simulation modeling practice and theory

System Modeling and SimulationSystem modeling and simulation is a critical process that helps organizations to design, develop, and test complex systems before they are implemented. It involves creating a virtual model of the system and simulating its behavior under different conditions to identify potential issues and optimize its performance. The use of system modeling and simulation has become increasingly important in various industries, including aerospace, automotive, defense, healthcare, and manufacturing. In this response, I will discuss the importance of system modeling and simulation, its benefits, challenges, and future trends.First and foremost, system modeling and simulation help organizations to reduce the risk of failure and save costs. By creating a virtual model of the system, engineers can identify potential issues and optimize its performance before it is implemented. This helps to reduce the risk of failure and minimize the cost of rework. For example, in the aerospace industry, system modeling and simulation are used to test the performance of aircraft before they are built. This helps to identify potential issues and optimize the design, which can save millions of dollars in development costs.Secondly, system modeling and simulation help organizations to improve their decision-making process. By simulating the behavior of the system under different conditions, engineers can evaluate the impact of different design choices and make informed decisions. This helps to reduce the risk of making costly mistakes and ensures that the system meets the requirements of the stakeholders. For example, in the healthcare industry, system modeling and simulation are used to evaluate the impact of different treatment options on patients. This helps doctors to make informed decisions and provide the best possible care to their patients.Thirdly, system modeling and simulation help organizations to improve their productivity and efficiency. By simulating the behavior of the system, engineers can identify potential bottlenecks and optimize the system's performance. This helps to improve productivity and reduce the time and cost of production. For example, in the manufacturing industry, system modeling and simulation are used to optimize the production process and reduce the time and cost of production.However, there are also challenges associated with system modeling and simulation. One of the biggest challenges is the complexity of the systems being modeled. As systems become more complex, it becomes increasingly difficult to create an accurate model and simulate its behavior. This can lead to inaccurate results and increase the risk of failure. Another challenge is the availability of data. In order to create an accurate model, engineers need access to a large amount of data. However, in some cases, data may not be availableor may be difficult to obtain.Looking into the future, there are several trends that are likely to shape the future of system modeling and simulation. One of the trends is the use of artificial intelligence (AI) and machine learning (ML) to improve the accuracy of models. AI and ML can help to identify patterns in data and create more accurate models. Another trend is the use of cloud computing to improve the scalability and accessibility of system modeling and simulation. Cloud computing allows engineers to access powerful computing resources and collaborate with others in real-time.In conclusion, system modeling and simulation are critical processes that help organizations to design, develop, and test complex systems before they are implemented. They help to reduce the risk of failure, improve decision-making, and improve productivity and efficiency. However, there are also challenges associated with system modeling and simulation, such as the complexity of the systems being modeled and the availability of data. Looking into the future, there are several trends that are likely to shape the future of system modeling and simulation, including the use of AI and ML and cloud computing.。

九分达人theory or practice Theory or Practice: Which is More Important in Mastery?Introduction:In the pursuit of mastering a skill or field, individuals often contemplate the balance between theory and practice. While theory provides the foundation, practical application tests and solidifies knowledge. Both theory and practice play significant roles in mastery. However, the question remains: which is more important? This essay will delve into this debate, exploring the importance of both theory and practice in various scenarios.Body:1. The Significance of Theory:Theory serves as the cornerstone of any field of knowledge. It provides the conceptual framework andunderstanding necessary for practical application. For instance, in scientific fields, theories such as relativity and quantum mechanics lay the groundwork for experimentation and technological advancements. Similarly, in academic disciplines like history and literature, theories provide critical analysis tools for interpreting and analyzing texts and events. Thus, theory is essential for individuals to grasp the underlying principles and concepts of a subject.2. The Power of Practical Application:While theory is crucial, practice is equally important in mastering a skill or field. Practical application allows individuals to put theory into action, enhancing their understanding and refining their abilities. Take a musical instrument, for example. Understand the theoretical aspects of playing a guitar, such as chords and scales, is essential. However, it is through consistent practice that individuals can develop muscle memory, speed, and creativity in playing.Only through practical application can individuals truly internalize and excel in a skill.3. Complementary Nature of Theory and Practice:Theory and practice are not mutually exclusive; instead, they work in tandem to facilitate mastery. Theoretical knowledge forms the foundation, guiding individuals in their practical endeavors. Conversely, practical experience enriches theoretical knowledge, deepening understanding by revealing the complexities and nuances that theory alone cannot capture. Achieving mastery requires a continuous cycle of learning, applying, and reflecting. As Albert Einstein once said, "The only source of knowledge is experience." Theory and practice are symbiotic, with each enhancing and reinforcing the other.4. The Balance between Theory and Practice:Finding the optimal balance between theory and practiceis essential for mastery. In some fields, such as academia orresearch, theory might hold more weight due to the need for deep understanding and original thinking. On the other hand, practical skills, like carpentry or cooking, requireextensive practice to refine dexterity and technique. In reality, the balance might vary depending on the context and individual goals. For example, an individual aspiring to become a successful entrepreneur may require a solidtheoretical understanding of business concepts and strategies. However, they must also gain practical experience bylaunching and operating their own ventures.5. The Importance of Context:The importance of theory and practice can also be influenced by the context in which they are applied. Incertain situations, theory might be prized more heavily than practice. For example, in academic settings, deep theoretical knowledge and critical thinking skills are often valued above practical proficiency. However, in the professional world,practical skills and experience are often considered more important for career success. Different fields and industries place varying degrees of emphasis on theory and practice, making it essential to adapt accordingly.Conclusion:In the quest for mastery, both theory and practice play significant roles. Theory provides the foundation, enabling individuals to understand fundamental concepts and principles. Meanwhile, practice transforms that theoretical knowledgeinto real-world skills through repetition, adaptation, and refinement. The ideal approach is to strike a balance between theory and practice, recognizing their complementary nature and leveraging them according to the context and goals. Ultimately, mastery lies in the integration of theory and practice, with each enriching the other in a continuous cycle of growth and development.。

entrepreneurship theory and practice中
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entrepreneurship theory and practice中文意思是：创业理论与实践。

