198毕马威--IT规划方案教程
毕马威-全套内部培训教程5
Course Modules
1. Proposal Basics 2. Proposal Process Proposal Theme Simulation Exercise 3. Proposal Writing - General Rules 4. Proposal Writing - Guidelines/Hints 5. Proposal Graphics 6. Executive Summary
CONSULTING
Who is This Guy, and Why Should I Listen to Him?
10+ Years of Personal Proposal Writing and Management Experience Built a KPMG Consensus, speaking to:
Managing Directors BDMs Senior Managers/Proposal Managers Experienced Proposal Team Members
CONSULTING
KPMG Proposal Process
No Established Official Process Yet Today’s Program Focuses on What Makes a Good Proposal Future Program Will Focus on Improving the Process
CONSULTING
Carl Rosenblatt Public Services 703 747-6508
Sharon Long Comm & Content
703 747-5490
IT规划方法论汉普咨询
业务策略 - 组织能力
汉普咨询IT规划方法强调业务与IT的紧密结合
差异
管理语言
弥合业务思想和技术思想之间的差异 使用共同的方法 培训 (知识, 文化)
技术语言
融合业务和技术语言
结合表示:
理解
• 从技术、组织和流 • 分析业务/IT现状与
程三个方面来系统 愿景之间的差距及
地评估企业当前的 改进方法
信息系统现状及相 • 建立未来业务和IT发
关能力
展的构想
• 流程重组建议
• 快速解决方案
汉普咨询IT规划方法
业务/IT 解决方案 策略及愿景 & IT能力
IT规划
关键业务需求 解决方案
IT管理运作
信息技术飞速发展，新的思想、技术、工具层出不穷，而信息技术人才严重短缺，在这样的环 境中信息部门如何有效地完成自己的使命？
企业信息化如何应对挑战？
高度复杂、飞速发展的信息技术
+ 管理问题的复杂性
议程
➢ IT规划方法介绍
➢ IT规划的核心思想 ➢ IT规划项目工作内容及工具
➢ IT规划项目运作
➢ 交流与讨论
吸取经验，高起点确立企业信息化目标
业务范围
重新定义
业
务 变
供应链
化
重新设计
程
度
业务流程
重新设计
革命性的
集成化应用 本地化应用
渐进性的
潜在收益面
做为一项管理改造工程，而非技术项目
• 企业信息化应做为管理改造工程，而不 仅仅是一项技术性的工作；
• 信息技术的应用做为一项管理手段的提 升，帮助企业从效率和效益方面得到改 进；
毕马威IT咨询
Publication date: July 2009
© 2009 畢馬威會計師事務所是香港一家合伙 制事務所，同時也是與瑞士合作組織畢馬威國 際相關聯的獨立成員所網絡中的成員。版權所 有，不得轉載。香港印刷。
© 2009 KPMG, a Hong Kong partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International, a Swiss cooperative. All rights reserved.
畢馬威會計師事務所的名稱和標識均屬於瑞士 合作組織畢馬威國際的註冊商標。
二零零九年七月印刷
© 2009 畢馬威會計師事務所是香港一家合伙制事務所，同時也是與瑞士合作組織畢馬威國際相關聯的獨立成員所網絡中的成員。版權所有，不得轉載。 © 2009 KPMG, a Hong Kong partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International, a Swiss cooperative. All rights reserved.
© 2009 畢馬威會計師事務所是香港一家合伙制事務所，同時也是與瑞士合作組織畢馬威國際相關聯的獨立成員所網絡中的成員。版權所有，不得轉載。
© 2009 KPMG, a Hong Kong partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International, a Swiss cooperative. All rights reserved.
IBM的IT战略规划方法论
I B M的I T战略规划方法论本页仅作为文档封面，使用时可以删除This document is for reference only-rar21year.MarchIBM的IT战略规划方法论IT S&P项目将分三个阶段进行第一阶段：检查业务策略，以便制订战略性的的IT方向和策略。
本阶段将提交一份《IT愿景报告》，其中包括在今后的5年时间里，采用什么行之有效的信息技术，应用在哪些业务领域，以发挥IT在该公司的业务目标实现中的作用。
第一阶段包括如下几方面的工作：-1. 了解该企业业务环境特别是行业环境、竞争对手，并鉴别可能的竞争者；2. 审视该企业当前的业务战- 略，以及构成公司竞争地位所做的权衡和取舍；3. 确定企业内部能力和关键业务流程；4. 评估当前企业的支持业务的IT解决方案；5. 以业界最优秀的实例为标杆，评估企业当前的关键流程。
IT策略将会指出今后五年里信息技术应用的、经过优先排序的和验证的初步构思。
同时，它也描述了5年后企业所期望的状态（也即IT愿景）。
另外，作为附加，在报告中也会指出需要改进的领域尤其是关键业务流程。
第一阶段将安排如下的培训和汇报：1. 对企业IT规划项目组进行为期一天的第一阶段方法论培训；2. 用两个半天时间，向未直接参与项目的企业其它高层管理人员作第一阶段输出汇报。
目的是给他们第一阶段建议的介绍，并希望达成一致。
第二阶段：将制订IT流程、组织管治、IT技术体系架构等方面的处理策略。
