一、精益六西格玛简述

    六西格玛是以数据和事实为驱动的管理。它把企业的所有的管理和改进转化为实际有效的行动，而不是停留在口头上和文件里，因而六西格玛是一种非常有效的质量管理和改进体系。然而建立这种改进体系所需成本也非同一般。比如，GE开始实施六西格玛第一年(1996年)的年投入就达2亿美元。此外，实施六西格玛需要强有力的领导层和一定的员工素质，那么六西格玛方法是否适合于我国众多的中小型EMS(电子制造服务)企业呢？

    当前，我国多数的中小型EMS企业的特点，一是没有雄厚的资金，家底薄，二是典型的制造型企业，利润空间小。

    鉴于这些特点，实施精益六西格玛更适合于众多中小型EMS企业的发展。精益六西格玛是精益生产和六西格玛两种改进体系的有机结合。

    精益生产首创于上世纪50年代的丰田公司，1990年由麻省理工学院提出。精益生产是以创造价值为目标，消除浪费的过程，它的最终目标是要以最优质量和最低成本的产品，最迅速地响应市场(客户)的需求。
 
    精益生产是继手工生产和大批量生产之后的第三种生产方式，是第二次生产管理方式的革命。它的核心是通过企业内部和企业间的流程化、协同合作，及时向客户提供个性化的产品(产品的种类和数量直接根据客户的要求来确定)。

    从这个角度出发，精益生产关注生产第一线的创新以适应快速的变化。它强调必须让前线员工参与到质量的控制和改进中来，最大限度地发挥他们的聪明才智。为此，企业投资的重点必须从机器设备转向人，必须使员工在智力和体力上不断完善自己。

    与传统的大批量生产相比，精益生产能够消耗较少的人力、空间、资金和时间，制造最少缺陷的产品以满足快速变化的客户需求。日本的优秀企业，特别是汽车制造业，在上个世纪六七十年代广泛实施精益生产，取得巨大成功。日本产品以高质量、低成本享誉全球。到了80年代，欧美和韩国及台湾的制造业也开始引入精益生产，并取得显著成效。

二、循序渐进，建立精益六西格玛体系

    六西格玛方法以可以有效地改进流程的质量，减少过程变异。精益生产可以有效地提高流程的效率，降低成本。把两者有机结合起来，精益六西格玛可以有效地提高流程的总体效率(高质量、高速度、低成本)，使企业的效益最大化。同时，由于精益六西格玛起步于精益生产，是一种渐进式的质量改进体系，对其投资也是渐进式的。因而不会对企业造成太大的资金压力。

    建立精益六西格玛改进体系就像建一座大厦，要循序渐进，步步为营。

    首先要建造牢固的地基。精益六西格玛体系的基石之一是5S体系。实际上，5S是一切工作的基础，在一个没有5S基础的企业中实施精益生产是不可想象的。 5S(整理、整顿、清洁、规范、素养)的最高境界是使员工养成“有规定按规定去做”的习惯(职业素养)。做不到这一点，5S基石就不牢固。精益六西格玛体系的另一基石是合理化建议和持续改进小组。这是“使前线员工参与到质量的控制和改进中来，最大限度地发挥他们的聪明才智”的保障。企业投资的重点必须从机器设备转向人，创造学习型组织环境，为员工提供不断学习、改进自我的机会。

    第二建造入口。精益六西格玛体系的出入口就是价值流分析(VSM)。首先描绘出当前价值流的状态，再由此设计出新的更合理的价值流，然后策划改进并实施。价值流分析是精益六西格玛改进的首要工具。

    第三建造支柱。精益六西格玛体系的三大支柱为：TQC(全面质量控制)、TPM(全面生产维护)和IE(工业工程)。

    第四建造挡风隔热墙。这就是精益六西格玛体系的几个要素：标准化管理、可视化管理、小批量/单件流、快速产品转换、物流管理、质量源管理、看板/拉动系统、U型/柔性布局和自主维护。

    第五封顶装饰。实施六西格玛持续改进，追求精益生产之完美指标。

    至此精益六西格玛的大厦建造完毕。大厦的整体质量由每个单元要素的质量和相互结合强度决定。建造牢固的大厦需要技艺高超的建筑师和所有参与者的艰苦劳动和共同努力。

三、精益六西格玛体系关键因素

    实际上，不同的公司有不同的基础。实施精益六西格玛所用工具和实施顺序也应因公司而异。为了建立高质量的精益六西格玛体系，应遵循以下九项基本原则：

   以客户为关注焦点
   强调自上而下的领导作用
   解决问题面向流程
   系统思维方法
   全员参与
   持续改进
   基于事实和数据的决策方法
   与供方互利的原则
   创造学习型组织环境
   高质量的精益六西格玛大厦将为企业和员工遮风避雨，抵御风险。它将为制造型企业赢得竞争优势和长久发展。

