A Review of Decision Support Systems for Manufacturing Systems (original) (raw)
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A Review Of Decisions Support Systems For Manufacturing Systems
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In the field of manufacturing systems automated data acquisition and development of technological innovations like manufacturing execution systems (MES), Enterprise Resource Planning (ERP), Advanced Planning Systems (APS) and new trends in Big Data and Business Intelligence (BI) have given rise to new applications and methods of existing decisionsupport technologies. Today manufacturers need an adaptive system that helps to react and adapt to the constantly changing business environment. The internal data processing system of a company can only offer minimum support because it is related to transactions. In this case, decision support systems (DSS) combine human skills with the capabilities of computers to provide efficient management of data, reporting, analytics, modeling and planning issues. DSS provide a distinction between structured, semi structured and unstructured data. In particular, a DSS reduces the quantity of data to a high quality structured amount; due to this, decisi...
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Flexible manufacturing systems (FMS) increase the efficiency of operations by improving cost, quality, and lead time while decision support and expert systems (DSS&ES) improve the effectiveness of manufacturing by assisting the decision maker in coordinating all the elements and increasing the overall utilization of the factory, particularly in managing a complex system of flexible manufacturing.
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With the increase of flexibility and production rates, the complexity of manufacturing systems reached a point where the operator in charge of the production activity control of the system is not able to forecast efficiently the impact of his decisions on the global performances. As a matter of fact, more and more Decision Support Systems (DSS) are developed, as much in literature or industrial applications. DSS have one common point: the initialization of their forecasting functionality is based on data coming from the manufacturing system. Furthermore, this feature is fundamental, as it has a direct impact on the accuracy of the forecasts. Considering the variety of input and output data, a data processing is necessary to adapt those coming from the manufacturing system. The aim of this paper is to present several design approaches enabling the integrator of a new manufacturing system to speed up the implementation, with the idea of automate and systematize the maximum design phases thanks the model driven engineering.
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Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation ...
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The evolving Information and Communication Technologies (ICTs) has not spared the manufacturing industry. Modern ICT based solutions have shown a significant improvement in manufacturing industries' value stream. Paperless manufacturing, evolved due to complete automation of factories. The chapter articulates various Machine-to-Machine (M2M) technologies, big data and data modelling requirements for manufacturing information systems. Manufacturing information systems have unique requirements which distinguish them from conventional Management Information Systems. Various modelling technologies and standards exist for manufacturing information systems. The manufacturing field has unique data that require capturing and processing at various phases of product, service and factory life cycle. Authors review developments in modern ERP/CRM, PDM/PLM, SCM, and MOM/MES systems. Data modelling methods for manufacturing information systems that include STEP/STEP-NC, XML and UML are also covered in the chapter. A case study for a computer aided process planning system for a sheet metal forming company is also presented.