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星期二, 六月 16, 2009
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星期二, 五月 26, 2009
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星期二, 一月 06, 2009
采购流程优化及供应商评估与管理
===================================================================================
采购流程优化及供应商评估与管理
===================================================================================
时间与地点：
2010年1月09-10日 上海
2010年1月20-21日 深圳
===================================================================================
课-程-前-言:
开源节流是经营企业两大法宝，采购就成为当今企业获取利润的第二源泉。企业产品开发、
保证质量、生产效率、成本控制等都离不开供应商的支持，现代企业竞争已是整个供应链的竞
争！供应商管理水平决定了企业的竞争力！供应商管理的挑战有哪些？在面对数十家供应商时
，我们应该如何分类管理？新供应商开发时，为什么经常选不准？为什么我们与供应商会经常
发生合同或订单纠纷？信得过的供应商还要不要加强日常管理？货期延误，我方有无责任？每
年的供应商绩效评估究竟发挥了多大的作用？
课-程-收-益：
供应商采购管理的挑战有哪些？
如何优化采购运作流程？
如何做好供应商的分类管理？
如何做好供应市场的调查？
如何选对我们的采购对象？
如何评估新供应商？
如何防范合同的纠纷？
如何做好供应商的日常管理？
如何评估现行供应商？
参-加-对-象：
高层管理者、采购、品管、物流、财务等部门及其他相关部门的职业经理。
培-训-形-式：
实操讲解-深度解析-情境模拟
===================================================================================
第一讲：供应商采购管理的挑战有哪些？
采购管理的KPI指标有哪些？ 三鹿公司真的没有发现吗？
采购管理的目标顺序应该如何？ 沃尔码采购与宝洁采购有何区别？
各种采购管理的目标差异 为什么采购部门'老受气'？
外资与民企相互学习些什么？ 如何提升我们的采购职业能力？
如何建立采购绩效考核制度？
第二讲：如何优化采购运作流程？
采购管理的基本流程是什么？ 各种采购的关键难点在哪里?
如何做好一次性的采购？ 如何做好商贸型采购?
不同采购流程的选择 采购流程不合理的后果
如何合理化? 职能说明书应包括哪些内容
如何编写岗位说明书? 采购人员的职责
职能说明书编制的原则 各职能的KPI指标应是什么？
第三讲：如何做好供应商的分类管理?
原料、运作采购的分类？
采购分类的作用是什么?
一、分散采购好还是集中采购好？
同一物品的单一与多家战略 经销商是多好还是少好？
集团的集中采购与分厂采购? 如何处理瓶颈物品和日常物品?
如何实现集团的集中采购?
二、买卖关系的类别？
两类关系的采购区别? 买卖关系的确定
三、降低采购成本的方法有哪些？ 如何分析采购供应风险?
四种供应链类型对采购的挑战? 不同供应市场的采购风险分析
采购'服务'的特点 采购服务的挑战
Pareto （帕累托）分类法 ABC的分类
第四讲：如何做好供应市场的调查？
1、市场规模 2、生产供应厂家的数量
3、市场进出的难易程度？ 4、行业政策与法规的影响
5、供应产品的分类 6、已应用的领域（客户）
7、市场竞争的特点？ 8、产品供应的区域性和性质性特点
9、营销模式 10、供应链特性
11、原材料供应的特点 12、行业的生命周期阶段
13、行业利润水平
如何撰写供应市场的调研报告？ XX公司供应市场调研报告
第五讲：如何选对我们的采购对象？
卖饮料与卖钢材的营销区别是什么? 供应商（重复性）的客户分类
供应商为何会报不同的价？ 供应商的能力与积极性－应首选哪个？
我们应该如何选择供应商？ 供应商选择的三步曲
第六讲：如何评估新供应商？
供应商评估的四大内容？ 不同类型供应商的评价侧重点
产品的现状及能力供应商的愿望 供应商的实力如何评估供应商的保障能力？
应该问谁？怎么问？ 如何权衡调研结果？评估指标的权重分析
第七讲：如何防范合同的纠纷？
为什么会产生合同纠纷？ 不同合同类型的纠纷
口头协议有效吗? 合同法的四项基本原则
要约与要约邀请？ 签了字的合同有效吗?
