.g. an Automated teller machine), while less normalized tables tend to be used in database applications that need to map complex relationships between data entities and data attributes (e.g. a reporting application, or a full-text search application).
Database theory describes a table's degree of normalization in terms of normal forms of successively higher degrees of strictness. A table in third normal form (3NF), for example, is consequently in second normal form (2NF) as well; but the reverse is not necessarily the case.
Although the normal forms are often defined informally in terms of the characteristics of tables, rigorous definitions of the normal forms are concerned with the characteristics of mathematical constructs known as relations. Whenever information is represented relationally, it is meaningful to consider the extent to which the representation is normalized.
Database normalization, sometimes referred to as canonical synthesis, is a technique for designing relational database tables to minimize duplication of information and, in so doing, to safeguard the database against certain types of logical or structural problems, namely data anomalies. For example, when multiple instances of a given piece of information occur in a table, the possibility exists that these instances will not be kept consistent when the data within the table is updated, leading to a loss of data integrity. A table that is sufficiently normalized is less vulnerable to problems of this kind, because its structure reflects the basic assumptions for when multiple instances of the same information should be represented by a single instance only.
Higher degrees of normalization typically involve more tables and create the need for a larger number of joins, which can reduce performance. Accordingly, more highly normalized tables are typically used in database applications involving many isolated transactions (e.g. an Automated teller machine), while less normalized tables tend to be used in database applications that need to map complex relationships between data entities and data attributes (e.g. a reporting application, or a full-text search application).
Database theory describes a table's degree of normalization in terms of normal forms of successively higher degrees of strictness. A table in third normal form (3NF), for example, is consequently in second normal form (2NF) as well; but the reverse is not necessarily the case.
Although the normal forms are often defined informally in terms of the characteristics of tables, rigorous definitions of the normal forms are concerned with the characteristics of mathematical constructs known as relations. Whenever information is represented relationally, it is meaningful to consider the extent to which the representation is normalized.
Problems addressed by normalization
An update anomaly. Employee 519 is shown as having different addresses on different records.
An insertion anomaly. Until the new faculty member is assigned to teach at least one course, his details cannot be recorded.
A deletion anomaly. All information about Dr. Giddens is lost when he temporarily ceases to be assigned to any courses.
A table that is not sufficiently normalized can suffer from logical inconsistencies of various types, and from anomalies involving data operations. In such a table:
The same information can be expressed on multiple records; therefore updates to the table may result in logical inconsistencies. For example, each record in an "Employees' Skills" table might contain an Employee ID, Employee Address, and Skill; thus a change of address for a particular employee will potentially need to be applied to multiple records (one for each of his skills). If the update is not carried through successfully—if, that is, the employee's address is updated on some records but not others—then the table is left in an inconsistent state. Specifically, the table provides conflicting answers to the question of what this particular employee's address is. This phenomenon is known as an update anomaly.
There are circumstances in which certain facts cannot be recorded at all. For example, each record in a "Faculty and Their Courses" table might contain a Faculty ID, Faculty Name, Faculty Hire Date, and Course Code—thus we can record the details of any faculty member who teaches at least one course, but we cannot record the details of a newly-hired faculty member who has not yet been assigned to teach any courses. This phenomenon is known as an insertion anomaly.
There are circumstances in which the deletion of data representing certain facts necessitates the deletion of data representing completely different facts. The "Faculty and Their Courses" table described in the previous example suffers from this type of anomaly, for if a faculty member temporarily ceases to be assigned to any courses, we must delete the last of the records on which that faculty member appears. This phenomenon is known as a deletion anomaly.
Ideally, a relational database table should be designed in such a way as to exclude the possibility of update, insertion, and deletion anomalies. The normal forms of relational database theory provide guidelines for deciding whether a particular design will be vulnerable to such anomalies. It is possible to correct an unnormalized design so as to make it adhere to the demands of the normal forms: this is called normalization. Removal of redundancies of the tables will lead to several tables, with referential integrity restrictions between them.
Normalization typically involves decomposing an unnormalized table into two or more tables that, were they to be combined (joined), would convey exactly the same information as the original table.
Background to normalization: definitions
Functional dependency: Attribute B has a functional dependency on attribute A i.e. A → B if, for each value of attribute A, there is exactly one value of attribute B. If value of A is repeating in tuples then value of B will also repeat. In our example, Employee Address has a functional dependency on Employee ID, because a particular Employee ID value corresponds to one and only one Employee Address value. (Note that the reverse need not be true: several employees could live at the same address and therefore one Employee Address value could correspond to more than one Employee ID. Employee ID is therefore not functionally dependent on Employee Address.) An attribute may be functionally dependent either on a single attribute or on a combination of attributes. It is not possible to determine the extent to which a design is normalized without understanding what functional dependencies apply to the attributes within its tables; understanding this, in turn, requires knowledge of the problem domain. For example, an Employer may require certain employees to split their time between two locations, such as New York City and London, and therefore want to allow Employees to have more than one Employee Address. In this case, Employee Address would no longer be functionally dependent on Employee ID.
Another way to look at the above is by reviewing basic mathematical functions:
Let F(x) be a mathematical function of one independent variable. The independent variable is analogous to the attribute A. The dependent variable (or the dependent attribute using the lingo above), and hence the term functional dependency, is the value of F(A); A is an independent attribute. As we know, mathematical functions can have only one output. Notationally speaking, it is common to express this relationship in mathematics as F(A) = B; or, A → F(A).