第二阶段将提交的《IT管治报告》，给出对当前IT的评估和一个崭新的IT运作模型。
根据第一阶段新建立的IT愿景，本阶段将会构建一个全面的IT体系结构，它是用以实现IT愿景的IT基础架构或技术蓝图。
这个IT体系结构将包括： 1. 系统基础架构2. 应用系统体系结构（一系列解决方案的集合）3. 网络结构在本阶段，将参考业界最好的IT应用实例，定义关键IT流程和组织管理结构，以确定IT运作模型，使IT为企业提供最佳的应有的作用。
KPMG培训资料
税务合规
税务合规要求
了解和掌握各项税务法规 和政策，确保企业税务合 规。
税务合规流程
建立完善的税务合规流程 ，包括税务申报、税款缴 纳、发票管理等，确保企 业按时履行纳税义务。
税务合规风险管理
及时发现和解决税务合规 风险，防止因违规行为导 识别
通过分析企业的经营状况和税收 环境，识别潜在的税务风险点。
KPMG培训资料
汇报人： 2023-12-23
目录
• KPMG简介 • 审计服务 • 税务服务 • 咨询服务 • 新技术与数字化转型 • KPMG培训与发展计划
01
KPMG简介
公司历史
01
1979年，KPMG在香港 成立合伙制事务所。
02
1981年，KPMG成为首 家获得财政部批准在中 国大陆开展业务的国际 会计师事务所。
案例一
某公司财务报表审计案例，包括审计 计划、风险评估、控制测试和实质性 程序等详细过程。
案例二
某集团合并财务报表审计案例，涉及 多个子公司和复杂的交易结构，如何 应对挑战和解决问题。
案例三
某上市公司舞弊调查案例，介绍如何 发现舞弊迹象、进行调查和报告处理 。
案例四
某跨国公司合规审计案例，强调合规 要求和跨国文化差异对审计工作的影 响。
人力资源管理
总结词
提供人力资源管理咨询，提升企业人才竞争力。
详细描述
KPMG的咨询团队专注于为企业提供人力资源管理咨询服务，包括组织结构设计 、职位管理、招聘与选拔、培训与发展、绩效管理等方面的支持。这些服务旨在 帮助企业建立高效的人力资源管理体系，提升人才竞争力。
市场调研与分析
总结词
进行市场调研与分析，为企业决策提供数据支持。
IT规划与信息化流程优化
阶段4：确立IT支撑点和IT愿景
IT/IS技术能力的评估
IT组织机构的评估
IT流程的评估
● 提供企业IT管理系统：建立IT管理系统架构；规划IT管理系统；评估IT管理 系统。 ● 管理IT的业务价值：设定IT的价值；进行研究；制定IT策略；验证IT服务和 基础设施；定义IT服务和基础设施；定义IT体系结构；制定跟踪IT计划。 ● 管理IT资产：IT财务管理；购买所需服务和组件；定价和管理客户合同；IT 资产管理；保密性管理；人力资源管理；技能组合管理。 ● 支持IT服务和方案：配置信息管理；可用性管理；支撑IT的外部设施管理； 备份与恢复管理；IT连贯性管理；效果与容量管理；问题管理。 ● 方案实现：了解方案的要求；设计解决方案；构建、集成解决方案；测试 方案；客户接受和认证。 ● 方案推行：定义变更管理；对变更做好计划；管理变更；实施变更 ● 提供运行服务：满足服务要求；配备资源以实现承诺；进行服务；维持服 务的能力。 ● 满足客户关系：了解客户需求；使IT服务的提供市场化；监控服务水平；给 客户提供IT运作支持；客户满意管理。
◆缺乏协同：文化与组织机构、业务与 IS / IT 团队、工作议程与目标。
◆需要设计新结构和框架（业务，应用 程序，组织等）。 ◆在新领域中创建知识和经验。
◆没有成功的评估标准。
◆对人员、流程和技术的关注不够。
IT规划的整体过程
阶段1：规划项目启动
咨询与企业合作模式共同挖掘需求
管理咨询与IT咨询公司定位
阶段2：广泛的项目调研明确需求
IT规划调研——样例
阶段3：战略审视
什么是战略和战略规划？
● 我们存在的目的是什么：我们在这里是为了什么？存 在的理由是什么？
● 我们的业务是什么：谁是我们的顾客，我们为顾客提 供什么产品服务，或者为顾客创造什么价值？ ● 我们的业务范围是什么：我们从事的领域是什么，在 这个领域内我们要处于什么样的地位？ ● 我们要到哪里去：我们期望我们的公司是什么样的？ 我们的公司应该是什么样的？
197毕马威--全套内部培训教程8个文件(如何写建议书)_KPMG全套内部培训教程6
“KPMG is unquestionably the best of the Big Five companies.”
CONSULTING
Proposal Process
First Draft (Blue Team) Review
Checkpoint for Partner/BDM/Proposal Manager Limited to proposal contributors only Draft should be complete, but can be rough in spots Focus on identifying serious weaknesses and
CONSULTING
Proposal Process
Why We Need Themes
Picture the mindset of the customer:
I don’t know who KPMG is I don’t know KPMG’s capabilities I don’t know KPMG’s related/relevant experience I don’t know your past performance I don’t know your technical approach I don’t know your management style I don’t know what you’re selling
Proposal Manager
– Day-to-day management of the proposal team, sections, and production; sets schedule and delegates assignments; ensures punctual completion of proposal tasks and overall proposal quality; owns the proposal creation process; works with Partner to create themes.
