NISTIR 8107

Current Standards Landscape for Smart 

Manufacturing Systems

Yan Lu

KC Morris

Simon Frechette

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8107
NISTIR 8107

Current Standards Landscape for Smart 

Manufacturing Systems

Yan Lu

KC Morris

Simon Frechette 

Systems integration Division

Engineering Laboratory

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8107

February 2016

U.S. Department of Commerce

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
i

Abstract 

Today’s manufacturers face ever-increasing demands of variability—greater customization, smaller lot sizes, 

sudden supply-chain changes and disruptions. Successful manufacturers will have to choose and incorporate 

technologies that help them quickly adapt to rapid change and to elevate product quality while optimizing use of 

energy and resources. These technologies form the core of an emerging, information-centric, Smart 

Manufacturing System that maximizes the flow and re-use of data throughout the enterprise. The ability of 

disparate systems, however, to exchange, understand, and exploit product, production, and business data rests 

critically on information standards. This report provides a review of the body of pertinent standards—a 

standards landscape—upon which future smart manufacturing systems will rely. This landscape comprises

integration standards within and across three manufacturing lifecycle dimensions: product, production system, 

and business. We discuss opportunities and challenges for new standards, and present emerging activities 

addressing these opportunities. This report will allow manufacturing practitioners to better understand those 

standards useful to integration of smart manufacturing technologies.

Disclaimer 

Certain commercial systems are identified in this paper. Such identification does not imply recommendation 

or endorsement by NIST; nor does it imply that the products identified are necessarily the best available for the 

purpose. Further, any opinions, findings, conclusions, or recommendations expressed in this material are those 

of the authors and do not necessarily reflect the views of NIST or any other supporting U.S. government or 

corporate organizations.
ii

CONTENTS 

1 Introduction........................................................................................................................................................1

2 Smart Manufacturing Ecosystem .......................................................................................................................2

2.1 Smart Manufacturing Capabilities..............................................................................................................2

2.2 Smart Manufacturing Ecosystem ...............................................................................................................4

2.3 Impacts of Standards..................................................................................................................................6

3 Manufacturing Standards landscape..................................................................................................................6

3.1 Standard Organizations..............................................................................................................................6

3.2 Product Development Lifecycle Standards.................................................................................................8

3.3 Production System Lifecycle Standards................................................................................................... 11

3.4 Business Cycle for Supply Chain Management........................................................................................ 16

3.5 Manufacturing Pyramid........................................................................................................................... 18

3.6 Standards Adoption and the Related Issues............................................................................................ 22

4 Standards Opportunities for Smart Manufacturing ........................................................................................ 24

4.1 Standards Needs...................................................................................................................................... 24

4.2 New Initiatives......................................................................................................................................... 27

4.2.1 Industrie 4.0..................................................................................................................................... 27

4.2.2 Internet of Things (IoT).................................................................................................................... 28

4.2.3 Cyber Physical Systems (CPS) .......................................................................................................... 29

4.2.4 Big Data and Cloud Manufacturing ................................................................................................. 29

4.2.5 Smart Manufacturing Initiatives in the U.S. .................................................................................... 29

4.2.6 SDO Smart Manufacturing Related Activities.................................................................................. 30

4.2.7 Sustainable Manufacturing Standards ............................................................................................ 30

5 Conclusion ....................................................................................................................................................... 31

6 References....................................................................................................................................................... 32
1

1 INTRODUCTION 

A manufacturer’s sustainable competitiveness depends on its capabilities with respect to cost, delivery, 

flexibility, and quality [1]. Smart Manufacturing Systems (SMS) attempt to maximize those capabilities by using 

advanced technologies that promote rapid flow and widespread use of digital information within and between 

manufacturing systems[2][3][4]. SMS are driving unprecedented gains in production agility, quality, and 

efficiency across U.S. manufacturers, improving long-term competitiveness. Specifically, SMS use information 

and communication technologies along with intelligent software applications to

1. Optimize the use of labor, material, and energy to produce customized, high-quality products for ontime delivery.

2. Quickly respond to changes in market demands and supply chains.

Smart manufacturing, different from other technology-based manufacturing paradigms, defines a vision of 

next-generation manufacturing with enhanced capabilities. It is built on emerging information and 

communication technologies and enabled by combining features of earlier manufacturing paradigms. Table 1 

shows the relationship between SMS and previous manufacturing paradigms.

Table 1: Smart Manufacturing and other manufacturing paradigms1, 2, 3, 4, 5 and 6

_______________________________

1 Strategos-International. Toyota Production System and Lean Manufacturing, http://www.strategosinc.com/toyota_production.htm 2 Flexible and reconfigurable manufacturing systems paradigms, Int J Flex Manuf Syst (2006) 17:261–276 DOI 10.1007/s10696-006-9028-7 3 Glossary of Sustainable Manufacturing Terms, EPA, http://archive.epa.gov/sustainablemanufacturing/web/html/glossary.html 4 DOE-FOA-0001263 Manufacturing innovation institute for smart manufacturing: advanced sensors, controls, platforms, and modeling for manufacturing. 5 Cloud-Based Manufacturing: Old Wine in New Bottles? , Proceedings of the 47th CIRP Conference on Manufacturing Systems 6 http://www.astri.org/technologies/initiatives/intelligent-manufaturing/
2

In 2014 in the United States, the President's Council of Advisors on Science and Technology (PCAST) issued a 

report that identified three top-priority transformative manufacturing technologies: Advanced Sensing, Control, 

and Platforms for Manufacturing; Visualization, Informatics and Digital Manufacturing Technologies; and 

Advanced Materials Manufacturing [5]. The first two of the technologies enhance the manufacturer’s ability to 

respond to information quickly and efficiently. They, in turn, rely on the effective information flow and system 

responsiveness that only standards can provide. The PCAST further noted that standards “spur the adoption of 

new technologies, products and manufacturing methods. Standards allow a more dynamic and competitive 

marketplace, without hampering the opportunity to differentiate. Development and adoption of standards 

reduce the risks for enterprises developing solutions and for those implementing them, accelerating adoption of 

new manufactured products and manufacturing methods.”

Standards are the building blocks that provide for repeatable processes and the composition of different 

technological solutions to achieve a robust end result. Standards come in many varieties and forms. 

Standards.gov [6] and OMB Circular A-119 [46] describe, in some detail, the variety of forms standards can take. 

The standards that we will discuss are primarily “voluntary consensus standards.” This means they are set by a 

standards organization based on the consensus of the partners who will be using them. In addition, these types 

of standards are enforced by voluntary compliance. Such standards are designed to open new market 

opportunities to their users. The standards supporting SMS range from those for information technology and 

communication through those that govern enterprises and supply chains.

This paper presents an SMS standards’ landscape based on a definition of a smart-manufacturing ecosystem

that encompasses three dimensions – product, production systems, and enterprise (business) systems. The 

landscape associates standards with the lifecycle phases in each dimension. Section 2 presents key capabilities 

and the manufacturing ecosystem as the convergence of the three different lifecycle perspectives in operational 

manufacturing systems. It also identifies areas where the integration of functions within and across these 

dimensions will result in systems that are more effective. Section 3 describes the landscape in terms of key 

standards’ organizations working in the area, types of standards in each of the three dimensions, and the 

manufacturing pyramid where the dimensions intersect. Finally, we discuss areas of opportunity for future 

standards in terms of the smart manufacturing capabilities.

2 SMART MANUFACTURING ECOSYSTEM 

Standards are fundamental for enabling SMS. Different standards contribute in different ways to enabling the 

capabilities of smart manufacturing systems. To generate an SMS landscape, we identify the standards as within 

scope based on whether a standard contributes to a capability, and analyze where, when, and for what purpose 

the standard is used. This section defines the key capabilities and presents a visualization of a smart 

manufacturing ecosystem. The following section presents the standards landscape for the ecosystem.

