03692nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001500162072002300177072001400200100008400214245016000298250001800458264006900476300006800545336002600613337002600639338003600665347002400701505029600725520150001021650004602521650003102567650002702598650002502625650003502650650004002685650001802725650002402743650002702767650004502794650003402839650006002873700007502933710003403008773002603042776003603068776003603104856004603140978-3-031-72255-4DE-He21320251030114646.0cr nn 008mamaa241228s2024    sz |    s    |||| 0|eng d  a97830317225547 a10.1007/978-3-031-72255-42doi 4aQ336 7aUN2bicssc 7aCOM0210002bisacsh 7aUN2thema1 aCheong, Michelle L. F.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aData and Decision Analytics for Business Operationsh[electronic resource] :bPrinciples, Problems, and Practice /cby Michelle L. F. Cheong, Ma Nang Laik.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVIII, 323 p. 186 illus., 59 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 a1. Introduction -- 2. Demand Forecasting -- 3. Inventory Management -- 4. Distribution Management -- 5. Capacity Planning -- 6. Optimization Theory -- 7. Special Optimization Problems -- 8. Workforce Planning And Scheduling -- 9. Heuristic Algorithms -- 10. Queuing Theory -- 11. Simulation.  aIn this book, readers will be exposed to the Data and Decision Analytics Framework which helps a business analyst to first identify the root cause of business problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions should be developed to solve the problems. To guide the reader through the learning and application of this framework, several cases are included in the book to illustrate the typical operations management problems faced by businesses. These cases are based on experiences in business domains such as retail, healthcare, transportation and logistics operations, and banking, and they are related to demand forecasting, inventory management, distribution management, capacity planning, resource allocation, workforce scheduling, and service system management. For each case, a complete mapping of the case into the Data and Decision Analytics Framework was done to explain how the framework was applied to derive the data insights from data analytics, to define the business objectives, make the necessary assumptions, and then develop the solution to the business problem. This book aims at senior-year undergraduate or graduate students studying industrial engineering, business management with a focus on operations, or data science. They will learn how to use data analytics to first analyze problems to identify the root cause of problems, before developing the solutions supported by decision analytics. 0aArtificial intelligencexData processing. 0aBusinessxData processing. 0aProduction management. 0aOperations research. 0aBusiness information services. 0aInformation technologyxManagement.14aData Science.24aBusiness Analytics.24aOperations Management.24aOperations Research and Decision Theory.24aBusiness Information Systems.24aComputer Application in Administrative Data Processing.1 aMa Nang Laik.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303172254708iPrinted edition:z978303172256140uhttps://doi.org/10.1007/978-3-031-72255-4