Business ecosystem modeling- the hybrid of system modeling and ecological modeling: an application of the smart grid
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Business ecosystem is popularly used to investigate a complex social system with the business perspective, and particularly contributes to the understanding of actors and their relations in the innovation research. However, the aspect of business ecosystem modeling is limited discussed in the literature, although the importance has emerged significantly in recent years due to the emphasis on cross-disciplinary research and digitalization with artificial intelligence. Therefore, this paper proposes a framework for business ecosystem modeling with the discussion of system engineering and ecological modeling. The domain of smart grid is selected to demonstrate how system engineering, especially standards and ontologies contribute to the business ecosystem modeling. The proposed framework of the business ecosystem modeling includes three parts and nine stages that combines theories from system engineering, ecology, and business ecosystem. Part I-Business ecosystem architecture development includes four stages which aims to identify a target business ecosystem and its elements (actors, roles, and interactions). Part II-Factor analysis includes two stages to identify potential changes (and the dimensions of the changes) in the ecosystem. Part III- Ecosystem simulation and reconfiguration aims to use simulations to investigate the transition of an ecosystem and the re-configurated ecosystem. The framework not only provides a systematic approach for modeling a business ecosystem but also provides a methodological foundation for research on the aspect of complex systems in the business ecosystem field.
KeywordsBusiness ecosystem System modeling System engineering Ecological modeling Standards Architecture design Simulation
The European Committee for Standardization
The European Committee for Electrotechnical Standardization
The Common International Classification for Ecosystem Services
The Department of Defence Architecture Framework
The European Telecommunications Standards Institute
The Swiss Federal Office for the Environment
GridWise Transactive Energy Framework
Information and Communication Technology
The Millennium Ecosystem Assessment
Mapping and Assessment on Ecosystems and their Services
The National Ecosystem Services Classification System
The National Institute of Standards and Technology
Ontology for Energy Management Applications
Smart Grids Architecture Mode)
- SG-CG/SP SG-CG
Sustainable Processes Work Group
Systems Modeling Language
The Economics of Ecosystems and Biodiversity
The Open Group Architecture Framework
Unified Modeling Language
System modeling is the process of developing abstract models of a system (Sommerville 2015). System modeling is popularly used in software engineering, especially during requirement engineering to help derive the requirements for a system. System modeling can visualize a system, specify the structure or behavior of a system, provide a template for system construction, etc. System modeling represents a system by either graphical notation, e.g. the popular used Unified Modeling Language (UML) or develops mathematical models of a system that are usually used as a detailed system specification. System modeling has been used in different domains, e.g. manufacturing system (Baines and Harrison 1999), organization (Dietz 1994), enterprise information system (Shen et al. 2004), market (Gebremedhin and Moshfegh 2004), etc. The application of system modeling in business mainly focuses on business process modeling (Greasley 2003) or strategic modeling (Cosenz and Noto 2016), which is the lack of a holistic view of the business world.
The term of the business ecosystem was introduced in 1993, by J. R. Moore (1993) to describe how the economic community works. Business ecosystem is defined as a large number of loosely interconnected participants who depend on each other for their mutual effectiveness and survival (Iansiti and Levien 2002). Business ecosystem has been popularly discussed since the 2000s, especially by the boost of the internet. Along with the internet, the term digital ecosystem was introduced, a digital business ecosystem is defined as ‘constructed when the adoption of internet-based technologies for business is on such a level that business services and the software components are supported by a pervasive software environment, which shows an evolutionary and self-organizing behavior’ (Peltoniemi and Vuori 2004a). The digital ecosystem has been applied to present the IT infrastructure (Iansiti and Richards 2006) or the software platform (Ceccagnoli et al. 2012).
However, there is little literature on the modeling aspect of the business ecosystem. For instance, (Manning et al. 2002) propose a 6-step approach to building a business ecosystem, but the approach is more like a guideline, not system modeling. Without system modeling, the investigation of the business ecosystem will be lack of visualization, clear structure of behaviors and specification that system modeling in software engineering can provide.
Therefore, this paper proposes an approach for systematic modeling of a business ecosystem. To develop and present this business ecosystem modeling approach, this paper chooses the smart grid business ecosystem as a case study. The reason for choosing the smart grid domain is because the smart grid is a well-defined and structured domain with a series of matured standards. Therefore, the investigation of system modeling in the smart grid can support the business ecosystem modeling development in this paper.
The proposed business ecosystem modeling approach includes three parts (Part I-Business ecosystem architecture development, Part II-Factor analysis, and Part III- Ecosystem simulation and reconfiguration). This paper mainly focuses on the introduction of the propose framework, and the three parts will be introduced in the future work.
This paper is organized as: Section II introduces the theory of system engineering with three aspects (system modeling process, domain ontology and system modeling languages). Section III presents the ecological modeling with the discussion of the biological ecosystem, ecosystem mapping and modeling. Section IV discusses the smart grid including smart grid architecture, smart grid actors and roles and value flows in the smart grid. Section V introduces the proposed framework for the business ecosystem modeling followed by conclusion in Section VI.
System Modeling Process
‘The elements, or parts, can include people, hardware, software, facilities, policies, and documents; that is, all things required to produce system-level results’.
‘The results include system-level qualities, properties, characteristics, functions, behavior, and performance’.
‘The value added by the system as a whole, beyond that contributed independently by the parts, is primarily created by the relationship among the parts; that is, how they are interconnected’.
