孕妇新年打扮:二十一世纪的管理，使用SPC的制造分析

We constantly hear that we are in a new era where the problem is no longer one of not being able to collect enough data on our manufacturing processes, but how to effectively use the massive volume of data we collect. 我们不断听到我们在一个新时代的问题不再是一个不能够收集到足够的数据对我们的生产流程，但如何有效地使用我们收集的数据量大。 One of the primary battle cries of this new era is, “Better analysis of the data is absolutely essential!” (Then, following that battle cry is usually a summary of someone's particular flavor of analysis technology or methodology.) Chances are, the term “KPI” is also mentioned, as it is explained how this technology contributes to the KPI process. 在这个新时代的主要战斗呐喊之一是，“更好地分析数据是绝对必要的！”（然后，下面的战斗口号是通常别人的分析技术或方法的特别风味的摘要。）机会是 ，这个词“KPI”还提到有，因为它是解释这项技术如何有助于KPI的过程 。

In keeping with that established path, let us examine an established technology in a new light. 在保持与该既定的路径，让我们来看看建立一个新的轻的技术。

Traditional Quality and Specification Analysis SPC 传统的质量和规格分析的SPC

Statistical Process Control (SPC) is a time honored and well demonstrated method of process management. 统计过程控制（SPC）是一种荣幸，并表现出良好的流程管理方法。 Everyone who has studied modern manufacturing knows of Dr. Deming and his early work establishing SPC as a standard practice in postwar Japan. 研究现代制造业的每个人都知道Deming博士和他的早期工作，建立标准的做法在战后日本SPC。 SPC has long been used for measuring and monitoring quality by the Quality departments and labs of most industrial manufacturing facilities. 最高人民法院长期以来一直用于测量和监测质量由质量部门和大多数工业生产设施的实验室 。

SPC has undergone periodic recasting and updates, such as Continuous Process Improvement (CPI) and Total Quality Management (TQM), as we learn more in each cycle of popularity. SPC经历了定期重塑和更新，如持续的过程改进（CPI）和全面质量管理（TQM），我们学会在每个周期的普及。 Certainly SPC is a key part of the Six Sigma and Lean Six Sigma processes that many manufacturing companies have invested in. 当然，最高人民法院的六西格玛和精益六西格玛过程中的关键部分，许多制造企业已投资的

Many of these SPC applications provide data analysis either “after the fact”, in the case of quality monitoring in a lab based on product samples at the end of a production process, or somewhat removed from the immediate “actionable decisions”, in the case of the Six Sigma processes. 这些SPC应用程序的许多无论是“事后”提供的数据分析，在实验室在生产过程结束，产品样本为基础的质量监测的情况下，或有些从眼前的“采取行动的决定“的情况下， 六西格玛过程。

Typical applications of the traditional SPC methods include successful quality/process management functions such as: 传统的SPC方法的典型应用包括成功的质量/过程管理功能，如：

• Routine SQC/SPC Reporting 常规SQC / SPC报告
• Process Monitoring and Improvement 过程监控和改进
• Analytical Method QC in Laboratories 在实验室分析方法QC
• Regulatory Compliance 合规性
• Supply Chain Customer Certification 供应链客户认证

Emerging Real-Time Operational Decision Support SPC Analysis 新兴的实时业务决策支持SPC分析

While the SPC analytical methods have met the challenge in these functions, it also is the right tool to meet the emerging battle cry for better analysis of available data. 虽然SPC分析方法满足这些功能的挑战，它也为更好地利用现有数据的分析，以满足新兴的战斗口号是正确的工具。 The time based, comparative analysis and visual presentation of the analysis that are unique to the SPC methods, enable manufacturers to better understand their processes and, more importantly to take immediate action based upon the analysis of the data. 的时间为基础，分析比较和分析SPC方法是唯一的视觉呈现，使制造商能够更好地了解他们的流程，更重要的是立即采取行动，根据数据分析 。 SPC is the appropriate technology and methodology to meet the current needs of manufacturing that call for data analysis to provide: SPC是适当的技术和方法，以满足当前的需求制造，为数据分析提供呼叫：