拓展：
《创业理论与实践》是一本关于创业领域的期刊，主要关注创新创业领域的研究论文、案例研究和实践经验。

该期刊旨在促进创业领域的学术研究和商业实践，支持和鼓励新的商业机会和创新思维。

从理论角度来看，《创业理论与实践》提供了许多关于创业的理论和模型，帮助创业者和投资者更好地了解创业过程和风险特征。

其中，最重要的理论包括创业心理学、创业生命周期理论、创业风险管理、创业融资等。

这些理论提供了创业者在业务规划、组织结构、市场营销、融资等方面的指导和支持，可以帮助创业者更好地发展和经营其公司。

此外，该期刊提供了许多实践经验和案例研究，为创业者提供了大量有用的信息和指导方针。

这些案例研究和实践经验覆盖了各种创业类型和行业，在创业形式、商业模式、团队建设、产品开发等方面提供了思路和灵感。

这些案例研究和经验分享也可以帮助创业者更好地理解市场和顾客需求，发现新的商业机会和发展方向。

从应用角度来看，《创业理论与实践》主要关注在现实商业环境中创业实践的落地和实现。

虽然许多理论和模型已经被提出，但是如何将这些理论转化为实际操作、如何应对市场变化和风险、如何保持创新和竞争力，这些都是创业者必须面对的问题。

因此，该期刊提供了许多有关创业实践的信息和实用工具，包括市场研究、竞争分析、商业计划书、团队管理等。

这些工具和信息可以帮助创业者更好地制定战略、管理公司和应对变化。

学士学位论文基于Witness的物流实验室生产物流系统建模与仿真学生姓名：指导教师：所在院系：所学专业：研究方向：东北农业大学中国·哈尔滨2015年6月NEAU B.A. Degree Thesis Registered Number：A07111049MODELING AND SIMULATINGON LOGISTICS LABORATORY PRODUCTION LOGISTICS SYSTEM BASED ON WITNESSName of Student：Wang JiananSupervisor:Wang YijiaoCollege：Engineering CollegeSpecialty:Industrial EngineeringResearch Field:Logistics ManagementNortheast Agricultural UniversityHarbin·ChinaJune 2015毕业设计（论文）原创性声明和使用授权说明原创性声明本人郑重承诺：所呈交的毕业设计（论文），是我个人在指导教师的指导下进行的研究工作及取得的成果。

尽我所知，除文中特别加以标注和致谢的地方外，不包含其他人或组织已经发表或公布过的研究成果，也不包含我为获得及其它教育机构的学位或学历而使用过的材料。

对本研究提供过帮助和做出过贡献的个人或集体，均已在文中作了明确的说明并表示了谢意。

作者签名：日期：指导教师签名：日期：使用授权说明本人完全了解大学关于收集、保存、使用毕业设计（论文）的规定，即：按照学校要求提交毕业设计（论文）的印刷本和电子版本；学校有权保存毕业设计（论文）的印刷本和电子版，并提供目录检索与阅览服务；学校可以采用影印、缩印、数字化或其它复制手段保存论文；在不以赢利为目的前提下，学校可以公布论文的部分或全部内容。

