Managers today are inundated by data. Often, the correct interpretation of these data can mean the difference between being on the cutting edge of a market and being left behind by the competition. To solve the complex business problems faced by organizations in the 21st century, managers are increasingly turning to management science — the application of statistical techniques, scientific method, and mathematical modeling — to analyze and solve the problems of the organization. Management science can help managers solve a wide range of problems in the workplace and to make better decisions concerning the operation and strategy of the business. Management science offers managers many tools to help analyze and interpret the data with which they are bombarded every day. Some of these tools include data mining, decision support systems, and forecasting techniques.
Keywords Customer Relationship Management; Data Mining; Decision Support System (DSS); Forecasting; Model; Probability; Scientific Method; Time Series Data
Management is the process of efficiently and effectively accomplishing work through the coordination and supervision of others. Effective management is much more than the effective application of such soft skills as management style, communication ability, and administrative prowess, however. Management also comprises the decisions that managers make to help gain or maintain a market share or to increase profitability within the marketplace. Although management decisions can be made subjectively based on a given manager's experience, the amounts of data that must be processed in order to do this well continue to increase with the proliferation of information technology and systems. As a result, managers are increasingly turning to management science — the application of statistical methods, scientific method, and mathematical modeling — to analyze and solve the problems of the organization.
21st Century Applications of Management Science
Management science has many practical applications in 21st century businesses. The use of probability theory, statistical techniques, and research methodology can help managers solve a wide range of problems in the workplace including optimizing cost/benefit tradeoffs in product design, improving the quality of services or products, forecasting the needs and trends of the marketplace, and improving the effectiveness of supply chain management. By applying mathematical statistical techniques to the real problems of the business world, managers can make better decisions concerning the operation and strategy of the business.
Management science allows organizations to apply scientific method in order to solve many problems in the business world. For example, descriptive statistics are useful in summarizing and describing data. In addition, management science also includes the application of business statistics to real world problems. This discipline is an applied form of mathematics and is a valuable tool for helping analyze and interpret data. In particular, the use of inferential statistics, a collection of techniques that allow one to make inferences about the data, including the ability drawing conclusions about a population from a sample.
Another tool of management science is building models, or the development of abstract representations of situations, systems, or subsystems. Model development typically occurs in two phases. First, a conceptual model is developed to describe the situation or system. Initial conceptual models tend to be broad or general representations without much detail but which span the range of variables to be considered. Models typically must be tested and refined until they represent the real world to the degree desired by the analyst or decision maker. Models are iteratively refined using observations of the real world so that they better reflect the underlying reality of the situaiton. When the conceptual model adequately reflects the major parameters of the real world, data can be gathered and quantitative techniques used to turn the conceptual model into a mathematical model.
In the 21st century, human beings are constantly bombarded with data that need to be sorted, analyzed, and interpreted. In the business world, the correct interpretation of data can mean the difference between being on the cutting edge of a market or being left behind by the competition. Management science offers managers many tools to help analyze and interpret the data with which they are bombarded every day. Data mining allows managers to examine large amounts of data for previously unseen patterns and relationships in order to help forecast the future of an industry or the needs of a market. Decision support systems help managers make reasoned decisions about the semi-structured and unstructured problems with which they are faced every day. Forecasting uses statistical tools to recognize trends and patterns in the data and provides managers with the empirical data necessary to help shape the growth and focus of the organization.
Data mining is the process of analyzing large collections of data to establish patterns and determine previously unknown relationships between the data. The results of data mining efforts are used to predict future behavior. Data mining can be used to help the business better understand the needs and desires of current and potential customers and to identify and acquire high-value customers. The information produced by data mining efforts can also be used to optimize store layout for increased sales by shelving items that are frequently purchased together in close proximity. In addition, data mining can be used to increase the profitability of the store or chain, increase return on investment, decrease the costs of advertising and promotions, monitor performance of the store or business, and detect fraud, waste, and abuse. By detecting previously unrecognized patterns in data, data mining can help managers to make better decisions regarding business problems, develop novel approaches to meeting current organizational objectives, or create a more profitable strategic plan for the future of the organization.
Although data mining is used to examine the relationships within a wide variety of data across many disciplines, one of the primary uses of data mining in business is to find unknown relationships within sets of customer data. Among other things, data mining is frequently used in business for customer relationship management. This is the process of identifying prospective customers, acquiring data concerning these prospective and current customers, building relationships with customers, and influencing their perceptions of the organization and its products or services. For example, a data mining effort might examine the types of products purchased on line by owners of home computers. One source of data that is increasingly mined for information comes from retail scanners. The customer cards issued by various retail stores (e.g., supermarkets, pet supply stores, drug stores) that many people carry in their wallets that give them discounts or points toward rewards also can be used to collect information that is used in data mining efforts. For example, a grocery store might use data mining software to analyze the purchases made by consumers over the course of a week to determine what items are purchased together. For example, if the store finds that the purchases of fresh sweet corn fluctuate with the purchase of ribs during the summer, they might place pre-shucked corn next to the ribs in the refrigerated meat case. This knowledge would also help the store determine whether or not to purchase more corn for the weeks when ribs were on sale. Alternatively, data mining could be used by the store or chain to push other products. Grocery stores often issue customers coupons at the checkout counter or send them to customer homes based on what purchases they have made. For example, previous purchases of Brand X paper towels might result in the generation of a discount coupon for a future purchase of Brand X paper towels or for a future purchase of the store's brand of paper towels.
Data mining applications...
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