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What Is Business Process Modeling?

1 October 2021 6 min read

IBM Cloud Education, IBM Cloud Education

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Business process modeling gives organizations a simple way to understand and optimize workflows by creating data-driven visual representations of key business processes.

Most enterprises have a pretty good idea of the various business processes powering their daily operations. However, when they need to ensure that those processes consistently drive optimal outcomes, “a pretty good idea” isn’t enough.

If an organization wants research and development (R&D) investments to produce sufficient returns, IT issues resolved with minimal downtime or a highly accurate lead qualification workflow, it needs to understand these processes on an objective and comprehensive level. Even the business users directly involved in these processes may lack total transparency into exactly what happens at every step of the way.

Business analysts can gain end-to-end views of the business process lifecycle through business process modeling , a business process management (BPM) technique that creates data-driven visualizations of workflows. These process models help organizations document workflows, surface key metrics, pinpoint potential problems and intelligently automate processes.

What is business process modeling?

A business process model is a graphical representation of a business process or workflow and its related sub-processes. Process modeling generates comprehensive, quantitative activity diagrams and flowcharts containing critical insights into the functioning of a given process, including the following:

Key aspects of business process modeling

Learn more by reading “Process Mining vs. Process Modeling vs. Process Mapping: What’s the Difference?”

How business process models are made

To fully understand business process modeling techniques, one must first understand the relevant business process modeling tools — event logs and process mining .

Most enterprise IT systems maintain event logs . These event logs are digital records that automatically track state changes and activities (i.e., “events”) within the system. Anything that happens within a system can be an event. The following are some common event examples:

Event logs track both the occurrence of events and information surrounding these events, like the device performing an activity and how long the activity takes. Event logs act as the inputs during the production of process models.

Process mining is the application of a data-mining algorithm to all of this event log data. The algorithm identifies trends in the data and uses the results of its analysis to generate a visual representation of the process flow within the system. This visual representation is the process model . Depending on the process targeted for modeling, process-mining algorithms can be applied to a single system, multiple systems or entire technological ecosystems and departments.

Business process modeling use cases

Process models offer unprecedented levels of transparency into company workflows, making them a key business process management tool. While process models can be leveraged in any scenario that requires analyzing business processes, these are some of the most common use cases:

Gaining 360-degree insight into processes

A single process model can contain a wealth of workflow data, allowing team members to analyze a workflow from multiple perspectives. Business analysts often use business process modeling to zero in on the following workflow components in particular:

Optimizing and standardizing processes

Process models accurately reflect existing workflow inefficiencies, making it easier to identify opportunities for process optimization. Once workflows have been optimized, businesses can use process modeling to standardize workflows across the entire enterprise. The model acts as a template for how processes should play out, ensuring that every team and employee approaches the same process in the same way. This leads to more predictable workflows and outcomes overall.

Assessing new processes

Process models can take the guesswork out of implementing and evaluating new business processes. By creating a model of a new process, business users can get a real-time look at how that workflow is performing, allowing them to make adjustments as necessary to achieve process optimization.

Analyzing resource usage

Process models can help companies track whether money and resource investments produce suitable returns. For example, by creating a model of the standard sales process, an organization can see how sales representatives are utilizing the tools and systems at their disposal. It may turn out that a certain tool is used much less frequently than anticipated, in which case, the organization can choose to disinvest from the tool and spend that money on a solution the sales team actually uses.

Communicating processes

Process models transform complex processes into concrete images, making it easier to disseminate and discuss processes throughout the organization. For example, if one department has a particularly efficient process for troubleshooting technical problems, the business can create a model of this process to guide implementation on an organization-wide scale. 

The benefits of business process modeling

Business process modeling arms an enterprise with objective business intelligence that supports more informed decisions for resource allocation, process improvement and overall business strategy. With a clear view of processes, enterprise teams can ensure that workflows always drive the desired results. As a result, operating costs are lower, revenue is higher and business outcomes are stronger.

Specifically, business process modeling allows companies to do the following:

Business process modeling and IBM

Process modeling forms a cornerstone of any automation effort or business process management initiative. Without comprehensive views of existing processes and their undergirding business logic, enterprises cannot effectively optimize and automate workflows at scale.

Take the next step:

IBM Blueworks Live is a cloud-based business process modeling software designed to help organizations discover business processes and document them in a collaborative fashion across multiple stakeholder groups. Teams can work together through an intuitive and accessible web interface to document and analyze processes. No download required.

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Business Process Knowledge

What it Doesn’t Look Like

Questions to Consider

Learning and Development Activities

Choose one or two activities that support your preferred learning style, or styles

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Suggestions for activities you can do on the job

With your Manager/Team Lead

Here are some ideas that can be pursued on the job, with some coordination. Use these reflective questions to gain more from your learning experience:

UBC Training Programs offered through Continuing Studies: http://www.tech.ubc.ca/

For UBCO course offerings, please visit the Events page. http://web.ubc.ca/okanagan/facultystaff/events.html

Consider working with a coach following training, to aid in anchoring your learning: http://www.hr.ubc.ca/coaching/

Choose to read one or two of the books listed below. Consider the reflective questions to enhance your learning:

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Modeling Business Process

Related terms:.

Business Process Models

David M. Bridgeland , Ron Zahavi , in Business Modeling , 2009

Why Model Business Processes?

