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In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.
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Analytics has become the technology driver of this decade. Companies such as IBM, Oracle, Microsoft, and others are creating new organizational units focused on analytics that help businesses become more effective and efficient in their operations. Decision makers are using data and computerized tools to make better decisions.
Even consumers are using analytics tools directly or indirectly Ch 5 Maths Class 10 Ex 5.2 Apk to make decisions on routine activities such as shopping, health care, and entertainment. New applications emerge daily in customer relationship management, banking and fi- nance, health care and medicine, sports and entertainment, manufacturing and supply chain management, utilities and energy, and virtually every industry imaginable.
The theme of this revised edition is analytics, data science, and AI for enterprise decision support. We highlight these technologies as emerging components of modern-day business analytics systems. AI tech- nologies have a major impact on decision making by enabling autonomous decisions and by supporting steps in the process of making decisions.
AI and analytics support each other by creating a synergy that assists decision making. The purpose of this book is to introduce the reader to the technologies that are generally and collectively called analytics or business analytics but have been known by other names such as decision support systems, executive information systems, and business intelligence, among others. We use these terms interchangeably.
This book pres- ents the fundamentals of the methods, methodologies, and techniques used to design and develop these systems. In addition, we introduce the essentials of AI both as it relates to analytics as well as a standalone discipline for decision support. The book primarily provides exposure to various analytics techniques and their applications.
The idea is that a student will be inspired to learn from how other organizations have employed analytics to make decisions or to gain a competitive edge. We believe that such exposure to what is being done with analytics and how it can be achieved is the key component of learning about analytics. In describing the techniques, we also introduce specific software tools that can be used for developing such applica- tions. The book is not limited to any one software tool, so the students can experience these techniques using any number of available software tools.
Specific suggestions are given in each chapter, but the student and the professor are able to use this book with many different software tools. Finally, we hope that this exposure and experience enable and motivate read- ers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct them to Teradata University Network and other sites as well that include team-oriented exercises where appropriate.
In our own teaching experience, projects undertaken in the class facilitate such exploration after the students have been exposed to the myriad of applications and concepts in the book and they have experienced specific software introduced by the professor.
It can also be used to teach two consecutive courses. For example, one course could focus on the overall analytics coverage. It could cover selective sections of Chapters 1 and 3�9. A second course could focus on artificial intelligence and emerging technologies as the enablers of modern-day analytics as a subsequent course to the first course. This second course could cover portions of Chapters 1, 2, 6, 9, and 10� The book can be used to offer managerial-level exposure to applications and techniques as noted in the previous paragraph, but it also includes sufficient technical details in selected chapters to allow an instructor to focus on some technical methods and hands-on exercises.
Despite the many changes, we have preserved the comprehensiveness and user friendliness that have made the textbook a market leader in the last several decades.
Finally, we present accurate and updated material that is not available in any other text. We next describe the changes in the eleventh edition. The book is supported by a Web site pearsonhighered. We provide links to additional learning materials and software tutorials through a special section of the book Web site.
With the goal of improving the text and making it current with the evolving technology trends, this edition marks a major reorganization to better reflect on the current focus on analytics and its enabling technologies. The following sum- marizes the major changes made to this edition.
Chapter 1 provides an introduction to the journey of decision support and enabling technologies. It begins with a brief overview of the classical decision making and decision support systems. Then it moves to business intelligence, followed by an introduction to analytics, Big Data, and AI.
We follow that with a deeper introduction to artificial intelligence in Chapter 2. Because data is fundamental to any analysis, Chapter 3 introduces data issues as well as descriptive analytics including statistical concepts and visualization. An on- line chapter covers data warehousing processes and fundamentals for those who like to dig deeper into these issues. The next section covers predictive analytics and machine learning.
Chapter 4 provides an introduction to data mining applications and the data mining process. Chapter 5 introduces many of the common data min- ing techniques: classification, clustering, association mining, and so forth. Chapter 6 includes coverage of deep learning and cognitive computing.
Chapter 7 focuses on. Chapter 8 covers prescriptive analytics including optimization and simulation. Chapter 9 includes more details of Big Data analytics.
It also includes introduction to cloud-based analytics as well as location analytics. Chapter 10 introduces robots in business and consumer applications and also stud- ies the future impact of such devices on society.
Chapter 11 focuses on collaboration systems, crowdsourcing, and social networks. Chapter 12 reviews personal assis- tants, chatbots, and the exciting developments in this space. Chapter 13 studies IoT and its potential in decision support and a smarter society. The ubiquity of wireless and GPS devices and other sensors is resulting in the creation of massive new data- bases and unique applications. Finally, Chapter 14 concludes with a brief discussion of security, privacy, and societal dimensions of analytics and AI.
We should note that several chapters included in this edition have been avail- able in the following companion book: Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson Hereafter referred to as BI4e.
The structure and contents of these chapters have been updated somewhat before inclusion in this edition of the book, but the changes are more significant in the chapters marked as new. Of course, several of the chapters that came from BI4e were not included in previous editions of this book. The major issues covered are protection of privacy, intellectual property, ethics, technical issues e.
We also cover the impact of these technolo- gies on organizations and people and specifically deal with the impact on work and. Special attention is given to possible unintended impacts of analytics and AI robots. We have optimized the book size and content by add- ing a lot of new material to cover new and cutting-edge analytics and AI trends and technologies while eliminating most of the older, less-used material.
We use a dedicated Web site for the textbook to provide some of the older material as well as updated content and links. Several chapters have new opening vignettes that are based on recent stories and events. These application case stories now include suggested questions for discussion to encourage class discussion as well as further explora- tion of the specific case and related materials.
New Web site links have been added throughout the book. We also deleted many older product links and references. Finally, most chapters have new exercises, Internet assignments, and discussion questions throughout.
The specific changes made to each chapter are as follows: Chapters 1, 3�5, and 7�9 borrow material from BI4e to a significant degree. New topics include. We have retained many of the enhancements made in the last editions and updated the content.
These are summarized next:. Most chapters include new links to TUN teradatauniversitynetwork. We encourage the instructors to reg- ister and join teradatauniversitynetwork. The cases, white papers, and software exercises available through TUN will keep your class fresh and timely. The TUN Web site provides software support at no charge. It also provides links to free data mining and other software. In addition, the site provides exercises in the use of such software.
