Request PDF on ResearchGate | On Feb 5, , Dursun Delen and others published Decision Support and Business Intelligence Systems (9th Edition). Request PDF on ResearchGate | Decision Support and Business Intelligence Systems / E. Turban [et al.]. | Contenido: Parte I Soporte de decisiones e. intelligence (BI), data mining, and the decision support systems (DSS) discussed in this chapter are used to min- imize uncertainty (the reverse of intelligence).
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Chapter 12 Business Intelligence and n Turban and Volonino Decision Support Systems t io ua Information Technology for Management al Improving. Intelligence Systems?9th Edition – Efraim Turban, Ramesh Sharda Course: ISQA / Decision Support Systems Syllabus Decision Support and Business Intelligence Systems (9th Edition) , by Turban, E. Sharda, R., and Delen, D., Pearson, ISBN Download as PDF, TXT or read online from Scribd. Flag for inappropriate Decision Support Systems and Business Intelligence, 2 Opening Vignette.
How have the capabilities of computing evolved over time? This overview can prepare a srudent to begin thinking about a term project in either area should the instructor require it right from the beginning of. Discussion of the increased capabilities of databases and the significant growth of user interfaces and models. Finally, we present acrurnte and updated m: These changes have been retained. We provide answers to these questions in Chapters 3 and 4. Download pdf.
Artificial Intelligence and Expert Systems, Applications of Expert Systems, Application Case Sample Applications of ES, Structure of Expert Systems, Application Case Knowledge Engineering, Technology Insights Difficulties in Knowledge Acquisition, Development of Expert Systems, Expert Systems on the Web, Application Case Banner with Brains: Advanced Intelligent Systems, Machine-Learning Techniques, Case-Based Reasoning, Application Case Genetic Algorithm Software, Fuzzy Logic and Fuzzy Inference Systems, Support Vector Machines, Intelligent Agents, Technology Insights Intelligent Agents, Objects, and ES, International Stock Selection, Application Case Management Support Systems: Emerging Trends and Impacts, Reality Mining, Virtual Worlds, Technology Insights Second Life as a Decision Support Tool, The Web 2.
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Won In any form or by any One Lake SUI. Many oC the designations by manufacr.. Where those doeI;tgnattom appe DecIsion support.
DedMon support and buslOOMlln! ISBN I. DcdsIonsupport systems.
Expcn ' yslems Ganputer. Delen, Du"un. T87 6S8. Dedicated to our spouses and children with love -The Authors User name: He is also the author of 20 books, including Efectrollic Commerce: He is also a consultant to major corporntions worldwide. Turban's current areas of interest are Web-based decision support systems, social commerce and collaborative decision making.
Ramcsh Sharda M. His current research inter- ests are in decision support systems, collaborative applications, and technologies for managing infonnation overload. Sharda is also a cofounder of iTradeFair. Durs un Ddell Ph. His research has appeared in major journals.
With Professor David Olson he recently published a book on advanced data mining tech- niques. Research and Applications and serves on the editorial boards of the Jourllal oj His research and teaching interests are in deci- sion support systems, data and text mining, knowledge management, business intelli- gence.
User name: Companies such as IBM, Ornde, Microsoft, and others are creating new organizational units focused on analytics that help businesses become more effective and efficient in their operations. As more and more decision makers become computer and Web literate, they are using more computerized tools to support their work.
The field of decision support systems DSS Jbusiness intelligence BI is evolving from its begirmings as primarily a personal-support and is quickly becoming a shared commodity across organizations.
The purpose of this book is to intnxiuce the reader to these technologies, which we call, collectively. The core technology is BI. We use these tenns interchangeably.
Most of the specific improvements made in this ninth edition concentrate on three areas: BI, data mining, and autom. Despite the many changes. We have also reduced the book's size by eliminating generic llmterial and by nuving llmterial to the Web site.
Finally, we present acrurnte and updated m: We first describe the changes in the ninth editiOil and rerum to expanding on the objectives and covernge latE'!
