Handling data problems in machine learning applications in supply chain management: a multiple-case study on the analysis of data augmentation approaches
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Stuttgart
Fraunhofer Verlag
2022
|
| Series: | Publication series on logistics and technologies
Vol. 10 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | 365 Seiten Illustrationen, Diagramme 24 cm x 17 cm |
| ISBN: | 9783839617861 3839617863 |
Staff View
MARC
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| 100 | 1 | |a Menden, Christian |e Verfasser |0 (DE-588)1136477322 |4 aut | |
| 245 | 1 | 0 | |a Handling data problems in machine learning applications in supply chain management |b a multiple-case study on the analysis of data augmentation approaches |c Christian Menden |
| 264 | 1 | |a Stuttgart |b Fraunhofer Verlag |c 2022 | |
| 300 | |a 365 Seiten |b Illustrationen, Diagramme |c 24 cm x 17 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Publication series on logistics and technologies |v Vol. 10 | |
| 502 | |b Dissertation |c Fakultät Sozial- und Wirtschaftswissenschaften der Otto-Friedrich-Universität Bamberg |d 2021 | ||
| 650 | 0 | 7 | |a Data Augmentation |0 (DE-588)4825966-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Supply Chain Management |0 (DE-588)4684051-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 653 | |a Fraunhofer SCS | ||
| 653 | |a Fraunhofer IIS | ||
| 653 | |a Machine Learning | ||
| 653 | |a Applied Mathematics | ||
| 653 | |a Transport Studies | ||
| 653 | |a Data Augmentation | ||
| 653 | |a Business mathematics and systems | ||
| 653 | |a Data Scientists | ||
| 653 | |a Data Analysts | ||
| 653 | |a Forscher | ||
| 653 | |a Logistiker | ||
| 653 | |a Disponenten | ||
| 653 | |a Maschinenbauer | ||
| 653 | |a Softwareentwickler | ||
| 653 | |a Unternehmensberater | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
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| 689 | 0 | 2 | |a Supply Chain Management |0 (DE-588)4684051-5 |D s |
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| 710 | 2 | |a Fraunhofer IRB-Verlag |0 (DE-588)4786605-6 |4 pbl | |
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Record in the Search Index
| _version_ | 1804183466359652352 |
|---|---|
| adam_text | OVERVIEW
OF
CONTENTS
1
INTRODUCTION
1
2
RESEARCH
DESIGN
17
3
RESEARCH
BACKGROUND
29
4
STATE
OF
RESEARCH
89
5
THEORETICAL
FOUNDATION
AND
METHODICAL
APPROACH
135
6
CASE
A:
STRATEGIC
SPARE
PARTS
DEMAND
FORECASTING
167
7
CASE
B:
TACTICAL
RECESSION
PREDICTION
193
8
CASE
C:
OPERATIVE
FREIGHT
VOLUME
FORECASTING
221
9
CONCLUDING
REMARKS
AND
OUTLOOK
247
REFERENCES
275
AP
PENDIX
307
TABLE
OF
CONTENTS
LIST
OF
FIGURES
........................................................................................
VII
LIST
OF
TABLES
.........................................................................................
XI
LIST
OF
ABBREVIATIONS
..................................................................................
XIII
1
INTRODUCTION
1
1.1
INTRODUCTION
TO
THE
RESEARCH
TOPIC
......................................................
2
1.2
RESEARCH
GOAL
.......................................................................................
6
1.3
INTRODUCTION
TO
THE
EMPIRICAL
FIELDS
..........................................................
8
1.4
STRUCTURE
OF
THE
DISSERTATION
...............................................................
11
1.5
A
NOTE
ON
TERMS
AND
CONVENTIONS
......................................................
13
1.6
BIBLIOGRAPHIC
NOTES
..............................................................................
14
2
RESEARCH
DESIGN
17
2.1
BRIEF
INTRODUCTION
TO
THE
ACADEMIC
DISCIPLINES
....................................
18
2.2
RESEARCH
CONCEPT
.................................................................................
20
2.3
INTRODUCTION
TO
CASE
STUDY
RESEARCH
...................................................
25
3
RESEARCH
BACKGROUND
29
3.1
MACHINE
LEARNING
AND
ITS
CONCEPTUAL
CLASSIFICATION
...........................