There are also functions of more than one independent variable--commonly, this is referred to as multivariable functions. This idea represents an attribute being functionally dependent on a combination of attributes. Hence, F(x,y,z) contains three independent variables, or independent attributes, and one dependent attribute, namely, F(x,y,z). In multivariable functions, there can only be one output, or one dependent variable, or attribute.
Trivial functional dependency: A trivial functional dependency is a functional dependency of an attribute on a superset of itself. {Employee ID, Employee Address} → {Employee Address} is trivial, as is {Employee Address} → {Employee Address}.
Full functional dependency: An attribute is fully functionally dependent on a set of attributes X if it is
functionally dependent on X, and
not functionally dependent on any proper subset of X. {Employee Address} has a functional dependency on {Employee ID, Skill}, but not a full functional dependency, because is also dependent on {Employee ID}.
Transitive dependency: A transitive dependency is an indirect functional dependency, one in which X→Z only by virtue of X→Y and Y→Z.
Multivalued dependency: A multivalued dependency is a constraint according to which the presence of certain rows in a table implies the presence of certain other rows: see the Multivalued Dependency article for a rigorous definition.
Join dependency: A table T is subject to a join dependency if T can always be recreated by joining multiple tables each having a subset of the attributes of T.
Superkey: A superkey is an attribute or set of attributes that uniquely identifies rows within a table; in other words, two distinct rows are always guaranteed to have distinct superkeys. {Employee ID, Employee Address, Skill} would be a superkey for the "Employees' Skills" table; {Employee ID, Skill} would also be a superkey.
Candidate key: A candidate key is a minimal superkey, that is, a superkey for which we can say that no proper subset of it is also a superkey. {Employee Id, Skill} would be a candidate key for the "Employees' Skills" table.
Non-prime attribute: A non-prime attribute is an attribute that does not occur in any candidate key. Employee Address would be a non-prime attribute in the "Employees' Skills" table.
Primary key: Most DBMSs require a table to be defined as having a single unique key, rather than a number of possible unique keys. A primary key is a key which the database designer has designated for this purpose
Higher degrees of normalization typically involve more tables and create the need for a larger number of joins, which can reduce performance. Accordingly, more highly normalized tables are typically used in database applications involving many isolated transactions (e.g. an Automated teller machine), while less normalized tables tend to be used in database applications that need to map complex relationships between data entities and data attributes (e.g. a reporting application, or a full-text search application).
Database theory describes a table's degree of normalization in terms of normal forms of successively higher degrees of strictness. A table in third normal form (3NF), for example, is consequently in second normal form (2NF) as well; but the reverse is not necessarily the case.
Although the normal forms are often defined informally in terms of the characteristics of tables, rigorous definitions of the normal forms are concerned with the characteristics of mathematical constructs known as relations. Whenever information is represented relationally, it is meaningful to consider the extent to which the representation is normalized.
Database normalization, sometimes referred to as canonical synthesis, is a technique for designing relational database tables to minimize duplication of information and, in so doing, to safeguard the database against certain types of logical or structural problems, namely data anomalies. For example, when multiple instances of a given piece of information occur in a table, the possibility exists that these instances will not be kept consistent when the data within the table is updated, leading to a loss of data integrity. A table that is sufficiently normalized is less vulnerable to problems of this kind, because its structure reflects the basic assumptions for when multiple instances of the same information should be represented by a single instance only.
Higher degrees of normalization typically involve more tables and create the need for a larger number of joins, which can reduce performance. Accordingly, more highly normalized tables are typically used in database applications involving many isolated transactions (e.g. an Automated teller machine), while less normalized tables tend to be used in database applications that need to map complex relationships between data entities and data attributes (e.g. a reporting application, or a full-text search application).
Database theory describes a table's degree of normalization in terms of normal forms of successively higher degrees of strictness. A table in third normal form (3NF), for example, is consequently in second normal form (2NF) as well; but the reverse is not necessarily the case.
Although the normal forms are often defined informally in terms of the characteristics of tables, rigorous definitions of the normal forms are concerned with the characteristics of mathematical constructs known as relations. Whenever information is represented relationally, it is meaningful to consider the extent to which the representation is normalized.
Problems addressed by normalization
An update anomaly. Employee 519 is shown as having different addresses on different records.
An insertion anomaly. Until the new faculty member is assigned to teach at least one course, his details cannot be recorded.
A deletion anomaly. All information about Dr. Giddens is lost when he temporarily ceases to be assigned to any courses.
A table that is not sufficiently normalized can suffer from logical inconsistencies of various types, and from anomalies involving data operations. In such a table:
The same information can be expressed on multiple records; therefore updates to the table may result in logical inconsistencies. For example, each record in an "Employees' Skills" table might contain an Employee ID, Employee Address, and Skill; thus a change of address for a particular employee will potentially need to be applied to multiple records (one for each of his skills). If the update is not carried through successfully—if, that is, the employee's address is updated on some records but not others—then the table is left in an inconsistent state. Specifically, the table provides conflicting answers to the question of what this particular employee's address is. This phenomenon is known as an update anomaly.
