Electrolux Case Essay Example

Electrolux Case Essay Electrolux, page 25-27: 1) Explain why the issues facing Electrolux were strategic? Long-term direction: More domestic and professional appliances Shift production to low-cost countries Close plants that are inefficient Reduce number of supplies, also purchase more from low-cost countries Build a strong global brand Scope: Electrolux focused on its key competences (domestic and professional appliances) Sold its outdoor division (mowers, chain saws, etc. ) Want to invest 2% of sales in product development to intensify product renewal and systematic development of brands Advantages over competitors: Global, recognized brand Business environment: Globalization (production in low-cost countries, production costs of all major producers will be the same, therefore more concentration on product development, brand-building, marketing) Market polarization (demand for basic products and higher-price products increases) Consolidation of retailers (more business with big chains and fewer traditional dealers) 2) Identify the main factors about the strategic situation of Electrolux. Globalization Market polarization Consolidation of retailers Cost reduction in order to compete with other big companies (Whirlpool) 3) Think about strategic choices for the company in relation to the issues that it has. Invest more in research and development Use more economies of scale, reduce number of unique products and increase â€Å"platforms† for easy adjustments/diversification of existing products Find new markets (developing world, BRIC-Countries) Show how the elements of Strategic Management differ in: b) a large multinational business We will write a custom essay sample on Electrolux Case specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Electrolux Case specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Electrolux Case specifically for you FOR ONLY $16.38 $13.9/page Hire Writer In a large multinational firm, the strategy needs to consider aspects and local circumstances of not only one country, but all countries they are operating in. When defining the long-term direction of an organization, these different situations (i. e. local business environment, political issues, laws, importance of business location) are not evenly important for the overall success of the company. Also, some parts of the organization have other problems than others, which need to be considered. The scope of activities in a multinational business can be very wide, with production in one country and demand for it in many others. Additionally, there can be a very diverse product mix, so that the organization needs to focus on keeping it adjusted with their overall long-term direction and if all business units are equally important to achieve this goal. Facing many different fields of competition in many incoherent surroundings is a difficult task. However, as a large multinational company, it can use its bargaining power, market share and economies of scale to improve its advantage in front of competitors. Having a unique product brings special know-how, skills and competences into the business. Therefore, these resources can be used to improve and develop products, services or whatever the company sells. Considering that an organization like this is part of what makes this world so globalized, it also needs to adapt to changes in its environment, which can be technological, political or influence the business in other ways. In order to stay successful, the company needs to act quickly, but with careful consideration, before the competition has used good opportunities. Since most multinational firms are listed at least on one stock exchange in order to finance their business, the strategy has to include the interests of shareholders. However, the strategy should not focus on improving share prices on a short term basis, while forgetting about the long-term direction and the possible harm that can be caused by a wrongly focused strategy. Other stakeholders like employees, local communities, etc. also have to be considered, since especially employees are the main factor in generating products and therefore, profits. Deciding on a global strategy for a multinational company is very difficult, complex and need to integrate many different situations and focus areas.

Data Warehouse

Data Warehouse Abstract Data warehousing, as a means of organizing enterprise information in order for businesses to manage knowledge and benefit from the knowledge acquired from possible analysis, is a common business venture in most firms today. Gone are the days when one large and expensive supercomputer would be used to manage an entire organization’s data.Advertising We will write a custom research paper sample on Data Warehouse specifically for you for only $16.05 $11/page Learn More Today, various Central Processing Units (CPUs) are available and at the disposal of the IT team. The beauty of this scenario is that the CPUs can be used simultaneously to perform completely different, but related tasks that are part of the major task and thus completing the major task in record time. One of the many advantages of data warehousing is the fact that these systems become a central data source after consolidation, which is accessible to end users and information deriv ation becomes simpler if not straightforward. Consequently, this element increases the efficiency of business transactions, which eventually draws the line between the firms with business acumen and those without. However, one inherent disadvantage follows data warehousing and it involves data mining. Ideally, data mining is the final stage of data warehousing because at this point, it is possible to gather all possible types of relational information from the system and determine links and relationships that were not decipherable before. As a result, the accuracy of queries increases and business output increases. However, this case does not apply in practice due to a few hitches that attach to this process of data mining. First, after completing the process of data mining, only a few users in the entire enterprise can actually get to use the procedure due to the high level of specialization required in its application. In fact, the number presently oscillates at a maximum of five. Given this scenario, unsurprisingly most organizations do not see the point of paying very expensively for a process that would only be used by five people in the firm. Therefore, they pay peanuts. On the other hand, data-warehouse builders know that they require a lot of upfront capital and heavy investment in time resources upfront before coming up with a data-mining algorithm, which is infamous for its complexity. This aspect coupled with the fact that it is virtually impossible to predict the resourcefulness of a data mining infrastructure from the onset and thus decapitating the technician from having a sales pitch, makes a very bad case for data mining, and yet its importance cannot be overemphasized.Advertising Looking for research paper on it? Let's see if we can help you! Get your first paper with 15% OFF Learn More This paper looks into several such poignant features of data warehousing and close with a few recommendations as well as forecasts into the future of data warehousing. Introduction Data warehousing is a rather new term for an old concept. In fact, it emerged in the 1990s where it was initially referred to as Decision Support System or Executive Information system. The father of data warehousing is one William Inmon and a co-innovator usually lined up beside him in reviews is Ralph Kimball. Several definitions exist to befit what has come to be accepted as data warehousing in the 21st century and these include â€Å"A Data warehouse is as organized system of enterprise data derived from multiple data sources designed primarily for decision making in the organization† (Bertman, 2005, p. 12). This definition brings out the idea of a myriad of sources of data, which is especially relevant because today, most organizations have a multiple of data sources. Moreover, it is essential in the customization of data warehousing to ensure that the data-warehousing infrastructure being set up including ETL tools (Extracti on, Transformation, Transportation