With the goal of improving the text, this ninth editiOil makes severnl enhancements to the major changes in the eighth editiOil. These changes have been retained. The new edition has malty timely additions, and dated content has been deleted. The following major specific changes have been m. The following chaptE'!
ClNlpter 7, '7e: The chapter provides a wide variety of Application Cases to make the subject inter- esting and appealing to the intended audience. We have made the book shorter by keeping the most conunonly used content. We also reduced the prefonnaned online content so that the book does not appear too dependent on this content. We reduced the number of references in each chapter.
Specifically, we streamlined the introductorr coverage of business intelligence and u"lta mining by deleting Chapter S and instead putting some of that content in Chapter 1. With this change, the reader can get an overview of the overall content through Chapter lboth decision suppon and BI technologies.
This overview can prepare a srudent to begin thinking about a term project in either area should the instructor require it right from the beginning of User name: The details of DSS are examined in Chapters 2 through 4. We deleted the chapters that were available as online chap- ters with the last edition and incorporated some of tbat coment into this edition. This edition includes one new author and an expanded role for an author from the last edition.
Building upon the excellent content that has been prepared by the authors of the previous edition Turban, Aronson, Liang, and Sharda , this edition was revised printarily by Ramesh Sharda and Dursun Delen. Both Ramesh and Dursun have worked extensively in DSS and data mining and have industry as well as research experience. Although the figures in the print edition have been retained from previous editions and new figures added for the new content. Adopters of the textbook will have access to a Web site that will include links to news stories, software, tutorials, and even YouTube videos related to topics covered in the book.
Almost all of the chapters have new opening vignenes and closing cases that are based on recent stories and events. For example, the closing case at the end of Chapter 2 asks the students to apply Simon's decision- making phases to bener understand the current economic conditions caused by the subprime mortgage mess in the United States.
In addition. New Web site links have been added throughout the book. We also deleted many older product links and references. Internet assigrunents. Other specific changes made to the ninth edition are sUllJITl. The PC-based version of the software is available for free to academics.
The chapter includes a concise introduction to this software and several exercises to help students learn to use the DSS builder software. This section was contributed by Dr. In addition, all the Microsoft Excel- related coverage has been updated to work with Microsoft Excel The presen- miion of me maieriai in ihis chapier foiiows a meihociicai approach ihai corresponds io the standardized process used for data-mining projects.
Specifically, it excludes text and Web mining which are covered in a separate chapter and significantly expands on cbta-mining methods and methodologies.
A new section on the explanation of ANN models via sensitivity analysis has been added to this chapter. For example. Chapter 13 now includes coverage of ne wer techniq ues. Besides streamlining and updating the coverage through a new opening vignette, a new closing case , and discussions throughout, it includes new sections on key performance Indicators KPI , operational metrics, Lean Six Slgm: W'e have retained m.
Most chapters include links to TUN tcradataunivcrsitync twork. We reduced the number of boxes by more than SO percent. Importa nt material was incorporated in the text. Only two types of boxes now exist: Applicatlon Cases and Technology In.
The TUN Web site provides software support at no charge. It also provides links to free data mining and other soft ware. Corporations reg- ularly develop distributed system. Managers can make bener decisions because they h.
Today's deci. The and c: Managers com- municate with computers and the Web by using a variety of handheld Wireless devices, including mobile phones and PDAs. These devices enable managers to access imponant infonnation and useful tools, communicate, and collaborate. Data warehouses and their analytical tools e. Decision suppon for groups continues to improve, with major new developments in groupware for enhancing collaborative work, anytime and anywhere.
Artificial intelligence methods are improving the quality of decision suppan and mve become embedded in many applications, ranging from automated pricing optimization to intelligent Web search engines. Intelligent agents perform routine tasks, freeing up time that decision makers can devote to imponant work. Developments in wireless technologies.
This course is designed to present the decision s upport: Another objective is to provide prac- tieing managers v. In additio n to tr: We also provide links to software rutori- als through a spec ial section of the Web site.
