Supply chain analytics for inventory management:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Logos
[2021]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | IX, 169 Seiten Diagramme |
| ISBN: | 9783832554002 |
Internformat
MARC
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| 245 | 1 | 0 | |a Supply chain analytics for inventory management |c Christiane Haubitz |
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| 264 | 4 | |c © 2021 | |
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Datensatz im Suchindex
| _version_ | 1804183392361644033 |
|---|---|
| adam_text | CONTENTS
LIST
OF
FIGURES
V
LIST
OF
TABLES
VII
LIST
OF
ABBREVIATIONS
IX
1
INTRODUCTION
1
1.1
MOTIVATION
.............................................................................................
1
1.2
OUTLINE
...................................................................................................
2
1.3
CONTRIBUTION
..........................................................................................
5
2
CONTROLLING
THE
TRANSITION
TO
OPTIMIZED
SPARE
PARTS
INVENTORY
POLICIES
7
2.1
INTRODUCTION
..........................................................................................
8
2.2
LITERATURE
REVIEW
................................................................................
11
2.3
PROBLEM
FORMULATION
AND
MATHEMATICAL
MODEL
...............................
14
2.3.1
THE
INVENTORY
SYSTEM
IN
STEADY-STATE
..................................
15
2.3.2
THE
INVENTORY
TRANSITION
........................................................
17
2.4
SOLUTION
APPROACHES
.............................................................................
22
2.4.1
COLUMN
GENERATION
APPROACH
.................................................
22
2.4.2
MARGINAL
ANALYSIS
APPROACH
.................................................
24
2.5
NUMERICAL
STUDY
....................................................................................
27
2.5.1
PERFORMANCE
EVALUATION
OF
THE
SOLUTION
APPROACHES
............
28
2.5.2
THE
VALUE
OF
CONTROLLING
THE
TRANSITION
...............................
33
2.6
CONCLUSION
.............................................................................................
45
APPENDIX
.......................................................................................................
47
2.A
CHARACTERISTICS
OF
(P
S
)
.......................................................................
47
2.B
COLUMN
GENERATION
.............................................................................
49
2.B.1
COMPUTING
AN
UPPER
BOUND
..................................................
49
2.B.2
GENERATING
NEW
SEQUENCES
BY
SOLVING
THE
SUBPROBLEM
...
50
2.C
MARGINAL
ANALYSIS
ALGORITHM
FOR
THE
INVENTORY
TRANSITION
................
53
2.D
DISTRIBUTION
SELECTION
FOR
SENSITIVITY
ANALYSIS
.................................
55
2.E
SENSITIVITY
ANALYSIS
-
MAXIMUM
PERIODIC
COST
SAVINGS
AND
MAXIMUM
PERIODIC
SYSTEM
FILL
RATE
DIFFERENCES
..................................................
57
3
THE
EFFECTS
OF
ALGORITHM
TRANSPARENCY
ON
THE
USE
OF
ADVICE
59
3.1
INTRODUCTION
...........................................................................................
60
3.2
LITERATURE
REVIEW
.................................................................................
62
3.3
HYPOTHESES
DEVELOPMENT
.....................................................................
65
3.4
EFFECTS
OF
ALGORITHM
TRANSPARENCY
ON
THE
USE
OF
ADVICE
......................
67
3.4.1
EXPERIMENTAL
DESIGN
..............................................................
67
3.4.2
EXPERIMENTAL
PROTOCOL
...........................................................
69
3.4.3
RESULTS
........................................................................................
70
3.5
DISCUSSION
AND
FUTURE
WORK
..................................................................
73
4
HOW
ALGORITHM
COMPLEXITY
DRIVES
THE
USE
OF
ADVICE
75
4.1
INTRODUCTION
...........................................................................................
76
4.2
LITERATURE
REVIEW
.................................................................................
78
4.2.1
THE
USE
OF
ALGORITHMIC
ADVICE
...............................................
79
4.2.2
ADVICE-TAKING
IN
DEMAND
FORECASTING
....................................
81
4.3
DEVELOPMENT
OF
HYPOTHESES
..................................................................
83
4.4
EXPERIMENTAL
STUDY
..............................................................................
85
4.4.1
EXPERIMENTAL
DESIGN
..............................................................
85
II
4.4.2
EXPERIMENTAL
PROTOCOL
...........................................................
88
4.5
RESULTS
...................................................................................................
89
4.5.1
VALIDATION
OF
TREATMENT
MANIPULATION
...................................
89
4.5.2
EFFECTS
OF
TRANSPARENCY
OF
SIMPLE
AND
COMPLEX
ALGORITHMS
ON
WEIGHT
ON
ADVICE
.................................................................
