The impact of lean operations on the Chinese manufacturing performance

Purpose - The purpose of this paper is to investigate the impact of lean operations practice and design on the Chinese manufacturing performance, using lean assessment data from 65 plants in various industries. Design/methodology/approach - Exploratory factor and regression analyses are used to examine the associations among operations practice, production design, and operations performance. Three constructs are developed, two for operations practice (human resources and supply chains) and one for production design. Findings - Factor analysis shows that three factors are sufficient to represent the lean performance dimensions of flow, flexibility, and quality. Regression analysis shows that the lean performance factors are strongly related to operations practice and production system design. Using lean factors and operations practice/design, our results indicate significant gaps in lean manufacturing practices among different industries, with the petroleum and hi-tech industries performing relatively best. In addition, the garment industry performs very well in flexibility, indicating it does not compete just on price, but also on rapid response. Finally, all industries perform well in quality, underlining the emerging economy character of China. These results support other recent findings of the positive impact of lean operations on the performance of the Chinese manufacturing sector. Research limitations/implications - The paper's findings, which are based on the experience of selected manufacturing plants in China, should not be interpreted as indicative of the characteristics of the Chinese manufacturing plants in general. Originality/value - This paper advances the evidence on the role of lean manufacturing in two ways. First, to derive more robust statistical results, the paper relies on primary lean assessment data, as opposed to secondary opinion survey data common to most other studies. Second, to obtain more general findings, the paper makes use of a wider set of relevant variables, both for assessing manufacturing practice and performance, than is usual in the literature.