The follOwing instroctor and student s upplements are avai bble on the book's Web site, pcatSollhlghc tt: Ire rated by difficulty level. TestGen is a comprehensive suite of tools for testing and assessment.
TeslGen fe: Both the Tesl Item File and teslgen software are available on the secure bculty section ci pt-"aJ"! PowerPoint slides are available that illuminate and build on key concepts in me text.
Faculty can download the PowerPoint slides from pcarsonhighc n. In addilion, a blog site will include continuous updates 10 each chapter including links 10 new material and related software. It will be available through lhe text web sile. Prefac", xix User name: Dozens of srudenTS participated in class testing of various chap- ters, software, and problems and assisted in collecting material.
It is not possible to name everyone who participated in this project, but our thanks go to all of them. Cenain indi- viduals made significant contributions, and they deserve special recognition. First, we appreciate the effons of those individuals who provided fOnllal reviews of the first through ninth editions school affiliations as of the date of review: Maryland Martin Grossm. University of New! Lou is W. University of Idaho Paul J.
Barbara WlXonl wrote the opening vignelte for Chapter 1 to illustrate the special relationship of this book to Teradata University Connection. Dan Power Dssrcsourccs. Major contributors for the previous editions include Mike Goul Arizona State University , whose contributions were included in Chapter S of the eighth edition, Leila A.
Halawi Bethune-Cookman College who provided material for the chapter on data warehousing, and Christy Cheung Hong Kong Baptist University wbo contributed to the chapter on knowledge management. McCarthy Quinnipiac. Third, the b ook benefited greatly from the efforts of many individuals who contributed advice and interesting material such as problems , gave feedback on material, or helped with class testing.
Merril Warkentin Northe: Fourth, several vendors cooperated by providing development andlor demonstration software: Greenwich, Connecticut , Expen Choice, Inc. Pittsburgh, Pennsylvania , Nancy dark of Exsys, Inc. Connecticut , Salford Sr-;tems New York , Guy Miner of Statsoft. Michael Goul. Utive Director; Barb Wixom. Associate Director. Susan Baxley, Program Director;: Sixth, many individuals helped us with administrative matters and editing, proof- reading.
The project beg: Judy Finally, the Prentlce Hall team is to be commended: Executive Editor Bob Hornn, who orchestrated this project; our editorial project manager Kelly Loftus, who kept us on the timeline; Kitty Jarrett, who copyedited the manuscript; and the production team, Karalyn Holland at Prentice and the staff at GGS nook Service: We would like to thank all these individuals and corpoT'Jtions.
Without their help, the creation of this book would not have been possible. Ramesh and Dursun want to specifically acknowledge the contributions of previous coauthors Jay Aronson and T, p, J. As this book "'elll to pK'SIl. Web slles 0 whieb we "'f SometImes Web sites. MOSI organlZat! If you have a problem ronneaing to a Web site that we mention. We apologize in advance for thls inmnvenience. These technologies have had a profound impact on corporate strategy, periolTT1ance, Rnd competitivene!
In Part I, we provide an overview of the whole book in one chapter. We cover several topics in this chapter. The first topic is managerial decision making and its computerized support; the second is frameworks for decision support. We then introduce business intelligence. We also provide brief coverage of the tools used and their implementation, as well as a preview of the entire book. Organizations, private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate.
Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational Processing these , in the framework of the needed decisions.
This book is about using business intelligence as computerized support for managerial decision making.
It concentrates both on the theoretical and conceptual foundations of decision support , as well as on the conunercial tools and techniques that are available. This chapter has the following sections: Each day. Managers focused on opti- mizing the use of railcars to get the most production out of their fIXed assets. Vice and enter into contracts with customers. On-time delivery became an important factor in the industry. Over time, Norfolk Southern responded 10 these industry changes by becoming a "scheduled railroad.
In this way, managers could predict when they could get a shipment 10 a customer. Norfolk Southern has always used a variety of sophisticated systems to run its business. Becoming a scheduled railroad, however. These new systems wen: TOP was deployn: Norfolk Southern's numerous sys- tems generate millions of n: Unfortunately, the company was not abie to simpiy tap into this data without risking significant impact on the systCfll.