91
4.5.3
PERCEIVED
APPROPRIATENESS
OF COMPLEXITY
AS
A
MODERATOR
OF
THE
EFFECTS
OF
ALGORITHM
TRANSPARENCY
ON
WEIGHT
ON
ADVICE
.
93
4.6
CONCLUSION
.............................................................................................
96
APPENDIX
......................................................................................................
99
4.
A
PRELIMINARY
STUDY
................................................................................
99
4.A.1
EXPERIMENTAL
DESIGN
..............................................................
99
4.A.2
EXPERIMENTAL
PROTOCOL
...............................................................
100
4.A.
3
RESULTS
...........................................................................................
101
4.B
PREREGISTERED
HYPOTHESES
.......................................................................
103
4.C
LABORATORY
EXPERIMENT
..........................................................................
107
4.C.1
TASK
DESCRIPTION
...........................................................................
107
4.C.2
INITIAL
FORECAST
..............................................................................
108
4.C.3
ALGORITHM
EXPLANATION
..............................................................
109
4.D
REGRESSIONS
WITH
CONTROLS
.......................................................................
113
5
INCREASING
ACCURACY
OF
LEAD
TIME
MASTER
DATA
WITH
MACHINE
LEARNING
115
5.1
INTRODUCTION
..............................................................................................
116
5.2
LEAD
TIME
PREDICTION
WITH
MACHINE
LEARNING
......................................
118
5.3
PROBLEM
SETTING
.......................................................................................
119
5.4
METHODOLOGY
.......................................................................................
121
5.4.1
LEAD
TIME
TYPES
.......................................................................
121
5.4.2
LEAD
TIME
PREDICTION
FRAMEWORK
...............................................
122
5.4.3
ALTERNATIVE
PLANNED
LEAD
TIMES
..................................................
124
III
5.4.4
MODELS
...........................................................................................
124
5.4.5
TRAIN/TEST
SPLIT
............................................................................
126
5.4.6
EVALUATION
METRICS
.....................................................................
128
5.5
EMPIRICAL
STUDY
........................................................................................
129
5.5.1
DATASET
........................................................................................
129
5.5.2
FEATURES
........................................................................................
133
5.5.3
SPARE
PARTS
ORDER
DISTRIBUTION
...................................................
133
5.5.4
MODEL
TRAINING
............................................................................
135
5.6
RESULTS
........................................................................................................
136
5.6.1
PERFORMANCE
OF
DIFFERENT
REGRESSORS
.........................................
136
5.6.2
FEATURE
IMPORTANCE
AND
PARTIAL
DEPENDENCE
...........................
136
5.6.3
PREDICTING
ORDER
LEAD
TIMES
.........................................................
140
5.6.4
PREDICTING
PLANNED
LEAD
TIMES
...................................................
140
5.6.5
PREDICTING
PLANNED
LEAD
TIMES
FOR
NEW
PARTS
...........................
141
5.7
IMPACT
ON
INVENTORY
PERFORMANCE
.........................................................
142
5.7.1
INVENTORY
POLICY
............................................................................
143
5.7.2
INFLUENCE
OF
LEAD
TIME
ACCURACY
ON
INVENTORY
PERFORMANCE
.
144
5.8
CONCLUSION
.................................................................................................
145
APPENDIX
...........................................................................................................
147
5.A
DATA
ANALYSIS
-
SEGMENTATION
...............................................................
147
6
CONCLUSION
149
6.1
SUMMARY
OF
KEY
RESULTS
............................................................................
149
6.2
CRITICAL
REVIEW
AND
FUTURE
RESEARCH
......................................................
152
BIBLIOGRAPHY
155
IV
|
| adam_txt |
CONTENTS
LIST
OF
FIGURES
V
LIST
OF
TABLES
VII
LIST
OF
ABBREVIATIONS
IX
1
INTRODUCTION
1
1.1
MOTIVATION
.
1
1.2
OUTLINE
.
2
1.3
CONTRIBUTION
.
5
2
CONTROLLING
THE
TRANSITION
TO
OPTIMIZED
SPARE
PARTS
INVENTORY
POLICIES
7
2.1
INTRODUCTION
.
8
2.2
LITERATURE
REVIEW
.
11
2.3
PROBLEM
FORMULATION
AND
MATHEMATICAL
MODEL
.
14
2.3.1
THE
INVENTORY
SYSTEM
IN
STEADY-STATE
.
15
2.3.2
THE
INVENTORY
TRANSITION
.
17
2.4
SOLUTION
APPROACHES
.
22
2.4.1
COLUMN
GENERATION
APPROACH
.
22
2.4.2
MARGINAL
ANALYSIS
APPROACH
.
24
2.5
NUMERICAL
STUDY
.