and Loading solutions) are compatible with all the data sources. Additionally, the definition touches on the issue of decision making as a primary focus when establishing a data-warehousing project. A second definition is slightly brief, viz. â€Å"†¦a data warehouse is a structured repository of historic data† (Kimball, Ross, Thornthwaite, Mundy, Becker, 2008, p. 32) The author of this definition adds that it is â€Å"†¦developed in an evolutionary process by integrating data from non-integrated legacy systems† (Kimball, Ross, Thornthwaite, Mundy, Becker, 2008, p. 32).Advertising We will write a custom research paper sample on Data Warehouse specifically for you for only $16.05 $11/page Learn More This definition is attractive for its introduction of the term â€Å"integrated†, because the main idea behind data warehousing is that the information that was previously archived in a jumble is reorganized to make sense in the form of tables and even graphs depending on the presentation format preferred by the end user. At this point, it is appropriate to introduce Inmon’s definition. As the father of data ware housing, his definition has attached a legendary thrill to data warehouse builders and other experts in the field and thus it has even been used in a devolved capacity to divide data warehousing into branches. He states, â€Å"A data warehouse is a subject-oriented, integrated, time variant, and anon volatile collection of data used in strategic decision making† (Inmon, 2003, p. 34). It is important to note the usage of several definitive words that have since achieved the status of â€Å"mandatory† features of a data warehouse including subject oriented, non-volatile, time variant, and integrated. Another definition reads, â€Å"A data warehouse is an electronic storage of an organization’s historical data for the purpose of analysis a nd interpreting† (Prine, 1998, p. 54). The interesting concept introduced by this final definition is the term â€Å"historical data†, which is a very important feature of data warehouses as shall be seen in the ensuing discourse. Additionally, the tasks of analysis and interpretation mentioned by this definition are very crucial features in the business of data ware housing. The next section provides a run through the definitions of other important terms outlined within this paper. Definitions OLAP: Online Analytical Processing refers to the procedure through which multidimensional analysis occurs.Advertising Looking for research paper on it? Let's see if we can help you! Get your first paper with 15% OFF Learn More OLTP: this term refers to a transaction system that collects business data and it is optimized for INSERT and UPDATE operations. It is highly normalized because the emphasis is on updating the system since transactions take precedence here and so the currency of the information is crucial for the relevance of the data. Data Mart: this term underscores a data structure designed for access. It is designed with the aim of enhancing end user access to information files stored in subject-order. For instance, in an organization there are numerous departments including IT, HR, Management, Finance, and Research among others. However, an organization may set up data marts on top of the hardware platform for each department, so that after data warehousing, there exists the traditional centralized data storage envisioned by the creators, but in addition to this, a next section in the architecture provides for data marts (Hackney, 2007, p. 45). These elements would in effect separate the info rmation into the relevant sub-sections based on the subject matter. ER Model: this model refers to an entry relationship model. In other words, a data modeling methodology whose aim is to normalize data by reducing redundancy. Dimensional Model: this model qualifies the data. The main goal is to improve data retrieval mechanism. It is ideal for data ware housing that is operated based on queries. A typical example would be keying in 1kg as a search term and how convoluted the results that one is likely to get would be. On the contrary, if one keys in: â€Å"1kg of soya (product) bought by Becker (customer) on 23rd November 2012 (date),† in effect, one has just introduced three dimensions- product, customer, and date. These are mutually independent and non-overlapping classifications of data (Imhoff, Galemmo, Geige, 2003, p.101). A fact underlines something that can be measured or quantified conventionally, but not always, in numerical values that can be aggregated. Star sc hema: this term refers to a technique used in data warehousing models in which one centralized fact table is used as the reference for all the dimension tables so that the keys (primary keys) from the entirety of dimension tables can flow directly into the fact table (as foreign keys of course) in which the measures are stored. The entity relationship represented diagrammatically resembles a star, hence the name. Different Types of Data Warehousing Architectures There are three main types of data warehousing architectures and these include: Data Warehouse Architecture (basic) Data Warehouse Architecture (with a Staging Area) Data Warehouse Architecture (with a staging area and a data mart) Data ware house architecture basic This structure comprises metadata, raw data, and summary data. Meta data and raw data are a classical feature of all operational systems, but the summary data makes the architecture to be a unique data warehouse material. Summaries pre-compile long operations in advance, for instance, they can grant an answer to a query on August sales (Imhoff White, 2011, p. 25). In oracle, a summary is also known as a materialized view and in term of granul-ity, it may be atomic, which is transaction oriented, lightly summarized, or highly summarized. Data Warehouse Architecture (with a Staging Area) This architectural type is relevant when there is a need to clean and process operational data before it is stored in the warehouse. This task can be done either programmatically, that is, with a program or using a staging area. A staging area simply refers to that â€Å"region of the architecture that simplifies building summaries and general warehouse management† (Jarke, Lenzerini, Vassiliou, Vassiliadis, 2003, p. 67). Data Warehouse Architecture (with a staging area and a data mart) This architecture type is ideal for the customization of a data warehouse for different groups within an organization. It adds â€Å"data marts to the staging area , where data marts are systems that are designed for a particular line of business† (Hackney, 2007, p.18). A good example is a case where a firm needs to separate inventories from sales and or purchases. At this point, it is important to introduce the concept of Business Intelligence for a better understanding of the working of database warehouses. Business intelligence covers information that is available for strategic decision making by businesses. In this setting, the data warehouse is simply the backbone or the infrastructural component (Prine, 1998, p. 39). Business intelligence includes the insight that is obtained upon the execution of a data mining analysis and other unstructured data, and this aspect explains the significance of content management systems because in an unstructured context, they organize the information logically for better analysis. When choosing a business intelligence tool, one needs to address the following considerations that advice the choice, v iz. increasing the costs, increasing the function ability, increasing the complexity of business intelligence, and decreasing the number of end users (Eliott, 2012). Interestingly, the most popular business intelligence tool is Microsoft Excel. This assertion holds due to several reasons including the fact that Ms Excel is cheap to acquire, and it is conveniently simple to use. In addition, the user does not have to worry whether the other user can decipher the information or figure out how the reports are to be interpreted (because the presentation is simple to interpret), and finally, Excel has all the functionalities that are necessary for the display of data (Barwick, 2012). Other tools include a reporting tool, which can be either custom built or commercial and