A comprehensive and flexible technology-support package is available to enhance the teaching and learning experience. The questions are rated by difficulty level, and the answers are referenced by book page number. You can manually or randomly view test questions and drag- and-drop to create a test. You can add or modify test-bank questions as needed. These conversions can be found on pearsonhighered.
The TestGen is also available in Respondus and can be found on www. PowerPoint slides are available that illuminate and build on key concepts in the text.
Faculty can download the PowerPoint slides from pear- sonhighered. Many individuals have provided suggestions and criticisms since the publication of the first edition of this book. Dozens of students 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. Certain indi- viduals made significant contributions, and they deserve special recognition. First, we appreciate the efforts of those individuals who provided formal reviews of the first through eleventh editions school affiliations as of the date of review :.
James T. Several individuals contributed material to the text or the supporting material. For this new edition, assistance from the following students and colleagues is grate- fully acknowledged: Behrooz Davazdahemami, Bhavana Baheti, Varnika Gottipati, and Chakradhar Pathi all of Oklahoma State University. Rick Wilson contrib- uted some examples and new exercise questions for Chapter 8. Pankush Kalgotra Auburn University contributed the new streaming analytics tutorial in Chapter 9.
Other contributors of materials for specific application stories are identified as sources in the respective sections. Many other colleagues and students have assisted us in developing previous editions or the recent edition of the companion book from which some of the content has been adapted in this revision. Some of that content is still included this edition.
Their assistance and contributions are acknowledged as well in chronological order. Dave Schrader contributed the sports examples used in Chapter 1. These will provide a great introduc- tion to analytics. Their help for BI 4e is gratefully acknowledged. The Tera- data Aster team, especially Mark Ott, provided the material for the opening vignette for Chapter 9. Abhishek Rathi of vCreaTek contributed his vision of analytics in the retail industry. Chapter 5. Sams, New York, provided material for the early editions; Larry Medsker American University , who contributed substantial material on neural networks; and Richard V.
McCarthy Quinnipiac University , who per- formed major revisions in the seventh edition. Previous editions of the book have also 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. Many individuals helped us with administrative matters and editing, proofreading, and preparation. Jon Outland assisted with the supplements.
Finally, the Pearson team is to be commended: Executive Editor Samantha Lewis who orchestrated this project; the copyeditors; and the production team, Faraz Sharique Ali at Pearson, and Gowthaman and staff at Integra Software Services, who transformed the manuscript into a book.
We would like to thank all these individuals and corporations. Without their help, the creation of this book would not have been possible. We want to specifically acknowl- edge the contributions of previous coauthors Janine Aronson, David King, and T. Liang, whose original contributions constitute significant components of the book.
Note that Web site URLs are dynamic. As this book went to press, we verified that all the cited Web sites were active and valid. Web sites to which we refer in the text sometimes change or are discontinued because compa- nies change names, are bought or sold, merge, or fail.
Sometimes Web sites are down for maintenance, repair, or redesign. If you have a problem connecting to a Web site that we mention, please be patient and simply run a Web search to try to identify the new site. Most times, the new site can be found quickly. Some sites also require a free registra- tion before allowing you to see the content.
We apologize in advance for this inconvenience. Ramesh Sharda M. He has worked on many spon- sored research projects with government and industry, and has also served as consultants to many organizations. Dursun Delen Ph. Prior to his academic career, he worked for a privately owned research and consultancy company, Knowledge Based Systems Inc. He served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management September 2�4, , in Seoul, South Korea and regularly serves as chair on tracks and mini-tracks at various business analytics and information systems conferences.
He is the co-editor-in-chief for the Journal of Business Analytics, the area editor for Big Data and Business Analytics on the Journal of Business Research, and also serves as chief editor, senior editor, associate editor, and editorial board member on more than a dozen other journals.
His consul- tancy, research, and teaching interests are in business analytics, data and text mining, health analytics, decision support systems, knowledge management, systems analysis and design, and enterprise modeling. Efraim Turban M. He is also a consultant to major corporations worldwide. T he business environment climate is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pres-sures that force them to respond quickly to changing conditions and to be in- novative in the way they operate.
Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways.
This book is about using business analytics and artificial intelligence AI as a computerized support portfolio for managerial decision making.
It concentrates on the. The book presents the fundamentals of the tech- niques and the manner in which these systems are constructed and used. We follow an EEE exposure, experience, and exploration approach to introducing these topics. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics.
In describing the techniques, we also give examples of specific software tools that can be used for devel- oping such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain.
To facilitate such exploration, we include exercises that direct the reader to Teradata University Network TUN and other sites that include team-oriented exercises where appropriate.
In our own teaching experi- ence, projects undertaken in the class facilitate such exploration after students have been exposed to the myriad of applications and concepts in the book and they have experi- enced specific software introduced by the professor. This introductory chapter provides an introduction to analytics and artificial intel- ligence as well as an overview of the book. The chapter has the following sections:. KONE is a global industrial company based in Finland that manufactures mostly eleva- tors and escalators and also services over 1.
The company employs over 50, people. Over 1 billion people use the elevators and escalators manufactured and serviced by KONE every day. If equipment does not work properly, people may be late to work, can- not get home in time, and may miss important meetings and events. The company has over 20, technicians who are dispatched to deal with the elevators anytime a problem occurs.
As buildings are getting higher the trend in many places , more people are using elevators, and there is more pressure on elevators to handle the growing amount of traffic. KONE faced the responsibility to serve users smoothly and safely. As we will see in Chapter 6, IBM installed cognitive abilities in buildings that make it possible to recognize situations and behavior of both people and equipment.
The sensors collect information and data about the elevators such as noise level and other equipment in real time. The systems also identify the likely causes of problems and suggest poten- tial remedies. Note the predictive power of IBM Watson Analytics using machine learning, an AI technology described in Chapters 4�6 for finding problems before they occur.
The KONE system collects a significant amount of data that are analyzed for other purposes so that future design of equipment can be improved. This is because Watson Analytics offers a convenient environment for communication of and collaboration around the data.
