Journal of Physics: Conference Series

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Application of lean six sigma in energy saving lamp assembly process

To cite this article: Tian Feng et al 2018 J. Phys.: Conf. Ser. 1053 012129

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Application of lean six sigma in energy saving lamp assembly process

Tian Feng¹, Yan Zhang, Xiangsai Wang and Yajie Li

Beijing University of Posts and Telecommunications No.10 Xitucheng Road, Haidian District, Beijing, China

1Email: fengtian@bupt.edu.cn

Abstract. Lean Six Sigma is an improvement strategy. Stimulated by global high-profile enterprises including CE, Motorola, Pfizer and Boeing, increasingly number of enterprises select it and promote its implementation. It can be observed, as an important measure, to study Lean Six Sigma and how to effectively apply the management approache of it gives preference to further the development of enterprises. Attributing to the high demand in energy-saving lamp, herein energy-saving lamps manufacturing industry is selected. Taking its one-off assembly yield as an instance, we integrate the data pre-treatment into the Lean Six Sigma Management Process, employ specific DMAIC implementation pattern and apply measuring system analysis, SIPOC analysis, fishbone diagram, FMEA analysis etc. For analysis to reduce in-process defectives, improve first time success ro, thereby to have the operation cost lowered, company quality level lifted and company competitiveness enhanced.

Introduction

Since the emergence of Six Sigma in Motorola, it has been defined as a technology to improve enterprisesrsquo; quality process management. It can provide impetus to lower quality cost to a large extent in the pursuit of “zero defective”, finally yield a higher financial effectiveness and breakthrough in enterprise competitiveness. Six Sigma Management has gradually developed into Lean Sigma Management which is combined with production and pure statistical technique. LEAN is such a most preferred mode that through the transformation of system structure, personnel organization, operation mode and market supply etc. It is implemented through PDCA (The meaning of PDCA is to divide quality management into four phases, which are planning, doing, checking and action). With the development of theories and practices, the theory of Six Sigma and LEAN are synthesized to have compensated for the imperfectness when either of them is individually applied. With DMAIC (Defining, Measuring, Analysing, Improving, and Controing), a specific pattern to push enterprises to continuously modify the Six Sigma, it represents the five phases of LEAN and Six Sigma Management. [1]

Research in the paper Management and Implementation of Six Sigma (2007) by Hezhen, Yuegang and Wang Lilin [2] indicate that the emergency of Six Sigma se per is an innovation in management and statistics. Generally, DMAIC process is adopted for follow-up existing process modification. And strictly speaking, Deming PDCA and DMAIC share no difference in nature. While at each stage of DMAIC, specific technologies and instrumental support are given, and voluminous methods and tools for modern management and statistics are generated. Therefore, Six Sigma is applied to have provided

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the system with the ability to detect, analyse and resolve problems as well as technology route and methods to proceed in parallel therewith.

Analysis on Key Factors of Six Sigmarsquo;s Success (2009) by Bo Xiangping and Fang Fei [3] reveales its research outcomes: key factors of Six Sigmarsquo;s success mainly focus on the scientificity of Six Sigma process implementation and goal orientation of projects featured by source-saving and customer-centre, except for the attention and participation of senior leaders.

Wang Gang [4] analysed the reasons for the decreasing performance of SV companies in the appliance factories and then started from the current operation of the company, using the Lean Six Sigma theory, Lean manufacturing tools, DMAIC Six Sigma and so on. The result is improving production efficiency, product qualification rate, cost savings. He summarized methods and experiences, which are of reference value and practical significance to other enterprises.

In Research on quality improvement of the carriage in limited quantities based on six sigma management, Wang Jing [5]used six sigma ideas and methods which are aimed at improving the business process and customers satisfaction, combing with the passion analysis and regression analysis. Combining with the actual case of bumping rate of water jacket core in ABC Company, Wang Xingxing [6] focused on the structure of Six Sigma DMAIC process and analyzed and studied the five

stages of Lean Six Sigma in A Case Study of Lean Six Sigma Application in ABC Company.

