Prediction of a standard requirement for the development of modular architecture-based gasoline turbo engine using time series analysis

Seung Hyun Chung, Joonyoung Park, Jin Yeong Um

Research output: Contribution to journalArticlepeer-review

Abstract

Environmental regulations have been tightened since Volkswagen's diesel gates. To cope with environmental regulations, eco-friendly cars should be developed, but due to the high price, it is difficult to be accepted by all consumers. As customer needs diversify, the product life cycle is shortening and needs to be addressed. Manufacturers need to innovate their processes to diversify their products and reduce lead times. This paper aims to predict long-term future engine torque to secure development efficiency and competitiveness of internal combustion engines, which are the major components of automobiles. Sales data for 20 years from 1998 to 2017 were used, with weights based on sales volume. Based on the above data, time series analysis was conducted using three methods, MA, ARIMA, and Fitted Regression. The results of the prediction for each segment were derived, and the engine can be used to plan the engine with appropriate torque.

Original languageEnglish
Pages (from-to)3547-3558
Number of pages12
JournalJournal of Green Engineering
Volume10
Issue number7
StatePublished - Jul 2020

Keywords

  • ARIMA
  • Data mining
  • Fitted Regression
  • Integrated Product Planning
  • Market Needs Forecasting
  • Time series analysis

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