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1 Introduction

Today’s global enterprises have faced with a growing increase in the competitiveness that forces them to adopt and develop new strategies and methods with relevant challenges in integrating product, process, and enterprise dimensions and life cycles (Subrahmanian et al. 2005; Americon and Antonio 2011; AbdulKadir et al. 2011; Barbau et al. 2012). The Cloud manufacturing is the proposed paradigm for today’s global manufacturing (Xu 2012; Shen et al. 2011). This new look considers a wide cloud of encapsulated manufacturing resources usually distributed over the globe with the capability of centralized management (Tao et al. 2010). This paradigm inherits the cloud computing paradigm (Lai et al. 2012; Bohm and Kanne 2011; Goscinski and Brock 2010) in which manufacturing agents distributed over the globe use the cloud services to fulfill their manufacturing operations requirements (Li et al. 2010). The first challenge to achieve the Cloud manufacturing paradigm is the global manufacturing data integration.

The integration of enterprise-level business systems with manufacturing systems is found to be one of the inevitable drivers for productivity and making businesses more responsive to supply chain demands (Zurawski 2007; Tu and Dean 2011; Koren 2010; Ahmadn and Cuenca 2012). Combination of systems and technologies designed to integrate the data and information of global enterprises is known to be one of the key competitive strategies for industries in the twenty-first century (Ang 1987, 1989; Hannam 1997; Shen et al. 2010; Rudberg and Olhager 2003). Considering that the product life cycle has been shorten, the constant communication and transmission of ideas between different sections throughout the product life cycle in different processes should be integrated to foster a fast and effective development process (Lu and Storch 2011; Abdelkafi et al. 2011; Tu and Dean 2011; Dangayach and Deshmukh 2006; Bloomfield et al. 2012).

However, integrating heterogeneous and autonomous data sources through enterprises’ structures which are numerous in number and also in approaches for data management is a significant challenge (Kosanke 2005; Bellatreche et al. 2006; Cutting-Decelle et al. 2007). This challenge increases the requirements for proposing researches for development of tools and techniques to integrate the various enterprises’ data sources (Young et al. 2005; Martin 2005). This integration should support the aforementioned sources for working based on their own data ontology design, structures, and procedures for data management and representation (Valilai and Houshmand 2012, 2013a, b). Moreover, the data integration should involve the integration of autonomous, distributed, and heterogeneous databases sources into a single data source associated with a global schema (Bellatreche et al. 2006; Wu et al. 2009; Paredes-Moreno et al. 2010).

The enterprise data integration is known to be a painful procedure due to problems like occurrence of different types of conflicts and not credible accumulation or storage of the information (Santarek 1998; Vassiliadis et al. 2001), challenges due to information sharing among the various management information systems (Zha and Du 2002; Wolfert et al. 2010), existence of a harmonized data model for appropriate representation of enterprise’s manufacturing discourse information (Zauner et al. 1993; Zhao et al. 2006), enterprises’ diverse requirements for information systems and IT rapid development which persuade information systems in different industry fields to becomes more complicated (Wu et al. 2012; Whitfield et al. 2012). Although different researches have been conducted to fulfill the enterprises’ requirements for integration, but still there is an essential need for a solution for enterprises’ data integration (Valilai and Houshmand 2010, 2011; Houshmand and Valilai 2012).

In the next section, the chapter studies the concepts of global manufacturing data integration. Discussing the manufacturing ontology model researches, the chapter studies the ISO-based standards approaches and reviews the related researches and solutions offered as an enabler approach in global manufacturing data integration. Talking over the axiomatic design theory, the chapter proposes an efficient solution for global manufacturing ontology model. Finally, the chapter proposes an implementation sample of the proposed idea for global manufacturing ontology model using ISO standard framework.

2 Global Manufacturing Data Integration in Cloud Manufacturing

Researches show that enterprises’ information is considered as a highly valuable asset (Paredes-Moreno et al. 2010; Bachlaus et al. 2008; Zhou et al. 2007; Wiendahl et al. 2007). However, the manufacturing enterprises’ data are often scattered over different areas, formats, and software packages (Zhang et al. 2011a, b; Scannapieco et al. 2004; Rudberg and Olhager 2003). This necessitates the proposal of solutions to manage the enterprises’ information from its various sources updated and integrated. Such solutions must include structure and procedures in a centralized and sophisticated manner to exploit their information effectively and profitably (Wang et al. 2010; Feng and Wu 2009; Patel et al. 2006). However, this integration is usually limited by the current state of technology in terms of the structure and functionalities of current manufacturing information systems (Lebreton et al. 2010; Hwang and Katayama 2009). Also, the proper solution for integration of manufacturing enterprises’ processes should cover a wide range of manufacturing discourses like integrating engineering design databases for product information (Jiao and Helander 2006), integration of business activities (Liu et al. 2011), supply chain integration (Bachlaus et al. 2008; Wang et al. 2010; Molina et al. 2007), and integration of information related to the manufacturing operation analysis (Hernandez-Matias et al. 2008).

