Supply Chain Analysis of A Tier Two Automotive Supplier - YU, Wenjie (2012)
Automotive is a highly competitive industry, in which numbers of sub suppliers, suppliers and OEMs are seeking for the access to shorten lead-time and zero inventories. The case company in the study is a second tier supplier in China that provides truck components to first tier suppliers in Europe, America, and Asia. Within its 135 kinds of products, the company only manufactures part of its products, while ordering half-finished parts of other products from third tier suppliers and assembly them to finished auto components in Hangzhou, China. Because of lack of communication and proper management, it has a long lead-time and inflexible production line. The lead-time for one order is more than 70 days, most of which are used for waiting for sub suppliers delivering half finished parts and transportation. Moreover, its forecasting system is lack of professional calculation. Historical data is its main forecasting factor. This paper attempts to help the company increase demand and make improvements for the production line. Before making detailed analysis of the company, a series of personal interview and case study are made first to give an overall description of the company. Furthermore, as forecasting is important in the make to order system, elements that may affect demand are listed in detail, including macro environment effects and inner company effects, to help make forecasting more accurate. The ability of making an accurate forecasting can also help to reduce its idle stock and build a flexible supply chain. To identify the drawbacks of this company, this study makes brief comparisons between it and four case companies, which are different automotive companies. Cases are selected because of their outstanding characteristics in managing production operations and achieving stockless supply in their production line. According on the comparisons between the case company and benchmarks, suggestions of reducing lead-time and eliminating inventory are given, based on company’s location, product characteristics, and industry features. Simple calculations are used to help the analysis. Besides, it is worth mentioning that the accuracy of demand forecasting can dramatically affect inventory level. Thus, recommendations of increasing forecasting accurate are also provided in this study.