There are circumstances in which certain facts cannot be recorded at all. For example, each record in a "Faculty and Their Courses" table might contain a Faculty ID, Faculty Name, Faculty Hire Date, and Course Code—thus we can record the details of any faculty member who teaches at least one course, but we cannot record the details of a newly-hired faculty member who has not yet been assigned to teach any courses. This phenomenon is known as an insertion anomaly.
There are circumstances in which the deletion of data representing certain facts necessitates the deletion of data representing completely different facts. The "Faculty and Their Courses" table described in the previous example suffers from this type of anomaly, for if a faculty member temporarily ceases to be assigned to any courses, we must delete the last of the records on which that faculty member appears. This phenomenon is known as a deletion anomaly.
Ideally, a relational database table should be designed in such a way as to exclude the possibility of update, insertion, and deletion anomalies. The normal forms of relational database theory provide guidelines for deciding whether a particular design will be vulnerable to such anomalies. It is possible to correct an unnormalized design so as to make it adhere to the demands of the normal forms: this is called normalization. Removal of redundancies of the tables will lead to several tables, with referential integrity restrictions between them.
Normalization typically involves decomposing an unnormalized table into two or more tables that, were they to be combined (joined), would convey exactly the same information as the original table.
Background to normalization: definitions
Functional dependency: Attribute B has a functional dependency on attribute A i.e. A → B if, for each value of attribute A, there is exactly one value of attribute B. If value of A is repeating in tuples then value of B will also repeat. In our example, Employee Address has a functional dependency on Employee ID, because a particular Employee ID value corresponds to one and only one Employee Address value. (Note that the reverse need not be true: several employees could live at the same address and therefore one Employee Address value could correspond to more than one Employee ID. Employee ID is therefore not functionally dependent on Employee Address.) An attribute may be functionally dependent either on a single attribute or on a combination of attributes. It is not possible to determine the extent to which a design is normalized without understanding what functional dependencies apply to the attributes within its tables; understanding this, in turn, requires knowledge of the problem domain. For example, an Employer may require certain employees to split their time between two locations, such as New York City and London, and therefore want to allow Employees to have more than one Employee Address. In this case, Employee Address would no longer be functionally dependent on Employee ID.
Another way to look at the above is by reviewing basic mathematical functions:
Let F(x) be a mathematical function of one independent variable. The independent variable is analogous to the attribute A. The dependent variable (or the dependent attribute using the lingo above), and hence the term functional dependency, is the value of F(A); A is an independent attribute. As we know, mathematical functions can have only one output. Notationally speaking, it is common to express this relationship in mathematics as F(A) = B; or, A → F(A).
There are also functions of more than one independent variable--commonly, this is referred to as multivariable functions. This idea represents an attribute being functionally dependent on a combination of attributes. Hence, F(x,y,z) contains three independent variables, or independent attributes, and one dependent attribute, namely, F(x,y,z). In multivariable functions, there can only be one output, or one dependent variable, or attribute.
Trivial functional dependency: A trivial functional dependency is a functional dependency of an attribute on a superset of itself. {Employee ID, Employee Address} → {Employee Address} is trivial, as is {Employee Address} → {Employee Address}.
Full functional dependency: An attribute is fully functionally dependent on a set of attributes X if it is
functionally dependent on X, and
not functionally dependent on any proper subset of X. {Employee Address} has a functional dependency on {Employee ID, Skill}, but not a full functional dependency, because is also dependent on {Employee ID}.
Transitive dependency: A transitive dependency is an indirect functional dependency, one in which X→Z only by virtue of X→Y and Y→Z.
Multivalued dependency: A multivalued dependency is a constraint according to which the presence of certain rows in a table implies the presence of certain other rows: see the Multivalued Dependency article for a rigorous definition.
Join dependency: A table T is subject to a join dependency if T can always be recreated by joining multiple tables each having a subset of the attributes of T.
Superkey: A superkey is an attribute or set of attributes that uniquely identifies rows within a table; in other words, two distinct rows are always guaranteed to have distinct superkeys. {Employee ID, Employee Address, Skill} would be a superkey for the "Employees' Skills" table; {Employee ID, Skill} would also be a superkey.
Candidate key: A candidate key is a minimal superkey, that is, a superkey for which we can say that no proper subset of it is also a superkey. {Employee Id, Skill} would be a candidate key for the "Employees' Skills" table.
Non-prime attribute: A non-prime attribute is an attribute that does not occur in any candidate key. Employee Address would be a non-prime attribute in the "Employees' Skills" table.
Primary key: Most DBMSs require a table to be defined as having a single unique key, rather than a number of possible unique keys. A primary key is a key which the database designer has designated for this purpose
Statement of Purpose
Statement of purpose
A statement of purpose, or personal statement, is a brief and focused essay about one's career or research goals, and is frequently required for applicants to universities, graduate schools, and professional schools. A statement of purpose (SoP) is a concise essay about one's career goals, identified means to achieve them and accomplishments so far towards those goals. It is a required document when applying for admission to most professional programs in the United States. Often, SoP is used as a yardstick to assess the capabilities of a prospective student in terms of critical thinking, analytical abilities, interests, aims and aspirations. It is a good way for an applicant to communicate with the admissions committee. Most admissions committees look for a short, crisp and ideologically clear SoP.