Why do we care about modeling business processes? As you will recall from Chapter 1 , business models in general are used for eight purposes: communication, training and learning, persuasion and selling, analysis, managing compliance, as requirements for developing software, executing directly as software, and knowledge management and reuse.

All eight purposes apply to business process modeling. Some businesses build business process models as part of their transformation initiatives to capture the way they perform their work today and the way they will perform work in the future. These models are used to communicate to the employees what will change and how the change will affect their day-to-day work lives. Sometimes models are used to train new employees so that they understand all the tasks they are expected to perform and the order in which they should perform them.

Process models are often analyzed . One business process is compared with others to see which process is best. Analysis helps us understand the cost involved with each process, how many people are needed, and where delays occur. Such analysis can also be used to persuade . If we think that outsourcing a business function is cheaper than keeping the function in-house, we can show process models with the function in-house and with the function outsourced and demonstrate the difference in cost. Sometimes process models are used to persuade clients or prospective clients—for example, to persuade a client that we understand his business and his challenges.

Process models are useful in managing compliance with a new regulation. By modifying an existing process (or by implementing a new process) we ensure that we are complying with a regulation. We can investigate the way we are doing work today and compare it to the work that needs to be accomplished to achieve compliance.

Business process models can provide us with information useful in capturing software requirements . By capturing the way users perform the work, we can understand their needs. We can investigate each activity in a process and determine whether the activity is supported by a software application today and whether it should be supported by an application in the future. We can trace the software requirements of the future applications back to the activities they support. ( Chapter 12 explores the relationship between business process activities and software requirements.)

Business processes can be executed as software . A business process executed as software becomes workflow. A user is presented with user interfaces that walk her through the steps she must perform. To execute a business process as workflow, a specialized tool must be used to convert from the business process to code that can be executed in a business process engine. The tool then ensures that the modeled workflow is realized and followed. Execution of business processes as workflow is explained in Chapter 12 .

When an organization practices knowledge management , it applies knowledge gleaned in one part of the organization to another part of the organization. Often this knowledge includes how to perform a business process. Business process models capture how work is performed and who performs it. They also show how a person interacts with others—both others within their organization and others external to it.

Variation in Business Processes

Mark von Rosing , Jonnro Erasmus , in The Complete Business Process Handbook , 2015

Managing Business Process Variances

The three approaches to capturing and modeling business process variance unsurprisingly result in varying degrees of managerial burden. Simply put, the amount of administrative control necessary is proportional to the amount of content created and the need for accurate traceability. The first approach, in which distinctive processes are created, results in the most architectural content, but does not really lend itself to maintaining traceability between the processes. A generic master process may obviously be documented, but its relationship to the variant processes can at best be a text-based reference. The other two approaches are better suited to maintaining traceability to the master process. The second approach, in which variants of the master process are created, will result in significantly more business processes, but at least commonality is encouraged by keeping the processes together in the model. The third approach will result in the fewest business processes and least content to manage, but is very difficult to capture in document format.

When considering management of the complete process life cycle, the need for different approaches for different processes is further enforced. Figure 6 shows how the various business models drive the value life cycle, which in turn drives the process life cycle.

business process model knowledge

Figure 6 . Business models and life cycle alignment. 18

The value and revenue models target innovation and align to the analysis and design phases of the process lifecycle. Thus, in the context of business process variance, business value, and revenue creation will drive the identification of unique variations in process and how those variations deliver value to the business. Innovation will eventually make way for a focus on efficiency and effectiveness, once a product or service reaches its midlife. Thus, the performance and service models will drive business process improvement and standardization. Eventually then, cost and operating models will be introduced to drive optimization and simplification. Thus, the management of business process variance is not only dependent on the business model and strategies, but also on the life cycle of the specific process and its resulting product or service. Early in the life cycle, when innovation is encouraged and freedom is sought, variation should be allowed. When the innovation delivers value, the core-differentiating competencies should be captured and treated as recognized business process variances. This approach ensures that the justification for the variance is captured to enable informed management thereof later in the process life cycle.

Regardless of the life-cycle phase of a process, it should be measured and managed. The process performance indicators will typically be associated to individual variants, to allow for comparison of the process variants. The more business process variance exists, the more management effort is necessary, because effectively the amount of architectural content is increased. As with all business processes the documentation, configuration, and interfaces of all variants must be managed. This task is significantly more difficult though; not only must alignment be maintained between the actual process, the documentation that describes it and what is expected of the process, but also traceability and commonality to the master process, if one exists. This requires establishment and maintenance of an additional relationship within the process content and appreciable attention from business process management.

Modeling Business Processes Using BPMN

Tim Weilkiens , ... Kim Nena Duggen , in OCEB 2 Certification Guide (Second Edition) , 2016

Goals of BPMN

BPMN pursues the following goals:

A standardized graphical notation exists for modeling business processes.

The notation can be understood by all stakeholders—from business analyst to process implementer.

Among other things, the notation allows for the mapping of a graphical notation in an executable XML-based process language—for example, Web Service Business Process Execution Language (WSBPEL).

The notation also allows an interchange of diagrams between tools, using an interchange format and execution semantics using a process engine .

If you sneak a peek at the specification, 1 it provides you with

all elements of the graphical notation,

the metamodel as a class diagram, and

mapping of BPMN on WSBPEL and diagram interchange formats (in the specification's appendix).