The consistent data model for various manufacturing ontology scope is one of the important aspects of global manufacturing data integration (Verstichel et al. 2011; Runger et al. 2011). This data model is vital to enable the exchange of information through manufacturing enterprises’ processes (Lampathaki et al. 2009). The concept of manufacturing ontology scope defines sets of data and their related structure which enables the experts to share information in a manufacturing domain of interest based on knowledge management and computer-supported cooperative work (Chang and Terpenny 2009; Curran et al. 2010). The manufacturing ontology can be modeled along with the rules and the relationships between the objects. This model enables the interoperability through the manufacturing domain (Blornqvist and Ohgren 2006; Alsafi and Vyatkin 2010).

2.1 Manufacturing Ontology Models

During the last decades, significant researches have been conducted to propose information model supporting decision-making processes throughout the manufacturing operations (Guerra-Zubiaga and Young 2006; Grubic and Fan 2010). Molina et al. (1995) proposed a consistent source of manufacturing information to support CAE users and software packages. This model enables the manufacturing capability of a manufacturing facility presentation. The proposed model considers three types of information, categorized as manufacturing resources, processes, and strategies. The proposed manufacturing data model can be implemented in four levels proposed as factory level, shop floor level, working cell level, and working station level. Uschold et al. (1998) proposed Enterprise Ontology named EO to improve the communication between humans through enterprise processes, provide a data model to support the end-user applications and support interoperability among them (Grubic and Fan 2010). The proposed model consists of five types of information, categorized as meta-Ontology and time, activity, plan, capability and resource, organization, strategy and marketing. The proposed ontology model lacks to cover the whole enterprise ontology data model; however, it has been used by the later researchers for further works.

AMICE. CIMOSA (1993), an organization consists of major companies, including users, vendors, consulting companies, and academia established a project in CIM concept. This project aimed to develop a solution for Computer Integrated Manufacturing Open Systems Architecture named CIMOSA. CIMOSA is an enterprise modeling framework which is able to support the enterprise integration of machines, computers, and people. The framework of CIMOSA is based on the system life cycle concept, and offers a modeling language, methodology, and supporting technology for enterprise integration (Berio and Vernadat 1999). AMICE consortium has promoted the application of CIMOSA for whole enterprise lifecycle support.

Steele et al. (2001) proposed an object-oriented approach to integrate engineering functions through enterprise processes like planning, analysis, and control of resources such as machine tools and fixtures. This object-oriented resource model links information from different enterprise ontology domains. The model establishes a one–to-one relation among the software packages and physical objects. Madni et al. (2001) proposed a data model foundation for designing, reinventing, managing, and controlling for collaborative and distributed enterprises. This model named IDEON, considers four types of information categorized as enterprise data model for environmental data exchange, organizational structure in the enterprises, enterprise processes, and resource and product. Feng and Song in (2003) proposed an object-oriented manufacturing process information model. The model is capable to elaborate the hierarchical structure of the information representing manufacturing processes by means of recursive definition. The proposed model consists of information concerning to manufacturing artifact, manufacturing activities, workpiece, manufacturing equipment, estimated cost and time, and manufacturing process sequences.

Guerra-Zubiaga and Young (2006) offered a manufacturing facility information and knowledge model named MFIKM. This model aimed to enable the management of information and knowledge related to the manufacturing facility. This model focused on process and resource knowledge in manufacturing enterprises. Lin and Harding (2007) proposed a knowledge representation scheme for general manufacturing system engineering called MSE ontology model. The model aims to facilitate the communication and information exchange through the enterprises. The proposed model focuses on information autonomy which enables the enterprise agents to maintain their own data structures. The model focuses on main types of information which can be named as process, resource, strategy, and projects.

Ye et al. (2008) proposed an ontology-based architecture to fulfill the problem of semantic integration through supply chain management concepts. This model, named supply chain ontology (SCO), considers the skeletal method to capture information concepts through the supply chain management processes. This model considers the main information types like supply chain structure, activity, resource, and performance. Tursi et al. (2009) proposed a novel model focusing on a product centric to represent its technical data. This model enables interoperability among applications which are involved in manufacturing enterprises. The researchers tried to include all technical data which may be needed through manufacturing operations. This model is based on a product ontology which formalizes all technical data and concepts contributing to the definition of a product.

