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Requirements for Supporting Enterprise Interoperability in Dynamic Environments Georg Weichhart

Abstract Interoperability in enterprise systems is currently discussed from a system theoretic point of view. In the conceptual work described here, two special instances of systems theory are used as a basis allowing to detail requirements for interoperability in dynamic environments. Chaos Theory and Complex Adaptive Systems Theory focus on the description of properties of dynamic systems where the global system’s behavior cannot be determined by summing up behaviors of system parts. First a connection between enterprise systems and the theories are established. The theories are then used as a lens for analyzing and discussing initial requirements for a platform that supports interoperability in a dynamic context.

Keywords E-learning Chaos theory Enterprise interoperability

Complex adaptive systems theory

1 Introduction Recently important steps towards the formulation of a scientific basis for interoperability have been made. General System Theory (GST) [1] has been used to discuss interoperability in enterprise systems [2–4]. One important aspect of interoperable systems, in contrast to both non-interoperable and fully integrated systems, is that interoperable systems are more resilient [5]. Non-interoperable systems do not provide the desired functionality. In fully integrated systems, a failure in one part lets the overall system fail, because of links to other dependent system parts [6].

G. Weichhart (&) Department of Communications Engineering—Business Informatics, Johannes Kepler University, Altenberger Straße 69 Science Park 3, 4040 Linz, Austria e-mail: [email protected]; [email protected] Metasonic AG, Münchner Str. 29–Hettenshausen, 85276 Pfaffenhofen, Germany

K. Mertins et al. (eds.), Enterprise Interoperability VI, Proceedings of the I-ESA Conferences 7, DOI: 10.1007/978-3-319-04948-9_40, Springer International Publishing Switzerland 2014

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In order to design and engineer a supporting environment for enterprise systems to establish and maintain sustainable interoperability in a dynamic environment, we aim at identifying requirements for a digital platform that facilitates interoperability on different levels. Two special systems theories which place special attention to dynamics are used to support the definition of requirements: • Chaos Theory • Complex Adaptive Systems Theory. The paper is structured as follows. First a brief introduction to systems theory with special attention to interoperability is given. This leads to the combined discussion of the two used system theories. Properties of the theories are discussed and linked to enterprise systems. Existing Interoperability approaches are used to discuss problems identified with respect to dynamic environments.

2 Systems Theories General System Theory (GST) [1] intends to support the identification of principles that are valid for many systems. GST facilitates the communication between different scientific disciplines. This is done by abstracting concepts of a discipline to form systems. This abstraction allows scientists of other disciplines to use elements and insights described by these systems. GST builds upon the notion that a system is an organization of connected parts, where each part and the overall system exhibits some behavior. A system is placed in an environment and may have a function and produce some outcome according to a system’s objectives [7]. Parts of a system are themselves systems. A system has a state and may be evolving over time, therefore it has a history. The following concept map shows system concepts and links them (Fig. 1). Naudet et al. [3] have identified a close relationship between GST and the research domain of interoperability. Due to the dynamics of today’s business environment, recognizing organizations and organizational networks as ‘‘static’’ systems is not sufficient [8]. In the following two special sub-theories of GST are used to characterize enterprise systems. Chaos theory and complex adaptive systems theory are theories which have their roots in GST (see above) and put emphasis on dynamic aspects. As there is no general agreement on the properties such systems have [9], selected properties described within these theories are briefly discussed below. These properties are used for establishing a conceptual link between enterprise systems and the theories. In the following each property is briefly discussed. This is followed by an example that highlights how every aspect is recognized in enterprise systems.

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Fig. 1 Concept map describing systems

2.1 Non-linear Interdependence In a complex system, parts are connected (structure) and show some (individual) behavior. However, due to some non-linearity either in the link or in the part’s behavior, the global behavior of the system may not be predicted by summing up the individual part’s behaviors. For supply chains, the example in the following exemplifies links between system parts visible as physical flow of goods, information flows, and links between the supply chains participants’ behaviors.

2.2 Path Dependence The ‘‘butterfly effect’’ (coined by Lorenz [9]) exemplifies that systems are sensitive to initial conditions. Depending on a small change in some part of the overall system, the system’s state evolves significantly different in a distant part. In that particular example, a butterfly’s wing causes changes in the airflow which amplifies over time and causes a thunderstorm somewhere else. From a supply chain management point of view, the so called ‘‘Beer Game’’ is used to exemplify how small changes in the demand, lead to a globally observable phenomenon, which is called ‘‘bullwhip effect’’ [10]. The following figure shows small changes of customer orders, which get more and more amplified down the supply chain. The more a company is located at the beginning of the chain, the more amplified order changes get. The (well designed) setting for this example includes communication and transport delays which lead to an amplification of beer bottles ordered. Supply Chain participants place higher orders than required, overcompensating delivery delays (Fig. 2).

