4 Standards Opportunities for Smart Manufacturing/智能制造标准的机遇

Most standards for manufacturing created in the last 30 years have already achieved a high degree of maturity; however, to enable SMS, further standards development is necessary. We identify several areas in the SM Ecosystem where standards can be extended or where new standards should be developed, and we identify some new initiatives focused on SMS that will spur the development of both SMS technology and standards.

大多数制造标准是在最近30年里产生的，已经具有了很高的成熟度；但是，面向智能制造系统仍有必要进行更多的标准开发。我们之前识别的智能制造生态系统的领域，将会是标准扩展或新标准开发的主要领域，我们还发现了一些智能制造系统关注的新的新兴领域也将刺激智能制造系统技术和标准的发展。

4.1 Standards Needs/标准需求

Full realization of SMS capabilities will require replacement of the classical manufacturing system architectural paradigm based on a hierarchical control model [45]. Figure 6 shows a new paradigm based on distributed manufacturing services, also called Cyber Physical Production Systems (CPPS).⁸ The paradigm shift is made possible by the introduction of smart devices accessible as services on a network, more embedded intelligence at every level, predictive analytics that enable responsive control, and cloud technology that enables virtualization of control and engineering functions at all hierarchical levels. With these capabilities in place, widespread automation across hierarchical levels using new approaches to control is a realistic possibility.

智能制造系统能力的全面实现需要在层次控制模型基础上替换传统制造系统架构的范式[45]。图6给出了一种机遇分布式制造服务的新范式，也被称作信息物理生产系统（CPPS）⁸。这个范式可以使智能设备以服务的形式接入网络、在各个层次中使用更多嵌入式智能、响应控制可使用预测分析、在所有层次中采用云技术实现虚拟控制与工程设计。由于这些能力的使用，使自动化用新的方法跨越各个层次实现控制成为可能。

The new service-oriented paradigm ultimately transforms the smart manufacturing ecosystem into a fully connected and integrated system, shown in Figure 7. All the manufacturing functions along three dimensions and in the manufacturing pyramid can be virtualized and hosted as services, except those time-critical and safety-critical manufacturing functions remaining at the shop floor level.

新的面向服务的范式根本上把智能制造生态系统转化为一个完全联通的集成的系统，如图7中所示。全部三个生命周期与制造金字塔中的制造功能都可虚拟化和部署为服务，除了车间层中那些有时间要求和安全要求的制造功能。

Existing manufacturing standards are far from being sufficient for the service-oriented smart manufacturing ecosystem. Areas that need new standards support include reference architecture, cybersecurity, factory networking, supply chain integration, and data transfer from factory floor to enterprise level. Table 16 lists these standards’ opportunities and the types of capabilities they support. Specifically, new or improved standards in these areas would improve capabilities associated with agility (A), quality (Q), productivity (P) and sustainability (S). The first column is the area of opportunity for new standards. The second column shows where the standards impact the SM Ecosystem—Product Lifecycle (PL), Production System Lifecycle (PSL), Business Cycle (BC), and Smart Production Pyramid (SPP). The third column shows how the standards map to SMS capabilities. Note that we present this not as a complete list, but rather as a starting point for exploration and discussion of the infrastructure of SM standards.

现有的制造标准距面向服务的智能制造生态系统还有较大的距离。需要新标准支持的领域包括：参考架构、信息安全、工厂网络、供应链集成、工厂层到企业层的数据传输。表16列出了这些标准的机遇和支持它们的能力的类型。应特别注意的是这些领域中新的或改进的标准应改进的能力应关联“敏捷（A）”、“质量（Q）”、“效率（P）”和“可持续（S）”。第一列是新标准机会的领域。第二列列出了标准对智能制造生态系统的影响——产品生命周期（PL）、生产系统生命周期（PSL）、业务生命周期（BC）和智能制造金字塔（SPP）。第三列列出了标准与智能制造系统能力的映射关系。注意，这里我们列出的并不是完整的清单，它更像探索的七点和智能制造标准的讨论的而基础。

As shown in the table, a high-level reference architecture for SMS, including functional models and architectural definitions, is needed to integrate functions within and across the extended enterprise, including between suppliers and customers. These models will form the basis for dynamic production capabilities and customization of end products.