2.1.1 ISO-based Data Model Standards for Manufacturing Integration

Considering the aforementioned researches to define a basis for data exchange and sharing, one of the best efforts is the work of the ISO committees during the last two decades (Valilai and Houshmand 2010; Cutting-Decelle et al. 2007; Carnahan et al. 2005; Kramer and Xu 2009). Through the committees’ researches, subcommittee 4 (SC 4) of technical committee (TC 184) focused on integration of manufacturing operations in the field of product information model (Jiang et al. 2010; Valilai and Houshmand 2011; Zhou et al. 2007; Panetto and Molina 2008; Zhao et al. 2009; Ray and Jones 2006). The outcome of this committee was proposed as the STEP standard—ISO 10303—which is improving under auspices of the International Standard Organization and is believed to be one of the most successful solutions (Liang and O’Grady 1998; Wang et al. 2009; Zhao and Liu 2008; Xu and Newman 2006; Panetto et al. 2012). Considering the problems encountered where the STEP standard is applied (Gielingh 2008; Ball et al. 2008; Lee et al. 2007; Pratt et al. 2005), different researches have been conducted to propose an efficient approach to overcome these limitations (Houshmand and Valilai 2012; Valilai and Houshmand 2010; Ma et al. 2009; Nylund and Andersson 2010).

Of other related activities of TC184/SC4, the PLib Standard, officially named ISO 13584 Standard series, has defined a model and an exchange format for digital libraries of technical components. PLib is fully interoperable with STEP and in its libraries, component representations may be defined as STEP data and vice versa; in STEP product data, PLib components may be represented by simple references (Maropoulos and Ceglarek 2010; Pierra et al. 1998). MANDATE (ISO 15531) is of other International Standard proposed for the computer-interpretable representation and exchange of industrial manufacturing management data. This standard is suitable not only for neutral file exchange, but also acts as a basis for implementing and sharing manufacturing management databases and archiving (Steele et al. 2001; Cutting-Decelle et al. 2007; Tolio et al. 2010; Cerovsek 2011). The purpose is to facilitate the integration between the numerous industrial applications by means of common standardized software tools. PSL standard (ISO 18629) aims to establish a neutral, high-level language for specifying processes and the interaction related to multiple process-related applications considering the manufacturing life cycle (Dzeng and Tommelein 2004; Chituc et al. 2008; Cutting-Deeelle et al. 2006).

2.2 Overview of Current Researches for Global Manufacturing Data Integration

Different researches have been conducted to propose solutions for global manufacturing data integration. To discuss the dominant proposed approaches, this chapter studies the scope which these approaches cover in the world of manufacturing ontology. Moreover, the structures of the proposed manufacturing ontology model are also discussed based on the approaches in aforementioned section.

Oztemel and Tekez (2009) by a reference model for intelligent manufacturing system, Chen et al. (2009) by a product lifecycle ontology model, and Steele et al. (2001) by an object-oriented resource model tried to propose global manufacturing data integration solutions to cover a wide range of manufacturing operations. These operations are mainly focused on computer-aided design, computer-aided manufacturing and production planning, and quality control processes. The researchers have developed their own manufacturing ontology model.

Wiesner et al. (2011) using a formal ontology named OntoCAPE for the domain of Computer-Aided Process Engineering with its application for the integration of design data in chemical engineering, Madni et al. (2001) using a four-categorized data model for computer-aided design processes, Molina et al. (1995) using a three-categorized data model for the domain of production planning and resource control and quality control, Zauner et al. (1993) using a relational database within integrated manufacturing model for the domain of quality control and management, and Ye et al. (2008) using a supply chain management ontology model for supply chain management processes provided solutions each on a specific manufacturing domain.

Zha and Du (2002), Johansson et al. (2004), Vichare et al. (2009), Valente et al. (2010), Valilai and Houshmand (2010), Zhang et al. (2011a, b), Kretz et al. (2011), Ridwan and Xu (2012), Valilai and Houshmand (2013a, b) provided solutions for manufacturing data integration based on ISO 10303 standard. The researchers manufacturing domain were mainly concentrated on computer-aided design, computer-aided manufacturing and CNC machining operations, and in some cases production planning and resource control operations. Considering the ISO 10303 shortcomings and imitations, some solutions were proposed in researches to facilitate the manufacturing data integration (Valilai and Houshmand 2010, 2013a, b).

Naciri et al. (2011) using a generic product modeling languages called (GPM), Tursi et al. (2009) using a product definition ontology model, and Uschold et al. (1998) using a five-categorized data model provided solutions for whole manufacturing lifecycle data integration. These researchers provided ontology models which were designed to be capable of covering all information requirements in different manufacturing operations.