作者签名：日期：学位论文原创性声明本人郑重声明：所呈交的论文是本人在导师的指导下独立进行研究所取得的研究成果。

学科专业简介计算系统结构专业是计算机科学技术学科所属的二级学科，是计算机科学技术最活跃的研究领域之一。

特别是最近几年，随着高性能计算机、高速计算机网络和嵌入式系统的广泛深入地研究，已形成诸多新的研究热点。

本专业从应用需求出发，围绕这些研究热点，通过数年的研究积累已形成稳定的研究方向，并取得一定的学术成果。

高性能计算机系统在许多领域中有着非常重要的应用，这些领域包括科学计算、建模与仿真、图像识别与处理、计算机辅助设计、网络通信、人工智能等。

近年来，并行处理已成为高性能计算机系统的关键技术。

容错技术和实时技术是保证计算机系统执行结果的逻辑正确性和时间正确性的重要技术。

这两种技术的结合将进一步提高计算机系统和基于计算机的应用系统的实时性（如时间可预测性）和可信性（包括可靠性、安全性和可测试性等）。

计算机网络和分布式系统的研究正朝着高速、高服务质量和无线网络方向发展。

在此基础上形成多媒体信息在网络中的传输及处理、网络计算环境的知识捕获和处理、计算机支持的协同工作（CSCW）、电子商务的协议与标准等等研究热点。

嵌入式系统被定义为以应用为中心、以计算机技术为基础、软硬件可裁剪、适应应用系统对功能、可靠性、成本、体积、功耗严格要求的专用计算机系统。

嵌入式处理器的应用软件是实现嵌入式系统的关键、软件要求固化存储，软件代码要求高质量、高可靠性、系统软件的高实时性是基本要求。

在制造工业、过程控制、通讯、仪器、仪表、汽车、船舶、航空、军事装备、消费类产品等方面均是嵌入式计算机应用领域。

本方向主要从事家庭网络、e-home、智能卡技术、嵌入式操作系统和开发环境的研究。

本专业近三年来在国内外重要核心学术刊物上发表学术论文近百篇，出版著作和教材数本。

承担国家自然科学基金项目、国家重点基础研究发展计划（973）项目和省部级等科学研究项目数十项。

获得省部级等科技进步奖和优秀教材奖多项。

每年有百万元研究经费。

本专业具有稳定的、分布合理的学术梯队，共有教师20人，其中教授3人，副教授7人。

Readings in Translation Practice and Theorys/n 英国诗选卞之琳译，1996，商务印书馆1 斯宾塞诗选胡家峦译胡家峦译，1997，漓江出版社2 华兹华斯抒情诗选杨德豫译，1996，湖南文艺出版社3 雪莱抒情诗选江枫译，1996，湖南文艺出版社4 惠特曼抒情诗选李野光译，1996，湖南文艺出版社5 狄金森抒情诗选江枫译，1996，湖南文艺出版社6 密尔顿抒情诗选金发燊译，1996，湖南文艺出版社7 彭斯诗情诗选袁可嘉译，1996，湖南文出版社8 莎士比亚抒情诗选梁宗岱译，1996，湖南文艺出版社9 拜伦抒情诗选杨德豫译，1996，湖南文艺出版社10 雪莱抒情诗选江枫译，1997，商务印书馆11 英美著名儿童诗一百首屠岸编译，1994，中国对外翻译出版公司12 英语爱情诗一百首黄杲忻译，1993，中国对外翻译出版公司13 圣经故事一百篇刘意青等译，1989，中国对外翻译出版公司14 名人演说一百篇石幼珊译，1987，中国对外翻译出版公司15 莎士比亚戏剧精选一百段黄兆杰编译，1989，中国对外翻译出版公司16 名人书信一百封黄继忠译，1987，中国对外翻译出版公司17 英美名诗一百首孙梁译，1987，中国对外翻译出版公司18 中国成语故事一百篇杨立义译，1991，中国对外翻译出版公司19 中国神话及志怪小说一百篇丁往道译，1991，中国对外翻译出版公司20 孙子兵法一百则罗志野译，1996，中国对外翻译出版公司21 中国历代笑话一百篇卢允中译，1991，中国对外翻译出版公司22 佛经故事一百篇张庆年译，1996，中国对外翻译出版公司23 中国古代案例一百则乔车洁玲译，1996，中国对外翻译出版公司24 中国历代短简一百篇谢百魁译，1997，中国对外翻译出版公司25 莎士比亚十四行诗一百首屠岸译，1992，中国对外翻译出版公司26 唐诗一百首张廷琛、魏博思译，1991，中国对外翻译出版公司27 唐宋词一百首许渊冲译，1991，中国对外翻译出版公司28 中国历代散文一百篇戴抗选等译，1996，中国对外翻译出版公司29 中国现代诗一百首庞秉钧译，1993，中国对外翻译出版公司30 京剧名唱一百段彭阜民、彭工译，2000，中国对外翻译出版公司31 柔巴依一百首黄杲忻译，1998，中国对外翻译出版公司32 英语口译——理论、技巧与实践潘能，1994，西安交通大学出版社33 口译：理论与实践，语言与交际李逵六，外语教学与研究出版社34 科技翻译技巧论文集中国翻译工作者协会，1987，中国对外翻译出版公司35 汉英翻译教程吕瑞昌、喻云根等，1983，陕西人民出版社36 英汉翻译练习集庄绎传，1984，中国对外翻译出版公司37 英汉翻译技能训练手册刘宓庆，1987，上海外语教育出版社38 唐诗三百首新译许渊冲、陆佩弦、吴钧陶等译，1988，中国对外翻译出版公司39 百岁人生随想录粟旺、黄黎，1994，中国对外翻译出版公司40 唐诗二百首英译徐忠杰，1990，北京语言学院出版社41 词百首英译徐忠杰，1986，北京语言学院出版社42 古诗英译翁显良，1985，北京出版社43 古今爱国抒情诗词选王知还，1995，中国对外翻译出版公司44 毛泽东诗词选许渊冲，1993，中国对外翻译出版公司45 毛泽东诗词1999，外文出版社46 人面桃花崔健等，1995，外文出版社47 实用口译手册钟述孔，1991，中国对外翻译出版公司48 汉英口译教程王逢鑫，1992，北京大学出版社49 经贸口译教程王学文等，1993，中国对外经济贸易大学出版社50 汉英——英汉经贸口译教程胡修浩等，1998，上海财经大学出版社51 汉译英口译教程（修订版）吴冰等，1995，外语教学与研究出版社52 大学英语口译（汉英）教程吴冰，1988，外语教学与研究出版社53 实用英语口译（英汉）新编崔永禄等，1994，南开大学出版社54 口笔译概论达妮卡·塞莱斯科维奇等/ 孙慧双译，1992，北京语言学院出版社55 实用英汉口译技巧朱佩芬，1995，华东理工大学出版社56 口译技巧：思维科学与口译推理教学法刘和平，2001，中国对外翻译出版公司57 英文翻译方法和实例刘天民，1976，香港宏业书局58 丰华瞻译诗集1997，上海外语教育出版社59 英汉翻译手册钟述孔，1997，世界知识出版社60 浮生六记林语堂译，1999，外语教学与研究出版社61 法窗译话陈忠诚，1992，中国对外翻译出版公司62 汉英科技法译指要冯志杰，1998，中国对外翻译出版公司63 困难见巧金圣华，1998，中国对外翻译出版公司64 英语理解与翻译林相周，1998，上海外语教育出版社65 英译中漫谈易江，2001，中央编译出版社66 文学翻译原理张今，1987，河南大学出版社67 英汉互译实用教程郭著章，1988，武汉大学出版社68 实用翻译教程冯庆华，1997，上海外语教育出版社69 英汉翻译津指陈生保，1998，中国对外翻译出版公司70 翻译与人生周兆祥，1998，中国对外翻译出版公司71 当代文学百家谈王寿兰编，1989，北京大学出版社72 现代英语翻译诀窍谭宝全，1997，上海交通大学出版社73 新实用汉译英教程陈宏微，1996，湖北教育出版社74 汉英翻译指导陆钰明，1995，上海远东出版社75 实用翻译教程范仲英，1994，外语教学与研究出版社76 翻译教程孙万彪，1996，上海外语教育出版社77 英汉翻译原理周方珠，1997，安徽大学出版社78 英汉互译实用教程自学指导手册郭著章，1998，武汉大学出版社79 