In addition, the analysis suggests how to optimize buildings and equip- ment operations. Finally, KONE and its customers can get insights regarding the financial aspects of managing the elevators. Salesforce also provides superb customer relationship management CRM.
The people�machine communication, query, and collaboration in the system are in a natural language an AI capability of Watson Analytics; see Chapter 6. Note that IBM Watson analytics includes two types of analytics: predictive, which predicts when failures may occur, and prescriptive, which recommends actions e.
KONE has minimized downtime and shortened the repair time. The owners can also optimize the schedule of their own employees e. All in all, the decision mak- ers at both KONE and the buildings can make informed and better decisions.
Some day in the future, robots may perform maintenance and repairs of elevators and escalators. To learn more, we suggest you view the following YouTube videos: 1 youtube. Sources: Compiled from J. Millions of Elevators. No Room for Downtime. It is said that KONE is embedding intelligence across its supply chain and enables smarter buildings.
Describe the role of IoT in this case. What makes IBM Watson a necessity in this case? What tools were included that relate to this case? Check IBM cognitive buildings. How do they relate to this case? Today, intelligent technologies can embark on large-scale complex projects when they include AI combined with IoT.
The capabilities of integrated intelligent platforms, such as IBM Watson, make it possible to solve problems that were economically and techno- logically unsolvable just a few years ago. The case introduces the reader to several of the technologies, including advanced analytics, sensors, IoT, and AI that are covered in this book.
This vignette also introduces us to two major types of analytics: predic- tive analytics Chapters 4�6 and prescriptive analytics Chapter 8. Several AI technologies are discussed: machine learning, natural language process- ing, computer vision, and prescriptive analysis. The case is an example of augmented intelligence in which people and machines work together.
The case illustrates the benefits to the vendor, the implementing compa- nies, and their employees and to the users of the elevators and escalators. Decision making is one of the most important activities in organizations of all kind� probably the most important one. Decision making leads to the success or failure of orga- nizations and how well they perform. Making decisions is getting difficult due to internal and external factors. The rewards of making appropriate decisions can be very high and so can the loss of inappropriate ones.
Unfortunately, it is not simple to make decisions. To begin with, there are several types of decisions, each of which requires a different decision-making approach. For ex- ample, De Smet et al. Chapter Therefore, it is necessary first to understand the nature of decision making. For a comprehensive discussion, see De Smet et al. Modern business is full of uncertainties and rapid changes. To deal with these, or- ganizational decision makers need to deal with ever-increasing and changing data.
This book is about the technologies that can assist decision makers in their jobs. For years, managers considered decision making purely an art�a talent acquired over a long period through experience i. Management was considered an art because a variety of individual styles could be used in approaching and successfully solving the same types of manage- rial problems. These styles were often based on creativity, judgment, intuition, and experience rather than on systematic quantitative methods grounded in a scientific ap- proach.
However, recent research suggests that companies with top managers who are more focused on persistent work tend to outperform those with leaders whose main strengths are interpersonal communication skills. It is more important to emphasize methodical, thoughtful, analytical decision making rather than flashiness and interper- sonal communication skills. Managers usually make decisions by following a four-step process we learn more about these in the next section :.
Define 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 recommend a potential solution to the problem.
Understand the decision you have to make. Collect all the information. Identify the alternatives. Evaluate the pros and cons. Select the best alternative. Make the decision. Evaluate the impact of your decision. To follow these decision-making processes, one must make sure that sufficient alterna- tive solutions, including good ones, are being considered, that the consequences of using these alternatives can be reasonably predicted, and that comparisons are done properly.
However, rapid changes in internal and external environments make such an evaluation process difficult for the following reasons:. Major decisions may be influenced by both external and.
An example is the trade war on tariffs. These range from competition to the genera and state. These need to be considered when changes are being made. The impact on the physical environment must be assessed in many decision-making situations. Other factors include the need to make rapid decisions, the frequent and unpredict- able changes that make trial-and-error learning difficult, and the potential costs of making mistakes that may be large.
These environments are growing more complex every day. Therefore, making deci- sions today is indeed a complex task. For further discussion, see Charles For how to make effective decisions under uncertainty and pressure, see Zane Because of these trends and changes, it is nearly impossible to rely on a trial- and-error approach to management.
Managers must be more sophisticated; they must use the new tools and techniques of their fields. Most of those tools and techniques are discussed in this book. Using them to support decision making can be extremely rewarding in making effective decisions. Further, many tools that are evolving impact even the very existence of several decision-making tasks that are being automated.
This impacts future demand for knowledge workers and begs many legal and societal impact questions. We will see several times in this book how an entire industry can employ analytics to develop reports on what is happening, predict what is likely to happen, and then make decisions to make the best use of the situation at hand.
These steps require an organiza- tion to collect and analyze vast stores of data. In general, the amount of data doubles every two years. From traditional uses in payroll and bookkeeping functions, computer- ized systems are now used for complex managerial areas ranging from the design and management of automated factories to the application of analytical methods for the eval- uation of proposed mergers and acquisitions.
Nearly all executives know that information technology is vital to their business and extensively use these technologies. Computer applications have moved from transaction-processing and monitoring ac- tivities to problem analysis and solution applications, and much of the activity is done with cloud-based technologies, in many cases accessed through mobile devices.
Managers must have high-speed, networked information systems wired or wireless to assist them with their most important task: making deci- sions. In many cases, such decisions are routinely being fully automated see Chapter 2 , eliminating the need for any managerial intervention. Besides the obvious growth in hardware, software, and network capacities, some devel- opments have clearly contributed to facilitating the growth of decision support and ana- lytics technologies in a number of ways:.
Many decisions are made today by groups whose members may be in different locations. Groups can collaborate and communicate readily by using collaboration tools as well as the ubiquitous smartphones. Collaboration is especially important along the supply chain, where partners�all the way from vendors to customers�must share information.
Assembling a group of decision makers, especially experts, in one place can be. Information systems can improve the collaboration process of a group and enable its members to be at different locations saving travel costs. More critically, such supply chain collaboration permits manufacturers to know about the changing patterns of demand in near real time and thus react to marketplace changes faster. For a comprehensive coverage and the impact of AI, see Chapters 2, 10, and Many decisions involve complex computations.
