Often, Kaizen teams for Lean Six Sigma lack the data and corresponding analysis needed to drive consensus and convince others to adopt the project recommendations. The Industrial Internet of Things (IIoT) facilitates fact-based decisions, a fundamental theme of both Six Sigma and Lean Manufacturing. IIoT provides data and analytics that could improve the effectiveness of Lean Six Sigma programs and Kaizen teams.
Six Sigma DMAIC process and impediments
通常，精益六西格玛Kaizen团队首先创建他们想要改进的过程的价值流映射（VSM）。VSM方法论为实际工作（而不是经理或支持工程师而不是经理或支持工程师）的过程中的当前状态的共识驱动了同意。当发现缺陷是问题时，六西格玛DMAIC（定义，测量，分析，改进和控制）进程发挥作用。
Kaizen teams can be thwarted, however, by impediments that block progress. Common causes of project failure include:
- 缺乏收集衡量阶段数据的手段。
- Using inappropriate analytics for the problem at hand like statistical analysis assuming a normal distribution curve when it isn’t normal.
- 返回旧的做事方式导致控制阶段弱。
IIot.can provide a means to overcome each of these impediments.
The measure phase involves data acquisition and numerical studies for parameters around the previously defined defect. This activity involves validating the measurement system, including the instrument’s accuracy, and understanding the potential sources of variation. Obtaining this fidelity requires a lot of data points—certainly hundreds, and maybe even millions of samples.
In the early days of Six Sigma (late 1980s and 1990s), teams could easily find processes running at the two sigma quality level. Here, a hundred measurements should contain 31 defects, which was often enough to guide the team to the root cause of the defect. Moving beyond three sigma—a need for today’s teams—requires much larger data sets (tens of thousands to millions).
With the higher sigma quality levels, it becomes impractical to obtain the needed measurements manually. This is because the high costs, labor intensity and extended elapsed time required for data acquisition often scuttle a project. Also, low-quality data overwhelms the true defects. Manual data acquisition has a higher error rate than the process step under examination. Studies performed decades ago to show the benefits of barcode data entry found 10 percent of manual entries containing 80 characters had an error—and that’s back when schools taught good penmanship.
Automated data acquisition is more effective, and IIoT fills this need. Also, IIoT can go beyond process data and add often previously unavailable equipment data that can have significant impact on defect rates.
分析超出了正态分布曲线
六西格玛的培训计划几乎总是使用假设正常分布曲线的统计分析。这可能是典型的两个和一些三个西格玛项目可以接受。不幸的是，现实世界环境通常具有不遵循正态分布曲线的模式。例如，没有用于确定资产的维护策略的设备故障模式均遵循正常曲线。
特别是ΣIGMA水平更高，分析需要超越假设正态分布曲线。IIT平台提供广泛的分析。有些人还具有快速识别包含许多参数的数据集中的关联（即，随着时间的推移各种I / O测量值）。确定问题的真实源是必要的，而不是并行效果（因果关系与相关性）。
提高阶段使用大量的methods to reduce defects—limited mainly by the team’s imagination and process knowledge. IIoT offers a means to continuously monitor the health of a process or equipment and generate an alert when it deteriorates. The most common application of IIoT involves monitoring an asset’s condition for predictive maintenance to prevent unplanned downtime.
IIot.can be used to help assure that the improvement sticks and prevent people from going back to the old way of doing things. The continuous monitoring using IIoT can be programmed to generate an alert when things start deteriorating—early enough so that preventive measures can be taken before defects occur. Alerts managed using ad hoc communications (phone call or hallway conversation) with people that can fix the problem are often lost—humans tend to forget. In the case of predictive maintenance, the recommended automated business process has the alerts sent directly to the maintenance planner who can assess, set priorities and schedule a repair in the enterprise asset management (EAM) system.
Lean Six Sigma programs have advanced beyond traditional manual data collection and simple statistical analysis assuming a normal distribution curve. IIoT offers a means to take these programs to a new level of effectiveness.
>>Ralph Rio,rrio@arcweb.com., is vice president of弧咨询组.
