Kamis, 22 November 2012

Inventory Management Solutions

Any organization that carries an inventory of products has a need to maintain accurate information on the movements of all these items to better serve its customers and run a profitable business. Whether you need to manage one supply closet or a multi-facility warehouse operation, the goal is to cut down on manual data entry costs, minimize your inventory write-offs and overstocks and increase efficiencies in your supply chain.

A complete inventory management solution will consist of management software, mobile computers and label printers. These tools will enable your business to track inventory levels and movements in your warehouse or stock room by item, serial number, or lot number. With full visibility into your entire incoming and outbound inventory, you can make better decisions to maintain dynamic stock levels that meet the demands of your customers and usage.
What are the Key Benefits of an Inventory Management System?
A proper inventory management solution will streamline your business by significantly reducing costs and waste.

  • Accuracy - Eliminate human error in inventory counting.

  • Speed - Reduce man hours by using automated data capture.

  • Accountability - Document shrinkage and loss to identify steps to reduce them.

  • Mobility - Make adjustments or replace damaged/unreadable labels on-the-spot using mobile     computers and printers.
What is Inventory Management?
Inventory management systems offer benefits to the operation of all types and sizes of businesses. An inventory management system decreases data entry errors, increases productivity and lowers operational costs. This is achieved by reducing or eliminating the hand-keying of data, manual physical inventories, backorders and lost sales due to inaccurate stock levels, inventory write-offs and shrinkage. With a properly set-up inventory system, you remove any ambiguity as to what is in your warehouse and where it is going.

Inventory management systems typically pay for themselves in less than one year. Most of the return on investment comes through saving on labor costs and reducing losses in sales due to lack of stock. Very few companies understand how inexpensive and simple it is to put an inventory management system in place. The first step in determining an inventory tracking system is to understand whether you are dealing with inventory or assets.

  • Inventory refers to objects that are sold, distributed, or otherwise consumed by a company. These     "temporary" objects include retail items and supplies. In this case, you may have 100 boxes of staples     in inventory and when you use one box, the tracked quantity decreases by one. You are not     concerned with which specific instance was sold but rather that 1 of 100 was removed. At the end of     the day, you want to know how many you have in stock, where it went and when to order more. This     detailed visibility into your inventory usage allows you to make adjustments to your ordering and     management process to increase efficiency.

  • Assets are "permanent" objects that a business uses internally, such as computers, tools, or     educational material. Although employees may check the object in or out to for a given project, or     even use at home, an asset ultimately belongs to the company and must be returned to the company.     An asset is always tracked as a unique item. Even though you may have 10 of the same PC or tool,     you are managing each instance as an individual item with its own unique barcode label or RFID     tag. The main concern is to have accurate information on each specific item like location, condition,     purchase date, value, custodian, last maintenance, etc.

To begin, compare your process with the chart below to determine if you need inventory or asset tracking.
Description Asset Inventory
Objects used internally, such as computers, tools, and educational materials •  
Track depreciation of company property •  
Track maintenance on company equipment •  
Objects are for sale or resale   •
Ability to track/monitor reorder levels   •
Objects are "temporary" and/or often replace, such a paper, pens and other consumables   •
Employees may check objects in and out • •
Inventory management solutions are driven (and limited by) the software that is used. Basic inventory functions are covered by all software, but depending on your need to have real-time updates to your system, some applications will be a better fit than others. The real strength of any application is how easily and in-depth it provides visibility into your inventory usage. Features like setting a minimum and maximum stock level for each item allows you to run a simple report to see what needs to be re-ordered. Inventory usage reporting is key to keeping your stock levels relevant to what your customers or company is buying/using. Another software concern is support for the number of users you will have. Some applications are geared towards small business with limited user licensing while others are completely open-ended to fit any business size. To have an effective inventory tracking solution, you'll need to make sure the inventory tracking software is the right fit for your business.
What Should You Address When Making an Inventory Management System?
When putting together an effective and suitable inventory management system, there are a few key questions to answer:

  • What type of inventory are you tracking? Cases vs. individual items?
  • Will you need to track batch, lot, or serial numbers? Expiration dates?
  • Is the inventory already labeled? Will you create your own sku or part numbers?
  • Do you need real-time updates or can batch collection be used?
  • Do you have a wireless network in place already?
  • What type of reporting will you need from the system?
  • How many users will you have? Desktop and mobile.
  • Will the inventory database need to sync with other software systems?
What are the Components of an Inventory Management System?
All inventory management systems consist of 4 core components:

  • Inventory Tracking Software - The software you use will determine how you track your inventory.     Support for real-time vs. batch updates and the depth of reporting will determine which application is     most appropriate for your business.