2.1 SMART MANUFACTURING CAPABILITIES 

Significant and positive relationships exist between manufacturing strategies and corporate competitive 

strategies [47]. To achieve corporate competitive goals, manufacturing systems should be developed with 

capabilities aligned to a firm’s competitive strategy, which usually consists of cost control and differentiation 

strategies of quality, delivery, innovation, service, and environmentally sustainable production. We classify key 

SMS-enabling capabilities into four categories including productivity, agility, quality, and sustainability (These 

characteristics are discussed in more detail in [14].) Table 2 shows a mapping of SMS capabilities to corporate 

competitive strategies.
3

To analyze the role of existing manufacturing standards, we summarize the key SMS capabilities as follows:

Productivity: Manufacturing productivity is defined as the ratio of production output to inputs used in the 

production process [89]. Productivity can be broken down further to labor productivity and material and energy 

efficiency. As production sizes increase, typically productivity increases; however, for SMS for which 

customization is a hallmark, productivity measures may need to be adjusted to be more inclusive of 

responsiveness to customer demand.

Agility: Agility is defined as “the capability of surviving and prospering in a competitive environment of 

continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by 

customer-designed products and services” [7]. Critical to the success of agile manufacturing are enabling 

technologies such as model-based engineering, supply chain integration, and flexible production systems with 

distributed intelligence. Traditional metrics to measure agility include On Time Delivery to Commit, Time to 

Make Changeovers, Engineering Change Order Cycle Time, and Rate of New Product Introduction [8]. New 

measures could include Delay Due to Supply Chain Change.

Table 2: Key Capabilities for Smart Manufacturing System

Quality: Traditional quality measures reflect how well finished products meet design specifications. In addition, 

for SMS, quality also includes measures of product innovation and customization. Traditional quality metrics 

include Yield, Customer Rejects/Returns, and Material Authorizations/Returns [8]. New quality measurement 

indicators for innovativeness and variety/product family and options/product to measure personalization 

degree are needed.
4

Sustainability: While time and cost as measures of productivity have been the traditional drivers for 

manufacturing, sustainability has taken on more importance. Measurement science for manufacturing 

sustainability is not as mature as for time and cost and is an active area of research [18] [19]. As productivity and 

agility of manufacturing systems increases, the necessity for better understanding and controlling the 

sustainability-related impacts of those systems increases. Manufacturing sustainability is defined in terms of 

environmental impact (such as energy and natural resources), safety and well-being of employees, and 

economic viability [9].

2.2 SMART MANUFACTURING ECOSYSTEM 

The Smart Manufacturing Ecosystem encompasses a broad scope of systems in the manufacturing business 

including production, management, design, and engineering functions. Figure 1 illustrates three dimensions of 

concern that are manifest in SMS. Each dimension—product (green), production system (blue), and business 

(orange)—is shown within its own lifecycle. The product lifecycle is concerned with the information flows and 

controls beginning at the early product design stage and continuing through to the end-of-life of the product. 

The production system lifecycle focuses on the design, deployment, operation and decommissioning of an entire 

production facility including its systems. The business cycle addresses the functions of supplier and customer 

interactions. Each of these dimensions comes into play in the vertical integration of machines, plants, and 

enterprise systems in what we call the Manufacturing Pyramid (Figure 5). The integration of manufacturing

software applications along each dimension helps to enable advanced controls at the shop floor and optimal 

decision-making at the plant and enterprise. The combination of these perspectives and the systems that 

support them make up the ecosystem for manufacturing software systems. Details of the lifecycle of the three 

dimensions, as well as the Manufacturing Pyramid, will be described in Section 3.

Figure 1. Smart Manufacturing Ecosystem
5

Historically, these dimensions have been dealt with as silos of concern. Indeed, integration along even one of 

these dimensions is a non-trivial challenge and is being actively worked on. We have observed that organizations 

that were formed to integrate single dimensions of this ecosystem are expanding in scope to address the digital 

thread across the dimensions (orange arrows in Figure 1). Paradigms such as continuous process improvement 

(CPI), flexible manufacturing (FMS), and design for manufacturing and assembly (DFMA) rely on information 

exchange between the dimensions as indicated in Figure 1. Tighter integration within and across the three 

dimensions will result in faster product-innovation cycles, more efficient supply chains, and more flexibility in 

production systems. The combination of these allows for optimal control of the automation and decision-making 

needed to make high quality, highly customized goods in tight synchronization with the demand for these goods 

[10].

Essentially, it is the seamless integrations within and across SMS dimensions and the manufacturing pyramid that 

lead to SMS capabilities. Table 3 shows the integration technologies highlighted in Figure 1 and the SMS capabilities 

supported by them.

Table 3: SMS Ecosystem and Capability Mapping
6

2.3 IMPACTS OF STANDARDS 

Standards are fundamental and valuable tools that can enable the adoption of technologies and innovations 

by business owners. Accordingly, they contribute to one or more SMS key capabilities. For example, on the 

product dimension, PLM standards contribute to both agility (by streamlining processes) and quality (by 

enabling the integration of different activities along the product and production system lifecycles). In the 

production system area, continuous commissioning (CCX) standards can improve machine performance and 

systems reliability to improve productivity, quality, and sustainability (through improved energy performance). 

Standards for electronic commerce such as the Open Applications Group Integration Specification (OAGIS) help 

streamline business processes between partners in the supply chain.

The next section presents a landscape of manufacturing standards on top of the SMS ecosystem where we see 

clearly existing manufacturing standards and how they can enhance SMS capabilities, if adopted.

3 MANUFACTURING STANDARDS LANDSCAPE 

Existing manufacturing standards provide “how-to” instructions for designers, engineers, builders, operators,

and decision makers to conduct disciplined activities within their domains. They also facilitate communication 

between stakeholders across domain borders, borders of the manufacturing system hierarchy, and between

lifecycle phases. Today, numerous national, regional, and international standards development organizations 

(SDOs) set manufacturing industry standards. This section provides a review of the types of standards for each of 

the three lifecycle dimensions with a focus on those standards that specifically address manufacturing 

information content. While some national or regional standards are considered, more emphasis is placed on 

international standards. In many cases, local SDOs may have direct liaisons with international SDOs. First, we 

provide an overview of the standards development organizations and then we position the standards within the 

ecosystem.7

3.1 STANDARD ORGANIZATIONS 

A completed standard is typically the result of one of several development processes. The more traditional of 

these is via a formally sanctioned SDO. These organizations facilitate consensus building and ensure that 

standards are openly available to organizations that wish to use them. In this category are both international 

standards bodies, such as ISO [20] IEC [30], ASME [21], and ASTM [22]; and national bodies including 

professional organizations, which define best practices for their practitioners. In the United States, ANSI [23]

certifies professional organizations to create standards in areas of specialization. 

The traditional consensus-building process used by SDOs can be quite time-consuming; as a result, other 

processes have emerged, including open source [24]. In the open-source process, a standard can come in the 

form of a specification describing the standard, a reference implementation of the standard, or both. These 

standards are often developed within a consortium, maintained collaboratively, and widely available to the 

public at large. Ownership of these standards remains a public trust of various sorts and they are open to 

interested participants. The licensing and governance models for the intellectual property contained in these 

standards vary greatly between projects. Open-source standards sometimes proceed to a more formal sanction 

by an SDO. An independent organization usually manages the open source process. Some vendor-driven 

_______________________________ 7 In the interest of brevity, we do not provide references for every standard described. They are easily accessible through an Internet search.
7

standardization communities have taken another approach whereby a vendor implementation of a proposed 

standard is selected to define the standard. The chosen implementation becomes the reference to which other 

vendors implement.

Multiple standards intended to work together are often defined as suites. Examples of this in the Information 

Technology (IT) world include the collection of standards from groups like WC3 [25] and IETF [26] that have 

aided the digital revolution. In the world of manufacturing, we find the ISO 10303 suite of standards (commonly 

known as STEP) for product data [27], the Quality Information Framework(QIF)[32] for streamlining quality 

testing capabilities, and the standards emerging from consortia such as the Open Application Group, Inc. (OAGi) 

for enterprise level applications [28]. These standards incorporate other, more fundamental, standards, e.g., the 

EXtensible Markup Language (XML) from W3C, and offer engineering and manufacturing content representation 

and interpretation. The Object Management Group (OMG) [38] also defines suites of standards that, for the 

most part, address underlying technologies rather than those specific to manufacturing. Some of these 

standards have become particularly useful for manufacturing application and are discussed later.