如表中所示，智能制造系统的高层级参考架构包括功能模型与架构定义，用来整合企业的功能包括供应商与客户。这些模型将规范动态生产能力和定制化最终产品。

Information models representing smart devices on the shop floor and manufacturing services are also needed to increase productivity and agility by supporting reconfiguration of equipment, as well as allowing more optimal health maintenance. A reference architecture for CPPS will enable development of production modules incorporating smart devices. As these systems of systems come into place, intelligent machine communication standards along with an architectural framework will allow automation of system-level controls and transparency of data from the lowest levels of manufacturing to higher control levels.

表现车间智能设备和制造服务的信息模型也需要提升设备重配置的效率和敏捷性，这样能够更加优化健康维护。CPPS的参考架构能够促进构成生产模块的智能设备开发。替换了这些系统后，遵从这个架构框架的智能机器的通信标准可以令自动化实现系统级的控制和制造层次中低层次向高层次的数据传递透明化。

This increase of automation possibilities brings a need for new types of interfaces for humans to interact with the machines. Much of the performance data for individual machines can be presented to people through dashboards that also enable direct control. Similarly, dashboards for monitoring and controlling system-wide performance are needed. Optimization of these interfaces is an area of active research, and related standards should accordingly follow. ISA formed an HMI committee to establish standards, recommend practices, and provide technical reports relating to human-machine interfaces (HMIs) in manufacturing and processing applications.

增长的自动化可能性带来了新的人机界面的类型。更多的单台机器的性能数据可以通过仪表盘展现给人，同时也可以直接指导控制。同样的，监控与控制系统范围绩效的仪表盘是必要的。这些接口的优化是一个积极研究的领域，与之相关的标准应该被遵循。ISA组织的HMI委员会为制造与工艺应用中的人机交互界面（HMI）制定标准、推荐案例、提供技术报告。

In addition, for production system design, operational data from manufacturing is needed to generate new designs and better process plans more quickly. Although it is an area of research, no explicit standards yet exist to assess production system capabilities and to link the results back to upstream activities in the lifecycle.

另外，对生产系统的设计，新设计和更好的工艺计划的快速生成需要制造中的操作数据。尽管该领域还处在研究阶段，没有明确的标准存在用来评估生产系统能力并将结果反馈到生命周期上游活动中去。

For product lifecycle management, AMP 2.0[5] recommends an ontology of data and artifacts that captures, stores, visualizes, searches, and shares both static and dynamic data, both along the product lifecycle and through the supply chain. The development of such a standard will enable more agility in the supply chain and reuse of products designs for rapid redesign.

对于产品生命周期管理，AMP 2.0[5]建议采用本体的数据与文件的方式对产品进行获取、存储、可视化、搜索，并在产品生命周期与供应链中进行静态与动态数据共享。开发这样的标准可能令供应链更敏捷、产品设计快速重设计的复用性。

Product lifecycle data combined with data from manufacturing processes can enable advanced analyses of the processes themselves, resulting in process improvement in terms of productivity, sustainability, and quality. For instance, analysis of product performance in the field can sometimes reveal quality issues in production.

来自制造过程的数据整合到产品生命周期数据中可以进一步分析过程本身，指导改善过程的效率、可持续性、质量。例如，现场的产品性能分析有时能够发现生产中的质量问题。

One vision for SMS is that products themselves can contain the history of how, when, and where they were manufactured. The MTConnect Institute is starting standards activities that will enable this type of traceability. Technology and standards for big data and cloud manufacturing will allow many types of advanced analysis and other functions to be provided on a service basis, thereby making them more readily accessible to manufacturers.

有一种智能制造系统的观点，产品本身可以包含它们什么时候在哪儿怎么样被制造的历史。MTConnect研究原正在进行这类可追溯类型的标准研究。支持制造的大数据和云计算的技术和标准能够让多种高级分析和其他的功能构成一个服务基准，令它们可以更容易接入制造端。

Standards related to sustainability evaluation for manufacturing systems are evolving along each of the dimensions described. Current practices for sustainability evaluation for manufacturing follow the Life Cycle Assessment (LCA) methodology standardized in the ISO 14000 series on environmental management. These standards operate from a management perspective and use a top-down approach to estimating sustainability impacts of different processes involved in goods production. In SMS, we envision more accurate measures of the sustainability impacts of each of the manufacturing processes based on measures of operational data for each process. These measures will allow more accurate accounting of the impacts of individual decisions at each production facility. Still, many challenges will exist since sustainability assessment, by its very nature, must address tradeoffs between many criteria. How this data can be used along each of the dimensions of the SMS ecosystem and how sustainability impacts are apportioned to the different aspects of production and the product are grand challenges for sustainability assessment. Standards are necessary to provide unambiguous and comparable data to support this decision-making process.