it is used for the running, creation, and scheduling of operations or reports (Kimball, Ross, Thornthwaite, Mundy, Becker, 2008, p. 67). Another tool is the OLAP tool, which is a favorite amongst advanced users because it features a multidimensional perspective of findings, and finally there is the Data mining tool that is for specialized users, hence the limitation to less than five users in an entire enterprise. Overall structure The primary features of a data warehouse are better relayed in a graphical format, but this section hopes to provide a comprehensive textual explanation of the same. At the beginning end, there exists data sources, which are archived in different formats, but they are largely unorganized and very general. The idea is to get them to the other end where in an idyllic scenario they are available to end users in data marts and the users are capable of deriving this information in the form of CDs, DVDs or flash drives. In a bid to get to that end, the data has to pass through data acquisition, which refers to retrieval of information from the data sources; that is, â€Å"a set of processes and programs that extract data for the data warehouse and operational data store from the operational systems† (Imhoff, Galemmo, Geige, 2003, p. 17). At this stage, features touching on cleansing, integrating, and transformation of data stand out. Next, the data, through data delivery, is moved to the open marts and ready for harvesting. Advantages of data warehousing This process makes the data more accessible in terms of accuracy so that end users do not fumble through scores of unsorted data in order to get a response to the queries that they are seeking to answer. Consequently, it makes the process of accessing that information cheaper and more efficient. It reduces the costs of acquiring this data because the accessibility means that users do not need to spend additional resources on fruitless tasks; in addition, these resources can be expended elsewhere. Another advantage is that it increases the competitive advantage of the enterprise that integrates it into its infrastructure. The data in a data warehouse can be used in multiple scenarios including in the production of reports for log term analyses, in producing reports meant to aggregate enterprise data, and finally for producing reports that are multidimensional; for instance, a query can be lodged on the profits accrued by month, product, and branch. The information stored in a warehouse provides a basis for strategic decision-making, it is available for access, and it is consistent. Additionally, it assists in introducing an organization to the continuous changes in information within the enterprise. Finally, it helps protect the data from abusers. Disadvantages of data warehousing Data warehousing is a very costly investment, which is bound to dig into the capital pool of the enterprise that is using it. Additionally, it takes a lot of time to get the project underway and finally see it to completion and this aspect could be anywhere between two to six months. The time becomes relevant because the data-warehousing infrastructure being installed may just end up obsolete by t he time it is getting into production. The very volatile nature of business is vulnerable to this new risk because in contemporary times, even the formerly static fields like finance are susceptible to multiple changes within such a period in order to increase sales. In such a scenario, at the onset of installation, the data warehousing technique may be relevant, but at the end of the project, it may have become obsolete. It is also very worrying that colleges and other institutions are churning out new experts in data warehousing every other day and the effect that this has on the industry is horrifying because these new brains are eager to apply what they have learnt ins school, yet have not practiced and they apparently lack quality experience. Ultimately, they install data warehouses that are slow or ineffective because of sticking to ideals that may not be practical in real life scenarios. Moreover, another disadvantage is the fact that due to the efficiency of the results of d ata warehousing, organizational users may be tempted to use the data warehouse inappropriately. This scenario occurs when the data warehouse is used to replace the operational systems or reports that are normally churned out by operational systems, or in analyzing the current operational results. It is noteworthy that these two systems are not supposed to be used interchangeably; on the contrary, they should be used complimentarily. OLTP and Data Warehousing Environments Before getting to the contrasts, it is important to create a background that is relevant to this discourse. With that in mind, a data warehouse â€Å"is a relational database, which is designed for queries and analyses rather than for transaction processing† (Imhoff, Galemmo, Geige, 2003, p.111). Consequently, it is comprised of historical data as well as data from other sources or in other word, which in most cases it falls in the category of unstructured data. The surrounding environment features the follo wing components: ETL solution This component comprises the extraction, transportation loading, and transformation stages that are required for unstructured data to be cleaned and transformed into an integrated block of information. Online Analytical Processing Engine (OLAP) This component underscores the reporting and analyzing system that processes business data. It is deliberately de-normalized in order to ensure fast data retrieval. As a result, instead of the update and insert features that are commonplace for OLTP, this system features SELECT operations that are ideal for queries (Jarke, Lenzerini, Vassiliou, Vassiliadis, 2003, p. 54). A good example would be in a department store scenario where at the Point-of-Sale, which is at the cashier’s stand where he or she looks at the price list that he or she has and deducts money from customers’ credit cards; therefore, this aspect amounts to a transaction and so OLAP is not in play (Hackney, 2007, p. 39). However, if the store manager were to require a list of out-of-stock products, he would turn to the OLAP operation to retrieve that data. Client analysis tools Other tools that are used in the management of the gathering of data and the consequent delivery to business users After landscaped the environs of a data warehouse to this end, it is important to look into the founding father’s perspective, as it shall form the basis of the contrast between OLTP and Data Warehousing Environments. As per William Inmon’s definition of warehouses mentioned above, four distinguishing features come to mind: Subject oriented During operation, where operation refers to data analysis, it is possible for the data warehouse to be programmed to act based on a particular subject, for example, sale of Ferraris. In this line of thought, it is thus possible to arrive at the best customer for Ferraris in June 2012. This aspect is known as subject orientation. Integrated This feature is in reference to an organization and so it is safe to say that it is an organizational feature. At this point, it is apparent that in an organizational context, there exist various sources of data. The cumulative effect of this aspect is that the bulk of the data will be disparate and inconsistent and thus the job of ensuring that this data goes through consolidation and alignment into a sensible platform belongs to the data warehouse (Bertman, 2005, p. 41). In the course of executing this task, various challenges are expected to emerge. These challenges should meet resolution and if the data warehouse is capable of getting to such a state where they are resolved, it qualifies as an integrated data warehouse. Time variant The idea behind data warehousing is to carry out an analysis that spans a given period and the width of its scope may be infinite. This aspect explains why data warehouses contain historical data ranging back years or decades. This element is very different from Online Transaction Pro cessing (OLTP) systems, which store historical data in archives to give room for current data. On the contrary, data warehousing analysts need a large data bundles in order to glean change over time, which underscore the concept of time variance. Non volatile This feature is in reference to the stability or performance of data once it has been loaded into the data warehouse. The data warehouse should have the ability to maintain the information in the state that it was entered initially. There should not be any deletions or other alteration or else the whole information would be jumbled and inaccurate to use in the analysis of business intelligence. Contrast between OLTP and Data Warehousing Environments Workload Data warehouses accommodate ad hoc queries, which is to say that the queries they deal with are random and unexpected. The ideal system should have the capacity to perform well in a wide array of possible questions in various categories. On the other hand, OLTP systems rely on the pre definition of key concepts. It follows that applications should be specifically tuned or designed for preset applications. Data modifications Data warehouses feature a regular update of the system through the ETL process (offering extraction, transportation, transformation, and loading solutions). The same is set to run nightly or weekly depending on organizational preferences. In a bid to accomplish this goal, the enterprise employs bulk-data-modification-techniques. However, the end users do not individually update the data warehouse. On the contrary, in OLTP systems, â€Å"the end users are responsible for system updates and they do this by way of routinely issuing individual modification statements to the database warehouse; consequently, the database is always up to date† (Reddy, Rao, Srinivasu, Rikkula, 2010, p.2869). Schema design Data warehouses â€Å"use fully or partially de-normalized schemas such as the star schema for optimal query performance† (Reddy, Rao, Srinivasu, Rikkula, 2010, p.2870). On the other hand, OLTP systems use normalized schemas for optimum updates with insert and delete functionalities and data consistency because they are transactional and the accuracy of current information is very critical. Typical operations For data warehouses, the typical operation is querying. They need the capacity to scan thousands or even millions of rows simultaneously to come up with the required search result load. A good example of such a demanding query is one that is in search: for instance, finding the total sales for all the cashiers for the last month. On the other hand, OLTP systems have a lighter burden to contend with in terms of the requirements of bulk. A transactional operation scans only a handful of records at a go. For instance, retrieve the current price for this customer’s order. Historical data Due to the nature and the intended use of data warehouses, it is relevant for them to store up to decades of information in a region that is easily accessible when queries are executed. Such a structure is ideal for historical analyses. On contrary, OLTP systems are just the opposite. They store up data for at most a few weeks or months and only retain historical data as is relevant for the current transaction. Moreover, this additional historical data is stored up in archives and a special retrieval process is necessary when it becomes relevant or necessary. Hardware and I/O Considerations in Data Warehouses Scalability It is important to ensure that the data warehouse grows as the data storage grows. In a bid to warrant this element, it would be wise to choose the RDBMS and hardware platforms that are adequately structured to handle large volumes of data with the most efficacies (Kimball, Reeves, Ross, Thornthwaite, 1998, p. 90). However, this move may be a difficult task to embark on in advance when it is still not apparent what amount of data shall be stored in the data warehouse in its maturity. This realization explains why it is also advisable to approximate the amount and use it as a basis in setting up the data warehouse. Parallel Processing Support It is necessary to refrain from using one CPU as the main processor and instead use multiple CPUs each performing a related part of the task separately but simultaneously (South, 2012, p. 67). RDBMS – Hardware combination This move becomes relevant because of the physical location of the RDBMs as it is strategically placed on top of the hardware platform and this aspect may bring issues with bugs and bugs fixing (Kimball Ross, 2002, p. 26). Ebay database warehouse (structure) Oliver Ratzesberger and his team in eBay are responsible for two of the world’s larges t data warehouses. The Greenplum data warehouse that is fully equipped with a data mart is comprised of 6.5 petabytes of user data, which translates to more than 17 trillion records, and â€Å"each day, an additional 150 billion new re cords are added and this amounts to 100 days of event data (Dignan, 2010, Para.12). The ultimate goal is to reach 90-180 days of event data. The working speed of these metrics is an impressive 200 MB/node/sec of I/O. This rate further improves due to a minimized number of concurrent end users. The second data warehouse is â€Å"a teradata warehouse with two (2) petabytes of user data, which is fed by tens of thousands of production databases† (Miller, Monash, 2009, Para.6). Its speed is 140 GB/sec of I/O, or 2 GB/node/sec. By aiming at resource partitions, eBay metrics rely on the workload management software to deliver on numerous Service –Level Agreements (SLA) simultaneously. Conclusion This paper has addressed the topic of data warehousing exhaustively. It has touched on the system’s definitions, characteristics, advantages and disadvantages, contrasts with OLTP and even hardware considerations. Finally, it has concluded by looking into eBay’s data w arehousing, which is the idyllic system that most organizations throughout the globe envy and would be wise to learn from. References Barwick, H. (2012). Security, Business Iintelligence critical for Australian CIOs in 2013:  Telstyle. Retrieved from http://computerworld.co.nz/news.nsf/security/security-bi-critical-for-australian-cios-in-2013-telsyte Bertman, J. (2005). Dispelling Myths and Creating Legends: Database Intelligence  Ã‚  Groups. Retrieved from scribd.com/doc/35922990/Dispelling-Myths Dignan, L. (2010). eBays Teradata implementation headed to 20 petabytes. Retrieved from zdnet.com/blog/btl/ebays-teradata-implementation-headed-to-20-petabytes/40082 Eliott, T. (2012). Rethinking Business Intelligence: 3 Big New Old Ideas. Retrieved from http://smartdatacollective.com/timoelliott/86496/rethinking-bi-3-big-new-old-ideas Hackney, D. (2007). Picking a Data Mart Tool. Retrieved from egltd.com/dmrarchive/1997-10.pdf Imhoff, C., Galemmo, N., Geiger, J. (2003). Mastering Dat a Warehouse Design :  Relational and Dimensional Technique. Indianapolis, IN: Oxford University Press. Imhoff, C., White, C. (2011). Self-Service Business Intelligence Empowering Users to  Generate Insights. Retrieved from sas.com/resources/asset/TDWI_BestPractices.pdf Inmon, W. (2005). Building the Data Warehouse. Indianapolis, IN: Wiley. Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P. (2003). Fundamentals of Data  Warehousing (2nd edn.). New York, NY: Springer. Kimball, R., Reeves, L., Ross, M., Thornthwaite, W. (1998). Data Warehouse  Lifecycle Toolkit: Expert methods for Designing, Developing, and Deploying Data  Warehouses. Indianapolis, IN: Wiley. Kimball, R., Ross, M. (2002). The Data Warehouse Toolkit: The Complete Guide to  Dimensional Modeling (2nd edn.). Indianapolis, IN: Wiley. Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., Becker, B. (2008). Data  Warehouse Toolkit: Practical Techniques for Building Data warehouse and Business Intelligen ce Systems (2nd edn.). Indianapolis, IN: Wiley. Miller, R., Monash, C. (2009). eBay’s two enormous data warehouses. Retrieved from dbms2.com/2009/04/30/ebays-two-enormous-data-warehouses/. Prine, G. (1998). Coherent Data Warehouse Initiative. London, UK: Unisys Presentations. Reddy, S., Rao, M., Srinivasu, R., Rikkula, S. (2010). Data Warehousing, Data Mining, OLAP and OLTP Technologies are Essential Elements to Support Decision-Making Process in Industries. International Journal of Computer Science and Engineering, 2(9), 2865-73. South, G. (2012). Small business: Savings lead to a Stellar business. New Zealand  Herald , 67.