BPMN supports three types of diagrams: the process diagram, that is, the operational sequence as a model, and the conversation and choreography diagrams that are used in special cases. For this reason, the focus of the BPMN is not on the following:

Tim Weilkiens , ... Andrea Grass , in OCEB Certification Guide , 2011

BPMN was defined to achieve the following goals:

Provide a standardized graphical notation for modeling business processes.

The notation can be understood by all stakeholders, from business analyst to process implementer.

The notation can also be mapped to executable XML-based process languages (such as Business Process Execution Language for Web Services, BPEL4WS 4 ).

If you sneak a peek at the specification, 5 it provides you with all elements of the graphical notation and a mapping of BPMN to BPEL4WS (see Appendix).

BPMN 1.x supports one single diagram type only: the Business Process Diagram (BPD). The BPD includes an image of the business processes. For this reason, the focus of the BPMN is not on the following:

Organization structure

Resource structure

Data and information models

Business strategies and business rules

Definition of an exchange format like BPEL4WS 6

Metamodel 7

The next section introduces you to BPMN using an example.

Business Processes and Information Flow

David Loshin , in Business Intelligence (Second Edition) , 2013

Benefits of the Business Process Model

There are some major benefits for building this model, such as:

Understanding an information flow provides logical documentation for the business process;

The review may uncover additional opportunities for adding value through different kinds of analytical processing;

Business modeling process helps in understanding business consumer data use; and

Business modeling helps in communicating user requirements to the implementation team.

When a formal framework is used to describe a process, not only does it ease the translation of user needs into system requirements, it also provides the manager with a high-level view of how control migrates throughout the system and how information flows through the business, both of which in turn help guide the dissection of the problem into implementable components.

More generally, an information flow, as embodied as part of a business process model, provides the following benefits:

Development road map . Identifying how information is used and diffused helps direct the development of interfacing between the discretized execution components as well as tracking development against the original requirements.

Operational road map . When the application is in production, the model provides a description of how any analytical data sets are populated as well as a launch point for isolating problems in operation. It can also be used to track and isolate data quality problems, map workflow and control back to information use, and expose opportunities for optimization.

Management control . This model provides a way to see how information propagates across the organization, to identify gaps in information use (or reuse), and to expose the processes involved in information integration.

Consideration of return on investment . This allows the manager to track the use of information, the amount of value-adding processing required, and the amount of error prevention and correction required to add value and to relate the eventual business value back to the costs associated with generating that business value.

Orchestration as Organisation

Malinda Kapuruge , ... Alan Colman , in Service Orchestration As Organization , 2014

4.4 Two-tier constraints

A service orchestration is a coherent environment in which the service consumers and service providers as business entities achieve their respective goals via their integration by a service aggregator. However, changes are inevitable. Therefore, during the runtime, the organisational behaviours, as described by behaviour units in Serendip, need to be altered to facilitate various change requirements. Irrespective of the source, the runtime changes need to be carried out both in the process definition level and in the process instance level [77, 251] [77] [251] . It is a challenge for a service orchestration to maintain its integrity amidst such changes. Therefore, it is important that the process modelling approach provides suitable measures to ensure that the changes are carried out within a safe boundary.

In this section, we discuss the importance of such a boundary for a safe modification and how to define one without unnecessarily restricting the possible modifications.

4.4.1 The boundary for a safe modification

A clearly defined boundary for safe modification not only safeguards the integrity of the organisation or composition but also helps to increase the confidence level when applying changes. Such confidence allows a service aggregator to further optimise the business processes without being held back by the possibility of possible violations of business constraints. In a service composition, optimisations need to be carried out to satisfy the business requirements of the service consumers and service providers.

For example, some service consumers may need faster and rapid completion of towing, repairing and other assistances. Certain assistance processes might need to deviate from the originally specified time period depending on the severity of the accident and the unforeseen customer requirements. Fulfilling such requirements can increase the reputation of RoSAS as the service aggregator. Inability to cater for the customer requirements may damage its reputation. However, the providers of bound services are also autonomous businesses and have their own limits in delivering services. Furthermore, the collaborations need to maintain certain temporal and causal dependencies between tasks. An adaptation from a service consumer point of view may lead to breaking certain dependencies, hindering the collaborations and impacting on the service delivery in the long run.

Similarly, an adaptation requested by one of the collaborating services might also impact on the customer goals. Thus, the service aggregator needs to support the adaptability in a careful manner. The possible adaptations need to be facilitated but without compromising the integrity of the composite from the perspectives of the consumers, partner services and aggregator. Consequently, the problem of supporting adaptations should be considered as one of finding a solution to meet customer demands within a safe boundary, as symbolised in Figure 4.15 . Therefore, we define the boundary for safe modification in terms of ‘a set of constraints formulated from the business requirements of the aggregator as well as the consumer and partner services of a service orchestration to avoid invalid modifications.’

business process model knowledge

Figure 4.15 . Boundary for safe modification.

The use of constraints in business process modelling has been extensively explored in the past. Condec [73, 150] [73] [150] is a constraint-based process modelling language that attempts to use constraints for business process modelling. In Condec, there is no specific flow of activities; instead, the activities are carried out as long as the execution adheres to the defined constraint model. The constraint model defines the constraints that should not be violated at a given time. This ensures the integrity while providing increased flexibility for the runtime execution. Regev et al. [76] also see the flexibility as the ability to change without losing the identity. The identity of a business is defined via a set of ‘norms’ and ‘beliefs’. Here, a norm is a feature that remains relatively stable and a belief is a point of view from a particular observer [252] .