  • Mobile Computer - Inventory tracking requires users to be on the move throughout your facility     making updates and changes. Depending on your environment and need for wireless communication     a range of devices can serve your process.

  • Wireless Infrastructure - Many businesses need inventory updates to occur in real-time and a     wireless network is a required tool to do so. No matter the size of your facility, a network can be scaled     to accommodate it.

  • Barcode Printer - In order to track items quickly and easily, they need to be labeled with a barcode.     Printing can be done at fixed units for high volume printing or on the move with mobile printers.
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What is Inventory Management?

Effective inventory management is all about knowing what is on hand, where it is in use, and how much finished product results.
Inventory management is the process of efficiently overseeing the constant flow of units into and out of an existing inventory. This process usually involves controlling the transfer in of units in order to prevent the inventory from becoming too high, or dwindling to levels that could put the operation of the company into jeopardy. Competent inventory management also seeks to control the costs associated with the inventory, both from the perspective of the total value of the goods included and the tax burden generated by the cumulative value of the inventory.
Balancing the various tasks of inventory management means paying attention to three key aspects of any inventory. The first aspect has to do with time. In terms of materials acquired for inclusion in the total inventory, this means understanding how long it takes for a supplier to process an order and execute a delivery. Inventory management also demands that a solid understanding of how long it will take for those materials to transfer out of the inventory be established. Knowing these two important lead times makes it possible to know when to place an order and how many units must be ordered to keep production running smoothly.
Calculating what is known as buffer stock is also key to effective inventory management. Essentially, buffer stock is additional units above and beyond the minimum number required to maintain production levels. For example, the manager may determine that it would be a good idea to keep one or two extra units of a given machine part on hand, just in case an emergency situation arises or one of the units proves to be defective once installed. Creating this cushion or buffer helps to minimize the chance for production to be interrupted due to a lack of essential parts in the operation supply inventory.
Inventory management is not limited to documenting the delivery of raw materials and the movement of those materials into operational process. The movement of those materials as they go through the various stages of the operation is also important. Typically known as a goods or work in progress inventory, tracking materials as they are used to create finished goods also helps to identify the need to adjust ordering amounts before the raw materials inventory gets dangerously low or is inflated to an unfavorable level.
Finally, inventory management has to do with keeping accurate records of finished goods that are ready for shipment. This often means posting the production of newly completed goods to the inventory totals as well as subtracting the most recent shipments of finished goods to buyers. When the company has a return policy in place, there is usually a sub-category contained in the finished goods inventory to account for any returned goods that are reclassified as refurbished or second grade quality. Accurately maintaining figures on the finished goods inventory makes it possible to quickly convey information to sales personnel as to what is available and ready for shipment at any given time.
In addition to maintaining control of the volume and movement of various inventories, inventory management also makes it possible to prepare accurate records that are used for accessing any taxes due on each inventory type. Without precise data regarding unit volumes within each phase of the overall operation, the company cannot accurately calculate the tax amounts. This could lead to underpaying the taxes due and possibly incurring stiff penalties in the event of an independent audit.
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INVENTORY MANAGEMENT AND CONTROL*



INVENTORY MANAGEMENT AND CONTROL concerns most managers of agricultural marketing and supply businesses, whether they are retail, wholesale, or service oriented. The value of a manager to an agricultural marketing and supply business depends on his ability to manage inventories effectively. The total cost of maintaining the desired inventory level must be held down to a reasonable figure, but the inventory must also be large enough to permit the company to effectively merchandise the products and services it sells. If the manager doesn't control his inventories to accomplish both of these objectives, the business may not be able to prosper or even to survive against competition. The information in this circular suggests to the manager ways on how best to do four things: Y How to control inventories. Y How to visualize the inventory costs to be included in determining how much inventories are costing the company. Y How to determine the level of inventory that is most profitable. Y How to determine how much to order and how often to order.

Controlling Inventories

Purchase systematically. Place orders for materials long enough beforehand so there will not be a shortage between ordering and delivery. Let the inventory become relatively low before reordering but keep enough on hand to meet current needs. There are costs associated with keeping large inventories. Likewise, there are costs if you deplete your stock. Don't hold “dead” lines or items.