Systems are complex, and system modeling approaches are different based on the aspects/layers of the systems required to be modeled. In system engineering, there are several well-known system modeling processes, e.g. the ISO 15288 (Industrial automation systems and integration- Integration of life-cycle data for process plants including oil and gas production facilities) (ISO 2013) and the system engineering formula by NASA (2007). In the ISO 15288, there are 25 processes defined to realize a system, starting from a statement of purpose (objective) and a set of top-level (stakeholder) requirements ending in operating and maintaining the system (Ruijven 2012). Meanwhile, NASA creates the single systems engineering formula (NASA n.d.) as:
Architecture development is an important part of system engineering because a system architecture is a conceptual model that defines the structure, behavior, and multiple views of a system (Jaakkola and Thalheim 2011). Among a number of the architecture development approaches, the TOGAF (The Open Group Architecture Framework) (The Open Group n.d.-a) and DoDAF (the Department of Defence Architecture Framework) (Chief Information Officer n.d.) are popularly used as the standards for the enterprise architecture (Tao et al. 2017).
How Ontologies Enable the Simulation Model Development Process (Benjamin et al. 2006)
Simulation Modeling Activity
Role of Ontologies
Establish purpose & scope
Capture needs, questions, objectives. Integrate across multiple perspectives. Map organization /mission goals to simulation goals.
Terminology harmonization to enable shared and clear understanding.
Formulate conceptual model
Validate system descriptions. Identify model boundaries. Identify the level of modeling abstraction. Identify model objects and roles. Determine model structure and logic.
Ontology knowledge is used to determine the unambiguously differentiated abstraction levels. The ontological analysis helps to map system objects to model objects and to identify appropriate object roles. The ontological analysis helps reason with system constraints to facilitate determination of model logic.
Acquire and analyze data
Identify data sources and data dictionaries. Perform data and text mining. Perform statistical analyses, data reduction (distribution fitting, etc.).
Ontologies play an important role in detailed data analysis, especially in disambiguating terminology and interpreting text data descriptions.
Design detailed model
Refine, detail, and validate model objects. Refine, detail, and validate model structure and model logic.
Ontologies will facilitate detailed analysis of objects and constraints including mapping the simulation model constraints to evidence/ specifications of real-world constraints in the domain descriptions.
An ontology is an inventory of the kinds of entities that exist in a domain, their salient properties, and the salient relationships that can hold between them (Benjamin et al. 1995). In a domain ontology, various kinds of objects (e.g., tools and employees), properties (e.g., being made of metal or professional expertice), and relations between tools and employees (e.g., produced by or working with) are defined (Benjamin et al. 2006).
Ontologies are important in intelligent system development for modeling interoperability, composition, and information exchange at the semantic level. Ontologies addresses four challenges at the semantic level: semantic inaccessibility, logical disconnectedness, consistency maintenance, and modeling at multiple levels of abstraction (Benjamin and Graul 2006). However, not all ontologies represent domains in the same way and at the same level of detail. In addition, ontologies use different vocabularies to describe the same concepts. Therefore, there is the need for a unified ontology as a standard that can be used for a specific domain, e.g. smart grid (Cuenca et al. 2017).
Step 1. Determine the domain and scope of the ontology
Step 2. Consider reusing existing ontologies
Step 3. Enumerate important terms in the ontology
Step 4. Define the classes and the class hierarchy
Step 5. Define the properties of classes—slots
Step 7. Create instances
Energy Domains Representation Level of Detail (Cuenca et al. 2017)
Infrastructure technical data
Energy consumption systems data
Energy performance data
Energy stakeholders’ data
Distributed energy sources data
Energy DR operations
Example of Reused Ontologies and Concepts (Cuenca et al. 2017)
Energy consumption systems data
HVAC systems, communication appliances, entertainment appliances, oce automation devices, lighting systems, white goods, acoustic systems, domotic network components, energy facilities, device state, device commands, equipment manufacturer, external and internal equipment
Appliances working modes and power proles, device manufacturer and device model.
Body care devices, pressing devices, water heating devices, charging devices, lighting systems, entertainment devices, cleaning devices and electrical appliance category.
System Modeling Languages and Tools
The UML class diagramming has been popularly used for the system modeling. It has been used in the smart grid system modeling for modeling the electricity market (e.g. the harmonized role model (E. a. e. ENTSO-E 2018)), IT infrastructure and multi-layer architecture (in the SGAM (Smart Grids Architecture Model) framework (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012)). The use case diagram is also used in the SGAM framework to present the design of the smart grid in an architectural viewpoint that allows both- specific but also neutral regarding solution and technology. SGAM framework also uses the sequence diagrams to describe the information objects which are exchanged between actors.
The term ecosystem was firstly used by A. G. Tansley in 1935, and he defined an ecosystem as ‘a particular category of physical systems, consisting of organisms and inorganic components in a relatively stable equilibrium, open and of various sizes and kinds’ (Tansley 1935). Later, the definitions of ecosystem emphasize more on ‘interaction’. For instance, the ecosystem is defined as the joint functioning and interaction (large quantities of matter, energy and information) of these two compartments (populations and environment) in a functional unit of variable size (Garcia, and Food, and A. O. o. t. U. Nations 2003).
According to (Ricard 2014), ‘the “species” is the ecosystem basis and refers to all organisms of the same kind. The members of a species living in a given area at the same time constitute a population. All the populations living and interacting within a particular geographic area make up a biological (or biotic) community. The living organisms in a community together with their non-living or abiotic environment make up an ecosystem’.
Based on the scale of ecological dynamics, ecosystems hierarchy includes ecozone, biome, ecosystem (ecological community, population, habitat/biotope), ecotone and niche (Ricard 2014).
The Main Types of Interspecies Interactions in Ecological Communities (Khan Academy 2016)
Organisms of two species use the same limited resource and have a negative impact on each other.
- / -
A member of one species, predator, eats all or part of the body of a member of another species, prey.
+ / -
A special case of predation in which the prey species is a plant.
+ / -
A long-term, close association between two species in which both partners benefit.
+ / +
A long-term, close association between two species in which one benefits and the other is unaffected.
+ / 0
A long-term, close association between two species in which one benefits and the other is harmed.
+ / -
Ecosystems are controlled both by external and internal factors, e.g. climate, soil, topography. External factors control the overall structure of an ecosystem and the way things work within it. Internal factors control ecosystem processes and are controlled by the feedback loops. Internal factors include decomposition, competition or shading, disturbance, succession and the types of species present.
