Procurement analytics: data-driven decision-making in procurement and supply management
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2023]
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| Schriftenreihe: | Springer series in supply chain management
22 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xiv, 268 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031432804 |
| ISSN: | 2365-6409 |
Internformat
MARC
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| 245 | 1 | 0 | |a Procurement analytics |b data-driven decision-making in procurement and supply management |c Christian Mandl |
| 264 | 1 | |a Cham |b Springer |c [2023] | |
| 300 | |a xiv, 268 Seiten |b Illustrationen, Diagramme | ||
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| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Springer series in supply chain management |v 22 |x 2365-6409 | |
| 650 | 4 | |a Procurement | |
| 650 | 4 | |a Supply Chain Management | |
| 650 | 4 | |a Operations Management | |
| 650 | 4 | |a Business Analytics | |
| 650 | 4 | |a Operations Research and Decision Theory | |
| 650 | 4 | |a Industrial procurement | |
| 650 | 4 | |a Business logistics | |
| 650 | 4 | |a Production management | |
| 650 | 4 | |a Business / Data processing | |
| 650 | 4 | |a Operations research | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-031-43281-1 |
| 830 | 0 | |a Springer series in supply chain management |v 22 |w (DE-604)BV043837793 |9 22 | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034912667&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
Datensatz im Suchindex
| _version_ | 1805071299239739392 |
|---|---|
| adam_text |
Contents Introduction. 1.1 The Status Quo of Procurement. 1.2 Digital Transformation of Procurement. 1.3 Technologies and Trends in Digital Procurement. 1.3.1 X-as-a-Service (XaaS) . 1.3.2 Platform Solutions. 1.3.3 Robotic Process Automation (RPA). 1.3.4 (Chat)Bots. 1.3.5 Natural Language Processing (NLP). 1.3.6 Text Mining. 1.3.7 Optimal Character Recognition (OCR) . 1.3.8 Electronic Data Interchange (EDI). 1.3.9 Blockchain. 1.3.10 Digital Twins and Multi-Agent Systems . 1.4 Data Analytics in Procurement. 1.5 Structure of the Textbook. References. 1 1 3 5 5 6 6 7 7 7 8 8 8 9 9 11 12 2
Fundamentals of Data Analytics. 2.1 Introduction . 2.2 Descriptive Analytics. 2.2.1 Data Collection and Consolidation. 2.2.2 Descriptive Statistics. 2.2.3 Correlation Analysis. . 2.2.4 Data Mining and Principal Component Analysis. 2.2.5 Data Visualization. 2.3 Predictive Analytics. 2.3.1 Accuracy Measures. 2.3.2 Methods of Predictive Analytics. 2.4 Prescriptive Analytics. 2.4.1 Accuracy Measures. 13 13 16 16 17 19 20 21 22 24 31 49 49 1 vii
viii Contents 2.4.2 Methods of Prescriptive Analytics. 2.4.3 Fundamental OptimizationProblems. References. 51 61 66 3 Data-Driven Spend Management. 3.1 Introduction . 3.2 Spend Classification . 3.2.1 Spend Cube. 3.2.2 ABC-XYZ Classification . 3.2.3 Kraljic Classification and Derivatives. 3.2.4 Geospatial Classification. 69 69 70 70 73 75 79 Al-Based Classification. 79 Procurement Performance Indicators. Spend Optimization. 3.4.1 Should-Cost Analysis. 3.4.2 Linear Performance Pricing (LPP). 3.4.3 Nonlinear Performance Pricing .