30
3.1.1
HISTORY
OF
MACHINE
LEARNING
...................................................
31
3.1.2
DEFINITIONS
OF
MACHINE
LEARNING
.............................................
35
3.1.3
DEMARCATIONS
TO
RELATED
TOPICS
.............................................
39
3.1.4
MACHINE
LEARNING
CONCEPTS
AND
METHODS
..............................
44
3.2
DATA
AUGMENTATION
AND
ITS
CONCEPTUAL
CLASSIFICATION
........................
64
3.2.1
HISTORY
OF
DATA
AUGMENTATION
................................................
66
3.2.2
DEFINITIONS
OF
DATA
AUGMENTATION
..........................................
70
3.2.3
DEMARCATIONS
TO
RELATED
TOPICS
.............................................
74
3.2.4
DATA
AUGMENTATION
CONCEPTS
AND
METHODS
...........................
76
4
STATE
OF
RESEARCH
89
4.1
LITERATURE
REVIEW
APPROACH
..................................................................
90
4.2
QUANTITATIVE
RESULTS
OF
THE
LITERATURE
REVIEW
....................................
96
4.3
QUALITATIVE
RESULTS
OF
THE
LITERATURE
REVIEW
.......................................
103
4.3.1
DISTINCTION
BY
DATA
QUANTITY
AND
DIMENSIONALITY
..................
103
4.3.2
DISTINCTION
BY
SUPPLY
CHAIN
PLANNING
HORIZONS
.....................
124
4.4
SUMMARY
AND
INTERIM
DISCUSSION
............................................................
130
5
THEORETICAL
FOUNDATION
AND
METHODICAL
APPROACH
135
5.1
MODEL
FRAMEWORK
.....................................................................................
136
IV
TABLE
OF
CONTENTS
5.2
THEORETICAL
FOUNDATION
........................................................................
142
5.2.1
DECISION
THEORY
AND
RATIONAL
EXPECTATIONS
...........................
142
5.2.2
FORECASTING
IN
DECISION
THEORY
................................................
143
5.3
METHODICAL
APPROACH
...........................................................................
147
5.3.1
CASE
SELECTION
...........................................................................
147
5.3.2
DATA
ANALYSIS
...........................................................................
151
5.3.3
EVALUATION
DESIGN
.....................................................................
159
5.4
INTRODUCTION
TO
THE
SELECTED
CASE
STUDIES
..........................................
160
5.4.1
INTRODUCTION
TO
CASE
A
............................................................
160
5.4.2
INTRODUCTION
TO
CASE
B
............................................................
163
5.4.3
INTRODUCTION
TO
CASE
C
............................................................
165
6
CASE
A:
STRATEGIC
SPARE
PARTS
DEMAND
FORECASTING
167
6.1
INTRODUCTION
TO
SPARE
PARTS
MANAGEMENT
..........................................
168
6.2
METHODICAL
APPROACH
...........................................................................
170
6.2.1
MASTER
DATA
CLUSTERING
...........................................................
173
6.2.2
DEMAND
DATA
CLUSTERING
........................................................
175
6.3
EMPIRICAL
ANALYSIS
.................................................................................
177
6.3.1
DATA
DESCRIPTION
....................................................................
177
6.3.2
CODE
DESCRIPTION
....................................................................
179
6.3.3
CLUSTERING
RESULTS
....................................................................
181
6.3.4
FORECASTING
EXERCISE
.................................................................
185
6.3.5
EMPIRICAL
RESULTS
....................................................................
186
6.3.6
DEPLOYMENT
.............................................................................
188
6.4
CONCLUDING
REMARKS
...........................................................................
188
7
CASE
B:
TACTICAL
RECESSION
PREDICTION
193
7.1
INTRODUCTION
TO
RECESSION
FORECASTING
................................................
194
7.2
METHODICAL
APPROACH
...........................................................................
196
7.3
EMPIRICAL
ANALYSIS
.................................................................................
199
7.3.1
DATA
DESCRIPTION
........................................................................
200
7.3.2
ESTIMATION
RESULTS
........................................................................
205
7.3.3
RECESSION
PREDICTION
..................................................................
211
7.4
CONCLUDING
REMARKS
..............................................................................