We also agree that defining such constraints when modelling business processes is essential to ensure their integrity. However, this book moves a step further by addressing how the constraints should be defined in an orchestration to achieve the maximum flexibility without unnecessarily restricting the possible changes to the runtime. We propose defining constraints in two different levels of a shared service composition, and thereby identify the minimal set of constraints that is applicable for a given modification.

4.4.2 The minimal set of constraints

A common practice is to globally associate constraints with a process model [76, 110, 139] [76] [110] [139] . A process model is valid as long as a set of constraints are not violated. However, this book discourages setting up a global set of constraints in a service orchestration, especially in shared online marketplaces where multiple business requirements are achieved using the same shared application infrastructure. Instead, the constraints should be specified in terms of a particular scope. We use the modularity provided by behaviour units and the process definitions as the basis for specifying the constraints. When a modification is proposed, only the applicable minimal set of constraints is considered to check the integrity. As shown later in this section, the minimal set of constraints is determined by considering linkage between behaviour units and process definitions.

Unlike a global set of constraints, which is same for all the modifications, a well-scoped constraint specification only uses the minimum relevant constraints. It follows that compared to the global specification of constraints, the probability of restricting even a possible modification is less in well-scoped constraints due to a lesser number of constraints considered. With this intention, we define constraints in two different levels in a Serendip orchestration, i.e., behaviour-level and process-level constraints.

Behaviour-level constraints are defined in behaviour units, e.g., in bTowing , to safeguard the collaboration aspects as expected by towing. A collaboration represented by the behaviour units can have certain properties or constraints that should not be violated by runtime modifications to the behaviour unit. A sample constraint (bTowing_c1) is shown in Listing 4.9 , which specifies that every car towing should eventually be followed by a payment . In other words, if event eTowSuccess has occurred, then it should eventually be followed by the event eTTPaid . Consequently, each process definition that refers to bTowing should respect this constraint. Note that the constraints are specified in the TPN-TCTL [253, 254] [253] [254] language, which is a language to specify TCTL properties of a Time Petri-Nets (TPN). Further details about the used constraint language can be found in [255, 256] [255] [256] . This language has been used due to its expressiveness in terms of specifying the causal relationships between two events and the available tool support [257] .

business process model knowledge

Listing 4.9 . Behaviour-level constraints.

Process-level constraints are defined to safeguard the goals represented by a process definition. A process definition addresses a requirement of a customer or a customer group. Therefore, these constraints reflect goals or norms as expected by the customer. Process constraints are defined within a process definition. A sample process-level constraint is shown in Listing 4.10 , which specifies that when a complaint is received, eventually it should be followed by a notification to the customer . Because the constraint is defined in pdGold , this constraint is applicable only for gold customers.

business process model knowledge

Listing 4.10 . Process-level constraints.

Figure 4.16 shows the relationship among constraints and the constituent process modelling concepts, process definitions and behaviour units.

business process model knowledge

Figure 4.16 . Meta-model: types of constraints.

The relevancy of an applicable minimal set of constraints is determined by the linkage among behaviour units and process definitions within the organisation. For a change in a behaviour unit B, which is shared by n process definitions PD i ( i =1, 2, 3, …, n ), the applicable minimal set of constraints (CS msc ) is:

CS B is the set of constraints defined in behaviour unit B and

CS PD i ( i =1, 2, 3, …, n ) is the set of constraints defined in the i th process definition PD i that refers to behaviour unit B.

Any applicable constraint set (CS msc ) is always a subset of the global set of constraints (CS global ), which is all the constraints defined over all the m behaviour units and k process definitions, i.e., CS msc ⊆CS global .

CS PD i ( i =1, 2, 3, …, k ) is the set of constraints defined in the i th process definition PD i .

CS B j ( j =1, 2, 3, …, m ) is the set of constraints defined in the j th behaviour unit B j .

4.4.3 Benefits of two-tier constraints

To elaborate the benefits of having two-tier constraints, consider that six constraints (c1, c2, …, c6) are defined within the scope of two process definitions ( pdGold and pdPlat ) and three behaviour units ( bRepairing , bTaxiProviding and bAccommodationProviding ), as shown in Figure 4.17 . Assume there is a modification proposed to pdGold due to a change requirement in the gold service consumer group to change the way the taxi assistance is provided. Consequently, the behaviour unit bTaxiProviding needs to be modified. However, another process definition pdPlat also uses the behaviour unit bTaxiProviding .

business process model knowledge

Figure 4.17 . Process-collaboration linkage in constraint specification.

Therefore, the change in the pdGold has an impact on the objectives of pdP l at. Consequently, the applicable minimal set of constraints that needs to be considered when identifying the impact of the modification include all the constraints defined in bTaxiProviding , pdGold and pdPlat. Therefore,

CS msc ={c2, c5, c6}

CS global ={c1, c2, c3, c4, c5, c6}

There is no requirement to consider the other constraints, i.e., c1, c3 and c4. Hence, such a scoping of constraints and the explicit linkage between process definitions and behaviour units avoid unnecessary restrictions and considerations in modifications in contrast to a global set of constraints. Only the applicable minimal set of constraints (CS csp ) is considered, which is always less or equal than the global set of constraints (CS global ).

