Forecasting Accessory Demand in the Automotive Industry

The automotive industry seeks effective ways to forecast consumer demand to avoid overstocking, waste, underproduction, and employee underperformance. Modeling future demand for vehicles is standard, however parts & accessories are a significant subset of overall automotive revenue. There is no industry standard for predicting the quantity of accessories sold or revenue. This paper seeks to use the best industry forecasting methods and research practices to build a predictive model that forecasts vehicle accessory sales. The time-series forecasting model utilizes Toyota Motor Corporation data in a first attempt to predict accessory sales

Forecasting for Nonlinear and Nonstationary Systems Using Intrinsic Functional Decomposition Models

The purpose of this study is to develop nonlinear and nonstationary time series forecasting methods to address modeling and prediction of real-world, complex systems. Particular emphasis has been placed on nonlinear and nonstationary time series forecasting in systems and processes that are of interest to IE researchers. Two new advanced prediction methods are developed using nonlinear decomposition techniques and a battery of advanced statistical methods. The research methodologies include empirical mode decomposition (EMD)-based prediction, structural relationship identification (SRI) methodology, and intrinsic time-scale decomposition (ITD)-based prediction. The advantages of using these prediction methods are local characteristic time scales and the use of an adaptive basis that does not require a parametric functional form (during the decomposition process). The utilization of SRI methodology in ITD-based prediction also provides a relationship identification advantage that can be used to capture the interrelationships of variables in the system for prediction application. The empirical results of using these new prediction methods have shown a significant improvement in the accuracy for customer willingness-to-pay and automobile demand prediction applications.Industrial Engineering & Managemen

Mobility in a Globalised World 2013

The term mobility has different meanings in the following science disciplines. In economics, mobility is the ability of an individual or a group to improve their economic status in relation to income and wealth within their lifetime or between generations. In information systems and computer science, mobility is used for the concept of mobile computing, in which a computer is transported by a person during normal use. Logistics creates by the design of logistics networks the infrastructure for the mobility of people and goods. Electric mobility is one of today‘s solutions from engineering perspective to reduce the need of energy resources and environmental impact. Moreover, for urban planning, mobility is the crunch question about how to optimise the different needs for mobility and how to link different transportation systems. In this publication we collected the ideas of practitioners, researchers, and government officials regarding the different modes of mobility in a globalised world, focusing on both domestic and international issues. We are grateful for the academic hospitality at the Stuttgart Media University for our conference 2013 "Mobility in a globalised world" in September 2013. We would like to thank Prof. Dr Johannes Maucher and Dr. Heiko Roßnagel for their technical support during our sojourn in Stuttgart