Keep track of inventories. When stock is received, be sure that what was ordered was delivered. Make sure that the amount received is added to the inventory. Physical inventories should be taken frequently to find out which items are not selling so you can discontinue them as quickly as possible, to spot shortages in merchandise that may be due to theft, to note deterioration that may occur, and to decide when to reorder.

Make someone responsible for checking the inventory. Delegate the responsibility for specific parts of the total inventory effort to the persons in the organization who are best qualified to do the job. Be sure those to whom you delegate responsibility know exactly what they are supposed to do.

Use storage facilities efficiently. Assign space to each item in stock. Arrange the storage area to permit the handling of stock with the least amount of effort and in such a way that stock can be easily found, the quantity determined and recorded, and the stock removed if necessary. Arrange the warehouse and sales area so the items that sell rapidly can be most easily picked up by the customer or restocked in the display area readily by the employees. Use mechanical means to handle and move supplies whenever the volume warrants it. This will reduce the amount of labor used in handling stock. Plan to use space interchangeably with seasonal items and thus reduce the cost of storage space.

Be aware of inventory turnovers. Know what the turnover of each commodity is and if possible compare this with the turnover of the same items by other similar firms. Inventory turnover ratio is determined by dividing the volume of sales of merchandise by the level of inventory at a point in time, such as the first of each month. For example, the sales of fertilizer in May amounted to $143,000 and the fertilizer inventory at the end of May was $16,300. The inventory turnover ratio for May thus was $143,000/$16,300, or 8.8 to 1. That is, there was an $8.80 turnover of fertilizer for every dollar’s worth in stock at the end of the period in question. A zero inventory, which would give you a ratio of infinity, would not be desirable because the objective of management is to maintain the level of inventory at a level that will permit the most effective merchandising. The most profitable inventory turnover ratio varies with each commodity. Generally, high inventory is needed for rapidly moving commodities if the merchandising effort is going to be efficient and effective. Good examples of this are feed and grain. Other commodities move more slowly but require that a small stock be on hand at all times. Farm machinery is a good example of this. The demand for some items, such as seed, is
seasonal and requires large inventories at certain times of the year. Increase selling efforts or reduce the average stock of slow-moving items. If an item can’t be sold, discontinue stocking it immediately. Dead items are real losers.

Know the costs of inventories. Because costs of inventories are very closely related to size of inventories, the manager should keep his inventory as small as possible consistent with a good merchandising program. The costs of carrying inventories can be a large percentage of the sale value of the inventory. The costs are often 20 to 25 percent or more of the total value of the inventory. The manager should know the costs of holding an inventory and then try to reduce the price of an item to the inventory holding cost to dispose of the items instead of holding them in inventory.

Avoid holding lines of merchandise that compete with one another. Stocking too many lines is asking for inventory problems. Choose the lines of merchandise carefully and then vigorously sell a limited number of lines. Duplication of items that occurs when the company carries multiple lines can more easily result in larger inventories and increased inventory holding costs.

Determining Inventory Costs Inventory costs are real but they are also difficult to determine because they cannot be taken directly from accounting records. Inventory costs for individual items make it necessary to prorate costs of equipment, space, labor for handling, utilities, insurance, taxes on land and buildings, depreciation on buildings and handling equipment, clerical help, unemployment insurance for certain personnel, social security for all “space,” “handling,” and “inventory service” personnel, and a proportionate share of administrative overhead. The cost of holding inventories may make the payoff so great that the manager can’t afford not to do it. Also, when inventory costs have been determined once, it is a much simpler task to make the necessary adjustments in each of the costs. One way to view the total annual cost of carrying inventory is as a percentage of total inventory value. For example, if a company’s average inventory is $25,000 and the average inventory carrying cost is 20 percent, it will cost the company $5,000 per year to carry an average inventory of $25,000. An inventory holding cost that is 20 percent of the average value of inventory is probably too low. Estimates of inventory holding costs for agricultural supply businesses usually range from 20 to 35 percent. The costs that need to be included in the total inventory carrying cost are:

Storage space costs. These include taxes on land and buildings; insurance on buildings; depreciation on buildings and warehouses owned; rent (if paid); materials for repairs and maintenance on buildings; utilities; and janitor, watchman, and maintenance costs.