In a research of Yu Hongbin [7], the application of advanced management methods was of vital importance to reduce the quality cost and improve the management level. Based on the current application of Six Sigma, this research put forward some problems that should be paid attention to in the future promotion of Six Sigma in China.

All these scholarsrsquo; researches indicate the increasingly vital role of Six Sigma has played in enterprises developme

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精益六西格玛在节能灯装配工艺中的应用

冯天¹，张燕，王湘思，李亚杰

中国北京市海淀区西土城路10号北京邮电大学

电子邮件:fengtian@bupt.edu.cn

摘要：精益六西格玛是一种改进策略。在CE、摩托罗拉、辉瑞、波音等全球知名企业的激励下，越来越多的企业选择并推动其实施。可见，作为一项重要的措施，研究精益六西格玛及其管理方法的有效应用，对企业的进一步发展具有重要意义。鉴于节能灯的高需求，本文选择节能灯制造业作为研究对象。采取一次性组装产量作为一个实例,我们将数据预处理集成到精益六西格玛管理过程,采用特定DMAIC实现模式并应用测量系统分析,SIPOC分析鱼骨图,FMEA分析等分析减少进程内的次品,提高第一次成功,从而降低操作成本,使公司质量水平提高,增强公司竞争力。

介绍

自六西格玛在摩托罗拉出现以来，它就被定义为一种改善企业质量过程管理的技术。它能在很大程度上为追求“零缺陷”而降低质量成本提供动力，最终产生更高的财务效益并且突破企业竞争力。六西格玛管理已逐步发展成为生产与纯统计技术相结合的精益西格玛管理。精益是一种最受欢迎的模式，它通过制度结构、人员组织、运作模式、市场供应等的转变，通过PDCA (PDCA的含义是将质量管理分为计划、实施、检查和行动四个阶段)来实现。它随着理论和实践的发展，综合了六西格玛理论和精益理论，弥补了两者单独应用时的不足。DMAIC(定义、测量、分析、改进和控制)是推动企业不断修改六西格玛的一种特定模式，它代表了精益和六西格玛管理的五个阶段^[1]。

何桢，岳刚，王丽林^[2]在论文《六西格玛管理与实施》(Management and Implementation of Six Sigma)中进行的研究表明，六西格玛本身的突发事件是一种管理和统计上的创新。一般采用DMAIC工艺对现有工艺进行后续修改。严格地说，Deming PDCA和DMAIC在性质上没有区别。在DMAIC的每个阶段，都提供了具体的技术和工具支持，并产生了大量现代管理统计的方法和工具。因此，应用六西格玛提供该系统具有检测、分析和解决问题的能力，以及与之并行的技术路线和方法。

曾由薄湘平, 方飞和Krabbendam K^[3]分析的六西格玛成功的关键因素及其研究结果:除了高层领导的关注和参与，六西格玛成功的关键因素主要集中在六西格玛过程的科学性的项目实施和节约资源的目标取向、客户中心。

王刚^[4]运用精益六西格玛理论、精益制造工具、DMAIC六西格玛等理论，分析了电器厂SV公司业绩下降的原因，并从公司目前的经营状况出发,其结果是提高了生产效率、产品合格率，节约了成本。他总结的方法和经验，对其他企业具有参考价值和现实意义。

王景^[5]在基于六西格玛管理小批量台架生产质量改进研究中，运用六西格玛思想和方法，结合激情分析和回归分析，旨在提高业务流程和客户满意度。王星星在精益六西格玛在ABC公司应用的案例分析^[6]中结合ABC公司水套芯磕碰伤率的实际案例，重点研究了六西格玛DMAIC工艺的结构，并对这五种工艺进行了分析研究。

精益六西格玛的阶段——以ABC公司精益六西格玛应用为例

在于洪彬^[7]的研究中所采用的先进管理方法对于降低质量成本，提高管理水平有着至关重要的作用。本研究基于六西格玛在中国的应用现状，提出了六西格玛在中国未来推广应注意的问题。