It is also known as Graduate School Essay. Other universities sometimes call it a "Letter of Intent", "Letter of Intention", "Statement of Intent", "Statement of Intention", "Statement of Interest", "Goals Statement", "Personal Statement", "Personal Narrative" or "Application Essay". The name can be just a name but often it influences content and length of the essay. Every university has its own regulations but most of the time it will be 1-2 pages.
A statement of purpose, or personal statement, is a brief and focused essay about one's career or research goals, and is frequently required for applicants to universities, graduate schools, and professional schools. A statement of purpose (SoP) is a concise essay about one's career goals, identified means to achieve them and accomplishments so far towards those goals. It is a required document when applying for admission to most professional programs in the United States. Often, SoP is used as a yardstick to assess the capabilities of a prospective student in terms of critical thinking, analytical abilities, interests, aims and aspirations. It is a good way for an applicant to communicate with the admissions committee. Most admissions committees look for a short, crisp and ideologically clear SoP.
It is also known as Graduate School Essay. Other universities sometimes call it a "Letter of Intent", "Letter of Intention", "Statement of Intent", "Statement of Intention", "Statement of Interest", "Goals Statement", "Personal Statement", "Personal Narrative" or "Application Essay". The name can be just a name but often it influences content and length of the essay. Every university has its own regulations but most of the time it will be 1-2 pages.
Friday, July 18, 2008
RETAIL
Supply Chain Management
By: Ismael Tabije
Supply chain management
By: Ismael Tabije
A supply chain, logistics network, or supply network is a coordinated system of organizations, people, activities, information and resources involved in moving a product or service in physical or virtual manner from supplier to customer. Its management deals with the process of planning, implementing, and controlling its operations with the purpose of satisfying customer requirements as efficiently as possible.
Supply chain management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of-consumption. It integrates supply and demand management within and across companies If a supply chain management system is properly implemented, strategic and competitive areas can be used to their full advantage. Fulfillment is enabled through efficient communication, ensuring that orders are placed with the appropriate amount of time available to be filled. The supply chain management system also allows a company to constantly see what is on stock and making sure that the right quantities are ordered to replace stock.
Logistics is enhanced, keeping the cost of transporting materials as low as possible consistent with safe and reliable delivery. Production can run smoothly as a result of fulfillment and logistics being implemented correctly without delays due to ordering and transportation. The company’s flexibility to respond to unforeseen changes in demand and supply is improved.
Because of this, a company has the ability to produce goods at lower prices and distribute them to consumers quicker than companies without supply chain management thus increasing the overall profit. And most especially, cooperation among supply chain partners and consumers is established ensuring mutual success.
Supply chain management must address the problems regarding distribution network configuration—number and location of suppliers, production facilities, distribution centers, warehouses and customers; distribution strategy—centralized versus decentralized, direct shipment, cross docking, pull or push strategies, third party logistics; information—integration of systems and processes through the supply chain to share valuable information, including demand signals, forecasts, inventory and transportation; and inventory management—quantity and location of inventory including raw materials, work-in-process and finished goods.
Supply chain management is a cross-functional approach to managing the movement of raw materials into an organization and the movement of finished goods out of the organization toward the end-consumer. Its success relies on the corporations’ focus on core competencies and being flexible. In some circumstances, operations should be outsourced to other corporations that can perform the activities better or more cost effectively.
In effect, the number of companies involved in satisfying consumer demand would increase, while reducing management control of daily logistics operations. Less control and more supply chain partners would lead to the creation of supply chain management concepts. The purpose of supply chain management is to improve trust and collaboration among supply chain partners, thus improving inventory visibility and improving inventory velocity.
Copyright 2007 Ismael D. Tabije
By: Ismael Tabije
Supply chain management
By: Ismael Tabije
A supply chain, logistics network, or supply network is a coordinated system of organizations, people, activities, information and resources involved in moving a product or service in physical or virtual manner from supplier to customer. Its management deals with the process of planning, implementing, and controlling its operations with the purpose of satisfying customer requirements as efficiently as possible.
Supply chain management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of-consumption. It integrates supply and demand management within and across companies If a supply chain management system is properly implemented, strategic and competitive areas can be used to their full advantage. Fulfillment is enabled through efficient communication, ensuring that orders are placed with the appropriate amount of time available to be filled. The supply chain management system also allows a company to constantly see what is on stock and making sure that the right quantities are ordered to replace stock.
Logistics is enhanced, keeping the cost of transporting materials as low as possible consistent with safe and reliable delivery. Production can run smoothly as a result of fulfillment and logistics being implemented correctly without delays due to ordering and transportation. The company’s flexibility to respond to unforeseen changes in demand and supply is improved.
Because of this, a company has the ability to produce goods at lower prices and distribute them to consumers quicker than companies without supply chain management thus increasing the overall profit. And most especially, cooperation among supply chain partners and consumers is established ensuring mutual success.
Supply chain management must address the problems regarding distribution network configuration—number and location of suppliers, production facilities, distribution centers, warehouses and customers; distribution strategy—centralized versus decentralized, direct shipment, cross docking, pull or push strategies, third party logistics; information—integration of systems and processes through the supply chain to share valuable information, including demand signals, forecasts, inventory and transportation; and inventory management—quantity and location of inventory including raw materials, work-in-process and finished goods.