合同将不得含以下条款？ 不可抗力的合理性确定
国际合同与国内合同的区别 《国际贸易术语解释通则》 INCOTERMS
第八讲：如何做好供应商的日常管理？
QA与QC的区别在哪里？ 采购供应链日益脆弱的原因
不同采购类型的管理区别 服务性采购的日常管理
如何确保供应商的履约性？ 采购价格与质量有无关系？
质量与价格的关系 如何提高抽样的准确性？
如何提高采购订单的稳定性？ 如何提高生产、采购系统灵活性？
如何与供应商保持好日常关系 为什么要建立一定程度的积极性冲突？
如何建立良性的冲突？ 买卖关系生命周期的六阶段
要有强烈的因果意识如何做好供应商的风险管理？
第一步：导致延误的诱因预见－鱼骨图法 第二步：各种诱因的风险分析
第三步：如何才能发现？ 第四步：发现后怎么办？
第九讲：如何评估现行供应商？
新供应商评估与现行供应商评估的区别有哪些？
绩效评估的原则
绩效评估的五大内容
一、操作性指标的评估
如何设计供应商月度评估表? 某公司的供应商月度评估表
二、运作服务性指标的评估
三、供应商的现场评审内容有哪些
如何判断供应商表现的好与坏 供应商的改进方案
好供应商的奖励制定 表现不佳的供应商？
===================================================================================
讲-师-介-绍: 张老师
张老师是改革开放后早期海归派讲师。1986年获美国Gerber公司的奖学金赴美国Michigan
State University (密西根州立大学) 留学，获硕士学位。毕业后,受聘于美国Heinz(亨氏)集团
公司。张老师曾先后受聘于美国亨氏公司、英国联合饼干公司、美国美赞臣公司等，曾任美赞臣
公司的技术及运作总监。从2000年开始,张老师开始自己创业，从事于多行业的经营管理。所以,
张老师既有世界500强企业职业经理人的丰富阅历,又有作为企业老板的心得体会。在二十多年的
职业生涯中，张老师曾接受过各种国际国内的职业培训。曾赴加拿大、美国、英国、新加坡、泰
国、菲律宾、马来西亚等国考察学习。曾任国家技术监督局食品标准化委员会成员和国家技术监
督局食品添加剂标准化技术委员会成员，参与、制定和审核国家级别的食品标准和食品添加剂标
准。曾任广东省食品工业协会的理事以及其它多项社会职务。 张老师的授课富有很强的激情,现
场感染力强，风趣,幽默,现场气氛活跃；思维敏捷,反应能力强,看问题较为深刻。张老师的课件
设计力求深度、实用；案例多为工具性案例,有很强的实操。课程内容跨度大,尽量吸取各个行业
的精粹，具有高度的浓缩性。
===================================================================================
学习费用:2,500/元（提供讲义、午餐、发票、茶点、赠送陈剑老师研发软件等）
您可信赖的合作伙伴：
------------------------------------------
M M C 管 理 学 院
全国统一服务热线：400-8899,628
华东地区专线：021-5109,9475
广东地区专线：020-3366,5697 / 3452,0981
电 邮：salesclub@ 126.com
===================================================================================
星期六, 一月 03, 2009
采购流程优化及供应商评估与管理
===================================================================================
采购流程优化及供应商评估与管理
===================================================================================
时间与地点：
2010年1月09-10日 上海
2010年1月20-21日 深圳
===================================================================================
课-程-前-言:
开源节流是经营企业两大法宝，采购就成为当今企业获取利润的第二源泉。企业产品开发、
保证质量、生产效率、成本控制等都离不开供应商的支持，现代企业竞争已是整个供应链的竞
争！供应商管理水平决定了企业的竞争力！供应商管理的挑战有哪些？在面对数十家供应商时
，我们应该如何分类管理？新供应商开发时，为什么经常选不准？为什么我们与供应商会经常
发生合同或订单纠纷？信得过的供应商还要不要加强日常管理？货期延误，我方有无责任？每
年的供应商绩效评估究竟发挥了多大的作用？
课-程-收-益：
供应商采购管理的挑战有哪些？
如何优化采购运作流程？
如何做好供应商的分类管理？
如何做好供应市场的调查？
如何选对我们的采购对象？
如何评估新供应商？
如何防范合同的纠纷？
如何做好供应商的日常管理？
如何评估现行供应商？
参-加-对-象：
高层管理者、采购、品管、物流、财务等部门及其他相关部门的职业经理。
培-训-形-式：
实操讲解-深度解析-情境模拟
===================================================================================
第一讲：供应商采购管理的挑战有哪些？
采购管理的KPI指标有哪些？ 三鹿公司真的没有发现吗？
采购管理的目标顺序应该如何？ 沃尔码采购与宝洁采购有何区别？
各种采购管理的目标差异 为什么采购部门'老受气'？
外资与民企相互学习些什么？ 如何提升我们的采购职业能力？
如何建立采购绩效考核制度？
第二讲：如何优化采购运作流程？
采购管理的基本流程是什么？ 各种采购的关键难点在哪里?