Nutrition Research Paper Example | Topics and Well Written Essays - 1000 words - 1

Nutrition - Research Paper Example It mainly consists of proteins, fats, water, carbohydrates as well as macro minerals (Instah, 2010). This topic will cover the importance of proper nutrition for children. Nutrition is vital for child’s development. It can also ward off many diseases and health related problems like obesity, weak bones, and diabetes. It also helps in development of children’s brain which is important for them as they are constantly learning new things during this period. The five nutrients mentioned below are most crucial for child health and body: Fiber: It is needed for healthy growth and proper nutrition. Fiber helps in dealing with the problem of constipation. Foods like legumes, vegetables and wheat are rich in fiber (Harvard School of Public Health, 2010). Antioxidant nutrients: It includes vitamin C, E, mineral selenium and beta-carotene. It helps in improving the child’s immune system. These nutrients are found in foods like tomatoes, cherries, carrots and spinach (Swanson, 1999). Calcium: It is one of the richest minerals found in body and is responsible for the growth of bones. It was found that children between four to eight years need 800 milligrams of calcium per day (Palo Alto Medical Foundation, 2010). Protein: It is another important nutrient for child body. It is present in every tissue of the body. Four to eight year old children need 19 milligrams of protein daily to meet the requirements of protein. It is also important during infancy (Lifeclinic, 2010). Iron: As iron helps in development of brain and its function, it is considered as one of the main nutrients in child’s diet. Deficiency of iron can lead to anemia which results in severe weakness. Foods like meat, seafood, chicken and spinach are rich in iron (Chillemi, 2005). Poor nutrition can result in health problems ranging from small health issues to fatal diseases. The main

What are the opinions of teachers and parents of the barriers to Literature review

What are the opinions of teachers and parents of the barriers to participation in physical activity within the early years (3-7 years) - Literature review Example Craigg and Cameron (2006) assert that kids who have a healthy physical lifestyle in their early years tend to carry the same routines for their entire life. Research shows that some diseases that show up in adulthood can indeed be linked to unhealthy lifestyle in the earlier years. Diseases such as diabetes and obesity have been associated with lack of physical activity in the earlier years of growth. The Institute of medicine (2004) estimates that there are more than 9million overweight children, 4.5million of whom are obese. Obesity in children is majorly associated with poor eating habits and lack of physical activity. Parents play a great role in nurturing physical activities in a child’s life since children spend more time at home during this face of their life. It is important for parents to recognize the need for their kids to participate in physical activities and they should strive to nurture this in them. Play activities should therefore be incorporated to kids in their early years; parents need to constantly talk on the need and importance of physical health to their kids. Research by the American Alliance for Health (2002) suggests that kids should be exposed to at least 60 minutes each of both structured and unstructured play activities each day. Structured activities mainly involve the planned activities involving instructions with clear guidelines while unstructured activities are spontaneous, arising when the child is exploring their surroundings. With the ever increasing competitive world, and the economic hardships that have emerged; many parents have been forced work outside their homes, hence preferring to enroll their kids to day-cares. These centers pose great avenues for the implementation of physical health and activities (Burdettee &Whitaker 2005b ).Teachers in day-care are in unique positions to encourage healthy physical life among the kids since they tend to spend most of

The research strategy and the limitations

The research strategy and the limitations 3.1 Introduction Methodology can generally be described as the analysis of, and rationale for, the particular method or methods used in a given study, and in that type of study in general (Jankowicz, 2005). This chapter is initially concerned with identifying the best research philosophy to be adopted, as this contains important assumptions about the way in which the world is viewed by the researcher, and it is these assumptions that underpin the chosen research strategy and its associated methods (Saunders et al 2009). This chapter therefore starts with an explanation of the philosophical approach chosen by the researcher; then the chapter details the research strategy, and this section also explains the limitations of using such methods. Further this section will explains the research design, method data collections, sampling methods and limitations. 3.2 Research Philosophy, axiology and approach 3.2.1 Research Philosophy The Data collection techniques most often used approach basis for this research project is a mix of critical realism and interpretivism. As Johnson and Clark (2006) note, as business and management researchers we need to be aware if the philosophical commitments we make through our choice of research strategy since this has significant impact not only on what we do but we understand what it is we are investigating. The Research Onion Source: Mark Saunders, Philip Lewis and Adrian Thornhill 2008 In selecting the research philosophy and the methodology it is appropriate to access the researchers relationship with the research topic (Fisher, 2004) and attitude towards knowledge and reality. Saunders et al (2009) suggests experiment or survey as appropriate research strategies for research testing hypotheses founded on existing theory. In the above figure the realism and Interpretivism with epistemological approach will suits the research topic. To interpret different account holders in concepts the interpretive approach will helps here and the realism will helps to find the connection between the different variables. 3.2.2 Research Axiology According to (Saunders et al, 2009) research axiology is a branch of philosophy that studies judgements about values. The role that owns values play in all stages of the research process is of great importance if the research results want to be credible (Heron 1996). Research is based on world views, cultural experience and upbringing in realism (Saunders et al, 2009). This is important and relevant to the research topic as it is undertaking in the organization which I worked before doing masters program. This topic has been personally motivated me to go through research question and have firsthand experience in knowing the difficulties facing by human resource in controlling attrition rate in BPO companies. 