Back in In , the data warehouse became a critical component of TOP. Norfolk Southern built a TOP dashboard application that pulls data from the data wan: The application uses visualization technology so that field managers can mon: The User name: And, ;n the past 5 years, railcar cycle time has decreased by an entire day, which trans- lates into millions of dollars ;n annual savings. Norfolk Southern bas an enterprise data warehouse, which means that once data is placed in the warehouse.
Although train and connection perfonnance data is used for the TOP applic3lion, the company has been able to levemge that dam for all kinds of other purposes. For example, the Marketing Depaltrnenl has developed an application called accessNS, which was built for Norfolk Southern customers who want visibility into Norfolk Southern's eXlensive transponation network. Customers want to know where their shipments are "right now"-and at times they want hiSlorical information: Where did my shipment come from?
How long did it I3ke to arrive? What were the problems along the route? Users can access currem dam. Deparunents across the company- from Engineering and Strategic Planning to Cost and Human Resourcesuse the enterprise data warehouse.
By combining employee demographic data e. Vices offices locations. Today, the Norfolk Southern dam warehouse has grown to a 6-terabyte system that manages an extensive amount of infonnation about the company's vast network of railroads and shipping se! Norfolk Southern uses the data warehouse to analyze trends. The dam warehouse provides information to over 3. Norl'olk Southern was the first railroad to offer self-se! Vice business intelligence.
The company was also one of the first railroads to provide a large variety of historical dam to external customers. How are information systems used at Norfolk Southern to suppon decision making? What type of information is accessible through the visualization applications? What type of information suppon is provided through accessNS? How does Norfolk Southern use the data warehouse for HR applications? Can the same dam warehouse be used for business intelligence and optimization applications?
Getting more out of a company's assets requires more timely and detailed understanding of its operations. We will see many examples of such applications throughout this book.
These include OI: IOurc", Contributed by Professors R1rl: Wixom Unlwrslty dVlrQlnla. Companies are moving aggressively 10 computerized suppon of their operations. To uoderst: This complexity creates opportunities on the one hand and proble ms on the othe! Take globalization as an example. Today, you can eas- ily find suppl iers and customers in many counllics, which tIleans rou can buy cheaper materials and sell more of your products and services: Business environment factors can be d ivided into four major categories: Note that the i me1lsity of IllOIit of these factors incre: In this kind of environment, managers muS respond quickly, innovate.
Jlatioos Market conditions """""""" "" I ClJnty and terroris! Managers may take other actions. Many, if nO! These and other response aaions are frequently facilitated by computeri zed DSS. In order to understand why computerized support is needed and how it is provided, especially for dedsion-making support, let 's look at managerial decision making. Sectio n 1. The resources are considered inputs, and attainment of goals is viewed as the output of the process.
The degree of success of the organization and the manager is often measured by the ratio of outputs to inputs. This ratio is an indi cation of the organization's productivity, which is a reflection of the orgalliztilional alld gerial performance.
The level of productivity or the success of management depends on the perform- ance of managerial functions , such as planning, organizing, directing, and controlling. To perform their functions , managers are engaged in a continuous process of making dedsions. Making a decision means selecting the best alternative from two or more solutions.
The Nature of Managers' Work Mintzberg's 20Cl8 daSliic study of top managers and several replicated studies suggest that managers perfonn 10 major roles that can be classified into three major categories: To perform these roles. This infomlation is delivered by networks, generally via Web technologies.
In addition to otxaining infomlation necessary to better perfonn their roles, nlan- agers use computers directly to support and improve decision making, which is a key task that is part of most of these roles.
Many managerial activities in all roles revolve around decision making. Mallagers, especially those til bigb managerial levels, are prima- rily decisioll makers. We review the dedsion-making process next but will study it in more detail in the next chapter. The Decision-Making Process For years, managers considered decision making purely an art- a talent acquired over a long period through experience Le.
Management was considered an art because a variety of individual styles could be used in approaching and successfully solving the same types of managerial problems. These styles were often based on creativity, judgment.
