LEADER 04824nam 2200661 450 001 9910463651303321 005 20200520144314.0 010 $a1-78017-243-5 010 $a1-78017-241-9 010 $a1-78017-242-7 035 $a(CKB)2670000000578858 035 $a(EBL)1713958 035 $a(SSID)ssj0001435901 035 $a(PQKBManifestationID)11853501 035 $a(PQKBTitleCode)TC0001435901 035 $a(PQKBWorkID)11434550 035 $a(PQKB)10700955 035 $a(MiAaPQ)EBC1713958 035 $a(CaSebORM)9781780172415 035 $a(Au-PeEL)EBL1713958 035 $a(CaPaEBR)ebr10993970 035 $a(CaONFJC)MIL666121 035 $a(OCoLC)898101370 035 $a(EXLCZ)992670000000578858 100 $a20141220h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProblem management $ean implementation guide for the real world /$fMichael G. Hall 205 $a1st edition 210 1$aWiltshire, England :$cBCS The Chartered Institute for IT,$d2014. 210 4$dİ2014 215 $a1 online resource (192 p.) 300 $aDescription based upon print version of record. 311 $a1-322-34839-1 311 $a1-78017-244-3 320 $aIncludes bibliographical references and index. 327 $aCover; Copyright; CONTENTS; FIGURES AND TABLES; AUTHOR; ACKNOWLEDGEMENTS; INTRODUCTION; WHAT THIS BOOK IS ABOUT; WHAT THIS BOOK IS NOT ABOUT; WHY READ THIS BOOK?; BIASES; SECTIONS; CONVENTIONS USED IN THIS BOOK; SECTION 1 - INTRODUCING PROBLEM MANAGEMENT; 1 WHAT IS PROBLEM MANAGEMENT?; OBJECTIVES; SCOPE; PROBLEM MANAGEMENT IS DIFFERENT FROM INCIDENT MANAGEMENT; 2 FACTORS FOR SUCCESS; CHALLENGES; MANAGEMENT SUPPORT; TRAINING; STAKEHOLDER MANAGEMENT; COMMUNICATION; CONSISTENT APPROACH; AGREEMENTS; 3 DEVELOPING THE BUSINESS CASE; WHY HAVE PROBLEM MANAGEMENT? 327 $aWHY IS A STRUCTURED APPROACH TO PROBLEM MANAGEMENT REQUIRED?SETTING OUT THE VALUE PROPOSITION; THE PLAN; THE CALL TO ACTION; SAMPLE BUSINESS CASES AND PLANS; SECTION 2 - IMPLEMENTING AND RUNNING PROBLEM MANAGEMENT; 4 THE IMPLEMENTATION PROJECT; IMPLEMENTATION APPROACH; PHASE ZERO: PLANNING AND PREPARATION; PHASE ONE: START-UP; PHASE TWO: CONSOLIDATION; PHASE THREE: STEADY STATE; FURTHER READING: ORGANISATIONAL CHANGE ISSUES; 5 ORGANISING PROBLEM MANAGEMENT AS A FUNCTION; THE ORGANISATIONAL MODEL USED; PEOPLE AND SKILLS; MAPPING THE PROBLEM MANAGEMENT TEAM TO THE PROCESS 327 $aGOVERNANCE AND PROBLEM ADVISORY BOARD6 REALISING THE BENEFITS OF PROBLEM MANAGEMENT; COMMITMENTS; CLASSIFICATION AND ROOT CAUSE CODE STRUCTURES; FIX THE PROBLEM; MAKE RESULTS AVAILABLE FOR FUTURE USE; EFFECTIVE COMMUNICATION; 7 METRICS, KEY PERFORMANCE INDICATORS AND REPORTING; INTRODUCTION; KEY PERFORMANCE INDICATORS; METRICS; REPORTING; 8 TOOL REQUIREMENTS; MANAGING THE WORKFLOW; MANAGING THE INFORMATION; REPORTING AND COMMUNICATION; AIDS TO SELECTION; 9 WHERE NEXT FOR PROBLEM MANAGEMENT?; PROBLEM MANAGEMENT WITHIN IT; PROBLEM MANAGEMENT OUTSIDE IT; WHERE TO NEXT FOR PROBLEM MANAGERS? 327 $aSECTION 3 - PROBLEM MANAGEMENT PROCESS AND TECHNIQUES10 PROCESS OVERVIEW; STATES; 11 DETECT AND LOG PROBLEMS; DETECTING PROBLEMS; REACTIVE PROBLEM MANAGEMENT; PROACTIVE PROBLEM MANAGEMENT; LOGGING PROBLEMS; 12 ASSESS, PRIORITISE AND ASSIGN PROBLEMS; ASSESS THE PROBLEM; CATEGORISATION; PRIORITISATION; ASSIGNMENT; 13 INVESTIGATION AND DIAGNOSIS; MAJOR INVESTIGATION FRAMEWORKS; SUPPORTING INVESTIGATION TOOLS; OTHER DEFINITIONS OF PROBLEM, PROBLEM MANAGEMENT, ROOT CAUSE ANALYSIS; ROOT CAUSE QUALITY: GETTING TO 'REAL' ROOT CAUSES; 14 ERROR RESOLUTION; FIND A SOLUTION; THE SOLUTION PROPOSAL 327 $aAPPROVALIMPLEMENTATION; 15 CLOSING PROBLEMS; REVIEW; THE DEFERRED STATE; CLOSURE; MAJOR PROBLEM REVIEW; CONCLUSION; ONLINE RESOURCES; SAMPLE BUSINESS CASES; SAMPLE IMPLEMENTATION PLAN; SAMPLE LAUNCH AND FAMILIARISATION TRAINING; ROOT CAUSE CODES; FURTHER READING; REFERENCES; INDEX; Back Cover 330 $aProblem management is the IT service management process that tends to return more benefits more quickly than any of the others. This book offers real-world guidance on all aspects of implementing and running an effective problem management function. It gives IT practitioners, consultants and managers the tools to add real value to their businesses. 606 $aProblem solving$xEconomic aspects 608 $aElectronic books. 615 0$aProblem solving$xEconomic aspects. 676 $a658.403 700 $aHall$b Michael G.