Ecosystem science is the study of inter-relationships among the living organisms, physical features, biochemical processes, natural phenomena, and human activities in ecological communities (NOAA 2018). Ecosystem science investigates the areas (not limited to) of ecosystem processes, population ecology and population dynamics, disturbance and resilience, nutrient cycle, decomposition and mineralization, ecological amplitude, ecology, environmental influences, biological interactions, biodiversity, and environmental degradation. According to (Gorham and Kelly 2018), types of ecosystems, environmental factors, levels of complexity, major plants and animals, and interspecific associations have been popularly discussed in the ecosystem science.
The theories of the biological ecosystem demonstrate that the terminologies, principles and methodologies in the biological ecosystem science can be modified and applied in other disciplines, e.g. system engineering and business ecosystem research.
Ecosystem mapping is the spatial delineation of ecosystems following an agreed ecosystem typology (ecosystem types), which strongly depends on mapping purpose and scale (European Union 2013). The ecosystem mapping may also include mapping of status (e.g. functioning and health) for monitoring and assessment of the ecosystem’s quality.
According to a coherent analytical framework proposed by EU ( 2013), the ecosystem classification and mapping apply two basic principles: typological (divides nature into ecosystem types – classes that can occur at more geographical locations, e.g., temperate broadleaf and mixed forests) and regional (describes ecosystems from a regional perspective e.g., Dinaric mixed forests) or their combination.
Ecosystem services are an important part of the ecosystem mapping that describe the ways that humans are linked to and depend on nature. There are different definitions of ecosystem services ((La Notte et al. 2017) summarizes the definitions provided in recent ecosystem services studies). For instance, MA (The Millennium Ecosystem Assessment) defines ecosystem services as “the benefits people obtain from ecosystems” (Millenium Ecosystem Assesment 2005), and ecosystem services are defined as the contributions that ecosystems make to human well-being by CICES (the Common International Classification for Ecosystem Services) (European Union 2013). The conceptions of ecosystem services have been discussed in different domains, e.g. natural science, economics and policy (Danley and Widmark 2016).
There are three international classification systems available to classify ecosystem services: MA (Millennium Ecosystem Assessment 2005), TEEB (The Economics of Ecosystems and Biodiversity n.d.) and CICES (n.d.). Ecosystem services categories in CICES are provisioning services, regulating and maintenance services, and cultural services (Haines-Young and Potschin 2018). CICES does not include the ‘supporting services’ in MA, but merges the ‘habitat services’ from TEEB with regulating services, in a category called ‘regulating and maintenance services’ (La Notte et al. 2017).
Compared to CICES that places greater emphasis on the ecological system, FEGS-CS (Final Ecosystem Goods and Services Classification System) (United States Environmental Protection Agency (EPA) n.d.) and NESCS (the National Ecosystem Services Classification System) (Landers 2015) mainly focus on benefits and beneficiaries (described in (Landers and Nahlik 2013)). Based on the comparison between CICES and FEGS-CS (La Notte et al. 2017; Bordt 2016), this paper recommends including benefits and beneficiaries in the business ecosystem modeling because benefits and beneficiaries are an important aspect in the business ecosystem. However, there is a lack of discussion regarding whether CICES and FEGS-CS can be compatible. The Swiss Federal Office for the Environment (FOEN) proposes an ‘Inventory of Final Ecosystem Goods and Services’ (FEGS) and include four Benefit categories (health, security production factors and natural diversity) (Staub et al. 2011). The FEGS aims to be integrated into the MA and CICES Classifications.
Mapping and assessment of ecosystems and their services
1) Mapping of the concerned ecosystem;
2) Assessment of the condition of the ecosystem;
3) Quantification of the services provided by the ecosystem; and
4) Compilation of these into an integrated ecosystem assessment
Indicators for mapping and assessing the condition of ecosystems
Pressures and environmental quality indicators
Ecosystem modeling is also called ecological modeling. An ecosystem model is an abstract representation of an ecological system which is studied to better understand the real system (Hall and Day 1990). Ecological modeling can be simply categorized as conceptual models and quantitative models. Generally, conceptual models are written as diagrams with boxes (represent state variables or condition of the ecosystem components) and arrows (illustrate relationships among state variables), and a quantitative model is a set of mathematical expressions of coefficients and data attached to the boxes and arrows in the conceptual models (Jackson et al. 2000). Quantitative models are more precise and specific about a system and can make predictions when they capture the key elements for system representation (Bondavalli et al. 2009). There are three main types of ecosystem modeling and the choice of model type and detail depends on the system studied, the questions asked, and the data available (Jackson et al. 2000).
Characteristics of agent-based and system dynamics models, and a hybrid model (Martin and Schlüter 2015)
System dynamics model
How do emergent system-level interaction (e.g. spatially, between individuals?)
How do stocks change or stabilize? (given that rates are constant) Which process/feedback is dominating?
How do changing process rates (impacted by decisions) affect dynamics?
How do changing stocks affect agent states/the distribution of traits?
Purposes in general for all: improve system understanding rather than prediction or forecasting
To identify mechanisms (specific interactions) that are responsible for emerging system-level patterns (disaggregated)
Generate hypotheses, exploration of micro-level behavior.
Investigate system-level dynamics (aggregated), stability properties of the system, loop dominance, explaining temporal dynamics, projection into the future.
Investigating different micro- or system-level mechanisms that drive certain dynamics. Generate hypotheses of system state-change (when does dominance of feedbacks change?) or structural development over time (when does an average trait of agents change?)
Micro-level interactions between entities, network structure (heterogeneous characteristics of individuals/actors, temporal discrete behavior), transient dynamics.
Processes driving accumulation in stocks at (sub-)system level, stable-states, feedbacks (balancing, amplifying), non-linearities.
