An ontology supported risk assessment approach for the intelligent configuration of supply networks

Risk is an ever present element in the daily operation of many organisations throughout the world. Success, profitability, competitive advantage and survival all depend upon an organisation’s ability to focus upon risk, then assess and derive the best course of action relative to their specific domain of operation and operating requirements. Many organisations are seeking ever better ways to conduct and grow their business, the servitisation of products, i.e. the combination of products and services into one offering (Vandermerwe and Rada 1988), is something that is becoming ever more popular and hence, the provision of Product-Service Systems (PSS). Organisations need to change traditional approaches to undertaking business, they must become more agile, more sensitive to the pace of change and aware of other factors that influence their operations, not just locally, but globally too. This applies to aspects such as the technological rate of change, economic issues and environmental sustainability and impact (Doualle et al. 2015; Hussain et al. 2016; Medini and Boucher 2016; Schmidt et al. 2015; Sousa-Zomer and Miguel 2016). Evermore legislation is being enforced both at national and global levels to promote and influence attitudes towards the environment and sustainability. When designing, developing and producing PSS for customers, many organisations interact and cooperate with numerous other organisations. Thus, to all intents and purposes they can be classed in some shape or form as collaborative networked organisations (CNO) as put forward by Camarinha-Matos (2009) and Camarinha-Matos et al. (2009). One key enabling factor that underlies CNO is that of interoperability, i.e., “the ability of two or more systems or components to exchange information and to use the information that has been exchanged” (IEEE Std. 610, 1990). To date, the issue and facilitation of interoperability for organisations still consumes large amounts of time and money (Brunnermeier and Martin 2002; European Commission 2008; Sampath and Hegde 2013; Huber 2014). Hence, to be able to deploy such an approach more quickly than competitors can often be a significant advantage, therefore, the development of approaches and technologies to achieve this can be of great importance. One domain where these can be applicable to and potentially derive benefit for organisations by enabling them to lower costs, adapt better to change and adopt technology faster is that of Global Production Networks (GPN) (Henderson et al. 2002; Coe et al. 2008; Coe and Hess 2012, 2013; Coe 2012). The development and supply of products and services on a global scale can be fraught with difficulties due to the sheer geographical scale and diversity of operations, but, also in part to the interoperation of the many and varied organisations, their domains and the information systems they employ to facilitate their businesses. Additionally, many forces can influence GPN, many of which cannot be influenced or controlled by actors within those GPN (Damgaard and Spencer 2005), these can be political forces (e.g. relationships, trade agreements, war), environmental forces (e.g. weather and its potential adverse effects), legal forces (e.g. standards and safety legislation), social forces (e.g. social unrest and workforce strikes) and economic forces (e.g. exchange rates and financial uncertainty) (Levy 2008; Coe et al. 2008; Reuter et al. 2016). Hence, an ability to understand the risks involved for a given GPN, where they apply and how to best mitigate these by the design, redesign and reconfiguration of an organisation’s GPN could potentially be of great benefit.

The FLEXINET project aims to provide services that support the design and provision of flexible interoperable networks of production systems that can be rapidly and accurately re-configured based on the implementation of new technologies. It applies advanced solution techniques to the provision of a set of Intelligent Production Network Configuration Services that can support the design of high quality manufacturing networks, understanding the costs and risks involved in network re-configuration, and then mitigating the impact of system incompatibilities as networks change over time. FLEXINET takes the fundamental view that complex manufacturing systems which involve multiple partners with multiple technological capabilities require a semantically rigorous formal foundation upon which to base the flexible re-configuration of manufacturing information systems networks that can meet the needs of Small to Medium Enterprises (SME) as well as Original Equipment Manufacturers (OEM), i.e., a company which manufactures a complex product from components bought from another manufacturer (Oxford English Dictionary 2016). The three key FLEXINET industrial end user partners are especially interested in understanding the impact of external demands, such as environmental regulations, on their business and most especially when related to the introduction of new product-service opportunities into their production networks. These three industrial partners represent the domains of food and drink, white goods and industrial pumps. For them, the availability, accessibility and usability of reliable data as well as the ability to use it for strategic and tactical decisions is of particular importance.