如何做好一次性的采购？ 如何做好商贸型采购?
不同采购流程的选择 采购流程不合理的后果
如何合理化? 职能说明书应包括哪些内容
如何编写岗位说明书? 采购人员的职责
职能说明书编制的原则 各职能的KPI指标应是什么？
第三讲：如何做好供应商的分类管理?
原料、运作采购的分类？
采购分类的作用是什么?
一、分散采购好还是集中采购好？
同一物品的单一与多家战略 经销商是多好还是少好？
集团的集中采购与分厂采购? 如何处理瓶颈物品和日常物品?
如何实现集团的集中采购?
二、买卖关系的类别？
两类关系的采购区别? 买卖关系的确定
三、降低采购成本的方法有哪些？ 如何分析采购供应风险?
四种供应链类型对采购的挑战? 不同供应市场的采购风险分析
采购'服务'的特点 采购服务的挑战
Pareto （帕累托）分类法 ABC的分类
第四讲：如何做好供应市场的调查？
1、市场规模 2、生产供应厂家的数量
3、市场进出的难易程度？ 4、行业政策与法规的影响
5、供应产品的分类 6、已应用的领域（客户）
7、市场竞争的特点？ 8、产品供应的区域性和性质性特点
9、营销模式 10、供应链特性
11、原材料供应的特点 12、行业的生命周期阶段
13、行业利润水平
如何撰写供应市场的调研报告？ XX公司供应市场调研报告
第五讲：如何选对我们的采购对象？
卖饮料与卖钢材的营销区别是什么? 供应商（重复性）的客户分类
供应商为何会报不同的价？ 供应商的能力与积极性－应首选哪个？
我们应该如何选择供应商？ 供应商选择的三步曲
第六讲：如何评估新供应商？
供应商评估的四大内容？ 不同类型供应商的评价侧重点
产品的现状及能力供应商的愿望 供应商的实力如何评估供应商的保障能力？
应该问谁？怎么问？ 如何权衡调研结果？评估指标的权重分析
第七讲：如何防范合同的纠纷？
为什么会产生合同纠纷？ 不同合同类型的纠纷
口头协议有效吗? 合同法的四项基本原则
要约与要约邀请？ 签了字的合同有效吗?
合同将不得含以下条款？ 不可抗力的合理性确定
国际合同与国内合同的区别 《国际贸易术语解释通则》 INCOTERMS
第八讲：如何做好供应商的日常管理？
QA与QC的区别在哪里？ 采购供应链日益脆弱的原因
不同采购类型的管理区别 服务性采购的日常管理
如何确保供应商的履约性？ 采购价格与质量有无关系？
质量与价格的关系 如何提高抽样的准确性？
如何提高采购订单的稳定性？ 如何提高生产、采购系统灵活性？
如何与供应商保持好日常关系 为什么要建立一定程度的积极性冲突？
如何建立良性的冲突？ 买卖关系生命周期的六阶段
要有强烈的因果意识如何做好供应商的风险管理？
第一步：导致延误的诱因预见－鱼骨图法 第二步：各种诱因的风险分析
第三步：如何才能发现？ 第四步：发现后怎么办？
第九讲：如何评估现行供应商？
新供应商评估与现行供应商评估的区别有哪些？
绩效评估的原则
绩效评估的五大内容
一、操作性指标的评估
如何设计供应商月度评估表? 某公司的供应商月度评估表
二、运作服务性指标的评估
三、供应商的现场评审内容有哪些
如何判断供应商表现的好与坏 供应商的改进方案
好供应商的奖励制定 表现不佳的供应商？
===================================================================================
讲-师-介-绍: 张老师
张老师是改革开放后早期海归派讲师。1986年获美国Gerber公司的奖学金赴美国Michigan
State University (密西根州立大学) 留学，获硕士学位。毕业后,受聘于美国Heinz(亨氏)集团
公司。张老师曾先后受聘于美国亨氏公司、英国联合饼干公司、美国美赞臣公司等，曾任美赞臣
公司的技术及运作总监。从2000年开始,张老师开始自己创业，从事于多行业的经营管理。所以,
张老师既有世界500强企业职业经理人的丰富阅历,又有作为企业老板的心得体会。在二十多年的
职业生涯中，张老师曾接受过各种国际国内的职业培训。曾赴加拿大、美国、英国、新加坡、泰
国、菲律宾、马来西亚等国考察学习。曾任国家技术监督局食品标准化委员会成员和国家技术监
督局食品添加剂标准化技术委员会成员，参与、制定和审核国家级别的食品标准和食品添加剂标
准。曾任广东省食品工业协会的理事以及其它多项社会职务。 张老师的授课富有很强的激情,现
场感染力强，风趣,幽默,现场气氛活跃；思维敏捷,反应能力强,看问题较为深刻。张老师的课件
设计力求深度、实用；案例多为工具性案例,有很强的实操。课程内容跨度大,尽量吸取各个行业
的精粹，具有高度的浓缩性。
===================================================================================
学习费用:2,500/元（提供讲义、午餐、发票、茶点、赠送陈剑老师研发软件等）
您可信赖的合作伙伴：
------------------------------------------
M M C 管 理 学 院
全国统一服务热线：400-8899,628
华东地区专线：021-5109,9475
广东地区专线：020-3366,5697 / 3452,0981
电 邮：salesclub@ 126.com
===================================================================================
星期五, 十一月 02, 2007
How To Do Research In the MIT AI Lab
How To Do Research In the MIT AI Lab
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY
AI Working Paper 316 October, 1988
How to do Research At the MIT AI Lab
by:
a whole bunch of current, former, and honorary MIT AI Lab graduate students
David Chapman, Editor
September, 1988.
Abstract:
This document presumptuously purports to explain how to do research. We give heuristics that may be useful in picking up the specific skills needed for research (reading, writing, programming) and for understanding and enjoying the process itself (methodology, topic and advisor selection, and emotional factors).
Copyright 
1987, 1988 by the authors.