Process of restructuring in a system which can focus either on a structure affecting the processes, or processes affecting the structure.
Tests for model calibration
Statistical pattern matching-can the model grow patterns that are found in reality?
Stability analysis-under which parameter setting can fixed points/equilibria occur? How stable are they?
Separate sub-system test (paradigm specific) and qualitative check for the coupled version.
Suitable and traditional analysis tools, typical experiments
Only through simulations, often with multiple repetitions because of stochastic elements: plotting group/system-level characteristics over time (average), evaluation a limited parameter range, describing transient dynamics.
Simple models through analytical tools (basins of attraction, bifurcation analysis, overall stability), and more complex through simulations (state space plots from simulations, evaluating stable-states, equilibria.)
Through simulations with a focus on either
1. change in structure/ parameters: how does it affect the dynamics?
2. Change in dynamics: how does it affect the structure?
Type of outcome
Emerging spatial/agent patterns, scenario comparison between structurally different model versions, system properties such as the average state of a population.
Aggregated system properties in terms of stability, loop dominance.
Time series of merging state-transitions.
Besides agent-based modeling, there is an increase in applying multi-agent simulation in ecology due to the growth in CPU power. In the field of ecosystem management, the interactions between ecological dynamics and social dynamics are examined, and modelers describe systems as a set of modules or compartments interlinked by flows (of matter, energy, or information) and controls (Bousquet and Le Page 2004). Multi-agent systems are used in ecology to investigate multiple agent interactions (Huhns and Stephens 1999). Multi-agent system originally came from the field of artificial intelligence, and is a complex system that is composed by more than one distributed agents, and these agents communicate to deal with problems which usually can’t be solved by a single agent (Dam and Lukszo 2006; Merdan et al. 2011).
Smart Grid Architecture
The concept of the smart grid is built upon the intersection of technology, people and infrastructure for intelligent generation, transmission, distribution and consumption of electricity (Geelen et al. 2013). A smart grid can be defined in terms of the socio-technical network with enhanced two-way communication and active management of information and energy flows that allow controlling the practices of distributed generation, storage, consumption and flexible demand (Wolsink 2012).
There are many different definitions of a smart grid that emphasizes different aspects (Ma et al. 2015). To have a holistic view of the smart grid with multiple stakeholders, multiple applications, multiple networks, it is necessary to investigate the smart grid architecture. There are several smart grid models have been proposed and discussed, e.g. the smart grid conceptual model introduced by The National Institute of Standards and Technology (NIST) (2018) and SGAM Framework developed by CEN (the European Committee for Standardization), CENELEC (the European Committee for Electrotechnical Standardization), and ETSI (the European Telecommunications Standards Institute) (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012).
Table Styles Terms and Definitions in the SGAM Framework (CEN-CENELEC-ETSI Smart Grid Coordination Group 2012)
SGAM Interoperability Layer
To allow a clear presentation and simple handling of the architecture model, the interoperability categories described in the GridWise Architecture model are aggregated in SGAM into five abstract interoperability layers.
One dimension of the Smart Grid Plane covers the complete electrical energy conversion chain, partitioned into 5 domains.
One dimension of the Smart Grid Plane represents the hierarchical levels of power system management, partitioned into 6 zones.
Smart Grid Actors and Roles
Domains and Actors in the Smart Grid Conceptual Model (The National Institute of Standards and Technology (NIST) 2010)
Actors in the Domain
The end users of electricity. May also generate, store, and manage the use of energy. Traditionally, three customer types are discussed, each with its own domain: residential, commercial, and industrial.
The operators and participants in electricity markets.
The organizations providing services to electrical customers and utilities.
The managers of the movement of electricity.
The generators of electricity in bulk quantities. May also store energy for later distribution.
The carriers of bulk electricity over long distances. May also store and generate electricity.
The distributors of electricity to and from customers. May also store and generate electricity.
Smart Grid domain: ‘is a high-level grouping of organizations, buildings, individuals, systems, devices or other actors that have similar objectives and that rely on—or participate in—similar types of applications. Communications among actors in the same domain may have similar characteristics and requirements. Domains may contain sub-domains. Moreover, domains have much overlapping functionality, as in the case of the transmission and distribution domains. Transmission and distribution often share networks and, therefore, are represented as overlapping domains’.
Actor: ‘a device, computer system, software program, or the individual or organization that participates in the smart grid. Actors have the capability to make decisions and to exchange information with other actors. Organizations may have actors in more than one domain. The actors illustrated here are representative examples but are by no means all of the actors in the smart grid. Each actor may exist in several different varieties and may actually contain other actors within them’.
System actors: cover functions or devices, for example, defined in the Interface Reference Model (IEC 61968–1) (IEC 2012). A system actor performs a task under a specific role.
A business actor: ‘specifies, in fact, a « Role » and will correspond 1:1 with roles defined in the eBIX harmonized role model (possibly some new roles will be required and added to the eBIX model)’.
An Example of the Roles and Descriptions in the Harmonized Role Model (E. a. e. ENTSO-E 2018)
Has overall responsibility for creating balance in the market and for handling transmission grid operation and ensuring stable electricity supply.
A party that operates one or more grids
A party responsible for installing, maintaining, testing, certifying and decommissioning physical meters.
However, the definition of a business actor corresponds 1:1 with a role (E. a. e. ENTSO-E 2018; IEC 2012) does not comply in all smart grid system. Unbundling is defined by EU (European Commission n.d.) as ‘the separation of energy supply and generation from the operation of transmission networks’. Comparatively, in the bundling electricity market structure, the energy supply and generation might not be separated from the operation of transmission network, and one actor can hold one or more than one roles. For instance, the actor-utility in the Chinese electricity market corresponds to the roles of transmission operator, distribution operator, and electricity supplier.
An actor: ‘represents a party that participates in a business transaction. Within a given business transaction an actor performs tasks in a specific role or a set of roles’.