3.2.3 Research Approach The research approach is aim at the high level management and middle level management employees of BPO companies and it can be done with both deductive and Inductive approach for analyzing the research data. Creswell (2002) suggests a number of particular criteria and perhaps the most important of the research approach are the emphasis of the research and nature of the research topic. According to Robson (2002) lists five sequential stages in deductive approach to progress positive results in quantitative analysis Deducting a hypothesis (a testable proposition about the relationship between two or more concepts or variables) from the theory; (Saunder 2009) Expressing the hypothesis in operational terms (that is, indicating exactly how the concepts or variables are to be measured), which propose a relationship between two specific concepts or variables; (Saunder 2009) Testing this operational hypothesis (this will involve one or more of the strategies) (Saunder 2009) Examining the specific outcome of the inquiry (it will either tend to confirm the theory or indicate the need for its modification); (Saunder 2009) If necessary. Modifying the theory in the light of the feelings. (Saunder 2009) Deductive approach will suits the research topic in conducting the survey questionnaire (Gill and Johnson 2002) to use structured methodology to facilitate replication and this approach is quicker to complete the data collection and it can predict the time schedules accurately and can be done in one take. Inductive approach will help this research to conduct semi-structured interviews with exit managers and supervisors in BPO companies through Skype online video conversation and understand the situation very quickly and instantly. Traditional research can be done with various methods of collecting the data in order to establish different views of phenomena (Easterby-Smith et al. 2008). Easterby-Smith et al. (2008) argues that knowledge of the different research traditions enables to adapt research design and these may be practical, involving, say, limited access to data, or due to lack of subject knowledge. Research topic will be very practical in conducting survey questionnaire with BPO employees and semi-conducted interviews with managers as Hakim (2000) uses an architectural metaphor to illustrate the choice of approach. She introduce the notion of the researchers preferred style, which rather like the architects, may reflect à ¢Ã¢â€š ¬Ã‚ ¦.the architects own preferences and ideasà ¢Ã¢â€š ¬Ã‚ ¦ and the stylistic preferences of those who pay for the work and have to live with the final result. 3.3 Research Strategy The strategy used for this dissertation is a Survey study. (Saunders et al, 2009) the Survey is usually associated with the deductive approach and it is very popular strategy in research and frequently used to answer who, what, where, how much and how many questions. Surveys are popular as they allow the collection of a large amount of data from a sizeable population in a highly economical way (Saunders 2009). Often obtained by using a questionnaire administered to a sample and standardized data to compare easy with the results given by BPO employees. According to Saunders et al, 2009 the survey strategy allows to collect quantitative data which can analyze quantitatively using descriptive and inferential statistics and using the survey strategy give more control over the research process and when the sampling is done with BPO employees and exit managers. 3.4 Research Design India BPO industry is still just in its developing edge except in manufacturing sector, which has already become a leader in Global offshore outsourcing lied to the low cost labor and semi-skilled labor pool. According to the India outsourcing associations report, it is believed that most of BPO projects are contracted by a small number of state- owned companies which accounts for around 70% of all revenue. Therefore, in this study, we only take those service providers with offshore experience into consideration. This research sources mainly based on a pre-structured questionnaire which derived from researcher Terdiman and Berg (2001)s Country Selection Model to collect primary data. Besides it, a limited number of online video interviews were also carried out for better understanding the survey by questionnaire. In order to minimize the bias of information given by responds, the secondary data came from available journals in the field as well as reports from field associations in both countries as the objective of this paper is to analyze the perception of the industry service providers rather than MNCs, BPO practices related to human resource management. The information and data obtained from the questionnaire survey was analyzed using the Country selection model from researcher Terdiman and Berg (2001). It served as basis model to analyzed information from questionnaire survey. The model was chosen because it critically covers the important reasons why human resource facing the problems in controlling attrition rate in BPO companies. Basically, it will test out the attractive factors relatively in BPO companies. The outcome of interview analyzed in line with theoretical models so bias can be minimized. Process of outsourcing model (source: Brown and Wilson, 2005) applied for interview outc ome analysis to identify which part still need to improve in order to have better performance. One approach which begins deductively makes use of data categories and codes that derive from the theoretical frameworks to analyze the collected data (Saunders et al, 2009). The implications of different data give a essential insight and guide for new human resource strategies in controlling the attrition rate and retain best employees. 3.5 Reliability Threats to reliability Reliability refers to the extent to which our data collection techniques or analysis procedures with yield consistent findings. (Easterby-Smith et al. 2008:109). Questionnaire used in this research will aims to find the new strategies in human resource management at BPO companies and the survey data will be more reliable and very confidentially. According to Robson (2002) there is a threat in doing semi structured interviews as we may get different results with different employees and the company will not allow revealing every information regarding HR policies to external media and peoples. 3.6 Research procedures Data collection consisted of a well-structured questionnaire considered as primary data in both BPO companies which I using for this research (Cognizant Technology Solutions Private Limited and Bank of America Continuum Solutions Private Limited) and the research will be done through online application Survey console. The sample needs to be large enough to be statistically significant and broad enough to avoid any limitations of common experiences. This is best satisfied using automated techniques, which also address challenges presented by time zone and availability of busy professionals. As the professionals concerned live out of suitcases but must be effective when travelling away from base locations, it is assumed a web-based survey is appropriate. The survey was built online using a wizard style interface to select question styles and validate responses. Survey Console simplifies building the survey with the inclusion of some common question sets, such as demographics, which can be tailored to the needs of the individual survey (e.g. question 1). Building took place during late July and early August 2010. Testing was conducted initially personally and subsequently by colleagues experienced in creating surveys. Survey Console provides a testing mechanism allowing a preview of the survey to be accessed and completed online, without storing the results. The testing includes the e-mail notification function. Colleagues were able to realistically confirm the time taken to complete the survey is within the intended 10 to 12 minute limit self-imposed during the design. Professional license will be used in this survey for reliable report with all survey results