制造系统可持续性评价的相关标准将沿着各维度展开。当前的制造可持续性评价惯例遵循ISO 14000系列环境保护标准中的生命周期评估（LCA）的方法论。这些标准以一种管理的观点操作并使用一种自上而下的方法评估良好制造中的不同过程对可持续性的影响。在智能制造系统中，我们期待更精确的可持续性影响的测量，能够测量每一个制造过程对可持续性的影响。这基于对制造过程的操作数据的测量。这些测量能够更精确的记录每个生产装置的独立决策的影响。然而，现在的可持续性评估还很原始，还存在很多挑战，必须明确多个标准之间的交换。如何在智能制造生产系统各维度中使用这些数据，如何将可持续性影响分解到不同生产和产品的不同方面去，这些是可持续性评价的巨大挑战。支持决策处理的数据确定与比较标准是必要的。

4.2 New Initiatives/新趋势

Most of the standards areas that we described are being extended to address SMS capabilities. Quite a few new initiatives worldwide have emerged to contribute to the standards and opportunities identified above.

我们所描述的标准领域大多扩展自智能制造系统的能力。一小部分是全球范围中的新倡议出现有助于标准和上述机遇。

4.2.1 Industrie 4.0/工业4.0

Industrie 4.0 is a key initiative in Germany containing a technical strategy for achieving SMS. The enablers of Industrie 4.0 are the internet, mobile computing, and cloud computing technologies. A goal of Industrie 4.0 is the creation of innovations including smart products, smart production systems, smart factories, and smart logistics running in a decentralized and dynamic fashion [56]. The Industry 4.0 working group recommended standardization and open standards for a reference architecture as the first priority for implementation [57]. Following this recommendation, the German Commission for Electrical, Electronic & Information Technologies (DKE) produced a standardization roadmap in 2014 [17]. In parallel, Platform Industrie 4.0 projects were established by a number of German associations to form interdisciplinary working groups on issues for future standardization. The result is the Reference Architectural Model (RAMI) 4.0 and the Industrie 4.0 components [58] that describe functional models for CPPS. These will serve industry as a basis for developing future products and business models in Germany.

工业4.0是德国的一项主动措施，包含了实现智能制造系统的技术策略。工业4.0的支撑技术是互联网、移动计算、云计算技术。工业4.0的目标是一系列的创新包括智能产品、智能生产系统、智能工厂和智能物流运行在一个自治和动态的环境下[56]。工业4.0工作组建议的标准化和基于一个参考架构的开放标准作为优先实施的内容[57]。根据这些建议，德国电子、电气和信息技术委员会（DKE）在2014年发布了标准化路线图[17]。同时，工业4.0平台项目由德国多家跨学科工作组联合建立，旨在解决未来的标准化问题。产出了参考架构模型（RAMI）4.0和工业4.0组件用来描述CPPS的功能模型。这些将作为未来德国产品与商业模型开发的基准服务于工业。

4.2.2 Internet of Things(IoT)/物联网

In the area of the Internet of Things (IoT), the Europe Union (EU) founded several projects to develop an IoT reference model and reference architecture. IoT-A, an EU Seventh Framework Project, created an architectural reference model envisioned as a foundation for the Internet of Things [62]. IoT@Work is another EU project led by Siemens AG that focuses on harnessing IoT technologies in industrial and automation environments [63]. Three main scenarios providing requirements for the IoT@Work architecture include agile manufacturing, large-scale manufacturing, and remote maintenance.