毕马威项目管理ppt
项目环境（6）
项目生命周期 项目定义 制定计划 计划实施 项目终止
定义
计划
实施
终止
项目开始
标准的项目生命周期
项目定义
项目环境
项目管理功能 项目定义 项目计划 项目控制
定义
•项目背景与目的 •明确项目利益相关人 •项目规则的制定和明确 •招募项目人员
•工作一览表 •责任矩阵 •沟通计划 •项目合约
利益相关人是项目成功的关键 每一位利益相关人都在项目的某些方面起重要作用并负具体责任
规则的制定和明确
项目规则是基础 一份好的项目规则能够影响我们的三个成功因素 目标一致 控制范围 领导支持
使每个人都能理解并认同项目规则的四个方法 发布项目书
发布项目一览表
设置责任矩阵
设立沟通计划
发布项目书 发布项目一览表 设置责任矩阵 设立沟通计划
工作一览表
规则的制定和明确
设置责任矩阵
列出项目主要活动 列出利益相关人 定义活动与利益相关人的关系 编制责任矩阵
发布项目书 发布项目一览表 设置责任矩阵 设立沟通计划
规则的制定和明确
制定沟通计划 沟通计划是指在恰当的时候给相关人员以恰当的信息
项目计划
在项目管理中最大的挑战在于项目计划的制定 ——项目计划有助于项目的组织管理
制定计划的工具
风险管理 工作明细结构图（WBS）
现实的进度表
动态的精确估算 项目平衡
现实的进度表
计划概要 编制项目说明 开发风险 编制工作分类结构 任务序列 工作量估算 计算初步进度表 资源分配 预算制定
项目规则
计划
范围和交付成果
企业IT规划咨询资料(doc 24页)
第八章企业IT规划第一节借IT出海，让美梦成真——逐梦公司IT战略规划咨询案例背景陈述这可是一家从诞生之日起就站在国际的高度参与全球化竞争的企业，从20世纪初由清朝政府创办至今，企业的发展就是中国航天科技工业发展的一个缩影。
从上个世纪初公司自行制造的中国第一架飞机，到70年代成功地发射了我国第一颗人造地球卫星，再到90年代末的第一艘无人试验飞船，近百年的沧桑历练了一个伟大的梦想:为人类征服太空的理想而奋斗！每一次里程碑式的成就，都让这个超大型企业向这个梦想更近了一步，于是，我们就给它起了个美丽的名字：逐梦航空机械制造公司。
随着航天科技的飞速发展，以及航天军工系统市场化改革的深入，逐梦提出了新的战略发展思路：根据制造业未来发展的趋势和中国航天业自身的特点，从企业实际出发，坚持以信息化带动工业化的原则，把信息技术的应用贯穿于工业化的始终。
应用先进制造技术，构筑柔性制造体系，实现公司“建成国际一流的航天制造企业”的战略目标。
不过，梦想与现实间的差距却让逐梦人清醒地认识到企业内部管理的诸多问题。
由于长年在计划体制下，按照上级的指示完成生产任务，逐梦公司的管理模式日益陈旧，面对新的竞争环境，市场导向意识明显不足；没有有效的激励约束机制，导致逐梦的技术创新和管理创新缺乏动力；从国际水平上看，逐梦公司同类产品的制造成本偏高，价格竞争处于劣势。
核心问题也就在这里：逐梦公司的市场在全球，对手是全世界航天业的巨头。
在国际同类公司普遍借助于IT技术、实现管理全面升级的竞争态势下，在中国国有控股企业IT咨询齐上马的国情影响下，逐梦公司当然不能落后。
于是，企业资源计划（ERP）、企业流程重组（BPR）、客户关系管理（CRM）、供应链管理（SCM），这些全球都炙手可热的“舶来”理论，摆到了逐梦公司高管层的办公桌前。
他们一方面观望着身边诸多大型企业蜂拥而上ERP、大搞企业信息化建设，另一方面琢磨着冷酷无情的官方数字：“ERP的实施成功率普遍在10％～20％之间，在发达国家的成功率也只有20%。
IT规划方法论(规划培训)_2
Han Consulting ( China) Ltd. 2006 Copyright
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3.3：应用系统选型比较
Han Consulting ( China) Ltd. 2006 Copyright
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3.3：应用系统选型比较
Han Consulting ( China) Ltd. 2006 Copyright
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2.2：信息化系统规划
市场支持系统
产品研发系统
制 造 系 统
分销
供应链计划系统
人力资源
绩效管理
知识管理系统
服务支持系统CRM
SCM
采购
PLM
狭义的ERP
企业
财务管理＼会计帐户
销售支持系统
Han Consulting ( China) Ltd. 2006 Copyright
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2.2：信息化系统规划
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1.3：业务流程梳理
Han Consulting ( China) Ltd. 2006 Copyright
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1.3：业务流程梳理
简化
控制
清除
集成
企业信息化
Han Consulting ( China) Ltd. 2006 Copyright
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1.4：应用系统详细需求
Han Consulting ( China) Ltd. 2006 Copyright
离港系统的运营维护
“ 西关－ 高新－ 咸阳” 三地财务系统的管理和维护
集团信息化建设的管理和规划
1.1：信息化现状评估
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实用的IT规划培训材料
IT战略
总体架构
（应用范围与部署方式）
关键流程
（核心管控与业务流程）
应用系统
（功能需求与数据结构）
2013-7-22
P. 5
企业战略决定了IT对板块业务的支撑策略，对核心能力建设 的支持方式，对战略阶段目标实现的任务与能力要求……
a
• 业务板块的总体战略 处置 培育 发展 保留 • IT对业务板块的支撑策略 • 如何分配有限的IT资源满足总体企业战略 • 优先支持重点发展的经营主业，平衡其他板块需要
2 4
8
9 10
监控停车收费系统
PDS系统
互联网 Internet
1 2 2 3 4
安检多媒体培训系统
咸阳机场
1 2 3 4 6 7 8 9
管理系统
1
2
1
VPN
集团总部
1
1
2
2 3 4
办公自动化系统 财务系统
基础设施
1 2 3 4
汉中航站
3 2 4 6
网络系统 安全系统 主机系统 桌面系统
安康航站
2 4
2013-7-22
P. 3
对客户企业相关要素的理解，是为了清晰信息化的需求所在
A. 客户企业理解
+
B. IT需求分析
2013-7-22
P. 4
企业的发展战略、管理模式和关键流程对于企业信息化的发 展战略、总体架构、应用系统等起着决定性的作用；
企业管理与业务运营
对IT的需求
公司战略 管理模式
（管控与业务模式）
5
2013-7-22
P. 16
如何有效的归集、呈现现有IT资产（时间维度）
•信息化建设继续向前推进， 启动信息化战略规划，逐渐 加大管理系统投资，实现信 息化的均衡发展； •信息化建设大力发展，业务系 统投资加大，信息系统对机场业 务运营支撑程度提高； •信息化建设刚刚起步，新 建信息系统比较少，信息系 统对机场业务和管理的支撑 程度低； •主要投资在集团本部和咸 阳机场的办公自动化系统和 各机场的财务系统； •主要投资在咸阳机场、榆林、 汉中等航站的业务系统，包括： 航显系统、广播系统、信息查询 系统、离港系统、安检信息系统、 集成系统等； •推进集团信息化战略规划 和基础架构及应用系统建设
KPMG培训资料
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系统优化建议
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06
业务运营
质量控制
质量保证
建立严格的质量保证体系，确 保服务质量和客户满意度。
质量检查
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风险管理解决方案
服务交付
01
02
03
服务标准
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Normative Design Of Organizations - Part I Mission Planning
Normative Design of Organizations—Part I:Mission PlanningGeorgiy M.Levchuk,Yuri N.Levchuk,Jie Luo,Krishna R.Pattipati,Fellow,IEEE,andDavid L.Kleinman,Fellow,IEEEAbstract—This paper presents a design methodology for synthe-sizing organizations to execute complex missions efficiently.It fo-cuses on devising mission planning strategies to optimally achieve mission goals while optimally utilizing organization’s resources. Effective planning is often the key to successful completion of the mission,and conversely,mission failure can often be traced back to poor planning.Details on subsequent phases of the design process to construct the mission-driven human organizations are discussed in a companion paper.Index Terms—Mission decomposition,mission planning,organi-zational design,resource allocation,scheduling.I.I NTRODUCTIONA.MotivationC HANGING patterns of today’s world impose new re-quirements for many modern organizations,ranging from military establishments to agile manufacturing systems and commercial enterprises.With the benefit of new technologies now under development,the competition will be won by an organization that will best utilize both its resources and its critical information to achieve its goals.This implies the need for much greater emphasis on realistic