Supply chain analytics: concepts, techniques and applications
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Palgrave Macmillan
[2022]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 377 Seiten |
| ISBN: | 9783030922238 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents 1 Introduction....................................................................................................... What Is a Supply Chain?..................................................................... 1.1.1 Why Do We Need a Supply Chain?........................................ 1.1.2 Structure of a Supply Chain................................... 1.1.3 Supply Chain Processes........................................................... 1.1.4 Supply Chain Flows................................................................. 1.2 Supply Chain Management................................................................. 1.3 Business Analytics............................................................................... 1.4 Supply Chain Analytics....................................................................... 1.4.1 SMART Goals of SCA............................................................. 1.4.2 SCA.......................................................................................... References................................................................................... 1.1 2 Data-Driven Supply Chains and Intro to Python................................... Data and Its Value in SCM................................................................... Data Source in Supply Chains............................................................ Big Data................................................................................................ Introduction to Python................................ 2.4.1 Python Downloads and
Installation........................................ 2.4.2 Python IDE and Jupyter Notebook........................................ 2.4.3 Essential Python Libraries....................................................... 2.4.4 Jupyter Notebook Optimization............................................... References....................................................................................................... 2.1 2.2 2.3 2.4 3 Data Manipulation.......................................................................................... 3.1 3.2 3.3 3.4 3.5 3.6 3.7 What Is Data Manipulation? ................................................................ Data Loading and Writing.................................................................... Data Indexing and Selection............................................................... Data Merging and Combination........................................................... Data Cleaning and Preparation........................................................... Data Computation and Aggregation.................................................. Working with Text and Datetime Data................................................. 1 1 2 2 4 6 7 9 12 13 15 19 21 21 24 30 31 33 33 34 38 43 45 45 47 50 55 62 67 75 XV
xvi Contents 4 Data Visualization.......................................................... 83 4.1 Data Visualization in Python................................................................ 83 4.2 Creating a Figure in Python................................................................. 85 4.3 Formatting a Figure............................................................................. 91 4.4 Plotting Simple Charts......................................................................... 93 4.5 Plotting with Seaborn..............................................................................100 4.6 Geographic Mapping with Basemap.................................................... 103 4.7 Visualizing Starbucks Locations.......................................................... 106 5 Customer Management................................................................................. 113 5.1 Customers in Supply Chains................................................................. 114 5.2 Understanding Customers..................................................................... 115 5.2.1 Benefits of a Customer-Centric Supply Chain.......................... 116 5.3 . Jlow to Build a Customer-Centric SC................................................... 117 5.3.1 Step One: Define Customers.................................................... 118 5.3.2 Step Two: Understand Customers’ Real Needs....................... 118 5.3.3 Step Three: Translate Needs into Product Features............... 119 5.3.4 Step Four: Design Supply Chain Processes.............................