References. 80 90 90 91 96 97 Data-Driven Supplier Management. 4.1 Introduction . 4.2 Sourcing Strategies. 4.2.1 Make or Buy. 4.2.2 Supplier Partnerships. 4.2.3 Global Sourcing Versus Local Sourcing. 4.2.4 Multi-sourcing Versus Single Sourcing. 4.3 Tender Management. . 4.3.1 Request-for-X (RFX). 4.3.2 Reverse Auctions. 4.3.3 Combinatorial Auctions. 4.4 Supplier Negotiations. 4.4.1 Basic Terms in Negotiations and Bargaining. 4.4.2 Contingency Compensation in Distributive Bargaining. 4.4.3 Psychological Negotiation Design. 4.4.4 Game-Theoretic Negotiation Design (Negotiation Games). 131 4.4.5
Negotiation Strategies and Technology. 4.5 Supplier Selection and Evaluation Models . 4.5.1 Weighted Sum Model (WSM). 4.5.2 Weighted Product Model (WPM) . 4.5.3 Analytic Hierarchy Process (AHP). 4.5.4 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). 144 4.5.5 Outranking Method (OM). 4.5.6 Decision Trees (DT). 4.5.7 Data Envelopment Analysis (DEA). 99 99 101 101 106 107 108 Ill Ill 114 122 127 127 129 131 3.2.5 3.3 3.4 4 136 137 138 140 141 147 150 150
Contents ix 4.5.8 Linear Programming (LP). 153 4.5.9 Method Comparison. 156 Supply Network Design. 156 4.6.1 Supply Chain Mapping. 157 4.6.2 Deterministic Supply Network Optimization. 158 4.6.3 Stochastic Supply Network Optimization. 161 4.6.4 Multi-echelon Supply Network Optimization. 164 4.6.5 Multi-objective Supply Network Optimization. 165 4.7 Supply Contracting and Coordination . 167 4.7.1 Consequences of Lacking Supply Chain Coordination. 167 4.7.2 Vertical Purchasing Agreements. 169 4.7.3 Supply Contracting Under Demand Risk. 172 4.7.4 Supply Contracting Under Asymmetric Information. 178 4.7.5 Service-Level Agreements (SLA). 179 4.7.6 Horizontal Purchasing Agreements. 179 References. 184 4.6 5 Data-Driven Inventory Management. 5.1 Introduction . 5.2 Inventory
Metrics. 5.3 Economic Order Quantity (EOQ). 5.3.1 Standard EOQ Model. 5.3.2 EOQ Model Under Quantity Discounts. 5.3.3 Dynamic Lot Sizing Model. 5.4 Safety Stock Planning . 5.4.1 Simple Safety Stock Rules. 5.4.2 Analytical Safety Stock Models. 5.5 Newsvendor Models. 5.5.1 Standard Newsvendor Model. 5.5.2 Data-Driven Newsvendor Model. 5.5.3 Feature-Based Newsvendor Model. 5.5.4 Machine Learning-Enabled Newsvendor Model. 5.5.5 Deep Learning-Enabled Newsvendor Model . 5.6 Dynamic Stochastic Inventory Control Policies. 5.6.1 (s,Q) Inventory Policy. 5.6.2 (s,S) Inventory Policy. 5.6.3 (t,S) Inventory Policy. 5.6.4 (t,Q) Inventory Policy.