A Role: ‘represents the intended external behavior (i.e. responsibility) of a party. Parties cannot share a role. Parties carry out their activities by assuming roles, e.g. system operator, trader. Roles describe external business interactions with other parties in relation to the goal of a given business transaction’.
These definitions can better represent the relations between actors and roles in a smart grid.
Value Flows in the Smart Grid
Besides the emphasis on actors and roles in both the business layer in the SGAM framework and in the conceptualization of the business ecosystem, interactions between actors are also important and well discussed in both the SGAM framework specification and business ecosystem litterature.
For instance, in the SGAM conceptual model, actors are connected by associations, the exchanged information is defined as business services, and actors interact through these business services. However, the description of the associations or business services is not detail discussed in the SGAM framework. Meanwhile, although processes/ interactions between actors are described in the harmonized role model (E. a. e. ENTSO-E 2018) and IEC standards (e.g. 61,850) (Energinet 2019), the described interactions in (E. a. e. ENTSO-E 2018; Energinet 2019) are mainly technical related information, and there is no methodology for systemically defining and describing the interactions between actors in the smart grid.
In the theory of the business ecosystem, especially the service ecosystem, value co-creation is well discussed for the understanding of interactions between actors, e.g. (Palumbo et al. 2017). Value co-creation is driven by the collaborative efforts of and interactions between actors in the ecosystem (Pop et al. 2018). Value can be tangible and intangible (Ciasullo et al. 2017), and at different levels: micro (e.g., households, organizations, etc.), meso (e.g., industries, communities, etc.), and macro (e.g., nations, global markets, etc.) (Frow et al. 2016). However, the types of value are not defined in the ecosystem theory, but value constitutesis an important part of the ecosystem modeling to define and clarify the interactions between actors.
The value chain takes an economic value perspective;
Value networks are primarily concerned with identifying the participants involved in creating and delivering value;
Lean value streams are all about optimizing business processes (primarily within a manufacturing context).
Value Stream Modeling Relationships with an external stakeholder value stream mapping
Decomposing the value stream into a sequence of value-creating stages
Mapping value stream stages to business capabilities and performing a gap analysis
Heat-mapping Scenario and decision-making processes
An Example of Value Strem of Acquiring Retail Product in TOGAf (The Open Group 2017)
Acquire Retail Product
The activities involved in looking for, selecting, and obtaining a desired retail product.
A retail shopper wishing to purchase a product.
Customers are able to locate desired products and obtain them in a timely manner.
Business ecosystem modeling
The investigation of the system engineering and biological ecosystem modeling shows that there are similarities and differences between these two fields. For instance, ecosystem theory focuses on interaction (ecosystem services), the ecosystem itself (e.g. the types of ecosystem, the assessment of the ecosystem), and the hierarchy of the ecosystem. Ecosystem mapping is similar as the system modeling, and ecosystem modeling is more abstract (usually mathematical modeling) and focuses one/several aspects in the ecosystem, and system modeling mainly focuses on the architecture of a system with ICT (Information and Communication Technology) auxiliary.
Although ecosystem theory discusses species, it mainly focuses on the interaction between species (interspecies and inner species). Comparatively, system engineering emphasizes actors and their roles. The interactions between actors are also discussed in the system engineering, but not systematic as in the ecosystem theory, and mainly use the UML diagrams to describe the interactions.
Standards are important in both ecosystem mapping and system engineering that allow better understanding and consistency in practice. However, there are concerns about which standards should be applied and whether there is a compatibility issue between standards. Meanwhile, different domains or theoretical/practice fields have their own ontologies that raise the inconsistency issue in the interdisciplinary research, especially, if there is ontological overlap across domains (e.g., different ontologies describe the same thing in different domains).
System modeling mainly aims to visualize the static status of a system, although the system changes are also discussed in system engineering. The ecosystem modeling aims to investigate the system dynamics by establishing models or simulations (usually mathematic modeling). The static status and dynamics are both important for the business ecosystem modeling.
The biological ecosystem theory (especially the ecosystem mapping) and system engineering (especially the system modeling) can contribute to the business ecosystem modeling for different aspects. The ecosystem mapping mainly contributes to the theoretical aspects, e.g. the categories of interactions and system dynamics. There are several standards in system modeling, e.g. ISO, SGAM, TOGAF, and UML that can contribute to the practices of the business ecosystem modeling.
The Framework and Steps of the Business Ecosystem Modeling
Business ecosystem modeling
Business ecosystem architecture development
Identify the boundary of a selected ecosystem.
Identify actors and their roles in the ecosystem.
Identify actors’ value propositions and business models.
Identify interaction between actors (different types of interactions.)
Investigate influential factors and their impact on the elements in the ecosystem (actors, roles, and interaction.)
Investigate potential changes in the ecosystem.
Ecosystem simulation and reconfiguration
Multi-agent based ecosystem modeling to identify ecosystem reaction towards the potential changes.
Ecosystem reconfiguration (including reconfiguration of actors, roles, and interaction) due to changes, system dynamics modeling might be applied at this stage.
Business model reconfiguration.
The Business Ecosystem Modeling With The Integration of System Modeling and Ecosystem Theory
Business ecosystem modeling
Part I- Stage 1
Identify the boundary of a selected ecosystem.
Domain oriented business ecosystem.
System architecture .
Types of ecosystem.
Part I- Stage 2
Identify actors and their roles in the ecosystem.
Categories of organisms (Lundberg and Moberg 2003)(producers, consumers, decomposers.)
Types of keystone species (Mills et al. 1993) (predators, mutualists, engineers.)
Part I- Stage 3
Identify actors’ value propositions and business models.
Business model (Zott and Amit 2013).
Value creation (Clarysse et al. 2014).
Ecosystem function and biodiversity (Duffy 2002).
Part I- Stage 4
Identify interaction between actors (different types of interactions.)
Value flows (Matthies et al. 2016) (monetary, product, information and intangible.)