• Ability to measure ROI on systems investment 系统的投资能力来衡量的投资回报率
• Timely and effective analysis summaries and reporting 及时和有效的分析总结和报告
• Predictive capability 预测能力
• Identifiable benefits (lower costs, higher yields) 可识别的好处（成本更低，产量更高）
• Support immediate actionable decision processes based upon results 支持立即采取行动的决策过程后的结果为基础
• High confidence to make process change for improvement 高的信心，使过程改进的变化
• Incorporation into a proactive process improvement program 积极主动的过程改进计划纳入
• Make it easy to get specific, role-based results for individual informational needs 可以很容易地得到具体的，基于角色的个人信息需求的结果
• Reduce complex calculations of aggregated data to meaningful and measurable information with context 减少复杂的计算，以有意义和可衡量的上下文信息的汇总数据
• Immediately measurable results of actions taken 立即采取的行动可衡量的结果

If the battle cry is for better analysis, then Real-Time, actionable, decision support analysis are the rally points. 如果战斗口号是为更好地分析，然后实时的，可操作的，决策支持分析的反弹点 。 Again, the SPC techniques, methods and characteristics lend themselves to these points. 再次，SPC技术，方法和特点本身对这些点。 The ability to provide the manufacturing operator with real-time SPC derived analysis, presented visually in a simple enough format to quickly identify “out of control” parameters, is the goal. 提供制造运营商，直观地呈现在一个足够简单的格式，以快速识别“失控”的参数，与实时SPC派生分析的能力，是我们的目标 。 This real-time presentation can take the form of a robust SPC application embedded within an HMI display or it could be in the form of a dedicated SPC data collection and analysis system on the manufacturing floor. 这种实时的演示，可以形成一个强大的SPC在HMI显示嵌入式应用，它可以在一个专门的SPC数据收集和分析系统对生产车间的形式。

In the HMI applications, SPC has often been viewed as an accessory function. 在HMI应用SPC往往被视为一个附件功能。 However, the emerging requirement for better analysis, to facilitate better process management and improvement, requires that fully developed SPC analysis and presentation tools be an embedded function within the HMI. 然而，新的要求，为更好地分析，以便更好地处理管理和改进，需要充分开发的SPC分析和演示工具在嵌入式HMI的功能。 This facilitates the most effective real-time data capture and analysis, provides the operator with immediate access to the visual presentation of the analysis, allows a mechanism for the operator to document preventative or corrective actions taken, and supports the emerging role of the HMI as a feed into integrated enterprise analytics. 这有利于最有效的实时数据采集和分析，提供经营者立即访问分析的视觉呈现，让操作文件采取预防或纠正措施的机制，并支持HMI的新兴作用一个饲料为一体的综合性企业分析。

SPC is fundamental in this emerging need of better data analysis. SPC是根本在这个新兴的需要更好的数据分析 。 It is how to make more effective use of all the data in historians, MES and ERP systems. 它是如何更有效地使用历史学家，MES和ERP系统中的所有数据。 It also allow for the reduction of complex, specialized process data into graphic visualizations which operations and management can quickly understand and from which informed action can be taken. 它还可以减少复杂的，到专业的过程中数据的图形可视化的经营和管理可以迅速了解并从中可以采取明智行动 。

Some of the particular derived benefits of Real-Time Decision Support SPC Analytics are; 一些特定的派生利益的实时决策支持SPC分析 ;

