Each specialization then becomes the homogeneous model for the particular sub-hierarchy which may again be specialized for a sub-hierarchy of the sub-hierarchy, and so forth. The hetero-homogeneous modeling approach resolves the dichotomy between tailored solutions for individual business entities and the demand for rigorous compliance with company-wide standard operating procedures.
An XML-based logical representation of MBAs allows for the semi- automated execution of multilevel business processes using information technology which produces event log data as the basis for performance analysis. The hetero-homogeneous nature of MBA models is beneficial to data analysis. Due to multilevel concretization, individual sub-hierarchies may capture performance measures at a finer granularity or record additional measures, thus bearing information which would be lost for analysis when restricted to homogeneous models.
At the same time, each sub-hierarchy is guaranteed to provide at least the data defined by the homogeneous model. Please e-mail for a copy: schuetz dke.
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Please write an e-mail to schuetz dke. As part of Signavio Business Transformation Suite, Process Manager captures, connects, and communicates how work is done and where decisions are made then delivers that information to Signavio Collaboration Hub.
Having visible processes and decisions across your organization will enable you to make better decisions, faster. This allows you to streamline processes and create new workflows—all while gaining insights into the consistency and efficiency of your business practices. A Signavio Process Manager license offers all the features of a professional process and decision modeling tool, including:.
Combining technical depth with an easy-to-use Interface, Signavio Process Manager puts your organization on the path to business excellence. Try it for yourself by registering now for a free day trial account. The cumulative effect of operational decisions has a high impact on the business outcome. With the Business Decision Management capabilities of Signavio Process Manager you can view decisions in the context of overall process management and have full control of your end-to-end decision management. Cost-intensive activities must be identified and cycle times have to be tested.
Customer Journey Mapping with Signavio connects the dots between your customer journey and the people, processes, decisions and IT systems that drive it. Diagrams can be connected to business process models, enabling processes and decisions to be managed in one platform. As with every diagram in Signavio, collaboration is facilitated through sharing of DRD for editing or comments and via publication in the Collaboration Hub.
All process models are saved in a central process repository.
The Signavio Process Manager provides a robust decision table capability, enabling users to combine processes and decisions in one integrated platform. Decision tables are vital for Business Decision Management as they contain the information, which underpins each decision. By defining the variables required for your decision in the decision table, the decision-making process is considerably simplified. Your organization also benefits from automatic checks for overlaps, gaps and inconsistencies in the table rules. With QuickModel, process documentation becomes easy!
Thanks to the spreadsheet-like working platform, all process participants can be involved in process design — even without previous experience. Simply fill out the table with start- and end-events, incoming and exporting documents, assign the different roles and define your process steps. Simultaneously, the tool generates a process model for you with BPMN 2. QuickModel-users can thus easily create process models and contribute to a process oriented organisation without requiring extra effort for training.
Just as everyone has their own style of writing, every modeler has their own modeling style. Even in the modeling standard BPMN 2. Modeling conventions help to guarantee a uniform and standardized way of modeling processes throughout an entire company or organization. Manage approvals individually! Our approach is comprised of Conceptualisation, Quantification and Interrogation stages as shown in Fig 1.
It is based on a standard Bayesian Network modelling technique but with a particular focus on integrating multiple data types representing the specific interests and concerns of different stakeholders. In the remainder of this article we discuss related research in this area, present our new model development approach, and describe its application to a real-life case study from the transport sector.
Our work concerns modelling of business processes. In this section we review some of the large number of related approaches for assessing business performance.
For example, Cost Benefit Analysis CBA has been used widely to assess projects for decision making in a wide range of business and non-business sectors. Recent applications of CBA include economic analysis of IT systems [ 8 ], the transport sector [ 9 , 10 ], medical fields [ 11 , 12 ] and ecological systems [ 13 , 14 ] among others. The CBA method aims to assess decision scenarios and quantify monetary gains against costs for each scenario [ 15 ].
Although CBA was highly successful in these application domains, it has been criticised for its limited ability to handle complex situations with multiple stakeholders [ 16 ]. The monetary conversion process in CBA often fails to distinguish between direct and indirect impacts by treating them equally and it provides the decision maker with a false sense of certainty [ 16 ]. Considering the problems with the CBA method for benefits that are not directly financial in nature such as social and environmental benefits , the Decision Rule method has been suggested as an alternative [ 17 ].
Multilevel Business Processes
The Decision Rule method works by applying a set of rules to evaluate alternatives and find the best one [ 18 ]. However, the Non-Compensatory Decision Rule approach suffers from disadvantages including a limited applicability and b missing important information. The compensatory approach has been criticised because a overly complex judgements are required for rule development, b overview arguments may be difficult to define based on rules, c overall preference measures may be too abstract, and d the trade-off principle may not be well accepted by decision makers [ 19 ].