The use of two-tier constraints also helps to identify the affected process definitions and behaviour units. This helps the change impact analysis processes. In this example, the process definitions pdPlat and pdGold and behaviour unit bTaxiProviding are affected. The other process definition pdSilv and the rest of the behaviour units are not affected. When violation happens as identified by impact analysis, a software engineer can pinpoint and perform corresponding actions only on the exact sections that are affected in a large service orchestration. Possible actions would be to discard the change or relax some constraints. Such a capability is possible due to the consideration of explicit linkage between the process definitions and behaviour units in the two-tier constraint validation process (see Section 6.4 ).

Literature Review

3.4.3 business rules integration.

One of the issues with the popular orchestration standards is the imperative or procedural nature of process modelling. The businesses operating in more dynamic environments challenge the imperative and procedural process modelling paradigm as it fails to successfully capture the unpredictability and complexity of businesses. Intrinsically, the business policies (rules) that are subject to frequent changes are intermingled with the process logic, leading to an unmanageable process/rule spaghetti [106] .

This consideration leads to more descriptive approaches such as the use of business rules [171] in modelling business processes. The business rule engines work hand-in-glove with process enactment engines to provide the required flexibility for runtime modifications. The business policies for achieving business goals are specified as business rules [172] . A widely accepted definition for a business rule is provided by the GUIDE group [173] . According to them ‘ a business rule is a statement that defines or constrains some aspects of the business. It is intended to assert business structure and to control or influence the behaviour of the business .’ Rather than defining these business assertions as part of business processes, they were defined as separate artefacts using more expressive rule languages. The required agility of dynamic changes to the business logic is achieved through dynamic changes to these rules as enabled by the advancements of rule engines and languages such as Jess [174] , JRules [175] and Drools [176] .

Traditionally, these rule engines are used to reason about the software behaviour based on the knowledge supplied as declarative rules. The policies were specified as event-condition-actions (ECA) rules [177] or IF-THEN (condition-action, CA) rules [176, 178] [176] [178] without requiring much knowledge of programming languages. Therefore, from the language comprehension point of view, business rules are much easier to understand for business users compared to traditional programming languages [179] . This is in fact a major step forward, as now the business users have the ability to specify and continuously change the business policies themselves in software systems according to a more natural and business-oriented language. In other words, the ability of reflecting the business know-how is now made more direct and agile. With the support of business rules management systems, the business policies are not hard-wired to the application logic, as is the case with traditional programming languages.

In order to exploit these advantages of business rules, Rosenberg et al. [179] propose integrating business rules with a service orchestration. A service orchestration specified in the BPEL language is integrated with business rules. Business rules are categorised into integrity rules, derivation rules and reaction rules . Such categorisation distinguishes the types of rules that can be used on a service orchestration. The integrity rules check data conformance, while derivation rules infer information from the knowledge available via inference and mathematical calculations. The reaction rules are ECA rules that perform actions in response to an event under a certain condition.

These rules are integrated with the orchestration using two interceptors called before and after . The before interceptor allows rules to be executed before a BPEL activity, whereas the after interceptor allows rules to be executed after a BPEL activity. These interceptions are specified in a mapping document, which maps process activities and rules. Such integration exploits the aforementioned advantages of business rules, including the ease of understanding and adaptation. Another important architectural contribution of this approach is the way in which the business rules engine is integrated with the orchestration engine. The integration is loosely coupled with the support of the business rules broker [180] . The business rules broker, which is a broker service, makes the business rules engine independent from the orchestration via a unified Web service interface. However, the points of adaptations need to be foreseen and cannot be changed during the runtime. Furthermore, there is limited support for controlling the changes and analysing the change impacts on business collaborations.

Several patterns on how business rules should be integrated with business processes has been introduced by Graml et al. [181] . The rules can be complex but the outcome, which is a simple Boolean value (yes/no), can be used to drive the process. Similar to the work of Vanderfeesten et al. [115] , this approach calls a rule service to evaluate rules before and/or after an activity of a process. From an architectural point of view, both share the same concepts of rule integration. However, this approach has made several improvements compared to the work of Rosenberg et al. [179] . First, the approach does not limit its adaptation capabilities to a mere rule evaluation. The introduced patterns specify several actions that can be taken based on the rule evaluation, including even more drastic changes such as insertion of sub-processes to the process flow. Second, the approach allows rules to take decisions based on a shared process context, which is important when multiple rules are collectively used to make decisions. Third, the post-processing of the BPEL code is done automatically via XSLT [182, 183] [182] [183] transformations, which reduces the engineering effort.

Rule-based BPMN (rBPMN) [184] is another rule and process integration effort. The rBPMN approach integrates BPMN processes with R2ML rules (reverse rule mark-up language) [185] . The adaptability is provided by dynamically changing rules. R2ML rules are used as gateways of BPMN and are connected to other parts of a process flow. The rBPMN approach claims to support all the change patterns proposed previously [181] ; however, rBPMN is more advanced because the rule integration is done at the meta-model level, leading to a definition of a rule-based process modelling language. The approach uses reaction rules that are capable of generating complete service descriptions. This is not usually supported by approaches that use the rule integration technique. The integrity rules are used to ensure the integrity of the overall control flow. However, the replacement of process fragments is limited to pre-identified places of a complete process flow.