汉译英难点解析500例汪福祥，1998，外文出版社80 桥畔译谈：翻译散论八十篇金圣华，1997，中国对外翻译出版公司81 汉英翻译讲评单其昌，1989，对外贸易教育出版社82 汉英翻译技巧单其昌，1990，外语教学与研究出版社83 英汉与汉英翻译教程柯平，1993，北京大学出版社84 译艺谈黄邦杰，1991，中国对外翻译出版公司85 中译英技巧文集1992，中国对外翻译出版公司86 文学翻译十讲刘重德，1991，中国对外翻译出版公司87 英美名著翻译比较喻云根，1996，湖北教育出版社88 从柔巴伊到坎特伯雷：英语诗汉译研究黄杲忻，1999，湖北教育出版社89 翻译与批评周仪、罗平，1999，湖北教育出版社90 汉英翻译教程喻云根，1983，山西人民出版社91 汉英时文翻译贾文波，2000，中国对外翻译出版公司92 词语翻译丛谈陈忠诚，1983，中国对外翻译出版公司93 词语翻译丛谈续编陈忠诚、吴幼娟，2000，中国对外翻译出版公司94 外事翻译：口译和笔译技巧徐亚军、李健英，1998，世界知识出版社95 好易学英汉口译方凡泉，2000，世界图书出版社96 英汉翻译教程庄绎传，1999，外语教学与研究出版社97 大学英汉翻译教程（修订版）王治奎主编，1999，山东大学出版社98 大学汉英翻译教程（修订版）王治奎主编，1999，山东大学出版社99 中国文学·现代诗歌卷（汉英对照）1998，中国文学出版社/外语教学与研究出版社100 中国文学·古代诗歌卷（汉英诗歌卷）1998，中国文学出版社/外语教学与研究出版101 明清散文选（汉英对照）偬仕，1999，中国文学出版社/外语教学与研究出版社102 唐诗选（汉英对照）偬仕，1999，中国文学出版社/外语教学与研究出版社103 闻一多诗文选（汉英对照）闻一多，1999，中国文学出版社/外语教学与研究出版104 古代笑话选（汉英对照）偬仕，1999，中国文学出版社/外语教学与研究出版社105 诗经选（汉英对照）偬仕，1999，中国文学出版社/外语教学与研究出版社106 宋词选（汉英对照）偬仕，1999，中国文学出版社/外语教学与研究出版社107 新编千家诗（汉英对照）袁行霈注解/许渊冲译，2000，中华书局108 新编英语口译教程厦门大学外文系/中英英语合作项目组编，1999，上海外语教育出版社109 英诗选译集孙大雨，1999，上海外语教育出版社110 中国名家散文精译（汉英对照）张梦井等，1999，青岛出版社111 英译中国现代散文选张培基，1999，上海外语教育出版社112 现代英汉翻译操作何刚强，1998，北京大学出版社113 汉英翻译基础陈宏薇，1998，上海外语教育出版社114 英汉翻译基础古今明，1997，上海外语教育出版社115 英译陶诗汪榕培，2000，外语教学与研究出版社116 散文佳作108篇（汉英/英汉对照）乔萍等，1999，译林出版社117 A Dream of Red Mansions(V ol.1-5) 杨宪益、戴乃迭译，1994，外文出版社118 Journey to the West(V ol. 1-3) W. J. F. Jenner 译, 1993，外文出版社119 The Scholars 杨宪益、戴乃迭译，1957，外文出版社120 Creation of the Gods（封神演义，1-2卷）顾执中译，1992，新世界出版社121 Outlaws of the Marsh（Vol.1-3）Sidney Shapiro 译，1980，外文出版社122 Three Kingdoms (V ol. 1-3) Moss Roberts 译，1994，外文出版社123 论语（汉英对照）丘文明，1997，世界知识出版社124 论语（汉英对照）赖波，1994，华语出版社125 西厢记许渊冲译，1997，湖南人民出版社126 左传（上下）胡志挥译，1996，湖南人民出版社127 庄子汪榕培译，1998，湖南人民出版社128 孟子汪榕培，1999，湖南人民出版社，外文出版社129 宋词三百首许渊冲译，1996，湖南人民出版社130 汉魏六朝诗三百首汪榕培，1998，湖南人民出版社131 楚辞许渊冲译，1994，湖南人民出版社32 坛经黄茂林译，1996，湖南人民出版社133 诗经许渊冲译，1993，湖南人民出版社134 尚书罗志野译，1997，湖南人民出版社135 孙子兵法Giles 译/ 程郁等校注，1993，湖南人民出版社136 老子Arthor Waley 译/ 傅惠生校注，1994，湖南人民出版137 汉英四书James Legge 译/ 刘重德校注，1992，湖南人民出版138 老子任继愈译，1993，外文出版社139 庄子冯友兰译，1989，外文出版社140 Selected Works of Mao Tse-tong (V ol.1-4) 1965，外文出版社141 Selected Works of Zhou Enlai(V ol.1-2) 1981，外文出版社142 Selected Works of Deng Xiaoping(V ol.1-3) 1994，外文出版社143 女神（The Goddesses）郭沫若著/巴恩斯、勒斯特译，2001，外文出版社144 茶馆（Teahouse）老舍著/霍华译，2001，外文出版社145 二马（Mr. Ma & Son: A Sojourn in London），老舍著/尤利叶·吉姆孙译，2001，外文出版社146 雷雨（Thunderstorm）曹禺著/王佐良、巴恩斯译，2001，外文出版社147 朝花夕拾（Dawn Blossoms Plucked at Dusk）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社148 故事新编（Old Stories Retold）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社149 呐喊（Call to Arms）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社150 鲁迅小说选（Selected Stories of Lu Xun）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社151 彷徨（Wandering）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社152 阿Q正传（The True Story of Ah Q）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社153 野草（Wild Grass）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社154 鲁迅诗选（Lu Xun Selected Poems）鲁迅著/杨宪益、戴乃迭译，2000，外文出版社155 春蚕，林家铺子（汉英对照）矛盾著/沙博理译，2001，外文出版社156 英语美文50篇（英汉对照）陶洁选编，2002，译林出版社157 汉英·英汉美文翻译与鉴赏刘士聪著，2002，译林出版社158 中国名家散文精译张梦井、杜耀文，1999，青岛出版社159 名作精译——《中国翻译》英译汉选萃杨平，1998，青岛出版社160 名作精译——《中国翻译》汉译英选萃杨平，2003，青岛出版社161 实用文本汉译英方梦之，2003，青岛出版社162 莎士比亚戏剧经典[中英对照全译本]，《哈姆莱特》等十种朱生豪译/ 杨小川编，中国国际广播出版社。