• Robust, easy to understand, high level of confidence 坚固耐用，易于理解，高水平的信心
• Identify, verify and reduce sources of variation 识别，验证和减少变异的来源
• Analyzes ongoing and immediate variation, not final product quality – process control not product control 分析正在进行的和直接的变化，而 ??不是最终产品的质量-过程控制，产品控制
• Applies to both the process and the product 适用于过程和产品
• Detects changes, shifts and unusual events 检测到的变化，变化和不寻常的事件
• Separates signals from noise 噪声分离信号
• Identifies causes of excessive variation 标识过多的变化的原因
• Monitors real-time results of continuous process improvement activities (pitch for CPI as one of the real ROI payoffs for SPC) 实时监视器持续的过程改进活动的结果（消费物价指数的间距作为一个真正的投资回报回报为 SPC）
• Predictive problem detection on a stable process 在稳定过程的预测问题检测
• Provides tool for documentation of compliance with customer supply chain requirements 提供与客户供应链的要求遵守的文件的工具

SPC Based Manufacturing Analytics 最高人民法院根据制造分析

Along with the battle cry for better analysis of data, there has been an increase in the use of Business Intelligence and Business Analysis tools, and the desire to integrate production data and analysis with business data and analysis. 随着战斗口号为更好地分析数据，已经在使用商业智能和商业分析工具，并整合与业务数据和分析生产数据和分析的愿望增加 。 This is all aimed at developing a better understanding of complete corporate performance. 这是所有旨在更好地了解开发一个完整的企业绩效 。

This new opportunity has created the need for analytical tools that can perform in this joint environment, and deliver to these goals. 这个新的机会创造了分析的工具，可以执行在本联合环境的需要，并提供实现这些目标。 SPC methods arise well to this new opportunity in the form of SPC based Manufacturing Analytics. SPC方法以及新的机会，这在基于SPC的制造分析的形式出现。

SPC based Manufacturing Analytics is statistical and rule based, providing the aggregation, analysis and role-based visualization and reporting of manufacturing data that enables users to better understand and improve their processes, identify and reinforce best practices, react quickly to process events, and anticipate potential problems before they affect product quality, yield, or cost. SPC的制造分析是基于统计和规则，提供聚合，分析和基于角色的可视化和制造业数据，使用户能够更好地理解和改善他们的流程，识别和强化最佳实践报告，迅速 ??作出反应，处理事件，并预测潜在的问题之前，它们会影响产品质量，产量，或成本 。 The key differentiating elements of the SPC based Manufacturing Analytics methodology for analyzing data are: 基于SPC的制造分析分析数据的方法的关键要素是 ：

• Statistically based 基于统计
• Focused on role based analysis and reporting 专注于基于角色的分析和报告
• Identifies significant events, separating out “noise” 标识显著事件，分离出的“噪音”
• Emphasis on visual presentation technique to enable quick analysis 强调视觉呈现技术，使快速分析
• Supports both reactive and predictive behavior 同时支持的反应和预测行为
• Easy enough to be implemented, maintained, and used by existing plant personnel 很容易实施，维护和现有工厂的工作人员所使用的
• Aggregates data from different sources while preserving statistical validity 来自不同来源的聚合数据，同时维护统计的有效性
• Supports the ISA S-95 Production Performance Analysis Activity Model, which outlines the need for robust systems, methodologies, and tools to improve the ability to make very informed decisions based upon extensive and varied analysis functions. 支持在ISA的S - 95生产性能分析活动模型，其中概述了强大的系统，方法和工具，需要提高的能力，使基于广泛和多样的分析功能非常明智的决策 。

What has been the result of the merging of these two levels of business analysis and manufacturing analysis are value parameters used to monitor the overall status and performance of an operation or enterprise. 已合并这两个层次的业务分析和生产分析值参数用于监视操作或企业的整体状态和性能的结果 。 These are expressed as Key Performance Indicators (KPIs), which are usually a single parameter consisting of an aggregation of financial, operational, and measured parameters to provide a meaningful and reliable KPI variable. 这些关键性能指标（KPI），这通常是一个单一的参数，包括财务，业务，和测量参数的聚集提供了一个有意义和可靠的KPI变量表示。 These variables are often monitored for a “good” or “bad” status in some sort of web-based visualization tool, such as a portal or dashboard. 这些变量通常是在一些基于Web的可视化工具，如一个门户或仪表板，的“好”或“坏”的状态监测。 The ability to contribute to or provision this web-based visualization function is a key component of the new analysis opportunity. 能力作出贡献或提供这个基于Web的可视化功能，是一个新的分析机会的关键组成部分。