这些学者的研究都表明六西格玛在企业发展中的作用越来越重要。正如通用电气最年轻的首席执行官杰克bull;韦尔奇所言:“六西格玛管理培训计划是下个世纪通用电气领导层的诞生地。

在节能灯制造行业，王宇乾等人于2011年发表了基于Minitab的六西格玛管理在节能灯工业工程与管理中的应用^[8]。他们的研究主要通过方差的双因素分析来选择相同的DMAIC模式来提高节能灯的装配率。本文在研究不完整的原始数据的基础上，对缺失数据进行预处理。

方法

• 1. 节能灯生产行业简介

节能灯又称节电灯泡、电灯泡、紧凑型荧光灯或集成荧光灯，是将荧光灯与镇流器(稳定器)组合而成的照明设备。由于现代社会对节能灯的巨大需求和节能灯技术的细微差别，工艺流程的完整性成为制造商的核心竞争力，即提高一次性组装节能灯的合格率。

• 1. 数据预处理

我们的分析是以2011年4月2组lamp输出不合格案例为例进行的。但此次处理的数据是基于Minitab的六西格玛管理在节能灯装配中的应用中提取出来的，因此4月18日至4月21日数据的缺乏导致了原始数据不完整。所以我们引入数据预处理来处理不完整问题。步骤如下：将已知数据输入Mathematica，利用插值函数和插值方法对缺失数据进行拟合，并对通过插值计算得到的数据进行测试，使其在可接受误差范围内。

图1

如图1所示，节能灯的生产从4月份的2组插值开始。

a NS是指后盖上没有灯光。

b GL为后盖漏气。

c ONS指的是上行线没有灯光。

d HLF是指热灯故障。

• 1. D(定义阶段)
     1. 一次性装配合格率现状。由图1可知，4月份2组共生产节能灯89804盏，其中不合格品1268盏，合格率达97%。图2帕累托图揭示了失败的主要原因。这意味着后盖没有照明是主要原因，几乎是一半的灯故障。

图2 Pareto原因图 图3 SIPOC节能灯生产过程

• • 1. 项目过程分析。我们制作了SIPOC模式如图3所示，清晰的回顾了整个流程，每个阶段的关键点项目和关键问题。对于节能灯的一次组装率，S(供应商)的零部件质量、I(输入)的装配方法和环境、P(工艺)的质量控制等过程中的各个阶段都会影响组装率。
    2. 节能灯的CTQ和基准。本例节能灯规格为EB1, 52 25W 127V 50-60HZ。在127V电压的前提下，功率合格范围为22.5-27.5W。本文旨在对节能用电数据进行分析和修正，降低灯具不合格率，希望实现合格率由97%提高到99%的目标。
  1. (测量阶段)

由于4月份节能灯生产数据量大，为了更好的分析不合格率及其原因，我们选择抽样测量。A、B两个操作者对10个采样节能灯进行3次测量，记录如下表1。

表1 U=100V测量系统数据分析

注:基于Minitab的六西格玛管理在节能灯装配中的应用数据

我们选择功率数据测量作为连续的数据，其原因是对系统测量的分析能够反映其综合状况。使用Minitab软件对上述数据的分析结果如图4和图5所示。

图4 GRamp;R的分析结果

图5 Ramp;R分析结果

从以上结果我们可以得出结论:

1. 测量系统的精度与工艺规范的精度之比为8.82%lt;10%，表明该仪器能满足规定的测量能力要求。
2. 根据iso9000的第5类，表明测量系统可以对可接受范围内的工艺变化进行有效的分类。
3. 第一个变异曲线图显示，大多数变异发生在组件之间。最后一幅图告诉我们，操作者之间没有差异，评估人员之间也没有差异。
   1. (分析阶段)

针对灯具失效分析目标和原因，选取一百多个一次性装配不合格灯具进行FMEA分析，进行失效检测。本次分析主要采用以风险优先级编号RPN为核心的简化的危害分析方法来进行。^[4]

根据对上述测量阶段影响因素的分析，将9个影响因素分为输入、过程和输出三个部分，分析结果如表2所示。

表2 FMEA分析