$0138304 712 02$aBCS, The Chartered Institute for IT, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463651303321 996 $aProblem management$91969494 997 $aUNINA LEADER 05904nam 22006615 450 001 9910558496703321 005 20251204105528.0 010 $a9783030909284$b(electronic bk.) 010 $z9783030909277 024 7 $a10.1007/978-3-030-90928-4 035 $a(MiAaPQ)EBC6942562 035 $a(Au-PeEL)EBL6942562 035 $a(CKB)21441195500041 035 $a(PPN)261518380 035 $a(BIP)83681689 035 $a(BIP)81838784 035 $a(DE-He213)978-3-030-90928-4 035 $a(EXLCZ)9921441195500041 100 $a20220329d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlgorithmic Decision Making with Python Resources $eFrom Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs /$fby Raymond Bisdorff 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (366 pages) 225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v324 311 08$aPrint version: Bisdorff, Raymond Algorithmic Decision Making with Python Resources Cham : Springer International Publishing AG,c2022 9783030909277 327 $aPart I: Introduction to the DIGRAPH3 Python Resources -- 1. Working with the DIGRAPH3 Python Resources -- 2. Working with Bipolar-Valued Digraphs -- 3. Working with Outranking Digraphs -- Part II: Evaluation Models and Decision Algorithms -- 4. Building a Best Choice Recommendation -- 5. How to Create a New Multiple-Criteria Performance Tableau -- 6. Generating Random Performance Tableaux -- 7. Who Wins the Election? -- 8. Ranking with Multiple Incommensurable Criteria -- 9. Rating by Sorting into Relative Performance Quantiles -- 10. Rating-by-Ranking with Learned Performance Quantile Norms -- 11. HPC Ranking of Big Performance Tableaux -- Part III: Evaluation and Decision Case Studies -- 12. Alice?s Best Choice: A Selection Case Study -- 13. The Best Academic Computer Science Depts: A Ranking Case Study -- 14. The Best Students, Where Do They Study? A Rating Case Study -- 15. Exercises -- Part IV: Advanced Topics -- 16. On Measuring the Fitness of a Multiple-Criteria Ranking -- 17. On Computing Digraph Kernels -- 18. On Confident Outrankings with Uncertain Criteria Significance Weights -- 19. Robustness Analysis of Outranking Digraphs -- 20. Tempering Plurality Tyranny Effects in Social Choice -- Part V: Working with Undirected Graphs -- 21. Bipolar-Valued Undirected Graphs -- 22. On Tree Graphs and Graph Forests -- 23. About Split, Comparability, Interval, and Permutation Graphs. 330 $aThis book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA). The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects, such as bipolar-valued digraphs and outranking digraphs. In eight methodological chapters, the second part illustrates multiple-criteria evaluation models and decision algorithms. These chapters are largely problem-oriented and demonstrate how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The book?s third part presents threereal-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs. The book is primarily intended for graduate students in management sciences, computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantile-rating algorithms, discussed and illustrated in several chapters, will be of practical interest to public and private performance auditors. 410 0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v324 606 $aOperations research 606 $aGraph theory 606 $aComputer science$xMathematics 606 $aMathematical statistics$xData processing 606 $aOperations Research and Decision Theory 606 $aGraph Theory 606 $aMathematical Applications in Computer Science 606 $aStatistics and Computing 615 0$aOperations research. 615 0$aGraph theory. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics$xData processing. 615 14$aOperations Research and Decision Theory. 615 24$aGraph Theory. 615 24$aMathematical Applications in Computer Science. 615 24$aStatistics and Computing. 676 $a005.133 676 $a658.4033 700 $aBisdorff$b Raymond$01220512 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910558496703321 996 $aAlgorithmic Decision Making with Python Resources$92824666 997 $aUNINA