217
8
CASE
C:
OPERATIVE
FREIGHT
VOLUME
FORECASTING
221
8.1
INTRODUCTION
TO
TRANSPORTATION
VOLUME
FORECASTING
..............................
222
8.2
METHODICAL
APPROACH
..............................................................................
224
8.2.1
A
NOTE
ON
NOTATION
....................................................................
226
8.2.2
FEATURE
SELECTION
METHODS
.........................................................
226
8.2.3
ML
METHODS
................................................................................
232
TABLE
OF
CONTENTS
V
8.2.4
EVALUATION
.....................................................................................
234
8.3
EMPIRICAL
ANALYSIS
....................................................................................
237
8.3.1
DATA
DESCRIPTION
........................................................................
237
8.3.2
CODE
DESCRIPTION
........................................................................
238
8.3.3
DATA
PREPARATION
........................................................................
239
8.3.4
HYPERPARAMETER
TUNING
................................................................
240
8.3.5
FEATURE
SELECTION
........................................................................
241
8.3.6
FORECASTING
EXERCISE
.....................................................................
243
8.4
CONCLUDING
REMARKS
.............................................................................
245
9
CONCLUDING
REMARKS
AND
OUTLOOK
247
9.1
SUMMARY
OF
RESULTS
................................................................................
248
9.2
LIMITATIONS
...............................................................................................
268
9.3
IMPLICATIONS
FOR
PRACTICAL
APPLICATIONS
..................................................
270
9.4
FUTURE
RESEARCH
OPPORTUNITIES
..............................................................
271
REFERENCES
275
APPENDIX
307
APPENDIX
A:
DETAILED
INFORMATION
ON
THE
STRUCTURED
LITERATURE
REVIEW
.
.
307
APPENDIX
B:
DETAILED
INFORMTION
ON
THE
MULTIPLE-CASE
STUDY
.......................
315
|
| adam_txt |
OVERVIEW
OF
CONTENTS
1
INTRODUCTION
1
2
RESEARCH
DESIGN
17
3
RESEARCH
BACKGROUND
29
4
STATE
OF
RESEARCH
89
5
THEORETICAL
FOUNDATION
AND
METHODICAL
APPROACH
135
6
CASE
A:
STRATEGIC
SPARE
PARTS
DEMAND
FORECASTING
167
7
CASE
B:
TACTICAL
RECESSION
PREDICTION
193
8
CASE
C:
OPERATIVE
FREIGHT
VOLUME
FORECASTING
221
9
CONCLUDING
REMARKS
AND
OUTLOOK
247
REFERENCES
275
AP
PENDIX
307
TABLE
OF
CONTENTS
LIST
OF
FIGURES
.
VII
LIST
OF
TABLES
.
XI
LIST
OF
ABBREVIATIONS
.
XIII
1
INTRODUCTION
1
1.1
INTRODUCTION
TO
THE
RESEARCH
TOPIC
.
2
1.2
RESEARCH
GOAL
.
6
1.3
INTRODUCTION
TO
THE
EMPIRICAL
FIELDS
.
8
1.4
STRUCTURE
OF
THE
DISSERTATION
.
11
1.5
A
NOTE
ON
TERMS
AND
CONVENTIONS
.
13
1.6
BIBLIOGRAPHIC
NOTES
.
14
2
RESEARCH
DESIGN
17
2.1
BRIEF
INTRODUCTION
TO
THE
ACADEMIC
DISCIPLINES
.
18
2.2
RESEARCH
CONCEPT
.
20
2.3
INTRODUCTION
TO
CASE
STUDY
RESEARCH
.
25
3
RESEARCH
BACKGROUND
29
3.1
MACHINE
LEARNING
AND
ITS
CONCEPTUAL
CLASSIFICATION
.
30
3.1.1
HISTORY
OF
MACHINE
LEARNING
.
31
3.1.2
DEFINITIONS
OF
MACHINE
LEARNING
.
35
3.1.3
DEMARCATIONS
TO
RELATED
TOPICS
.
39
3.1.4
MACHINE
LEARNING
CONCEPTS
AND
METHODS
.
44
3.2
DATA
AUGMENTATION
AND
ITS
CONCEPTUAL
CLASSIFICATION
.
64
3.2.1
HISTORY
OF
DATA
AUGMENTATION
.