References. 187 187 188 191 191 193 196 197 197 198 202 202 205 206 209 209 210 211 212 213 214 215 Data-Driven Risk Management. 6.1 Introduction. 6.2 Supply Disruption Risk. 6.2.1 Measuring Supply Disruption Risk. 6.2.2 Measures for Risk Mitigation. 217 217 219 220 223 6
Contents X 7 6.3 Commodity Price Risk. 6.3.1 Measuring Commodity Price Risk . 6.3.2 Stochastic Modeling of Commodity Prices. 6.3.3 Measures for Risk Mitigation. 6.4 Exchange Rate Risk. 6.4.1 Measuring Exchange Rate Risk. 6.4.2 Measures for Risk Mitigation. References. 229 230 234 236 260 260 263 265 Conclusion. 267 |
| adam_txt |
Contents Introduction. 1.1 The Status Quo of Procurement. 1.2 Digital Transformation of Procurement. 1.3 Technologies and Trends in Digital Procurement. 1.3.1 X-as-a-Service (XaaS) . 1.3.2 Platform Solutions. 1.3.3 Robotic Process Automation (RPA). 1.3.4 (Chat)Bots. 1.3.5 Natural Language Processing (NLP). 1.3.6 Text Mining. 1.3.7 Optimal Character Recognition (OCR) . 1.3.8 Electronic Data Interchange (EDI). 1.3.9 Blockchain. 1.3.10 Digital Twins and Multi-Agent Systems . 1.4 Data Analytics in Procurement. 1.5 Structure of the Textbook. References. 1 1 3 5 5 6 6 7 7 7 8 8 8 9 9 11 12 2
Fundamentals of Data Analytics. 2.1 Introduction . 2.2 Descriptive Analytics. 2.2.1 Data Collection and Consolidation. 2.2.2 Descriptive Statistics. 2.2.3 Correlation Analysis. . 2.2.4 Data Mining and Principal Component Analysis. 2.2.5 Data Visualization. 2.3 Predictive Analytics. 2.3.1 Accuracy Measures. 2.3.2 Methods of Predictive Analytics. 2.4 Prescriptive Analytics. 2.4.1 Accuracy Measures. 13 13 16 16 17 19 20 21 22 24 31 49 49 1 vii
viii Contents 2.4.2 Methods of Prescriptive Analytics. 2.4.3 Fundamental OptimizationProblems. References. 51 61 66 3 Data-Driven Spend Management. 3.1 Introduction . 3.2 Spend Classification . 3.2.1 Spend Cube. 3.2.2 ABC-XYZ Classification . 3.2.3 Kraljic Classification and Derivatives. 3.2.4 Geospatial Classification. 69 69 70 70 73 75 79 Al-Based Classification. 79 Procurement Performance Indicators. Spend Optimization. 3.4.1 Should-Cost Analysis. 3.4.2 Linear Performance Pricing (LPP). 3.4.3 Nonlinear Performance Pricing .
References. 80 90 90 91 96 97 Data-Driven Supplier Management. 4.1 Introduction . 4.2 Sourcing Strategies. 4.2.1 Make or Buy. 4.2.2 Supplier Partnerships. 4.2.3 Global Sourcing Versus Local Sourcing. 4.2.4 Multi-sourcing Versus Single Sourcing. 4.3 Tender Management. . 4.3.1 Request-for-X (RFX). 4.3.2 Reverse Auctions. 4.3.3 Combinatorial Auctions. 4.4 Supplier Negotiations. 4.4.1 Basic Terms in Negotiations and Bargaining. 4.4.2 Contingency Compensation in Distributive Bargaining. 4.4.3 Psychological Negotiation Design. 4.4.4 Game-Theoretic Negotiation Design (Negotiation Games). 131 4.4.5
Negotiation Strategies and Technology. 4.5 Supplier Selection and Evaluation Models . 4.5.1 Weighted Sum Model (WSM). 4.5.2 Weighted Product Model (WPM) . 4.5.3 Analytic Hierarchy Process (AHP). 4.5.4 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). 144 4.5.5 Outranking Method (OM). 4.5.6 Decision Trees (DT). 4.5.7 Data Envelopment Analysis (DEA). 99 99 101 101 106 107 108 Ill Ill 114 122 127 127 129 131 3.2.5 3.3 3.4 4 136 137 138 140 141 147 150 150
Contents ix 4.5.8 Linear Programming (LP). 153 4.5.9 Method Comparison. 156 Supply Network Design. 156 4.6.1 Supply Chain Mapping. 157 4.6.2 Deterministic Supply Network Optimization. 158 4.6.3 Stochastic Supply Network Optimization. 161 4.6.4 Multi-echelon Supply Network Optimization. 164 4.6.5 Multi-objective Supply Network Optimization. 165 4.7 Supply Contracting and Coordination . 167 4.7.1 Consequences of Lacking Supply Chain Coordination. 167 4.7.2 Vertical Purchasing Agreements. 169 4.7.3 Supply Contracting Under Demand Risk. 172 4.7.4 Supply Contracting Under Asymmetric Information. 178 4.7.5 Service-Level Agreements (SLA). 179 4.7.6 Horizontal Purchasing Agreements. 179 References. 184 4.6 5 Data-Driven Inventory Management. 5.1 Introduction . 5.2 Inventory
Metrics. 5.3 Economic Order Quantity (EOQ). 5.3.1 Standard EOQ Model. 5.3.2 EOQ Model Under Quantity Discounts. 5.3.3 Dynamic Lot Sizing Model. 5.4 Safety Stock Planning . 5.4.1 Simple Safety Stock Rules. 5.4.2 Analytical Safety Stock Models. 5.5 Newsvendor Models. 5.5.1 Standard Newsvendor Model. 5.5.2 Data-Driven Newsvendor Model. 5.5.3 Feature-Based Newsvendor Model. 5.5.4 Machine Learning-Enabled Newsvendor Model. 5.5.5 Deep Learning-Enabled Newsvendor Model . 5.6 Dynamic Stochastic Inventory Control Policies. 5.6.1 (s,Q) Inventory Policy. 5.6.2 (s,S) Inventory Policy. 5.6.3 (t,S) Inventory Policy. 5.6.4 (t,Q) Inventory Policy.