in structured format with graphs, standard deviation and with unlimited questions. And secondary data for this study was drawn from wide resources according to Yin (2003)s suggestion that information obtained from interviewees are often subjective, therefore, secondary data is necessary to be gained from companys published report and others. As author fortunately have some contacts with people who working in this field. It made it possible to have chance for face to face interview with some of relevant persons. Therefore, The research data for this study will be also draw based on qualitative data that can generally be defined as non-numerical data or data that have not been quantified (Saunders et al, 2009). The character of this data is mostly primary data which is defined as material that you have gathered yourself (Jankowicz, 2005). It is always useful to gain information face to face to add bonus to primary data that conducted based on well- structured questionnaire. Requesting gaining access email from BPO company Cognizant Technology Solutions Private Limited. Request email sent to Manager BPO operations in Cognizant Technology Solutions Private Limited. Approval email received from BPO Company for conducting survey Email received from Mr. Hari Krishna (Operations manager) to conduct survey through online tool with their employees. Online survey invitation to BPO employees Survey console can generate automatically an invitation emails to all the BPO employees by sending a link in the email to do the survey and the average time track can be done through online survey. Sampling methods Qualitative enquiry is very often about depth, nuance and complexity, and understanding how these work. Therefore, the act of concussing through sampling is likely to be as strategic as it is practical (Mason.J, 2002). In order to obtain relevant information for the study, 2 companies with offshore BPO have only been carefully selected for a questionnaire survey even though it was random selected based. Besides that, semi-structured interviews will be arranged to obtain an insight HRM in BPO industry. The overall response rate for the questionnaire was 95% through survey console and 5% in semi-structured interviews. 3.7 Limitations to the research The important limitations need to be highlighted in this research. Firstly the research will be taken in online web application Survey console there is a chance to do twice or more times the survey by the same person and Secondly there will be Internet constraint may be some people cant do the survey due to company lock on the private websites in the company and no internet access at their homes. The sample size apparently will be third limitation to the project with a small number of sample selected discredit the accuracy, reliability of the information obtained. 3.6 Ethinical considerations According to (Robson 2002; Sekaran 2003) the key stage of this research is to gain the access from the organizations to conduct the survey. In this research the data which is collected by all BPO employees will be stored with very confidential and do not reveal at any cost to the external clients. And the approach for gaining access and collecting data is very professional manner through online application. 3.7 Summary The philosophical approach to the research is given and the appropriate research methods to reflect this approach have been explained and justified. Equally the rationale for the rejection of inappropriate methods has also been explained. The research design including the procedures, strategy and reliability of the research are also described with ethical considerations when conducting the research survey. Therefore this chapter will laid the options for collecting the data and analyze the research methods in conducting survey, findings and analysis can be described in chapter 4.

Should Doctors Be Tried in Consumer Courts

The Indian Supreme Court has ruled that doctors can be sued for medical negligence in consumer courts set up under India's consumer protection act of 1986. The landmark judgment, delivered last week, caps the nine year old controversy over whether doctors providing medical services to patients on payment of fees can be held liable under this act. Responding to appeals by doctors against earlier judgments by state high courts, the Supreme Court ruled that patients aggrieved by deficiencies in medical services rendered for payment can claim damages Should doctors be tried in consumer courts? This issue requires much debate, a lot of questioning, also a lot of introspection. But before we do that, let me ask, why do we limit ourselves to consumer courts Why are we afraid to move to criminal courts if the quantum of crime is culpable enough to be tried only in criminal courts When I say this, I wish to emphasize on the point that doctors are after all human beings. They are bound to get carried away by pleasure of life, greed for money and desire for a lavish lifestyle. This may encourage the devils inside them, they may switch over to illegal activities, may mint quick money and fulfill their hiddendesires. Somebody, at this instance, may like to raise a point that doctors are human beings also when they are performing complex surgeries. They are bound to make small mistakes, forget a glove back in the stomach of a patient, inadvertently give a wrong injection, prescribe unknowingly a useless drug. Agreed!!! I don’t suggest harsh punishment for such situations. But I wish that we do not neglect this issue in totality and that too for two reasons. One, because if we allow a doctor to got scott free after leaving a surgical instrument into a patient’s abdomen, we are encouraging his carelessness. There is one in every chance that this mistake may repeat itself and the doctor will never repent for it. Another reason I discourage this practice is- A black sheep in the herd of dedicated doctors, may, under the veil of â€Å"Non purposeful mistake†; Do illegal activities. He, for his unethical gains, perform acts as – prescribe a banned drug to his patient and when he get access to it, supply it potential buyers abroad. Not only this, on pretext of a small operation, he may remove vital organs from the body of a patient while the patient remains in dark. Unlawful activities, but with a lesser criminal intention, could be purposefully keeping a patient in ICU although there is no need of it!!! This point reminds me of a case, where in a patient’s family proved themselves smart enough for the doctors. Let me give a first hand description of this, although in brief. A patient on death bed, with serious kidney malfunction was admitted in a government hospital. Observing the serious condition of the patient, he was instantaneously recommended to a better equipped private hospital. Unfortunately, he breathed his last on the way. Promptly the private hospital issued a death certificate. Unsatisfied with the callous attitude of doctors from government hospitals he was again admitted in the government hospital. The hospital declared him dead after two days, issued a huge bill along with a death certificate. Armed with two death certificates issued on two different dates, the family published the horror story in media channels. This story is another reason why I did not blame only private hospitals and private doctors of illegal bunglings, because that would have shielded all government employed doctors. I blame this situation on the society as a whole and government policies in particular. Society because every 1 in 5 families of a patient