However, recent research suggests that companies with top managers who are more focused on persistent work almost dullness tend to outperform those with leaders whose main strengths are interpersonal conununication skills Kaplan et aI. It is more important to emp hasize methodical , thoughtful , analytical decision making rather than flashiness and interpersonal communication skills.
CompilO'd from H. Managers usually make decisions by following a four-step process we learn more about these in Chapter 2: Define the problem j. To follow this process, one must make sure that sufficient alternative solutions are being considered. However, the envirorunenml f: Because of mese trends and changes, it is nearly impossible to rely on a trial-and- error approach to ma.
Managers must be more sophisti cated; they must use the new lools and techniques of their fields. Most of those tools and techniques are discussed in mis book. In the following section. Il rok Why h: Describe the four steps managers take in making a decision. Computer applications have moved from transaction processing and moniloring activ- ities to problem analysis and solution applications, and much of the a. Managers must have high-speed, networked infor- mation wireline or wireless to assist them with their most important b sk: A computer enables the decision maker to perform many cornputMion: Ti,,,e]r decision: With a computer, mousands of alternatives can be evaluated in seconds.
Furthel"more, the benefits--to-cost ratio of computers and the speed of executions are constantly increasing. Many decisions are made today by groups whose members may be in different locations. Groups can collaborate and communicate readily by using Web-based tools.
Collaboration is especially important along the supply chain, where panners--aU the way from vendors to custOfl1CfS--fI1USt share information. Computerized s upport can User name: In addition, computerized support can increase the productivity of staff support e. Decision makers can also increase their productivity by using software optimiZ3tion tools that help determine the best way to run a business see Chapter 4. Many decisions involve complex computa- tions. Data for these can be stored in different databases anywhere in the organ- ization and even possibly at Web sites outside the organizat ion.
The data may include text. It may be necessary to transmit data quickly from distant locations. Computers can search. Large data warehouses, like the one operated by Wal -Malt, contain terabytes and even petabytes of data.
Computers can provide extremely great storage capability for an y type of digital information. Special methods, including parallel computing, are available to organize, search, and mine the data.
The costs related to daL"l warehousing are declining. Computers can improve the quality of decisions made. For example, more daw can be accessed, mOTe alternatives can be evaluated, forecasts can be improved, risk analysis can be perfonned quickly, and the views of expens some of whom are in remote locations can be collected quickly and at a reduced cost. Expertise can even be derived directly from a computer system using anificial intelligence methods discussed in Part III and also Chapter With computers, decision makers can perfornl complex simulations, check many possible scenarios, and assess diverse impacts quickly and economically.
Omlpetition today is based not just cu price but also on quality, timeliness, customiZ3tion of pnxiucts, and customer support. In addition, organizations must be able to frequently and rapidly change their mode of operation, reengineer processes and structures, empower employees, and innOv. Decision support tedmologies such as intelligent systems can empower people by allowing them to make good decisions quickly, even if they lack some knowledge.
According to Simon , the human mind has only a limited ability to process and store infor- mation. People sometimes fmd it difficult to recall and use information in an error-free fashion due to their cognitive limits. The tenn cognitive limits indicates that an indi- vidual"s problem-solving capability is linuted when a wide range of diverse infcnnation and knowledge is required.
Since the development of the Internet and Web servers and tools, there have been dramatic changes in how decision ntakers are supported.
Using wireless technology, managers can access infonnation anytime and from anyplace. JHgence improve the collaborntion process of a group and enable its members to be at dif- ferent locations saving travel costs. In addition, computerized suppon can the productivity of staff support e.
Decision makers can also increase their productivity by using software optimization tools that help determine the best way to run a business see Chapter 4. The data may include text, sound, graphics. Computers can provide extremely great storage capability for any type of digital information. Special methods. The C05ts related to data warehousing are declining. For example, more data can be accessed, more alternatives can be evaluated, forecasts can be improved.
