Artificial Data Generation for Smart Manufacturing Systems: Discrete Event Simulation, Traceability, and Process Mining

Construction companies are increasingly utilizing sensing technologies to automatically record different steps of the construction process in detail for effective monitoring and control. This generates a significant amount of event data that can be used to learn the underlying behavior of agents in a construction site using process mining. While process mining can be used to discover the real process and identify and analyze deviations and bottlenecks in operations, it is a backward-looking approach. On the other hand, discrete event simulation (DES) provides a means to forecast future performance from historical data to enable proactive decision-making by operation managers relating to their projects. However, this method is largely unused by the industry due to the specialized knowledge required to create the DES models. This paper thus proposes a framework that extends the utility of collecting event data and their process models, by transforming them into DES models for forecasting future performance. This framework also addresses another challenge of using DES relating to its inability to update itself as the project progresses. This challenge is addressed by using the Bayesian updating technique to continuously update the input parameters of the simulation model for the most up-to-date estimation based on data collected from the field. The proposed framework was validated on a real-world case study of an earthmoving operation. The results show that the process mining techniques could accurately discover the process model from the event data collected from the field. Furthermore, it was noted that continuous updating of DES model input parameters can provide accurate and reliable productivity estimates based on the actual data generated from the field. The proposed framework can help stakeholders to discover the underlying sequence of their operations, and enable timely, data-driven decisions regarding operations control.

Business process management is a popular approach that has caught the attention of several researchers over the years. BPM has been extended to Business Process Intelligence in order to add more intelligence to managing business processes. Process Mining is one of the emerging areas of Business Process Intelligence, which aims to automatically discover, verify and improve business processes. Nowadays, the emergence of smart environment. The data that occurs in this environment are more varied and are kind of unstructured data. In this sense, applying process mining, to analyze processes in a smart context is a challenging task, since the process mining techniques relies on a structured event log. In this paper, we present a comparative study of existing approaches on applying process mining techniques in smart BPM, as a first step towards developing a new approach to discover and analyze business processes from sensor data perspective.KeywordsProcess Mining (PM)Smart environmentsSensor logSmart BPM

Process Mining comprises a consolidated set of techniques that aim at discovering, checking and improving processes. Originally designed to cope with business processes, process mining techniques fed on log files automatically generated by enterprise resource planning (ERP) systems and software alike. However, nowadays, with the unstoppable deployment of the Internet of Things, multiple events are continuously generated and stored by distributed, highly uncoupled and heterogeneous devices able to sense their surroundings and report precise and timely updates on the environment. By embracing the Internet of Events, recently available contextual information opens the door to the application of process mining techniques in new domains, hence, generating opportunities for the analysis of contextual data with multiple implications in hot research areas such as, smart environments (e.g., smart cities, smart buildings, smart homes), cognitive systems, and industry 4.0. In this article, we discuss the opportunities and challenges of process mining in these new, events-rich, context-aware environments.

PurposeProcess mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.Design/methodology/approachStandard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.FindingsResults show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.Originality/valueThis paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

This paper describes a business process analysis method that helps determining if a business process complies with the requirements put forward by enterprise ontology’s transaction pattern. The method starts by discovering the business process through the application of process mining techniques to the events that are generated by the applications that support the execution of the process. This step discovers the actual implementation of the process from its event trace. Next, the discovered process is analysed against enterprise ontology’s transaction pattern to determine whether the process complies with the structure and sequencing of its coordination and production acts. The paper shows that combining process mining with enterprise ontology contributes to the analysis of business processes, especially in terms of determining the boundaries of authority and responsibility of the process. The feasibility and the limitations of the method are discussed using a case study that analyses a semi-automated business process.

Process Mining analyzes event logs from business processes to enhance understanding, verification, and improvement of these processes. Event logs typically originate from multiple sources and require extensive preprocessing before applying process mining techniques. This preprocessing includes filtering, composing, joining events into traces, and fixing or calculating attribute values. Existing process mining tools offer ad-hoc manual actions through graphical interfaces and simple domain-specific languages, but these methods have limitations, and there is no standardized approach for processing event logs. The primary contribution of this work is the Process Query Language (PQL), a domain-specific language (DSL) for processing event logs that surpasses current techniques. We begin by analyzing several existing DSLs concerning key success factors, followed by an evaluation of current business process DSLs to identify their limitations. Based on this analysis, we propose the PQL specification as a draft technical standard. PQL’s design decisions are validated through an interpreter implementation and its application to numerous use cases.