suggestopaedia in theory and practiceSUGGESTOPEDIA IN THEORY AND PRACTICEIntroductionSuggestopedia is a system of language teaching that uses elements of both suggestion and psychology to create an environment that is conducive to improved language acquisition. The approach is based on the idea that when individuals are placed in an environment and encouraged to learn new things, they can construct new knowledge and learn faster. The aim of suggestopedia is to create an optimal learning environment which is enjoyable and stimulating, thus increasing the learner's motivation and making the learning process easier. TheorySuggestopedia is based on the idea that the environment in which language is learned plays a crucial role in the successful acquisition of a new language. The design of the learning environment is one of the main aspects of the method. In suggestopedia, the environment is arranged in a way that is both stimulating and pleasant. This is achieved by creating a pleasant atmosphere with comfortable and attractive furniture, relaxing music, and the use of soft lighting. The idea is to create an environment that stimulates the senses and encouragesa relaxed and receptive learning attitude.Another important aspect of suggestopedia is the use of suggestion. This involves using both verbal and non-verbal cues to emphasize the importance of certain words and phrases and to encourage the learner to focus their attention on them. The teacher will also use body language and facial expressions to further reinforce the lesson.PracticeIn order to apply suggestopedia in practice, the teacher has to be aware of the different aspects of suggestopedia and how they interact. The teacher must create an environment and atmosphere conducive to learning as well as be able to effectively use verbal and non-verbal cues to emphasize certain words and phrases. The teacher must also be able to create interesting and stimulating activities that will appeal to the learners.In the classroom, the teacher should encourage the learners to interact with one another and promote a relaxed and comfortable attitude. The teacher should also use activities that involve both speaking and listening, and should encourage the learners to use the new language.ConclusionSuggestopedia is an effective and enjoyable method for language learning. The environment, the use of suggestion, and the activities should all be taken into consideration when applying suggestopedia in practice. When used correctly, suggestopedia can help learners acquire a new language quickly and easily.。