119 5.3.5 Step Five: Build Efficient Logistics Systems........................... 120 5.4 Cohort Analysis...................................................................................... 121 5.4.1 What Is a Cohort?...................................................................... 121 5.4.2 What Is Cohort Analysis?......................................................... 121 5.4.3 Steps for Cohort Analysis....................... 122 5.4.4 Cohort Analysis Example in Python........................................ 123 5.5 RFM Analysis ...................................................................................... 129 5.5.1 What Is RFM?...........................................................................129 5.5.2 What Is RFM Analysis?............................................................. 130 5.5.3 Steps for RFM Analysis........................................................... .131 5.5.4 RFM Analysis Example in Python............................................. 133 5.6 Clustering Algorithms............................................................................139 5.6.1 К-Means Algorithm.................................. .*................................140 5.6.2 Customer Segmentation with K-Means................................... 145 5.6.3 DBSCAN..............................................................................151 5.6.4 Gaussian Mixture Model........................................................... 155 References.................................................................................... 162 6 Supply
Management....................................................................................... 163 6.1 Procurement in Supply Chains............................................................... 164 6.1.1 Vertical Integration...................................................................... 164 6.1.2 Outsourcing................................................................................. 165 6.2 Supplier Selection.......................................................... 168 6.3 Supplier Evaluation................................................................................ 171 6.3.1 Supplier Capability Assessment................................................. 173
Contents xvii Supplier Relationship Management...................................................... 174 6.4.1 Managing Tiered Supply Network............................................ 175 6.5 Supply Risk Management..................................................................... 178 6.5.1 Step One: Risk Identification..................................................... 179 6.5.2 Step Two: Risk Assessment....................................................... 179 6.5.3 Step Three: Develop Risk Response Strategies........................180 6.5.4 Step Four: Continuous Monitoring and Regular Review.... 180 6.6 Supplier Selection Examples................................................................. 182 6.6.1 Coffee Quality Example........................................................... 182 6.7 Regression Algorithms........................................................................... 192 6.7.1 Linear Regression..................................................................... 192 6.7.2 Support Vector Machines...........................................................196 6.7.3 Decision Trees........................................................................... 202 6.7.4 Cross-Validation......................................................................... 206 6.7.5 Random Forests......................................................................... 208 6.7.6 Model Fine-Tuning...................................................................210 6.7.7 Extra-Trees................................................................................. 214
References......................................................................................................... 218 6.4 7 Warehouse and Inventory Management.....................................................219 7.1 Warehouse Management....................................................................... 220 7.1.1 What Is Warehouse Management?............................................220 7.1.2 Warehouse Management System.............................................. 222 7.1.3 Benefits of WMS...................... 223 7.1.4 Warehouse Management Performance Measurement........... 224 7.2 Inventory Management........................................................................ 225 7.2.1 What Is Inventory?..................................................................... 225 7.2.2 The Purpose of Holding Inventory............................................ 226 7.2.3 What Is Inventory Management?.............................................. 228 7.2.4 Inventory Management Methods.............................................. 229 7.3 Warehouse Optimization....................................................................... 236 7.3.1 Introduction to PuLP................................................................. 236 7.3.2 Warehouse Optimization with PuLP....................................... 240 7.4 Classification Algorithms....................................................................... 243 7.4.1 Logistic Regression................................................................... 243 7.4.2 Classification Performance
Measurement............................... 248 7.4.3 Dealing with Imbalanced Dataset............................................ 253 7.4.4 Linear Support Vector Classifier.............................................. 258 7.4.5 Random Forest Classifier................................. 260 7.4.6 Boosting Methods for Classification........................................262 References......................................................................................................... 269
