|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|107487||2017||12 صفحه PDF||سفارش دهید||10235 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 183, Part A, January 2017, Pages 223-234
This paper verifies the argument that arcs of integration or supply chain integration (SCI) configurations differ across different industries. It further develops statistical methods to compare âbalancedâ and âunbalancedâ arcs of integrations and determines performance outcomes of different arcs of integration in three Thai industries. Survey data collected from 151 automotive, 82 electronics and 115 food manufacturers in Thailand are examined using cluster analysis, analysis of variance (ANOVA) and novel approaches to statistically differentiate balanced and unbalanced SCI configurations and their performance implications. The analyses conclude the existence of balanced arcs of integration with uniform levels of supplier integration (SI), internal integration (II), and customer integration (CI), as well as unbalanced arcs of integration with an emphasis on CI in the automotive and electronics industries. The food industry has no balanced arc of integration; some food manufacturers emphasize SI and II. These findings confirm differences across industries and add further insights in terms of how arcs of integration with different SCI strengths and emphases could lead to differences in delivery, quality, cost, flexibility, and innovation performance. Based on the data from these Thai industries, the findings from the different industries allow practitioners to benchmark SCI implementation and identify suitable arcs of integration for achieving desirable performance outcomes. In addition to statistically validating the differences amongst the SCI configurations and providing crucial empirical evidence to verify industrial differences, the paper demonstrates the benefit of analysing SCI configurations based on separate industrial samples and provides empirical evidence to drive new theoretical development.