2.2.1 Discussion of Researches for Global Manufacturing Data Integration

Considering the current approaches for global manufacturing data integration as shown in Table 1, the approaches can be discussed in three main groups. The first group of the approaches has developed their own data model structures. These data model structures enable the integration of manufacturing data through specific manufacturing processes but these approaches cannot insure the necessary improvements and refinements due to their own developed methodologies. This group raises a risk of reliability for future manufacturing domain improvements and extensions. The second group of the approaches has used international standard. These approaches can insure continuous and ongoing processes for data model improvements but the solutions do not propose any approaches to overcome the aforementioned criticisms discussed for international standards. Furthermore, the researchers have concentrated on a limited scope of manufacturing operations in the area of design and manufacturing which is indeed a small scope of global manufacturing operations.

Table 1 Current researches of global manufacturing data integration solutions
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The third group of the approaches has proposed whole manufacturing lifecycle data integration solutions. However, like the first group, these approaches cannot insure the necessary improvements and refinements due to their own developed manufacturing otology model structures. So this group also raises a risk of reliability for future improvements and manufacturing domain extensions due to the improvements and developments in manufacturing discourses.

Considering the aforementioned studied researches, two major characteristics can be proposed for a global manufacturing data integration solution. A global manufacturing data integration solution should insure the capability to support whole manufacturing discourses data integration and also should support ongoing improvement procedures due to the improvements and developments in manufacturing discourses. Proceeding, the chapter discusses an efficient framework named axiomatic design theory. Using this theory, the chapter proposes a manufacturing ontology model to efficiently fulfill the major characteristics of the global manufacturing data integration.

2.3 Axiomatic Design Theory

Considering the complexity of the characteristics of a global data integration solution in the aforementioned section, the chapter focuses on an efficient approach to propose an idea for the design of a road map. This chapter considers the axiomatic design axioms as an efficient approach to propose a manufacturing ontology model. The axiomatic design framework provides the fundamental axioms for analysis and decision making. It also proposes a systematic approach to the design process, which has usually employed empirical methods (Suh 1990; Albano and Suh 1994; Bae et al. 2002; Shirwaiker and Okudan 2011). The axiomatic design framework consists of two axioms which are very effective in both the conceptual design stage and the detailed design stage (Brown 2005; Cebi and Kahraman 2010; Cebi et al. 2010). The axiomatic design theory insures an efficient approach for fulfilling “what we need” by “How we are going to satisfy the requirements”.

In axiomatic design framework, the first axiom is called the Independence Axiom and the second axiom is called the Information Axiom. The first axiom, Independence Axiom, proposes that the independence of functional requirements (FRs) should always be maintained, where FRs are defined as the minimum set of independent requirements that characterizes the design goals through functional domain. The second axiom, Information Axiom, proposes that the design having the smallest information content is the best design among those designs that satisfy the Independence Axiom (Suh 2001; Chen and Feng 2003; Togay et al. 2008; Ferrer et al. 2010; Cebi and Kahraman 2010). A formal representation of the Independence Axiom is possible (Gazdik 1996; Goncalves-Coelho and Mourao 2007; Heoa and Lee 2007; Peck et al. 2010), if the set of FRs is regarded as an m-component vector and the set of DPs—which represent the design parameters to satisfy the FRs—as an n-component vector in physical domain. Then the following relationship holds:

$$FR = [A]DP$$
(1)

Besides the FRs in functional domain and DPs in physical domain, the axiomatic design uses parameter values called PVs in the process domain as shown in Fig. 1. The Tis domain is used as a check to see if a reasonable manufacturing process and process variables exist which are beneficial in concurrent or simultaneous engineering (Brown 2005). Where analyzing the functional domain and physical domain, the “product design” is accomplished. Finally, moving from the physical to the process domain, “process design” is accomplished (Goncalves-Coelho and Mourao 2007).

Fig. 1
figure 1

The axiomatic design domains known (Brown 2005)

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In Eq. 1, [A] is a mn-design matrix. The structure of this matrix makes it possible to assess the quality of the design. The following alternatives are possible:

  • Decoupled design:

    $$\forall (m,n) \in {\rm N},\,m \ne n:A_{mn} = 0$$
    (2)

This design is called the best possible design. In this state, every functional requirement is contingent on one design parameter only. So, there is a one-to-one mapping between every member in the sets of DP and FR, through A. The parameters affecting the object designed are not coupled to one another through the design. Often, only simple mechanisms and structures can be shown to meet the criteria of decoupled design (Heoa and Lee 2007; Thielman et al. 2005).