Supply chain management is a cross-functional approach to managing the movement of raw materials into an organization and the movement of finished goods out of the organization toward the end-consumer. Its success relies on the corporations’ focus on core competencies and being flexible. In some circumstances, operations should be outsourced to other corporations that can perform the activities better or more cost effectively.
In effect, the number of companies involved in satisfying consumer demand would increase, while reducing management control of daily logistics operations. Less control and more supply chain partners would lead to the creation of supply chain management concepts. The purpose of supply chain management is to improve trust and collaboration among supply chain partners, thus improving inventory visibility and improving inventory velocity.
Copyright 2007 Ismael D. Tabije
RETAIL
By: John Mehrmann
Reverse logistics in supply chain management
By: John Mehrmann
The evolution of reverse logistics for manufactured products is developing in direct proportion to the rapid advancements in technology and the subsequent price erosion of products as new and improved products enter the supply chain at a faster pace. With such thin margins and so much competition, mismanagement of the supply chain can be devastating. Those organizations with the infrastructure to capture and compare the composite value of components with real time intelligent analysis and disposition based on changes in refurbishment cost, resale value, spare parts, repair and overall demand will not only become more profitable, but such flexibility and scalability will allow them to outmaneuver and eliminate the competition.
This is a case of modern Darwinism. It is survival of the fittest. It requires collaboration integration within Supply Chain Logistics, or appears on the endangered species list. Even the mighty predator, the Tyrannosaurus Rex, was doomed to extinction by the constant progress of evolution. Today, technology drives evolution at an astounding pace. The ability to capture, migrate, integrate and facilitate the intelligent analysis of data is akin to the invention of fire. This is what will separate the companies who can walk upright from the ones that will be stuck in the tar pits of slow response.
The early days of Reverse Logistics were measured by convenience and customer accommodations. The focus was on the front end of the return process, the ability for consumers to be able to return unwanted or defective merchandise. The ability to facilitate a consumer return was a courtesy that turned into a compelling competitive differentiator in retail. The companies that did not support consumer returns found themselves at a strategic disadvantage to those that did, and were eventually forced to adopt the same consumer conveniences or lose those customers to the competition.
It did not take long for retail merchants to seek the same concessions from manufacturers and distribution channels. Stock rotation became a normal condition of business, and processes for returning defective merchandise became standard practice. Although this is accepted as commonplace today, it has not always been this way. Even today there are cultural differences with regards to consumer returns, especially for product that is not defective and returned because of 'customer remorse'.
As the cost of Reverse Logistics continued to increase, and as the methods of transportation became more sophisticated, manufacturers and distributors began to look for alternatives in transportation for savings. Planning and consolidating freight for return products was identified as a way to reduce expenses related to fuel and labor. This also led to detailed analysis of transportation options, like truck, air and railway. In Supply Chain Logistics business you are either the one driving the truck, the one pumping the gas, or the one paying the other two.
The next step in the evolution of Reverse Logistics was the experimentation and cost comparison between multiple local hubs and single consolidated returns centers. The simple analysis for savings contrasted the costs of warehouse space and manpower to the amount of freight and transportation fees for handling the back end of the Supply Chain. Other factors also played a significant role in the financial analysis, including volume, material costs and inventory controls.
As the costs of Reverse Logistics continued to rise, the importance of returning refurbished merchandise to market also became more significant. Organizations began to place financial significance on the devaluation of product for every day lost in transportation, handling, processing or warehousing. As technology and features improved, price and demand for aging product diminished, as did the ability to recoup costs from returns. Speed to return to market could be measured in resale value.
In the next step of Reverse Logistics evolution, there was an awakening and realization that reverse logistics is only a portion of the entire back-end services solution. Consolidation meant more than merely consolidating returns, it meant consolidation of activities related to back-end support operations. Manufacturers began to consolidate spare parts and materials in the same warehouse as the returned merchandise, discovering that it is less expensive to move parts and packing materials across an aisle than across state lines. Spare parts used to refurbish returns were placed in the same building. Taking this concept one step further, manufacturers began
to consolidate depot warranty repair operations inside the same facility to maximize the utilization of parts, labor, warehouse and materials. This activity often required collaboration between previously diverse management and operational groups within large organizations. The collaborative effort reduced expenses for all participating departments and groups within the organizations.
The next major step in the evolution of Reverse Logistics is collaboration with partners and external resources. It is a greater awakening and realization of integration with the entire Supply Chain by leveraging data exchange. It begins with an understanding of the value of the components that comprise a completed manufactured product, the Bill of Materials (BOM). The Bill of Materials is also used by manufacturers to forecast, procure and manage an inventory of spare parts that are used for repair. Frequently, the combined cost of the individual components exceeds the cost of the original retail product. Furthermore, due to price erosion, the cost to repair some products exceeds the cost of replacing the entire unit. Manufacturers must make quick financial decisions regarding the return on investment to refurbish returned products, repair or replace defective warranty products, and the potential resale value for refurbished products returned to market. Manufacturers must also weigh the potential cost if inventory for procuring spare parts to support warranty, extended warranty and out of warranty regulations. To be truly effective, manufacturers must make these decisions before the returned product enters the reverse logistics supply chain, not after it is in it.