xviii Contents 8 Demand Management.................................................................................... 271 8.1 Demand Management........................................................................... 272 8.2 Demand Forecasting............................................................................. 273 8.3 Time Series Forecasting.............................. 275 8.3.1 Time Series Components.......................................................... 275 8.3.2 Traditional Time Series Forecasting Methods........................ 277 8.4 Machine Learning Methods................................................................... 302 8.4.1 Univariate vs Multivariate Time Series................................... 302 8.4.2 Random Forest Regression...................................................... 303 8.4.3 XGBoost................................................................................... 308 8.4.4 Learning Rate............................................................................. 311 References......................................................................................................... 318 9 Logistics Management.................................................................................. 319 VI Logistics Management........................................................................... 320 9.1.1 What Is Logistics Management?.............................................. 320 9.1.2 Main Logistics Management Activities.................................... 322 9.2 Modes of Transport in
Logistics.......................................................... 323 9.2.1 Chargeable Weight...................................................................... 323 9.2.2 Product Value Density............................................................... 325 9.3 Logistics Service Providers................................................................... 327 9.3.1 Freight Companies..................................................................... 327 9.3.2 Freight Carriers................................. 328 9.3.3 Freight Forwarders.................................................................. .328 9.3.4 Third Party Logistics (3PL) providers...................................... 328 9.3.5 Fourth Party Logistics (4PL) Companies........................ 329 9.4 Global Logistics Management............................................................... 329 9.4.1 Incoterms.................................................................................... 331 9.5 Logistics Network Design..................................................................... 332 9.5.1 Location Decisions..................................................................... 333 9.5.2 Centralization vs De֊centralization .. Λ................................. 335 9.5.3 Logistics Network Design Example with PuLP.................... 337 9.6 Route Optimization.............................................................. 342 9.6.1 Travelling Salesman Problem.................................................. 342 9.6.2 Vehicle Routing Problem............................... 348 9.6.3 Route
Optimization Example: The Cambridge Pizza Store.................................................................................. 350 References........................... 369 Index.......................................................................................................................... 371
|
| adam_txt |
Contents 1 Introduction. What Is a Supply Chain?. 1.1.1 Why Do We Need a Supply Chain?. 1.1.2 Structure of a Supply Chain. 1.1.3 Supply Chain Processes. 1.1.4 Supply Chain Flows. 1.2 Supply Chain Management. 1.3 Business Analytics. 1.4 Supply Chain Analytics. 1.4.1 SMART Goals of SCA. 1.4.2 SCA. References. 1.1 2 Data-Driven Supply Chains and Intro to Python. Data and Its Value in SCM. Data Source in Supply Chains. Big Data. Introduction to Python. 2.4.1 Python Downloads and
Installation. 2.4.2 Python IDE and Jupyter Notebook. 2.4.3 Essential Python Libraries. 2.4.4 Jupyter Notebook Optimization. References. 2.1 2.2 2.3 2.4 3 Data Manipulation. 3.1 3.2 3.3 3.4 3.5 3.6 3.7 What Is Data Manipulation? . Data Loading and Writing. Data Indexing and Selection. Data Merging and Combination. Data Cleaning and Preparation. Data Computation and Aggregation. Working with Text and Datetime Data. 1 1 2 2 4 6 7 9 12 13 15 19 21 21 24 30 31 33 33 34 38 43 45 45 47 50 55 62 67 75 XV
xvi Contents 4 Data Visualization. 83 4.1 Data Visualization in Python. 83 4.2 Creating a Figure in Python. 85 4.3 Formatting a Figure. 91 4.4 Plotting Simple Charts. 93 4.5 Plotting with Seaborn.100 4.6 Geographic Mapping with Basemap. 103 4.7 Visualizing Starbucks Locations. 106 5 Customer Management. 113 5.1 Customers in Supply Chains. 114 5.2 Understanding Customers. 115 5.2.1 Benefits of a Customer-Centric Supply Chain. 116 5.3 . Jlow to Build a Customer-Centric SC. 117 5.3.1 Step One: Define Customers. 118 5.3.2 Step Two: Understand Customers’ Real Needs. 118 5.3.3 Step Three: Translate Needs into Product Features. 119 5.3.4 Step Four: Design Supply Chain Processes.