is willing enough to have an illegal organ transplant or blood transfusion from an illegal source; if it guarantees the welfare of the patient. This encourages doctors to switch to illegal means for any need, small or big. And the government policies are equally responsible because the scenario since independence, by and large remains the same. There has been no increase in the numbers of opportunities available to this class of people whom we call doctors. Instead we expect them to serve the masses, with minimal(or sometimes even nil) infrastructure, no incentives and when compared to their better offs in IT and MANAGEMENT sectors, only a handful are able to give competition. But before blaming anyone else, Is it not the moral and ethical responsibility of a doctor to serve selflessly his fellow beings? Is this not the first thing taught in medical colleges?!! May be I am keeping my foot on one more debatable topic.!!!! ? Now let me stir the pot a bit more. People die, as sure as they are born, they will die. Its a fact of life and one had better learn to accept it. People do not die ONLY because of doctors mistakes. If my memory does not fail me, the lady, whose death caused the JJ Hospital incident, was 75 years old. No details about what she was suffering from, on how long she was ailing, or the condition she was brought to the hospital in. Despite lacking these vital dfetails, you are ready to accept that the death was due to negligence. That is being unfair. Years ago, when I was still a houseman, we had a patient brought in with a myocardial infarction. The man had suffered a couple of infarctions over the years and his family was well aware that the next one could be fatal. Despite our best efforts he died. The family accepted this and were taking the body away, when the youngest son arrived. This guy had been out drinking, and made such a tamasha, threatening to kill the doctors, screaming abuse etc. Eventually the security had to physically throw him out of teh hospital. Often these dramatic and explosive incidents take place to â€Å"demonstrate† grief, however phony it may be. I have witnessed many such incidents, and feel that even if there was negligence, the Consumer Protection Courts are available to redress any complaint. Beating up pepole is not the solution to the problem. Hi rocrab – long time no see , no hear from you on forums†¦Ã¢â‚¬ ¦ OK so you are a Doctor right So lets first establish the fact that you are biased- right or wrong Ok second point – the JJ hospital fiasco was uncalled for thats for sure. And it also highlighted the plight of 100's of overworked doctors and their meagre stipends. But the fact remains is that you do something wrong – you get punished. I do not think Consumer courts should try Doctors. As in lawyers – they should be debarred from practising medicine if found guilty. Anyways the point I want to bring out strongly is that :-1. People do not get in on merit – they pay huge donations – eg – Manipal college. – Totally shameless. 2. You pay to get in – you can also pay to pass. 3. If you do not know what you are doing – naturally you are literally ‘playing God'. So what are we left with – QUACKS by the dozen. That is why I think if some fear is instilled such foul play will lessen. Am starting another new topic on another issue that is close to my heart after the Jessica Lal forum. The recent strike by resident doctors in Mumbai's JJ Hospital because a relative of a victim who died – slapped a doctor caused headlines for several days. My take on this matter is that the Public slaps, verbal and physical abuse,vandalising of hospitals etc is due to the fact that there is no other way to bring negligent Doctors to book in this country. That is why there are more and more instances of public outrage. What is more dangerous is the fact that these doctors who have caused such deaths – CONTINUE to treat patients and put them at risk with their ‘little' knowledge. And last night NDTV debated about this because a one Dr Saha is suing 4 Kolkotta doctors for Rs 77. 7 crore for negligence for his wife's death. So my question is Should such Doctors be tried for negligence and be punished in a court of law or not? If so what sort of court should try them – like the military have a Martial law ! How long can we allow these so called ‘quacks' to get away with murder 18 Mar 2006 07:03 pm 14 Rumhona, Totally agree with what you have said about money taking precedence over merit. Medical colleges operating out of rented premises, lacking basic infrastructure. â€Å"Deemed† universities, sprouting like mushrooms after the rains, with no one to check or monitor the quality of teh education they provide or the â€Å"doctors† they produce. What is practiced by such â€Å"doctors† is criminal, no question of negligence. Unfortunately we have two sets of laws in India, one for the rich and one for the poor, this has been amply demonstrated in the recent past. Despite all this each case has to be judged on its merits. Negligence as defined by law, â€Å"is not doing that which a reasonable doctor would do†. Now what a reasonable doctor would do, is there in black and white, in teh text books. But no text book can describe ALL the medical conditions and the complicating factors. Medicine is also subjective, so things can get tricky. Every view put forward by an expert will be refuted by another equally qualified expert. (An expert being one who knows more and more about less and less, till eventually he knows every thing about nothing. )Finally, the quality of medicine practiced in India is pretty miserable. My wife who is diabetic and hypertensive, was nearly killed by a consultant, who could not see beyond the infection he was treating. Eventually, I had to bring her down to my hospital where she began recovering in 24 hours.

Bmw Research Paper Essay

BMW is enhancing the travel experience for drivers and passengers while also launching a series of new platforms The BMW Group – one of Germany’s largest industrial companies – is also one of the most successful car and motorcycle manufacturers in the world and 2011 was its best year to date. With almost 1. 7 million vehicles sold, the BMW Group is the world’s leading premium manufacturer in terms of sales volume. Its three automobile brands, BMW, MINI and Rolls-Royce, and the BMW and Husqvarna motorcycles brands led to record sales of â‚ ¬68. 8bn. During 2011, the company introduced five new BMW models across the 1,3,5 and 6 series as well as the Mini Coupe and the Rolls-Royce 102EX, the first electric vehicle in the ultra-luxury segment. In addition, the company also launched a new sub brand – BMW i – that includes the i3 all-electric and i8 plug-in hybrid concept cars due for launch in 2013. However, as well as selling more products than ever and expanding production capacity, especially for the all-important China market, the company also kicked off a number of strategic partnerships for the future. These included the start of the BMW Peugeot Citroen Electrification joint venture, the acquisition of a strategic investment in SGL Carbon SE and the cooperation with Toyota Motor Corporation in basic research for battery cell technology. Although a significant manufacturer, BMW is not part of a larger company like its main competitor Audi. As BMW invests heavily in innovation to continue to produce the ultimate driving experience, keeping its power options open is key, so as the shift towards electric continues to gather speed these development partnerships are vital elements in the company’s growth strategy.