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E-raamat: Business Process Modeling, Simulation and Design

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(University of Colorado at Boulder, USA), (Lund University, Sweden)
  • Formaat: 542 pages
  • Sari: Textbooks in Mathematics
  • Ilmumisaeg: 07-Dec-2018
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781351667258
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Business Process Modeling, Simulation and DesignSuurem pilt
  • Formaat: 542 pages
  • Sari: Textbooks in Mathematics
  • Ilmumisaeg: 07-Dec-2018
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781351667258
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Business Process Modeling, Simulation and Design, Third Edition provides students with a comprehensive coverage of a range of analytical tools used to model, analyze, understand, and ultimately design business processes.

The new edition of this very successful textbook includes a wide range of approaches such as graphical flowcharting tools, cycle time and capacity analyses, queuing models, discrete-event simulation, simulation-optimization, and data mining for process analytics.

While most textbooks on business process management either focus on the intricacies of computer simulation or managerial aspects of business processes, this textbook does both. It presents the tools to design business processes and management techniques on operating them efficiently.

The book focuses on the use of discrete event simulation as the main tool for analyzing, modeling, and designing effective business processes. The integration of graphic user-friendly simulation software enables a systematic approach to create optimal designs.
Preface xiii
1 Introduction to Business Process Design
1(26)
1.1 What Is a Business Process?
1(9)
1.1.1 Process Types and Hierarchies
3(1)
1.1.2 Determinants of the Process Architecture
4(1)
1.1.2.1 Inputs and Outputs
5(1)
1.1.2.2 Flow Units
5(1)
1.1.2.3 Network of Activities and Buffers
6(2)
1.1.2.4 Resources
8(1)
1.1.2.5 Information Structure
9(1)
1.1.3 Workflow Management Systems
9(1)
1.2 The Essence of Business Process Design
10(6)
1.2.1 Incremental Process Improvement and Process Design
12(1)
1.2.2 An Illustrative Example
13(3)
1.3 Business Process Design, Overall Business Performance, and Strategy
16(2)
1.3.1 Business Process Design and Overall Business Performance
16(1)
1.3.2 Business Process Design and Strategy
17(1)
1.4 Why Do Inefficient and Ineffective Business Processes Exist?
18(2)
1.5 Summary
20(1)
Discussion Questions and Exercises
21(5)
References
26(1)
2 Process Management and Process-Oriented Improvement Programs
27(50)
2.1 Process Management and the Power of Adopting a Process View
27(15)
2.1.1 Phase I: Initialization
29(1)
2.1.1.1 Process Ownership
30(1)
2.1.1.2 Analyzing Process Boundaries and Interfaces
31(2)
2.1.2 Phase II: Definition
33(1)
2.1.3 Phase III: Control
34(1)
2.1.3.1 Establishing Control Points
34(1)
2.1.3.2 Developing and Implementing Measurements
35(1)
2.1.3.3 Feedback and Control
36(1)
2.1.4 An Illustrative Example: Managing a Document Distribution Process
36(1)
2.1.4.1 Assign Process Ownership
37(1)
2.1.4.2 Analyze Boundaries and Interfaces
37(2)
2.1.4.3 Define the Process
39(1)
2.1.4.4 Establish Control Points
40(1)
2.1.4.5 Develop and Implement Measures
40(1)
2.1.4.6 Perform Feedback and Control
41(1)
2.1.4.7 Summary and Final Remarks
41(1)
2.2 Six Sigma Quality Programs
42(10)
2.2.1 Six Sigma Definitions
42(2)
2.2.2 The Six Sigma Cost and Revenue Rationale
44(1)
2.2.2.1 The Cost or Efficiency Rationale
44(2)
2.2.2.2 The Revenue or Effectiveness Rationale
46(1)
2.2.3 Six Sigma in Product and Process Design
47(1)
2.2.4 The Six Sigma Framework
48(1)
2.2.4.1 Top Management Commitment
48(1)
2.2.4.2 Stakeholder Involvement
49(1)
2.2.4.3 Training
49(1)
2.2.4.4 Measurement System
50(1)
2.2.4.5 The Improvement Methodology
50(2)
2.2.5 Concluding Remarks: Key Reasons for the Success of Six Sigma
52(1)
2.3 Business Process Reengineering
52(14)
2.3.1 Reengineering and Its Relationship with other Earlier Programs
54(3)
2.3.2 A Brief History of Reengineering
57(1)
2.3.3 When Should a Process Be Reengineered?
58(2)
2.3.4 What Should Be Reengineered?
60(1)
2.3.4.1 Dysfunction
61(1)
2.3.4.2 Importance
62(1)
2.3.4.3 Feasibility
62(1)
2.3.5 Suggested Reengineering Frameworks
63(3)
2.4 Revolutionary versus Evolutionary Change
66(3)
2.5 Summary
69(2)
Discussion Questions and Exercises
71(3)
References
74(3)
3 A Framework for Business Process Design Projects
77(34)
3.1 Step 1: Case for Action and Vision Statements
79(2)
3.2 Step 2: Process Identification and Selection
81(2)
3.3 Step 3: Obtaining Management Commitment
83(1)
3.4 Step 4: Evaluation of Design Enablers
83(5)
3.4.1 Example: The Internet-Enabling Change at Chase Manhattan Bank
85(1)
3.4.2 Example: New Technology as a Change Enabler in the Grocery Industry
86(2)
3.5 Step 5: Acquiring Process Understanding
88(3)
3.5.1 Understanding the Existing Process
88(2)
3.5.2 Understanding the Customer
90(1)
3.6 Step 6: Creative Process Design
91(11)
3.6.1 Benchmarking
93(2)
3.6.2 Design Principles
95(7)
3.6.3 The Devil's Quadrangle
102(1)
3.7 Step 7: Process Modeling and Simulation
102(3)
3.8 Step 8: Implementation of the New Process Design
105(1)
3.9 Summary
106(1)
Discussion Questions and Exercises
107(2)
References
109(2)
4 Basic Tools for Process Design
111(40)
4.1 Process Flow Analysis
113(12)
4.1.1 General Process Charts
114(1)
4.1.2 Process Flow Diagrams
115(2)
4.1.3 Process Activity Charts
117(1)
4.1.4 Flowcharts
118(3)
4.1.5 Service System Maps
121(4)
4.2 Workflow Design Principles and Tools
125(15)
4.2.1 Establish a Product Orientation in the Process
125(2)
4.2.2 Eliminate Buffers
127(1)
4.2.3 Establish One-at-a-Time Processing
128(2)
4.2.4 Balance the Flow to the Bottleneck
130(4)
4.2.5 Minimize Sequential Processing and Handoffs
134(1)
4.2.6 Establish an Efficient System for Processing of Work
135(5)
4.2.7 Minimize Multiple Paths through Operations
140(1)
4.3 Additional Diagramming Tools
140(2)
4.4 From Theory to Practice: Designing an Order-Picking Process
142(1)
4.5 Summary
143(1)
Discussion Questions and Exercises
143(6)
References
149(2)
5 Managing Process Flows
151(36)
5.1 Business Processes and Flows
151(7)
5.1.1 Throughput Rate
153(1)
5.1.2 Work-in-Process
154(2)
5.1.3 Cycle Time
156(1)
5.1.4 Little's Law
157(1)
5.2 Cycle Time and Capacity Analysis
158(10)
5.2.1 Cycle Time Analysis
158(1)
5.2.1.1 Rework
159(1)
5.2.1.2 Multiple Paths
160(1)
5.2.1.3 Parallel Activities
160(4)
5.2.2 Capacity Analysis
164(1)
5.2.2.1 Rework
164(1)
5.2.2.2 Multiple Paths
165(1)
5.2.2.3 Parallel Activities
165(3)
5.3 Managing Cycle Time and Capacity
168(4)
5.3.1 Cycle Time Reduction
168(2)
5.3.2 Increasing Process Capacity
170(2)
5.4 Theory of Constraints
172(6)
5.4.1 Drum-Buffer-Rope Systems
177(1)
5.5 Summary
178(1)
Discussion Questions and Exercises
178(7)
References
185(2)
6 Introduction to Queuing Modeling
187(54)
6.1 Queuing Systems, the Basic Queuing Process, and Queuing Strategies
189(7)
6.1.1 The Basic Queuing Process
190(1)
6.1.1.1 The Calling Population
191(1)
6.1.1.2 The Arrival Process
192(1)
6.1.1.3 The Queue Configuration
192(2)
6.1.1.4 The Queue Discipline
194(1)
6.1.1.5 The Service Mechanism
194(1)
6.1.2 Strategies for Mitigating the Effects of Long Queues
195(1)
6.2 Analytical Queuing Models
196(44)
6.2.1 The Exponential Distribution and its Role in Queuing Theory
197(4)
6.2.1.1 The Exponential Distribution, the Poisson Distribution, and the Poisson Process
201(1)
6.2.2 Terminology, Notation, and Little's Law Revisited
202(4)
6.2.3 Birth-and-Death Processes
206(10)