This way of integrating rules along with an orchestration engine can be problematic in terms of understanding the overall process flow. The adaptations carried out by the rules can lead to many complex flows that may harm the integrity of the process. This is especially the case in such environments as service brokering and SaaS, in which requirements of many tenants/consumers need to be satisfied within the same composite service instance. In such environments, it is important to facilitate the capture of commonalities and variations systematically. Furthermore, special attention should be given to control the adaptations so that the integrity of the overall service orchestration is protected.

Overall, while business rules languages provide a powerful way to express business policies in a business user-friendly manner and even in natural languages [122] , careful attention needs to be given to ‘ how ’ these rules should be integrated with a process flow. It should be noted that flexibility is not an explicit characteristic of business rules development. Boyer et al. states that ‘ the agility is not given with business rules development, (rather) we need to engineer it ’ [186] . The same applies for integration of business rules with business process modelling and enactment. The separation of adaptation concerns should be done in a manner that the overall understanding of the process is preserved. Furthermore, providing modularity and improving reuse are also important.

The use of software product lines for business process management: A systematic literature review

Roberto dos Santos Rocha , Marcelo Fantinato , in Information and Software Technology , 2013

2.1 Business process management

BPM (Business Process Management) has been presented as a key factor to the success of an IT infrastructure prepared for today’s organizational demands [15] . Moreover, BPM is seen as a competitive edge for the organizations, as with it they can determine and exhibit their maturity level [16] .

According to van der Aalst et al. [4] , BPM includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. BPM can therefore be considered an extension of classical Workflow Management approaches and systems [4] . Several specification and modeling languages and tools have been proposed to be used in BPM, from which the BPMN (Business Process Model and Notation) language [20] has become the ‘de facto’ standard language to represent business processes. Nevertheless, other languages such as UML Activity Diagrams have also been used for modeling business processes [21,22] .

A business process consists of a set of tasks performed in a specific sequence to achieve a common business goal [13,23] . The BPM lifecycle includes several phases, such as [4,5] : (a) business process modeling; (b) business process model instantiation; (c) business process enactment and administration; (d) business process monitoring and auditing; and, (e) business process assessment and optimization. In the last phase, the execution history can be analyzed, looking for ways to improve the business process, which leads to business process remodeling, restarting the cycle all over again [13] . Considering the markets’ current dynamics, each sequence in such lifecycle is usually completed in a very short time, due to the constant need for new versions of the business processes running in the organizations [24] .

In order to make the management and integration of business processes possible, from a technical point of view, different technologies have been proposed, including, not so recently, the middleware frameworks such as CORBA, DCOM and Java-RMI [25] , which were properly used in the intra-organizational context. As the need for interoperability has evolved towards interorganizational cooperation, the existing solutions failed to meet their objectives [25] . Such limitation was finally resolved when SOA and their implementations emerged [26] , mainly through the web services technology, offering new perspectives to this need and providing, for example, the composition of e-services through WS-BPEL (Web Services Business Process Execution Language) [27] to enable the execution of business processes.

Applications of neuro fuzzy systems: A brief review and future outline

Samarjit Kar , ... Pijush Kanti Ghosh , in Applied Soft Computing , 2014

8 NFS in manufacturing and system modeling

Manufacturing system comprise of equipment, products, people, information, control and support functions for the competitive development to satisfy market needs. The term may refer to a range of human activity, from handicraft to high tech, but is most commonly applied to industrial production in which raw materials are transformed into finished goods on a large scale. Manufacturing takes turns under all types of economic systems. System modeling concerns modeling the operation of an unknown system from a set of measured input output data and has a wide range of applications in various areas such as control, power systems, communications, and machine intelligence. Systems modeling may be used in different ways as part of a process for improving and understanding of a situation, identifying problems or formulating opportunities and supporting decision making. In business and IT development the term “systems modeling” has multiple meaning such as functional modeling, business process modeling, enterprise modeling etc. Applications in this category includes autonomous vehicles, gear industry, underwater robotics, anti lock braking system, supply chain management, unmanned flight control, pneumatic system, software development time estimation, time varying system etc.

