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|a OHX
|b eng
|e rda
|c OHX
|d FIE
|d OCLCO
|d SZR
|d OCLCF
|d UtOrBLW
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|a EEMR
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| 050 |
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4 |
|a HD30.23
|b .G733 2019
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| 072 |
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7 |
|a HF
|2 lcco
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| 082 |
1 |
4 |
|a 658.472
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| 100 |
1 |
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|a Greasley, Andrew,
|e author.
|0 http://id.loc.gov/authorities/names/n97068464
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| 245 |
1 |
0 |
|a Simulating business processes for descriptive, predictive, and prescriptive analytics /
|c Andrew Greasley.
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| 264 |
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1 |
|a [Boston, Mass.?] :
|b De G Press,
|c [2019]
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| 264 |
|
4 |
|c ©2019
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| 300 |
|
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|a x, 341 pages :
|b illustrations ;
|c 24 cm
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
|
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|a unmediated
|b n
|2 rdamedia
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| 338 |
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|a volume
|b nc
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references (pages 336-337) and index.
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| 505 |
0 |
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|a Frontmatter -- Preface -- Acknowledgments -- About the author -- part 1: Understanding simulation and analytics. Analytics and simulation basics -- Simulation and business processes -- Build the conceptual model -- Build the simulation -- Use simulation for descriptive, predictive and prescriptive analytics -- part 2: Simulation case studies. Case study: a simulation of a police call center -- Case study: A simulation of a "Last Mile" logistics system -- Case Study: A simulation of an enterprise resource planning system -- Case study: A simulation of a snacks process production system -- Case study: A simulation of a police arrest process -- Case study: A simulation of a food retail distribution network -- Case study: A simulation of a proposed textile plant -- Case study: A simulation of a road traffic accident process -- Case study: A simulation of a rail carriage maintenance depot -- Case study: A simulation of a rail vehicle bogie production facility -- Case study: A simulation of advanced service provision -- Case study: Generating simulation analytics with process mining -- Chapter 18. Case study: Using simulation with data envelopment analysis -- Case study: Agent-based modeling in discrete-event simulation -- Appendix A -- Appendix B -- Index.
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| 520 |
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|a This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker. --
|c Provided by publisher.
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| 650 |
|
0 |
|a Decision making
|x Simulation methods.
|0 http://id.loc.gov/authorities/subjects/sh2009123011
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| 650 |
|
0 |
|a Management
|x Statistical methods.
|0 http://id.loc.gov/authorities/subjects/sh2008107306
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| 650 |
|
0 |
|a Business intelligence.
|0 http://id.loc.gov/authorities/subjects/sh85018300
|
| 650 |
|
7 |
|a Business intelligence.
|2 fast
|0 (OCoLC)fst00842723
|
| 650 |
|
7 |
|a Decision making
|x Simulation methods.
|2 fast
|0 (OCoLC)fst00889066
|
| 650 |
|
7 |
|a Management
|x Statistical methods.
|2 fast
|0 (OCoLC)fst01007232
|
| 776 |
0 |
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|z 9781547400690
|i PDF version
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| 776 |
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|y .b140081781
|b 210707
|c 201030
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|b 201109
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|t 0
|
| 952 |
f |
f |
|p Can Circulate
|a Michigan State University-Library of Michigan
|b Michigan State University
|c MSU Main Library
|d MSU Main Library
|t 0
|e HD30.23 .G733 2019
|h Library of Congress classification
|i Printed Material
|m 31293037213570
|n 1
|
