چارچوب مدل سازی یکپارچه برای پشتیبانی از تشخیص سیستم تولید برای بهبود مستمر
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|6828||2008||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Robotics and Computer-Integrated Manufacturing, Volume 24, Issue 2, April 2008, Pages 187–199
This paper proposes an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators (KPIs) that highlight possible areas for improvement. The method combines five important concepts: hierarchical structure, quantitative/qualitative analysis, data modelling, manufacturing database and performance indicators. It enables methods to build a full information model of the manufacturing system, from the shopfloor functional structure to the basic production activities (operations, transport, inspection, etc.). The proposed method is based on a modified IDEF model that stores all kind of quantitative and qualitative information. A computer-based support tool has been developed to connect with the IDEF model, creating automatically a relational database through a set of algorithms. This manufacturing datawarehouse is oriented towards obtaining a rapid global vision of the system through multiple indicators. The developed tool has been provided with different scorecard panels to make use of KPIs to decide the best actions for continuous improvement. To demonstrate and validate both the proposed method and the developed tools, a case study has been carried out for a complex manufacturing system.
The high competitiveness of modern industry leads companies to a continuous refinement of their manufacturing processes. Time and motion studies and continuous quality improvement programs are very useful tools in the study of manufacturing systems. However, the high number of strategies, techniques and methods which can be implemented (JIT, TQC, TPM, SMED, QFD, etc.) make analysis of these systems difficult. The reasons are the complexity of the manufacturing system and the high number of implied factors. In many cases, the results obtained from conventional analysis are lacking in a detailed description of the system's current state. The effort that implies the use of process analysis charts, data summary panels, modelling tools, check lists or the use of quality tools is wasted due to a lack of integration of this information in subsequent phases. Learning from the information structuring mechanisms provided by the system modelling and from the flexibility of the relational databases, this paper sets out a methodology for modelling manufacturing systems. This methodology allows a rapid analysis of the production and quality activities, and the creation of a data repository used in the evaluation of activities and in the exploitation of the system indicators. The developed methodology integrates the data acquisition cycle, graphical analysis and system evaluation in a single environment. The objective is to identify the activities without added value, the production capacity used and the technical–economical indicators of the system, mainly those related to productivity and costs. The application of this method implies it is oriented to supporting decision-making tasks in continuous improvement action planning that characterises new manufacturing strategies. The proposed method has been used as a conceptual base in the development of reference software architecture and new software oriented to the analysis and improvement of manufacturing systems. The developed method, architecture and tools have been used for the study and evaluation of a complex production plant, so the results obtained have validated the proposed method.
نتیجه گیری انگلیسی
In the last 20 years, several methodologies, models and tools have been developed and applied to the analysis and optimisation of manufacturing systems in order to propose general improvements. Many of these aids make extensive use of techniques that stem from the field of systems engineering, such as data modelling, simulation, decision-making support, expert systems or reference models. Process modelling tools are based on informal notation, lack mathematical rigor, and are static and quantitative, and are therefore difficult to be used for analysis. Manufacturing analysis is in fact a complex task due to the difficulty involved in integrating the different types of data to be analysed—such as quality, time, costs, resource capacity, productivity, flexibility or improvements—within a single analysis environment. The extent of this problem, the lack of data integration and the widely divergent goals of these analyses are hindering the development of common, and standardised methods and tools. This paper proposes an integrated modelling framework for manufacturing analysis systems that can increase the capacity of modelling tools for rapidly creating a structured database, thereby obtaining key performance indicators (KPIs) that highlight possible areas for improvement. Manufacturing systems are complex arrangements of physical entities characterised by measurable parameters that must be recognised in order to evaluate the performance of the system. The proposed method is able to create a quantitative and qualitative information model using IDEF0. This model is the first stage for creating a full datawarehouse of the manufacturing activities. A specific decision-making support tool for managing performance indicators has been developed to use this data structure establishing a standard interface that can be used by any modeller and simulator. The flexibility of the proposed method and the use of an open architecture based on standards allow the integrated modelling framework to be applied in different manufacturing systems having a wide range of problems. Their application has an optimal implementation in manufacturing systems where there is a need to measure indicators in order to take decisions. Examples of these situation are systems with characteristics such as non-optimal layout, high buffers between machines, unbalanced production, overproduction, wastes of time (delays and transport) or high ratios of defective products. To summarise, the main application areas of the method are: • Rapid diagnosis of manufacturing systems, especially when the goal of analysis is to implement strategies for excellence like lean manufacturing or continuous improvement. The easy way to apply the method and the rapid return of results makes the system especially useful for SMEs due to the high cost of traditional system diagnosis consultant services. • Design of a full database model of the productive system to be used to get KPIs. This information may be very useful for taking decisions about new equipment or the design of new productive systems. The flexibility of the method allows the possibility of importing data from different sources of information such as simulation software or spreadsheets. • Non-quality and value analysis. Traditional cost models are not able to generate the right information for production managers about the non-quality cost or the added value of each manufacturing activity. The work presented is part of a project currently under development in the Manufacturing Department of the Polytechnic University of Madrid in collaboration with SMEs belonging to the timber, aeronautical and automotive industries. The final aim is the proposal of a methodology and tools for the analysis of manufacturing systems that can be used by the maximum number of manufacturing enterprises from different industrial sectors.