Data, information and knowledge management in the field of smart business
Payam Faghihi; Mehrdad Kazerooni
Abstract
AbstractAccelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, ...
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AbstractAccelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the current research presents a new methodology for multi-agent production control based on integration with ERP, which improves the capabilities of the system in the face of the above deficiencies. The research method employed in this study is qualitative, and developmental-applicative, aiming to enhance the integration of multi-agent production control systems with ERP. The objective is to improve the flow of material, production, and the quality of semi-finished products on the production line by considering the parameters that influence them. The key accomplishment of this research is the development of a reliable production control methodology that encompasses three components: a data exchange framework, tools, and implementation. These components are derived from existing ERP information systems that are functionally mature and designed based on best practices with a focus on maintenance, modification, and performance, aiming to minimize errors. The developed methodology offers a practical and agile solution for enhancing production control using an ERP system, with a lower implementation cost than the implementation of a commercial ERP system with a separate multi-agent system. IntroductionAccelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the presented research introduces a versatile methodology developed to enhance the efficiency of data and material flow control within a production system. The methodology emphasizes the role of data flow in regulating material flow, making it agile and autonomous.The innovation lies in elevating the role of ERP modules from process flow reporting to that of decision-making software agents, aligning with the common nature of both systems. Consequently, higher levels of data integration between the production system and the Multi-Agent Production Control System (MAPCS) integrated with ERP are achieved, leveraging agent technology and best practices from ERP modules.This approach enables real-time responsiveness to changes in the production system, establishing an agile production control methodology capable of managing material flow dynamics. Furthermore, it represents a step toward addressing current MAPCS limitations.Literature ReviewThe advent of affordable computer technology marks a pivotal moment in the adoption of advanced IT-based production control systems (Karrer, 2012). Leveraging technologies that continually monitor and gather information concerning the real-time status of production systems, such as machines equipped with sensors actively participating in the production process and offering virtual representations of the production system's state, enhances data integrity for improved decision-making in production control (Huang, 2022).Over the last decade of the 20th century, agent technology emerged, giving rise to agent-based production planning and control models and extensive research into technology development based on these principles (Bär, 2022; Groß et al., 2021).Agent-based systems represent the next generation of software, capable of dynamic adaptation to the evolving business environment and addressing a wide array of production system challenges (Mesbahi et al., 2014). However, they do present ongoing challenges, including limitations in system state comprehension, restricted control, reduced decision-making reliability, and a generally increased risk of errors in design and implementation (De la Prieta et al., 2019; Balaji & Srinivasan, 2010).Concurrently, Enterprise Resource Planning (ERP) systems emerged as IT-based solutions in the final decade of the 20th century, witnessing rapid expansion in research and implementation across various organizations (Scharf et al., 2022; De Brabander et al., 2022; Febrianto & Soediantono, 2022; Senaya et al., 2022).The integration of agents with ERP systems holds the promise of enhancing ERP intelligence, allowing them to autonomously interact with their environment and execute self-directed actions while collaborating with other systems (Faghihi & Kazerooni, 2023).This paper introduces a novel solution: the development of a Multi-Agent Production Control Methodology (MAPCM) integrated with ERP system that encompasses three components: data exchange framework, tools, and implementation.MethodologyIn this study, a developmental-applicative research method has been employed with the goal of building upon the findings of prior fundamental research. The objective is to enhance and refine various aspects, including behaviors, methods, tools, devices, structures, and patterns. This iterative process aims to address the practical needs of the society's industries.Additionally, to gather the desired data, a qualitative research method has been employed. This approach is particularly useful for tackling complex problems and deriving meaningful, easily comprehensible conclusions accessible to a wide audience.Results4.1. Data exchange frameworkThe development of the Final MAPCM integrated with ERP framework proceeded in a systematic four-layer approach. To enhance comprehension of the progress in each stage and the data exchange within these layers, we represent the first layer's data in black, while the data from the second and third layers are depicted in blue and red, respectively.4.1.1. Layer 1: A Framework for streamlining production control data exchangeFigure 1, illustrates an exemplary data-exchange framework for production control, which serves as the foundation for the proposed framework (Frazzon et al., 2018). This framework leverages a Manufacturing Execution System (MES) as the central data hub, facilitating seamless data exchange to bridge the physical manufacturing and production system with a multi-agent system.The data-exchange framework, depicted in Figure 2, emphasizes the implementation of real-time inventory distribution, dispatching limitations, and delivery constraints throughout the production process. Also, effectively addresses the dynamic handling of inventory distribution and delivery constraints in response to unplanned and unscheduled maintenance operations. This capability is achieved through the collaborative efforts of the inventory control and the maintenance modules of the ERP system. After upgrading the ERP quality control module to a software agent, it conducts three-phase quality checks utilizing data from both human and cyber-physical systems. (Figure 3):- Phase 1:This phase is dedicated to assessing the quality of raw materials and consists of two sections:The quality of incoming warehouse inventoryThe quality of warehouse inventory during storage periods- Phase 2:Semi-product quality control during the manufacturing process- Phase 3:Quality of finished productsFigure 3. MAPCM integrated with ERP – based on quality control framework 4.1.4. Layer 4: Final MAPCM integrated with ERP frameworkThe final MAPCM integrated with ERP framework (Figure 4) was developed through concurrent implementation and application of the preceding layers.Figure 4. Final MAPCM integrated with ERP framework 4.2. ToolsCyber-physical systems offer rich sensory data. A network of sensors continuously monitors the condition of machine tools on the shop floor and tracks the work-in-progress status in the production system.4.3. ImplementationWhile constructing complex software agents from the ground up using Agent-Oriented Programming (AOP) languages can be challenging due to the skills and knowledge required, readily accessible agent-building toolkits like JAFMAS, JATLite, ZEUS, and Sodabot provide valuable alternatives.DiscussionAgent-based approaches are essential for future production control systems due to their decentralized decision-making, flexibility, and complexity-reducing capabilities. Integrating ERP modules into software agents and enabling data exchange and direct interactions among these agents can enhance self-management and intelligence in production systems. This integration reduces implementation costs compared to using separate commercial ERP software and a multi-agent system. Furthermore, real-time soft sensors become more accessible and user-friendly due to the software-based nature of production control agents.ConclusionThe developed methodology offers a practical, cost-effective, and agile solution to enhance production control through ERP integration. By harnessing the synergistic capabilities of agents and ERP modules for monitoring, decision-making, and control, the limitations of traditional MAPCS models have been resolved. This transition results in autonomous production control systems that reduce reliance on human intervention. This methodology leverages well-established ERP information systems, following best practices to minimize errors, and enhance maintenance, modification, and performance, ultimately striving for error reduction.