ISO and IEC are working on standards of significant importance to smart manufacturing. Within ISO, the 

technical committee on automation systems and integration (TC184) has two subcommittees (SC) that are of 

particular interest in our landscape: SC4 and SC5. SC4 focuses on industrial data standards – primarily those 

related to product data including ISO 10303. SC5 focuses on interoperability, integration, and architectures for 

automation applications. Both subcommittees have new standards for smart manufacturing in development.

IEC, which historically has served the electronics industry, has developed standards that have broader 

applicability beyond the electronics industry. For example, IEC TC 65 focuses on standards for industrial process 

control and automation and is active in addressing integration between product data and production processes. 

ISO/IEC Joint Technical Committee (JTC) 1 on information technology deals with a large number of 

standardization topics in IT for manufacturing systems including sensor and device networks and user interfaces. 

Consequently, these types of standards are also included in our landscape.

A number of consortia are developing standards and best practices in the SMS area including OAGi, 

MTConnect [29], ProSTEP iViP [81], OPC Foundation [31], DMSC [32], and MESA [33]. Standards and best 

practices from these organizations are sometimes proposed in ISO and IEC to facilitate broader dissemination 

and adoption. OAGi and MTConnect make standards freely available as open source for downloading to the 

public. PDES Inc., for example, is an industrial consortium that undertakes projects related to product data 

exchange for design. Their projects are driven by its members’ specific needs for data integration standards. 

Results are fed into the ISO 10303 standards for product definition and others as appropriate. The work often 

addresses resolving issues surrounding the implementation of those standards. The OPC Foundation originally 

developed standards that allowed device providers to integrate their products into a Microsoft-based platform. 

OPC Foundation has since evolved into an independent standards organization with its own certification and 

testing program. Dimensional Metrology Standards Consortium (DMSC) [32] sets standards including QIF, which 

is fundamental for assuring that the entire quality-measurement process—consisting of product measurement 

design, planning, rules, resources, programming, results, and summary statistics—is fully integrated and 

traceable.

APICS [35], ASTM, MESA, IPC [79], and ISA [34] are industrial professional societies or trade associations 

working to advance the state-of-the-art in their fields. Their work includes standards, as well as educational and 

other activities. The APICS scope is supply chain and operations management. A part of APICS, known as the 

Supply Chain Council, produced a series of reference documents to provide guides on best practices for the 
8

supply chain industry. This rich set of information includes definitions for a wide range of performance metrics 

for manufacturing operations, many of which can be applied outside of the supply chain context. ASTM 

addresses manufacturing and materials, products and processes, systems, and services. MESA is concerned with 

the production level of Manufacturing Operations Management (MOM) or Manufacturing Executions Systems 

(MES) and the integration of information systems from the enterprise level into the manufacturing operation. 

IPC originated with the electronics industry, specifically focusing on printed circuit boards, but, as with many of 

the other organizations, its scope has expanded to address service and supply chain topics as well. ISA focuses 

on automation, specifically as applied to “engineering and technology to improve the management, safety, and 

cybersecurity of modern automation and control systems used across industry and critical infrastructure.”[34]

While each of these organizations brings a different perspective, their scopes intersect in our focus areas -

product, production system, and business information flows.

Other noteworthy standards come from more academically oriented professional societies, which typically are

ANSI accredited. These include the standards from IEEE [37] and ASME. 

3.2 PRODUCT DEVELOPMENT LIFECYCLE STANDARDS 

Figure 2. Standards along the Product Lifecycle

The product lifecycle in the context of the smart manufacturing ecosystem includes 6 phases as shown in 

Figure 2: Design, Process Planning, Production Engineering, Manufacturing, Use and Service, and End-of-Life and 

Recycling. The existing standards, particularly in the areas of Computer Aided Design (CAD), Computer Aided 

Manufacturing (CAM), and Computer Aided Technology (CAx) generally have greatly improved engineering 

efficiency [11]. In addition, these standards enhance modeling accuracy and reduce product innovation cycles, 

thus contributing directly to manufacturing system agility and product quality. Advancements in this area have

resulted in a new product development paradigm known as model-based engineering or enterprise, or MBE [51].
9

Figure 2 also shows a set of select standards related to the product lifecycle phases. These standards are 

classified into five categories: Modeling Practice, Product Model and Data Exchange, Manufacturing Model Data, 

Product Category Data, and Product Lifecycle Data Management.

Modeling practice standards define digital product-definition data practices for both two-dimensional (2D) 

drawings and three-dimensional (3D) models. There are several standards available worldwide that define 

symbols and rules for dimensioning and tolerancing. The predominant standards are ASME (American Society of 

Mechanical Engineers) Y14.5 GD&T (Geometric Dimensioning and Tolerancing) and International Organization 

for Standardization, ISO/TC 213 GPS (Geometrical Product Specification) with ISO/TC 10 Technical Product 

Documentation. The ISO (GPS) standards are typically address a single topic. The ASME Y14.5 standard combines 

a set of topics for GD&T in one standards document. In addition to GD&T, ASME Y14.36M and ISO 1302 are 

established standards for communicating surface texture control requirements and defining surface texture 

properties. The joint IEC/ISO standard 81714 defines graphical symbols for use in technical documentation of 

products. Table 4 lists selected modeling practice standards from ASME and ISO. There are also standards for 

specific manufacturing domains such as electronics product modeling practice including IPC-D-325 for printed 

boards, assemblies, and supporting drawings. 

Table 4: Modeling Practice Standards
10

Product model and data exchange standards include ISO standards and de facto standards, as shown in Table 

5. These standards capture the representation of product and engineering information to enable data exchange 

between CAD software from different vendors. STEP is unique in that it addresses a broader range of 

information than is needed for CAD representations. The STEP architecture includes underlying methods for the 

definition and exchange of data and an architecture for integrating the data definitions into implementable units, 

called Application Protocols (AP), where CAD information is connected to product structure, as well as a wide 

array of other engineering and lifecycle information elements. The most commonly implemented and used APs 

are 203 for aerospace, defense and AP 214 for automotive [52]. The most recent part of STEP to be released is 

AP 242 edition 2, titled “Managed Model Based 3D Engineering” [48][52]. This standard merges AP 203 and AP 

214 and incorporates requirements for data exchange in the areas of model-based development, product data 

management (PDM), product and manufacturing information (PMI), and long-term archiving of digital data. AP 

242 is complementary to other visualization exchange formats such as JT (ISO 14306) and PRC (ISO 14739).

Another set of standards for product modeling is used in additive manufacturing areas. The Stereo Lithography

(STL) file format is widely used for rapid prototyping, 3D printing and additive manufacturing. ISO 52915,

Additive Manufacturing File Format (AMF), is an open standard proposed to describe color, materials, lattices, 

and constellations of a 3D object that allows more complex descriptions of products, beyond basic geometry.

Table 5: Product Model and Data Exchange Standards

Manufacturing model data standards, in contrast to the product model standards that focus on product 

design, define the data needed to manufacture a product from a design (Table 6). ISO 6983, or G-Code, is the 

most widely used Numerical Control (NC) programming language. ISO 14649 was developed to overcome the 

problems of ISO 6983 by defining a data model to enable a link between manufacturing operations and the 

original CAD geometry data. STEP AP 238 is designed to extend ISO 14649 in order to integrate more tightly with 

product design definitions. It can exchange explicit toolpath descriptions along with part, stock, fixture geometry, 

tool descriptions, GD&T, and PDM information. 
11

Table 6: Manufacturing Model Data Standards

Product catalog data standards support the description of particular instances of products or parts in a 

uniform, vendor-neutral way. Examples of product catalog data standards include ISO 13584, 15926-Part 4 and 

22745. The first two of these focus on methods for catalog definition. ISO 13854 specifies the principles to be 

used for defining characterization classes of parts and properties of parts independent from any particular 

supplier-defined identification. ISO 22745 provides guidelines for the development of terminology for open 

technical dictionaries and inventory and catalog systems. In contrast, 15926 Part 4 represents a specific domain 

and defines the engineering terms required to design, build, and operate an oil and gas production facility.