modeling of distributed organizations in which the human participants are the focus. In large-scale organizations that involve humans,decision-making and operational functions are distributed among team members who coordinate their actions in order to achieve their common goal.Since the processing capabilities of a human are limited,the distribution of information,resources,and activities among decision-makers(DMs)in an organization must be set up to guarantee that the decision-making and operational load of each DM remains below the DM’s capacity thresholds.In a highly competitive and distributed environment,a proper balance among information acquisition,decision hierarchy, and resource allocation,in short,a proper organizational structure and its processes,is critical to superior organizational performance.Manuscript received January2,2001;revised May5,2001.This work was supported by the Office of Naval Research under Contracts N00014-93-1-0793, N00014-98-1-0465and N00014-00-1-0101.This paper was recommended by Associate Editor S.Ghosh.G.M.Levchuk,J.Luo,and K.R.Pattipati are with the Department of Elec-trical and Computer Engineering,University of Connecticut,Storrs,CT06269 USA(e-mail:krishna@).D.L.Kleinman is with the Naval Post Graduate School,Monterrey,CA93943 USA(e-mail:kleinman@).Y.N.Levchuk is with Aptima Inc.,Woburn,MA01801USA(e-mail: levchuk@).Digital Object Identifier10.1109/TSMCA.2002.802819Over the years,research in team decision-making has demon-strated that an organization operates best when its structure and processes match the corresponding mission environment[43], [49]–[51].Consequently,it has been concluded that the opti-mality of an organizational design ultimately depends on the actual mission structure and on the key attributes of the environ-ment in which the organization operates[6].This premise has led to the application of systems engineering techniques to the process of designing human organizations[37],[38],[51].The systems engineering approach to organizational design is as fol-lows.First,a quantitative model describing the mission and the organizational constraints is built.Next,different criteria used to judge the optimality of an organization are combined into a(possibly nonscalar)objective function.Finally,an organiza-tional design is generated to optimize the objective function. In this paper,we present a methodology for modeling mis-sions and for synthesizing the concomitant optimal organiza-tions.We introduce a three-phase iterative optimization process that derives an optimized organizational design for a given mission structure and organizational constraints.In the first (mission-planning)phase of our design process,the optimal al-location of mission tasks to organization’s platforms(physical resources)is determined to optimize the mission schedule.In the second phase,a three-way DM-platform-task allocation is derived to minimize the coordination and workload overhead and its impact on the mission schedule.In the third phase, other dimensions of organizational structure(e.g.,information acquisition and communication structures,decision hierarchy) are optimized to fulfill the design objectives.Following a de-scription of our modeling and design methodology,the paper focuses on the development of algorithms for mission-plan-ning phase.The algorithms to optimize other dimensions of organizational design are presented in a companion paper.B.Related ResearchOver the past few years,mathematical and computational models of organizations have attracted a great deal of interest in various fields of scientific research(see[43]for review).The mathematical models have focused on the problem of quanti-fying the structural(mis)match between organizations and their tasks.The notion of structural congruence has been generalized from the problem of optimizing distributed decision-making in structured decision networks[51]to the multiobjective opti-mization problem of designing optimal organizational structures to complete a mission,while minimizing a set of criteria[37], [38].As computational models of decision-making in orga-nizations began to emerge[9],[55],[64],the study of social1083-4427/02$17.00©2002IEEEnetworks(SSN)continued to focus on examining a network structure and its impact on individual,group,and organiza-tional behavior[67].Most models,developed under the SSN, combined formal and informal structures when representing organizations as architectures(e.g.,see the virtual design team [42]and ORGAHEAD[9]),and in many empirical studies, the informal subsumed the formal.In addition,a large number of measures of structure and of the individual positions within the structure have been developed[59],[65],[66]. Application of systems engineering techniques to the process of designing human organizations led to several graph-de-composition and combinatorial optimization algorithms to synthesize congruent organizational structures(i.e.,struc-tures that are in some sense“matched”with their mission task environment)[37],[38],[51]and to several