Data for these can be stored in different databases anywhere in the organization and even possibly outside the organization. The data may include text, sound, graphics, and video, and these can be in different languages.
Many times it is neces- sary to transmit data quickly from distant locations. Systems today can search, store, and transmit needed data quickly, economically, securely, and transparently. See Chapters 3 and 9 and the online chapter for details. Large data warehouses DWs , like the ones operated by Walmart, contain huge amounts of data. The costs related to data storage and mining are declining rapidly.
Technologies that fall under the broad category of Big Data have enabled massive data coming from a variety of sources and in many different forms, which allows a very different view of organizational performance that was not pos- sible in the past. See Chapter 9 for details. With more data and analysis technologies, more alternatives can be evaluated, forecasts can be improved, risk analysis can be performed quickly, and the views of experts some of whom may be in remote locations can be collected quickly and at a reduced cost.
Expertise can even be derived directly from analytical systems. With such tools, decision makers can perform complex simulations, check many possible scenarios, and assess diverse impacts quickly and economically. This, of course, is the focus of several chapters in the book. See Chapters 4�7. The human mind has only a limited ability to process and store information.
People sometimes find it difficult to recall and use information in an error-free fashion due to their cognitive limits. Computerized systems enable people to overcome their cognitive limits by quickly accessing and processing vast amounts of stored infor- mation. For coverage of cognitive aspects, see Chapter 6.
Organizations have gathered vast stores of informa- tion about their own operations, customers, internal procedures, employee interac- tions, and so forth through the unstructured and structured communications taking place among various stakeholders.
Knowledge management systems KMS have become sources of formal and informal support for decision making to manag- ers, although sometimes they may not even be called KMS. Technologies such as text analytics and IBM Watson are making it possible to generate value from such knowledge stores. See Chapters 6 and 12 for details. Using wireless technology, managers can access information anytime and from any place, analyze and interpret it, and communicate with those using it.
This perhaps is the biggest change that has occurred in the last few years. The speed at which information needs to be processed and converted into decisions has truly changed expectations for both consumers and businesses.
These and other capabilities have been driving the use of computerized decision support since the late s, especially since the mids. The growth of mobile technologies, social media platforms, and analytical tools has enabled a different level of information systems IS to support managers.
This growth in providing. We will first study an overview of technologies that have been broadly referred to as BI. From there we will broaden our horizons to introduce various types of analytics. Because of the complexities in the decision-making process discussed earlier and the environment surrounding the process, a more innovative approach is frequently need. A major facilitation of innovation is provided by AI. Almost every step in the decision-making process can be influenced by AI.
AI is also integrated with analytics, creating synergy in making decisions Section 1. Why is it difficult to make organizational decisions? Describe the major steps in the decision-making process. Describe the major external environments that can impact decision making.
What are some of the key system-oriented trends that have fostered IS-supported. List some capabilities of information technologies that can facilitate managerial deci- sion making. In this section, we focus on some classical decision-making fundamentals and in more detail on the decision-making process. These two concepts will help us ground much of what we will learn in terms of analytics, data science, and artificial intelligence.
Decision making is a process of choosing among two or more alternative courses of action for the purpose of attaining one or more goals. According to Simon , mana- gerial decision making is synonymous with the entire management process. Consider the important managerial function of planning.
Planning involves a series of decisions: What should be done? By whom? Managers set goals, or plan; hence, planning implies decision making. Other managerial functions, such as organizing and controlling, also involve decision making. It is advisable to follow a systematic decision-making process. Simon said that this involves three major phases: intelligence, design, and choice. He later added a fourth phase: implementation.
Monitoring can be considered a fifth phase�a form of feedback. However, we view monitoring as the intelligence phase applied to the imple- mentation phase. A conceptual picture of the decision-making process is shown in Figure 1. It is also illustrated as a decision support approach using modeling.
There is a continuous flow of activity from intelligence to design to choice see the solid lines in Figure 1. Modeling is an essential part of this process. The seemingly chaotic nature of following a haphazard path from problem discovery to solution via decision making can be explained by these feedback loops. The decision-making process starts with the intelligence phase; in this phase, the decision maker examines reality and identifies and defines the problem.
Problem owner- ship is established as well. In the design phase, a model that represents the system is constructed. This is done by making assumptions that simplify reality and by writing down. The model is then validated, and criteria are de- termined in a principle of choice for evaluation of the alternative courses of action that are identified.
Often, the process of model development identifies alternative solutions and vice versa. The choice phase includes the selection of a proposed solution to the model not necessarily to the problem it represents. This solution is tested to determine its viability. When the proposed solution seems reasonable, we are ready for the last phase: imple- mentation of the decision not necessarily of a system.
Successful implementation results in solving the real problem. Failure leads to a return to an earlier phase of the process. In fact, we can return to an earlier phase during any of the latter three phases. The intelligence phase begins with the identification of organizational goals and objectives related to an issue of concern e.
Problems occur because of dissatisfaction with the status quo. Dissatisfaction is the result of a difference between what people desire or expect and what is occurring.
In this first phase, a decision maker attempts to determine whether a problem exists, identify its symptoms, determine its magnitude, and. Organization objectives Search and scanning procedures Data collection Problem identification Problem ownership Problem classification Problem statement.
Often, what is described as a problem e. Because real-world problems are usually complicated by many interrelated factors, it is sometimes difficult to distinguish between the symptoms and the real problem.
New opportunities and problems certainly may be uncovered while investigating the causes of symptoms. The measurement of productivity and the construction of a model are based on real data. The collection of data and the estimation of future data are among the most difficult steps in the analysis. As a result, the model is made with and relies on potentially inaccurate estimates.
As a result, revenues,. To overcome this difficulty, a present-value approach can be used if the results are quantifiable. If this is not the case, the nature of the change has to be predicted and included in the analysis.
When the preliminary investigation is completed, it is possible to determine whether a problem really exists, where it is located, and how significant it is. A key issue is whether an information system is reporting a problem or only the symptoms of a problem. For example, if reports indicate that sales are down, there is a problem, but the situation, no doubt, is symptomatic of the problem.
