Combining Machine Learning with Human Knowledge for Delivery Time Estimations
Lade...
Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2021
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Deviations in a global supply chain directly affect a retailer’s on-time delivery performance, causing availability problems and lower customer satisfaction. If the variation can be anticipated and more accurate lead-times estimated, proactive measures can be taken to decrease the impact. Existing estimation approaches use machine learning algorithms based on historical data to determine a lead-time value for the future. However, those approaches can only handle knowledge available in a machine-readable form, while expert knowledge about the domain is not considered during the actual prediction. Therefore, this thesis describes three novel approaches used for delivery time predictions that combine a machine learning model with human input. The proposed logic covers two phases: learning based on actual delivery data and capturing human knowledge to cover exceptional situations not reflected in historical data. The proposed models and the resulting estimates were evaluated using deliveries from a retail company. This thesis shows that the pure machine learning model delivers better results than a combination of humans and machines. On the one hand, it is due to the difficulty of incorporating the complexity of human knowledge into the algorithm in a suitable way. On the other hand, the effect is caused by the human tendency to generalize and exaggerate. Although the pure machine learning model delivers superior estimation accuracy than the human-machine combination, the systematic analysis of the results presents insights for future development in this area.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
Lochbrunner, M. (2021). Combining Machine Learning with Human Knowledge for Delivery Time Estimations [Hochschule für Wirtschaft FHNW]. https://irf.fhnw.ch/handle/11654/48594