Using Lean Six Sigma to Improve Call Center Operations
By Robert Gettys

  Employees knew that the service in their third-party call center had deteriorated in recent years. Their job was to handle queries from independent business owners about financial services offered by the call center's client. As in many call centers, the job was considered highly stressful because of expected response times and resolution.
  Initially, no one at the call center knew exactly how bad the problem was – all they knew was that the client was considering canceling the contract. While certain data were collected (time that representatives were available to answer calls, hold time, etc.), there were no data connected to the goals of resolving 75 percent of the inquiries the first time around (first-call resolution) and 90 percent of inquiries within five days (five-day resolution). Furthermore, unknown to the call center, a key decision maker at the client was taking data on how many calls she received each week from people who were unable to get answers from the call center. The number of these so-called escalated calls had grown to a mean of 15 per week.
  When faced with this kind of problem, many companies just lay off staff in an attempt to increase productivity of the remaining group, hire more staff without solving the underlying problem or try to improve results by forcing people to be on the phone more. This company took a different tack: They turned to a Lean Six Sigma expert for help with the goal of improving performance to a level that the client would find acceptable, and thereby assuring renewal of the contract.

Analysis
  In the course of doing a basic process analysis, the Lean Six Sigma expert discovered:
• The majority of calls that could not be resolved on the first call required some research by the service representatives.
• The service representatives were primarily judged on whether they were available to answer. This limited the time they could devote to research open issues. As a result, many calls that could not be resolved right away were often never resolved.
• Customers whose inquiries were not answered within a few days would call back. This increased the call volume, inflated the numbers of calls that could not be resolved on the first call, and led to multiple entries in the computer system for the same problem.
  Furthermore, though the system collected a lot of data, it was unreliable and of little use in terms of understanding performance or making improvements. For example, the data was not random; rather, one person would be designated to track their calls one day and someone else would do it another day. Also, the company had never developed a good way to track notes that could be used for follow up, so no one could be sure if or when inquiries were resolved.
  Baseline data showed that the company was falling far short of its goal, achieving only a 50 percent first-call resolution rate and 62 percent five-day resolution rate.

  Tools
  The Lean Six Sigma team approached the problem in the beginning using standard methodology. One of the first challenges was creating a useful measurement system, including developing and testing operational definitions for first-call resolution and five-day resolution.
  The first measurement system analysis (MSA) testing    the application of these definitions failed  reproducibility. This failure actually was a major moment in the life of the project. All of the operators had done the job for at least a year, yet each had a different interpretation of what was acceptable – even after working together to create the operational definitions. More precise definitions followed, a second MSA was successful, and measurement could begin.
  As expected, the hypothesis tests demonstrated a negative linear relationship between the five-day resolution outcome metric and the available-to-answer process metric. Specifically, the data showed that the representatives with the highest available-to-answer rates had the lowest resolution rate. Interestingly, there was no relationship (r-squared of less than 20 percent) between available-to-answer and first-call resolution – so just increasing the time that people were available to answer calls would not necessarily drive up the first-call resolution rate.
  Using direct observation, random listening to calls (recorded so as not to skew the activity on the call) and fishbone diagramming and analysis, some root causes began to surface.

Actions
  To better meet the needs of the process, the work unit decided to:
• Split the team into two sections: Part of the staff would only take calls and the rest would do the research to resolve the issues.
• Representatives would rotate through the two groups, with daily metrics designed for success, collected individually and reported in a central location. Significant drops in first-call resolution now immediately trigger follow-up action.
• Have the IT staff set up the computer system to use an unused field in the screens to capture research information and notes leading to the ability to monitor not only the issues needing research, but also the age of those issues.
• Forward any calls that were not resolved within four days to management for action.
  Results
  As shown in the following figure, these changes had an immediate and dramatic impact on performance. Within weeks, the first-call resolution rate rose to from 50 percent to 90 percent and the five-day resolution rate rose from 62 percent to 98 percent. Equally important, the secret escalated calls metric that the client was keeping on how many calls she got per week dropped from 15 per week to fewer than 1 per month.
 Figure 1: Center Call Performance Metrics

The client – who once described this company as the worst service provider it had ever known?– later wrote an article for an internal newsletter celebrating the provider's improvement.

Conclusions
  Call centers are not only ubiquitous, but also a hot bed for customer dissatisfaction. The    performance can make or break any service provider's     indexes of customer loyalty. Only by honing in on what the client needs (or another department, if the call center is internal), building a process around those needs and collecting measurement on key factors can a call center be an asset to the organization as a whole. At that point, other data (why people are calling, utilization of its associates, hold times, etc.) become a critical and trusted part of understanding the entire enterprise.
  Customer service representatives need to be able to answer the phone. They need to resolve questions quickly. Hold time needs to be minimal and at or under the customer's expectation. Yet these important metrics, taken alone, with little or no regard to other client-affecting service level indicators, can lead to a loss of business.
  While this project was not solely responsible for saving the contract, within four months of this project's completion date, the client renewed the existing contract for five more years.