Handling costs. These include depreciation on equipment; fuel for equipment; maintenance and repair of equipment and insurance and taxes on equipment.

Risk costs on inventory. These include insurance on inventory; obsolescence of inventory; physical deterioration of inventory; pilferage; and losses resulting from inventory price declines.

Inventory service costs. These include taxes on inventory; labor costs of handling and maintaining stock clerical costs for inventory records; contribution to Social Security by employer based on prorated time devoted to inventories by employees; unemployment compensation insurance based on prorated time of “inventory involved” personnel; employer contribution to pension plans, and group life, health, and accident insurance programs based on prorated time of “inventory involved” personnel; and an appropriate proportionate share for administrative overhead, including all taxes, Social Security, pension, and employer contributions to insurance programs for administrative personnel who are involved.

Capital costs. These include interest on money invested in inventory; interest on money invested in inventory handling and control equipment; and interest on money invested in land and buildings to store inventory (if land and buildings are owned).

Cost summary. The information about the hypothetical company that follows shows how a manager can develop a better understanding of how he can use the knowledge he has about inventory holding costs to make better management decisions. The company management thinks they have a high cost of inventory holding but they don't know exactly what that cost is. Consequently they ask the manager to compute this cost. The average inventory value in 1971 was $25,000. This figure is arrived at by adding the quarterly inventory values, which were $21,000 on March 31, $33,000 on June 30, $29,000 on September 30, and $18,000 on December 31, to get a total of $101,000. This results in a quarterly average of $25,250, which is rounded to $25,000. The manager computed the holding costs on the average inventory and found that they were as follows:
Obsolescence cost based on the value of the average inventory ......... $1,500.00
Cost of capital on the average inventory ........................................... $2,500.00
Deterioration of average inventory or its prevention .................................... $1,250.00
Handling and distribution costs of average inventory ............................. $2,000.00
Transportation................................... $250.00
Taxes on average inventory ............. $187.50
Insurance of average inventory… $125.00
Storage facilities cost on average
inventory ........................................... $500.00
TOTAL.............................................. $8,312.50

The manager also had available the average industry-wide inventory holding costs listed below:
Obsolescence................................ 4 to 7%
Cost of capital ................................ 8 to 12%
Deterioration or its prevention ....... 4 to 5%
Handling and distribution ............... 2 to 3%
Transportation................................ .5 to 1%
Taxes ............................................. .5 to .75%
Insurance ....................................... 0.25%
Storage facilities ............................ .25 to .75%
TOTAL.........................................1..9.5 to 29.75%

The manager now compared his company’s costs with these percentages by computing the percentages that each of the company’s average inventory holding costs were of his total inventory costs. These computations yielded the following information:
Obsolescence....... $1,500 is 6% of $25,000
Cost of capital ....... 2,500 is 10% of 25,000
Deterioration or its prevention ............. 1,250 is 5% of 25,000
Handling and distribution ............ 2,000 is 8% of 25,000
Transportation....... 250 is 1% of 25,000
Taxes .................... 187.50 is .75% of 25,000
Insurance .............. 125 is .5% of 25,000
Storage facilities ... 500 is 2% of 25,000
TOTAL.......... $8,312.50 is 33.25% of $25,000

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Rabu, 14 November 2012

Data Warehouse


From Wikipedia, the free encyclopedia
This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (February 2008)
 