During the last decade a number of researchers have contributed their innovations in this category. Kim and Yuh [66] in 2002 described fuzzy membership function based neural network for autonomous underwater vehicles of which the dynamics were highly coupled, non-linear and time varying. They achieved this by using on-line training algorithm with neural network. Then in 2004, Wang et al. [67] developed a neuro fuzzy diagnostic system for monitoring gear function. They used constrained gradient reliability algorithm to facilitate decision making process which significantly improved the diagnostic accuracy. Chen et al. [184] extended the Takagi–Sugeno (T–S) fuzzy model representation for the stability analysis of nonlinear interconnected systems with multiple time-delays using linear matrix inequality (LMI) theory. In the year 2005 many researchers [68–72,137,138] started their journey in this category. Kim et al. [68] started working on underwater vehicle dynamics using NF controller. They described a fast on-line neuro fuzzy controller for underwater robots of which the dynamics are highly nonlinear, coupled, and time-varying. The neuro fuzzy controller was based on the Fuzzy membership function neural network (FMFNN) with varying learning rate. Roy [69] proposed an approach based on adaptive neuro fuzzy inference system for predicting surface roughness of the workpiece in turning operation for set of given cutting parameters such as cutting speed, feed rate and depth cut mainly used in manufacturing industry. Ouyang et al. [70] developed a TSK-type neuro fuzzy network technique for deriving a model from a given set of input-output data for system modeling problems. Kaitwanidvilai and Parnichkun [71] in their paper investigated two types of controller: adaptive neuro fuzzy model reference controller (ANFMRC) and hybrid ANFMRC to enhance the controller performance for the pneumatic system. Pneumatics is a section of technology that deals with the study and application of pressurized gas to produce mechanical motion mainly used in industries and factories. Yang et al. [72] developed a neuro-fuzzy controller with self-organized optimal fuzzy rules and membership functions to illustrate the effectiveness of the neuro fuzzy system on vibration control of a hard spring system. Hsiao et al. [137] derived a stability criterion for nonlinear multiple time-delay interconnected systems via Lyapunov's direct method. Authors proposed a systematic design using Takagi–Sugeno fuzzy controller to ensure the stability of nonlinear multiple time-delay interconnected systems. To overcome the effect of modeling errors between nonlinear multiple time-delay subsystems and Takagi–Sugeno fuzzy models with multiple time delays, Hsiao et al. [138] proposed a robustness design of fuzzy control using a model-based approach and Lyapunov's direct method. In 2006, Raad and Raad [73] developed neuro fuzzy controller to carry out adaptive channel reservation in micro-cellular networks with general cell dwell times and call holding times where handover rates were expected to be high and non-poissonian. Chen [175] provided the stability conditions and controller design for a class of structural and mechanical systems represented by Takagi–Sugeno fuzzy models. Vesselenyi et al. [74] in 2007 presented some modeling applications on a pneumatic actuator containing a force and a position sensor. Later in 2008, Marza et al. [75] aimed at building and evaluating a neuro-fuzzy model to estimate software projects development time which is one of the challenging tasks for software developers. Authors used MATLAB 7.4 to process the fuzzy logic, neural network and neuro-fuzzy systems. Topalov et al. [76] presented a paper using a new neuro-fuzzy adaptive control approach for development of nonlinear dynamical systems, coupled with unknown dynamics, modeling errors and various sorts of disturbances. This scheme consisted of a conventional proportional plus derivative (PD) controller and a neuro-fuzzy feedback controller. After a few months the authors modified their view on anti lock braking system using a new variable structure systems-based (VSS-based) [77] on-line learning algorithm for parameters adaptation. Sayedhoseini et al. [78] in 2010 developed an approach based on adaptive neuro fuzzy inference system for measurement of agility in supply chain management while agility is accepted as a winning strategy for growth, even a basis for survival in certain business environments, the idea of creating agile supply chain have become a logical step for companies. An agile supply chain (ASC) is frequently considered as a dominant competitive advantage. Kurnaz et al. [79] in their paper described an ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) with the help of three distinct operating modules implemented in MATLAB. Perez et al. [131] presented a neuro-fuzzy controller that has been applied to intelligent transportation systems. The purpose of this controller was to improve the response of a vehicle propelled by a gasoline motor. Chen [167] presented a fuzzy Lyapunov method to derive the stability conditions for an interconnected fuzzy system which represents real structural systems. Chen [172] provided a systematic and effective framework for the control of time-delayed nonlinear structural systems subjected to external excitations. He constructed global fuzzy logic controller based on T–S fuzzy controller design techniques. In 2011, Abiyev et al. [80] designed a type-2 TSK based fuzzy neural system (FNS) structure for identification of dynamic time-varying plants and equalization of time-varying channel. The usage of type-2 fuzzy sets enables the system to cope with uncertainties and to handle uncertain information effectively. Chen et al. [139] proposed an intelligent adaptive controller to handle the computational burden and dynamic uncertainty of multivariable systems to make the model-based decoupling approach simpler to implement in a real-time control system. In this study the adaptive controllers were based on fuzzy systems and the initial parameter vector values are based on the genetic algorithm. Chen [176] developed a neural-network (NN) based approach which combines Tagagi–Sugeno fuzzy control for the purpose of stabilization and stability analysis of nonlinear systems. Farooq et al. [81] designed a neuro fuzzy approach for the autonomous vehicle speed controller where the system knowledge representation and the learning mechanism made it easier to adapt the environment and control the speed to avoid the traffic congestion and collision. Topalov et al. [82] developed a Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Recently Mahdaoui et al. [83] used NF diagnostic approach along with TSK/Mamdani model and pattern recognition technique in manufacturing system for fault diagnosis. In last year (2012) Kayacan et al. [84] modeled intelligent control of tractor implement system by using type 2 fuzzy neural network (T2FNN). Authors used a novel sliding mode control theory based learning algorithm for training purpose of T2FNN. Chen [161] addressed the stability conditions for nonlinear systems with multiple time delays to ensure the stability of building structure control systems based on the fuzzy Lyapunov method. The delay independent conditions were derived via the traditional Lyapunov and fuzzy Lyapunov methods for multiple time-delay systems as approximated by the Takagi–Sugeno fuzzy model. Yeh et al. [181] proposed a method using neural-network fuzzy control mechanism for a time-delay chaotic building system where a novel stability condition was derived using the fuzzy Lyapunov method. Authors used both the Tagaki–Sugeno fuzzy model and parallel distributed compensation (PDC) scheme in the controller design. The researchers’ motivation behind the research work was to improve the performance in manufacturing system and system modeling. The methodology, domain and article type of manufacturing and system modeling is categorized in Table 7 .