物联网（IoT）领域，是由欧盟的多个项目开发的物联网参考模型和参考架构。IoT-A，欧盟第7框架项目，创建了一个架构参考模型作为物联网的基础[62]。IoT@Work是另一个由西门子AG领导的欧盟项目，关注于将物联网技术应用到工业和自动化环境中[63]。IoT@Work架构提供了满足三个主要的应用场景需求，包括敏捷制造、大规模制造和远程维护。

In the U.S., the Industrial Internet Consortium (IIC)[36] founded by GE, IBM, CISCO, Intel, and AT&T is a transatlantic cousin of Industrie 4.0. IIC is concerned with anything that can be connected to the internet, provide data as feedback, and raise efficiency. Its scope is larger than Industrie 4.0 in that it addresses not only manufacturing systems, but also energy, healthcare, and infrastructure. Unlike Industrie 4.0, which works on standards directly, IIC has set a goal to “define and develop the reference architecture and frameworks necessary for interoperability” and which might help set future standards. Table 17 shows a comparison between Industrie 4.0 and IIC from [60].

在美国，由GE、IBM、CISCO、Intel和AT&T联合组成的工业互联网联盟（IIC）[36]是工业4.0大洋彼岸的近亲。IIC关注让任何事物能够连接到互联网，提供数据反馈，籍此提高效率。它的范围比工业4.0更广，它不只关注制造系统，还关注能源、健康、基础设施。不像工业4.0那样直接开展标准工作，IIC制定了一个目标“定义和开发互操作性所需的参考模型和框架”，这样可以帮助制定未来的标准。表17列出了工业4.0和工业互联网联盟形式的比较[60]。

Meanwhile, the Open Interconnect Consortium (OIC), founded by leading technology companies like Samsung, Cisco, GE, and Intel, is proposing an open-source solution to enable device-to-device connectivity for IoT [61]. OIC focuses on building a common communications standard and sponsors the IoTivity project to build an open-source reference implementation of those specifications. The adoption of the OIC standard is expected to begin in consumer electronics and expand over time to industrial applications.

同时，开放互联联盟（OIC）有领先的技术企业成立，如三星、Cisco、GE和Intel，致力于为IoT提供一套令设备与设备之间能够互联的开源解决方案[61]。OIC关注与建立一套通用的通讯标准和提倡IoT类项目以开源的形式提供它们这类规范的实现。OIC试图首先在消费类电子产品中率先使用OIC标准，然后向工业应用拓展。

Open Machine communication standards are one of the key enablers of IoT implementation. The diversified IoT use scenarios mean that there will be no single ‘winner’ in terms of Machine-to-Machine (M2M) standards. Initiatives such as OneM2M, HyperCat, OMA LightweightM2M, Eclipse M2M and Weightless[70] have potential to be de facto M2M standards[65]. Specifically, Eclispe SCADA will provide connectivity to a variety of industrial devices and offer a monitoring system to create alarms and events and record historical data and a framework to build custom user interfaces and visualizations for those functions[71]. A new ETSI(European Telecommunications Standards Institute) Technical Committee is also developing standards for M2M Communications in cellular segment for IoT applications in industrial automation, health care, and supply chains[64].

开放机器通讯标准是支持物联网实现的一项关键标准。在物联网多样化的使用场景中，意味着机器与机器（M2M）标准没有单一的“赢家”。OneM2M、HyperCat、OMA LightweightM2M、Eclipse M2M和Weightless[70]都有机会成为M2M事实标准[65]。特别是Eclipse SCADA能够提供连接大量工业设备的能力并提供一个监控系统能够创建报警和事件、记录历史数据和一个建立定制用户界面的框架和上述功能的可视化功能[71]。新的欧洲电信标准机构（ETSI）的技术委员会业正在为物联网开发蜂窝M2M通讯标准，以期应用于工业自动化、医疗和供应链[64]。

4.2.3 Cyber Physical System(CPS)/物理信息系统

While the IoT deals with unique, identifiable, and internet-connected physical objects, cyber-physical systems efforts are concerned with the nature of cyber-physical coupling and the system of systems characteristics of software-controlled systems. Standards for CPS include a reference architecture, common services and functional models, semantics, security and safety standards, and standard interfaces for system-to-system interactions. A public working group led by NIST is working on terminology and a reference architecture for CPS [72]. CPS research and standards development are being worked on in multiple NIST Laboratories in programs on advanced manufacturing, cybersecurity, buildings and structures, disaster resilience, and smart grid. NIST efforts include work on Industrial Control Systems (ICS) as well. In Europe, the EU has invested significantly in CPS through its ARTEMIS and ECSEL JU programs and Smart CPS projects under the Horizon 2020 plan [73]. The Association for German Engineers founded Technical Committee 7:20 - Cyber-Physical Systems to support standards development in CPS from the perspective of automation technology [74].