Many present-day companies carry out a huge amount of daily operations through the use of their information systems without ever having done their own enterprise modeling. Business process mining is a well-proven solution which is used to discover the underlying business process models that are supported by existing information systems. Business process discovery techniques employ event logs as input, which are recorded by process-aware information systems. However, a wide variety of traditional information systems do not have any in-built mechanisms with which to collect events (representing the execution of business activities). Various mechanisms with which to collect events from non-process-aware information systems have been proposed in order to enable the application of process mining techniques to traditional information systems. Unfortunately, since business processes supported by traditional information systems are implicitly defined, correlating events into the appropriate process instance is not trivial. This challenge is known as the event correlation problem. This paper presents an adaptation of an existing event correlation algorithm and incorporates it into a technique in order to collect event logs from the execution of traditional information systems. The technique first instruments the source code to collect events together with some candidate correlation attributes. Based on several well-known design patterns, the technique provides a set of guidelines to support experts when instrumenting the source code. The event correlation algorithm is subsequently applied to the data set of events to discover the best correlation conditions, which are then used to create event logs. The technique has been semi-automated to facilitate its validation through an industrial case study involving a writer management system and a healthcare evaluation system. The study demonstrates that the technique is able to discover an appropriate correlation set and obtain well-formed event logs, thus enabling business process mining techniques to be applied to traditional information systems.

Process mining in healthcare: A literature review

MO3 COMPARING METHODS OF DATA SYNTHESIS: UPDATING PARAMETERS OF AN EXISTING PROBABILISTIC COST-EFFECTIVENESS MODEL

MO1 ADVANTAGES AND DISADVANTAGES OF MARKOV MODELS VERSUS DISCRETE EVENT MODELS FOR SIMULATING PATIENT FLOWS

Current paper proposes a custom solution for detecting deviations in process execution by analyzing event logs of running cases. The monitored business processes are extracted with process mining techniques. The behavior of the current alert system is illustrated on a case study, the event process logs resulted from the execution of a software development process used in the IT department of a large automotive company. Enhancing the level of control in project management activities, improving the business process execution by detecting deviations in real time and helping organizations to perform better by providing guidance to the people during task execution are the main reasons for designing the current solution. The statistical information about detected deviations represents the input for an analysis phase having as major scope business process improvements. The alert system was designed as a Web based application and is presented as a Business Activity Monitoring solution operating with process mining concepts.

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

This exploratory study analyzes student behavior in a Massive Open Online Course (MOOC). MOOCs represent a global educational phenomenon transforming teaching and inspiring new research perspectives on learning methods in higher education institutions. Understanding how students organize their learning sequences and how these relate to their academic performance is crucial for optimizing digital educational processes. The objective of this study is to identify and characterize the learning sequences performed by students during their study sessions in a MOOC, using process mining (PM) techniques. The methodology involved analyzing a dataset collected between July 2017 and January 2018, comprising 27,922 students and approximately 3.5 million recorded interactions. Process mining techniques were employed to examine these learning sequences. Results indicate that most interactions correspond to assessments and video lectures, while forums were the least utilized activity. Additionally, two student profiles were identified: “Comprehensive” learners, who follow expected sequences and engage in longer, more intensive study sessions, and “Strategic” learners, who prioritize assessments. This study advances the current understanding of online learning and situates its findings within the broader literature by contrasting them with similar classification patterns reported by other authors.

The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain private information. Data protection regulations restrict the use of such event logs for analysis purposes. One way of circumventing these restrictions is to anonymize the event log to the extent that no individual can be singled out using the anonymized log. This paper addresses the problem of anonymizing an event log in order to guarantee that, upon disclosure of the anonymized log, the probability that an attacker may single out any individual represented in the original log, does not increase by more than a threshold. The paper proposes a differentially private disclosure mechanism, which oversamples the cases in the log and adds noise to the timestamps to the extent required to achieve the above privacy guarantee. The paper reports on an empirical evaluation of the proposed approach using 14 real-life event logs in terms of data utility loss and computational efficiency.

Process mining-driven analysis of COVID-19’s impact on vaccination patterns