27
2.5.1
PERFORMANCE
EVALUATION
OF
THE
SOLUTION
APPROACHES
.
28
2.5.2
THE
VALUE
OF
CONTROLLING
THE
TRANSITION
.
33
2.6
CONCLUSION
.
45
APPENDIX
.
47
2.A
CHARACTERISTICS
OF
(P
S
)
.
47
2.B
COLUMN
GENERATION
.
49
2.B.1
COMPUTING
AN
UPPER
BOUND
.
49
2.B.2
GENERATING
NEW
SEQUENCES
BY
SOLVING
THE
SUBPROBLEM
.
50
2.C
MARGINAL
ANALYSIS
ALGORITHM
FOR
THE
INVENTORY
TRANSITION
.
53
2.D
DISTRIBUTION
SELECTION
FOR
SENSITIVITY
ANALYSIS
.
55
2.E
SENSITIVITY
ANALYSIS
-
MAXIMUM
PERIODIC
COST
SAVINGS
AND
MAXIMUM
PERIODIC
SYSTEM
FILL
RATE
DIFFERENCES
.
57
3
THE
EFFECTS
OF
ALGORITHM
TRANSPARENCY
ON
THE
USE
OF
ADVICE
59
3.1
INTRODUCTION
.
60
3.2
LITERATURE
REVIEW
.
62
3.3
HYPOTHESES
DEVELOPMENT
.
65
3.4
EFFECTS
OF
ALGORITHM
TRANSPARENCY
ON
THE
USE
OF
ADVICE
.
67
3.4.1
EXPERIMENTAL
DESIGN
.
67
3.4.2
EXPERIMENTAL
PROTOCOL
.
69
3.4.3
RESULTS
.
70
3.5
DISCUSSION
AND
FUTURE
WORK
.
73
4
HOW
ALGORITHM
COMPLEXITY
DRIVES
THE
USE
OF
ADVICE
75
4.1
INTRODUCTION
.
76
4.2
LITERATURE
REVIEW
.
78
4.2.1
THE
USE
OF
ALGORITHMIC
ADVICE
.
79
4.2.2
ADVICE-TAKING
IN
DEMAND
FORECASTING
.
81
4.3
DEVELOPMENT
OF
HYPOTHESES
.
83
4.4
EXPERIMENTAL
STUDY
.
85
4.4.1
EXPERIMENTAL
DESIGN
.
85
II
4.4.2
EXPERIMENTAL
PROTOCOL
.
88
4.5
RESULTS
.
89
4.5.1
VALIDATION
OF
TREATMENT
MANIPULATION
.
89
4.5.2
EFFECTS
OF
TRANSPARENCY
OF
SIMPLE
AND
COMPLEX
ALGORITHMS
ON
WEIGHT
ON
ADVICE
.
91
4.5.3
PERCEIVED
APPROPRIATENESS
OF COMPLEXITY
AS
A
MODERATOR
OF
THE
EFFECTS
OF
ALGORITHM
TRANSPARENCY
ON
WEIGHT
ON
ADVICE
.
93
4.6
CONCLUSION
.
96
APPENDIX
.
99
4.
A
PRELIMINARY
STUDY
.
99
4.A.1
EXPERIMENTAL
DESIGN
.
99
4.A.2
EXPERIMENTAL
PROTOCOL
.
100
4.A.
3
RESULTS
.
101
4.B
PREREGISTERED
HYPOTHESES
.
103
4.C
LABORATORY
EXPERIMENT
.
107
4.C.1
TASK
DESCRIPTION
.
107
4.C.2
INITIAL
FORECAST
.
108
4.C.3
ALGORITHM
EXPLANATION
.
109
4.D
REGRESSIONS
WITH
CONTROLS
.
113
5
INCREASING
ACCURACY
OF
LEAD
TIME
MASTER
DATA
WITH
MACHINE
LEARNING
115
5.1
INTRODUCTION
.
116
5.2
LEAD
TIME
PREDICTION
WITH
MACHINE
LEARNING
.
118
5.3
PROBLEM
SETTING
.
119
5.4
METHODOLOGY
.
121
5.4.1
LEAD
TIME
TYPES
.
121
5.4.2
LEAD
TIME
PREDICTION
FRAMEWORK
.
122
5.4.3
ALTERNATIVE
PLANNED
LEAD
TIMES
.
124
III
5.4.4
MODELS
.
124
5.4.5
TRAIN/TEST
SPLIT
.
126
5.4.6
EVALUATION
METRICS
.
128
5.5
EMPIRICAL
STUDY
.
129
5.5.1
DATASET
.
129
5.5.2
FEATURES
.
133
5.5.3
SPARE
PARTS
ORDER
DISTRIBUTION
.
133
5.5.4
MODEL
TRAINING
.