SPC based Manufacturing Analytics methodologies would allow for a system to be created that monitors the stability and change of all the parameter components contributing to the KPI, which would allow the detection of a change in one key KPI component before the KPI itself shows to be out of range. SPC的制造分析方法，将要创建的系统，让显示器的稳定性和变化贡献的KPI，这将允许在一个关键的KPI组件变化检测前KPI本身显示出所有参数组件范围。 The visual presentation of this detection could then be displayed not as just a “good” (green) or “bad” (red) status, but even as a “potentially getting worse” (yellow) status. 便可以显示，不只是一个“好”（绿色）或“坏”（红色）的状态，甚至“可能越来越差”（黄色）状态，但这种检测的视觉呈现。 Existing KPI analysis and reporting systems do not have access to all the parameters or the ability to represent all the components in this fashion, such that operations and management can quickly identify, or even predict, early signs of detrimental change to take action against. 现有的KPI分析和报告制度，没有获得的所有参数，或能够代表所有的组件，以这种方式，这样操作和管理，可以快速识别，甚至预测，不利变化的早期迹象采取行动对。

Choosing an SPC Analysis Platform 选择SPC分析平台

A good SPC platform should be able to accommodate all three levels of analytics outlined here for the manufacturing environment. 一个良好的最高人民法院平台，应能容纳3个这里的生产环境中的水平分析。

• Traditional – SPC Quality and Specification Analysis 传统- SPC的质量和规格分析
• Emerging – Real-Time Operational Decision Support SPC Analysis 新兴-实时业务决策支持SPC分析
• New – SPC Driven Manufacturing Analytics 新- SPC驱动的制造分析

Additionally, this platform must be architecturally structured to allow for growth and expansion around the different levels of need and phased implementation a manufacturer may require. 此外，这个平台必须构架结构，允许各地不同层次的需要和分阶段实施的制造商可能需要的增长和扩张 。 This requires a modular and component based approach that allows easy integration into existing and disparate systems, with an open and standards based approach to accessing the data to be analyzed. 这需要一个模块化和基于组件的方法，使易于集成到现有的和不同的系统，一个开放和基于标准的方法来访问数据进行分析 。 A system that can provide this function of real-time SPC analytics, coupled with role specific, visual reporting, can support the full range of analytics and decision support for all levels from plant floor through management. 一个系统，可以提供这种实时SPC分析的功能，作用具体化，视觉报告，可以支持的全方位的分析和决策支持，从车间通过管理各级。

Case studies 案例研究

The following examples illustrate how SPC-based Manufacturing Analytics is being implemented to get real benefits and improve the operating process. 下面的例子说明如何SPC为基础的制造分析正在实施中得到实实在在的好处，并提高操作过程中 。

Flexible Packaging – Offline and online real-time SPC analysis supports continuous improvement program 软包装-离线和在线实时SPC分析支持持续改进计划

A large international flexible packaging manufacturer produces many different products of a similar form for various food industry customers. 一个大型的国际软包装制造商类似的形式为各种食品行业的客户产生了许多不同的产品 。 Product specifications are slightly different for each customer, and tight monitoring of the production against these specifications is necessary to meet supply chain requirements. 产品规格为每一个客户略有不同，并严密监测对这些规范的生产需要，以满足供应链需求 。 Once the production line is running, the goal is keep the line running, and only make necessary changes to the equipment to ensure that the product remains within specs. 一旦生产线运行，我们的目标是不断线运行，只有设备进行必要的修改，以确保产品仍符合规格 。

By using SPC analysis of electronically collected measurement samples on a real-time basis, and then presenting the results to the operator in a very basic “in or out” of specification/control visual display, the operators were able to detect and make appropriate adjustments to keep the production line running and achieve reduced variation and reduction in returned product. 通过实时的基础上使用SPC电子测量样品收集的分析，然后提出一个非常基本的“或”规格/控制视觉显示运营商的结果，运营商能够检测并作出适当的调整保持生产线的运行，实现减少的变化，并在返回的产品减少。