The premise of this paper is to show the development of a reference ontology that can support ‘what-if’ GPN scenarios for a given set of constraints focusing upon the aspect of risk. The reference ontology has been built using both top-down and bottom-up approaches to enable the sharing of multi-contextual and multi-domain information and support the design and manufacture of PSS. The development of the reference ontology levels has utilised existing ontologies and international standards to aid their creation in a top down manner, whilst, a number of end user domain ontologies specially created for the project have helped inform and influence the structure and content of all the levels within the reference ontology. These have been created utilising: sets of end user requirements from the three industrial domains, a number of use cases created specifically for the project that represent those domains and three case studies, one for each of the end users. The reference ontology is detailed and presented within the paper, to which, its common-logic description is set out and explained. The application of this reference ontology is then illustrated with screenshots of the developed applications together with results obtained from testing and feedback. The main aim of the reference ontology is to support interoperability between information systems within multi-domain contexts for GPN configuration.

This paper is laid out in the following format. The literature review is put forward in “Literature review” section. The methodological point of view for the development of the research is set out in “Method” section. “The FLEXINET ontology and its development to support risk” section details the ontological approach that has been developed. “Results” section presents the results of the research and “Discussion” section contains a discussion about the pertinent issues involved. The paper is brought to a close with conclusions and further work contained in “Conclusions” section.

The current corpus of scientific literature when considering the subject of interoperability, presents wide and varied attention to the research, development and application of ontologies to develop new approaches and technologies to problem domains. A large amount of this research reports upon ontologies that are built to support specific contexts with exact aims, each of these having been built wholly within their respective contexts from the ground up, thereby meaning they are domain specific and cannot profit from exposure to different viewpoints by being able to represent more than just one. The issue with this is that when considering interoperability and communication between different systems, contexts and domains, the seamless transfer of data, information and knowledge is unachievable, due to the fact that the very premise of understanding different viewpoints and domains and how they interrelate is the cornerstone to achieving interoperability (Borgo and Leitão 2004). This can be addressed by the development and application of a reference ontology (core ontology) so as to construct a common basis for the sharing of information and knowledge between multiple domains to therefore enable interoperability. Figure 1 presents a view upon the classification of the different types of ontologies. Foundation ontologies are high level ontologies (sometimes called upper ontologies or top-level ontologies) that comprise generic (domain independent) concepts, relationships and axioms that are able to represent and relate to any context dependent concept. As such, they are context independent and have very few constraining axioms. Examples of foundation ontologies are: the Standard Upper Ontology (SUO) (Neches et al. 1991), the Suggested Upper Merged Ontology (SUMO) (Niles and Pease 2001), Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) (Gangemi et al. 2002) and Cyc-Ontology (Matuszek et al. 2006). Core ontologies are ontologies that have been specialised to some extent and are therefore broadly context dependent, but represent a number of different domains (Nardi et al. 2015). They are based upon and utilise concepts and relationships that exist within foundation ontologies. These ontologies still contain a minimal set of generalised concepts. These can be used as reference ontologies to be employed as building blocks to promote interoperability for much more domain specific and contextually dependant ontologies, yet enable communication between them due to the shared ‘common’ core concepts used to build them. Examples of core ontologies are: the Core Product Model (CPM) from the National Institution of Standards and Technology (NIST) (Foufou et al. 2005), the Manufacturing Core Ontology (MCO) (Chungoora and Young 2011a; Chungoora et al. 2012, 2013), the Manufacturing Core Concepts Ontology (MCCO) (Usman et al. 2011), the Manufacturing Information Systems (MIS) ontology (Hastilow 2013) and the UFO-S ontology (Nardi et al. 2015). Domain ontologies (Guarino 1998a, b) are ontologies that are wholly context dependant and are thus very specialised for their intended representation and purpose, these apply to specific activities.