Expenise can even be derived directly from a computer system using anificial intelligence methods discussed in Part III and also Chapter With computers. Competition today is based nOl just on price but also on quality. In addition, organizations must be able to frequently and rapidly change their mode of operation, reengineer processes and structures, empower employees, and innovate in order to adapt to their dJanging environnlents.
Dedsion support technologies such as intelligent systems can empower people by allo""lng them to make good decisions quickly. Computerized systems enable people to overconJe their cognitive limits by quickly accessing and processing vast amounts of stored infonna-. U "'PIt. Since the development of the Internet and Web servers and tools. Most important, the Web provides 1 access to a vast body of data, infonnation, and knowledge available around the world; 2 a common, useT"friendly graphical user interfuce GUI that is easy to learn to use and readily available; 3 the ability to effectively collabornte with remote partners; and 4 the availability of intelligent Using wireless technology, managers can access infonnation an 'lime and from anyplace.
Next, we present an early frame- work for decision suppan. Section 1. How have the capabilities of computing evolved over time? So How can a computer help overcome the cognitive limits of humans? Gorry and Scott-Morton created and u sed this frnmework in the early s, and the framework then evolved into a new technology called DSS. Two dimensions are the degree of structuredness and the types of control. Structured processes are routine and typically repetitive problems for which Slandan!
Unstructured processes are fuzzy, complex problems for which there are no cut-and-dried solution methods. Simon also described the dccislon- making process with II throe-phase process of inrcfllgmu: Later, a fourth phase was added: The four phases are defined as follows: This phase involves selecting 3 course of action from among those av:. This phase involves ad: The relationships among the four ph: ISCS are shown in Figure 1.
An uns tnlc nu"l. In a suuc turt--d problc. TIte procedures fo r obtaining the beSi or at least a good enough solution are knovm. Whether the problem involves finding an appropriate invemory level or chClO! Common objectives a re cost minimization and profit maximi zation. Semistructured problc lils rail between structured and unstructured problems, having some structured clenlt"nts and SOUle unstroctured elements.
Keen and Scott-Monon me ntioned trading Ixwlds, setting marketing budgets for consumer products, and performing capilli acquisition lnalysis as semistructured problems. Jtcgorics Problems or Opportunities Implementation Deploy: The initial purpose of this matrix was to suggest different types of computerized suppon to different cells in the matrix.
Human intellect and a different approach to computer technologies are necessary. They proposed the use of a supponive infonna- tion system. Note that the more structured and operational control-oriented tasks such as those in cells I, 2, and 4 are usually performed by lower-level managers, whereas the tasks in cells 6, 8, and 9 are the responsibility of top executives or highly trained specialists.
Computer Support for Structured Decisions Computers have supported structured and some semistructured decisions, especially those that involve operational and managerial control, since the s.
Operational and managerial control decisions are made in all functional areas, especially in finance and production i. Structured problems, which are encountered repeatedly, have a high level of struc- mre. It is therefore possible to abstract. For example, a make-or-buy decision is one category. Other examples of categories are Clpital budgeting, allocation of resources, distribution. For each category of decision, an easy-to-apply prescribed model and solution approach have been developed, generally as quantitative formulas.
This approach is called managemellt science. Therefore, it is possible to use a scientifIC approach to automating portions of managerial decision making. The MS process adds a new step 2 to the process described in Section 1. Defme the problem i.
Construct a model that describes the real-world problem. Identify possible solutions to the modeled problem and evaluate the solutions. Compare, choose, and reconmlend a potential solution to the problem. MS is based on mathematical modeling i.
Modeling involves transforming a real-world problem into an appropriate pro- totype strucrure model. Computerized methodologies can fmd solutions to the standard Cltegory models quickly and efficiently see Chapter 4. Some of these, such as linear progranuning, are deployed directly over the Web. ADS , sometinles also known as decisioll User name: Application Case 1. The company had a year-old pricing and promotion system that was very labor intensive and that could no longer keep up with the pricing decisions required in the fast- paced grocery market.