Manufacturers have the ability to gather data on activities that drive demand. Contributors to demand planning include failure rate or rate of repairs that require spare parts. At the very front end, potential return trends and potential repair trends can be identified by customer technical support or customer care phone calls. Quality analysis of returns and defective products can also be used to identify demands for spare parts planning. Resale value trends for refurbished products and seasonal sales cycles can be used to predict demand and resale value for refurbished products, and if the product is cost effective to refurbish or repair. In some cases the parts can actually be sold for more greater margin than the whole product. At the very least, parts can be harvested from return products to mix and match repair of other defective return products, avoiding expensive spare parts procurement when applicable. All of these factors contribute to planning the demand for a refurbished product or the component parts.
Once you know the demand and resale value for component parts and whole units, then it is only a matter of maintaining an intelligent planning engine that uses the input to analyze the Bill of Materials for returned products. Before the merchandise enters the Reverse Logistics Supply Chain, make an immediate and intelligent decision regarding the value and intended disposition of the whole unit or the component parts. In some cases the product will be scheduled for de-configuration to feed refurbishing activities or develop a spare parts inventory for warranty repairs. In other cases, the units may be expedited for refurbishing and resale. Some products may be scheduled to be environmentally scrapped for materials. Product may even be de-configured at the retail location to support local customer demands and thereby avoid freight entirely. Whatever the final result may be, the decision can be made before the product enters the Reverse Logistics Supply Chain cycle, as long as the intelligent engine is provided with continuously updated and accurate information. The new problem and the new solution is knowing what you sold, who wants to return it and what it is really worth, before you own it again.
It's hard to believe that there are still companies that invest millions of dollars each year in tools to forecast procurement and inventory management of spare parts, without accurately forecasting and managing the largest single source of surplus components that results from return merchandise. There are still organizations that struggle to achieve freight savings purely by negotiation or consolidation, without a achieving a balanced approach to freight avoidance, localization and intelligent de-configuration disposition. In the competitive landscape of rapidly evolving technology, mass production and eroding profit margins, managing the total cost of the supply chain and the composite value of the components is essential to cost reduction and financial survival. To ignore this aspect of reverse logistics can not only be costly, it can be fatal for an organization.
For Consumer Electronics and Computer products, the Reverse Logistics handling requirements are further complicated by compliance and regulations like RoHS, WEEE, controls on Lead based and Mercury materials, just to name a few. Recognizing these component parts is absolutely essential to the proper management of the intelligent engine that directs the disposition of returns immediately upon notification that merchandise may enter the returns cycle. Proper management is not only financially rewarding, but in the case of hazardous materials, it is the law.
In service, to be competitive is to be the first to provide the services that would otherwise put you out of business. If you can do this, you will place your competitors out of business, or at least have them working for you. Gathering the data that pertains to customer call centers, extended warranty services, spare parts, resale value, parts procurement costs and impending returns often requires extensive collaboration, integration and data exchange. To be successful, it is often necessary to partner with multiple organizations and experts to leverage best practices in a collaborative environment. The companies that collaborate, integrate and optimize date exchange will enjoy the competitive advantages of improved profit margins and precision management. The organizations that do not participate will be remembered as fabulous fossils.
Words of Wisdom:
"In Supply Chain Logistics business you are either the one driving the truck, the one pumping the gas, or the one paying the other two."
"In service, to be competitive is to be the first to provide the services that would otherwise put you out of business. If you can do this, you will place your competitors out of business, or at least have them working for you." - John Mehrmann.
"The old ways are dead. And you need people around you who concur.That means hanging out more with the creative people, the freaks, the real visionaries, than you're already doing. Thinking more about what their needs are, and responding accordingly. Avoid the dullards; avoid the folk who play it safe. They can't help you any more. Their stability model no longer offers that much stability. They are extinct, they are extinction." - Hugh Macleod.
About the Author:
John Mehrmann is a freelance author, industry expert and President of Executive Blueprints Inc, an organization dedicated to developing human capital and personal growth.
Reverse logistics in supply chain management
By: John Mehrmann
The evolution of reverse logistics for manufactured products is developing in direct proportion to the rapid advancements in technology and the subsequent price erosion of products as new and improved products enter the supply chain at a faster pace. With such thin margins and so much competition, mismanagement of the supply chain can be devastating. Those organizations with the infrastructure to capture and compare the composite value of components with real time intelligent analysis and disposition based on changes in refurbishment cost, resale value, spare parts, repair and overall demand will not only become more profitable, but such flexibility and scalability will allow them to outmaneuver and eliminate the competition.
This is a case of modern Darwinism. It is survival of the fittest. It requires collaboration integration within Supply Chain Logistics, or appears on the endangered species list. Even the mighty predator, the Tyrannosaurus Rex, was doomed to extinction by the constant progress of evolution. Today, technology drives evolution at an astounding pace. The ability to capture, migrate, integrate and facilitate the intelligent analysis of data is akin to the invention of fire. This is what will separate the companies who can walk upright from the ones that will be stuck in the tar pits of slow response.