119 5.3.5 Step Five: Build Efficient Logistics Systems. 120 5.4 Cohort Analysis. 121 5.4.1 What Is a Cohort?. 121 5.4.2 What Is Cohort Analysis?. 121 5.4.3 Steps for Cohort Analysis. 122 5.4.4 Cohort Analysis Example in Python. 123 5.5 RFM Analysis . 129 5.5.1 What Is RFM?.129 5.5.2 What Is RFM Analysis?. 130 5.5.3 Steps for RFM Analysis. .131 5.5.4 RFM Analysis Example in Python. 133 5.6 Clustering Algorithms.139 5.6.1 К-Means Algorithm. .*.140 5.6.2 Customer Segmentation with K-Means. 145 5.6.3 DBSCAN.151 5.6.4 Gaussian Mixture Model. 155 References. 162 6 Supply
Management. 163 6.1 Procurement in Supply Chains. 164 6.1.1 Vertical Integration. 164 6.1.2 Outsourcing. 165 6.2 Supplier Selection. 168 6.3 Supplier Evaluation. 171 6.3.1 Supplier Capability Assessment. 173
Contents xvii Supplier Relationship Management. 174 6.4.1 Managing Tiered Supply Network. 175 6.5 Supply Risk Management. 178 6.5.1 Step One: Risk Identification. 179 6.5.2 Step Two: Risk Assessment. 179 6.5.3 Step Three: Develop Risk Response Strategies.180 6.5.4 Step Four: Continuous Monitoring and Regular Review. 180 6.6 Supplier Selection Examples. 182 6.6.1 Coffee Quality Example. 182 6.7 Regression Algorithms. 192 6.7.1 Linear Regression. 192 6.7.2 Support Vector Machines.196 6.7.3 Decision Trees. 202 6.7.4 Cross-Validation. 206 6.7.5 Random Forests. 208 6.7.6 Model Fine-Tuning.210 6.7.7 Extra-Trees. 214
References. 218 6.4 7 Warehouse and Inventory Management.219 7.1 Warehouse Management. 220 7.1.1 What Is Warehouse Management?.220 7.1.2 Warehouse Management System. 222 7.1.3 Benefits of WMS. 223 7.1.4 Warehouse Management Performance Measurement. 224 7.2 Inventory Management. 225 7.2.1 What Is Inventory?. 225 7.2.2 The Purpose of Holding Inventory. 226 7.2.3 What Is Inventory Management?. 228 7.2.4 Inventory Management Methods. 229 7.3 Warehouse Optimization. 236 7.3.1 Introduction to PuLP. 236 7.3.2 Warehouse Optimization with PuLP. 240 7.4 Classification Algorithms. 243 7.4.1 Logistic Regression. 243 7.4.2 Classification Performance
Measurement. 248 7.4.3 Dealing with Imbalanced Dataset. 253 7.4.4 Linear Support Vector Classifier. 258 7.4.5 Random Forest Classifier. 260 7.4.6 Boosting Methods for Classification.262 References. 269
xviii Contents 8 Demand Management. 271 8.1 Demand Management. 272 8.2 Demand Forecasting. 273 8.3 Time Series Forecasting. 275 8.3.1 Time Series Components. 275 8.3.2 Traditional Time Series Forecasting Methods. 277 8.4 Machine Learning Methods. 302 8.4.1 Univariate vs Multivariate Time Series. 302 8.4.2 Random Forest Regression. 303 8.4.3 XGBoost. 308 8.4.4 Learning Rate. 311 References. 318 9 Logistics Management. 319 VI Logistics Management. 320 9.1.1 What Is Logistics Management?. 320 9.1.2 Main Logistics Management Activities. 322 9.2 Modes of Transport in
Logistics. 323 9.2.1 Chargeable Weight. 323 9.2.2 Product Value Density. 325 9.3 Logistics Service Providers. 327 9.3.1 Freight Companies. 327 9.3.2 Freight Carriers. 328 9.3.3 Freight Forwarders. .328 9.3.4 Third Party Logistics (3PL) providers. 328 9.3.5 Fourth Party Logistics (4PL) Companies. 329 9.4 Global Logistics Management. 329 9.4.1 Incoterms. 331 9.5 Logistics Network Design. 332 9.5.1 Location Decisions. 333 9.5.2 Centralization vs De֊centralization . Λ. 335 9.5.3 Logistics Network Design Example with PuLP. 337 9.6 Route Optimization. 342 9.6.1 Travelling Salesman Problem. 342 9.6.2 Vehicle Routing Problem. 348 9.6.3 Route
Optimization Example: The Cambridge Pizza Store. 350 References. 369 Index. 371 |
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| index_date | 2024-07-03T19:46:07Z |
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| language | English |
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| spelling | Liu, Yang Verfasser (DE-588)1101472219 aut Supply chain analytics concepts, techniques and applications Kurt Y. Liu Cham Palgrave Macmillan [2022] xix, 377 Seiten txt rdacontent n rdamedia nc rdacarrier Datenanalyse (DE-588)4123037-1 gnd rswk-swf Supply Chain Management (DE-588)4684051-5 gnd rswk-swf Production management Business information services supply chain analytics supply chains business management data science logistics management business analytics data science for business supply chain management Supply Chain Management (DE-588)4684051-5 s Datenanalyse (DE-588)4123037-1 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-92224-5 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=033580332&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Supply chain analytics concepts, techniques and applications |
| title_auth | Supply chain analytics concepts, techniques and applications |
| title_exact_search | Supply chain analytics concepts, techniques and applications |
| title_exact_search_txtP | Supply chain analytics concepts, techniques and applications |
| title_full | Supply chain analytics concepts, techniques and applications Kurt Y. Liu |
| title_fullStr | Supply chain analytics concepts, techniques and applications Kurt Y. Liu |
| title_full_unstemmed | Supply chain analytics concepts, techniques and applications Kurt Y. Liu |
| title_short | Supply chain analytics |
| title_sort | supply chain analytics concepts techniques and applications |
| title_sub | concepts, techniques and applications |
| topic | Datenanalyse (DE-588)4123037-1 gnd Supply Chain Management (DE-588)4684051-5 gnd |
| topic_facet | Datenanalyse Supply Chain Management |
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