Product lifecycle data management focuses on the needs of long-term retention and access to data 

consistently throughout the product lifecycle. The best-known product lifecycle data management standard is 

ISO 10303 AP239, also known as Product Life Cycle Support (PLCS). AP 239 is designed for the exchange of 

complex-product for life-long support, i.e., the information needed and created during the use and maintenance 

of products. PLM XML is an emerging open format from Siemens for facilitating PLM. PLM XML is designed as a

lightweight, extensible, and flexible format for sharing product design and function data in a collaborative 

environment [53]. The related LOTAR (LOng Term Archiving and Retrieval) International Project has an even 

broader scope. The LOTAR focus is long-term access to digital product and technical information through the 

development of standards-based archival and retrieval mechanisms. The results of that effort are recommended 

practices on how standards can be applied to long-term archiving of products and associated design information.

3.3 PRODUCTION SYSTEM LIFECYCLE STANDARDS 

“Production systems” here refers to collections of machines, equipment, and auxiliary systems organized to 

create goods and services from various resources.[49] Most product-model and modeling-practice standards are 

applicable to production system development as well. However, as one of the most complex product systems 

that is intended to manufacture goods, production systems have a number of unique standards that are 

fundamental to achieving SMS. Production systems usually have a much longer lifecycle than the goods that 

they produce. Additionally, they need to be frequently reconfigured, and thus have unique needs with respect 

to their design. The unique SMS capabilities discussed here are made possible by automation standards for 

system control and maintenance. In this subsection, we focus on standards supporting complex system 

modeling, automation engineering, and operation and maintenance (O&M) perspectives of production systems.

Typical lifecycle phases for a production system as shown in Figure 3 include Design, Build, Commission, O&M,

and Decommission. Production systems and facilities are typically designed to manufacture a family of products. 

The degree of manufacturing flexibility—how adaptive a production system is in responding to market and 
12

supply chain changes—is a key design decision that not only impacts cost but also influences the length of the 

production system lifecycle. The commissioning phase involves testing the entire production system, including 

equipment, plant and facility, and handing off the system for operation. The system is verified to function

according to the design objectives and specifications. After commissioning, the production system enters O&M–

a steady state of tactical operations and strategic maintenance activities. When radical internal or external 

changes are introduced, for example, if the main subsystems crash or the targeted product group is phased out 

of the market, the life of the product system comes to an end. At this point, production permanently ceases or 

the system is recycled. The steady-state O&M stage is typically the longest phase of a production system’s 

lifecycle. During this period, production systems are managed to best transform material, energy, and labor into 

products. In this phase, adaptation planning, re-commissioning, and continuous commissioning are conducted to 

maintain and improve system performance and search for ways to reconfigure the system to adapt to changes 

in the market and supply chain.

Areas of standards that support production lifecycle activities include Production System Model Data and 

Practice, Production System Engineering, O&M, and Production Lifecycle Management. Since the O&M stage is 

the longest phase, the standards for O&M and lifecycle management merit special attention. Specifically, 

standards supporting manufacturing operations are discussed in detail in the Manufacturing Pyramid subsection.

Figure 3. Standards for Production System Lifecycle
13

Production system model data and practice standards provide information models for factory and production 

system design. They enhance information exchange among stakeholders, and enable virtual commissioning, 

which can improve manufacturing agility and reduce manufacturing cost. Besides CAx standards, several 

international standards specific to production system modeling and data exchange exist. The standards in this 

area can be classified into two domains as shown in Table 7: manufacturing resource and process, and 

building/facility modeling. ISO 10303 AP 214 has been shown to be able to represent different aspects of a 

manufacturing system in development [53]. ISO 10303 AP 221 defines functional data for, and schematic 

representation of, process plants. ISA 95 defines an equipment hierarchy model and models manufacturing 

processes [34]. ISO 18629 defines a Process Specification Language (PSL) aimed at identifying and formally 

defining and structuring the semantic concepts intrinsic to the capture and exchange of process information 

related to discrete manufacturing. IEC 62832(Digital Factory) defines a comprehensive network of digital models, 

methods, and tools to represent the basic elements and automation assets, as well as the behavior and 

relationships between these elements/assets. The digital factory concept includes five views of information: 

Construction (C), Function (F), Performance (P), Location (L) and Business (B). ISO 17506 defines an open 

standard for exchanging digital assets among various graphics software applications for plant geometry 

representation and kinetics simulation. The Core Manufacturing Simulation Data (CMSD) Information Model was 

developed by NIST and standardized by the Simulation Interoperability Standards Organization (SISO) to define a 

Table 7: Production System Modeling and Practice Standards
14

data-interface specification for efficient exchange of manufacturing lifecycle data in a simulation environment.

PLC Open XML provides standards to represent programmable logic control (PLC) including sequences of actions,

internal behavior of objects, and Input/Output (IO) connections. IEC 62337 defines specific phases and 

milestones in the commissioning of electrical, instrumentation, and control systems in the process industry. IEC 

61987 defines a standard to facilitate understanding of process measurement and control equipment 

descriptions when transferred from one party to another.

Production system engineering standards can interconnect engineering tools from different disciplines, e.g., 

system engineering, mechanical plant engineering, electrical design, process engineering, process control 

engineering, Human Machine Interface (HMI) development, PLC programming, and robotic programming. In the 

production system engineering category, there are several important standards playing critical roles in 

improving production system engineering efficiency, as shown in Table 8. While most of these standards serve 

very specific functions, two of them are fundamental methods, which underpin much of the work in production 

system engineering. The Systems Modeling Language (SysML) [38] from OMG is a general-purpose modeling 

language for systems engineering applications. It supports the specification, analysis, design, verification, and 

validation of a broad range of systems and systems-of-systems, and is particularly useful for large-scale projects 

such as production systems. Modelica [92] is an object-oriented, declarative, multi-domain modeling language. 

Modelica is widely used in applications that model complex physical systems and particularly to model the 

mechanical, electrical, electronic, hydraulic, thermal, control, electric power, or process-oriented 

subcomponents of production systems.

In the production system automation engineering area, IEC 61131 is a widely adopted standard for

programmable controllers, including equipment requirements and tests, communication, functional safety, 

programming languages, and their implementation guidelines. Specifically, IEC 61131-3 is the most widely 

adopted standard defining the programming languages for PLCs, embedded controls, and industrial personal 

computers (PCs). IEC 61499 is an open standard for distributed control and automation, upon which entire 

applications can be built from Function Blocks (FB) with event triggers. This standard has not been accepted by 

industry, even though it is highly promoted by the academic community, because it does not provide a solid 

framework for the next generation of industrial automation systems [54]. IEC 61804 defines FB for process 

control. IEC 62714, AutomationML, interconnects engineering tools from different disciplines, e.g., mechanical 

plant engineering, electrical design, process engineering, process control engineering, HMI development, PLC 

programming, and robotic programming, etc. [12]. AutomationML incorporates different standards through 

strongly typed links across the formats including CAEX (IEC 62424) for properties and relations of objects in their 

hierarchical structure, COLLADA for Graphical attributes, and Kinematics and PLCopen XML for logic items. IEC 

also has a set of standards to model and configure production equipment, such as IEC 62453-2 for field device 

tool interface specification, and IEC 61804-3 specifying Electronic Device Description Language (EDDL). ISO 

18828 is under development by ISO TC184/SC4 for standardized procedures for production systems engineering.

For production engineering practice, IEC 61508 is the international standard for electrical, electronic, and 

programmable electronic safety-related systems. It specifies requirements for ensuring that systems are 

designed, implemented, operated, and maintained to standards for safety integrity levels (SIL). IEC 61511 is a 

technical standard that specifies practices in the engineering of systems that ensure the safety of an industrial 

process through the use of instrumentation. This standard is process-industry specific within the framework of 

IEC 61508. ISO 13849 provides safety requirements and guidance on the principles for the design and integration 

of safety-related parts of control systems, including the design of software.
15

Table 8: Production System Engineering standards

Production lifecycle data management standards define general models of data integration, sharing, exchange, 

and hand-over for lifecycle support of production facilities. A selected set of important production lifecycle data 

management standards are shown in Table 9. A study of information modeling [53] found that ISO 10303 AP 239 

(PLCS) has the most potential to model a manufacturing system for its lifecycle. However, guidance is needed 

regarding how to use PLCS for representing domain-specific objects such as machining centers [90]. ISO 15926 is 

the most widely used production lifecycle data management standard in the process industry. ISO 15926 -1 

defines a class structure of various phenomena or occurrences that exist in time and space in that industry.