In line with development of a methodological approach for the research the context and phenomenon were studied to see if they would best fit a quantitative or qualitative viewpoint (Galliers 1991). Quantitative viewpoints focus upon the formulation of facts by way of numerical analysis of data, whereas, qualitative viewpoints focus upon understanding people and their actions within social and organisational settings (Easterby-Smith et al. 1991; Sarantakos 1993), additionally it is suited to the use of multiple methods to establish different views of phenomena. Thus, when considering the FLEXINET research project context and its scope, the best fit was a qualitative approach due to the fact the information and knowledge would be studied and that there would not be a lot of hard quantitative data to be gathered. To accompany this, a choice of philosophical perspective was necessary as this can affect assumptions about the collection of data and information. There are three paradigms or epistemologies that are generally applied to a research approach, those of positivist, interpretive and critical (Orlikowski and Baroudi 1991). Positivist research decrees that only what can be observed and measured is valid (Comte 1853), interpretive research tries to understand phenomena through the meanings that people assign to them (Boland 1985; Walsham 1993) and critical research seeks to determine and understand the status quo and thereby question theoretical and conceptual knowledge. It was deemed that the most appropriate would an interpretative perspective, as it was necessary to study and understand the three distinct industrial end users’ points of view. When analysing the various qualitative research methods that fitted a deductive approach, the methods of action research, subjective or argumentative research, futures research and role playing were rejected due to the constraints of the research project and the availability of the industrial end users. Hence, the research method of grounded theory (Glaser and Strauss 1967) was chosen. This enabled a deductive and iterative approach to be adopted to ground the theories in the data and information collected. Furthermore, it was thought that a single method alone might not account for all of the aspects that were being studied, so a mixed methods approach (Creswell 2008) was adopted. There are a number of well-founded methodologies that can be used for the development of an ontology, some of the more commonly accepted ones are those of TOVE (Uschold and Gruninger 1996), the Enterprise Model Approach (Uschold et al. 1998) and METHONTLOGY (Fernandez et al. 1997), together with the methodology of Noy and McGuiness (2001). Each of these has specific approaches for the study of data, information and knowledge to help the development of an ontology for any given domain or field of interest. The Noy and McGuiness (2001) methodology was chosen based upon the positive experience the authors have had in applying it to previous ontological research efforts and the outcomes that had been achieved. Furthermore, it possesses similar elements expounded by the other stated methodologies.

For the purposes of this paper, two main research questions were posited, in line with a deductive, qualitative approach, these were:

A collaborative decision support environment is being developed as part of the FLEXINET project, this enables GPN to be designed, configured and then evaluated from multiple perspectives. These cover a wide range of applications from managing new ideas, through product-service configuration and business modelling and onto global production network configuration and risk analysis. A start point for the risk analysis is to have a potential global production network on which to work. The facilities and their flows that are to comprise such a network are first visualised in a global environment as illustrated in Fig. 11, with each of the blue icons representing a supplier, production facility or customer, with the knowledge of the network held in a knowledge base built on the ontology. The risk application then explores specific risk scenarios based on the GPN scenario with added risk factors of interest.

An exemplar GPN scenario has been created representing the a drinks company. It is comprised of a producer that incorporates both a Cider Fermentation Plant and a Bottling Plant, the intended Customers and the suppliers involved in the GPN, those being: Suppliers of Apples, Suppliers of Yeast, Suppliers of Sugar, Suppliers of Flavourings and Suppliers of Sugar. This combined GPN scenario including risk scenario information is shown in Fig. 12. Here, the arrows, whilst clearly following the flows of materials, in fact represent the sensitivity of one node’s performance to disruption in another node’s performance, represented visually by the numbers at the end of the arrows. Also, the numbers shown in the top left corner of each node represents the level of risk for each node; the number in the bottom left corner of each node represents the inoperability impact of the risk on the node, but propagated through the network. Initially these numbers are set to zero but potentially range from zero up to one.

To help illustrate the knowledge base and show some of the instances that populate it, Fig. 13 shows a screenshot of the instances of the property ‘Scenario’ within IODE and Fig. 14 depicts a number of instances for the property ‘Inoperability’, again within IODE.

Two Risk Scenarios are set out for the GPN as illustrated in Figs. 15 and 16. Note that the boxes in the figure are coloured to add a visual reference to the levels of inoperability impact. Very low inoperability impact is coloured green as in Fig. 11, yellow represents a low inoperability impact, orange a medium level inoperability impact and red represents a high inoperability impact. Scenario one is a ‘Supply Failure’ in Suppliers of Sugar with an risk factor of 0.7 along with the supplier of apples having a low risk of 0.25 and supplier of yeast having a very low risk of 0.15. This scenario results in propagated inoperability impact in the cider fermentation plant of 0.41 as illustrated in Fig. 15. Scenario two is a ‘Supply Failure’ in Suppliers of Apples with a risk factor of 0.5 along with low risk factors of 0.15 and 0.25 for the supplier of yeast and the supplier of sugar respectively. In this case the propagated inoperability impact in the cider fermentation plant is 0.34 as illustrated in Fig. 16. From the knowledge base and ontology point of view what is important is that all these values can be stored and reused as required.