当物联网正在解决物理对象的唯一性、可识别和连接互联网的问题的同时，信息物理系统致力于解决信息与现实的连接的性质和具有软件控制系统系统特征的系统。CPS标准包括惨喽架构、通用服务和功能模型、语义、安全标准、系统与系统间交互的接口标准。一个由NIST领导的公开工作组正在制定CPS的参考架构和术语[72]。CPS研究和标准开发正在多个NIST实验室中同时开展，如先进制造、信息安全、结构与构建、灾难恢复性、智能网格。NIST努力开展工业控制系统（ICS）的工作。在欧洲，欧盟已经重点投入CPS，通过了ARTEMIS & ECSEL JU计划⁹与Horizen 2020计划中的智能CPS项目[73]。德国工程师协会成立技术委员会7.20-信息物理系统从自动化技术的角度支持CPS标准制定。

4.2.4 Big Data and Cloud Manufacturing/大数据与云制造

The amount of data in manufacturing systems is exploding. Big-data analytics enables continuous innovation and process improvement of manufacturing systems, and has been recognized as a key enabler of SMS [80]. With a cloud-computing infrastructure, manufacturers gain the ability to access software and real-time data at lower cost and to respond quicker to customer issues. The IEEE Standards Association has introduced a number of standards related to big-data and cloud applications, including IEEE 2200-2012, IEEE 6136, and IEEE P2302. ISO/IEC JTC 1 recognized data analytics as an important future area for focus and established a Study Group on Big Data to identify standards gaps and propose standardization priorities to serve as a basis for future JTC 1 work [76]. NIST established a public working group to propose a reference architecture and identify standards related to Big Data, a fundamental technology for SMS [42]. While technology development in this area will have a huge impact on manufacturing, none of these activities are specifically directed at manufacturing. In May 2015, NIST and OAGI jointly held a Workshop on Open Cloud Architectures for Smart Manufacturing [78].

制造系统中的大量数据已经爆炸了。大数据分析可以使制造系统持续创新和改善过程，并且被认为是实现智能制造系统的关键技术[80]。使用云计算基础设施，制造企业能够低成本、快速使用软件和实时数据解决自身的问题。IEEE标准协会已经介绍了大量的大数据和云应用的而标准，包括IEEE 2200-2012、IEEE 6136和IEEE P2302。ISO/IEC JTC 1将数据分析作为未来的重点领域，建立了大数据学习组以研究标准之间的缝隙和未来ISO JTC 1标准化工作的优先顺序[76]。NIST成立了一个公开的工作组来研究大数据的参考架构和相关标准，将大数据作为智能制造系统的基础技术[42]。随着这一领域的技术发展，将对制造产生巨大的影响，但这些活动并不是特别针对制造领域的。在2015年5月，NIST和OAGI共同举办了智能制造开放云架构的研习会[78]。

4.2.5 Smart Manufacturing Initiatives in the U.S./美国在智能制造方面的积极活动

While most of the existing consortia and professional societies in the U.S. are addressing SMS in some ways, several industrial consortia formed to address broader, overarching, needs of SMS. The oldest of these is the Smart Manufacturing Leadership Coalition (SMLC), a non-profit organization committed to the creation of a scaled, shared, infrastructure called the Smart Manufacturing Platform [75]. SMLC activities will help set future standards in integrating SM applications. Subsequently, the U.S. government initiated a series of institutes to support U.S. manufacturing. These institutes collectively called the National Network of Manufacturing Institutes, or NNMI, address different challenge areas for advanced manufacturing. The Digital Manufacturing and Design Innovation Institute (DMDII) most closely aligns with the SMS needs for information flow throughout an enterprise to enable the SMS capabilities—agility, quality, productivity, and sustainability. DMDII has issued three rounds of project calls in areas of strategic importance, including intelligent machine communication standards and cyber-physical manufacturing operating systems. In 2014, the U.S. Department of Energy announced intention to create another institute for clean-energy manufacturing based on smart manufacturing technology, including advanced sensors, controls, platforms, and modeling technology for energy efficiency.

