Managing Supply Chains Using Business Intelligence

Organizations are deploying business intelligence (BI) systems to enable analysis of data assets for establishing management decisions. Corporate data captured using enterprise systems (ESs) are leveraged through BI to evaluate digital information for deploying business strategies. This study investigates use of BI in organizations for managing supply chain operations. The current BI practices of manufacturing firms are evaluated for transforming transactional data captured through ESs into organizational knowledge in pursuit of realizing supply chain goals. Findings from a case study reveals that although manufacturing firms have identified business analytic as one of the major necessities for organizational effectiveness, these companies often lack clarity in aligning key measurable against their business processes to utilize vital ES data. This results in underutilization of BI tools and the data assets for establishing business decisions. However, more and more companies are now deploying BI strategies for impromptu decision making in managing supply chains.falsePublishedAuckland, New Zealan

Critical business intelligence practices to create meta-knowledge

In order to successfully implement strategies and respond to business variations in real-time, business intelligence (BI) systems have been deployed by organisations that assist in focused analytical assessments for execution of critical decisions. Although businesses have realised the significance of BI, few studies have explored their analytical decision-enabling capabilities linked to organisational practices. This study investigates the BI practices critical in creating meta-knowledge successfully for strategy-focused analytical decision-making. First, key BI suppliers are interviewed to develop an understanding of their BI capabilities and current deployment practices. Subsequently, two large BI implementation case studies are conducted to examine their practices in data transformation process. Findings reveal that BI practices are highly context-specific in mapping decisions with data assets. Complimentary static and dynamic evaluations provide holistic intelligence in predicting and prescribing a more complete picture of the enterprise. These practices vary across firms in their effectiveness reflecting numerous challenges and improvement opportunities.Publishe

Using discrete event simulation for scheduling and long range capacity planning of a high volume press shop

The thesis expresses the essential requirement for and the use of Discrete Event Simulation (DES) in a high volume press shop. The press shop produces blanks and panels for the body shop, which manufactures three car models. DES is used to combat the battle between shop efficiency and low inventory. The process used to choose the most appropriate software package is described and then current situation in the press shop is discussed. The procedures involved in model creation follow set model construction guidelines. There are several assumptions made, which together with the constraints of the system, provide the limitations of the inputs facing the system. There is a trade off between model complexity and accuracy, so the setting of the constraints and assumptions often provided difficult decisions. Validation of the model is very important, so this was a lengthy process, involving using a series of dummy buffers to check inputs such as cycle times and batch quantities. The validated model is used to monitor the methods used to reduce inventory on the shop floor over a period of eight weeks and then used for 'What If? Scenarios, to ascertain the systems capacity and inventory levels underdifferent conditions. The scenarios include using volumes that are 100% higher on some models than the current situation and 20% less than currently. The findings are examined and proposals made for the introduction of the proposed volumes where possible. Findings of the scenarios highlight bottlenecks in the shop and areas for improvement. Using the model, the schedules can be changed quickly and easily to try and eliminate the bottlenecks and improve capacity. Conclusions discuss the problems encountered during the modelling process as well as the benefits. The integration of DES into the current scheduling processes in the shop poses no problems and the model will be used as an aid for capacity planning in the future

Automatic Generation Of Supply Chain Simulation Models From Scor Based Ontologies

In today\u27s economy of global markets, supply chain networks, supplier/customer relationship management and intense competition; decision makers are faced with a need to perform decision making using tools that do not accommodate the nature of the changing market. This research focuses on developing a methodology that addresses this need. The developed methodology provides supply chain decision makers with a tool to perform efficient decision making in stochastic, dynamic and distributed supply chain environments. The integrated methodology allows for informed decision making in a fast, sharable and easy to use format. The methodology was implemented by developing a stand alone tool that allows users to define a supply chain simulation model using SCOR based ontologies. The ontology includes the supply chain knowledge and the knowledge required to build a simulation model of the supply chain system. A simulation model is generated automatically from the ontology to provide the flexibility to model at various levels of details changing the model structure on the fly. The methodology implementation is demonstrated and evaluated through a retail oriented case study. When comparing the implementation using the developed methodology vs. a traditional simulation methodology approach, a significant reduction in definition and execution time was observed

Metodología de implantación de modelos de gestión de la información dentro de los sistemas de planificación de recursos empresariales. Aplicación en la pequeña y mediana empresa

La Siguiente Generación de Sistemas de Fabricación (SGSF) trata de dar respuesta a los requerimientos de los nuevos modelos de empresas, en contextos de inteligencia, agilidad y adaptabilidad en un entono global y virtual. La Planificación de Recursos Empresariales (ERP) con soportes de gestión del producto (PDM) y el ciclo de vida del producto (PLM) proporciona soluciones de gestión empresarial sobre la base de un uso coherente de tecnologías de la información para la implantación en sistemas CIM (Computer-Integrated Manufacturing), con un alto grado de adaptabilidad a la estnictura organizativa deseada. En general, esta implementación se lleva desarrollando hace tiempo en grandes empresas, siendo menor (casi nula) su extensión a PYMEs. La presente Tesis Doctoral, define y desarrolla una nueva metodología de implementación pan la generación automática de la información en los procesos de negocio que se verifican en empresas con requerimientos adaptados a las necesidades de la SGSF, dentro de los sistemas de gestión de los recursos empresariales (ERP), atendiendo a la influencia del factor humano. La validez del modelo teórico de la metodología mencionada se ha comprobado al implementarlo en una empresa del tipo PYME, del sector de Ingeniería. Para el establecimiento del Estado del Arte de este tema se ha diseñado y aplicado una metodología específica basada en el ciclo de mejora continua de Shewhart/Deming, aplicando las herramientas de búsqueda y análisis bibliográfico disponibles en la red con acceso a las correspondientes bases de datos