  • Quasi-coupled design:

    $$(\forall (m,n) \in {\rm N},\,m > n:A_{mn} = 0)\,\text{or}\,(\forall (m,n) \in {\rm N},\,m < n:A_{mn} = 0)$$
    (3)

In this design, the elements on the one side of the diagonal of [A] are equal to nil. Unfortunately, there is a one-to-many mapping between the sets of DP and FR, through A. This design is sequence dependent and can behave like a decoupled design if things are done in the correct order (Gazdik 1996; Kulak et al. 2005; Yi and Park 2005).

  • Coupled design:

    $$\exists (m_{1} ,n_{1} ),(m_{2} ,n_{2} ) \in {\rm N},\,(m_{1} > n_{1} )\,\text{and}\,(m_{2} < n_{2} ):A_{{m_{1} n_{1} }} \ne 0\,\text{and}\,A_{{m_{2} n_{2} }} \ne 0$$
    (4)

This design is the least desirable alternative. There is a many-to-many mapping between the sets of DP and FR, through [A]. Any design parameter affects not only the design requirement it is supposed to affect, but also other functional requirements, and is itself affected by them (Hirani and Suh 2005; Janthong et al. 2010). As the second axiom, Information Axiom states, among the designs which satisfy the Independence Axiom, the design that has the smallest information content is the best design (Gazdik 1996; Pappalardo and Naddeo 2005; Ogot 2011). The axiomatic design framework proposes the decomposition of the high-level FRs to develop the design details by zigzag approach (Goncalves-Coelho and Mourao 2007; Stiassnie and Shpitalni 2007). This approach includes moving from FRs domain to DPs domain to illustrate the DPs which fulfill the FRs and then going back to FRs domain to determine the lower-level FRs when needed. This process is continued until the highest-level FRs are satisfied completely.

3 An Idea for Global Cloud Manufacturing Ontology Model

3.1 Defining the First-Level FR and DP

To propose a global manufacturing data integration solution, the chapter uses the axiomatic design framework approach. The overall decomposition approach is shown in Fig. 2. As stated earlier, the high-level FR should be defined. Then using the decomposition approach, we should move to DPs zone to clarify the high-level DP. The chapter then should go back to FR zone to elaborate the high-level FR based on lower-level FRs. Considering the characteristics of global manufacturing data integration solution in the aforementioned section, the authors define the first-level FR as:

Fig. 2
figure 2

The axiomatic design zigzag approach for manufacturing ontology model

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  • FR0 “There is a need to integrate the whole global manufacturing discourses”

The above definition describes the essential need of integrating the different discourses in the global manufacturing paradigm. The solution should be capable of integrating the different manufacturing ontology aspects through whole manufacturing processes. Moving to the DP zone, the authors propose the first-level DP as:

  • DP0 “Develop a global manufacturing ontology model”

The first-level DP proposes the main contribution of the chapter. The authors intend to propose a global manufacturing ontology model. The global manufacturing ontology model enables the whole discourses in the global manufacturing operations. As shown in Fig. 2, the authors continue the decomposition processes by moving back to the FR zone. The first-level FR0 should be decomposed to lower-level FRs and then the authors continue the zigzag movement to reach to the required DPs to fulfill the main FR0.

3.2 Defining the Second-Level FRs and DPs

To decompose the first-level FR0, the authors consider the following three second-level FRs as shown in Fig. 2:

  • FR01 “The manufacturing ontology model should support all information requirements of manufacturing discourses”

  • FR02 “The manufacturing ontology model should support the improvements and developments in manufacturing discourses”

  • FR03 “The manufacturing ontology model should have an efficient management structure”

FR01 fulfills the first characteristic of a global manufacturing ontology model. FR01 insures the capability of a proposed solution to cover all data requirements of manufacturing processes. FR02 and FR03 insure the second characteristics of a global manufacturing ontology model. As stated earlier, there is a need to support ongoing improvement procedures due to the improvements and developments in the manufacturing discourses. FR02 and FR03 insure that the global manufacturing integration will be maintained when new manufacturing discourses are introduced or any improvement occurs.

To fulfill the second-level FRs, the authors move to DPs zone and define the second-level DPs as shown in Fig. 2:

  • DP01 “The manufacturing ontology model supports the manufacturing discourses using a resource, process and product centric approach”

  • DP02 “The manufacturing ontology model uses a development/improvement structure”

  • DP03 “The manufacturing ontology model uses a modular approach to manage the model”

The relation among FRs zone and DPs zone in the second-level is shown in Eq. 5. FR01 is fulfilled by DP01. The authors propose an ontology model using a resource-, process-, and product-centric approach. The idea of using a three-categorized approach based on resource, process, and product is supported in recent researches (Lemaignan et al. 2006; Tolio et al. 2010; Valente et al. 2010). FR02 is fulfilled by DP02. To manage the improvements and developments in manufacturing discourses, the authors propose a development/improvement structure. DP02 insures the capability of the global manufacturing ontology to align itself as any improvement or development should be encountered. Finally FR03 is fulfilled by DP02 and DP03. To support an efficient management structure, the authors propose both the development/improvement structure and also using a modular approach which insures the efficient management of manufacturing ontology model (Houshmand and Valilai 2012, 2013; Valilai and Houshmand 2013a, b).

$$\left[ {\begin{array}{*{20}c} {FR01} \\ {FR02} \\ {FR03} \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} X & 0 & 0 \\ X & X & 0 \\ X & X & X \\ \end{array} } \right]\left[ {\begin{array}{*{20}c} {DP01} \\ {DP02} \\ {DP03} \\ \end{array} } \right]$$
(5)

3.3 Defining the Third-Level FRs and DPs

Getting back to FR zone, the authors propose the third-level FRs decomposing the FR01 based on the second-level DPs as shown in Fig. 2:

  • FR011 “The manufacturing ontology model should support all information requirements of products in manufacturing domain”

  • FR012 “The manufacturing ontology model should support all information requirements of resources in manufacturing domain”

  • FR013 “The manufacturing ontology model should support all information requirements of processes in manufacturing domain”

The third-level FRs for FR01 categorize the first characteristics of a global manufacturing ontology model in three information requirement scopes based on the DP01. FR011 states the need for a consistent product information model, while FR012 and FR013 look for a resource and process information model. Fulfillment of FR011, FR012, and FR013 insures the capability of proposed solution for the first characteristic of global manufacturing ontology model (Tolio et al. 2010). The authors propose the third-level FRs decomposing the FR02 based on the second-level DPs as shown in Fig. 2:

  • FR021 “The manufacturing ontology model should provide a hierarchical structure to enable manufacturing domain analysis to data requirements”

  • FR022 “The manufacturing ontology model should insure an integrated data structures for new data requirements of manufacturing discourses”

As stated in DPs zone, DP02 proposes a development/improvement structure. This structure should insure the capability of global manufacturing ontology model to fulfill the new manufacturing world information requirements. The authors propose the FR021 as a need which enables the manufacturing world analyze for obtaining the needed information requirements. This structure should use a hierarchical structure which analyzes the manufacturing world requirements and leads to the required information structure. The FR022 is proposed to insure that the needed data requirements are included in the global manufacturing ontology model to fulfill the FR021. The authors propose the third-level FRs decomposing the FR03 based on the second-level DPs as shown in Fig. 2:

  • FR031 “ The manufacturing ontology model should support a modular approach to analyze the manufacturing world information requirement”

  • FR032 “The manufacturing ontology model should support a modular approach to fulfill the manufacturing information requirement”

As stated in DPs zone, DP03 proposes a modular approach to enable an efficient structure for global manufacturing ontology model. The authors propose two third-level FRs to decompose the second-level FR03. FR031 states the need for modularity when analyzing the manufacturing world information requirement, while the FR032 focuses on modularity in data structures which are designed to fulfill the information requirements.

Moving to DPs zone, the authors define the third-level DPs to fulfill the third-level FRs as shown in Fig. 2. The relation among the third-level FRs and DPs is shown in Fig. 3. As considered, the relation matrix among the FRs and DPs is a low-triangular matrix which obeys the Quasi-coupled design. To fulfill FR0111, FR012, and FR013, the following DPs are introduced in the third-level zone:

Fig. 3
figure 3

The proposed manufacturing ontology model FRs and DPs relation

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  • DP011 “Use a product integrated model”

  • DP012 “Use a resource integrated model”

  • DP013 “Use a process integrated model”

To fulfill the requirements for an information model of manufacturing ontology, the authors propose the application of integrated models in the area of product, resource, and process through DP011, DP012, and DP013. DP011 enables product data integration. DP012 focuses on supporting manufacturing resources involved in flow controls of a shop floor or in a factory and DP013 focuses on supporting manufacturing process data. As discussed earlier, the idea of categorizing the manufacturing ontology model based on a three-dimensional approach of resource, process, and product is supported in recent researches (Lemaignan et al. 2006; Tolio et al. 2010; Valente et al. 2010).

To fulfill the requirements of FR021 and FR022, the authors propose two third-level DPs as shown in Fig. 3:

  • DP021 “Use a manufacturing activity analysis, defined scope and mapping rules to data object structures”

  • DP022 “Use a unique set of data objects”

To fulfill the third-level FR021, the authors propose DP021. This DP insures a hierarchical structure to enable manufacturing world analysis to data requirements. DP021 proposes the hierarchical structure which starts with manufacturing activity analysis and then leads to formal description of the mapping to the data object structures. Using this hierarchical structure, a manufacturing ontology model can analyze a manufacturing discourse and leads to the data structures for storing and retrieving operations.

