Separation of decision modeling and BPMN
Posted: Mon Dec 23, 2024 9:43 am
Decision Model and Notation (DMN) site was designed to complement the Business Process Model and Notation (BPMN) and specific concerns about implementing decisions associated with various process models. Some process models include decisions encoded throughout the flow structure and data flow elements. In this article, we will examine the separation between decision modeling and BPMN and determine which decision model should capture the elements of decision making. Finally, we will examine how it plays a role in collaboration networks.
What is decision modeling?
To begin decision modeling, start with the decision: “What is the question that needs to be answered?” Then, add the inputs (considerations) needed to answer the question. Include all possible combinations of values and outcomes (conclusions). Now ask the question: “How does the outcome build on the considerations?” For each consideration that does not include immediately observable data, consider what would be required to create the input as the outcome of the sub-decision. Continue playing out all possible combinations and effects until each consideration is configured into verifiable data and does not require additional sub-decisions.
Think of a monthly wine subscription box. The rules that determine whether an incoming order is accepted should be designed to be unambiguous. For example, if the question is “Accept order,” the outcome should have only two possible values, “Yes” or “No.” Additionally, legal factors should be considered:
if the customer is of legal drinking age in the state where he or she placed his or her order.
If their condition allows the delivery of alcohol.
If the shipping address is outside the delivery parameters, for example outside the United States.
By adding all these factors together, the wine of the month company can make an unambiguous decision whether or not to accept the order.
Decision modeling for order acceptance
When you break down decision modeling into sub-decisions, it becomes easier to reuse them independently. On the other hand, DMN can focus on decision tables and support decision variations and special considerations. If DMN does not include predictive models, decision trees, rule sets, or even dashboards, it may miss its target. When processes become more complex, multiple decisions can affect the outcome. Since the context of decisions can impact the outcome, this aspect must be considered when designing the process. Therefore, it opposes the declarative protocols of DMN. Therefore, consistency with decision and process models must also become a priority. Here are some scenarios involving DMN and BPMN:
Process without decisions but with Processes involve various interdependent decisions. The philippines mailing list course and outcome depend on the conclusions. To improve efficiency, it is essential to ensure that no decision is made twice.
The processes are knowledge intensive and are only used to decide on a single outcome.
For example, imagine you are applying for a business loan and starting a new process. Once the bank receives the necessary financial data, it must decide whether the customer’s background qualifies them for a loan. The criteria are based on financial health to minimize the risk of default. If the customer meets the criteria, the loan is accepted. If not, the loan is rejected. However, if the eligibility criteria change, the process model and input data must be modified.
Modeling the decision distinct from the BPMN
When looking at BPMN 2.0 you get a more standard definition of calling business process rules . The "business rule task" uses the inputs and outputs of the invoked tasks. Furthermore, these inputs and outputs can serve as results for decisions. Furthermore, sub-decisions contribute to an overall business decision. So it makes sense that DMN supports BPMN 2.0.
What is decision modeling?
To begin decision modeling, start with the decision: “What is the question that needs to be answered?” Then, add the inputs (considerations) needed to answer the question. Include all possible combinations of values and outcomes (conclusions). Now ask the question: “How does the outcome build on the considerations?” For each consideration that does not include immediately observable data, consider what would be required to create the input as the outcome of the sub-decision. Continue playing out all possible combinations and effects until each consideration is configured into verifiable data and does not require additional sub-decisions.
Think of a monthly wine subscription box. The rules that determine whether an incoming order is accepted should be designed to be unambiguous. For example, if the question is “Accept order,” the outcome should have only two possible values, “Yes” or “No.” Additionally, legal factors should be considered:
if the customer is of legal drinking age in the state where he or she placed his or her order.
If their condition allows the delivery of alcohol.
If the shipping address is outside the delivery parameters, for example outside the United States.
By adding all these factors together, the wine of the month company can make an unambiguous decision whether or not to accept the order.
Decision modeling for order acceptance
When you break down decision modeling into sub-decisions, it becomes easier to reuse them independently. On the other hand, DMN can focus on decision tables and support decision variations and special considerations. If DMN does not include predictive models, decision trees, rule sets, or even dashboards, it may miss its target. When processes become more complex, multiple decisions can affect the outcome. Since the context of decisions can impact the outcome, this aspect must be considered when designing the process. Therefore, it opposes the declarative protocols of DMN. Therefore, consistency with decision and process models must also become a priority. Here are some scenarios involving DMN and BPMN:
Process without decisions but with Processes involve various interdependent decisions. The philippines mailing list course and outcome depend on the conclusions. To improve efficiency, it is essential to ensure that no decision is made twice.
The processes are knowledge intensive and are only used to decide on a single outcome.
For example, imagine you are applying for a business loan and starting a new process. Once the bank receives the necessary financial data, it must decide whether the customer’s background qualifies them for a loan. The criteria are based on financial health to minimize the risk of default. If the customer meets the criteria, the loan is accepted. If not, the loan is rejected. However, if the eligibility criteria change, the process model and input data must be modified.
Modeling the decision distinct from the BPMN
When looking at BPMN 2.0 you get a more standard definition of calling business process rules . The "business rule task" uses the inputs and outputs of the invoked tasks. Furthermore, these inputs and outputs can serve as results for decisions. Furthermore, sub-decisions contribute to an overall business decision. So it makes sense that DMN supports BPMN 2.0.