It is critical to know the real problem. Sometimes it may be a problem of perception, incentive mismatch, or organizational processes rather than a poor decision model. To illustrate why it is important to identify the problem correctly, we provide a clas- sical example in Application Case 1.
This story has been reported in numerous places and has almost become a classic example to explain the need for problem identification. Ackoff as cited in Larson, described the problem of manag- ing complaints about slow elevators in a tall hotel tower. After trying many solutions for reducing the complaint�staggering elevators to go to different floors, adding operators, and so on�the manage- ment determined that the real problem was not.
So the solution was to install full-length mirrors on elevator doors on each floor. Baker and Cameron An important approach classifies problems according to the degree of struc- turedness evident in them. This ranges from totally structured i. Solving the simpler subproblems may help in solving a complex problem.
Also, seemingly poorly structured problems sometimes have highly structured subproblems. Just as a sem- istructured problem results when some phases of decision making are structured whereas other phases are unstructured, and when some subproblems of a decision- making prob- lem are structured with others unstructured, the problem itself is semistructured.
As a de- cision support system is developed and the decision maker and development staff learn more about the problem, it gains structure. A problem exists in an organization only if someone or some group takes the responsibility for attacking it and if the organization has the ability to solve it.
The assign- ment of authority to solve the problem is called problem ownership. For example, a man- ager may feel that he or she has a problem because interest rates are too high.
Because interest rate levels are determined at the national and international levels and most manag- ers can do nothing about them, high interest rates are the problem of the government, not a problem for a specific company to solve. The problem that companies actually face is how to operate in a high interest-rate environment. For an individual company, the interest rate level should be handled as an uncontrollable environmental factor to be predicted.
When problem ownership is not established, either someone is not doing his or her job or the problem at hand has yet to be identified as belonging to anyone. It is then important for someone to either volunteer to own it or assign it to someone. The design phase involves finding or developing and analyzing possible courses of action. These include understanding the problem and testing solutions for feasibility. A model of the decision-making problem is constructed, tested, and validated.
Let us first define a model. If the real problem is identified as perceived waiting time, it can make a big difference in the proposed solutions and their costs. For example, full-length mirrors probably cost a whole lot less than adding an elevator! Sources: Based on J. Baker and M. Science, 24, pp. Hesse and G.
Woolsey MODELS A major characteristic of computerized decision support and many BI tools notably those of business analytics is the inclusion of at least one model. The basic idea is to perform the analysis on a model of reality rather than on the real system. A model is a simplified representation or abstraction of reality. It is usually simplified because reality is too complex to describe exactly and because much of the complexity is actually irrel- evant in solving a specific problem.
For a mathematical model, the variables are identified and their mu- tual relationships are established. Simplifications are made, whenever necessary, through assumptions. For example, a relationship between two variables may be assumed to be linear even though in reality there may be some nonlinear effects. A proper balance be- tween the level of model simplification and the representation of reality must be obtained because of the cost�benefit trade-off.
A simpler model leads to lower development costs, easier manipulation, and a faster solution but is less representative of the real problem and can produce inaccurate results. However, a simpler model generally requires fewer data, or the data are aggregated and easier to obtain. Choice is the critical act of decision making. The choice phase is the one in which the actual decision and the commitment to follow a certain course of action are made.
The boundary between the design and choice phases is often unclear because certain activi- ties can be performed during both of them and because the decision maker can return frequently from choice activities to design activities e. The choice phase includes the search for, evaluation of, and recommendation of an appropriate solution to a model. A solution to a model is a specific set of values for the decision variables in a selected alternative.
Choices can be evaluated as to their viability and profitability. Each alternative must be evaluated. If an alternative has multiple goals, they must all be examined and balanced against each other. Sensitivity analysis is used to determine the robustness of any given alternative; slight changes in the parameters should ideally lead to slight or no changes in the alternative chosen. What-if analysis is used to explore major changes in the parameters. Goal seeking helps a manager determine values of the decision variables to meet a specific objective.
These topics are addressed in Chapter 8. And change must be managed. User expectations must be managed as part of change management. The definition of implementation is somewhat complicated because implementation is a long, involved process with vague boundaries. Simplistically, the implementation phase involves putting a recommended solution to work, not necessarily implementing a computer system.
Many generic implementation issues, such as resistance to change, degree of support of top management, and user training, are important in dealing with information system�supported decision making.
Indeed, many previous technology- related waves e. Management of change is almost an entire discipline in itself, so we recognize its impor- tance and encourage readers to focus on it independently. Implementation also includes. The importance of project manage- ment goes far beyond analytics, so the last few years have witnessed a major growth in certification programs for project managers. See pmi. Implementation must also involve collecting and analyzing data to learn from the previous decisions and improve the next decision.
This is especially true for any public policy decisions. We need to be sure that the data being used for problem identification is valid. Sometimes people find this out only after the implementation phase. The decision-making process, though conducted by people, can be improved with computer support, which is introduced next.
The early definitions of decision support system DSS identified it as a system intended to support managerial decision makers in semistructured and unstructured decision situ- ations. DSS was meant to be an adjunct to decision makers, extending their capabilities but not replacing their judgment.
DSS was aimed at decisions that required judgment or at decisions that could not be completely supported by algorithms. Not specifically stated but implied in the early definitions was the notion that the system would be computer based, would operate interactively online, and preferably would have graphical output capabilities, now simplified via browsers and mobile devices.
An early framework for computerized decision support includes several major con- cepts that are used in forthcoming sections and chapters of this book. Gorry and Scott- Morton created and used this framework in the early s, and the framework then evolved into a new technology called DSS. Gorry and Scott-Morton proposed a framework that is a 3-by-3 matrix, as shown in Figure 1.
The two dimensions are the degree of structuredness and the types of control. Structured processes are routine Byjus Class 6 Maths Chapter 3 Eng and typically repetitive problems for which standard solution methods exist.
Unstructured processes are fuzzy, complex problems for which there are no cut-and-dried solution methods. An unstructured problem is one where the articulation of the problem or the solu- tion approach may be unstructured in itself. In a structured problem, the procedures for obtaining the best or at least a good enough solution are known. Whether the problem involves finding an appropriate inventory level or choosing an optimal investment strat- egy, the objectives are clearly defined.