Table 9: Production lifecycle data management standards
16

Due to the generic character of the PLCS data model in 15926, the 15926 standard defines a reference data 

model in Part 4 providing more useful entities for representing process plants. ISO 16739 defines a common 

data model for building lifecycle support that can be applied to manufacturing facilities. The emerging IEC 62890 

defines standards for lifecycle management for systems and products used in industrial process measurement, 

control and automation.

O&M standards define data processing, communication, and presentation standards for condition monitoring 

and diagnostics of machines, for maintaining the adequate performance of the system, and for searching ways 

to improve performance. Table 10 shows a selected set of O&M standards. Standards supporting production 

O&M include MIMOSA Open Systems Architecture for Enterprise Application Integration (OSA-EAI) 

specifications and condition-based maintenance (CBM) specifications, which are widely used in the Oil and Gas 

industry. ISO 13374 also defines data processing, communication, and presentation standards for condition 

monitoring and diagnostics of machines. The upcoming ASME B5.59-2 will address performance and capabilities 

of machine tools at any time in their lifecycles, e.g., during specification, after acceptance testing, or during 

operation. ASME B5.59-2 addresses only information related to the machine tool itself and does not include 

process-related information [53].

Table 10: Production System O&M Standards

3.4 BUSINESS CYCLE FOR SUPPLY CHAIN MANAGEMENT 

Electronic commerce is critical today to enable any type of business, or commercial transaction, and always 

involves information exchange between stakeholders. Figure 4 shows the Plan-Source-Make-Deliver-Return 

cycle for managing the manufacturing supply chain. Standards for interactions among manufacturers, suppliers, 

customers, partners, and even competitors include general business modeling standards (shown in Table 11), 

and manufacturing specific modeling standards and corresponding message protocols. These standards are the 

key to enhancing supply chain efficiency and manufacturing agility. Here, we highlight three sets of 

manufacturing-specific standards critical for integration: APICS Supply Chain Operations Reference (SCOR), Open 

Applications Group Integration Specification (OAGIS), and MESA’s B2MML.
17

Figure 4: Standards through the supply chain cycle

Table 11: General standards employed for modeling and executing business processes
18

SCOR is a process reference model from the Supply Chain Council (SCC) (now part of APICS) and is a de facto 

standard that identifies and promotes best practices in the management and operation of supply chain activities 

across many industries [13]. SCOR is a management tool spanning the supply chain from the supplier's suppliers

to the customer's customers. The model describes the business activities associated with all phases of satisfying 

a customer's demands. The model uses an approach based on three pillars: process modeling and reengineering, performance measurements, and best practices. The SCOR model is text-based and, therefore, not 

directly usable for automation.

OAGIS includes a suite of engineering and business message specifications called Business Object Documents 

(BODs), which define common content models and messages for communication between business applications. 

OAGIS also defines guides for implementation. OAGIS content spans many industries and functions, including 

electronic commerce, manufacturing, logistics, Customer Relationship Management, and Enterprise Resource 

Planning (ERP.) It includes not only specific formats for common types of messages used in manufacturing 

domains, but also mechanisms for extending and tailoring the standards for specific needs. The standard can 

serve as building blocks for customized data exchanges. MESA B2MML is a well-adopted implementation of the 

ISA 95 data models. It facilitates the integration of ERP and supply chain management systems with 

manufacturing systems such as control systems and MES.

3.5 MANUFACTURING PYRAMID 

The Manufacturing Pyramid is the core of the SM Ecosystem and is where the product lifecycle, production 

lifecycle, and business cycle converge and interact. In a smart operation, autonomous and intelligent machine 

behaviors—including self-awareness, reasoning and planning, and self-correction—are key, but information 

resulting from these behaviors must flow up and down the Pyramid. This integration from machine to plant to 

enterprise systems is vital and critically depends upon standards. Standards-enabled SM integration allows 1) 

access to field and plant data for making quick decisions and optimizing production throughput and quality, 2) 

accurate measures of energy and material use, and 3) improved shop floor safety and enhanced manufacturing 

sustainability.

In Figure 5, we divide integration standards based on the ISA 95[34] hierarchy, which has also been included 

within ISO/IEC 62264 [30]. ISA 95 is a commonly used reference model for developing automated interfaces

between enterprise and control systems. This standard was developed for global manufacturers and designed 

for applicability to all industries and for batch, discrete, and continuous processes alike.
19

Figure 5: Standards aligned to the ISA95 model

Table 12: Enterprise level standards
20

Enterprise level: Table 12 shows important enterprise-level manufacturing standards. ISO 15704 defines 

requirements for enterprise reference architectures and methodologies. The ISO standards 19439 and 19440 

listed in Figure 5 define the framework and specify characteristics of the core constructs needed for enterpriselevel activities. ISO 20140 establishes an overview and general principles of a method for assessment of 

environmental influence of manufacturing systems. OMG’s Business Process Model Notation (BPMN) is a 

graphical representation often used for specifying processes in a manufacturing business process model. The 

Predictive Model Markup Language (PMML) developed by the Data Mining Group (DMG) [93] is a format to 

define statistical and data mining models. PMML can be adopted at the MOM level as well. The Decision Model 

and Notation (DMN) standard is being developed by OMG to close the gap between business decision design 

and decision implementation. OAGIS defines a common content model for enterprise application integration.

MOM level: Manufacturing operations management, or MOM, refers to applications that control plant level 

operations. Table 13 shows some important MOM level standards. IEC 62264 is an international standard for 

enterprise control system integration and is based upon ISA 95. IEC 62264 defines activity models, function 

models, and object models in the MOM domain. Business to Manufacturing Markup Language (B2MML),

published by the MESA, is an implementation of IEC 62264 to link ERP and Supply Chain Management (SCM)

systems with manufacturing systems such as Manufacturing Execution Systems (MES). ISO 22400 defines key 

performance indicators (KPIs) used in manufacturing operations management. QIF is a suite of standards 

enabling the flow of information within computer-aided quality measurement systems. PMML from DMG could 

be applied at this level to support MOM functions.

Table 13: MOM level standards

SCADA and Device level: SCADA-level (Supervisory Control And Data Acquisition) and device-level standards in 

Figure 5 are considered shop floor standards. While these levels were once more distinct, the distinction of 

interest now is between time and safety critical activities and non-time-critical activities. At shop floor level, 

there is usually an organized hierarchy of control systems consisting of HMI, PLC, and field components such as

sensors and actuators. PLCs are usually connected to HMI via a non-time-critical communications system such as 
21

Ethernet/IP, DeviceNet, ControlNet, PROFINET, and EtherCAT. A fieldbus, such as PROFIBUS, CAN bus, HART and 

Modbus, links the PLCs to the field components. A serial communications protocol, Modbus is also often used to 

connect a supervisory computer with a remote terminal unit (RTU) in SCADA) systems. While communication 

profiles of real-time Ethernet-based protocols like EtherCAT, PROFINET and Ethernet/IP are captured by IEC 

61874, fieldbuses such as Foundation Fieldbus and PROFIBUS are specified in IEC 61158.

In addition to the communication protocols discussed above, there are several important integration 

standards linking shop floor control to MES and enterprise level systems, such as OPC and OPC UA (Unified 

Architecture), MTConnect, PackML and BatchML. OPC UA is an industrial machine-to-machine communication 

protocol based on Service-Oriented Architecture (SOA). Furthermore, OPC UA provides a flexible information 

model framework for creating and exposing customized information in a standard way, in those cases where 

users of OPC UA have agreed on the customizations. Some of these customizations are contained in companion 

standards for different application areas—for example OPC UA for ISA 95, OPC UA for Field Device Integration 

(FDI), OPC UA for Analyzer Devices (ADI), and OPC UA for PLCOpen (IEC 61131-3). MTConnect is used to access

real-time data from shop floor manufacturing equipment such as machine tools. ISA 88 is a standard for the 

batch processing industry, and defines the physical model, procedures, and recipes. It was adopted by the IEC in 

IEC 61512. PackML is a packaging standard used in the batch processing industry as part of ISA 88 standards.

