Core manufacturing simulation data (CMSD)

The International Journal of Advanced Manufacturing Technology, 2009

Computer-based simulation is one of the most valuable aids for manufacturing systems design, yet its use remains limited. The main reason for this is that current manufacturing systems are extremely complex and the user-friendly capabilities provided even by the most advanced simulation tools are not sufficient to cope with such complexity. On this basis, the paper explores the development of an improved tool to ease and speed up simulation modelling of complex manufacturing systems. A simulation interface, created and currently used at a major automotive manufacturer, is considered and a rigorous assessment of the extent to which this interface can support the simulation modelling process is provided. The paper evaluates the viability to use the interface as a basis for a general purpose simulation modelling tool capable of coping with any complex manufacturing systems, analyses its potential values, and proposes developments that can support the uptake of simulation techniques within the manufacturing industry.

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

Discrete Event Simulation (DES) is ranked among the top three tools for management support. However, it lags in becoming the successful tool in the industry that many experts have predicted. In this paper, sixteen projects accomplished in the area of DES have been analyzed in order to find the reasons for this delay. Most important is the lack of reliable manufacturing data in companies. This is due to inadequate practices within the organization, thus forcing users to build simulation models with estimated data. The paper also answers other questions as to why DES is an underutilized decision tool. DES is an information-intensive tool for decision-making, but has weak support concerning working procedures within organizations. Continuous generation of manufacturing data at all levels has to be supported by the working procedure in order to increase the use of DES as an everyday tool. How to improve this situation also is discussed.

International Journal of Internet Manufacturing and Services, 2013

As manufacturing systems are often costly to develop and operate, simulation technology has been demonstrated to be an effective tool for improving manufacturing system design and the efficiency of manufacturing operations and maintenance. However, effectively and efficiently using simulation remains an important issue in the manufacturing industry. One of the principal reasons is the difficulty in processing, organising, and making use of the production-related information about the manufacturing system to be simulated. The difficulty stemmed from no standard representation for such data, making the effort to input data such as product attributes, processing times, and material quantity requirements into simulations complex, time consuming, and error prone. The core manufacturing simulation data (CMSD) standards, SISO-STD-008-2010 and SISO-STD-008-01-2012, specifically address this data representation issue. CMSD enables the import/export of many different types of factory data into computer simulations and other manufacturing systems. This paper describes the motivation for creating CMSD, standardisation efforts for CMSD, and related research efforts. This paper also describes how CMSD could be used as an integration mechanism in a sustainable manufacturing test-bed being developed at the National Institute of Standards and Technology. The test-bed will be a virtual manufacturing environment to support the testing and validation of sustainability metrics, assessment methods, and tools for use in manufacturing.

Proceedings of the Winter Simulation Conference, 2002

Standard interfaces could help reduce the costs associated with simulation model construction and data exchange between simulation and other software applications-and thus make simulation technology more affordable and accessible to a wide range of potential industrial users. Currently, small machine shops do not typically use simulation technology because of various difficulties and obstacles associated with model development and data translation. This paper provides an overview of work currently underway at the National Institute of Standards and Technology (NIST) to develop a software architecture, standard data interfaces, and a prototype generic machine shop simulator that can be readily, reconfigured for use by a large number of small machine shops. It also reviews prior work in this area and describes future work.

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003

Small machine shops typically do not have the resources to develop custom simulations of their operations or data translators to import their data from other manufacturing software applications. This paper presents an overview of an information model currently under development at the National Institute of Standards and Technology (NIST) to address this problem. The model provides neutral data interfaces for integrating machine shop software applications with simulation. The information model provides mechanisms for describing data about organizations, calendars, work, resources, schedules, parts, process plans, and layouts within a machine shop environment. The model has been developed using the Unified Modeling Language (UML) and the Extensible Markup Language (XML).

SIMULATION, 2004

Even though we have moved beyond the Industrial Age and into the Information Age, manufacturing remains an important part of the global economy.There is a need for the pervasive use of modeling and simulation for decision support in current and future manufacturing systems, and several challenges need to be addressed by the simulation community to realize this vision. First, an order of magnitude reduction in problem-solving cycles is needed. The second grand challenge is the development of real-time, simulation-based problem-solving capability. The third grand challenge is the need for true plug-and-play interoperability of simulations and supporting software. Finally, there is the biggest challenge facing modeling and simulation analysts today: that of convincing management to sponsor modeling and simulation projects instead of, or in addition to, more commonly used manufacturing system design and improvement methods such as lean manufacturing and six sigma.

Simulation modelling has been identified as one of the most powerful techniques available for the analysis and design of complex manufacturing systems. A number of manufacturing simulators have been designed in an effort to make it easier to use the simulation approach in the manufacturing environment. This paper will attempt to describe and evaluate the characteristics of some of the available manufacturing simulators as well as possible ways to alleviate some of the inherent disadvantages. This paper will also report on the preliminary design philosophy and specifications of a manufacturing simulation program generator (FACSIM) presently under development.

Across a system's lifecycle, the greatest advantage of using simulation is found in the design and planning phase. By detecting and correcting poor systems design prior to system installation, manufacturing organizations can save significant system re-configuration costs. However, data availability is a major obstacle to modeling new systems because the machines are either yet to be built or are inaccessible to model builders. This paper investigates some of the mechanisms to compensate for the lack of downtime data when constructing simulation models for new manufacturing systems. It proposes methods that can validate the designs of new systems and plan for required resources to support them.

2010

Discrete-event simulation (DES) has mainly been used as a production system analysis tool to evaluate new production system concepts, layout and control logic. Recent developments have made DES models feasible for use in the day-to-day operational production and planning of manufacturing facilities. Operative simulation models provide manufacturers with the ability to evaluate the capacity of the system for new orders, unforeseen events such as equipment downtime, and changes in operations. A simulation-based Decision Support System (DSS) can be used to help planners and schedulers organize production more efficiently in the turbulent global manufacturing. This paper presents the challenges for development and the efforts to overcome these challenges for the simulation-based DSS. The major challenges are: 1) data integration 2) automated simulation model creation and updates and 3) the visualization of results for interactive and effective decision making. A recent case study is also presented.

Process efficiency is definitively a critical factor for manufacturing enterprises and the literature review clearly shows how researchers and operations managers consider simulation as a useful tool to study and optimize production processes. Nevertheless, even the studies that celebrate the simulation as the best approach for analyzing, designing and improving manufacturing processes, highlight some important limits that prevent the diffusion of simulation tools outside universities and research centers boundaries. The literature review suggested to concentrate on the design of a new modeling framework for simulating manufacturing processes that implements a structure and a working logic much closer to real production systems. This paper presents some key elements for developing a standard framework for simulating and modeling manufacturing systems, showing how a different modeling approach can allow to reproduce the actual dynamics of a generic production process, natively replicating both information flow and physical material flow. Snapshot of a simulation tool, still in development, are presented as well.