The authors also use the DP021 to fulfill the third-level FR022 and moreover propose another third–level DP defined as DP022. This DP provides an integrated and consistent data structure for all manufacturing discourses. DP022 enables FR011, FR012, and FR013 defined at the third-level to have distinct structures for defining the manufacturing discourses activity models but insures their integrity from sets of similar data objects. Using this structure, the improvements and developments in manufacturing discourses can be managed by an integrated data structure, while giving flexibility in analyzing the manufacturing world activity.

To fulfill the requirements of FR031 and FR032 completely, the authors propose two third-level DPs as shown in Fig. 3:

  • DP031 “Use a modular approach for manufacturing scope and mapping rules through defined modules”

  • DP032 “Use a modular approach to map data to data objects through defined modules”

To fulfill the third-level FR031, the authors apply the DP011 to DP013 and DP021 to DP022. Moreover, the DP031 is introduced to accomplish the FR031 fulfillment. This DP states that the efficient management of a global manufacturing ontology model needs a modular approach. DP031 uses a modular structure in analyzing manufacturing world activities and leading to the formal description of the mapping to consistent data objects.

Besides DP031 to fulfill the third-level FR032, the authors propose the DP032. DP032 focuses on using a modular approach in selecting the applicable constructs from the data objects. This modularity should encompass defined data object modules which are applicable for different analyzed manufacturing world activities.

Considering the overall structure of the approach proposed by the chapter as shown in Fig. 3, the authors proposed DP011, DP012, and DP013 three-level DPs to fulfill the three-level FRs including FR011, FR012, and FR013. This forms the first characteristic of a global manufacturing ontology model. Using the other proposed three-level DPs, the chapter fulfills the second characteristic of a global manufacturing ontology model. The overall relation among the FRs and DPs are satisfying the first axiom of the axiomatic design theory in a Quasi-coupled design form.

4 Implementation of the Structure in the Process Domain

In this section the chapter demonstrates the capabilities of the proposed global manufacturing ontology model by presenting the proper parameter values for DPs in process domain. As stated earlier, PVs in axiomatic design process domain express the required tools to implement the proposed DPs. Considering that the proposed DPs are fulfilling the first axiom of the axiomatic design theory, the chapter uses ISO-based structures as required PVs to fulfill the proposed DPs. This section will show that the proposed PVs relation with DPs insure the first axiom of the axiomatic design theory. This section can be one of the feasible implementation methods of global manufacturing ontology model.

Referring to DPs zone, the authors define the third-level PVs to fulfill the third-level DPs as shown in Fig. 4. The relation among the third-level DPs and PVs is shown in Fig. 5. As considered, the relation matrix among the DPs and PVs is a low-triangular matrix which obeys the Quasi-coupled design. To fulfill the DP0111, DP012, and DP013, the following PVs are introduced in the third-level zone:

Fig. 4
figure 4

The proposed PVs for global manufacturing ontology model

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Fig. 5
figure 5

The relation among proposed PVs and DPs for global manufacturing ontology model

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  • PV011 “Use ISO 10303”

  • PV012 “Use ISO 15531”

  • PV013 “Use ISO 18629”

To fulfill the requirements of the product-, resource-, and process-integrated model, the authors propose the application of three international standards in the area of product, resource, and process through PV011, PV012, and PV013. PV011 focuses on ISO 10303 (STEP) standard to enable product data integration. PV012 focuses on ISO 15531 (MANDATE) supporting manufacturing resources and manufacturing flow management like flow controls in a shop floor or in a factory and PV013 focuses on ISO 18629 (PSL) standards to support manufacturing process data.

To fulfill the requirements of DP021 and DP022, the authors propose two third-level PVs as shown in Fig. 4.:

  • PV021 “Use AAM, ARM, AIM and IRs hierarchical structure”

  • PV022 “Use a unique set of IRs”

To fulfill the third-level DP021, the authors propose PV021. This PV insures a hierarchical structure to enable manufacturing world analysis to data requirements. PV021 proposes the hierarchical structure of ISO-based data integration which starts with manufacturing activity analysis known as Application Activity Model (AAM) and then leads to defined scopes and functional requirements known as Application Reference Model (ARM) and finally using Application Interpreted Models (AIM) as the formal description of the mapping between the ARMs and the Integrated Resource (IR) models. Using this hierarchical structure, a manufacturing ontology model can analyze a manufacturing discourse and leads to the data structures for storing and retrieving operations.

