Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Book Review

A Scholarly Review of “Error Control for Network-On-Chip Links” (Authors: Bo Fu and Paul Ampadu, 2012)Fu, B.; and Ampadu, P. 2012. Error Control for Network-On-Chip Links.Springer Science+Business Media, LLC, New York, NY, USA.Available: <http://dx.doi.org/10.1007/978-1-4419-9313-7>

Managing OEE to Optimize Factory Performance

"If you can not measure it, you can not improve it."(Lord Kelvin) It is a common opinion that productivity improvement is nowadays the biggest challenge for companies in order to remain competitive in a global market [1, 2]. A well-known way of measuring the effectiveness is the Overall Equipment Efficiency (OEE) index. It has been firstly developed by the Japan Institute for Plant Maintenance (JIPM) and it is widely used in many industries. Moreover it is the backbone of methodologies for quality improvement as TQM and Lean Production. The strength of the OEE index is in making losses more transparent and in highlighting areas of improvement. OEE is often seen as a catalyst for change and it is easy to understand as a lot of articles and discussion have been generated about this topic over the last years. The aim of this chapter is to answer to general questions as what to measure? how to measure? and how to use the measurements? in order to optimize the factory performance. The goal is to show as OEE is a good base for optimizing the factory performance. Moreover OEE’s evolutions are the perfect response even in advanced frameworks. This chapter begins with an explanation of the difference between efficiency, effectiveness and productivity as well as with a formal definition for the components of effectiveness. Mathematical formulas for calculating OEE are provided too. After the introduction to the fundamental of OEE, some interesting issues concerning the way to implement the index are investigated. Starting with the question that in calculating OEE you have to take into consideration machines as operating in a linked and complex environment. So we analyze almost a model for the OEE calculation that lets a wider approach to the performance of the whole factory. The second issue concerns with monitoring the factory performance through OEE. It implies that information for decision-making have to be guaranteed real-time. It is possible only through automated systems for calculating OEE and through the capability to collect a large amount of data. So we propose an examination of the main automated OEE systems from the simplest to high-level systems integrated into ERP software. Even data collection strategies are screened for rigorous measurement of OEE. The last issue deals with how OEE has evolved into tools like TEEP, PEE, OFE, OPE and OAE in order to fit with different requirements. At the end of the chapter, industrial examples of OEE application are presented and the results are discussed

Tuotemallien tarkistuksen metriikan kehitys ja automaatio

A lot of interest and research has been focused on product quality and it is recognized as a crucial aspect of engineering. The quality of product models can also be seen as essential in engineering workflow especially in systems based on downstream data. Model quality effects not only the models accuracy and modifiability but also the agility of the whole engineering systems. Careful and thorough verification plays an important part in effecting product model quality. Verifying product models and designs manually can be laborious and time-consuming process. By automating parts of the verification process, benefits can be seen in the time frame and end results of the verification. The goal of the thesis is to develop metrics and automation for product model verification. Development of metrics is executed by researching literature for model quality metrics and construct a set of metrics for the company. Furthermore, the possibilities of product model verification automation are studied and a working automated model verification tool shall be created based on the metrics. The tool is intended be used in the current modeling environment. The outcomes of this thesis are a list of product quality dimensions with their corresponding metrics and a customized PTC ModelCHECK check that can automatically identify issues in product models. Quality dimensions were identified based on company needs and literature research. ModelCHECK platform was chosen for verification tool development as the software is readily available for the company which means it is a cost-effective way of utilizing automated product model verification in current design environment.Tuotteiden laatuun on jo pidemmän aikaa kiinnitetty paljon huomiota insinööriprosesseissa ja tutkimuksessa. Myös tuotemallien laatu voidaan nähdä insinöörityön kannalta elintärkeässä asemassa, erityisesti systeemeissä jotka perustuvat alaspäin virtaavaan tietoon. Mallien laatu vaikuttaa muun muassa sen tarkkuuteen ja muokattavuuteen sekä koko mallinnus- ja suunnittelujärjestelmän ketteryyteen. Huolellinen ja läpikotainen tarkistus on tärkeä osa tuotemallien laadun kehittämistä. Mallien manuaalinen tarkastaminen voi olla työlästä ja aikaavievää. Käyttämällä automaatiota tarkistuksen apuna, voidaan saavuttaa etuja tarkistuksen nopeudessa ja lopputuloksessa. Tämän diplomityön tavoitteena on kehittää tuotemallien tarkastuksen metriikkaa ja automaatiota. Metriikan kehitys perustuu kirjallisuustutkimukseen sekä muun muassa haastatteluissa kartoitettuihin yrityksen tarpeisiin. Tavoitteena on luoda tuotemalleille metriikkaa, joita vasten niiden ominaisuuksia voidaan arvioida. Myös tarkistuksen automaatiota tutkitaan ja tavoitteena on luoda automaattinen työkalu, jota voidaan käyttää yrityksen tämän hetkisessä suunnittelujärjestelmässä. Tutkimuksen lopputuloksena syntyi lista tuotemallien laadun ulottuvuuksista niihin liitetyillä metriikoilla ja metriikan mukainen PTC ModelCHECK tarkistuspohja 3D-malleille, joka löytyy automaattisesti virheitä malleista. ModelCHECK valittiin työkaluksi, koska se on valmiiksi saatavilla yrityksen nykyisessä mallinnusjärjestelmässä, joilloin automatisointi on erittäin kustannustehokasta