6.2.4 The M/M/1 Model
216(4)
6.2.5 The M/M/c Model
220(3)
6.2.6 The M/M/c/K Model
223(4)
6.2.7 The M/M/c/∞/N Model
227(4)
6.2.8 Queuing Theory and Process Design
231(2)
6.2.8.1 Determining WC
233(1)
6.2.5.2 Determining SC
234(1)
6.2.8.3 A Decision Model for Designing Queuing Systems
234(6)
6.3 Summary
240(1)
Appendix 6A Mathematical Derivations and Models with Generally Distributed Service Times
241(274)
6A.1 Mathematical Derivations of Key Results
241(2)
6A.1.1 The Exponential Distribution (Section 6.2.1)
241(1)
6A.1.2 Birth-and-death processes (6.2.3)
241(1)
6A.1.3 The M/M/1 Model (6.2.4)
242(1)
6A.2 Queuing Models with Generally Distributed Service Times
243(2)
6A.2.1 The M/G/1 Queuing Model
243(1)
6A.2.2 The M/G/∞ queuing model
244(1)
Discussion Questions and Exercises
245(11)
References
256(1)
7 Introduction to Simulation
257(36)
7.1 Simulation Models
259(2)
7.2 Monte Carlo Simulation
261(4)
7.3 Discrete-Event Simulation
265(2)
7.4 Getting Started in Simulation Modeling
267(5)
7.4.1 Step 1: Defining the Problem
267(1)
7.4.2 Step 2: Understanding the Process
268(1)
7.4.3 Step 3: Determining Goals and Objective
269(1)
7.4.4 Step 4: Obtaining Support from Management
269(1)
7.4.5 Step 5: Choosing Simulation Software
270(1)
7.4.6 Step 6: Determining Data Requirements and Availability
270(1)
7.4.7 Step 7: Developing Assumptions about the Problem
271(1)
7.4.8 Step 8: Determining Desired Outputs
271(1)
7.4.9 Step 9: Building the Simulation Model
271(1)
7.4.10 Step 10: Project Kickoff
272(1)
7.5 An Illustrative Example
272(7)
7.6 Spreadsheet Simulation of a Process
279(2)
7.7 Successful Simulation in Practice
281(3)
7.8 When Not to Simulate
284(3)
7.9 Summary
287(1)
Discussion Questions and Exercises
287(5)
References
292(1)
8 Modeling and Simulating Business Processes with ExtendSim
293(64)
8.1 Developing a Simulation Model---Principles and Concepts
294(2)
8.1.1 Model Verification
296(1)
8.1.2 Model Validation
296(1)
8.2 ExtendSim Elements
296(4)
8.3 ExtendSim Tutorial: A Basic Queuing Model
300(4)
8.4 Basic Data Collection and Statistical Analysis
304(5)
8.5 Adding Randomness to Processing Times and the Use of Attributes
309(5)
8.6 Adding a Second Underwriting Team
314(3)
8.7 Modeling Resources and Resource Pools
317(4)
8.8 Customizing the Animation
321(1)
8.9 Calculating Activity-Based Costs
322(4)
8.10 Cycle Time Analysis
326(2)
8.11 Modeling Advanced Queuing Features
328(7)
8.11.1 Blocking
330(1)
8.11.2 Balking s
330(2)
8.11.3 Reneging
332(1)
8.11.4 Priorities and Priority Queues
333(2)
8.12 Modeling Routing in Multiple Paths and Parallel Paths
335(7)
8.12.1 Multiple Paths
336(4)
8.12.2 Parallel Paths
340(2)
8.13 Model Documentation and Enhancements
342(2)
8.14 Summary
344(1)
Discussion Questions and Exercises
344(11)
References
355(2)
9 Input and Output Data Analysis
357(80)
9.1 Dealing with Randomness
358(2)
9.2 Characterizing Probability Distributions of Field Data
360(10)
9.2.1 Goodness-of-Fit Tests
363(1)
9.2.2 Using Stat::Fit for Distribution Fitting
364(3)
9.2.3 Choosing a Distribution in the Absence of Sample Data
367(3)
9.3 Random Number Generators
370(4)
9.3.1 The Runs Test
373(1)
9.4 Generation of Random Variates
374(3)
9.5 Analysis of Simulation Output Data
377(15)
9.5.1 Nonterminating Processes
379(2)
9.5.2 Terminating Processes
381(2)
9.5.3 Confidence Intervals
383(1)
9.5.3.1 Confidence Interval for a Population Mean
384(2)
9.5.4 Sample Size Calculation
386(3)
9.5.5 Comparing Output Variables for Different Process Designs
389(3)
