Abstract
This research presents an analysis of the shipments forecast of automotive sensors in three regions where such products are sold and shipped of 15,000 unique products and more than 100 million of units shipped last year. The Sensors company is one of the world’s leading suppliers of sensing solutions for automotive brands with operations and business centers in 11 countries so it’s very important to have a forecasting analysis based on time series as historical data is basement and estimating for 18 + months decisions. Models used in this paper are Holt-Winters, Cross Correlation and Simulation. Basically, as output of the Holt-Winters model, results show a constantly increasing forecast for the coming years when done a seasonable additive algorithm. With this model output, a prediction is performed having the forecasting till 2028 showing a stable increased of the quantity of auto sensors that are manufactured delivering to all the regions where the automotive sensors are shipped to.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fleming W (2001) Overview of automotive sensors. IEEE Sens J 1. https://doi.org/10.1109/7361.983469
Sensata Technologies web portal. https://sensata.com/about
Sparks D (2013) MEMS for automotive and aerospace applications. Woodhead publishing series in electronic and optical materials, pp 78–105. https://doi.org/10.1533/9780857096487.1.78
Oracle Business Intelligence. Marketing, Sales & Forecasting dashboard.
Cowpertwait PS, Metcalfe AV (2009) Introductory Time Series with R. Springer, New York. https://doi.org/10.1007/978-0-387-88698-5
Armstrong JS, Green KC (2018) Forecasting methods and principles: evidence-based checklists. J Glob Scholars Mark Sci 28(2):103–159
University of Texas at El Paso, El Paso, TX, USA, ISSN 1870–4069 Santana A, Nieves C, Objetos en R: Series temporales
Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice, 2nd edition. OTexts, Melbourne, Australia. https://otexts.com/fpp2/
Bermudez J, Vercher E, Segura J (2007) Holt-winters forecasting: an alternative formulation applied to UK air passenger data. J Appl Stat. https://doi.org/10.1080/02664760701592125
Askari H, Khajepour A, Behrad M, Lin Z (2019) Embedded self-powered sensing systems for smart vehicles and intelligent transportation. Nano Energy 66. https://doi.org/10.1016/j.nanoen.2019.104103
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Anaya-Villalvazo, F., Ochoa-Zezzatti, A., Cruz-Mejía, O., Diaz, J. (2021). Technical Analysis of Shipments in an Automotive Company to Forecast Sales Volumes. In: Ochoa-Zezzatti, A., Oliva, D., Juan Perez, A. (eds) Technological and Industrial Applications Associated with Intelligent Logistics. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-68655-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-68655-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68654-3
Online ISBN: 978-3-030-68655-0
eBook Packages: EngineeringEngineering (R0)