Table 7 . Manufacturing and system modeling.

Using Business Process Modeling and Tools

Summer kay september 15, 2020 business process.

Business Process Modeling

Great organizations continually improve their processes over time, striving to make them as efficient and cost-effective as possible. Business process modeling is a tool that organizations use to evaluate their current processes.  It allows the creator to visualize a more efficient process.  In this post, we explain how business process modeling techniques give you the knowledge to improve performance across your enterprise.

What is business process modeling?

Business process modeling is a technique that involves creating a visual depiction of a business process . This is typically achieved by using business process modeling tools like flowcharts and universal business modeling process notation (also known as BPMN ).

Next, business process modeling is used to identify improvements in an organization’s business processes or workflows. It does this by mapping two different iterations of a given process. The first is the process as it currently exists without implementing any changes. The second is what the process will be once improvements have been made.

It is possible to manually sketch out the steps in a process. This method is, however, far more time-consuming, and less effective than leveraging an automation solution like business process modeling software . With software organizations can easily create and run a process model to identify areas for improvement.

As written in a previous blog post, business process mapping techniques , “you can’t manage what you don’t measure.”

Business process modeling vs business process mapping

The terms business process modeling and business process mapping are often used interchangeably. While the techniques are similar, there is a difference. Business process modeling is generally used to map out low-level processes. In other words, the purpose of the diagram is to provide a somewhat broad overview of how a process works.

Business process mapping, however, can be used to create both broad and highly detailed diagrams. Where maps are detailed, their purpose is to provide operating procedures to guide stakeholders on the efficient completion of a process. Note that both business process modeling and business process mapping are used as part of a broader initiative like business process management .

The benefits of business process modeling

Business process modeling is a highly effective technique that offers organizations a broad range of benefits. Some of these benefits include:

Consider this example of an organization:

“… when reviewing a business process flow chart, a company’s senior management team may recognize that departments that offer incentive programs, such as employee of the week, are more productive than those that do not. As a result, they may choose to provide incentives to employees company-wide.”

Understanding, streamlining and automating processes results in more efficient and more profitable businesses. Through the use of process mapping, information is readily available for important decision making.  By graphically depicting workflows, the organization gains transparency in the various workflows throughout the business. Having this information at the ready creates a strong foundation for business agility and speeds the go-to-market efforts for new products and services.  

Modeling Elements

Companies use a standardized set of elements provided by the business process modeling notation to model processes. As BPM.com noted in a 2016 white paper, there are four basic types of elements:

Business Process Modeling Tools

There are many different business process modeling tools that can be used to improve workflows, making them more efficient and cost-effective.

SIPOC Diagrams

A SIPOC diagram is a tool used in the Six Sigma methodology. SIPOC is an acronym that helps stakeholders to identify the key elements of a process improvement project. The elements are the suppliers, inputs, process that is being improved, outputs, and the customers that receive the outputs.  

Business Process Model and Notation (BPMN)

BPMN diagrams are business process modeling tools that were developed by the Business Process Management Initiative (BPMI). The technique is like UML diagrams and is a standardized method for creating flowcharts – a step by step diagram of a process. Thus, when creating a process model, you use the elements specified under the BPMN methodology.  

Unified Modeling Language (UML) Diagrams

UML is a developmental modeling language that is used to provide a standardized way to visually represent a system. Diagrams include a system’s actors, actions, roles, and classes and help to gain a better understanding of or to document a system. UML was created in 1994 and its rapid rise in popularity led to it being published as an approved ISO standard in 2005.  

Value Stream Mapping

Value stream mapping is a business process modeling tool used to analyze the existing and future states of a process. These maps show all critical steps as well as the flow of materials and information through a process.  

An IPO, or input-process-output model , is a functional graph that identifies inputs, outputs, and required processes. The inputs consist of the information or materials that are introduced into a business process. This triggers the tasks that are required to produce the outputs that are the objective of a business process.

Gantt Charts  

Gantt charts are simplistic diagrams that provide a visualization of the overall time taken to complete a task or process. More specifically, Gantt charts can show the start and end times/dates of a process, the required tasks, and how long each took to complete.

Modeling process improvements

As evidenced by the different process modeling tools that we discussed in the last section, there are different ways to utilize the method. There are, however, some general steps that are used when the goal is to improve a process. These include:

BPM software features

We mentioned above that business process modeling is not a standalone method. Rather it is used as part of a larger initiative like business process management (BPM). As such, a process modeler is an essential feature of a BPM software solution .

A process modeler allows users to design business processes using an intuitive and easy to use drag and drop feature. Users can drag and drop tasks as well as decision points directly on the modeling canvas. Additional elements like forms, users, and data connectors can also be added. It is also important for users to be able to test their new process designs. This is accomplished with a process validation engine. Process validation engines allow users to test their new process designs to ensure they are working prior to deployment.

ProcessMaker offers an industry leading low-code business process management software that gives organizations access to powerful business process modeling tools. ProcessMaker has helped organizations around the globe to transform their business processes.

bpm Business process management Process modeling

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