The early days of Reverse Logistics were measured by convenience and customer accommodations. The focus was on the front end of the return process, the ability for consumers to be able to return unwanted or defective merchandise. The ability to facilitate a consumer return was a courtesy that turned into a compelling competitive differentiator in retail. The companies that did not support consumer returns found themselves at a strategic disadvantage to those that did, and were eventually forced to adopt the same consumer conveniences or lose those customers to the competition.
It did not take long for retail merchants to seek the same concessions from manufacturers and distribution channels. Stock rotation became a normal condition of business, and processes for returning defective merchandise became standard practice. Although this is accepted as commonplace today, it has not always been this way. Even today there are cultural differences with regards to consumer returns, especially for product that is not defective and returned because of 'customer remorse'.
As the cost of Reverse Logistics continued to increase, and as the methods of transportation became more sophisticated, manufacturers and distributors began to look for alternatives in transportation for savings. Planning and consolidating freight for return products was identified as a way to reduce expenses related to fuel and labor. This also led to detailed analysis of transportation options, like truck, air and railway. In Supply Chain Logistics business you are either the one driving the truck, the one pumping the gas, or the one paying the other two.
The next step in the evolution of Reverse Logistics was the experimentation and cost comparison between multiple local hubs and single consolidated returns centers. The simple analysis for savings contrasted the costs of warehouse space and manpower to the amount of freight and transportation fees for handling the back end of the Supply Chain. Other factors also played a significant role in the financial analysis, including volume, material costs and inventory controls.
As the costs of Reverse Logistics continued to rise, the importance of returning refurbished merchandise to market also became more significant. Organizations began to place financial significance on the devaluation of product for every day lost in transportation, handling, processing or warehousing. As technology and features improved, price and demand for aging product diminished, as did the ability to recoup costs from returns. Speed to return to market could be measured in resale value.
In the next step of Reverse Logistics evolution, there was an awakening and realization that reverse logistics is only a portion of the entire back-end services solution. Consolidation meant more than merely consolidating returns, it meant consolidation of activities related to back-end support operations. Manufacturers began to consolidate spare parts and materials in the same warehouse as the returned merchandise, discovering that it is less expensive to move parts and packing materials across an aisle than across state lines. Spare parts used to refurbish returns were placed in the same building. Taking this concept one step further, manufacturers began
to consolidate depot warranty repair operations inside the same facility to maximize the utilization of parts, labor, warehouse and materials. This activity often required collaboration between previously diverse management and operational groups within large organizations. The collaborative effort reduced expenses for all participating departments and groups within the organizations.
The next major step in the evolution of Reverse Logistics is collaboration with partners and external resources. It is a greater awakening and realization of integration with the entire Supply Chain by leveraging data exchange. It begins with an understanding of the value of the components that comprise a completed manufactured product, the Bill of Materials (BOM). The Bill of Materials is also used by manufacturers to forecast, procure and manage an inventory of spare parts that are used for repair. Frequently, the combined cost of the individual components exceeds the cost of the original retail product. Furthermore, due to price erosion, the cost to repair some products exceeds the cost of replacing the entire unit. Manufacturers must make quick financial decisions regarding the return on investment to refurbish returned products, repair or replace defective warranty products, and the potential resale value for refurbished products returned to market. Manufacturers must also weigh the potential cost if inventory for procuring spare parts to support warranty, extended warranty and out of warranty regulations. To be truly effective, manufacturers must make these decisions before the returned product enters the reverse logistics supply chain, not after it is in it.
Manufacturers have the ability to gather data on activities that drive demand. Contributors to demand planning include failure rate or rate of repairs that require spare parts. At the very front end, potential return trends and potential repair trends can be identified by customer technical support or customer care phone calls. Quality analysis of returns and defective products can also be used to identify demands for spare parts planning. Resale value trends for refurbished products and seasonal sales cycles can be used to predict demand and resale value for refurbished products, and if the product is cost effective to refurbish or repair. In some cases the parts can actually be sold for more greater margin than the whole product. At the very least, parts can be harvested from return products to mix and match repair of other defective return products, avoiding expensive spare parts procurement when applicable. All of these factors contribute to planning the demand for a refurbished product or the component parts.
Once you know the demand and resale value for component parts and whole units, then it is only a matter of maintaining an intelligent planning engine that uses the input to analyze the Bill of Materials for returned products. Before the merchandise enters the Reverse Logistics Supply Chain, make an immediate and intelligent decision regarding the value and intended disposition of the whole unit or the component parts. In some cases the product will be scheduled for de-configuration to feed refurbishing activities or develop a spare parts inventory for warranty repairs. In other cases, the units may be expedited for refurbishing and resale. Some products may be scheduled to be environmentally scrapped for materials. Product may even be de-configured at the retail location to support local customer demands and thereby avoid freight entirely. Whatever the final result may be, the decision can be made before the product enters the Reverse Logistics Supply Chain cycle, as long as the intelligent engine is provided with continuously updated and accurate information. The new problem and the new solution is knowing what you sold, who wants to return it and what it is really worth, before you own it again.
It's hard to believe that there are still companies that invest millions of dollars each year in tools to forecast procurement and inventory management of spare parts, without accurately forecasting and managing the largest single source of surplus components that results from return merchandise. There are still organizations that struggle to achieve freight savings purely by negotiation or consolidation, without a achieving a balanced approach to freight avoidance, localization and intelligent de-configuration disposition. In the competitive landscape of rapidly evolving technology, mass production and eroding profit margins, managing the total cost of the supply chain and the composite value of the components is essential to cost reduction and financial survival. To ignore this aspect of reverse logistics can not only be costly, it can be fatal for an organization.