BatchML is an implementation of ISA 88 for linking batch control systems to MES. The standards supporting

shop floor level manufacturing systems are shown in Table 14.

Table 14: SCADA and device level standards
22

Cross-levels: As shown on the right side of Figure 5, several standards cross all the levels and define

manufacturing system security, quality management processes, energy management, and environmental 

management. Table 15 lists several cross-level manufacturing standards. ISA/IEC-62443, formerly ISA 99, is a 

series of standards, technical reports, and related information that defines procedures for implementing 

electronically secure industrial automation and control systems. The concept of manufacturing and control 

systems electronic security is applied in the broadest possible sense, encompassing all types of plants, facilities, 

and systems in all industries. The ISO 9000 family of quality management systems standards is designed to help 

manufacturers ensure that they meet the needs of customers and other stakeholders while meeting statutory 

and regulatory requirements related to a product. ISO 50001 specifies the requirements for establishing, 

implementing, maintaining and improving energy management systems for a manufacturing business. ISO 14000 

is a series of environmental management standards containing guidance on how to systematize and improve 

environmental management efforts.

Table 15: Cross-level standards

3.6 STANDARDS ADOPTION AND THE RELATED ISSUES 

Standards play a big role in economic growth globally. Roughly, 80% of global merchandise trade is affected by 

standards and by regulations that embody standards [84]. In the United States, the economic impact of 

standards is not tracked [85]. Based on a UK study published in 2005, the Empirical Economics of Standards, 

standards make an annual contribution of 2.5 billion to the UK economy and 13% of the growth in labor 

productivity is attributed to the role of standards [86]. A study of the economic benefits of standardization 

undertaken by the German Institute for Standardization (DIN) and the German Federal Ministry of Economic 

Affairs and Technology from 1997-2000 based on 700 companies found the benefit of standards to the national 

economy is greater than $15 billion per year [87]. They also found that companies that participate in standards 

development have a head start on their competitors in adapting market demands and new technologies.

Standards are key enablers for manufacturing. They provide a basis on which to bring together specialized 

expertise to create highly efficient manufacturing systems. The sum total of standardization efforts in the 

manufacturing field is immense. Standards provide data definitions, detailed models of the information 

relationships, and interface protocols for all three of the lifecycles we describe. They support product designs 

and management, production system design and operations, and integration into business value chains. These 
23

are the foundations on which information can flow throughout the levels of manufacturing control and between 

partners in the manufacturing enterprise including the various software vendors and device providers that 

participate by providing their expertise to these systems in the form of commercial products. Standards allow 

for a clear separation of concerns among the participants, effectively bringing down costs while increasing 

reliability and efficiency.

Across some of the lifecycles and in some industry sectors standards are more mature than in others. In discrete 

manufacturing, which is characterized by individual parts that are assembled into final products, standards along 

the product lifecycle are fairly well positioned to support Smart Manufacturing. In the world of continuous 

manufacturing, however, standards are not as well established for process product data management. 

Production systems engineering standards are more diversified and can typically be applied in both categories of 

manufacturing. Within the manufacturing pyramid, communication standards are well established but 

interoperability among systems is somewhat limited, meaning that manufacturers typically are locked into a 

single vendor solution. Along the business lifecycle, several well-established standards exist however; the level 

to which information is able to interconnect with the production systems is quite limited. Other gaps in the 

ability of standards to address smart manufacturing needs exist and are discussed in detail in next section.

Beyond the standards gaps two other barriers to standards adoption inhibit the growth of SMS.

1) Lack of tracking of standards and standards adoption. The huge number of standards can be misleading,

as many standards are never used. The number of standards that are actively being sold constitute a 

small part of those offered for sale, meaning many standards out there are not being used. In 1996, a 

NIST report identified 25-30% of the US standards as obsolete [83] and certainly, this number has grown. 

SDOs rarely inactivate standards once they have been published. The result is that manufacturers and 

their support network, in the form of software and device providers, are left trying to navigate the 

standards without a compass to help them find their way.

2) Overlap and redundancy between standards. Three practices cause overlap and redundancy. First

standards from national, regional, international standards systems are sometimes identical, equivalent 

or in some other way related resulting in confusion as to the most authoritative source. Secondly, 

standards in the same technical areas but in different application sectors are defined independently. For 

example, material-testing methods defined for different industry sectors sometimes are not consistent. 

For the US, the third redundancy comes from the pluralistic standards system. In a pluralistic standards 

system, no one body is sanctioned to provide standards. The system reflects cultural individualization 

and an in-grained belief in a market-driven economy. In US, there are 600 SDOs maintaining ongoing 

standardization programs. As the number of standards produced expands, more opportunity for overlap 

and redundancy results. Standards organizations are not only competing with one another to write 

standards, they are sometimes writing conflicting standards, thus defeating the purpose. In the words of 

an old programmer, “Are we creating spaghetti code in our standards?”

To tackle the issues of untracked and overlapping standards, harmonization and collaboration among SDOs are

necessary. Harmonized standards as defined by ISO are “equivalent standards on the same subject approved by 

different standardization bodies that establish interchangeability of products, processes and services, mutual 

understanding of test results or information provided according to these standards" [94].

Historically, there have been several attempts at standards harmonization. The first international harmonization 

occurred when ISO was formed in 1947. Before the World War II, standards throughout the world were national 

in scope, often set by government-funded national standards bodies. Standards were developed to serve a 
24

country's particular needs and to benefit the domestic supplier. However, standards for similar products 

approved by different national standards bodies create difficulties for global companies. Trade barriers result in 

that products designed for one market may be blocked out of other markets based on the different national 

requirements. ISO was created to facilitate trade by opening up the markets globally [91]. We now see a trend 

for many SDOs to be global. Many of the organizations we have mentioned have evolved into international 

organizations from their national origins, often dropping the original meanings of their acronymized names at 

the same time. In the US government policy shifted away from government specific standards (known as Federal 

Information Processing Standards or FIPS and United States Military Standards or MILSPECS) to adopting 

publically developed standards from SDOs. A decade ago, 80% of all standardization activities of the European 

standards bodies focused on national work, with the remainder related to international development efforts. 

Today, this ratio is reversed: 80% of standards work is international [88]. Where overlap exists between 

standards organizations, it is typical to have at least a liaison that is responsible for mediating differences. SDOs 

will often establish joint working groups as well.

To summarize, in this age of smart manufacturing demands for further standards harmonization and a proactive 

way to develop and implement ubiquitous standards are greater than ever before. For manufacturers to 

embrace emerging information and communication technology, serve global markets and innovative business 

models, and provide rapid evolutions of product designs and new technology, strong standards underpinnings 

that are clear and unambiguous are needed. The standards landscape presented here identifies areas of 

technical interaction between the different standards efforts.

4 STANDARDS OPPORTUNITIES FOR SMART MANUFACTURING 

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.

4.1 STANDARDS NEEDS 

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.

_______________________________ 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.
25

Figure 6: Decomposition of the automation hierarchy with distributed services [55]

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.

Figure 7: Service-oriented smart manufacturing ecosystem

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 
26

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.

Table 16: Opportunities for Standards for SM Capabilities

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.

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.

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 
27

provide technical reports relating to human-machine interfaces (HMIs) in manufacturing and processing

applications.

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.

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.

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.

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.

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.

4.2 NEW INITIATIVES 

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.

4.2.1 Industrie 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 
28

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.

4.2.2 Internet of Things (IoT) 

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, largescale manufacturing, and remote maintenance.

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].

Table 17: Comparison of Industrie 4.0 and the Industrial Internet Consortium [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 opensource 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.
29

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 [66], HyperCat [68], OMA LightweightM2M [67], Eclipse M2M [68] and Weightless 

[70] have potential to be de facto M2M standards [65]. Specifically, Eclipse 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, heath care, and supply chains

[64].