References. 187 187 188 191 191 193 196 197 197 198 202 202 205 206 209 209 210 211 212 213 214 215 Data-Driven Risk Management. 6.1 Introduction. 6.2 Supply Disruption Risk. 6.2.1 Measuring Supply Disruption Risk. 6.2.2 Measures for Risk Mitigation. 217 217 219 220 223 6
Contents X 7 6.3 Commodity Price Risk. 6.3.1 Measuring Commodity Price Risk . 6.3.2 Stochastic Modeling of Commodity Prices. 6.3.3 Measures for Risk Mitigation. 6.4 Exchange Rate Risk. 6.4.1 Measuring Exchange Rate Risk. 6.4.2 Measures for Risk Mitigation. References. 229 230 234 236 260 260 263 265 Conclusion. 267 |
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| index_date | 2024-07-03T23:29:47Z |
| indexdate | 2024-07-20T04:35:36Z |
| institution | BVB |
| isbn | 9783031432804 |
| issn | 2365-6409 |
| language | English |
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| spelling | Mandl, Christian Verfasser (DE-588)1196573107 aut Procurement analytics data-driven decision-making in procurement and supply management Christian Mandl Cham Springer [2023] xiv, 268 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Springer series in supply chain management 22 2365-6409 Procurement Supply Chain Management Operations Management Business Analytics Operations Research and Decision Theory Industrial procurement Business logistics Production management Business / Data processing Operations research Erscheint auch als Online-Ausgabe 978-3-031-43281-1 Springer series in supply chain management 22 (DE-604)BV043837793 22 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034912667&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Mandl, Christian Procurement analytics data-driven decision-making in procurement and supply management Springer series in supply chain management Procurement Supply Chain Management Operations Management Business Analytics Operations Research and Decision Theory Industrial procurement Business logistics Production management Business / Data processing Operations research |
| title | Procurement analytics data-driven decision-making in procurement and supply management |
| title_auth | Procurement analytics data-driven decision-making in procurement and supply management |
| title_exact_search | Procurement analytics data-driven decision-making in procurement and supply management |
| title_exact_search_txtP | Procurement analytics data-driven decision-making in procurement and supply management |
| title_full | Procurement analytics data-driven decision-making in procurement and supply management Christian Mandl |
| title_fullStr | Procurement analytics data-driven decision-making in procurement and supply management Christian Mandl |
| title_full_unstemmed | Procurement analytics data-driven decision-making in procurement and supply management Christian Mandl |
| title_short | Procurement analytics |
| title_sort | procurement analytics data driven decision making in procurement and supply management |
| title_sub | data-driven decision-making in procurement and supply management |
| topic | Procurement Supply Chain Management Operations Management Business Analytics Operations Research and Decision Theory Industrial procurement Business logistics Production management Business / Data processing Operations research |
| topic_facet | Procurement Supply Chain Management Operations Management Business Analytics Operations Research and Decision Theory Industrial procurement Business logistics Production management Business / Data processing Operations research |
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