Adding decision support to workflow systems by reusable standard software components

Worley, J.; Castillo, Gutiérrez; Geneste, Laurent; Grabot, Bernard

doi:10.1016/s0166-3615(02)00063-5

Cited by 27 publications

(16 citation statements)

References 16 publications

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“…Additionally, [26] analyzes dependencies between KPIs by applying machine learning techniques. The approach described in [5] offers a toolbox of decision rules and methods to enable and ease the development of decision support systems.…”

Section: Related Workmentioning

confidence: 99%

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Enriching Business Process Models with Decision Rules

Catalkaya¹,

Knuplesch²,

Chiao³

et al. 2014

Business Process Management Workshops

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Abstract. Making the right decisions in time is one of the key tasks in every business. In this context, decision theory fosters decision-making based on well-defined decision rules. The latter evaluate a given set of input parameters and utilize evidenced data in order to determine an optimal alternative out of a given set of choices. In particular, decision rules are relevant in the context business processes as well. Contemporary process modeling languages, however, have not incorporated decision theory yet, but mainly consider rather simple, guard-based decisions that refer to process-relevant data. To remedy this drawback, this paper introduces an approach that allows embedding decision problems in business process models and applying decision rules to deal with them. As a major benefit, it becomes possible to automatically determine optimal execution paths during run time.

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Section: Related Workmentioning

confidence: 99%

“…There exist very few approaches that have already applied decision theory or process mining for optimizing decision-making in the context of business processes [5][6][7]. However, none of them meets all of the aforementioned requirements.…”

Section: Introductionmentioning

confidence: 99%

Enriching Business Process Models with Decision Rules

Catalkaya¹,

Knuplesch²,

Chiao³

et al. 2014

Business Process Management Workshops

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“…Worley, etc. [11] developed an expert system generator, a neural network simulator, and a case-based reasoning module to provide answers for the users. Due to both supply chain decision support systems only specific to the enterprise's supply chain, they have the disadvantage of inflexibility.…”

Section: Technology Backgroundmentioning

confidence: 99%

Intelligent Laboratory Resource Supply Chain Conceptual Network Model with Process and Information Integration, Visibility and Flexibility

Hsu

Chao

2009

Algorithms and Architectures for Parallel Processing

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Abstract. This paper proposes a web-based laboratory resource supply chain conceptual model for an educational institution to increase the process and information integration, visibility and flexibility. The proposed model utilizes a reasoning engine with fuzzy, parallel fuzzy rules, and de-fuzzy processes to decide the optimal purchase ordering quantity and the best constant stocks in the laboratory. The fuzzy takes the crisp input data through the characteristic function and maps the input data into its corresponding membership degree. The fuzzy rules are processed with different degree of membership and all rules in the system are processed before triggering an action. The de-fuzzy takes each item's purchase ordering degree of membership through the singleton output membership function and generates corresponding crisp data. The model allows users keying in their required experimental materials via the Web Site, uses the database management system to integrate all related information, and applies the fuzzy reasoning engine to generate the final purchase order reports to support the executor making the optimal decisions.

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“…For example, Worley et al (Worley et al 2002) developed a system that allows the implementation of workflow and groupware facilities and the communication between modules is achieved through a database which provides the integration with the main information system. Grundstein (Grundstein 1994) applied Al methods and techniques to capitalise company knowledge to work more effectively supporting knowledge transfer.…”

Section: Artificial Intelligence Based Systemsmentioning

confidence: 99%

“…Noh et al (Noh et al 2000) created a CBR approach to cognitive map-driven tacit knowledge management. However, Worley et al (Worley et al 2002), Grundstein (Grundstein 1994), Shehab and Abdalla (Shehab and Abdalla 2002) and Noh et al (Noh et al 2000) agree that work with knowledge is a difficult task and it is important to move forwards the exploration of new ways for knowledge representations, particularly with tacit knowledge.…”

Section: Artificial Intelligence Based Systemsmentioning

confidence: 99%

A manufacturing model to enable knowledge maintenance in decision support systems

Guerra-Zubiaga

Young

2006

Journal of Manufacturing Systems

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DEDICATIONThis thesis is dedicated to:My beloved wife Claudia and son David Alejandro, without whose moral support this work would not have been completed. Your presence in my life has enabled me to achieve all that I have. With all my love to them.To my parents who taught me the merit of education and for the generous love they gave me throughout my life.To my parents-in-law for their love, encouragement and support.To my relatives because they are important in my life.To God, thank you for everything YOU always give me. A very special thanks to Aysin, Dean, Shilpa, Roberto, Alex, Adrian, and their respective families for their hospitality, which helped us to have a second home. Claudia, David Alejandro and myself express our sincere gratitude for your friendship.Thanks to God for letting me get to the end of this journey. IV ABSTRACTThe product development process, within a typical manufacturing company, utilises huge amounts of knowledge related to manufacturing and design activities. Knowledge based systems are increasingly being used to support manufacturing and design decisions. These systems are important tools for obtaining a competitive advantage and leverage using company "know-how". However, it is important to define suitable knowledge structures in the creation of these decision support systems. Due to the significant volume of knowledge generated in the manufacturing and design stage, there is a need to create structures and methods that readily manage and maintain the knowledge in order to a) assure the long-term use of these systems b) improve the company's competitiveness.The research reported in this thesis explores and defines a Manufacturing Facility Information and Knowledge Model (MFIKM) allowing a) the ability to store and manage various types of knowledge, b) the capturing of valuable new knowledge using a knowledge maintenance method. The understanding of an information and knowledge infrastructure using different types of knowledge categorisation has been explored. The major emphasis has been placed on understanding the facility knowledge structure related to processes and resources supporting process planning decisions. Using a knowledge maintenance life cycle as a method to maintain knowledge, it was possible to capture new and valuable machining knowledge using different types of representations. Knowledge models and methods are essential in the definition of structures to support manufacturing decisions allowing knowledge management and maintenance.It has been shown that the knowledge structures defined for the new model can serve as a source and repository for different types of knowledge allowing the support of manufacturing decisions with up-to-date knowledge. The framework defined enables the structuring of facility knowledge, processes, and resources, as super classes; improving the understanding of the relationships and dependencies among them, and allowing accessibility depending on the characteristics of each. A UML tool helped in the creation of new structures detailing attr...

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Adding decision support to workflow systems by reusable standard software components

Cited by 27 publications

References 16 publications

Enriching Business Process Models with Decision Rules

Enriching Business Process Models with Decision Rules

Intelligent Laboratory Resource Supply Chain Conceptual Network Model with Process and Information Integration, Visibility and Flexibility

A manufacturing model to enable knowledge maintenance in decision support systems

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