SIMULATION MODELLING OF BUSINESS PROCESSESRay J. Paul, Vlatka Hlupic, George M. GiaglisBrunel University, Department of Information Systems and ComputingUxbridge, Middlesex UB8 3PHTel: 01895 – 203 374Fax: 01895 – 251 686E-mail: Ray.Paul@ABSTRACTIncreasingly, organisations need to adapt to new conditions and competitive pressures. Various change management approaches such as business process re-engineering have been developed to meet this perceived need. This paper investigates the potential of simulation modelling for modelling business processes. After a discussion on business processes related issues, an overview of business process modelling methods is presented. The usability of simulation modelling for evaluating alternative business process strategies is investigated. Finally, a framework for business process simulation is proposed.INTRODUCTIONIt is claimed that the increasing competitive pressures that organisations face encourages them to minimise the time it takes to develop the product, bring products to market and service customers whilst maximising profits. This pressure has made Business Process Re-engineering (BPR) a popular topic in organisational management and created new ways of doing business (Tumay, 1995). BPR relates to the fundamental rethinking and radical redesign of an entire business system to achieve significant improvements in performance of the company.Many leading organisations have conducted BPR in order to improve productivity and gain competitive advantage. For example, a survey of 180 US and 100 European companies found that 75% of these companies had engaged in significant re-engineering efforts in the past three years (Jackson, 1996). However, despite the number of companies involved in re-engineering, the rate of failure in re-engineering projects is over 50% (Hammer and Champy, 1993). Some of the frequently cited problems related to BPR include the inability to predict the outcome of a radical change, difficulty in capturing existing processes in a way that can be seen by multidisciplinary team members, shortage of creativity in process redesign, cost of implementing the new process, or inability to recognise the dynamic nature of the processes.Many authors argue that one of the major problems that contribute to the failure of BPR projects is a lack of tools for evaluating the effects of designed solutions before implementation (Paolucci et al, 1997), (Tumay, 1995). Mistakes brought about by BPR can only be recognised once the redesigned processes are implemented, when it is usually difficult and costly to correct wrong decisions. Although the evaluation of alternative solutions might be difficult, it is essential in order to reduce some of the risks associated with BPR projects.Simulation modelling appears to offer great potential for modelling and evaluating alternative business processes. Simulation uses a symbolic representation of processes in order to determine the path and flow of state transitions in ways that can be made persistent, replayed, dynamically analysed and reconfigured into alternative scenarios (Scacchi, 1997). For example, simulation models can dynamically model different samples of parameter values such as arrival rates or service intervals which can help in the discovery of process bottlenecks of and suitable alternatives. Simulation models can provide a graphical display of process models that can be interactively edited and animated to show process dynamics.This paper investigates the potential of simulation modelling to model business processes. We start by a discussion related to business processes and their definitions.A brief overview of business process modelling methods is presented. We then investigate the usability of simulation modelling for evaluating alternative business process strategies, and propose a framework for business process simulation. The conclusions outline the main findings of this research.DEFINING BUSINESS PROCESSESThere is no clear or agreed definition of “business process”in the literature. For example, Hammer and Champy (1993) define a process as “a set of activities that, taken together, produces a result of value to a customer.” According to Davenport and Short (1990) a business process is “a set of logically related tasks performed to achieve a defined business outcome”. Pall (1987) defined a process as “the logical organisation of people, materials, energy, equipment, and procedures into work activities designed to produce a specified end result (work product).”The BPR on-line learning centre states that “business processes are simply a set of activities that transform a set of inputs into a set of outputs (goods or services) for another person or process using people and tools.” Ferrie (1995) defines processes as being “a definable set of activities which from a known starting-point achieve a measurable output to satisfy an agreed customer need.” On the other hand, Earl (1994) define a process as “a lateral or horizontal form, that encapsulates the interdependence of tasks, roles, people, departments and functions required to provide a customer with a product or service.”According to Omrani (1992) a process is “a cycle of activities, which taken together achieve a business objective”. Davenport and Short (1993) define a process as “a structured, measured set of activities designed to produce a specified output for a particular customer or market. It implies a strong emphasis on how work is donewithin an organisation”. In another publication, Davenport (1993) defines a process as “an ordering of work activities across time and place, with a beginning, an end, and clearly identified inputs and outputs.”Saxena’s (1996) definition of a business process declares that a process is “a set of inter related work activities characterised by specific inputs and value added tasks that produce specific outputs”. Talwar (1993) defines a process as “any sequence of pre-defined activities executed to achieve a pre-specified type or range of outcomes.”According to Alter (1996) a business process is referred to as “a related group of steps or activities that use people, information and other resources to create value for internal or external customers. The steps are related in time and place, have a beginning and end, and have inputs and outputs”.It is apparent from an analysis of the above definitions of business processes that there is no consensus. However, some common elements can be identified in a majority of definitions. These elements relate to the process itself (usually described as transformation of input, workflow, or a set of activities), process input, and process output (usually related to creating value for a customer, or achieving a specific goal).Re-engineering business processes involve changes in people, processes and technology over time. As these changes happen over time, simulation appears to be a suitable process modelling method. The interaction of people with processes and technology results in an infinite potential number of scenarios and outcomes that are not possible to predict and evaluate using static process modelling methods.BUSINESS PROCESS MODELLING TOOLS AND METHODSThere are multitudes of approaches, methodologies, and techniques to support BPR design efforts (Wastell et al, 1994), (Harrison and Pratt, 1993). Kettinger et al (1997) conducted an empirical review of existing methodologies, tools, and techniques for business process change and developed a reference framework to assist positioning of tools and techniques that help in re-engineering