在当下的美国，大多数现有的联盟和专业团体都在用各自的方法探索智能制造系统（SMS），一些工业联盟组织研究智能制造系统（SMS）更广泛的、包罗万象的需求。其中最古老的是智能制造领导联盟（SMLC），一个非营利性组织，致力于建立一个可伸缩的，共享的、称为智能制造平台的基础设施[75]。SMLC的活动将有助于整合智能制造应用制定未来的标准。随后，美国政府设立了一系列机构，支持美国制造业。这些机构统称为国家制造网络机构或称NNMI，解决先进制造的不同挑战。数字制造与设计创新研究所（DMDII）与智能制造系统的需要结合最紧密，它将贯穿于企业中的信息流构成智能制造能力——敏捷、质量、效率和可持续性。DMDII已经发起了三轮战略重点领域的项目，包括智能机的通信标准、信息物理制造操作系统。2014年，美国能源部宣布打算设立另一个基于智能制造技术的清洁能源制造研究机构，包括提高能源效率的先进感知、控制、平台和建模技术。

The National Institute of Standards and Technology (NIST) has several initiatives addressing Smart Manufacturing. NIST is heavily engaged in efforts to develop new standards for the Digital Thread [39], Model-Based Enterprise [40], smart manufacturing design and analysis [95], additive manufacturing[97] and robotics[96]. NIST leads an effort to define requirements eventually leading to standards for cloud-based services for manufacturing. NIST work on cyber security for supply chains and industrial systems will have great importance for manufacturers [43]. Finally, NIST coordinates the networking of the NNMIs within the U.S. [44].

美国国家标准与技术研究所（NIST）有多项针对智能制造的举措。NIST正在积极开发数字线程[39]的新标准、企业模型[40]、智能制造设计与分析[95]、增材制造[97]和机器人[96]。NIST致力于定义基于云服务的制造标准的需求，以领导云制造标准的制定。NIST开展供应链和工业系统的信息安全研究工作，这对制造企业非常重要[43]。最后，NIST协调美国的国家制造网络机构的网络[44]。

4.2.6 SDO Smart Manufacturing Related Activities/标准组织的智能制造相关活动

Various SDO activities are starting to focus explicitly on the needs for and impacts of the technologies fundamental to SMS—IoT, cloud computing, Big Data, and analytics. To help ensure the existence of adequate standards support for SM, in 2014 the IEC Standardization Management Board (SMB) set up a new Strategic Group, SG 8: Industry 4.0 – Smart Manufacturing. Its scope includes defining terminology, summarizing existing standards and standardization projects in progress, and developing a common strategy for implementation of smart manufacturing [59]. SG 8 will also foster relationships between IEC (TC3, TC 65) and institutions like ISO (TC 184), ISA, and IEEE on SM standards development. In 2015, the ISO Technical Management Board (TMB) passed a resolution to form an ISO/TMB Strategic Advisory Group on Industry 4.0/Smart Manufacturing. The SAG is tasked to provide a definition of, and give an overview on, available standards, use cases, and current work related to Industry 4.0/Smart manufacturing; to identify possible gaps where additional standards are needed; and to make recommendations on actions to be taken by TMB [82]. In the Fall of 2014, MESA launched the Smart Manufacturing Working Group to better orchestrate their projects related to Smart Manufacturing. Outputs from this group will include things such as expansion of MESA's 'Collaborative Manufacturing Dictionary' and a library of 'Manufacturing Business Processes' and 'Use Cases' that map production processes across internal operating departments and supply chains [74]. OAGI also established a Smart Manufacturing working group to develop multi-tiered supply chain collaboration guidelines and standards for engineered components to improve cost, quality, agility and more. Similarly, ASTM has established a Smart Manufacturing Advisory Board to guide their efforts.