The knowledge environment, supported by the ontology, provides values into applications by the use of queries, an example of which is shown below. This query example states ‘for a given scenario, list the suppliers that exist and their inoperability values’. As shown, an organisation (?organisation) with inoperability (?inop) as an output role (?output_role) is queried for. Moreover, the organisation must be a member of a GPN (?GPNmember) and be a part of the scenario (?scenario). It is required that an organisation must have an output role, inoperability has a value (?inopValue) and that inoperability plays the role of an output for a scenario.

Applying this query to both Risk Scenario one and two, a set of inoperability values were calculated and are depicted in Table 4. This shows the resultant inoperability impact values as presented in the respective Figs. 14 and 15. Note in the table that the ontology has been created to support fuzzy numbers although the example has not used this facility. Therefore the inoperability values are listed as triples, but the same number three times.

Such an approach as illustrated, allows end users to add, edit and remove risks within risk scenarios (utilising the knowledge base) to then be able to assess the resultant calculated levels of inoperability between different scenarios, thereby bringing about the ability to understand and make better informed decisions when designing and formulating potentially beneficial and resilient GPNs.

The research and results presented within this paper have been brought about by a lengthy and in-depth approach to the problem domain performed as part of the FLEXINET project. What can be deduced from this is that the research questions set out in the methodology have been answered successfully. A heavyweight approach utilising first order logic has been developed and successfully represents risk knowledge relating to GPN. Furthermore, these ontology information structures have been populated utilising end user industrial domain knowledge. These have then been queried to provide answers to risk related issues to then potentially help end users to design, configure and reconfigure GPN for PSS. Within this paper a simple risk scenario has been created to illustrate the approach and how it may be applied. By way of this, the ontological structures developed at levels 2 and 4 have been validated using this demonstration. What can be drawn from this is that the ontology is able to represent potentially diverse and relevant risk scenarios to aid the modelling and management of complex GPNs.

The results obtained thus far in the early stages of end user testing have provided good results. The FLEXINET reference ontology approach shown within the paper has multiple parts that relate to and influence a risk ontology. However, they also relate to other areas of PSS related to the strategic and tactical decisions concerning the configuration of production networks. Extensions to this that deal with operational decisions have not been addressed and are still needed as further work.

In approaching the development of a reference ontology to facilitate interoperability for PSS and GPN, few examples have been found in the field of manufacturing industry, nor business and enterprise. Moreover, when considering the application of ontologies to the domain of risk, examples do exist, but, do not address the domain of GPN, or the application of reference ontologies to risk within that domain.

The FLEXINET reference ontology and the risk ontology within it, have been developed utilising end user requirements, information and knowledge from three different and distinct industrial contexts. Furthermore, these developments have been influenced by current existing international standards that are applicable to the approach and related research material within the domain. Therefore, the developmental approach has forged together through an iterative manner, both top down and bottom up views to bring about the ontological approach described in this paper.

The industrial end users that are associated with the FLEXINET research project have expressed a real need to assess risk within a GPN and between different configurations of a given network. Thus, the approach developed within this paper puts forward a viable and new approach to the assessment of risk for multiple risk scenarios based upon a range of possible GPN scenarios.

The FLEXINET reference ontology that has been developed is focused upon supporting the decision making processes often found at the early stages of product development. Its main aim is to support interoperability between systems and domains, by way of the intelligent configuration of a network of products or PSS for GPN.

The research defines a reference ontology that can support risk assessment for GPN in an interoperable manner. This is due to the fact that risk scenarios are dependent scenarios that rely upon GPN scenarios, thereby providing an ontological link to other aspects of GPN design and configuration as depicted within Fig. 3, together with the concepts of facilities and location, which are necessary to be able to define risk factors for a GPN. These enable an interoperable risk application, in that it can interact with the other applications needed to configure and design GPN utilizing the ontology.

Level 2 of the reference ontology is detailed for risk, defining the more generic concepts that represent risk, production network, scenario, location, indicators and metrics.

Level 4 of the reference ontology is described, this defines the more specialised concepts and relationships for scenario and risk, showing how they relate to GPN scenario, facility, location, indicators and metrics.

Together, these contributions can provide a basis for organisations to build and develop interoperable information communication systems so as to enhance risk assessment approaches when considering ‘what if’ questions for different combinations of risk scenarios.

The development of the FLEXINET collaborative application suite, the supporting software services and reference ontology is still on-going. The breadth and depth of the reference ontology will continue to be enhanced and extended to meet the demands of new application functionalities. This will include extending the boundaries for it to be representative of the full range of economic and risk assessment. Hence, while this paper demonstrates the potential for such an approach there is an on-going requirement to expand the reference ontology.