4.2.3 Cyber Physical Systems (CPS) 

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].

4.2.4 Big Data and Cloud Manufacturing 

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].

4.2.5 Smart Manufacturing Initiatives in the U.S. 

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 
30

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.

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], ModelBased 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].

4.2.6 SDO Smart Manufacturing Related Activities 

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.

4.2.7 Sustainable Manufacturing Standards 

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. 
31

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.

5 CONCLUSION 

In order to realize the vision of smart manufacturing, the classical manufacturing system architectural 

paradigm based on a hierarchical control model must be replaced. A new paradigm based on distributed 

manufacturing services is evolving. This paradigm shift is brought on by the introduction of smart devices 

accessible as services on a network, embedded intelligence at every level, predictive analytics, and cloud 

computing technology. All of these technologies depend on standards.

Standards are a fundamental component of the evolution to smart manufacturing. Standards allow for 

systematic, repeatable, and efficient manufacturing systems. Standards support the participation of a wide 

range of organizations from small manufacturers to large multi-national corporations. Moreover, smart 

manufacturing technologies enabled by standards allow for participation of a wide range of manufacturers, 

regardless of size or location.

This report presents an examination of the standards landscape for smart manufacturing. The landscape 

comprises standards used to integrate within and across three manufacturing lifecycle dimensions – the product

lifecycle, the production system lifecycle, and the business (enterprise) lifecycle. In order to study existing 

standards and how they can be applied to smart manufacturing, we have defined a Smart Manufacturing 

Ecosystem to provide a consistent context for analyzing existing standards and establishing future standards 

requirements for smart manufacturing. In this report, the SMS Ecosystem is used as a basis to categorize and 

evaluate existing standards for application to smart manufacturing systems, present a comprehensive view of 

emerging SMS standardization activities, and identify areas where new standards activities are needed to enable 

the implementation of SMS.

The report concludes that existing manufacturing standards are insufficient to fully enable smart 

manufacturing, especially in the areas of cybersecurity, cloud-based manufacturing services, supply chain 

integration, and data analytics. Traditional standards development efforts, primarily focused on incremental 

improvements of existing standards, cannot keep pace with the speed of evolving technology. SDOs, national 

manufacturing initiatives, and industrial consortia are identifying requirements for new smart manufacturing 

standards.

The report identified the following as priority areas for standards advancement to enable smart 

manufacturing:

• SMS reference model and reference architecture

• Internet of Things (IoT) reference architecture for manufacturing

• Manufacturing service models

• Machine to machine communication

• PLM/MES/ERP/SCM/CRM integration
32

• Cloud manufacturing

• Manufacturing sustainability

• Manufacturing cybersecurity

Finally, the report examined sources of new requirements for standards to support smart manufacturing,

including national and international manufacturing initiatives, the Internet of Things initiative, standards 

development organization manufacturing programs, and the emergence of sustainable manufacturing 

requirements.

6 REFERENCES 

[1] Global Agenda Council on the Future of Manufacturing , The Future of Manufacturing: Driving 

Capabilities, Enabling Investments, URL: 

http://www.cggc.duke.edu/pdfs/Future_of_Manufacturing_Driving_Capabilities.pdf, Access date 

March 14, 2015

[2] S. S. Shipp, N. Gupta, B. Lal, J. A. Scott, C. L. Weber, M. S. Finnin, M. Blake, S. Newsome, and S. Thomas, 

2012, Emerging global trends in advanced manufacturing, Institute for Defense Analyses, Virginia, US.

[3] J. Davis,

T. Edgar, J. Porter, J. Bernaden, M. Sarli, Smart Manufacturing, manufacturing intelligence and 

demand-dynamics performance. Computers & Chemical Engineering, 2012, 47,145-156.

[4] SMLC, Implementing 21st Century Smart Manufacturing: Workshop Summary Report, June 2011, 

http://www.energetics.com 

/resourcecenter/products/roadmaps/Documents/SMLC_Smart_Manufacturing.pdf, Access date 

March 8, 2015.

[5] PCAST AMP Steering Committee Report - Accelerating U.S. Advanced Manufacturing, October 27, 

2014.

[6] NIST Standards.gov, What are Standards? http://www.nist.gov/standardsgov/definestandards.cfm.

[7] Gunasekaran, Agile Manufacturing: The 21st Century Competitive Strategy, Elsevier, 2001 

[8] LNS Research, 2013. 28 Manufacturing Metrics that Actually Matter, 

http://blog.lnsresearch.com/blog/bid/188295/28-Manufacturing-Metrics-that-Actually-Matter-TheOnes-We-Rely-On.

[9] International Trade Administration, How does Commerce define Sustainable Manufacturing? 2010, 

http://www.trade.gov/competitiveness/sustainablemanufacturing/how_doc_defines_SM.asp.

[10]PCAST AMP Steering Committee Report - Accelerating U.S. Advanced Manufacturing, Annex 1-10: 

Transformative Manufacturing Technology, October 27, 2014.

[11]P. Witherell, B. Kulvatunyou, and S. Rachu, Toward the Synthesis of Product Knowledge across the 

Lifecycle, Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition

IMECE2013, November 13-21, 2013, San Diego, California, USA

[12]S. Faltinski, O. Niggemann, N. Moriz, A. Mankowski, AutomationML: From Data Exchange to System 

Planning and Simulation, 2012 IEEE International Conference on Industrial Technology (ICIT), Athens, 

Greek.

[13]P. Bolstorff, R. Rosenbaum (2003). Supply Chain Excellence: A Handbook for Dramatic Improvement 

Using the SCOR Model. AMACOM Div American Mgmt Assn. ISBN 0-8144-0730-7.

[14]D. Kibira, KC Morris, Background Study of Methods and Tools for Performance Assurance of Smart 

Manufacturing Systems, forthcoming NISTIR.

[15]CIMOSA, CIM Open System Architecture. URL: 

http://www.pera.net/Methodologies/Cimosa/CIMOSA.html, Access date March 8, 2015.
33

[16]R. Sessions, A Comparison of the Top Four Enterprise-Architecture Methodologies, URL: 

https://msdn.microsoft.com/en-us /library/bb466232.aspx. Access date March 8, 2015.

[17]German Standardization Roadmap, Industry 4.0. URL: 

https://www.dke.de/de/std/documents/rz_roadmap%20industrie%204-0_engl_web.pdf, Access date 

March 14, 2015.

[18]S. Rachuri, KC Morris, U. Roy, D. Dornfeld, S. Kumara, "Sustainable Manufacturing Program Workshop 

Report," NISTIR 7975, National Institute of Standards and Technology, November 2013.

[19]"Sustainable Manufacturing – A Business Perspective: A Technology Roadmap," University of Kentucky, 

Institute for Sustainable Manufacturing, Feb. 2015.

[20]International Organization for Standardization (ISO), http://www.iso.org.

[21]American Society for Mechanical Engineers, (ASME) https://www.asme.org

[22]ASTM International, http://astm.org

[23]American National Standards Institute (ANSI), http://ansi.org

[24]Open Source Initiative, http://opensource.org/osr-intro

[25]World Wide Web Consortium (W3C), http://www.w3.org

[26]Internet Engineering Task Force (IETF), http://ietf.org

[27]ISO 10303-1:1994, “Industrial automation systems and integration - Product data representation and 

exchange - Part 1: Overview and fundamental principles,” International Organization for 

Standardization, 12/01/1994

[28]Open Applications Group Inc. (OAGi), http://www.oagi.org

[29]MTConnect Institute, http://mtconnect.org

[30]International Electrotechnical Commission (IEC), http://www.iec.ch

[31]OPC, https://opcfoundation.org

[32]Dimensional Metrology Standards Consortium (DMSC), http://www.dmsc-inc.org

[33]Manufacturing Enterprise Solutions Association International (MESA), http://mesa.org

[34]International Society of Automation (ISA), https://www.isa.org

[35]APICS, http://www.apics.org

[36]Industrial Internet Consortium (IIC), http://www.industrialinternetconsortium.org

[37]Institute of Electrical and Electronics Engineers (IEEE), http://www.ieee.org

[38]The Object Management Group, http://www.omg.org/

[39]NIST, Enabling the Digital Thread for Smart Manufacturing,

http://www.nist.gov/el/msid/infotest/digital-thread-manufacturing.cfm

[40]NIST, Model-Based Enterprise Summit 2014, http://www.nist.gov/el/msid/mbesummit_2014.cfm

[41]National Institute of Standards and Technology (NIST), http://www.nist.gov

[42]NIST Big Data Program, http://bigdatawg.nist.gov/

[43]NIST Cybersecurity Framework, http://www.nist.gov/cyberframework

[44]www.manufacturing.gov

[45]G. Vogel-Heuser, G. Kegel, K. Bender, and K. Wucherer, Global information architecture for industrial 

automation. Automatisierungstechnische Praxis (atp), Oldenbourg-Verlag, Muenchen, 2009.

