Topic
Process modeling
About: Process modeling is a research topic. Over the lifetime, 11639 publications have been published within this topic receiving 223996 citations. The topic is also known as: process simulation.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, an improvement framework which incorporates the characteristics of the three approaches has been developed, and its use in a real case study has been described and used in a case study.
Abstract: In recent years, three key topics under the big umbrella of business process improvement (BPI) have been continuous process improvement (CPI), business process reengineering (BPR), and business process benchmarking (BPB). Each has received much attention and has been supported by a considerable amount of literature and empirical research and findings from business consultants and academics. Within the manufacturing domain, these three topics have been accepted by many manufacturing process analysts striving to improve productivity and efficiency of companies. However, organization structures in manufacturing enterprises are complex and involve many different processes. Their needs may be quite different. One process may require an incremental improvement in critical areas or technology updating in its existing operation while others may need a total enterprise‐wide process revamp. In other words, CPI, BPR, and BPB’s usefulness and applicability may not be universal; one or a combination of the two or three may be more appropriate, depending on the process, organization and its environment. An improvement framework which incorporates the characteristics of the three approaches has been developed. This paper describes the methodology, SUPER, and its use in a real case study.
89 citations
Posted Content•
TL;DR: This paper proposes a logic-based verification method that is based on a well-known formalism, i.e., propositional logic, and demonstrates that logic- based workflow verification is capable of detecting process anomalies in workflow models.
Abstract: The increasing complexity of business processes in the era of e-business has heightened the need for workflow verification tools. However, workflow verification remains an open and challenging research area. As an indication, most of commercial workflow management systems do not yet provide workflow designers with formal workflow verification tools. We propose a logic-based verification method that is based on a well-known formalism, i.e., propositional logic. Our logic-based workflow verification approach has distinct advantages such as its rigorous yet simplistic logical formalism and its ability to handle generic activity-based process models. In this paper, we present the theoretical framework for applying propositional logic to workflow verification and demonstrate that logic-based workflow verification is capable of detecting process anomalies in workflow models.
89 citations
TL;DR: A new monoclonal antibody purification template comprised of flocculation-based clarification, capture by continuous multi-column protein A chromatography and flow-through polishing offers a robust, single-use manufacturing solution while significantly reducing overall cost of goods.
89 citations
TL;DR: A genetic algorithm-based ANN model is proposed for the turning process in manufacturing Industry that satisfies all the accuracy requirements and is found to be a time-saving model.
Abstract: Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.
89 citations
24 Sep 2007
TL;DR: This work proposes an approach to business process modeling through reuse of existing business process artifacts - process fragments and provides a rich formalism for business process description based on π-calculus and ontologies as a basis of the approach.
Abstract: Business process models are created by business users with an objective to capture business requirements, enable a better understanding of business processes, facilitate communication between business analysts and IT experts, identify process improvement options and serve as a basis for derivation of executable business processes. Designing a new process model is a highly complex, time consuming and error prone task. In order to address this problem, we propose an approach to business process modeling through reuse of existing business process artifacts - process fragments. In addition, we provide a rich formalism for business process description based on π-calculus and ontologies as a basis of the approach. The formalism integrates different workflow perspectives and thus exposes the complete process model description to expressive querying and reasoning.
89 citations