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Business Analytics Principles, Concepts, and Applications: What, Why, and How [Kõva köide]

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  • Formaat: Hardback, 368 pages, kõrgus x laius: 232x178 mm
  • Ilmumisaeg: 15-May-2014
  • Kirjastus: Pearson FT Press
  • ISBN-10: 0133552187
  • ISBN-13: 9780133552188
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Business Analytics Principles, Concepts, and Applications: What, Why, and HowSuurem pilt
  • Formaat: Hardback, 368 pages, kõrgus x laius: 232x178 mm
  • Ilmumisaeg: 15-May-2014
  • Kirjastus: Pearson FT Press
  • ISBN-10: 0133552187
  • ISBN-13: 9780133552188
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780133552188.html
Märksõnad:
Learn everything you need to know to start using business analytics and integrating it throughout your organization.Business Analytics Principles, Concepts, and Applications brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives.They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making.Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning.Unlike most competitive guides, this text demonstrates the use of IBMs menu-based SPSS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.A valuable resource for all beginning-to-intermediate-level business analysts and business analytics managers; for MBA/Masters degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.

Muu info

Responding to a shortage of effective content for teaching business analytics, this text offers a complete, integrated package of knowledge for newcomers to the subject.

 

The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives.

 

Business Analytics Principles, Concepts, and Applications offers a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making.

 

Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning.
Preface xvi
Part I: What Are Business Analytics 1(14)
Chapter 1 What Are Business Analytics?
3(12)
1.1 Terminology
3(4)
1.2 Business Analytics Process
7(3)
1.3 Relationship of BA Process and Organization Decision-Making Process
10(2)
1.4 Organization of This Book
12(1)
Summary
13(1)
Discussion Questions
13(1)
References
14(1)
Part II: Why Are Business Analytics Important 15(28)
Chapter 2 Why Are Business Analytics Important?
17(12)
2.1 Introduction
17(1)
2.2 Why BA Is Important: Providing Answers to Questions
18(2)
2.3 Why BA Is Important: Strategy for Competitive Advantage
20(3)
2.4 Other Reasons Why BA Is Important
23(3)
2.4.1 Applied Reasons Why BA Is Important
23(1)
2.4.2 The Importance of BA with New Sources of Data
24(2)
Summary
26(1)
Discussion Questions
26(1)
References
26(3)
Chapter 3 What Resource Considerations Are Important to Support Business Analytics?
29(14)
3.1 Introduction
29(1)
3.2 Business Analytics Personnel
30(3)
3.3 Business Analytics Data
33(3)
3.3.1 Categorizing Data
33(2)
3.3.2 Data Issues
35(1)
3.4 Business Analytics Technology
36(5)
Summary
41(1)
Discussion Questions
41(1)
References
42(1)
Part III: How Can Business Analytics Be Applied 43(122)
Chapter 4 How Do We Align Resources to Support Business Analytics within an Organization?
45(18)
4.1 Organization Structures Aligning Business Analytics
45(9)
4.1.1 Organization Structures
46(5)
4.1.2 Teams
51(3)
4.2 Management Issues
54(6)
4.2.1 Establishing an Information Policy
54(1)
4.2.2 Outsourcing Business Analytics
55(1)
4.2.3 Ensuring Data Quality
56(2)
4.2.4 Measuring Business Analytics Contribution
58(1)
4.2.5 Managing Change
58(2)
Summary
60(1)
Discussion Questions
61(1)
References
61(2)
Chapter 5 What Are Descriptive Analytics?
63(30)
5.1 Introduction
63(1)
5.2 Visualizing and Exploring Data
64(3)
5.3 Descriptive Statistics
67(5)
5.4 Sampling and Estimation
72(6)
5.4.1 Sampling Methods
73(3)
5.4.2 Sampling Estimation
76(2)
5.5 Introduction to Probability Distributions
78(2)
5.6 Marketing/Planning Case Study Example: Descriptive Analytics Step in the BA Process
80(11)
5.6.1 Case Study Background
81(1)
5.6.2 Descriptive Analytics Analysis
82(9)
Summary
91(1)
Discussion Questions
91(1)
Problems
92(1)
Chapter 6 What Are Predictive Analytics?
93(26)
6.1 Introduction
93(1)
6.2 Predictive Modeling
94(3)
6.2.1 Logic-Driven Models
94(2)
6.2.2 Data-Driven Models
96(1)
6.3 Data Mining
97(5)
6.3.1 A Simple Illustration of Data Mining
98(1)
6.3.2 Data Mining Methodologies
99(3)
6.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Analytics Step in the BA Process
102(12)
6.4.1 Case Study Background Review
103(1)
6.4.2 Predictive Analytics Analysis
104(10)
Summary
114(1)
Discussion Questions
115(1)
Problems
115(2)
References
117(2)
Chapter 7 What Are Prescriptive Analytics?
119(20)
7.1 Introduction
119(1)
7.2 Prescriptive Modeling
120(2)
7.3 Nonlinear Optimization
122(7)
7.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Step in the BA Analysis
129(5)
7.4.1 Case Background Review
129(1)
7.4.2 Prescriptive Analysis
129(5)
Summary
134(1)
Addendum
134(1)
Discussion Questions
135(1)
Problems
135(2)
References
137(2)
Chapter 8 A Final Business Analytics Case Problem
139(26)
8.1 Introduction
139(1)
8.2 Case Study: Problem Background and Data
140(1)
8.3 Descriptive Analytics Analysis
141(6)
8.4 Predictive Analytics Analysis
147(11)
8.4.1 Developing the Forecasting Models
147(8)
8.4.2 Validating the Forecasting Models
155(2)
8.4.3 Resulting Warehouse Customer Demand Forecasts
157(1)
8.5 Prescriptive Analytics Analysis
158(5)
8.5.1 Selecting and Developing an Optimization Shipping Model
158(1)
8.5.2 Determining the Optimal Shipping Schedule
159(2)
8.5.3 Summary of BA Procedure for the Manufacturer
161(1)
8.5.4 Demonstrating Business Performance Improvement
162(1)
Summary
163(1)
Discussion Questions
164(1)
Problems
164(1)
Part IV: Appendixes 165(170)
A Statistical Tools
167(34)
A.1 Introduction
167(1)
A.2 Counting
167(4)
A.3 Probability Concepts
171(6)
A.4 Probability Distributions
177(16)
A.5 Statistical Testing
193(8)
B Linear Programming
201(40)
B.1 Introduction
201(1)
B.2 Types of Linear Programming Problems/Models
201(1)
B.3 Linear Programming Problem/Model Elements
202(5)
B.4 Linear Programming Problem/Model Formulation Procedure
207(10)
B.5 Computer-Based Solutions for Linear Programming Using the Simplex Method
217(10)
B.6 Linear Programming Complications
227(5)
B.7 Necessary Assumptions for Linear Programming Models
232(1)
B.8 Linear Programming Practice Problems
233(8)
C Duality and Sensitivity Analysis in Linear Programming
241(22)
C.1 Introduction
241(1)
C.2 What Is Duality?
241(2)
C.3 Duality and Sensitivity Analysis Problems
243(15)
C.4 Determining the Economic Value of a Resource with Duality
258(1)
C.5 Duality Practice Problems
259(4)
D Integer Programming
263(8)
D.1 Introduction
263(1)
D.2 Solving IP Problems/Models
264(4)
D.3 Solving Zero-One Programming Problems/Models
268(2)
D.4 Integer Programming Practice Problems
270(1)
E Forecasting
271(24)
E.1 Introduction
271(1)
E.2 Types of Variation in Time Series Data
272(4)
E.3 Simple Regression Model
276(5)
E.4 Multiple Regression Models
281(3)
E.5 Simple Exponential Smoothing
284(2)
E.6 Smoothing Averages
286(2)
E.7 Fitting Models to Data
288(3)
E.8 How to Select Models and Parameters for Models
291(1)
E.9 Forecasting Practice Problems
292(3)
F Simulation
295(8)
F.1 Introduction
295(1)
F.2 Types of Simulation
295(7)
F.3 Simulation Practice Problems
302(1)
G Decision Theory
303(32)
G.1 Introduction
303(1)
G.2 Decision Theory Model Elements
304(1)
G.3 Types of Decision Environments
304(1)
G.4 Decision Theory Formulation
305(1)
G.5 Decision-Making Under Certainty
306(1)
G.6 Decision-Making Under Risk
307(4)
G.7 Decision-Making under Uncertainty
311(4)
G.8 Expected Value of Perfect Information
315(2)
G.9 Sequential Decisions and Decision Trees
317(4)
G.10 The Value of Imperfect Information: Bayes's Theorem
321(7)
G.11 Decision Theory Practice Problems
328(7)
Index 335
Marc J. Schniederjans is the C. Wheaton Battey Distinguished Professor of Business in the College of Business Administration at the University of Nebraska-Lincoln and has served on the faculty of three other universities. Professor Schniederjans is a Fellow of the Decision Sciences Institute (DSI) and in 20142015 will serve as DSIs President. His prior experience includes owning and operating his own truck leasing business. He is currently a member of the Institute of Supply Management (ISM), the Production and Operations Management Society (POMS), and Decision Sciences Institute (DSI). Professor Schniederjans has taught extensively in operations management and management science. He has won numerous teaching awards and is an honorary member of the Golden Key honor society and the Alpha Kappa Psi business honor society. He has published more than one hundred journal articles and has authored or coauthored twenty books in the field of management. The title of his most recent book is Reinventing the Supply Chain Life Cycle, and his research has encompassed a wide range of operations management and decision science topics. He has also presented more than one hundred research papers at academic meetings. Professor Schniederjans is serving on five journal editorial review boards, including Computers & Operations Research, International Journal of Information & Decision Sciences, International Journal of Information Systems in the Service Sector, and Journal of Operations Management, Production, and Operations Management. He is also serving as an area editor for the journal Operations Management Research and as an associate editor for the International Journal of Strategic Decision Sciences and International Journal of the Society Systems Science and Management Review: An International Journal (Korea). Professor Schniederjans has served as a consultant and trainer to various business and government agencies.

Dara G. Schniederjans is an assistant professor of Supply Chain Management at the University of Rhode Island, College of Business Administration. She has published articles in journals such as Decision Support Systems, Journal of the Operational Research Society, and Business Process Management Journal. She has also co-authored two text books and co-edited a readings book. She has contributed chapters to readings utilizing quantitative and statistical methods. Dara has served as a guest co-editor for a special issue on Business Ethics in Social Sciences in the International Journal of Society Systems Science. She has also served as a website coordinator for Decisions Sciences Institute. She currently teaches courses in Supplier Relationship Management and Operations Management.

Christopher M. Starkey is an Economics student at the University of Connecticut-Storrs. He has presented papers at the Academy of Management and Production and Operations Management Society meetings. He currently teaches courses in Principles of Microeconomics and has taught Principles of Macroeconomics. His current research interests include macroeconomic and monetary policy, as well as other decision-making methodologies.