Data Warehouse Overview
In computing, a data warehouse (DW or DWH) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from multiple disparate sources. Data warehouses store current as well as historical data and are commonly used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting.
The typical ETL-based data warehouse uses staging, integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[1]
A data warehouse constructed from an integrated data source systems does not require ETL, staging databases, or operational data store databases. The integrated data source systems may be considered to be a part of a distributed operational data store layer. Data federation methods or data virtualization methods may be used to access the distributed integrated source data systems to consolidate and aggregate data directly into the data warehouse database tables. Unlike the ETL-based data warehouse, the integrated source data systems and the data warehouse are all integrated since there is no transformation of dimensional or reference data. This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems.
Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.
This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
Benefits of a data warehouse
A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:
  • Maintain data history, even if the source transaction systems do not.
  • Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.
  • Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.
  • Present the organization's information consistently.
  • Provide a single common data model for all data of interest regardless of the data's source.
  • Restructure the data so that it makes sense to the business users.
  • Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.
  • Add value to operational business applications, notably customer relationship management (CRM) systems.
A Generic Data Warehouse Environment
The environment for data warehouses and marts includes the following:
  • Source systems that provide data to the warehouse or mart;
  • Data integration technology and processes that are needed to prepare the data for use;
  • Different architectures for storing data in an organization's data warehouse or data marts;
  • Different tools and applications for the variety of users;
  • Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes.
In regards to source systems listed above, Rainer states, “A common source for the data in data warehouses is the company’s operational databases, which can be relational databases” (130).
Regarding data integration, Rainer states, “It is necessary to extract data from source systems, transform them, and load them into a data mart or warehouse” (131).
Rainer discusses storing data in an organization’s data warehouse or data marts. “There are a variety of possible architectures to store decision-support data” (131).
Metadata are data about data. “IT personnel need information about datasources; database, table, and column names; refresh schedules; and data usage measures (133).
Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers (Rainer, 127). A “data warehouse” is a repository of historical data that are organized by subject to support decision makers in the organization (128). Once data are stored in a data mart or warehouse, they can be accessed.
Rainer, R. Kelly (2012-05-01). Introduction to Information Systems: Enabling and Transforming Business, 4th Edition (Page 129). Wiley. Kindle Edition.v
History
The concept of data warehousing dates back to the late 1980s[2] when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as legacy systems), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "data marts" that were tailored for ready access by users.
Key developments in early years of data warehousing were:
  • 1960s — General Mills and Dartmouth College, in a joint research project, develop the terms dimensions and facts.[3]
  • 1970s — ACNielsen and IRI provide dimensional data marts for retail sales.[3]
  • 1970s — Bill Inmon begins to define and discuss the term: Data Warehouse
  • 1975 — Sperry Univac Introduce MAPPER (MAintain, Prepare, and Produce Executive Reports) is a database management and reporting system that includes the world's first 4GL. It was the first platform specifically designed for building Information Centers (a forerunner of contemporary Enterprise Data Warehousing platforms)
  • 1983 — Teradata introduces a database management system specifically designed for decision support.
  • 1983 — Sperry Corporation Martyn Richard Jones defines the Sperry Information Center approach, which while not being a true DW in the Inmon sense, did contain many of the characteristics of DW structures and process as defined previously by Inmon, and later by Devlin. First used at the TSB England & Wales
  • 1984 — Metaphor Computer Systems, founded by David Liddle and Don Massaro, releases Data Interpretation System (DIS). DIS was a hardware/software package and GUI for business users to create a database management and analytic system.
  • 1988 — Barry Devlin and Paul Murphy publish the article An architecture for a business and information system in IBM Systems Journal where they introduce the term "business data warehouse".
  • 1990 — Red Brick Systems, founded by Ralph Kimball, introduces Red Brick Warehouse, a database management system specifically for data warehousing.
  • 1991 — Prism Solutions, founded by Bill Inmon, introduces Prism Warehouse Manager, software for developing a data warehouse.
  • 1992 — Bill Inmon publishes the book Building the Data Warehouse.[4]
  • 1995 — The Data Warehousing Institute, a for-profit organization that promotes data warehousing, is founded.
  • 1996 — Ralph Kimball publishes the book The Data Warehouse Toolkit.[5]
  • 2000 — Daniel Linstedt releases the Data Vault, enabling real time auditable Data Warehouses warehouse.
Dimensional vs. Normalized approach for storage of data
There are two leading approaches to storing data in a data warehouse — the dimensional approach and the normalized approach.
The dimensional approach, whose supporters are referred to as “Kimballites”, believe in Ralph Kimball’s approach in which it is stated that the data warehouse should be modeled using a Dimensional Model/star schema. The normalized approach, also called the 3NF model, whose supporters are referred to as “Inmonites”, believe in Bill Inmon's approach in which it is stated that the data warehouse should be modeled using an E-R model/normalized model.
