摘 要
中国商用车轮胎市场的规模随着商用车市场的规模增长不断扩大，
保持着较快的发展速度。商用车作为一种重要的车型，为保障交通运
输、支持整个中国经济发展，同时也为拉动相关产业做出了积极重要的
贡献。中国宏观经济的发展会拉动商用车市场的发展，刺激商用车轮胎
的总体需求，但与此同时，受全球经济一体化，以及中国各地区、各省
市内部经济发展水平差异的影响，商用车及其轮胎市场的需求也变得难
以估计。繁多的产品规格以及不断变化的售后市场替换需求导致企业销
售需求预测的不确定性，给企业的生产计划、运营、物料管理、产品配
送等多个环节造成了困难，这无形中抬高了企业的成本，降低了企业的
竞争能力。

本文通过对商用车轮胎在配套市场以及售后替换市场的不同需求特
征进行分析研究，同时借助 S 轮胎公司在欧亿·体育（中国）有限公司中的影响力及其多年的内
部数据，通过市场调研以及对数据的充分挖掘分析，运用线性回归、指
数平滑等数理分析方法试图找到宏观经济数据 GDP 与商用车市场发展的
联系，进而通过细分市场计算，对整个商用车轮胎市场的需求做出预
测，并建立商用车轮胎市场需求的预测模型。为企业解决需求预测不确
定性的难题，提高企业供应链以及生产运营效率，降低成本，最终目标
是提高企业在整个欧亿·体育（中国）有限公司中的竞争力。

关键词：商用车，轮胎市场，需求预测，线性回归，数据模型
ABSTRACT
Along with the growth of the commercial vehicle market expanding, the
scale of China’s tire market for commercial vehicle maintained a rapid pace
of development. Commercial vehicles as an important model not only
support the transportation and China’s economic development, but also make
a positive and important contribution to driving-related industries. China’s
macroeconomic development will undoubtedly boost the development of the
commercial vehicle market and stimulate overall demand for commercial
vehicle tires. However, at the mean time, due to the global economic
integration and the unbalance of economic progress among China different
regions, provinces and municipalities, commercial vehicles and its tire
market demand become more and more difficult to predict. The wide range of
product specifications and frequent changes of demand in after-sales
replacement market lead to enormous uncertainty of sales forecasting, which
causes considerable troubles in production planning, operations management,
and product distribution. In turn, production cost is increased, which hurts the
competitiveness of enterprises.
Using S company’s influence in the tire industry and its internal data,
this thesis develops scientific methods to analyze the OEM and after-sales
replacement markets for commercial vehicle tires through marketing research
and data analysis. I employed linear regression and exponential smoothing
methods to link macroeconomic indices and the commercial vehicle volume
such as production output and ownership of the commercial vehicle market.
Then I established a market segmentation model for predicting the entire
commercial vehicle tire market. Finally the forecasting uncertainty is reduced,
which can be used to help improve efficiency and reduce costs in supply
chain and manufacturing operations.
KEY WORDS
：
Commercial vehicle, Tire Market, Linear Regression,
Demand forecasting, Data model