Common objectives are cost minimization and profit maximization. Semistructured problems fall between structured and unstructured problems, hav- ing some structured elements and some unstructured elements.
Keen and Scott-Morton mentioned trading bonds, setting marketing budgets for consumer products, and performing capital acquisition analysis as semistructured problems. The initial pur- pose of this matrix was to suggest different types of computerized support to differ- ent cells in the matrix. Gorry and Scott-Morton suggested, for example, that for making semistructured decisions and unstructured decisions, conventional management information systems MIS and management science MS tools are insufficient.
Human intellect and a different approach to computer technologies are necessary. They proposed the use of a supportive information system, which they called a DSS. Note that the more structured and operational control-oriented tasks such as those in cells 1, 2, and 4 of Figure 1. Operational and managerial control decisions are made in all functional areas, especially in finance and production i. Evaluating credit Preparing budget Laying out plant Scheduling project Designing reward system Ncert Solutions Of Class 10th Maths Chapter 8 Instruction Categorizing inventory.
Building a new plant Planning mergers and acquisitions Planning new products Planning compensation Providing quality assurance Establishing human resources policies Planning inventory. Planning research and development Developing new technologies Planning social responsibility. Managing finances Monitoring investment portfolio Locating warehouse Monitoring distribution systems.
Structured problems, which are encountered repeatedly, have a high level of struc- ture, as their name suggests. It is therefore possible to abstract, analyze, and classify them into specific categories. For example, a make-or-buy decision is one category. Other examples of categories are capital budgeting, allocation of resources, distribution, pro- curement, planning, and inventory control decisions. For each category of decision, an easy-to-apply prescribed model and solution approach have been developed, generally as quantitative formulas.
Therefore, it is possible to use a scientific approach for automat- ing portions of managerial decision making. Solutions to many structured problems can be fully automated see Chapters 2 and It is usually necessary to develop customized solutions.
However, such solutions may benefit from data and information generated from corporate or external data sources. He lives in Toronto. He's living in New York at the moment. They 9 to Canada thirty years When A sandwich. When they first 'O , they" any Why.. For two weeks. They worry about me. How much 1 My brother. How long? The blue one.
It's mine. Toronto and for a Master's degree, and then I hope to get a good job. Grammar Reference 1. Choose the correct word. Listen to the sentences. S Not a lot I studied English at school, but I didn't learn much.
If the word is WJMKrl sho ut 1! Now I'm stud ying in a language school here. If the word Is Wbo'sl shout 2! S The Shakespeare School of English.
Quest ions about you I A good name! Your English is very good now. S Thank you very much. My teacher's called David. He's great. Write it in. What you like doing in your free time? S Well, actually, I was a teacher, a history teacher.
I taught children 2 Do you like listening music? S Sometimes as many as I Goodness! That's a lot. S Usually I go every two months, but this mo nth my brother is 8 What your teacher wearing tod ay? I'm very excited I'm going to show him round.. Find examples of present, past, and future tenses in the interview. Roleplay the interview with a partner. Discuss your list with a partner. Look at the pictures. Who are they talking to?
Who are they talking about? When and where did they meet their oldest fr iend? What did they like about them? Make notes after each conversation. Answer the questions about the people.
How many? Check your answers with a partner. How do you think most couples meet? Look at the chart and match a line with a percentage. This week How did they meet? Will they ever meet again? Did anything surprise you? Talk about couples you know. How did!. Look at the pictures and read the introduction. Group B Read wh at Dominic says about Sa11 ,. Answer the questions in your group.
Were they both nervous when they met? What do Ch 12 Of Maths Class 10 Workbook Sally and Dominic have in common? What don't ther have in common? What nappened next? Take a class vote. What did they do? He was friendl y, tall. She smiled a lot. She has a lovely attractive. We laughed togethe r from t he start, smile and amazing green eyes. I think she was a bit think because we were both a bit nervous.
I loved her red dress - it was very red indeed. Wha IJI you talk abOut? So many things - places What did yot. Everything - travel, we both we want to travel to, such as South America. Sally hates it; sport. Unfortunately Dam doesn't play much spo rt, but I hate it , Sally loves it, but I am training to run the marathon he's going to run the marathon t his year. His acting - for charity; t he theatre, I have a small part in a small theatre I don't often go to the theatre so I didn 't have a lot to say.
Any difficult moments? I couldn't decide how to Any difficult moments? Not really. Oh yes, I could greet him when we first met. I shook his hand and he see t hat the waiter knew it was a blind date. That tried to kiss my cheek. That was a bit embarrassi ng, was embarrassing. Very good. I like a girt who Good table manners? Yes, very. He couldn 't use enjoys her food and she could use chopsticks. I was chopsticks, but he tried. Best thing about him?
He was chatty and funny. He Best thing about her? The green eyes! And she was didn' t just talk about himself, he asked me questions. It really easy to talk to. She was interested and interesting. She didn't just talk about sport. Did you go on somewhere? Just to the square Did you go on somewhere? Well , we didn't go far. There was a piano with a notice W e found a piano - they are all over the city at the moment 'Please play me' - so Dam did.
He can play the piano with signs saying 'Please play me'. I played, but I'm not very very well. It was a great good. Sally sang, she can't sing at all. We made a terrible way to end the evening. It was good fun. Then she caught the bus home. He lives out of town , Marks out of She can't sing, but I like her. Would you like to meet again? She left Marks out of 10?
I liked him more and more as the evening Vocabu lary progressed. Sally was interested Sally was interesting I because she was funny and made him laugh. Thank you. Use a dict ionary. Verbs of similar meaning Prepositions I Choose the correct verb fo r each line. He comes from Istanbul He never shuts up! Where can r some sun cream? Words with two meanings 4 Look at these sentences.
I met my husband on a blind date. I Listen to some sample answers. Read the expressions. Where are the people? A 'Hi, Anna. Is that seat freer B 'I'm fine. How are you? Pay attention to stress and intonation. See you later! What's the problem? Of course. Perhaps another time. Same to you. I can't come tonight. About 9. How do you do? You're here now. I 8 Bye! Have a good weekend. My pleasure. Good mormng. Pleased to meet you. Here's to your new job!
