Kiosk Strategic Demand Forecasting with Scenario Planning: A case study at SITA

Master Thesis (2022)
Author(s)

J. LIU (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

J. Rezaei – Coach (TU Delft - Transport and Logistics)

Adam Pel – Mentor (TU Delft - Transport and Planning)

M.Y. Maknoon – Graduation committee member (TU Delft - Transport and Logistics)

Faculty
Civil Engineering & Geosciences
Copyright
© 2022 JIshan LIU
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 JIshan LIU
Graduation Date
19-12-2022
Awarding Institution
Delft University of Technology
Programme
Civil Engineering | Transport and Planning
Faculty
Civil Engineering & Geosciences
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Abstract

SITA is the world’s leading specialist in air transport communications and information technology which works with around 400 air transport members and has 2800 customers in 190 countries. TS6 kiosk is the newest generation of its kiosk family and is facing a complicated situation now. Usually, the production of a kiosk is using make-to-order methods. However, if this method is adopted, the customers of SITA can not receive their productions within the expected periods of time. Besides this, SITA also can not get the procurement discount from the suppliers if they only purchase a low volume and do not make changes. In order to solve this problem, demand forecasting is conducted using the historical sales data of the TS6 kiosk. Through the literature review, suitable qualitative forecasting methods and quantitative forecasting methods which were mainly used in the FMCG industry are got together and combined to increase the forecast accuracy in this research to come out with the final forecast result of the TS6 kiosk. This research also explores the possibility of demand forecasting for the slow-moving consuming industry. By successfully conducting the final forecast, the result can help SITA to shorten procurement lead time so as to meet customers’ expectations as well as save total costs.
KeyWords: Demand Forecasting, Quantitative Forecasting Methods, Qualitative Forecasting Methods, ARIMA, SARIMA, Residual Analysis.

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