ENLARGE IMAGE 放大图片

The operators and quality inspectors have information directly available to them from this interface. 运营商和质量检查员从这个接口直接提供给他们的信息 。 They are able to view summary historic data, as well as real-time SPC charts to analyze the process. 他们能够查看历史数据汇总，以及实时SPC图表分析过程。 These role specific reports give the staff complete access to standard operating procedures and support uniform workflows. 这些作用的具体报告给员工完全获得标准作业程序，并支持统一的工作流程。

SPC is used to establish process control limits and support quality control initiatives. SPC是用于建立过程控制的限制和支持的质量控制措施 。 Engineering uses the software for process studies and to work process capability into specifications. 工程使用的软件过程研究和工作规范的过程能力 。 The approach is part of the continuous process improvement programs which reduce variation, decrease scrap, and reduce product overages. 该方法是降低变异，减少废料，并降低产品过剩持续的过程改进计划的一部分 。

As a result of proactively using SPC, the plant has been able to improve operations by limiting weight variation during the manufacturing process. 作为一个积极使用SPC的结果，该厂已被限制在生产过程中的重量变化，能提高操作。 Over the past two years, the difference between the target weights versus actual weight was measured and benchmarked. 在过去的两年中，目标权重与实际重量之间的差额是衡量和基准。 The measured results showed a cumulative cost savings of over $200,000 since the program started. 测量结果显示，累计超过20万美元的成本节约，自该计划开始。

FOOD SAFETY – Use SPC Analytics for early prediction 食品安全-用于早期预测的SPC分析

A poultry processing plant monitors regulated bacteria such as E. coli and Salmonella. 家禽加工厂的监控监管细菌如大肠杆菌和沙门氏菌等 。 When the pathogen level exceeds the limit, the required action is costly and time consuming. 当病原体的水平超过了极限，需要采取的行动是昂贵和费时。 Exceeding the safe levels can happen quickly, with little time for corrective action. 超出安全水平，可以很快发生，很少有时间纠正行动 。 Setting arbitrary “reaction” limits can lead to a tradeoff between false positives and missing signals. 设置任意的“反应”的限制，可能会导致误报和失踪信号之间的权衡。

The fact that they actively use SPC and actively manage the process enables fast and informed response. 事实上，他们积极使用SPC和积极管理的过程，使反应快速，并告知 。 Applying Manufacturing Analytics using SPC-based event and pattern rule violation detection results in early detection of an unstable process, predicting the likelihood of an event, and enabling corrective action and process improvement to prevent reoccurrence. 应用制造早期发现一个不稳定的过程中使用SPC基于事件和模式规则的冲突检测结果的分析，预测事件的可能性，使纠正行动和工艺改进，以防止再次发生。

By using continuous process improvement, the poultry plant was able to develop very high capability production (Cpk = 12.2) with regard to pathogen levels. 通过持续不断的过程改进，家禽厂能够制定关于病原体的水平非常高的能力生产（CPK = 12.2 ）。 This provides a large amount of manufacturing head room. 这提供了一个大量生产的头部空间。 Because of this high capability process management, the plant has a substantial leeway when a process destabilizing event occurs, and can continue to produce wholesome food while the process engineers are returning the process to control. 正因为如此高的能力，过程管理，工厂具有相当的余地，当一个进程不稳定事件发生，并可以继续生产有益健康的食物，而工艺工程师返回的过程控制。

Food Packaging – Using SPC Analytics to manage a co-packer's performance. 食品包装-使用SPC分析管理共同包装机的性能。

A dairy processing facility uses SPC Analytics to monitor the fill weights of their products. 乳品加工设施使用SPC分析，以监控其产品的填充重量。 They use SPC control charts to monitor a KPI that indicates whether the fill weights fall within a Maximum Allowable Variation (MAV) ratio. 他们用SPC控制图监控KPI表示是否填写重量范围内允许的最大变化（MAV）的比例下降。 This enables them to maximize the product yield while controlling the risk of MAV violation. 这使他们能够最大限度地提高产品的产量，同时控制微型飞行器违反风险。