In a dimensional approach, transaction data are partitioned into "facts", which are generally numeric transaction data, and "dimensions", which are the reference information that gives context to the facts. For example, a sales transaction can be broken up into facts such as the number of products ordered and the price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order.
A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly. Dimensional structures are easy to understand for business users, because the structure is divided into measurements/facts and context/dimensions. Facts are related to the organization’s business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008).
The main disadvantages of the dimensional approach are:
  1. In order to maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated, and
  2. It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business.
In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by subject areas that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joins. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented (Kimball, Ralph 2008). The main advantage of this approach is that it is straightforward to add information into the database. A disadvantage of this approach is that, because of the number of tables involved, it can be difficult for users both to:
  1. join data from different sources into meaningful information and then
  2. access the information without a precise understanding of the sources of data and of the data structure of the data warehouse.
It should be noted that both normalized – and dimensional models can be represented in entity-relationship diagrams as both contain joined relational tables. The difference between the two models is the degree of normalization.
These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree (Kimball, Ralph 2008).
In Information-Driven Business (Wiley 2010),[6] Robert Hillard proposes an approach to comparing the two approaches based on the information needs of the business problem. The technique shows that normalized models hold far more information than their dimensional equivalents (even when the same fields are used in both models) but this extra information comes at the cost of usability. The technique measures information quantity in terms of Information Entropy and usability in terms of the Small Worlds data transformation measure.[7]
Top-down versus bottom-up design methodologies
Bottom-up design
Ralph Kimball, a well-known author on data warehousing,[8] is a proponent of an approach to data warehouse design which he describes as bottom-up.[9]
In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes. Though it is important to note that in Kimball methodology, the bottom-up process is the result of an initial business-oriented Top-down analysis of the relevant business processes to be modelled.
Data marts contain, primarily, dimensions and facts. Facts can contain either atomic data and, if necessary, summarized data. The single data mart often models a specific business area such as "Sales" or "Production." These data marts can eventually be integrated to create a comprehensive data warehouse. The integration of data marts is managed through the implementation of what Kimball calls "a data warehouse bus architecture".[10] The data warehouse bus architecture is primarily an implementation of "the bus", a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.
The integration of the data marts in the data warehouse is centered on the conformed dimensions (residing in "the bus") that define the possible integration "points" between data marts. The actual integration of two or more data marts is then done by a process known as "Drill across". A drill-across works by grouping (summarizing) the data along the keys of the (shared) conformed dimensions of each fact participating in the "drill across" followed by a join on the keys of these grouped (summarized) facts.
Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse. The most important management task is making sure dimensions among data marts are consistent. In Kimball's words, this means that the dimensions "conform".
Some consider it an advantage of the Kimball method, that the data warehouse ends up being "segmented" into a number of logically self-contained (up to and including The Bus) and consistent data marts, rather than a big and often complex centralized model. Business value can be returned as quickly as the first data marts can be created, and the method gives itself well to an exploratory and iterative approach to building data warehouses. For example, the data warehousing effort might start in the "Sales" department, by building a Sales-data mart. Upon completion of the Sales-data mart, the business might then decide to expand the warehousing activities into the, say, "Production department" resulting in a Production data mart. The requirement for the Sales data mart and the Production data mart to be integrable, is that they share the same "Bus", that will be, that the data warehousing team has made the effort to identify and implement the conformed dimensions in the bus, and that the individual data marts links that information from the bus. Note that this does not require 100% awareness from the onset of the data warehousing effort, no master plan is required upfront. The Sales-data mart is good as it is (assuming that the bus is complete) and the Production-data mart can be constructed virtually independent of the Sales-data mart (but not independent of the Bus).
If integration via the bus is achieved, the data warehouse, through its two data marts, will not only be able to deliver the specific information that the individual data marts are designed to do, in this example either "Sales" or "Production" information, but can deliver integrated Sales-Production information, which, often, is of critical business value. An integration (possibly) achieved in a flexible and iterative fashion.
Top-down design