strategy, people, management, structure, and technology dimensions of business processes. Some of the most widely used process modelling techniques includes IDEF, role activity diagramming, process flowcharting, and hierarchical coloured Petri nets (Kettinger et al, 1997). Although simulation is also mentioned as one of the modelling methods in a comprehensive survey conducted by Kettinger et al (1997), the authors identified a need for more user-friendly multimedia process capture and simulation software packages that could allow easy visualisation of business processes and enable team members to participate in modelling efforts. Table 1 summarises the main types of business processes modelling tools available as presented in Gladwin and Tumay (1994).Type ofModelling ToolGeneral Description ExamplesFlow Diagramming Tools These tools help defineprocesses and workflows by connecting textdescriptions ofprocesses to symbols.ABC Flowcharter,Process Charter,EasyFlow,FlowCharting3.CASE tools These tools provide aconceptual frameworkfor modelling processdefinitions andhierarchies.Design/IDEF, Workflow Analyser, Business Design facility, Action Workflow.Simulation Modelling Tools These tools providedynamic, stochastic andanimation analysiscapabilityServiceModel,SimProcess,Extend+BPR,BPMATTable 1: Business process modelling toolsBusiness process modelling tools are continuously being released on the software market. Many of these tools represent business processes by graphical symbols, where individual activities within the process are shown as a series of rectangles and arrows. Most software tools for business process modelling have an origin in a variety of process mapping tools that provide the user with a static view of the processes being studied. Some of these tools provide basic calculations of process times. More sophisticated tools allow some attributes to be assigned to activities and enable some sort of process analysis. However, most of these tools are not able to conduct “what if “analysis, show dynamic changes in business processes, or evaluate the effects of stochastic events and random behaviour of resources. All of these are possible using simulation models of business processes. Simulation software tools are able to model the dynamics of the processes, such as the build up of queues, and show it visually, which can then enhance the generation of creative ideas concerning the redesign of the existing business processes.Analysis of the literature reveals that there no comprehensive, scientifically grounded design methodology to structure, guide, and improve organisational design efforts. Many authors argue that one of the major problems that contributes to the failure of business process change projects is a lack of tools for evaluating the effects of designed solutions before implementation (for example, Paolucci et al 1997, Tumay 1995). Mistakes about brought by business change can only be recognised once the redesigned processes are implemented, when it is usually difficult and costly to correct wrong decisions. Although a complete evaluation of alternative solutions is difficult, some attempt is essential in order to reduce some of the risks associated with business change projects. This argument is in line with van Meel and Sol (1996), who advocate the development of computer-based models of business processes as a crucial mechanism to support the process of experimentation with alternative business structures. Such models could be particularly useful for prototyping and accelerating process conceptualisation. This can then eliminate costly and time consuming trial and error approaches usually taken in the absence of adequate business process modelling tools.Simulation is one of the most widely used techniques in operational research and management science (Law and Kelton 1991). However, there are relatively few examples of using simulation for business process modelling available in the literature.A majority of these publications were written by simulation modelling practitioners rather than business analysis specialists. So, the potential of simulation for businessprocess modelling has yet to be recognised by the business community. SIMULATION AND BUSINESS PROCESS MODELLINGThe basic idea behind simulation is simple (Doran and Gilbert, 1994): We wish to acquire knowledge and reach some informed decisions regarding a real-world system (the business). But the system is not easy to study directly. We therefore proceed indirectly by creating and studying another entity (the simulation model), which is sufficiently similar to the real-world system so that we are confident that some of what we learn about the model will also be true of the system. In other words, the simulation model is used as a vehicle for experimentation, often in a ‘trial and error’way to demonstrate the likely effects of various policies. Those policies producing the best results in the model should be implemented in the real system (Pidd, 1992).Simon (1973) argues that one of the most important uses of computers is ‘to model complex situations and to infer the consequences of alternative decisions to overcome bounded human rationality’. We argue that computer-based models of business processes can help overcome the inherent complexities of studying and analysing businesses, and therefore contribute to a higher level of understanding and thereby improving these processes.In terms of the business environment, simulation models usually focus on an analysis of specific aspects of an organisation, for example production or finance. Perhaps the most widely known application area of simulation in the business arena is for modelling manufacturing operations, where the complexity and dynamic behaviour of the system is the main reason for using simulation to facilitate system design and assess operating strategies (Carrie, 1988). Examples of the use of simulation for manufacturing, production and operations management can be found in (Hlupic and Paul, 1994) and (Ceric and Hlupic, 1993). Another category is financial modelling, which is mainly concerned with risk analysis (Seila and Banks 1990). Only a few articles focus on modelling the whole spectrum of organisations and adopt a process-based approach in so doing.The use of business process modelling tools is usually focused on modelling current business process, without a systematic approach to evaluating business process alternatives. Gladwin and Tumay (1994) discovered that over 80% of BPR projects used static flowcharting tools for business process modelling. The static modelling tools, which are predominately used, are deterministic and do not enable the evaluation of alternative re-designed processes.Simulation models, on the other hand, can incorporate and depict the dynamic and random behaviour of process entities and resources. The physical layout and interdependencies of resources used in the processes under consideration can be shown visually, and the flow of entities among resources can be animated using simulation as a modelling tool.A FRAMEWORK FOR BUSINESS PROCESS SIMULATIONSimulation models provide quantitative information that can be used for decision-making. As such, they are regarded as problems understanding tools rather than problem solving tools. There are several characteristics of simulation that make this method suitable for business process modelling:•A process approach in simulation modelling terminology relates to a time-ordered sequence of interrelated events which describes the entire experience of an entity as it flows through the system (Law and Kelton, 1991). This approach can be related to some of the definitions of business processes presented in section 2 above.