Continue them if you can and act them to the class. Good mOnlillgl d Thanks a lot. I'm excited, but a bit nervous. What about that? U'f If s: goUsg to raill 'gaill i Thanks. Are you doing anything special? I missed the bus. II1II Listen and check. What's r-emarkabJe about them? Who likes going to clubs? How old is she? What does she look like? What does her fami ly think of her? Howald was he when he started it? What does his charity do? Whkh two present tenses are used in the texts?
Find examples of both. Which refers to all time? Which refers to now? He makes a lot of money. He kas his own company She's making another Single. She's having a good time. Is have got more formal or informal? More spoken or written? Grammar Rmrenct 1. Ask and answer questions about Ruth and Fraser. His company makes jam - 3 How many childrenlhave? The business is growing fast - four flavours at the me,mEmt, aD Listen and check.
Does she like parties for old people with live being famous? What do her friends think of her job? Complete the sentences. I've got 2 I an old woman in an old people's home And we're trying to get into 3 Because it me happ '! What does he like about his work? What does he say about friends and family? Complete the interviewer's lines. What are they talki ng about? How are the forms different? Ask and answer questions about these things. With a partne,r. State verbs Check it 4 Some verbs don't usually take the Present Continuou s.
Complete S Tick. D Angela live with her parents. D Where you go on holiday? Sorry: 2 I'm thirsty! Where did you get it? D She no works here anymore. He waits for a bus. I think he's ver y clever: D He's at the bus stop. He's waiting for a bus. I her. S 0 I'm liking black coffee. He a house in Mayfair. D 1 like black coffee. What's the matter?
You don't look a day over 60! Match a Verb and a Phrase. Phrase Phl'ilse play I lib playiftg sa I like shopping in the High Street, but mainly I shop onf.. Indian , I think. I three times a week. What is your idea of a perfect day? Make notes. Talk about your perfect day. What are the people doing? Why are they happy? I What does happiness depend on? Do you agree? Write one of these beadings above each section.
Whit do f OUthink! Work in groups. What makes you happy? Do you know how to make yourself happier? And you can actually be happier. It just need. Do the quiz and find out how happy you are. Bring in forma tio n and pictures to class. TeU the others about your person. I usually h a holiday at least once a year. I get pleasure from lots of different things - art, nature. Read the ideas. Do rou agree or disagree? A good neighbour is someone who Discuss fOur idOlS in small groups.
Two neishbours 3 IIII! Read the- questions. Answer the questions. What doesn't he wear? What time does he get up? Where does she live? What's he doing now? Check rour answers in small groups. How does he answer questions What differences are there? S In your groups.. Roleplay a conversation between Alfie and Mrs Crumble where they actually get to know each other. A Hello. I'm Alfie. You're Mrs Crumble, aren't you? C Oh, Alfie, hello. I don't usually see you in the mornings.. Listen to the conversations between two students and two teachers.
The teachers are trying to be friendly. Which conversatio n is more successful? I miss. They're new. It's a interesting city. How does B keep the conversation going? Cover B, then A. Remember the extra lines. Keeping a conversation going S Work with a partner.
Begin a conversation with one of these lines. Keep the conversation going as long as possible. What's in the news? Which are regular? Which are irregular? Amazing journey ends after 6, miles Ed Stafford ' bWlm. Camana on the Pacific coast of Peru. Write the questions. The Uow far did Ed walk? Practise the questions and answers with rour partner. S Read Cho 's story.
Who is Cho? Put the verb in brackets in the Past Simple or the Past Continuous. Complete these sentences. I Coo was working in the forest when he Ed's blog 7 Write the questions. Ask and answer them with your partner. The snake's go in and out.
I was terrified. One bite and you're dead in 3 hours. His companion was Gadiel 'Cha' Sanchez be on their land. We 10 leave as fast as Rivera, a forestry worker from Peru. Cho said, 'When I first met Ed, I was working in the forest. I thought he was crazy, but I wanted to help him and be his guide. I explained he was an adventurer and he was walking hammock last night hying to the Amazon. They decided he was crazy, Monkeys 12 saeam in the trees, and millions of mosquitos 13 buzz round my The Past Simple expresses a completed action in the past.
I " take a sleeping piI and finaIy Ed walked the Amazon. He beean his journey in Cho was working in the forest when he met Ed. Compare these sentences. Go online and fmd out more about Ed.
Were your I had a shower last night. What else did you learn? I was having a shower when the phone rang. Grammar Reftrtn t 3. Tell a partner. We stayedjn a hotel They stopped Listen and practise the sentences. Notice the pronunciation of was and were.
What was she wearing? They weren't enjoying the party. Discussinx grammar Talking about the news What do When we arrived, me WI5 making some coffee. When we arrived, she made some coffee. I read a good book in bed last night. Texting Woman I was reading a good book in bed last night. Chinese vase sells 2 While 1 shopped I was shopping this morning. Read the article on 'I was skiing I skied and I hit I was hitting a tree.
Tell your story to the 8 Did you have I Were you haVing a good holiday? DON'T read it! The other students can ask questions. Which of these news topics interests you most? Do you listen to the radio? Which station?
What is the first story about? The second? Write a number Choose one of the news stories. What else do you want to know? Think of more questions. Write the questions on the board. S l1li Listen to the news stories. Which questions were answered? One student should write the exact words on the board. The other students help. You Micsed out a word. That icn't how you spell ttI. Do some research. In the next lesson, bring in pictures and articles.
Tell the class about the story. Be prepared to answer questions. What makes you lose your cool? What made him lose hi s cool? In groups, write some sentences about the story. Compare ideas. As the Airbus A was taxiing slowly on the runway, a passenger stood up to get her luggage. Mr Slater told her to sit down, but she refused The businesswoman was taking her case out of the overhead locker when it hit Mr Slater on the head.
He started bleeding, and it was then that the flight attendant lost his temper. He marched to the front of the cabin and spoke furiously over the plane's PA system, saying, 'That's enough! After 28 years in this business, I quit! Mr Slater then ran to his car and drove home. Police arrested Mr Slater at his home a short time later.