各个不同的SDO都启动了针对智能制造系统、物联网、云计算、大数据和分析的需求和基础技术的研究活动。为帮助确保智能制造得到足够的现行标准的支持，2014年IEC标准化管理委员会（SMB）成立了一个新的战略小组，SG 8：工业4.0 – 智能制造。它的范围包括定义术语，总结了现有的标准和标准化项目的进展，并制定一个共同的智能制造实施战略[59]。SG 8也将建立IEC（TC3，TC65）和其他制定智能制造标准的研究机构之间的关系，如ISO（TC 184）、ISA、IEEE。2015年，ISO技术管理委员会（TMB）通过了一项决议，成立一个面向工业4.0和智能制造的ISO/TMB战略咨询小组。该战略咨询小组（SAG）的任务是对“工业4.0/智能制造”提供一个定义，并给出一个概述、可用的标准、使用案例、及当前的相关工作；识别可能需要补充的标准需求；并向TMB[82]提供行动建议。在2014的秋天，MESA启动的智能制造工作组更好地安排他们智能制造的相关项目。该组的产出包括“协同制造字典”和“制造业务流程库”和关联到生产流程与内部操作部门和供应链的“用例”[74]。OAGi也同样成立了一个智能制造工作组，制定多层次的供应链合作的准则和工程组件以改进成本、质量、敏捷性和其他方面。同样的，ASTM已经建立了一个智能制造顾问委员会指导他们的工作。

4.2.7 Sustainable Manufacturing Standards/绿色制造标准

Typically, sustainability is discussed from three perspectives: environment, economic, and social. The focus of our study is on data and information that can be collected by a manufacturing organization rather than on organizational policies and practices. In 2008, ASTM formed a committee on Sustainability and subsequently formed a subcommittee specifically addressing Sustainable Manufacturing. While the standards of this subcommittee are not yet complete, we expect an initial set on the near-term horizon for enabling analysis of how manufacturing systems are impacting sustainability and can be improve in this respect. A focus of the ASTM standards is on characterizing manufacturing processes for environmental sustainability assessment. Sustainability is inherently a complex area in which multiple tradeoffs must be considered. In order to evaluate those trade-offs, accurate data reflecting the impact of individual activities and processing leading to the creation of some good or service is necessary. Until now, such data was very difficult and costly to obtain. Direct measure of the use of physical resources is now quantifiable and thus the focus of the standardization activities. In addition, ISO 22400 has initiated an addendum standard on KPIs for energy management specifically. A wide range of other activities focuses on assessing social factors related to sustainability. These include organizational practices and policies and do not fall within the scope of this study. Economic aspects of sustainability are also not specifically addressed, but the data gathered for SMS will be ultimately useful in these assessments as well. For instance, when trying to understand issues of resource efficiency, one must take an economic viewpoint and factor that against measures of resource utilization.

通常情况下，可持续发展可从三个角度进行讨论：环境，经济和社会。我们的研究集中在是数据和信息，这样可以由一个制造组织收集，而不是从组织的政策和做法中入手。2008年，ASTM成立了一个委员会对可持续性，随后成立了一个小组委员会专门解决可持续制造。虽然这个小组委员会的标准还没有完成，我们期望制定一个短期目标，分析制造系统如何影响可持续性，并研究如何改进。ASTM标准的重点是生产过程的环境可持续性评价的描述。可持续性本质上是一个复杂的领域，在该领域中必须考虑多个方面的权衡。为了评估这些权衡，通过准确的数据反映了单个活动和过程导致好的或服务的创新的影响是必要的。到现在为止，获得这样的数据是非常困难和昂贵的。直接测量的物理资源的使用是可以量化的，因此标准化活动成为重点。此外，ISO 22400已经发起了一项针对能源管理的KPIs补充标准。广泛的其他活动侧重于评估与可持续发展有关的社会因素。这些包括组织的做法和政策的内容，不属于本研究的范围。可持续性的经济方面也没有特别的解决，但为智能制造系统收集的数据将最终使用在这些评估种。例如，当试图了解资源效率的问题时，必须采取相对于测量资源利用的一种经济的观点和因素。

⁸. Smart devices are at the core of the area of technology development that has become known as Cyber Physical Systems, or CPS, of which CPPS is a part. ↩

⁹. ARTEMIS-IA: ARTEMIS Industry Association is the association for actors in Embedded Intelligent Systems within Europe.
ECSEL-JU: Electronic Components and Systems for European Leadership - Joint Undertaking. 电子原件和系统欧洲领导组织-共同事业。
ARTEMIS-JU和ENIAC-JU与2014年6月合并成为ECSEL-JU项目。 ↩