LEADER 00000nam a2200589Ki 4500
001 on1143218617
003 OCoLC
005 20200717185501.2
006 m o d
007 cr cnu---unuuu
008 200305s2019 maua ob 001 0 eng d
020 9781547400713|q(electronic book)
020 1547400714|q(electronic book)
020 |z9781547416745
020 |z1547416742
035 (OCoLC)1143218617
040 N$T|beng|erda|epn|cN$T
049 RIDW
050 4 HD30.23|b.G733 2019eb
072 7 HF|2lcco
082 04 658.472|222
090 HD30.23|b.G733 2019eb
100 1 Greasley, Andrew,|0https://id.loc.gov/authorities/names/
n97068464|eauthor.
245 10 Simulating business processes for descriptive, predictive,
and prescriptive analytics /|cAndrew Greasley.
264 1 [Boston, Mass.?] :|bDe G Press,|c[2019]
264 4 |c©2019
300 1 online resource (x, 341 pages) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
340 |gpolychrome|2rdacc
347 text file|2rdaft
504 Includes bibliographical references (pages 336-337) and
index.
505 0 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.
520 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. --|cProvided by
publisher.
588 0 Print version record.
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Decision making|xSimulation methods.|0https://id.loc.gov/
authorities/subjects/sh2009123011
650 0 Management|xStatistical methods.|0https://id.loc.gov/
authorities/subjects/sh2008107306
650 0 Business intelligence.|0https://id.loc.gov/authorities/
subjects/sh85018300
650 7 Decision making|xSimulation methods.|2fast|0https://
id.worldcat.org/fast/889066
650 7 Management|xStatistical methods.|2fast|0https://
id.worldcat.org/fast/1007232
650 7 Business intelligence.|2fast|0https://id.worldcat.org/fast
/842723
655 4 Electronic books.
776 08 |iPrint version:|aGreasley, Andrew.|tSimulating business
processes for descriptive, predictive, and prescriptive
analytics.|d[Boston, Mass.?] : De G Press, [2019]
|z9781547416745|w(DLC) 2019937567|w(OCoLC)1126282340
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=2330456|zOnline ebook via EBSCO. Access
restricted to current Rider University students, faculty,
and staff.
856 42 |3Instructions for reading/downloading the EBSCO version
of this ebook|uhttp://guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 00 |d20200727|cEBSCO|tEBSCOebooksacademic NEW June-July 17
7032|lridw
994 92|bRID