[46]https://www.whitehouse.gov/omb/circulars_a119/#3

[47]K. Amoako-Gyampah, M. Acquaah, Manufacturing strategy, competitive strategy and firm 

performance: An empirical study in a developing economy environment - International Journal of 

Production Economics, 2008

[48]STEP AP242 Project, www.ap242.org

[49]Encyclopedia Britannica. In Encyclopedia Britannica Online: http:// www.britannica.com, 2015

[50]Rajesh ATTRI, Sandeep GROVER, A comparison of production system life cycle models, Front. Mech. 

Eng. 2012, 7(3): 305–311
34

[51]J. Lubell,S.P. Frechette, R.R. Lipman, F.M. Proctor, J.A. Horst, M. Carlisle,P.J. Huang, 2013. ModelBased Enterprise Summit Report, NIST Technical Note 1820, U.S. Department of Commerce

[52]A. Barnard Feeney, S.P. Frechette, V. Srinivasan; “A portrait of an ISO STEP tolerancing standard as an 

enabler of smart manufacturing systems;” 13th CIRP Conference on Computer Aided Tolerancing; May

2014;

[53]A.V. Euler-Chelpin, Information modelling for the manufacturing system life cycle, Doctoral Thesis, 

KTH Royal Institute of Technology, Stockholm, Sweden, 2008

[54]K. Thramboulidis, IEC 61499 vs. 61131: A Comparison Based on Misperceptions, ournal of software 

engineering and applications 6(08), December, 2012

[55]VDI/VDE-Gesellschaft Mess und Automatisierungstechnik (GMA). Cyber-physical systems: Chancen 

und nutzen aus sicht der Automation, Thesen und Handlungsfelder, April 2013.

[56]Germany Trade and Invest, Industrie 4.0 Smart Manufacturing for the Future, 

http://www.gtai.de/GTAI/Content/EN/Invest/_SharedDocs/Downloads/GTAI/Brochures/Industries/in

dustrie4.0-smart-manufacturing-for-the-future-en.pdf

[57]Recommendations for implementing the strategic initiative INDUSTRIE 4.0, 

http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Mater

ial_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf

[58]The Reference Architectural Model Industrie 4.0 (RAMI 4.0), 

http://www.zvei.org/Downloads/Automation/ZVEI-Industrie-40-RAMI-40-English.pdf

[59]Connecting machines, IoT and the Cloud, http://www.iec.ch/etech/2015/etech_0515/ind-1.htm, May 

2015.

[60]K. Bledowski, The Internet of Things: Industrie 4.0 vs. the Industrial Internet

https://www.mapi.net/research/publications/industrie-4-0-vs-industrial-internet, June, 2015.

[61]Open Interconnect Consortium, http://openinterconnect.org/

[62]Internet of Things Architecture, http://www.iot-a.eu/public

[63]https://www.iot-at-work.eu/index.html

[64]ETSI, Machine to Machine Communications, http://www.etsi.org/index.php/technologiesclusters/technologies/m2m

[65]M2M World news, Standards will be critical for the emergence of the Internet of Things, 

http://m2mworldnews.com/2014/07/31/80797-standards-will-be-critical-for-the-emergence-of-theinternet-of-things/, 2014

[66]OneM2M, http://www.onem2m.org/

[67]Open Mobile Alliance, http://openmobilealliance.org/about-oma/work-program/m2m-enablers/

[68]Eclipse IoT, http://iot.eclipse.org/standards

[69]HyperCat, http://www.hypercat.io/

[70]Weightless, http://www.weightless.org/

[71]Eclipse SCADA, http://www.eclipse.org/eclipsescada/

[72]NIST, Cyber-Physical Systems, http://www.nist.gov/cps/

[73]H2020 call1 2014 - topic ICT1 "Smart Cyber-Physical Systems" - Overview of selected projects, 

http://ec.europa.eu/digital-agenda/en/news/h2020-call1-2014-topic-ict1-smart-cyber-physicalsystems-overview-selected-projects

[74]VDI, Fachausschuss 7.20 - Cyber-Physical Systems, https://www.vdi.de/technik/fachthemen/messund-automatisierungstechnik/fachbereiche/anwendungsfelder-der-automation/gma-fa-720-cyberphysical-systems/

[75]MESA, Smart Manufacturing Working Group, 

http://mesa.org/en/committeeactivities/smartmanufacturingworkinggroup.asp

[76]Smart Manufacturing Leadership Coalition, https://smartmanufacturingcoalition.org/
35

[77]NIST Workshop on Open Cloud Architectures for Smart Manufacturing, 

http://www.nist.gov/el/msid/opencloudarch.cfm

[78]Lydon, B., Automation & Control Trends in 2015, http://www.automation.com/automationnews/article/automation-control-trends-in-2015

[79]IPC, http://ipc.org

[80]Accenture, How the industrial internet is changing the competitive landscape of industries, 

https://www.accenture.com/us-en/insight-industrial-internet-competitive-landscape-industries.aspx

[81]ProSTEP iViP Association, http://www.prostep.org/nc/en.html

[82]ANSI, http://www.ansi.org/news_publications/news_story.aspx?menuid=7&articleid=1862af9b-7157-

44fa-b43b-ee3b3e9029ba

[83]B. Roben, Standards Activities of Organizations in the United States, NIST Special Publication 806, 1996 

Edition.

[84]Arden L. Bement , “Standards and Infrastructure: Foundations of Manufacturing Competitiveness”, 

http://www.nist.gov/director/speeches/bement_032703.cfm

[85]Overview of the U.S. Standardization System, 

http://www.standardsportal.org/usa_en/standards_system.aspx

[86]The Empirical Economics of Standards JUNE 2005, DTI Economics Paper no.12, June 2005, 

http://www.sis.se/upload/632555702720125533.pdf

[87]Economic Benefits of Standardization , DIN - German Institute for Standardization, 2000,

http://www.din.de/blob/89552/68849fab0eeeaafb56c5a3ffee9959c5/economic-benefits-ofstandardization-en-data.pdf

[88]Standards Learn, http://www.standardslearn.org/

[89]T. J. Coelli, D. S. P. Rao, C.J. O'Donnell, G.E. Battese, An Introduction to Efficiency and Productivity 

Analysis, Springer, 2005

[90] A. V. Euler-Chelpin, Information modelling for the manufacturing system life cycle, Doctoral Thesis, 

KTH – Production Engineering, Stockholm, Sweden, 2008, ISSN 1650-1888

[91]A. Zuckerman, International Standards Desk Reference: Your Passport to World Markets, American 

Management Association, 1997

[92]https://www.modelica.org/

[93]The Data Mining Group, http://dmg.org/

[94]Global Harmonization of Standards, http://www.okstate.edu/ind-engr/step/WEBFILES/Papers/ 

Global_Harm_body.htm

[95]NIST Smart Manufacturing Systems Design and Analysis Program, http://www.nist.gov/el/msid/

syseng/smsda.cfm

[96]NIST Robotic Systems for Smart Manufacturing Program, http://www.nist.gov/el/isd/ms/rssm.cfm

[97]NIST Measurement Science for Additive Manufacturing Program, http://www.nist.gov/el/isd/

sbm/msam.cfm