9.6 Modeling and Analysis of Process Design Cases
392(15)
9.6.1 Process Design of a Call Center for Software Support
393(1)
9.6.1.1 Modeling, Analysis, and Recommendations
394(3)
9.6.2 Design of a Hospital Admissions Process
397(10)
9.7 Summary
407(1)
9.8 Training Cases
407(12)
9.8.1 Case 1: Improving the X-Ray Process at County Hospital
407(1)
9.8.1.1 Part I: Analyzing the Current Process Design
408(2)
9.8.1.2 Part II: Suggest and Evaluate a New Process Design
410(1)
9.8.2 Case 2: Process Modeling and Analysis in an Assembly Factory
410(4)
9.8.3 Case 3: Redesign of a Credit Applications Process
414(1)
9.8.4 Case 4: Redesigning the Adoption Process in a Humane Society
414(2)
9.8.4.1 Part I
416(1)
9.8.4.2 Part II
416(1)
9.8.5 Case 5: Performance Analysis and Improvement of an Internet Ordering Process
417(2)
Appendix 9A Hypothesis Testing, Confidence Intervals, and Statistical Tables
419(7)
9A.1 Goodness-of-Fit Tests (Section 9.2.1)
419(1)
9A1.1 The Chi-Square Test
419(3)
9A.1.2 The Kolmogorov-Smirnov Test
422(3)
9A.2 Confidence Interval for a Population Proportion (Section 9.5.3)
425(1)
9A.3 Hypothesis Testing (Section 9.5.5)
426(6)
9A.4 Statistical Tables
429(3)
Exercises
432(4)
References
436(1)
10 Optimizing Business Process Performance
437(36)
10.1 Business Process Optimization
437(2)
10.2 The Role of Simulation Optimization in Business Process Management
439(5)
10.3 Simulation Optimization with ExtendSim
444(13)
10.3.1 Tutorial: Process Optimization with ExtendSim
447(7)
10.3.2 Alternative Optimization Models
454(3)
10.4 Optimization of Process Simulation Models
457(4)
10.4.1 Configuring a Hospital Emergency Room Process
457(2)
10.4.2 Staffing Levels for a Personal Insurance Claims Process
459(2)
10.5 Summary
461(1)
Appendix 10A Evolutionary Computation
462(1)
Exercises
463(8)
10A.1 Simulation Optimization Projects
464(1)
10A.2 Emergency Room Staffing
464(2)
10A.3 Call Center Configuration
466(2)
10A.4 Loan Application Process
468(2)
10A.5 Process with Multiple Job Types and Deadlines
470(1)
References
471(2)
11 Business Process Analytics
473(42)
11.1 Competing on Analytics
475(4)
11.2 Business Process Management Systems
479(13)
11.2.1 Business Rules
481(1)
11.2.2 Data Mining
482(6)
11.2.3 Monitor and Control
488(1)
11.2.4 Process Mining
489(3)
11.3 Process Benchmarking
492(9)
11.3.1 Graphical Analysis of the Ratio Model
495(1)
11.3.1.1 Efficiency Calculation
496(2)
11.3.2 Linear Programming Formulation of the Ratio Model
498(3)
11.3.3 Learning from Best-Practice Organizations
501(1)
Appendix 11A Excel Add-In for Data Envelopment Analysis
501(6)
Discussion Questions and Exercises
507(5)
References
512(3)
Epilogue 515(2)
Index 517
Manual Laguna is a Media One Professor of Management Science at the Leeds School of Business in the University of Colorado. He received his doctoral degree in Operations Research and Industrial Engineering from the University of Texas at Austin. He has more than one hundred publications in data analytics methods and applications, and is the editor-in-chief of the Journal of Heuristics.

Johan Marklund is a Professor of Production Management at Lund University, Faculty of Engineering in Sweden. He holds a PhD in Production Management and BSc in Business Administration from Lund University, and a MSc in Industrial Engineering and Management from Linköping University. He has published in numerous scientific journals and his research interests include inventory theory, supply chain management and logistics.
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