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Packaging Demand Forecasting in Logistics using Deep Neural Networks
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Background: Logistics have a vital role in supply chain management and those logistics operations are dependent on the availability of packaging material for packing goods and material to be shipped. Forecasting packaging material demand for a long period of time will help organization planning to meet the demand. Using time-series data with Deep Neural Networks for long term forecasting is proposed for research. Objectives: This study is to identify the DNN used in forecasting packaging demand and in similar problems in terms of data, data similar to the available data with the organization (Volvo). Identifying the best-practiced approach for long-term forecasting and then combining the approach with identified and selected DNN for forecasting. The end objective of the thesis is to suggest the best DNN model for packaging demand forecasting. Methods: An experiment is conducted to evaluate the DNN models selected for demand forecasting. Three models are selected by a preliminary systematic literature review. Another Systematic literature review is performed in parallel for identifying metrics to evaluate the models to measure performance. Results from the preliminary literature review were instrumental in performing the experiment. Results: Three models observed in this study are performing well with considerable forecasting values. But based on the type and amount of historical data that models were given to learn, three models have a very slight difference in performance measures in terms of forecasting performance. Comparisons are made with different measures that are selected by the literature review. For a better understanding of the batch size impact on model performance, experimented three models were developed with two different batch sizes. Conclusions: Proposed models are performing considerable forecasting of packaging demand for planning the next 52 weeks (∼ 1 Year). Results show that by adopting DNN in forecasting, reliable packaging demand can be forecasted on time series data for packaging material. The combination of CNN-LSTM is better performing than the respective individual models by a small margin. By extending the forecasting at the granule level of the supply chain (Individual suppliers and plants) will benefit the organization by controlling the inventory and avoiding excess inventory.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Deep Learning, Forecasting, Logistics
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18471OAI: oai:DiVA.org:bth-18471DiVA, id: diva2:1337390
External cooperation
Volvo Group Trucks Operation
Subject / course
DV2572 Master´s Thesis in Computer Science
Supervisors
Available from: 2019-07-24 Created: 2019-07-14 Last updated: 2019-07-24Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
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  • Other locale
More languages
Output format
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