The dairy uses a co-packer to package some specialty products. 乳品使用共同打包机，包中的一些特色产品 。 The dairy qualifies the co-packer by using SPC to evaluate their process dependability and capability to meet specifications. 乳品资格使用SPC来评价他们的过程中的可靠性和能力，以满足规范的合作打包机 。 Production fill weights are routinely collected and SPC results reported to the dairy as part of their supply chain quality management. 生产填充重量，定期收集和SPC的结果报告，作为其供应链的质量管理的一部分的乳制品。

Since both the vendor and customer are using the same analytics and charts, they can more effectively collaborate to improve the process and yields. 由于供应商和客户都使用相同的分析和图表，它们可以更有效地合作，提高的过程和产量。 In addition, the dairy can use the co-packer's quality deliverables to manage their label weight regulatory compliance. 此外，奶制品可以使用合作打包机的质量交付来管理他们的标签重量的合规性 。

OEE - Use of SPC to validate OEE calculations OEE - SPC用于验证OEE的计算

SPC and process capability methods increase value and usability of OEE values. SPC和过程能力的方法增加的OEE值的价值和可用性。 The OEE KPI can be treated like any other process parameter with trend and capability monitoring and analysis. OEE的KPI可以像对待任何其他工艺参数的趋势和能力，监测和分析 。 Using SPC methods to analyze and chart the OEE values provides far more actionable information than a simple annunciator on the management dashboard. 使用SPC方法去分析和图表的OEE值比一个简单的报警器的管理仪表盘上提供更为可操作的信息。 Not only does the SPC chart and trend analysis deliver more and better decision support on the OEE KPI, you can drill down and study the behavior of the individual OEE components, availability, performance and yield. SPC图表和趋势分析，不仅提供更多，更好的决策支持的OEE的KPI，您可以向下钻取和研究的个人的OEE组件行为，可用性，性能和产量。

KPI Component Analysis KPI成分分析

A plant uses large, continuous process ovens needs to develop a meaningful and reliable KPI consisting of financial, operational, and measured parameters to accurately represent the total energy costs. 一个工厂使用大量的，持续的过程烤炉需要制定一个有意义的和可靠的KPI，包括财务，业务，和测量参数，准确地反映总的能源成本。 The existing KPI system was unable to monitor all the necessary component variables and visually display the KPI, such that operations could react to early signs of change. 现有的KPI体系是无法监控所有的必要组成部分变量，并直观地显示KPI，这样操作可能反应变化的早期迹象。

By applying MA using SPC-driven, role-based visualization reporting techniques, the plant could create a system that monitored the stability and change of all the parameter components contributing to the Energy Consumption KPI, which allowed the detection of a change in one key KPI component before the KPI itself showed to be out of range. 通过使用SPC驱动的，基于角色的可视化报告技术应用马，工厂可以创建一个系统，监测的稳定和改变能源消耗的KPI，这使得检测在一个关键的KPI的变化作出贡献的所有参数组件前KPI本身的组件显示范围 。

Conclusion 结论

Modern control systems, plant floor data collection, and laboratories generate large volumes of process data. 现代控制系统，工厂车间数据采集和实验室产生大量的过程数据。 Unless this data is analyzed and usefully reported to all the staff involved in production and plant management, it will not be useful for operational management decision making. 除非这个数据分析和有益的报告向所有参与生产和工厂管理人员，会不会有用的经营管理决策。 Actively using SPC and Manufacturing Analytics enables this data to be effectively used to manage the enterprise. 积极使用SPC和制造分析，使这些数据被有效地用于管理企业 。 Tightly coupled analytics will make control systems a core component of 21st century process and enterprise management systems. 紧密耦合的分析将使控 ??制系统的21世纪的过程中，企业管理系统的核心组件。