135
5.6
RESULTS
.
136
5.6.1
PERFORMANCE
OF
DIFFERENT
REGRESSORS
.
136
5.6.2
FEATURE
IMPORTANCE
AND
PARTIAL
DEPENDENCE
.
136
5.6.3
PREDICTING
ORDER
LEAD
TIMES
.
140
5.6.4
PREDICTING
PLANNED
LEAD
TIMES
.
140
5.6.5
PREDICTING
PLANNED
LEAD
TIMES
FOR
NEW
PARTS
.
141
5.7
IMPACT
ON
INVENTORY
PERFORMANCE
.
142
5.7.1
INVENTORY
POLICY
.
143
5.7.2
INFLUENCE
OF
LEAD
TIME
ACCURACY
ON
INVENTORY
PERFORMANCE
.
144
5.8
CONCLUSION
.
145
APPENDIX
.
147
5.A
DATA
ANALYSIS
-
SEGMENTATION
.
147
6
CONCLUSION
149
6.1
SUMMARY
OF
KEY
RESULTS
.
149
6.2
CRITICAL
REVIEW
AND
FUTURE
RESEARCH
.
152
BIBLIOGRAPHY
155
IV |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Haubitz, Christiane |
| author_GND | (DE-588)1287799906 |
| author_facet | Haubitz, Christiane |
| author_role | aut |
| author_sort | Haubitz, Christiane |
| author_variant | c h ch |
| building | Verbundindex |
| bvnumber | BV047837669 |
| classification_rvk | QP 530 |
| ctrlnum | (OCoLC)1302314731 (DE-599)DNB1246612968 |
| 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.BV047837669 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:10:57Z |
| indexdate | 2024-07-10T09:22:43Z |
| institution | BVB |
| institution_GND | (DE-588)1065538812 |
| isbn | 9783832554002 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033220812 |
| oclc_num | 1302314731 |
| open_access_boolean | |
| owner | DE-945 DE-N2 DE-355 DE-BY-UBR |
| owner_facet | DE-945 DE-N2 DE-355 DE-BY-UBR |
| physical | IX, 169 Seiten Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Logos |
| record_format | marc |
| spelling | Haubitz, Christiane Verfasser (DE-588)1287799906 aut Supply chain analytics for inventory management Christiane Haubitz Berlin Logos [2021] © 2021 IX, 169 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Dissertation Universität Köln Supply Chain Management (DE-588)4684051-5 gnd rswk-swf Lagerhaltung (DE-588)4034081-8 gnd rswk-swf Bestandsmanagement (DE-588)4423366-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Ersatzteilbestand (DE-588)4121214-9 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Bestandsmanagement (DE-588)4423366-8 s Supply Chain Management (DE-588)4684051-5 s Datenanalyse (DE-588)4123037-1 s DE-604 Lagerhaltung (DE-588)4034081-8 s Ersatzteilbestand (DE-588)4121214-9 s Logos Verlag Berlin (DE-588)1065538812 pbl DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033220812&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Haubitz, Christiane Supply chain analytics for inventory management Supply Chain Management (DE-588)4684051-5 gnd Lagerhaltung (DE-588)4034081-8 gnd Bestandsmanagement (DE-588)4423366-8 gnd Datenanalyse (DE-588)4123037-1 gnd Ersatzteilbestand (DE-588)4121214-9 gnd |
| subject_GND | (DE-588)4684051-5 (DE-588)4034081-8 (DE-588)4423366-8 (DE-588)4123037-1 (DE-588)4121214-9 (DE-588)4113937-9 |
| title | Supply chain analytics for inventory management |
| title_auth | Supply chain analytics for inventory management |
| title_exact_search | Supply chain analytics for inventory management |
| title_exact_search_txtP | Supply chain analytics for inventory management |
| title_full | Supply chain analytics for inventory management Christiane Haubitz |
| title_fullStr | Supply chain analytics for inventory management Christiane Haubitz |
| title_full_unstemmed | Supply chain analytics for inventory management Christiane Haubitz |
| title_short | Supply chain analytics for inventory management |
| title_sort | supply chain analytics for inventory management |
| topic | Supply Chain Management (DE-588)4684051-5 gnd Lagerhaltung (DE-588)4034081-8 gnd Bestandsmanagement (DE-588)4423366-8 gnd Datenanalyse (DE-588)4123037-1 gnd Ersatzteilbestand (DE-588)4121214-9 gnd |
| topic_facet | Supply Chain Management Lagerhaltung Bestandsmanagement Datenanalyse Ersatzteilbestand Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033220812&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT haubitzchristiane supplychainanalyticsforinventorymanagement AT logosverlagberlin supplychainanalyticsforinventorymanagement |