graph-the-oretic measures of task complexity[36].Potential benefits of a structural match predicted by the normative model,as well as the ability of a proposed design process to find this match,have been tested empirically in a computer-mediated team-in-the-loop experiment with human DMs in a distributed dynamic decision-making(DDD-III)simulator of Joint CFig.1.Three-phase organizational design process.expertise and workload threshold constraints on available DMs, and assigns each group to an individual DM to define the DM-resource allocation.Thus,the second phase delineates the DM-platform-task allocation schedule and,consequently,the individual operational workload of each DM.Phase III:Finally,Phase III of the design process completes the design by specifying a communication structure and a de-cision hierarchy to optimize the responsibility distribution and inter-DM control coordination,as well as to balance the control workload among DMs according to their expertise constraints. Each phase of the algorithm provides,if necessary,a feedback to the previous stages to iteratively modify the task-resource al-location and DM-platform-task schedule.Phase I of our design process essentially performs mission planning,while Phases II and III construct the organization to match the devised courses of action.III.M ISSION P LANNINGA mission analysis that details required courses of action by specifying a sequence of tasks,defining resource-to-task allocation,and a time-line for all task activities constitutes a mission plan.Planning problem is investigated in artificial intelligence and behavioral science area(see[2]).Planning models in military human behavior representation have been extensively studied and several specific planning tools have been developed,including adaptive combat modeling[22], commander’s visual reasoning tool[4],dismounted infantry computer-generated force[27],computer-controlled hostiles for SUTT(small unit tactical trainer)[53],fixed-wing attack-soar and soar-intelligent forces[32],[33],man–machine integrated design and analysis system[3],naval simulation system[61], automated mission planner[35],to name a few.A.Mission StructureA fundamental question underlying a distributed or-ganizational design—“who should do which part of the mission?”—implies that the mission must be decomposable into a set of entities.These entities are generally referred to as tasks.Definition1:A Task is an activity that entails the use of relevant resources(provided by organization’s platforms)and is carried out by an individual DM or a group of DMs to accom-plish the mission objectives.Every task in itself represents a“small mission,”and can oftentimes be further decomposed into more elementary tasks.A mission decomposition diagram can be built to represent a hierarchical structure among the mission tasks.Different decomposition techniques(e.g.,goal decomposition,functional decomposition,domain decomposition)represent different starting points of defining tasks and provide different task types required to complete the mission.The designer’s choice of a particular decomposition technique and model granularity (number of tasks in the mission decomposition)must be con-tingent on the computational efficiency of the design process and its supporting algorithms.Task attributes quantify various properties of the mission tasks that detail the specifics of task execution.They provide quantitative characteristics for the mission structure and specify the implications of commitment to task processing on both machine and human resources of an organization.In our model,we characterize every missiontaskin a state space that specifies the concomitant“distance”required for suc-cessful processing oftaskFig.2.Geographical constraints and mission tasks for an experiment with DDD-III simulator.One of the most popular modeling methodologies employs a graph formalism to describe the mission structure.The graph formalism is used to construct the dependency among the tasks of a mission.Examples of different mission decomposition tech-niques used to design the organizational structure for an exper-iment with DDD-III simulator(see[39]for details)are shown in Figs.3and4.Task parameters are shown in Table I. Definition2:A Task Graph is a dependency diagram that details the following interrelationships between tasks:1)task precedence;2)inter-task information flow;3)input–output relationships between tasks.A directed acyclic task-precedence graph represents the plan to execute the mission.The examples of task graphs and inter-task dependencies for an Experiment with DDD-III simulator[39]are shown in Figs.5–7.anizational Constraints and Design OutputIn defining an organization,we differentiate between two classes of entities:1)decision-makers(DMs)and2)resources. Organization’s resources that represent nonhuman physical assets are called platforms.Definition3:A Decision-Maker(DM)is an entity with infor-mation-processing,decision-making,and operational capabili-ties that can control the necessary resources to execute