Social network analysis (Ashton 2008).
Flows (e.g., information exchange.)
Associations (in UML diagram.)
Service-oriented architecture (in TOGAF.)
Intra-specific & inter-specific ecological interaction.
Matter, energy and information flows.
Part II- Stage 1
Investigate influential factors and their impact on the elements in the ecosystem (actors, roles, and interaction.)
Motivation and strategy (in ArchiMate.)
System thinking (Rubenstein-Montano et al. 2001).
machine logic (Polic and Jezernik 2005).
Assessment and indicator of ecosystem conditions.
Part II- Stage 2
Investigate potential changes in the ecosystem.
Emergence and co-evolution (Peltoniemi and Vuori 2004b).
Risk management/ assessment (Sage and Rouse 2014).
Ecosystem change (Elmqvist et al. 2003).
Part III- Stage 1
Multi-agent based ecosystem simulation to identify ecosystem reaction towards the potential changes.
Multi-agent-based models (Lurgi and Estanyol 2010).
System dynamics (Karnopp et al. 2012).
Multi-agent based modeling.
Part III- Stage 2
Ecosystem reconfiguration (including reconfiguration of actors, roles, and interaction) due to changes.
Business ecosystem lifecycle (Rong et al. 2015).
System lifecycle management (Sage and Rouse 2014).
Evolution of ecosystems (Azaele et al. 2006).
Part III- Stage 3
Business model recofiguraation.
Business model innovation (Chesbrough 2010).
The history of the business ecosystem field shows that the concepts of ecology and system engineering have been applied in the field, and especially the term and fundamental elements of the ‘business ecosystem’ originally came from ecology. However, the ‘business’ aspect is still the main focus in the business ecosystem field, not so much attention of the ‘system’ aspect in the research compared to the fields of system engineering and ecology. This paper proposes a framework for business ecosystem modeling to fill this gap.
The proposed framework includes three parts and nine stages that combine theories from system engineering, ecology, and business ecosystem. Part I-Business ecosystem architecture development includes four stages which aims to identify a target business ecosystem and its elements (actors, roles, and interactions). Part II-Factor analysis includes two stages to identify potential changes (and the dimensions of the changes) in the ecosystem. Part III- Ecosystem simulation and reconfiguration aims to use simulations to investigate the transition of an ecosystem and the re-configurated ecosystem.
The proposed framework not only provides a systematic approach for modeling a business ecosystem, but also provides a methodological foundation for research on the aspect of complex systems in the business ecosystem field. Meanwhile, it brings the practices of business and engineering together, therefore, provides a common language for a better understanding across the business ecosystem, system engineering and innovation management.
The application of the smart grid not only demonstrates a well-defined and structured system that was missing in the business ecosystem, but also presents the importance of standards in the system engineering. Meanwhile, the SGAM framework illustrates the complexity of the smart grid, and indicates that the business ecosystem modeling for a complex system as the smart grid should consider the layers of several interactive business ecosystems that might cross multi-domains. Therefore, the importance of ontology design is obvious in the business ecosystem modeling, and the design of the business ecosystem ontology should include both domain ontology (e.g. standards in the domain) and business ontology.
There are many subjects in the fields of ecological modeling and system engineering, and lots of them can contribute to the business ecosystem modeling. This paper does not cover all these subjects, and mainly aims to provide a prompt for an open thread. Meanwhile, although each part in the proposed framework combines theories from the fields of system engineering and ecology, detail description and examples are recommended in the future work, especially the application of multi-agent systems and system dynamics in the business ecosystem. Multi-agent systems and system dynamics have been popularly applied in the system engineering and ecological modeling for different purposes. However, the application of these two in the business ecosystem is limited, especially without a domain focus, e.g. (Lurgi and Estanyol 2010; Marín et al. 2007; Camarinha-Matos and Collaborative Business Ecosystems and Virtual Enterprises 2013).
ZM carried out the the paper design and writing. The author read and approved the final manuscript.
This paper is completed under no funding.
Consent for publication
The author declares that she has no competing interests.
- Benjamin P, Graul M (2006) A framework for adaptive modeling and ontology-driven simulation (FAMOS) (defense and security symposium). SPIEGoogle Scholar
- Bordt M (2016) Concordance between FEGS-CS and CICES V4.3. The United Nations Statistics Division Available: https://unstats.un.org/unsd/envaccounting/workshops/ES_Classification_2016/FEGS_CICES_Concordance_V1.3n.pdf
- Buitelaar P, Olejnik D, Sintek M (2004) A protégé plug-in for ontology extraction from text based on linguistic analysis. In: European Semantic Web Symposium. Springer, pp 31–44Google Scholar
- Camarinha-Matos LM, Collaborative Business Ecosystems and Virtual Enterprises (2013) IFIP TC5 / WG5.5 Third Working Conference on Infrastructures for Virtual Enterprises (PRO-VE’02) May 1–3, 2002. Springer US, SesimbraGoogle Scholar
- CEN-CENELEC-ETSI Smart Grid Coordination Group. “Smart Grid Reference Architecture,” CEN (the European Committee for Standardization), CENELEC (the European Committee for Electrotechnical Standardization), and ETSI (the European Telecommunications Standards Institute) 2012, Available: https://ec.europa.eu/energy/sites/ener/files/documents/xpert_group1_reference_architecture.pdf Google Scholar