For Consumer Electronics and Computer products, the Reverse Logistics handling requirements are further complicated by compliance and regulations like RoHS, WEEE, controls on Lead based and Mercury materials, just to name a few. Recognizing these component parts is absolutely essential to the proper management of the intelligent engine that directs the disposition of returns immediately upon notification that merchandise may enter the returns cycle. Proper management is not only financially rewarding, but in the case of hazardous materials, it is the law.
In service, to be competitive is to be the first to provide the services that would otherwise put you out of business. If you can do this, you will place your competitors out of business, or at least have them working for you. Gathering the data that pertains to customer call centers, extended warranty services, spare parts, resale value, parts procurement costs and impending returns often requires extensive collaboration, integration and data exchange. To be successful, it is often necessary to partner with multiple organizations and experts to leverage best practices in a collaborative environment. The companies that collaborate, integrate and optimize date exchange will enjoy the competitive advantages of improved profit margins and precision management. The organizations that do not participate will be remembered as fabulous fossils.
Words of Wisdom:
"In Supply Chain Logistics business you are either the one driving the truck, the one pumping the gas, or the one paying the other two."
"In service, to be competitive is to be the first to provide the services that would otherwise put you out of business. If you can do this, you will place your competitors out of business, or at least have them working for you." - John Mehrmann.
"The old ways are dead. And you need people around you who concur.That means hanging out more with the creative people, the freaks, the real visionaries, than you're already doing. Thinking more about what their needs are, and responding accordingly. Avoid the dullards; avoid the folk who play it safe. They can't help you any more. Their stability model no longer offers that much stability. They are extinct, they are extinction." - Hugh Macleod.
About the Author:
John Mehrmann is a freelance author, industry expert and President of Executive Blueprints Inc, an organization dedicated to developing human capital and personal growth.
Wednesday, July 16, 2008
Glossary of DBMS
Attribute
An attribute is a named column of a relation. Relation
A relation is a table with columns and rows. Cardinality
The cardinality of a relation is the number of tuples it contains. File-based System
A collection of application programs that perform services for the end-users such as the production
of reports. Each program defines and manages its own data. Database
A shared collection of logically related data (and a description of this data), designed to meet the
information needs of an organization. DBMS
A software system that enables users to define, create, and maintain the database and provides
controlled access to this database. Relation Database
A collection of normalized relations. Degree
The degree of a relation is the number of attributes it contains. Domain
A domain is the set of allowable values for one or more attributes. Reserved words
A reserved word in SQL is a word that has a specific meaning to SQL, such as SELECT, FROM,
TABLE, etc. By convention, these are written in capital letters. User-defined words
A user-defined word in SQL is a word that the user has chosen to name tables, columns, and other
variables. Literals
A literal in SQL is a string. That is, a set of characters (case-sensitive) that makes up a
non-numeric piece
of information, written exactly as it is written in the database. Literals are enclosed in single
quotes in SQL. Column Identifier
A column identifier may be either a colum name or a column number that identifies an element of the
SELECT list by its position within the list, 1 being the first (left-most) element in the list, 2 the
second element in the list, and so on. Nested Query
Nested query is when a SELECT statement embedded within another SELECT statement. Scalar Nested Query
Scalar nested query returns a single column and a single row; that is, a single value. Row Nested Query
Row nested query returns multiple columns, but again only a single row. Table Nested Query
Table nested query returns one or more columns and multiple row. Tuple
A tuple is a row of a relation.
An attribute is a named column of a relation. Relation
A relation is a table with columns and rows. Cardinality
The cardinality of a relation is the number of tuples it contains. File-based System
A collection of application programs that perform services for the end-users such as the production
of reports. Each program defines and manages its own data. Database
A shared collection of logically related data (and a description of this data), designed to meet the
information needs of an organization. DBMS
A software system that enables users to define, create, and maintain the database and provides
controlled access to this database. Relation Database
A collection of normalized relations. Degree
The degree of a relation is the number of attributes it contains. Domain
A domain is the set of allowable values for one or more attributes. Reserved words
A reserved word in SQL is a word that has a specific meaning to SQL, such as SELECT, FROM,
TABLE, etc. By convention, these are written in capital letters. User-defined words
A user-defined word in SQL is a word that the user has chosen to name tables, columns, and other
variables. Literals
A literal in SQL is a string. That is, a set of characters (case-sensitive) that makes up a
non-numeric piece
of information, written exactly as it is written in the database. Literals are enclosed in single
quotes in SQL. Column Identifier
A column identifier may be either a colum name or a column number that identifies an element of the
SELECT list by its position within the list, 1 being the first (left-most) element in the list, 2 the
second element in the list, and so on. Nested Query
Nested query is when a SELECT statement embedded within another SELECT statement. Scalar Nested Query
Scalar nested query returns a single column and a single row; that is, a single value. Row Nested Query
Row nested query returns multiple columns, but again only a single row. Table Nested Query
Table nested query returns one or more columns and multiple row. Tuple
A tuple is a row of a relation.
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