mission tasks,provided that such an execution will not violate the con-comitant capability thresholds.A maximal number of available DMs is specified.An ex-ample of DM responsibilities is shown in Fig.8.Definition4:A Platform is a physical asset of an organiza-tion that provides resource capabilities and is used to processtasks.For each platform),we defineits maximal velocityavailable on platformFig.5.Primary mission task graph for an experiment with DDD-IIIsimulator.Fig. 6.Expanded mission task graph for an experiment with DDD-IIIsimulator.Fig.7.Examples of interrelationships among tasks for an experiment with DDD-III simulator.among DMs by decomposing a mission into tasks and assigning these tasks to individual DMs who are responsible for their planning and execution.An overlap in task processing (wherein two or more DMs share responsibility for a given task while each possesses the capability to individually execute atask)Fig.8.Example of DM responsibilities.TABLE III LLUSTRATIONOFP LATFORM P ARAMETERS FOR E XPERIMENT W ITHDDD-III SIMULATORgives the team a degree of freedom to adapt to uneven demand by redistributing the load.The critical issues in team task processing are:what should be done,who should do what,and when .These questions are generally answered by mission planning that corresponds to mission-modeling phase followed by Phase I of our design process outlined in Fig.1.In general,DMs are provided with limited resources with which to accomplish their objectives.The distribution of these resources among DMs,and the assignment of these resources to seek information and to process tasks are key elements in an organization’s design.Team members must dynamically coor-dinate their resources to process their individual tasks while as-suring that team performance goals are met.The critical issues in team resource allocation are:who should own or transfer a specific resource,when ,and for how long .These questions are answered in Phase II of our design process.In addition to assigning to each DM his share of information,resources,and activities,the organizational design must expli-cate a decision hierarchy among DMs that designates their con-trol responsibilities (through command authority)and that regu-lates the inter-DM coordination (by assigning the responsibility of resolving decision ambiguities among coordinating DMs).The organizational design can also specify a communication structure among DMs to facilitate coordination and distributed information processing required for completing the mission.Acommunication structure and a decision hierarchy are devised in Phase III of our design process.IV .P HASE I:R ESOURCE -T ASK A LLOCATIONA.Scheduling in Organizational Design:Motivation and Problem DefinitionA successful scheduling of tasks,obtained from mission decomposition,to available organizational resources (plat-forms)under resource requirement and task inter-dependency constraints is a key determinant of organizational performance.Low computational complexity of algorithms for solving this NP-hard problem is a highly desirable feature.In the following sections,we present polynomial list scheduling algorithms and local search techniques to obtain an efficient near-optimal plat-form-task assignment for the Phase I of our design procedure.Conceptually,the scheduling phase of the organizational design process is as follows.A set of tasks with specified processing times,resource requirements,locations,and prece-dence relations must be executed by a set of platforms with given resource capabilities,ranges of operation,and velocities.Tasks are allocated to groups of platforms in such a way that,for each such platform package to task assignment,the vector of task’s resource requirements is componentwise less than or equal to the aggregated resource capability of the platform group.The task processing can begin only when the processing of all its predecessors is completed and all platforms from the group assigned to this task have arrived at the appropriate location.In our model,we assume that a resource can only process one task at a time.Platforms are to be routed among the tasks so that the overall mission completion time (i.e.,the completion time of the last mission task)is minimized.An output of the scheduling phase specifies a platform-task assignment for our organizational design,delineating task start times,and platform-task routing.B.Mathematical Formulation of the Scheduling Problem The scheduling problem associated with the Phase I of our three-phase organizational design process is defined by the fol-lowing variables:Assignmentvariables:otherwise.and (that is,making them real num-bers in the [0,1]range)produces a linear programming problem(LP)with the number of variables equaltoOther variations of problem formulation are possible.For ex-ample,there may exist a delay between processing of two tasks on the same platform(“adjustment”delay).The opposite of this situation is when the delay occurs only when tasks are processed on different platforms(communication