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Hybrid Modelling
Published in Andrew Greasley, Simulation Modelling, 2023
The use of process mining involves obtaining and extracting event data to produce an event log and transforming the event log into a process model termed process discovery. The process model can then be used to check the conformance of the system with the process design and to measure the performance of the process. In terms of event log construction, the data required to make an event log can come from a variety of sources, including collected data in spreadsheets, databases and data warehouses or directly from data streams. The minimum data required to construct an event log consists of a list of process instances (i.e. events), which are related to a case identification number and, for each event, a link to an activity label. Activities may reoccur in the event log, but each event is unique, and events within a case need to be presented in order of execution in the event log so that causal dependencies can be derived in the process model. It is also usual for there to be a timestamp associated with each event in the event log. Additional attributes associated with each event may also be included, such as the association of a resource required to undertake the event and the estimated cost of the event.
Process Excellence
Published in James William Martin, Operational Excellence, 2021
Figure 5.5 shows that process models are virtual representations that correspond to a real process. The interrelationships of the operations within a process may initially be unknown or poorly understood. The dynamic or complex performance of integrated processes may not be completely understood without creating models. Operational relationships are usually complicated and not obvious because real systems have ambiguity and time delays between the event occurrences and when their outputs are seen. This makes it difficult to understand relationships between cause and effect within a process. Therefore, mapping of processes along with their parameters and decision rules into a virtual model will be useful for understanding and improving operational performance. The advantages of a using a process model are that its structure and operational components can be easily modified and event frequency can be compressed to enable numerous evaluations of process modifications to identify an optimized final state.
Quality and Service Assurance, Testing, and Governance–Risk–Compliance (GRC) within Big Data
Published in Unhelkar Bhuvan, Big Data Strategies for Agile Business, 2017
The quality of business processes is verified and validated by creating visual models and then applying the quality techniques of walk-throughs, inspections, and audits. Business processes make use of applications and analytics to help the end users achieve their goals. Therefore, the quality of business processes depends on the way the users perceive their achievements. Understanding, documenting, and presenting the visual models of the business processes to the end users and incorporating their feedback in an iterative manner is an Agile way to enhance the quality of business processes. Process modeling standards (such as Unified Modeling Language [UML] and Business Process Model and Notation [BPMN]) and corresponding modeling tools further help in improving the quality of business processes.
Principles and applications of mathematical and physical modelling of metallurgical processes
Published in Mineral Processing and Extractive Metallurgy, 2020
A process model consists of process steps of similar nature as common blocks. Thus, a process model is a description of a process as an abstract combination of these blocks and is used to describe how the actual process works. Since one wants to obtain quantitative results from the model, the behaviour of the block is typically described by mathematical equations representing the process taking place together with known properties and knowledge about the process. The purposes of process models are to describe, explain, and/or predict what happens in a process. The major individual objectives include: to increase fundamental understanding of a process; to assist in scale-up; and to be used in process control and optimisation.
Exploiting a combined process mining approach to enhance the discovery and analysis of support processes in manufacturing
Published in International Journal of Computer Integrated Manufacturing, 2023
Giovanni Lugaresi, Antonio Davide Ciappina, Monica Rossi, Andrea Matta
As qualitatively described in Figure 3, by aligning the filtered event log traces with the discovered process model, it is possible not only to compute the process quality metrics and to detect whether there are discrepancies between the process model and the event log, but some process performance indicators can be immediately measured from both control flow and time perspectives. The results of the alignments can provide indicators on how many traces are replayed through certain paths of the process model, to immediately detect the most and less frequent paths of the process.
A process model of a logistics system as a basis for optimisation programme implementation
Published in International Journal of Logistics Research and Applications, 2018
Optimisation of processes is based on a process model of an enterprise, metrics of processes and mathematical models. Implementation of processes, resulting in achievement of target values of metrics of processes, calculated with an optimisation model, is possible subject to the existence of an organisational structure supporting the processes (Ilin, Levina, and Antipin 2013). Hence, it is appropriate to suggest an organisational structure supporting the considered processes. The proposed organisational structure is introduced in Figure 1.