- CEN-CENELEC-ETSI Smart Grid Coordination Group (2014) Document for the M/490 Mandate: Smart Grids Methodology and New Applications. Energy Networks Association Available: http://www.energynetworks.org/assets/files/electricity/engineering/Standards/SGCG%20Reports%20071014/SGCG_WGMethod_Sec0076_INF_ReportforComments(incl_annexes).pdf
- Chief Information Officer (n.d.) The DoDAF Architecture Framework Version 2.02. Available: https://dodcio.defense.gov/Library/DoD-Architecture-Framework/. Accessed July, 2019
- CICES (n.d.) Structure of CICES. Available: https://cices.eu/cices-structure/. Accessed July, 2019
- Cuenca J, Larrinaga F, Curry E (2017) A unified semantic ontology for energy management applications. WSP/WOMoCoE@ ISWCGoogle Scholar
- Dam KHV, Lukszo Z (2006) Modelling Energy and Transport Infrastructures as a Multi-Agent System using a Generic Ontology. In: 2006 IEEE international conference on systems, Man and Cybernetics, vol 1, pp 890–895Google Scholar
- E. a. e. ENTSO-E, "The Harmonised Electricity Market Role Model," 2018, Available: https://www.entsoe.eu/publications/electronic-data-interchange-edi-library/Pages/default.aspx Google Scholar
- Energinet (2019) Specification of IEC 61850 Information Exchange between DER and Power System Actors, including TSO, DSO and BRP. Energinet. DK Available: https://energinet.dk/-/media/F0AB7E801EFA45FD8A98358874336484.pdf?la=da&hash=9133E7378D59E9B0A4F9B40D0F00B5091DBC1B08
- European Commission (n.d.) Market legislation. Available: https://ec.europa.eu/energy/en/topics/markets-and-consumers/market-legislation. Accessed July, 2019
- European Union, "Mapping and Assessment of Ecosystems and their services- an analytical framework for ecosystem assessments under action 5 of the EU biodiversity strategy to 2020," 2013, Available: http://ec.europa.eu/environment/nature/knowledge/ecosystem_assessment/pdf/MAESWorkingPaper2013.pdf Google Scholar
- Garcia SM, Food, and A. O. o. t. U. Nations (2003) The Ecosystem Approach to Fisheries: Issues, Terminology, Principles, Institutional Foundations, Implementation and Outlook. Food and Agriculture Organization of the United NationsGoogle Scholar
- Greasley A (2003) Simulation Modelling for business. Routledge, LondonGoogle Scholar
- Haines-Young R, Potschin M (2012) CICES version 4: response to consultation. Centre for Environmental Management, University of Nottingham Available: https://cices.eu/content/uploads/sites/8/2012/09/CICES-V4_Final_26092012.pdf
- Haines-Young R, Potschin M (2013) Common international classification of ecosystem services (CICES): consultation on version 4, august–December 2012. In: EEA framework contract no EEA/IEA/09/003. School of Geography, University of Nottingham, Nottingham Available: https://cices.eu/content/uploads/sites/8/2012/07/CICES-V43_Revised-Final_Report_29012013.pdf Google Scholar
- Haines-Young R, Potschin M (2018) Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. CICES, Nottingham Available: https://cices.eu/content/uploads/sites/8/2018/01/Guidance-V51-01012018.pdf Google Scholar
- Hall CAS, Day JW (1990) Ecosystem modeling in theory and practice: an introduction with case histories. University Press of ColoradoGoogle Scholar
- M. Horridge (2018) Protégé 5.5.0-beta-3 available. Available: http://protege-project.136.n4.nabble.com/Protege-5-5-0-beta-3-available-td4671107.html. Accessed July, 2019
- Huhns MN, Stephens LM (1999) Multiagent systems and societies of agents. In: Gerhard W (ed) Multiagent systems. MIT Press, pp 79–120Google Scholar
- Iansiti M, Levien R (2002) The new operational dynamics of business Ecosystems: implications for policy, operations and technology strategy, vol 03-030. Harvard Business School Working PaperGoogle Scholar
- International Electrotechnical Commission (IEC) (2012) IEC 61968-1:2012 Application integration at electric utilities - System interfaces for distribution management. In Application integration at electric utilities - System interfaces for distribution management (pp. 137): International Electrotechnical CommissionGoogle Scholar
- International Organization for Standardization (ISO) (2013) ISO 15926-1:2004 Industrial automation systems and integration — Integration of life cycle data for process plants including oil and gas production facilities — Part 1: Overview and fundamental principles (pp. 18): iso.orgGoogle Scholar
- Jaakkola H, Thalheim B (2011) Architecture-driven Modelling methodologies. In: presented at the Proceedings of the 2011 conference on information Modelling and knowledge bases XXIIGoogle Scholar
- Karnopp DC, Margolis DL, Rosenberg RC (2012) System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems. WileyGoogle Scholar
- Khan Academy (2016) Interactions in communities- Overview of competition, predation, herbivory, mutualism, commensalism, and parasitism. Available: https://www.khanacademy.org/science/biology/ecology/community-ecosystem-ecology/a/interactions-in-communities. Accessed July, 2019Google Scholar
- Knublauch, H (2004) Ontology-driven software development in the context of the semantic web: An example scenario with Protege/OWL. 1st International workshop on the model-driven semantic web (MDSW2004), Monterey, California, USA. Annex XVII (7) 381–40Google Scholar
- D. Landers (2015) National Ecosystem Services Classification System (NESCS): Framework Design and Policy Application. Available: https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=310592&Lab=NHEERL Google Scholar
- Landers DH, Nahlik AM (2013) Final ecosystem goods and services classification system (FEGS-CS)," in "EPA/600/R-13/ORD-004914. U.S. Environmental Protection Agency, Washington, DC Available: https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=NHEERL&dirEntryId=257922&CFID=139720405&CFTOKEN=59303549&jsessionid=cc303f3c529fb2342dd56e52d7c80353e2a2 Google Scholar