2.3 Strange Attractor and Bifurcation In a dynamic system, a (strange) attractor is a part which attracts other independent parts. The force of attraction is dependent on the distance between these parts.

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Fig. 2 Bullwhip effect in supply chains

Fig. 3 Bifurcation in a logistic map [11, p. 34]

Parts getting close to the attractor remain close. The paths are influenced by attractors and are not fixed or predetermined. A bifurcation point marks a moment in time where a system’s part comes under the influence of another attractor changing its state. The concept of bifurcation is shown in the following figure. It illustrates a system’s transition by varying a single parameter (x-axis). On the left the system is in its beginning single steady state. The branches illustrate a period in which the system begins to fluctuate around two, and in the following more states. System parts are influenced by more and more attractors, and the system is getting more complex over time [12] (Fig. 3).

2.4 Active Agents In a complex adaptive system, ‘‘great many independent agents are interacting with each other in a great many ways’’ [13, p. 11]. The agents follow their individual rules how to interact with other agents. This interaction between agents is a local event. Agents may have sensors and actors to interact with their local environment.


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Fig. 4 Properties of the theories in use (cf. [17])

Yet it is important to understand that there is no global control flow, but there are only local interactions. In the view taken, agents in supply networks are enterprises taking part in these organizational networks [8]. Within organizations these agents are human agents [14]. All agents (independent on the observed system scale) act independent and interact with other agents.

2.5 Self-Organization and Emergent Behavior The lack of global control enables agents to act self-controlled and self-organize as group. As mentioned above, interactions are local to agents taking part in that interaction. Interactions with or without taking the higher system level state into account, facilitates emergent behavior on a higher system level (see above bifurcation). In a social system, an agent’s behavior influences the environment and vice versa [15]. Over time, agents learn from each other, for example through copying successful behavior. However, individual and group learning paths and learning results are not predictable. The performance of a group does not only depend on individuals, but also on the interaction between individuals. Learning and improvement are results of self-organization of individual agents and of groups of agents. Depending on the level observed an agent may be a learning organization taking part in several supply networks [8], or a human agent working in an organization [14]. The following concept map (based on the approach by Novak and Cañas [16]) brings together the above discussed concepts and their relationships (Fig. 4).

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3 Existing Interoperability Support In order to detail and structure the discussion of a dynamic system, we use the above described properties as lenses. This way we conceptualize requirements for supporting enterprise systems to reach and maintain interoperability. However, the theories will not provide direct input to a solution (cf. [17]). This means, chaos theory and complex adaptive systems theory are used to describe dynamic properties of the environment in which the enterprise system is placed. The enterprise system has to cope with these dynamic properties. However, it has to be remarked, that the enterprise system might be for example a department within an enterprise, or an enterprise within a supply network.

3.1 Systemic Interoperability Systemic approaches to enterprise interoperability have been already mentioned above [2–4]. Interoperability problems are discussed within frameworks along the following dimensions and categories (see for example [4, 18]): Interoperability Interoperability Interoperability Interoperability

barriers concerns approaches solution timing

Conceptual | Organizational | Technological Business | Process | Service | Data Federated | Unified | Integrated A-Priori solutions | A-Posteriori solutions

Barriers stop systems to be interoperable. Concerns describe the level of the enterprise system on which interoperability problems occur. Approaches provide strategies for overcoming the barriers. Solution timings refer to the circumstances when interoperability problems may be tackled. A-priori solutions are approaches that allow to anticipate problems and to overcome barriers before systems are build. A-posteriori solutions are approaches that allow to identify and correct problems after they occur in the running system [18].

3.2 Enterprise Architectures and Enterprise Modeling for Integration In the following we discuss existing interoperability support approaches like Enterprise Modeling (EM)/Enterprise Architecture (EA) and their (potential) role in a platform for supporting interoperability. However, we take a special look on arising challenges when interoperability of sub-systems is the objective for a system in a dynamic environment. This allows the identification of shortcomings of existing approaches and to propose modifications/enhancements of existing approaches to overcome challenges.