delays)with no delay for processing by the same platform.This has relevance in mul-tiprocessor scheduling with inter-processor communication de-lays[5].Another variation is the existence of time windows for processing each task(that is,the earliest start times,called re-lease times,and the latest end-times,called deadlines,define opportunity windows for tasks).In this case,the objective func-tion involves the minimization of earliness-tardiness penalties (that is,the penalties resulting from processing tasks outside of their time-windows).In our problem,we assume that task-pro-cessing times are fixed.In the real world,situations may arise when task-processing times depend on the amount of resources allocated to them.The objective then is to achieve a tradeoff between processing tasks as fast as possible and using as little resources as possible.Another complication is that a task can begin to be processed when the assigned platforms are within a specified distance of this task(depending on the task and ranges of platforms).In this case,the problem assumes the form of the shortest covering path problem[13].Other realistic constraints, such as the ability of tasks to move during the mission,and platforms having expendable resources(such as fuel,firepower, supplies,etc.)can be included.Since the static scheduling problem is NP-hard in its general forms[24],the research in this area has mainly focused on ob-taining heuristic scheduling algorithms with good performance. Most of the heuristic scheduling methods can be classified as ei-ther a clustering algorithm or a list-scheduling algorithm.Local search techniques are used to further improve the quality of the schedule.The clustering algorithm divides the task set into clusters to be assigned to the processing elements.This method can be used only when there is no resource sharing in task processing(that is,no multiplatform task processing).A list scheduling method assigns priorities to tasks.They are executed according to the priorities and precedence constraints.A local search technique improves the quality of the schedule by task reassignments and shifting tasks in the processing sequence or in the critical path (CP)(longest processing sequence)[19],[23].List scheduling algorithm selects a ready node(a node be-comes ready when all its predecessors are executed)according to the task priority information.The following typical methods for deciding task priority coefficients were developed:level as-signment(LA),critical path(CP),and weighted length(WL) [60].This will be explained in more detail in Section IV-D. When a task is selected,it is to be assigned to platforms for processing.In our case,task-resource requirement vector re-sults in multiplatform task processing.As will be shown in Sec-tion IV-D,this problem can be formulated as a multidimensional knapsack problem.The one-dimensional knapsack problem was shown to be NP-hard,but pseudopolynomial algorithms exist for this problem(see[46]for a review).D.Multidimensional Dynamic List Scheduling MethodThe multidimensional dynamic list scheduling(MDLS)finds the platform-task allocation and mission schedule by sequen-tially assigning tasks to platforms until task set is exhausted. MDLS heuristic has two main steps:Step1:Select the task to be processed.Step2:Select the group of platforms to be assigned to it for processing.Task Selection:In the first step,a ready task is selected(a task becomes ready when all its predecessors have been com-pleted).The selection is determined by the current assignment information and precedence structure.The selection is made ac-cording to the priority coefficients assigned by using one of the three algorithms:1)critical path(CP);2)level assignment (LA);or3)weighted length(WL)[60].The complexity of calcu-lating task priority coefficients is the same for each algorithm, and it isO(are calculated for each task given the task prece-dence graph and the task processing times(see Exit Path algo-rithm in[60]).In the list-scheduling algorithm,a ready task is selected with thelargest(2)where is the set of direct successors oftasknormalized by the maximum WLamong them.The WL algorithm selects a ready task with thelargest ischosen(or ties are broken arbitrarily).Another variation of this approach is to assign priorities ac-cording to weighted CP lengthis mini-mized subject to resource requirement constraintsdefine the costof assigning platform.The group with,andminimal aggregated cost is selected.The problem becomesWhen a task is assigned,platform-task related assignment andscheduling information is updated,as well as the activity coef-ficients of the platforms.The starting time of the selected taskis the current time;is the last task processed by plat-formFig.9.MDLS results for an experiment usingRExample (continued):For our experiment with the DDD-III simulator,MDLS algorithms based on CP and LA methods (as well as weighted CP variation of WL)produced the same optimal-length schedules (see Fig.9).Breaking the ties by choosing a schedule with the