- Lang JM, Benbow ME (2013) Species Interactions and Competition. Nat Educ Knowledge 4(8)Google Scholar
- Ma Z, Asmussen A, Jørgensen BN (2015) Industrial consumers' acceptance to the smart grid solutions: Case studies from Denmark. In: Smart Grid Technologies - Asia (ISGT ASIA), 2015 IEEE Innovative, pp 1–6Google Scholar
- Maes J et al (2018) Mapping and Assessment of Ecosystems and their services- an analytical framework for mapping and assessment of ecosystem condition in EU. Publications office of the European Union, Luxembourg Available: http://ec.europa.eu/environment/nature/knowledge/ecosystem_assessment/pdf/5th%20MAES%20report.pdf Google Scholar
- Manning B, Runge B, Thorne C, Moore G (2002) Demand driven: 6 steps to building an ecosystem of demand for your business. McGraw-Hill Companies, New YorkGoogle Scholar
- Marín CA, Stalker I, Mehandjiev N (2007) Business Ecosystem Modelling: Combining Natural Ecosystems and Multi-Agent Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 181–195Google Scholar
- Martin R, Schlüter M (2015) "Combining system dynamics and agent-based modeling to analyze social-ecological interactions—an example from modeling restoration of a shallow lake," (in English), Frontiers in Environmental Science, Original Research vol. 3, no. 66, 2015-October-13Google Scholar
- Millenium Ecosystem Assesment (2005) Ecosystems and Human Well-being: Synthesis. Millenium Ecosystem Assesment, Washington, DC Available: https://www.millenniumassessment.org/documents/document.356.aspx.pdf Google Scholar
- Millennium Ecosystem Assessment (2005) Overview of the Milliennium Ecosystem Assessment. Available: https://www.millenniumassessment.org/en/About.html#. Accessed July, 2019Google Scholar
- Moore JF (1993) The death of competition: leadership and strategy in the age of business Ecosystems. Harper PaperbacksGoogle Scholar
- NASA (2007) NASA Systems Engineering Handbook. NASA, Washington, DCGoogle Scholar
- NASA (n.d.) Chapter 2: The Systems Engineering (SE) Process. Available: https://www.nasa.gov/pdf/598887main_Auburn_PowerPoints_SE.pdf
- Noy NF, McGuinness DL (2001) Ontology Development 101: A Guide to Creating Your First Ontology. Stanford University Available: https://protege.stanford.edu/publications/ontology_development/ontology101.pdf
- Osterwalder A (2004) The business model ontology: a proposition in a design science approachGoogle Scholar
- Peltoniemi M, Vuori E (2004a) Business ecosystem as the new approach to complex adaptive business environments. Frontiers of E-business research, TampereGoogle Scholar
- Peltoniemi M, Vuori E (2004b) Business ecosystem as the new approach to complex adaptive business environments. In: FeBR 2004: Frontiers of e-business research 2004. Tampere University of Technology and University of Tampere, Tampere, pp 267–281Google Scholar
- Rechtin E (2017) Systems architecting of organizations: why eagles Can't swim. CRC PressGoogle Scholar
- Ricard M (2014) Ecological principles and function of natural ecosystems. MIO-ECSDE, Amfissa Available: http://mio-ecsde.org/erasmus-IP-2014/trainers/day%2002-Ricard.pdf Google Scholar
- Sage AP, Rouse WB (2014) Handbook of systems Engineering and management. WileyGoogle Scholar
- Sommerville I (2015) Software Engineering, 10th edn. Pearson, p 816Google Scholar
- Staub C, Ott W, Heusi F, Klingler G, Jenny A, Häcki M, Hauser, A (2011) Indicators for ecosystem goods and services: framework, methodology and recommendations for a welfare-related environmental reporting. Environment, 17.Google Scholar
- SysML Forum (n.d.) SysML FAQ: What is SysML?, What is MBSE?, Who created SysML? Available: https://sysmlforum.com/sysml-faq/. Accessed July, 2019
- SysML.org (n.d.) Commercial, Free & Open Source SysML Modeling Tools. Available: https://sysml.org/sysml-tools/. Accessed July, 2019
- SysMLtools.com (n.d.) Free & Commercial SysML Tools for MBSE. Available: https://sysmltools.com/. Accessed July 2019
- The Economics of Ecosystems and Biodiversity (TEEB) (n.d.) About TEEB. Available: http://www.teebweb.org/about/. Accessed July, 2019
- The GridWise Architecture Council, "GridWise Transactive energy framework," 2015, Available: https://www.gridwiseac.org/pdfs/te_framework_report_pnnl-22946.pdf Google Scholar
- The National Institute of Standards and Technology (NIST) (2010) NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0. U.S. Department of Commerce Available: https://www.nist.gov/sites/default/files/documents/public_affairs/releases/smartgrid_interoperability_final.pdf
- The National Institute of Standards and Technology (NIST) (2018) NIST Smart Grid Conceptual Model, Gaithersburg Available: https://www.nist.gov/sites/default/files/documents/2018/09/10/draft_smart_grid_conceptual_model_update.pdf
- The Open Group (2014) Open Group Standard: Service-Oriented Architecture Ontology Version 2.0. Reading, UK Available: https://publications.opengroup.org/downloadable/download/link/id/MC42MTQ1MjYwMCAxNTYyMTgyODA1NDA2NTQ1NDE5MzEwMTU1/ Google Scholar
- The Open Group (n.d.-a) The TOGAF® Standard, Version 9.2 Overview. Available: https://www.opengroup.org/togaf. Accessed July, 2019
- The Open Group (2017) TOGAF® Series Guide- Value Streams. Reading, UKGoogle Scholar
- United States Environmental Protection Agency (EPA) (n.d.) Final Ecosystem Goods and Services Classification System (FEGS-CS). Available: https://www.epa.gov/eco-research/final-ecosystem-goods-and-services-classification-system-fegs-cs. Accessed July, 2019
- W3C (2015) Ontology editors. Available: https://www.w3.org/wiki/Ontology_editors. Accessed July, 2019
- Wieringa R, Engelsman W, Gordijn J, Ionita D (2019) A Business Ecosystem Architecture Modeling Framework. In: presented at the 21st IEEE Conference on Business Informatics Moscow, Russia, July 15–17, 2019 Available: https://research.e3value.com/research/publications/ Google Scholar
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