What did the femal e passenger do? How did he react? How did Steven Slater leave the plane? Was this a very important story? After each one, answe r the questions Why do you think it was in the newspapers? Retell the story in more detail. What do you think? Why was it such big news for a week? Do you think he paid a fine or went to prison?
Look at the article on piSS for the answer. Do you think this was fair? How does the Steven Slater story illustrate this? Slater has messages from millions of people Us! Steven Slat Peopfe wrote how much they admired him. Including other cabin As he was leaving a Bronx police station, aaw. He could face up to How did they show their suppo rt? Why d id the public admire him?
W hat did other cabin c rew say? Ex-flight attendant Folk hero Slater relaxes on the beach to get TV Show Ex-flight attendant Steven Slater baseball cap 35 he talked to spent the weekend relaxing on Steven Slater is in talks to get his own excited fans on the beach near 1M beach. He was having a his home in New York. TV production company Stone Entertainment wants to give the couple of beers and enjoying Yesterday supporters shouted. What actually happened on the beach? What did Steven Slater do to deserve being ca1led a folk hero?
Underline the adverbs. Match a verb in A Try to remember the sentences. S Correct the word order in these sentences. Is this a fast train to london? I worlc hord and play hord. She speaks very weU English. Slow down! You drive too bst! She's a very hard worker. I sot up Complete 5 Never r can remember her name. I cleM complete quiet good slow bad honest my periect 6 Put the adverbs in the correct place in the sentences.
I My grandma is 75, and she goes swimming. S forget something Heat it gently. When it is ready, serve the scrambled eggs with toast. They're on Facebook. My dad's on Facebook. When's your birthday? What's your date of birth? What year were you born? DEI Listen and compare. Whirs the date today? July 25 - ft's MY wtddi"9 ivusary. When did you last.. Apiece of Con you come for dinner?
Who had the healthiest diet? For brMktad I, SOMa ur Do they cook any of their food? Claus Bonrich 33 and his wife Elvira 28 all! Claus 15 a software programmer and Elvira works in a health food shop. In many ways their lifo! They want to live until they all! And they believe they can do this by following an American health pian called the 'Calorie Restriction Diet: Claus and Elvira eat a lot of raw food.
They steam some food but they don't fry, grill, or roast anything. Which can't you count? Label the nouns look at the expressions of quantity in A, B. Which Countable and Uncountable. Which go wTth uncountables? Which go wTth both? How many When do we use them? Correct the senteoca. There are fffiIfl ' books in my bag. X There', I 1 look at these sentences. Is there any orange juke? Can I have some orange juke? Read and complete the questions and answers about the diet with the nouns from exercise 2.
Do you think the Bonrichs eat and drink the things in the box? Discuss with your partner and complete the lists. A No, we don't eat any eat some at all. A Yes, of course, we eat lots of raw 5 Q Don't you cook any vegetables at all? Sometimes we steam a few "'ud and a little 6 Q And what do you drink? A About 1, A That's about 1, fewer than most people. Practise the questions and 6. Listen and find out if your ideas were correct.
Will the Bonrichs live answers with your partner. How old were they? Just half a dozen. Two or three. He's a millionaire. My grandfather lived until he was years old.
He was a shopkeeper. He had a son and a daughter. The daughter is my mother. The family lived above -4 Complete the lines with the correct word. My grandfather made the best fish and chips in the area. I met who knows you! There's in my eye! He worked hard, 'Let me look. He didn't retire until he was 78 years old.
I cou1dn't find I liked: lID Listen and check. Practise them with a partner. S l1li Listen. There is a word missing in each sentence. Call out what it is. Say the complete sentence.
Join the lines about the grandfather with the. My ptdf. He lived in north of EJ18Iand. Find some examples of these rules in the text. His family lived above shop. Ittmido Some people came by bus to the shop. Read the lines aloud to a partner. Dis U5Sins grammar Work with a partner. We had best time ever. I work at home on my computer. S I do all my shopping on lnternet.
Check it 3 Find one mistake in each sentence and correct it. I He's postman, so he has breakfast at 4. S ' Where are the children? What are they? Where did people in your country eat and drink hundreds of years ago? Look at the pictures and the Fact Files. What's unusual about the three restaurants?
Group A Read about Dinner in the Sky. Group C Read about sBaggers Restaurant. Answer the questions about your restaurant. Is it good? SOm up in the air 6 Are there any problems? They sit at a huge table w hich hangs from listening a crane fifty metres in the air. It's not a good idea for people who are afraid of heights or for those who don't :5 o g Listen to people who visited the restaurants. The twenty-two Answer these questions after each person.
What was good about it? The restaurant opened in Belg ium in , but now has What wasn't so good? What do they say about the other guests? David Ghysels, the Belgian organizer says, 'We realized that people were bored with going to the same old Ale:u. They wa nted to try something different. The sky's the limit with us! The food is delicious, but most guests don't feel like eating until after a few drinks! Then they also get the courage to look down at the ground w here tiny people are looking up in amazement and waving.
What do you think! Dinner in the Sky is very exciting and the food is good, Which would you like to visit? For example, even in quiet weather conversation is difficult because of the wind.
Do yoo eat out? How often? Whafs your favourite resturant? Guests shout to each other across the table. Also, the Do you know any unusual restaurants? Tell the class. You can't go to the until the table descends again. Difficult for some! But later, back on earth, after a visit to the loa, the guests have a great experience t o talk about. It's a resta urant w it h no Indian Ocean. It haa means 'pearl' in the Maldivian waiters to serve you.
You do everyth ing for you rself with la nguage and the guests are like pearls in a glass oyster. It opened in and is the first automated restaurant in the world. However, 's Baggers credit card and go to sit at is easy to get to. You don't need to be a swimmer or a a big, round table with t hree or four scuba diver, but you do have to wear formal clothes.
You computer screens. The manager, Carl ton 5chieck says, 'We have used You don 't see the chefs. They are in t he kitchen high above aqua rium technology to put diners face-to-face with the you. They're real men, not machines at least not yet. The sh. Our guests are speechless at the colour and beauty food is all freShly cooked and when it is ready it is put in a o f th e underwater world.
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