Developing Taiichi Ohno’s Mental Model for Waste Identification in Nontraditional Applications

Ashley C. Yarbrough, Gregory A. Harris, Gregory T. Purdy, Nicholas Loyd
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引用次数: 1

Abstract

The growth of technology in the manufacturing domain is compelling industry to digitally transform with little to no guidance on what constitutes value-added and nonvalue-added data and information. However, the Toyota production system (TPS) approach, which has proven successful for decades in identifying wastes in physical manufacturing processes, can provide some insights. Extensive research has been conducted on the history of Toyota and the concepts and tools of the TPS, but there is no documentation of how Taiichi Ohno approached problems and developed the classification of wastes (the 7 Wastes) which led to the concepts and tools for continuous improvement that are collectively called the TPS. This article deconstructs literature on Ohno and the Toyota story to reconstruct the mental model that Ohno used to identify and categorize physical production waste in Toyota’s manufacturing operations. The mental model attributed to Ohno proposed in this work is then generalized into a framework for identifying and eliminating both physical and nonphysical wastes in systems. Manufacturing companies and researchers can utilize the framework to foster the same thinking that Ohno used to identify nonvalue-added activities in production processes. Applying the described framework to data and information flows will allow for the discovery of wastes that were once hidden and will lead to the development of tools for improving the data and information needed to support manufacturing in a Smart Manufacturing environment.
在非传统应用中发展大野耐一的废物识别心智模型
制造业领域技术的发展迫使行业进行数字化转型,但几乎没有关于什么是增值和非增值数据和信息的指导。然而,丰田生产系统(TPS)方法可以提供一些见解,该方法在识别物理制造过程中的浪费方面取得了数十年的成功。对丰田的历史以及TPS的概念和工具进行了广泛的研究,但没有关于大野耐一如何处理问题并开发废物分类(7种废物)的文件,这些分类导致了统称为TPS的持续改进的概念和工具。本文解构了关于大野耐一和丰田故事的文献,重建了大野耐一用于识别和分类丰田制造业务中物理生产浪费的心智模型。在这项工作中,Ohno提出的心理模型被推广到一个框架,用于识别和消除系统中的物理和非物理浪费。制造公司和研究人员可以利用这个框架来培养大野耐一用来识别生产过程中非增值活动的相同思维。将所描述的框架应用于数据和信息流将允许发现曾经隐藏的浪费,并将导致工具的开发,以改进支持智能制造环境中制造所需的数据和信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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