Bill Inmon, one of the first authors on the subject of data warehousing, has defined a data warehouse as a centralized repository for the entire enterprise.[10] Inmon is one of the leading proponents of the top-down approach to data warehouse design, in which the data warehouse is designed using a normalized enterprise data model. "Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. In the Inmon vision, the data warehouse is at the center of the "Corporate Information Factory" (CIF), which provides a logical framework for delivering business intelligence (BI) and business management capabilities.
Inmon states that the data warehouse is:
Subject-oriented
The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.
Non-volatile
Data in the data warehouse are never over-written or deleted — once committed, the data are static, read-only, and retained for future reporting.
Integrated
The data warehouse contains data from most or all of an organization's operational systems and these data are made consistent.
Time-variant
For An operational system, the stored data contains the current value.
The top-down design methodology generates highly consistent dimensional views of data across data marts since all data marts are loaded from the centralized repository. Top-down design has also proven to be robust against business changes. Generating new dimensional data marts against the data stored in the data warehouse is a relatively simple task. The main disadvantage to the top-down methodology is that it represents a very large project with a very broad scope. The up-front cost for implementing a data warehouse using the top-down methodology is significant, and the duration of time from the start of project to the point that end users experience initial benefits can be substantial. In addition, the top-down methodology can be inflexible and unresponsive to changing departmental needs during the implementation phases.[10]
Hybrid design
Data warehouse (DW) solutions often resemble the hub and spokes architecture. Legacy systems feeding the DW/BI solution often include customer relationship management (CRM) and enterprise resource planning solutions (ERP), generating large amounts of data. To consolidate these various data models, and facilitate the extract transform load (ETL) process, DW solutions often make use of an operational data store (ODS). The information from the ODS is then parsed into the actual DW. To reduce data redundancy, larger systems will often store the data in a normalized way. Data marts for specific reports can then be built on top of the DW solution.
It is important to note that the DW database in a hybrid solution is kept on third normal form to eliminate data redundancy. A normal relational database however, is not efficient for business intelligence reports where dimensional modelling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The DW effectively provides a single source of information from which the data marts can read, creating a highly flexible solution from a BI point of view. The hybrid architecture allows a DW to be replaced with a master data management solution where operational, not static information could reside.
The Data Vault Modeling components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both 3rd normal form and star schema. The Data Vault model is not a true 3rd normal form, and breaks some of the rules that 3NF dictates be followed. It is however, a top-down architecture with a bottom up design. The Data Vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which when built, still requires the use of a data mart or star schema based release area for business purposes.
Data warehouses versus operational systems
Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an entity-relationship model. Operational system designers generally follow the Codd rules of database normalization in order to ensure data integrity. Codd defined five increasingly stringent rules of normalization. Fully normalized database designs (that is, those satisfying all five Codd rules) often result in information from a business transaction being stored in dozens to hundreds of tables. Relational databases are efficient at managing the relationships between these tables. The databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. Finally, in order to improve performance, older data are usually periodically purged from operational systems.
Evolution in organization use
These terms refer to the level of sophistication of a data warehouse:
Offline operational data warehouse
Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented data
Offline data warehouse
Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data are stored in a data structure designed to facilitate reporting.
On time data warehouse
Online Integrated Data Warehousing represent the real time Data warehouses stage data in the warehouse is updated for every transaction performed on the source data
Integrated data warehouse
These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems.[11]
Sample applications
Some of the applications of data warehousing include:
  • Agriculture[12]
  • Biological data analysis
  • Call record analysis
  • Churn Prediction for Telecom subscribers, Credit Card users etc.
  • Decision support
  • Financial forecasting
  • Insurance fraud analysis
  • Logistics and Inventory management
  • Trend analysis
See also
References
1.      ^ Patil, Preeti S.; Srikantha Rao; Suryakant B. Patil (2011). "Optimization of Data Warehousing System: Simplification in Reporting and Analysis". IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (Foundation of Computer Science) 9 (6): 33–37.
2.      ^ "The Story So Far". 2002-04-15. Retrieved 2008-09-21.
3.      ^ a b Kimball 2002, pg. 16
4.      ^ Inmon, Bill (1992). Building the Data Warehouse. Wiley. ISBN 0-471-56960-7.
5.      ^ Kimball, Ralph (1996). The Data Warehouse Toolkit. Wiley. ISBN 0-471-15337-0.
6.      ^ Hillard, Robert (2010). Information-Driven Business. Wiley. ISBN 978-0-470-62577-4.
7.      ^ http://mike2.openmethodology.org/wiki/Small_Worlds_Data_Transformation_Measure
8.      ^ Kimball 2002, pg. 310
9.      ^ "The Bottom-Up Misnomer". 2003-09-17. Retrieved 2012-02-14.
10.  ^ a b c Ericsson 2004, pp. 28–29
11.  ^ "Data Warehouse".
12.  ^ Abdullah, Ahsan (2009). "Analysis of mealybug incidence on the cotton crop using ADSS-OLAP (Online Analytical Processing) tool, Volume 69, Issue 1". Computers and Electronics in Agriculture 69: 59–72. doi:10.1016/j.compag.2009.07.003.
Further reading

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