Cost Optimization in Disruption Conditions: A Case Study in Small Medium Enterprise

Purpose: The objective of this study was to design a cost optimization model that offers production improvement for SMEs.                                                                                                          Theoretical framework: Several studies related to production system disruption management have been conducted, with the majority focusing on large companies. However, small and medium enterprises (SMEs) have limitations compared to large companies. Repairability is considered for cost optimization.                                                    Design/methodology/approach:  This research designed a cost optimization model that offers production improvement with repairability process for SME.   Findings: There is a need for repairability given the disruption caused by defective products in SMEs. There is a clear difference in total profit between the current state without repairability and proposed conditions with repairability. SMEs suffer massive losses in the absence of repairs, assuming they do not consider repairing defective products with a production defect rate of approximately 15%. The current state produces many downgraded products. However, repairability still needs to be improved to increase profits.   Research, Practical & Social implications: The study implied that there is a need to consider repairability for product defects at SMEs, especially those with a 15% product defect rate. The use of the proposed model optimizes profit and is designed to increase production capacity based on product improvements. Repairability was considered in this research, considering that SMEs are more susceptible to disruptions compared to large companies.   Originality/value: The novelty of this paper is adding process repairability to the cost optimization model for SMEs in the textile sector, then considering the product downgrade under the conditions in SMEs

Integración de sistemas ERP para el monitoreo de planes y detección de eventos disruptivos en cadenas de suministros

Los sistemas de Planificación de Recursos Empresariales (ERP Enterprise Resource Planning) se definen como un conjunto de aplicaciones que permiten gestionar de manera integrada los procesos de negocio de las empresas. En la actualidad, los sistemas ERP carecen de flexibilidad suficiente para generar Cadenas de Suministros (CS) flexibles capaces de reaccionar rápidamente a eventos disruptivo. Es por ello, en este trabajo se propone un servicio MASM-ERP que integra los planes de abastecimiento, producción y distribución definidos en los ERP de las empresas involucradas en una CS. También permite monitorear la ejecución de estos planes y notificar la ocurrencia de eventos disruptivos a los actores pertinentes. El monitoreo durante la ejecución de planes permite anticiparse a los cambios que podrían tener lugar en el horizonte de tiempo considerado y mejorar los procesos de toma de decisión.IX Workshop Innovación en Sistemas de Software (WISS).Red de Universidades con Carreras en Informática (RedUNCI

SLA-aware operational efficiency in AI-enabled service chains: challenges ahead

Service providers compose services in service chains that require deep integra tion of core operational information systems across organizations. Additionally, advanced analytics inform data-driven decision-making in corresponding AI-ena-bled business processes in today’s complex environments. However, individual partner engagements with service consumers and providers often entail individu-ally negotiated, highly customized Service Level Agreements (SLAs) comprising engagement-specific metrics that semantically differ from general KPIs utilized on a broader operational (i.e., cross-client) level. Furthermore, the number of unique SLAs to be managed increases with the size of such service chains. The resulting complexity pushes large organizations to employ dedicated SLA management sys-tems, but such ‘siloed’ approaches make it difficult to leverage insights from SLA evaluations and predictions for decision-making in core business processes, and vice versa. Consequently, simultaneous optimization for both global operational process efficiency and engagement-specific SLA compliance is hampered. To address these shortcomings, we propose our vision of supplying online, AI-supported SLA analyt-ics to data-driven, intelligent core workflows of the enterprise and discuss current research challenges arising from this vision. Exemplified by two scenarios derived from real use cases in industry and public administration, we demonstrate the need for improved semantic alignment of heavily customized SLAs with AI-enabled operational systems. Moreover, we discuss specific challenges of prescriptive SLA analytics under multi-engagement SLA awareness and how the dual role of AI in such scenarios demands bidirectional data exchange between operational processes and SLA management. Finally, we discuss the implications of federating AI-sup-ported SLA analytics across organizations

Agent-based monitoring service for management of disruptive events in supply chains

Schedules of supply chains are generated with buffers to absorb the effect of disruptive events that could occur during their execution. Schedules can be systematically repaired through specific modifications within buffers by using appropriate decision models that consider the distributed nature of a supply chain. To this aim, information of disruptive events at occurrence or in advance allows decision models to make better decisions. To detect and predict disruptive events along a schedule execution, a service- oriented monitoring subsystem that uses a reference model for defining monitoring models was proposed. This subsystem offers services for collecting execution data of a schedule and environment data, and assessing them to detect/anticipate disruptive events. Because of the distributed nature and the complexity of these services functionalities, this paper presents an agent-based approach for their implementation. This technology allows dealing with supply chain monitoring by structuring monitoring subsystem functionalities as a set of autonomous entities. These entities are able to perform tailored plans created at execution time to concurrently monitor different schedules. A case study is described to try out the implemented prototype system.Fil: Fernández, Érica Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Toledo, Carlos Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Galli, Maria Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Salomone, Hector Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Chiotti, Omar Juan Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI