Artificial intelligence based prediction models: sales forecasting application in automotive aftermarket

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance.

Unsettled Issues on Sensor Calibration for Automotive Aftermarket Advanced Driver-Assistance Systems

Many automotive industry safety advocates have been pushing for greater market penetration for active safety and advanced driver-assistance systems (ADAS), with the goal of ending deaths due to car crashes. However, there are far-reaching implications for the collision repair, specialty equipment, and performance aftermarket sectors—after a collision or modification, the ADAS system functionality must be preserved to maintain, driver, passenger, and road user safety. To do this, sensor recalibration and ADAS functional safety validation and documentation after repair, modification, or accessorizing are necessary. Unsettled Issues on Sensor Calibration for Automotive Aftermarket ADAS tackles the challenges of accelerating the pace of ADAS implementation; increasing industry understanding of systems, sensors, software, controllers; and minimizing the overwhelming variety of sensor calibration procedures and automaker targets. Additionally, this report addresses the liability concerns that are challenging the industry as it seeks to move forward safely.

Development of Mobile Sales Force System for an Automotive Aftermarket Company

The aftermarket automotive company has a set of documents that is called Customer Information Folder (CIF). The CIF is used by the salesmen to record every activity, observation and sales made with their customers. Currently, the CIF is a pile of papers, in which paper misplacement can occur and data can be mixed up, and salesmen must return to the office at the end of the day to write down what they have done. The objective of this research is to create a mobile sales force system with a web application back-end that can help the company handle customer data and the system can also assist in managing others such as sales, orders, stocks and commissions. Based on request from the company, an ERP system is planned to be developed, starting from customer data (CIF / SFA), orders, stocks and commissions, which are developed for this research. The result of this research shows that the new system can help provide and manage data relating to customers, orders, stocks and commissions. Secondly, the new system can reduce paper usage by half through the simulation. Thirdly, the new system can provide organized, accurate and trustworthy data. Fourthly, the new system can help the Sales Department and company owner in determining sales trends and buying trends. Lastly, the new system is shown to have fulfilled user expectation as there are positive gaps between expectation and perception.

Automotive Aftermarket Forecast in a Changing World: The Stakeholders’ Perceptions Boost!

This study presents a methodology for forecasting the medium– and long–term real revenues of the automotive post–sales service sectors, assuming the automobile industry is nowadays undergoing a deep process of transformation. There are several conditioning factors, usage as well as environmental reasons, that makes past times an unreasonable guide for a future forecast. Firstly, we estimate, using regression models, the most important variables for the automobile sector that will affect the long–term forecasts of the automotive aftermarket’s revenues. Secondly, we apply participatory methods to quantify the impact of the new conditioning factors. This is a research tool used for the Spanish automotive aftermarket. Our results indicate how stakeholders’ perceptions modulate the forecasts for those economic sectors involved in a disrupted changing business model.