A. I. Laboratory Working Papers are produced for internal circulation, and may contain information that is, for example, too preliminary or too detailed for formal publication. It is not intended that they should be considered papers to which reference can be made in the literature.
A whole lot of people at MIT
科学网-How to do research
科学网-How to do research
How to do research How to do research? And Related Issues of Ph.D Education
"Can you tell me how to do research?",
"Please tell me what topics should I pick to do my ph.d.thesis? "
are two questions I periodically receive from young researchers and students in China.
There are two difficulties associated with answering these two questions.
1. The first question is open-ended. To answer it honestly and accurately will
require several hours of explanation. In fact, the process could take several
years of face-to-face interaction between an advisor and a ph.d student. This is
really what a ph.d. education involves. It is not the number of courses taken or
the number of papers published. But something about the quality of and taste
in research. I'll have more to say about this point below in connection with
trying to answer this first question here .
2. Behind the second question lie a unintentional misconception by many young
workers about "research" that probably has its origin in the martial arts novels
of Chinese literature (武侠小说). In typical novels of this genre, the
hero/heroine learns powerful martial arts technique through some secret
magical instruction passed down from a master or a long lost instruction book.
As a result, overnight s/he was able to defeat his/her enemy and achieve
success. Thus, many students are constantly looking for that "magical
formula" for research. Another common criticism of Chinese university
education by Westerners is that most students learn to answer questions or
pass exams extremely well, but there is little training in posing questions and
formulating problems which are really the important parts and more than half
of research. Growing up in such environment, it is natural for Chinese
students to seek the magical formula for research success. It is also my
purpose to address this issue here
To give an answer to the first question, you can approach it at three levels. At the first
level, your aim is to be that one scientific person in a century, e.g., the discoverer of the
theory of relativity. I have no experience or advice to give at that level. All I can say is
"good luck". At the next level, you want to be the founder of a research topic or make the
breakthrough on a difficult problem. Again, there is no magic here. For otherwise,
breakthrough will become commonplace. However, I do have one suggestion that worked
well for me and which is generally applicable. This I offer below:
I participated in a panel discussion 8 years ago which involves my answers to several
general questions posed to me at the panel and which still apply today. ( The 1999 IEEE
Conference on Decision and Control panel on "At the Gate of the Millennium – Are we
in control?" Published in IEEE Control Systems Magazine Vol.20, #1, Feb, 2000)
BEGINNING OF QUOTE
1. What are the 5 most notable research results in systems and control theory in the
past 100 years?