The authors also use the PV021 to fulfill the third-level DP022 and moreover propose another third-level PV defined as PV022. This PV provides an integrated and consistent data structure for all manufacturing discourses from the ISO-based integrated data structures. PV022 enables the DP011, DP012, and DP013 defined at the third level to have distinct structures for defining the manufacturing discourses activity model through AAM and ARM structures but insures their integrity from sets of similar IRs. Using this structure, a structural ontology development framework is insured and the improvements and developments in manufacturing discourses can be managed by integrated data structures, while giving flexibility in analyzing the manufacturing world activity through AAM and ARM structures.

To fulfill the requirements of DP031 and DP032 completely, the authors propose two third-level PVs as shown in Fig. 3:

  • PV031 “Use modular approach of AAM and ARM structure”

  • PV032 “Use a modular approach for MIM structure to interpret manufacturing data to IRs”

To fulfill the third-level DP031, the authors apply PV011 to PV013 and PV021 to PV022. Moreover, the PV031 is introduced to accomplish the DP031 fulfillment. This PV states that the efficient management of a global manufacturing ontology model needs a modular approach. This modular approach has been discussed in recent researches as a solution for shortcomings arisen by using the ISO standards like STEP standard (Houshmand and Valilai 2013; Valilai and Houshmand 2013a, b). PV031 uses a modular structure for analyzing manufacturing world activities and leading to the formal description of the mapping between the ARMs and the Integrated Resource (IR) models.

Besides PV031 to fulfill the third-level DP032, the authors propose the PV032. PV032 focuses on using a modular approach in selecting the applicable constructs from the integrated resources as baseline conceptual elements. This modularity is achieved through the baseline constructs with additional constraints and relationships which are specified by entities containing local rules, refined data types, global rules, and specialized textual definitions.

Considering the overall structure of the proposed PVs by the authors as shown in Fig. 4, three ISO standards, as the three-level PVs, are proposed to fulfill the three-level DPs including DP011, DP012, and DP013. This results in forming the first characteristics of a global manufacturing ontology model. Using the other proposed three-level PVs, the authors fulfill the second characteristics of a global manufacturing ontology model. The overall relation among the DPs and PVs are satisfying the first axiom of the axiomatic design theory in a Quasi-coupled design form.

5 Conclusion and Discussions

Today’s global enterprises have faced with a growing increase in the competitiveness that forces them to adopt and develop new strategies and methods with relevant challenges in integrating product, process, and enterprise dimensions and lifecycles. Considering the Cloud manufacturing paradigm as a solution for today’s global manufacturing enterprises, this chapter discussed the first requirement of the Cloud manufacturing paradigm known as global manufacturing integration. The integration of enterprise-level business systems with manufacturing systems is found to be one of the inevitable drivers for productivity and making businesses more responsive to supply chain demands. However, integrating heterogeneous and autonomous data sources through enterprises’ structures which are numerous in number and also in approaches for data management is a significant challenge. In this chapter, the concepts of global manufacturing data integration have been studied comprehensively. Discussing the dominant researches for solutions offered as enabler approaches in global manufacturing data integration, the authors proposed the axiomatic design theory capability to propose an efficient solution for global manufacturing ontology model. Two main characteristics of the solution are described as:

  1. 1.

    Providing a consistent manufacturing lifecycle data integration model.

  2. 2.

    The required structure and procedures to fulfill improvements and developments in manufacturing discourses.

Using the axiomatic design theory, the authors started from the first-level functional domain “There is a need to integrate the whole global manufacturing discourses”. Zigzagging among design parameter domain and functional domain, the authors decomposed the first-level functional requirement and proposed a solution in design domain while fulfilling the axiomatic design first axiom. A section is provided to obtain a candidate implementation structure for the proposed global manufacturing ontology model in the process domain. This implementation benefits from international standard organization standards like ISO 10303, ISO 15531, and ISO 18629 to provide a consistent manufacturing lifecycle integrated data structure. Moreover, to form an efficient approach to fulfill the improvements and developments in manufacturing discourses, the authors benefited from the international standard organization efforts for providing modular integration standard frameworks.

Considering the cloud manufacturing paradigm, the authors recommended the proposal of frameworks which use the proposed Cloud manufacturing ontology model for manufacturing data integration services in further researches. The solutions should define an efficient structure which enables the agents engaged in different manufacturing discourses for manufacturing data integration in the form of services. Also as the proposed ontology model insures a consistent information model for improvement and developments in manufacturing discourses, the proposal of collaborative and interoperable platforms especially based on Cloud manufacturing paradigm is suggested. These researches perform the next steps toward achieving the Cloud manufacturing paradigm in global manufacturing.