•Simulation models can be easily modified to follow changes in the real system and as such can be used as a decision support tool for continuous process improvement.•A simulation model of non-existing business processes can be developed and used for process design (rather than for redesign).•Simulation models can capture the behaviour of both human and technical resources in the system.•Simulation models can incorporate the stochastic nature of business processes and the random behaviour of their resources.•The visual interactive features of many simulation packages available on the market enable a graphical display of the dynamic behaviour of model entities, showing dynamic changes in state within processes.T he benefits of using simulation for business process modelling are numerous. For example, organisations can react more quickly to market changes, because simulating the effects of redesigned processes before implementation can improve the chances of getting the processes right at the first attempt. Visual interactive simulation backed up by a variety of graphical output reports can show the benefits of redesigned processes, which can be used for business process re-engineering “buy-in”, and for communicating the structure of new processes to other employees. Simulation can be used for focusing “brainstorming” meetings, where various new ideas can be tested using a simulation model, and informed decisions can be made. A simulation model of business processes can determine a potential bottleneck area and ascertain which resources are critical.T he process of developing simulation models of business processes can be divided into several distinct steps that have to be followed, from the identification of a need, to providing recommendations. Although these steps are necessarily described sequentially, they are executed iteratively, and several individual steps are usually repeated until they produce a suitable outcome. A framework for carrying out business process simulation consists of the following steps (summarised in Table 2):S tep 1 - Defining Modelling ObjectivesO nce it has been decided to use simulation for business process modelling, it has to be decided what is the required outcome of modelling and which information the model should provide. For example, the objective of modelling might be to evaluate the effects of downsizing or allocating particular tasks within processes to different employees.S tep 2 - Deciding on Modelling BoundariesI n this stage, it has to be decided which processes (or parts of a large process) should be incorporated in the model. This is to be determined on the basis of the importanceof certain processes or a need to redesign inefficient processes, and on the basis of the suitability of particular processes to be captured in a simulation model.S tep 3 - Data Collection and AnalysisD epending on the scale of modelling, a certain amount of important data about the processes being modelled needs to be collected and analysed in order to be incorporated in a model. Data is usually collected through discussions with experts and particularly with people involved in the processes to be modelled, through observation of the existing processes and through studying the documentation about processes. Data collected needs to be analysed using standard statistical procedures such as distribution fitting (Law and Kelton, 1991).S tep 4 - Business Process Simulation Model DevelopmentO nce the relevant data about the business processes is collected and analysed, a simulation model is developed using a simulation software package. This should be done through an iterative process where a simple model is initially developed, which is then expanded and refined until an acceptable model is obtained.S tep 5 - Model TestingA fter each iterative step in the model development, “models in progress”should be thoroughly tested using as many model verification techniques as feasible. Some of the most commonly used verification approaches include black box validation (for example testing model components) and white box validation (for example testing input distributions, static and dynamic logic) (Pidd, 1992).S tep 6 - Model ExperimentationA fter acceptable testing, experimentation with the model can commence. Formal experimental design seems to be appropriate where there are a number of alternative ways of performing the same process (Darnton and Darnton, 1997). General rules related to the design of experiments include:•random errors should be reduced,•experiments should be designed in such a way to include a wide range of alternatives so that recommendations could be valid for a range of organisational units,•the experiment should be as simple as possible,•a sound statistical analysis should be applied without making unrealistic assumptions related to the nature of business processes.Step 7 - Output AnalysisOutput results obtained during experimentation should be analysed using standard statistical techniques for simulation output analysis (Law and Kelton, 1991) related to the estimation of the values of the output variables.Step 8 - Business Process Change RecommendationsThe simulation model output analysis is used as a basis for making recommendations regarding business process change or improvement.Step Description1Defining Modelling Objectives2Deciding on Modelling Boundaries3Data Collection and Analysis4Business Process Simulation ModelDevelopment5Model Testing6Model Experimentation7Output Analysis8Business Process Change RecommendationsTable 2: A framework for business process simulation SUMMARY AND CONCLUSIONSThis paper investigated the potential of simulation modelling for modelling business processes. Following a discussion related to business processes and their definitions, a brief overview of business process modelling methods was presented. The usability of simulation modelling for evaluating alternative business process strategies was investigated, and a framework for business process simulation proposed.There are many reasons why simulation modelling should be used as a process modelling tool. For example, a new business process might involve a decision about capital investment that is difficult to reverse. It is usually too expensive to experiment with the real business processes, especially if business process change relates to the entire organisation. In many cases the variables and resources for new processes are not determined or understood, and the process of simulation model development can help in understanding some of these issues. The value of simulation depends on the model validity and the likelihood that the results of the model experiments would be replicated and implemented in the real processes.Using simulation models can improve various skills, from technical to decision making. Good simulation models can lead to replicable results where assumptions are explicit and can overcome limitations inhuman reasoning (Darnton and Darnton, 1997). However, if models are not adequately developed and validated, then assumptions about the behaviour of real processes are likely to be wrong. It might also be difficult to achieve a required precision for a model due to a lack of data or to the highly unpredictable behaviour of some process resources. Finally, simulation model development might be costly. It takes time and requires specialist skills not widely available. 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