The "test of time" and traditions of history rule out mentioning anything
developed in the past 25 years or involving living persons. Furthermore, scientific
discovery often is a matter of standing on the shoulder of others. To single out specific
results do not seem to be fair to others who laid the foundation. Instead, I propose to list
couple of ideas that seems to me have influenced the development of our field in a major
way
1. The fundamental role of probability and stochastic process in system work.
2. The concept of what constitutes a solution to a problem, namely, that which can
be reduced to another routinely solved problem such as numerical integration
3. The notion of dynamics and feedback in all their ramifications
The first item represents how knowledge from outside the field influenced our research
while the third states what specific concepts our field contributed to other fields. The
second item deals with how practices in science and mathematics are changed by
technology. These notions are generic and have parallel in other topics and fields.
2. What are future research milestones (for the next 5-10 years) which will have a
most significant impact on the field? Comment on what we should be doing now
and in the near future to make such accomplishments possible. Also discuss your
thoughts on:
i.Education issues. How should we be training our students to meet the future
challenges?
ii.Technology issues. What we see coming up that will change the control
landscape.
iii.Computational tools.
Scientific crystal balling has a notorious record in the past. The dust heap of past
predictions is filled with gross miscalculations and estimations by noted scientists with
the best of intentions. Let me try to approach the question "what's next in control systems
in the 21st century?" in a somewhat different way. During my travel and lectures, I am
often asked by young scientist/engineers starting out in their careers on what are
profitable avenues of research to pursue. One is often tempted to point to ones own
current research topic, which by definition must be the most interesting things to do.
However, this is selfish and dangerous advice. My considered advice, which I myself
have followed, is this:
"GO FIND A REAL WORLD PROBLEM THAT A GROUP OF PEOPLE IS
EAGER TO SOLVE, THAT HAPPENS TO INTEREST YOU FOR WHATEVER
REASON, AND THAT YOU DON'T KNOW MUCH ABOUT.
MAKE A COMMITMENT TO SOLVE IT BUT NOT A COMMITMENT
TO USE TOOLS WITH WHICH YOU HAPPEN TO BE FAMILIAR"
Such an approach has several immediate advantages. First, if you are successful
then you have some free built-in Public Relations. Unsolicited testimonial by others is the
best kind of publicity for your work. Second, most probably you have discovered
something new or have found a new application of existing knowledge. In either case,
you can try to generalize such discovery later into a fruitful research area for which you
will be credited with its founding. Third, in a new problem area there is generally less
legacy literature you will have to learn and reference. Fourth, a new problem area is like a
newly discovered mine. For the same effort you can pick up more nuggets lying near the
surface than digging deep in a well-worked out mineshaft. By the same reasoning, the
probability of serendipity at work is also by definition higher in a new area. Lastly, even
if you are unsuccessful in solving the original problem, you will have at the very
minimum learned something new which will increase the chance of your success in
future tries.
My own personal experiences whether it was differential games, manufacturing
automation, perturbation analysis in discrete event simulation, or ordinal optimization
reinforces the above belief. Above all, faith in the ability of the future generation of
scientists and engineers makes me an optimist in saying, "the best is yet to be, you ain't
seen nothing yet". It is fine to make predictions and to look forward, but there is no need
to get too obsessed with divining the future.
As far as technology and computational issues are concerned, I believe I have
already given my answer in the recent op-ed piece published in the June '99 issue of the
IEEE Control System Magazine "The No Free Lunch Theorem and the Human-Machine
Interface". I shall simply add by repeating what I said at my Bellamn Award acceptance
"the subject of control which is based on mathematics, enabled by computers, is about to
have a new birth of freedom under computational intelligence".
END OF QUOTE.
I now wish to elaborate on the last sentence in the above advice about making a
commitment. In trying to solve a problem, one is always tempted to use tools with which
one is familiar. This is very natural. Our training in school for the most part deals with
tools. Exercises associated with such learning are always designed to fit these tools. As a
result when we tackle a problem outside the textbook, our first instinct is to reach for
these tools.