66
3.2.2
DEFINITIONS
OF
DATA
AUGMENTATION
.
70
3.2.3
DEMARCATIONS
TO
RELATED
TOPICS
.
74
3.2.4
DATA
AUGMENTATION
CONCEPTS
AND
METHODS
.
76
4
STATE
OF
RESEARCH
89
4.1
LITERATURE
REVIEW
APPROACH
.
90
4.2
QUANTITATIVE
RESULTS
OF
THE
LITERATURE
REVIEW
.
96
4.3
QUALITATIVE
RESULTS
OF
THE
LITERATURE
REVIEW
.
103
4.3.1
DISTINCTION
BY
DATA
QUANTITY
AND
DIMENSIONALITY
.
103
4.3.2
DISTINCTION
BY
SUPPLY
CHAIN
PLANNING
HORIZONS
.
124
4.4
SUMMARY
AND
INTERIM
DISCUSSION
.
130
5
THEORETICAL
FOUNDATION
AND
METHODICAL
APPROACH
135
5.1
MODEL
FRAMEWORK
.
136
IV
TABLE
OF
CONTENTS
5.2
THEORETICAL
FOUNDATION
.
142
5.2.1
DECISION
THEORY
AND
RATIONAL
EXPECTATIONS
.
142
5.2.2
FORECASTING
IN
DECISION
THEORY
.
143
5.3
METHODICAL
APPROACH
.
147
5.3.1
CASE
SELECTION
.
147
5.3.2
DATA
ANALYSIS
.
151
5.3.3
EVALUATION
DESIGN
.
159
5.4
INTRODUCTION
TO
THE
SELECTED
CASE
STUDIES
.
160
5.4.1
INTRODUCTION
TO
CASE
A
.
160
5.4.2
INTRODUCTION
TO
CASE
B
.
163
5.4.3
INTRODUCTION
TO
CASE
C
.
165
6
CASE
A:
STRATEGIC
SPARE
PARTS
DEMAND
FORECASTING
167
6.1
INTRODUCTION
TO
SPARE
PARTS
MANAGEMENT
.
168
6.2
METHODICAL
APPROACH
.
170
6.2.1
MASTER
DATA
CLUSTERING
.
173
6.2.2
DEMAND
DATA
CLUSTERING
.
175
6.3
EMPIRICAL
ANALYSIS
.
177
6.3.1
DATA
DESCRIPTION
.
177
6.3.2
CODE
DESCRIPTION
.
179
6.3.3
CLUSTERING
RESULTS
.
181
6.3.4
FORECASTING
EXERCISE
.
185
6.3.5
EMPIRICAL
RESULTS
.
186
6.3.6
DEPLOYMENT
.
188
6.4
CONCLUDING
REMARKS
.
188
7
CASE
B:
TACTICAL
RECESSION
PREDICTION
193
7.1
INTRODUCTION
TO
RECESSION
FORECASTING
.
194
7.2
METHODICAL
APPROACH
.
196
7.3
EMPIRICAL
ANALYSIS
.
199
7.3.1
DATA
DESCRIPTION
.
200
7.3.2
ESTIMATION
RESULTS
.
205
7.3.3
RECESSION
PREDICTION
.
211
7.4
CONCLUDING
REMARKS
.
217
8
CASE
C:
OPERATIVE
FREIGHT
VOLUME
FORECASTING
221
8.1
INTRODUCTION
TO
TRANSPORTATION
VOLUME
FORECASTING
.
222
8.2
METHODICAL
APPROACH
.
224
8.2.1
A
NOTE
ON
NOTATION
.
226
8.2.2
FEATURE
SELECTION
METHODS
.
226
8.2.3
ML
METHODS
.
232
TABLE
OF
CONTENTS
V
8.2.4
EVALUATION
.
234
8.3
EMPIRICAL
ANALYSIS
.
237
8.3.1
DATA
DESCRIPTION
.
237
8.3.2
CODE
DESCRIPTION
.
238
8.3.3
DATA
PREPARATION
.
239
8.3.4
HYPERPARAMETER
TUNING
.
240
8.3.5
FEATURE
SELECTION
.
241
8.3.6
FORECASTING
EXERCISE
.
243
8.4
CONCLUDING
REMARKS
.