The system also limited the company's ability to e xecute more sophisticated pricing strategies. Giant was interested in executing its pricing strategy more consistently based cu a defmitive set of pricing rules pricing rules in remiI might include rela- tionships between national brands and private-label brands, relationships between sizes, ending digits such as '"9," etc.
In the past, many of the rules were kepi: Giant Foods worked with DemandTec to deploy a system for its pricing decisions. It can handle large numbers of price changes, and it can do so without increasing staff. The system allows Giant Foods to codify pricing rules with "narural language" sentences rather than having to go through a techni- cian.
The system also has forecasting capabilities. These capabilities allow Giant Foods to predicr the impact of pricing changes and new promotions before they hit the shelves. Giant Foods decided to implement the system for the entire store chain.
The system has allowed Giant Foods to become more agile in its pricing. It is now able to react to competitive pricing changes or vendor cost changes on a weekly basis rather than when resources become available. Giant's productivity has doubled because it no longer has to increase staff for pricing changes.
Giant now focuses on '"maintaining profitability while satisfying its customer and maintaining its price image. ADS initially appeared in the airline industry, where they were calJed revellue I".. Today, many service industries use similar pricing models. In contrast with management science approaches, which provide a model -based solution to generic strucrured problems e. The following are examples of b usiness rules: ADS attempt to automate highly repetitive decisions On order to justify the computerization COSl , based on business rules.
ADS are mostly suitable for frontline e mployees who can see me customer information online and frequently must make quick deci. Computer Support for Unstructured Decisions Unstructured p ro bl ems can be only partially supported by standard computerized quantitative methods.
However, such solutions may benefit from data and information generated from cor- porate or external data sources see Part III and Chapter Intuition and judgment may playa large role in these types of decisi ons. Computer Support for Semistructured Problems Solving semistructured problems may involve a combination of standard solution proce- dures and human judgment, MS can provide n"KXIels for the portion of a decision-making iJiOblem i.
FOi ihe linSi. These capabilities help managers 10 better understand the nature of problems and mus to make better decisions. In Chapter 2. Section l. S Review Questi o ns 1. Provide two examples of each.
Ddine operalKmal control. What are the nine cells of the decision framework? Explain what each is for. How can computers provide support for making structured decisions? How can computers provide support to He defmed dc Decision support systems couple the intellecmal resources of individuals with the capabilities of the computer to improve the quality of decisions.
It is a computer-based support system for management decision makers who deal with semistructured problems. Note that the teml decisioll support system, like mallagement information system MIS and other terms in the field of IT, is a content-free expression i. Therefore, there is no universally accepted definition of oss. We present additional definitions in Chapter 3. Actually, DSS can be viewed as a conceptual methodology-that is, a broad, umbrella tenn.
DSS as an Umbrella Term The tenn DSS can be used as an umbrella term to describe any computerized system that supports decision making in an organiwtion. An organization may have a knowledge management system to guide all its personnel in their problem solving. Another organiza- tion may have separate support systems for marketing, fmance, and accounting; a supply- chain management SCM system for production; and several expert systems for proouct repair diagnostics and help desks.
DSS encompasses them all. The problem to be solved was unstructured, but the initial analysis was based on the decision maker's strucrured defmition of the situation, using an MS approach.
The DSS was built using data. The development plat- fonn was a spreadsheet. The DSS provided a quick what-if analysis see Chapter 4. FurthemlOre, the DSS was flexible and responsive enough to allow managerial inruition and judgment to be incorporated into the analysis. The conlpany sells products in hundreds of proouC! In this highly competitive retail market, it is important to have enough product quantity on hand to provide a high service level to customers, but excess inventory costs money, so developing an optimal inventory policy is essential in achieving a decent profit.
GSK User name: GSK collects all of its sales and demand infor- mation through the supply chain planning system. From this system, it extracted the historical forecast and demand, month.
GSK developed a model that takes these inputs and estimates the safety stock measured in weeks forward carryover [WFC. The model can determine how changes in the safety stock affect the customer level. The entire model was built using a spread- sheet tool Microsoft Excel. The Excel-based DSS enables the company to evaluate many situ- ations. Because of the stochastic nature of demand for many products, the model includes a simulation feature, which all ows the company to analyze the impact of uncertainty in demand on the optimal safety stock level.