In the BPM approach, a business enterprise is viewed as a set of interacting processes representing various functions where the processes may be further decomposed [ 22 ]. An early BPM framework was proposed by Curtis et. Static modelling has been criticised firstly for assuming that business processes can only be designed in rational and technical terms, i. Dynamic BPM overcomes some of these issues by introducing concepts such as interdependent, interactive, boundary-crossing, and super-ordinate goals in the modelling process [ 25 ]. BPM has been used for complex system modelling in diverse areas including education [ 26 ], manufacturing [ 27 ], information technology [ 28 ] and business management [ 29 ] among others.
Simulation based Business Process Modelling approaches are also used to model complex systems [ 30 ]. Dynamic BPM suffers from a few disadvantages for modelling real world complex systems: i it may lead to the neglect of the socio-political dimension of a business process, as there is an implied belief that a business process can only be approached in logical and rational terms; ii such approaches obviously have a cost, so the time and skills required to build a dynamic computer model of simple systems may not add any value over simple flowcharts or spreadsheets; and iii it ignores the feedback loops that may determine the behaviour of many real-world business processes [ 22 ].
Multi Criteria Decision Making MCDM methods have been applied in many practical decision making situations, by practitioners and academics [ 31 ], including portfolio management [ 32 ], energy management [ 33 ], ecology [ 34 ], etc. MCDM methods generally compare several decision options against multiple and often conflicting criteria to provide decision outcomes in terms of ranks or overall scores.
Complex systems are also modelled using MCDM approaches including object-oriented modelling [ 35 ], modelling socio-economic processes [ 36 ], modelling production-inventory-supply chain systems [ 37 ], maintenance process modelling [ 38 ], etc. MCDM methods have the ability to incorporate the performances of decision alternatives under various criteria in easy-to-use processes for finding the best decision. MCDM methods may be able to quantify system performances from diverse measurements of subsystem performances but are unable to maintain the interrelationships for decision purposes as the overall decision outcome is obtained by combining the available information and the individual information is lost in this process [ 31 ].
The multi-agent technique is a relatively new complex system modelling paradigm that utilises the autonomy and characteristics of various entities in a system along with their relationships with each other [ 39 ].
The multi-agent technique has been widely used in supply chain management [ 40 , 41 ], manufacturing [ 42 — 44 ], and environment and ecology [ 45 , 46 ]. Advantages of multi-agent modelling include the ability to model a system in a realistic form, inclusion of heterogeneity while incorporating behaviours of different agents, flexibility and scalability of the developed models, and the ability to incorporate local objectives within the systems [ 39 ]. On the other hand some known disadvantages include extensive data requirements [ 47 ] and effort required for modelling, and resulting models which are developed for specific contexts and have very limited generic usage [ 48 ].
The various decision support and complex system modelling approaches discussed above each have distinct advantages and disadvantages and have been used in diverse application domains. These approaches have been used to develop models with specific stakeholder requirements in mind but they have limitations in some aspects of flexibility in usage and the extensibility necessary for developing multifaceted models.
In a multifaceted modelling approach the same model incorporates information related to all the stakeholders and can highlight and show those factors appropriate for a particular stakeholder while also revealing the interconnections and decision impacts on other stakeholders.
For example an organisation may have multiple units operations, finance, customer service, etc.
Multifaceted Modelling of Complex Business Enterprises
If a complex system model for the organisation is developed by following the organisational structure it will contain operational, performance and resource information for each department. To maximise the value of the modelling effort, the resulting model should also be sufficiently flexible that it can be used for purposes not anticipated at the time of its construction, and it must be maintainable as the business grows and evolves.
In our approach we use Bayesian Network modelling. A Bayesian Network modelling formalism has the inherent ability to model multifaceted complex systems by virtue of its simplicity and generality.
They are used in various research and practical areas [ 49 — 52 ]. The advantages of this approach include the ability to model complex interrelations between factors and the sensitivity of factors on decision outcomes [ 53 ], the ability to perform scenario analyses, to undertake sophisticated interrogations of the system, and to include other sources of information in the model, such as observational and experimental data, results from previous experiments, knowledge from published literature, expert judgements, and so on.
However, such a formal modelling notation alone is not useful without a well-defined strategy for constructing, employing and maintaining models based on the data available from the business enterprise. In the following section we present a novel, multifaceted approach for modelling complex business enterprises based on a Bayesian Network notation. Here we describe our development approach for constructing multifaceted complex system models from diverse available data in three stages: i Conceptualisation, ii Quantification, and iii Interrogation.
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