245
9
CONCLUDING
REMARKS
AND
OUTLOOK
247
9.1
SUMMARY
OF
RESULTS
.
248
9.2
LIMITATIONS
.
268
9.3
IMPLICATIONS
FOR
PRACTICAL
APPLICATIONS
.
270
9.4
FUTURE
RESEARCH
OPPORTUNITIES
.
271
REFERENCES
275
APPENDIX
307
APPENDIX
A:
DETAILED
INFORMATION
ON
THE
STRUCTURED
LITERATURE
REVIEW
.
.
307
APPENDIX
B:
DETAILED
INFORMTION
ON
THE
MULTIPLE-CASE
STUDY
.
315 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Menden, Christian |
| author_GND | (DE-588)1136477322 |
| author_facet | Menden, Christian |
| author_role | aut |
| author_sort | Menden, Christian |
| author_variant | c m cm |
| building | Verbundindex |
| bvnumber | BV047874301 |
| classification_rvk | QP 505 |
| ctrlnum | (OCoLC)1304485491 (DE-599)DNB1253098905 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV047874301 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:20:52Z |
| indexdate | 2024-07-10T09:23:53Z |
| institution | BVB |
| institution_GND | (DE-588)4786605-6 |
| isbn | 9783839617861 3839617863 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033256752 |
| oclc_num | 1304485491 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | 365 Seiten Illustrationen, Diagramme 24 cm x 17 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Fraunhofer Verlag |
| record_format | marc |
| series | Publication series on logistics and technologies |
| series2 | Publication series on logistics and technologies |
| spelling | Menden, Christian Verfasser (DE-588)1136477322 aut Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches Christian Menden Stuttgart Fraunhofer Verlag 2022 365 Seiten Illustrationen, Diagramme 24 cm x 17 cm txt rdacontent n rdamedia nc rdacarrier Publication series on logistics and technologies Vol. 10 Dissertation Fakultät Sozial- und Wirtschaftswissenschaften der Otto-Friedrich-Universität Bamberg 2021 Data Augmentation (DE-588)4825966-4 gnd rswk-swf Supply Chain Management (DE-588)4684051-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Fraunhofer SCS Fraunhofer IIS Machine Learning Applied Mathematics Transport Studies Data Augmentation Business mathematics and systems Data Scientists Data Analysts Forscher Logistiker Disponenten Maschinenbauer Softwareentwickler Unternehmensberater (DE-588)4113937-9 Hochschulschrift gnd-content Data Augmentation (DE-588)4825966-4 s Maschinelles Lernen (DE-588)4193754-5 s Supply Chain Management (DE-588)4684051-5 s DE-604 Fraunhofer IRB-Verlag (DE-588)4786605-6 pbl Publication series on logistics and technologies Vol. 10 (DE-604)BV040633845 10 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=d5fa505758b546639591098e4b0a4269&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033256752&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20220308 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Menden, Christian Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches Publication series on logistics and technologies Data Augmentation (DE-588)4825966-4 gnd Supply Chain Management (DE-588)4684051-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4825966-4 (DE-588)4684051-5 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches |
| title_auth | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches |
| title_exact_search | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches |
| title_exact_search_txtP | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches |
| title_full | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches Christian Menden |
| title_fullStr | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches Christian Menden |
| title_full_unstemmed | Handling data problems in machine learning applications in supply chain management a multiple-case study on the analysis of data augmentation approaches Christian Menden |
| title_short | Handling data problems in machine learning applications in supply chain management |
| title_sort | handling data problems in machine learning applications in supply chain management a multiple case study on the analysis of data augmentation approaches |
| title_sub | a multiple-case study on the analysis of data augmentation approaches |
| topic | Data Augmentation (DE-588)4825966-4 gnd Supply Chain Management (DE-588)4684051-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Data Augmentation Supply Chain Management Maschinelles Lernen Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=d5fa505758b546639591098e4b0a4269&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033256752&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV040633845 |
| work_keys_str_mv | AT mendenchristian handlingdataproblemsinmachinelearningapplicationsinsupplychainmanagementamultiplecasestudyontheanalysisofdataaugmentationapproaches AT fraunhoferirbverlag handlingdataproblemsinmachinelearningapplicationsinsupplychainmanagementamultiplecasestudyontheanalysisofdataaugmentationapproaches |