Managers have found the DSS to be very helpful in making inventory decisions. Dedskm suppan Systems. How can a thorough risk analysis, like the one in Application Case 1. How can the judgment factors be elicited, quantified, and worked into a model? How can the results be presented meaningfully and convincingly to the execu- tive?
What are what- if questions? How can the Web be used to access and integrate appropriate data and models? We provide answers to these questions in Chapters 3 and 4. The DSS concepts introduced in Chapter 3 provide considerable insights to software vendors that develop decision support tools, to builders that construct specific decision support applications. Every problem that has to be solved and every opponunity or strategy to be analyzed requires some data. Data are the first component of the DSS architecrure see Figure 1.
Data related to a specific situation are manipulated by using mooels see Chapters 3 and 4. These models, which are the second component of the DSS architecture, can be Slandard e.
Some systems have a knowledge or intelligence 0! Interfacing with the system via a user interface is the fifth component of the DSS architecture. When creating a DSS. In many DSS, the components are standards and can be purchased. But in other situations, especially unstructured ones, it is neces- sary to custom build some or all of the components. The details of the major components are provided in Chapter 3. The two major types are the nuxlel-oriemed DSS.
As PC technology advanced, a new generation of managers evolvedone that was comfortable with computing and knew that technology can directly help make intelligent business decisions faster.
New tools such as OlAP, data warehousing, data mining, and intelligent systems, delivered via Web technology, added promised capabil- ities and easy access to tools. These tools started to appear under the names BI and business allalytics in the mid- I? Provide two definitions 0: Describe DSS as an umbrdla term. How is the term DSS used in the academic world?
As the enterprise-wide systems grew, maruJgers were able to access user-friendly reports that enabled them to make decisions quickly. These systems, which were generally called executive infomtation systems EIS , then began to offer additional visualization, alerts, and perforntance measurement capabilities.
By , the major commercial products and services appeared under the umbrella term business intelligence BO. Pan of me conli.. By analyzing historical and current data. S coined by the Gartner Group in the mkI-Is.
During mat period, reporting. Sorne of the capabilities Introduced were dyruunic multidimensioo: So, the original concept of EIS was tran. Figure 1. It illustrates the evolution of BI as well. The most sophis. We will study severnl of these capabilities in more detail in Ch.
The relationship among these com- ponents is illustrated in Figure 1. We will discuss these in detail in Olapters 5 through 9. Norice that the data warehousing environment is ntainly the responsibility of techni- cal staff, whereas the analytical environment also known as business allalylics is the realm of business users.
Any user can connect to the system via the user interface, such as a browser, and top managers may use the BPM componem and also a dashboard. Some business analytics and user interface tools are introdu ced briefly in Section 1. However, one set of tools, illlelligent systems see Chapters 12 and 13 , can be viewed as a futuristic component of BI. Originally, the data warehouse included only historical data that were organized and summarized, so end users could easily view or manipulate data and information.
Today, soille data warehouses include current data as well, so they can provide real-time decision suppon see Chapter 8. These tools and teclmiques fit into two major categories: Decision Support and Business Intelligence Systems.. Pearson Education. Peter B.. The Savy. Jeffrey Jaffe 9th McGraw-Hill. Decision Support and Business Intelligence Systems? World Cases are new to the 9th edition. Publications Ltd.. Provide brief.. Custom Pearson Mar '11 Required. Sharda R. Dursu n Delen 9th Pearson incl.
Provide brief. Decision Support Systems Module 4: Development Process Universitas Narotama Surabaya 25 years. Aronson J Management Information Systems. Randolph W Westerfield. Building Business Intelligence 4: Decision Support and Arti? Brainpower for Your School Of Management Brainpower for Your Accounting.???????? Management Information Systems 9th edition.
He taught courses in accounting. Liang T. Decision support systems Executive information systems Please share this free experience to your friends on your social network to prove that we really send free books!
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