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To improve organizational interoperability in a complex adaptive system is a challenge. Existing approaches and languages for enterprise modeling (e.g. UEML [19]), and business process modeling (ARIS [20]) support the creation of detailed descriptions. The aim of the models is to support enterprise integration (EI). These models may be used as a point of reference to unify the views of agents involved in the modeled enterprise system. But not only enterprise modeling results are of importance. According to Vernadat [21], EI also has the objectives to enable and facilitate communication and coordination in order to allow (independent) actors to collaboratively fulfill the enterprise’s goals more efficient and effective. Models facilitate communication through the design of abstractions. The process of modeling facilitates knowledge exchange (i.e. local interactions between agents). For example, Oppl [22] has designed an interactive environment which allows multiple modelers to articulate their views and supports the agents to ‘‘negotiate’’ a unified model. Enterprise Architectures (EA) aim at providing a uniform representation and supporting the integration of different domains across the enterprise [23]. EA takes an IT centric perspective and supports a unified way to model different parts of an organization including: organizational structures and processes on business level, information systems and applications on IT level, and software and hardware infrastructure on technology layer [23].

3.3 Challenges of Dynamic Environments While the above approaches support a certain level of integration within enterprise systems, enterprise modeling and enterprise architecture approaches show some shortcomings when being analyzed using the theories discussed above as lenses. Integrated systems exhibit less resilience than interoperable systems (see also above) [5]. This is of importance if a dynamic environment is assumed. A unified single model is used as point of reference. With larger enterprise systems the modeling process, requires so much effort that when the models are finished, reality has moved on and the models are obsolete. This hampers pursuing enterprise integration goals [24].

3.3.1 Non-linear Interdependence The interdependence between parts may not be determined a-priori. An approach is needed which enables modeling, monitoring and simulation of flows, links and connections. Due to the non-linearity the global behavior cannot be predicted by observing individual parts. It is necessary to integrate possibilities for monitoring parts and links. The global behavior needs to be simulated based on the monitoring data. Modeling in dynamic systems is only one part of a continuous process.

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3.3.2 Path Dependence Any model or architecture (as any artificial artifact) has a history and at the same time the process of modeling becomes part of history. The model will influence upcoming, future solutions. This influence may have negative or positive consequences for consecutively modeled models.

3.3.3 Strange Attractor and Bifurcation Any model or architecture hopefully becomes some sort of ‘‘strange attractor’’ in the sense of influencing decisions and decision processes in order to fit to the model. A new created model however might influence decisions taken in enterprise systems to move towards a different direction. This model will trigger a bifurcation point for the system. A different state will be reached by the system after passing this point. Care has to be taken that this process will not suppress the function of the enterprise system, but support the system to better reach its objectives.

3.3.4 Active Agents Many modeling environments support a single modeler at a time. However, it has to be assumed that multiple agents are concurrently working on a particular model or architecture. A modeling approach has to allow modules which may be modified concurrently by active agents. The modeling language also has to be simple enough to enable agents with diverse backgrounds to participate in the process.

3.3.5 Self-Organization and Emergent Behavior In order to support self-organization of active agents working concurrently on a model or architecture, a communication infrastructure is needed. This support system should allow working in parallel and the identification and negotiation of (interoperability) solutions (a posteriori and a priori).

4 Supporting Interoperability in Dynamic Environments There are some challenges which need to be tackled in order to support interoperability in dynamic enterprise environments. Interoperability seen from this point of view is a process that follows the evolution of the (enterprise) system. It is assumed that the different sub-systems of


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any enterprise system are in flux.1 Non-interoperability between two or more subsystems may emerge at any time. Integration is not a solution to this type of system, as any small change in one system part would require actions to be taken at any other part. Interoperability of these parts would assume some level of ‘‘autonomy’’ of each part and provide the means to allow loosely coupled parts. Yet the same point of view requires monitoring and simulation facilities which allow to continuously update the models. For maintaining the interoperability of enterprise systems (active) agents in the system require support to recognize events that lead to non-interoperability situations. This support may take the form of Key Performance Indicators, or might be hardware sensors which monitor the ‘‘state’’ of the enterprise. Agents are required to communicate with others to self-organize (possible within a small group) and negotiate a course of action for reaching an interoperable state. In order to understand the paths taken during the discussions, negotiations and decisions by the agents, the history of the models needs to be recorded. This history will support other agents to understand the paths taken by the team working on interoperability. The initial work described here is a possible next step following other research on systemic and sustainable interoperability. The major difference to existing approaches is the special point of view which assumes a dynamic, complex and chaotic environment. Acknowledgments This work has been partly funded by the European Commission through the Project Marie Curie—Industry and Academia Partnerships and Pathways (IAPP) program project: IANES (Grant Agreement No. 286083). The authors wish to acknowledge the Commission for their support. For more information on the IANES project see http://www.ianes.eu.

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