least multiplatform task pro-cessing,the PWE procedure outputs the assignment-schedule shown in Fig.10.These results are based on the platform-task assignment obtained by usingcoefficientsplatform-taskassignment.V .S IMULATION R ESULTSAs stated earlier,the scheduling problem is NP-hard,which means that optimal solution takes exponential time in problem parameters (such as number of tasks,platforms,resources,and precedence constraints).Fig.12(,,Fig.12.Box plot of optimality ratios of heuristic algorithms.Number of tasks=10.Fig.13.Box plot of MDLS algorithms CPU times.Number of tasks =10.Fig.14.Box plot of PWE algorithms CPU times.Number of tasks =10.Simulation results for up to 90tasks for heuristic algorithms only [withcoefficientpriorities produces scheduleswith the shortest length,whilecoefficientspriorities produces scheduleswith the shortest length,whilecoefficientsFig.17.Average CPU time of PWE algorithms.Number of simulations =500.TABLE IIIO PTIMALITY OF H EURISTIC ALGORITHMSVI.S UMMARY AND F UTURE R ESEARCHIn this paper,we presented guidelines for model-driven synthesis of optimized organizations for a specific mission.The primary contributions of this paper include a formal method for representing missions and human–machine organizations,a three-phase iterative design procedure to devise an optimized organizational structure and its mission processing strategy,and a description of the mission-planning phase (i.e.,spec-ifying mission task structure and defining task processing schedule and resource utilization scheme).We also presented an overview of the state-of-the-art in different domains of organizational design.The potential of applying systems engineering approach to designing organizations is enormous,which was clearly shown by the experiments [18],[26].This approach to designingman-machine systems allows for replacement of cumbersome centralized control with decentralized control and autonomy.Strict mathematical problem formulations provide the founda-tion for exploring ways to solve design problems efficiently and with the required degree of optimality to make best use of available time and computational resources.The latter is especially important for designing dynamic algorithms that help humans to adapt.However,the field of model-based organizational design is in its infancy.The researchers lack a detailed classification of the design objectives and principles in building human–machine systems,as well as an understanding of the inner workings of a human organization.Some of these issues,including modeling a human DM as an integral part of a man–machine system and a detailed methodology for optimizing DM-resource allocation,inter-DM communication,and DM decision hierarchy,are presented in Part II of this paper.These methods,together with mechanisms for adaptation,including algorithms for on-line strategy adaptation and structural reconfiguration,form the basis for our continuing research in this area.A PPENDIXMDLS A LGORITHMInitializationis a direct successorofis a direct predecessorofbe the corresponding group oftasks.foreachFind thesetSelectSTEP4.Platform Group Selection.Find thesetdountilend ifend whileSTEP6.GroupAssignment.I man–machine integration design and analysis systems(MIDAS)software detailed design document,”NASA Ames Research Center,Moffett Field,CA,NASA Contractor Rep.177593.[4]M.J.Barnes and B.G.Knapp,“Collaborative planning aids for dis-persed decision-making at the brigade level,”in Proc.1st Annu.Symp.Advanced Displays and Interactive Displays Federated Laboratory,Adelphi,MD,Jan.28–29,1997.[5]S.K.Baruah,“The multiprocessor scheduling of precedence-con-strained task systems in the presence of interprocessor communicationdelays,”Oper.Res.,vol.46,no.1,pp.65–72,1998.[6]R.M.Burton and B.Obel,Strategic Organizational Diagnosis andDesign:Developing Theory for Application,2nd ed.Norwell,MA:Kluwer,1998.[7]L.G.Bushnell,D.Serfaty,and D.L.Kleinman,“Team informationprocessing:A normative-descriptive approach,”in Science of Commandand Control:Coping With Uncertainty,J.L.Boyes and S.J.Andriole,Eds.Washington,DC:AFCEA International 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IT规划基本步骤
（1）设备运行层：是确保发电业务能够被有效地、有效率 地执行的过程，设备或装置的运行参数和状态，是实时数 据的主要信息源。 （2）过程控制层：采集设备运行层设备或装置的运行参数 和状态等实时数据，为运行提供指导。 （3）信息事务层：是为实现企业目标有效地利用资源的具 体过程，积累的各种业务处理和管理基础数据，是非实时 数据的主要信息源。 （4）信息管理层：对采集的各种跨平台的基础数据重新组 合和加工，构成数据仓库，进行深层数据挖掘、多维数据 分析。 （5）决策层：确立企业的经营战略，即确定企业目标、方 针，制定战略规划、人力资源规划、财务管理计划等方面 的政策和原则。
1．1 中国企业信息化发展现状分析 信息技术是企业发展的助推器，许多 企业都寄希望于IT技术，也即希望信息化技 术能够为企业的发展贡献一份重要的 力量 。而事实上，IT 技术已经成为企业提高工 作效率和质量，缩短工作流程，加快信息 流的重要工具，为企业打造新的 业务模式 提供了可能。
1．2国内企业IT发展的缺陷和存在的不足 国内大型企业大多数在20世纪90年代开 始致力于信息化的建设，但是大多数企业对 信息化仍属于“懵懂”阶段，在信 息化建 设过程中走了 弯路。企业内部管理层对信 息化的认识也存在不足。最近几年，国内企 业对信息化的理解开始逐渐 有所转变，特 别是在国家大力提倡“信息化 带动工业化 ”以来，很多企业开始理性的看待信息化， 开始认识到信息化是 需要规划的，是需要 整体地去理解的。
2．3国内外企业对标阶段
本阶段的工作目标为：通过对相关行业信息化发 展现状的调研分析，找出企业在信息化方面的差 距，进行分析和比较 ，作为制定信息技术 战略规 划的基准。 本阶段的主要任务为：评估企业信息的标准化、 安全性和共享能力；评估企业信息与应用系统、 信息基础设施；评估 企业信息技术管理的 组织结 构、管理流程、信息技术服务；研究国内多：先 进企业的管理模式，信息化管理的相关案例 分析 ，提供相关行业信息技术的发展趋势和 领先实践 ，并提出可借鉴的建议。