But as engineers who ultimately must solve real world problems or as academic
engineers who aspire to pioneer breakthrough research, such an approach is often not the
best way. Basically, you are "a solution in search of a problem to fit". Too often, we bend
the problem to fit the solution.
Instead, it has been my experience that we should take the problem as it presents itself
and not form any pre-conceived idea on how to solve it. Confronting a problem on its
terms promotes looking at it in the simplest terms since we have nothing but common
sense to guide us. New ideas and knowledge, the bloodline of research, are often
discovered this way. Such ideas in addition will be practically relevant and significant
since they are discovered in the process of solving a real world problem as opposed to
extending or generalizing a given mathematical formula.
By the same token, when one derives a formula via analysis, it is useful to ask the
question: "how is this formula useful to an engineer/user? ". A user is not particularly
interested or impressed by the elegance of your derivation but more interested in how the
formula will help him; the relative importance of various parameters in the formula; any
critical threshold which affects performance, etc. A formula will only be used if the user
can understand its significance
In commerce, the adage is that to be successful, you must serve your customers well. As
an academic in an engineering department or a researcher in an industrial company, we
should always remember who are our ultimate customers and make a commitment to
serve them well.
Finally, a purely theoretical paper no matter how elegant will be thoroughly understood
and appreciated by only a few people. But everyone can appreciate the significance of a
real world application. For academic engineers, if you want your work widely admired
and used, you must adapt your theory to practice and not the other way around.
Lastly, most of the published research by most of us belong to the third level which I
denote as incremental research within an established framework. These are still important
works. These efforts provide continuation and transfer of knowledge from one generation
to another, extension, addition and clarification our knowledge base, and above all
justification for our existence. To do this kind of research, you must learn the prior
literature by consulting reviews, tutorials, and related published papers. However, you
should be warned that even if you read the papers on the day they are published, you are
at least 18 month out of date due to the delays in reviewing and publishing. This is where
your advisor comes in and the reason to go to conferences. An advisor, expert in a given
area, is aware of the latest results in the area long before they are published. Other
researchers are eager to sent new and unpublished results to these experts/authorities for
comments and to establish priority. And to maintain his/her expertise, such advisor keeps
up with the literature. If you are students of such as advisor, you benefit accordingly.
Secondly, conference proceedings are faster form of publication than archival journals.
Also the real benefit of going to a conference is the chance to informally gather the latest
information on topics of research. When you are working on the cutting edge of a
research topic, you must spend effort to keep up communications with others interested in
the same topic. "closing the door in order to built a car (冈门造车)" is not a
recommended way to do research which knows no national boundaries. The existence of
the Internet ameliorated the problem of information gathering and transmission for the
individual to some extent. Students in developing countries and developed countries are
more on a even playing field. However, there is nothing to replace the personal attention
of an expert advisors and mentor.
Speaking about advisors and mentors, there are different philosophies on the role played
by a Ph.D thesis advisor. One extreme school of thought (rather prevalent in China but
rare in leading universities elsewhere) views the role of an advisor simply as a
grader/examiner. Students are supposed to be self-sufficient (自生自灭). The advisor
merely makes sure that all rules are observed, requirements satisfied and the student's
thesis meet certain minimal standards. This way, a person can supervise ten, twenty, or
even fifty Ph.D. students. This makes life easy for the advisor but is bad for the scientific
standards of the profession. It also tends to hide the incompetence of the advisor. At the
other extreme, the role of an advisor is more akin to that between a kung-fu master and
disciple – it is a very serious commitment and responsibility on the part of the advisor
lasting over many years and sometimes over a lifetime. In such cases, taking on a Ph.D.
student is a time consuming task for at least five years. The advisor works closely with
the student and tries to convey and teach many things that are not in the textbooks but
important to the student in his/her career and life development. An advisor working in
this Socrates' teaching mode can handle at most five or six Ph.D. students simultaneously
when he is working full time. My own philosophy is more inclined towards this old
fashioned school of thought since I believe except for geniuses, first rate scholars are
taught and created this way.
As to the standard used in a Ph.D. thesis, I have three:
a) Portion of the thesis must be accepted for publication in a leading journal (not
just any SCI listed journal) of the field. Top scholars of a field usually agree on
what set of journals are leading in a given field. This requirement is not only an
independent check of the contribution of the thesis but also serve to publicize the
product of the university to the world.
b) The advisor should learn something new from the student thesis.
c) The advisor should not be ashamed to admit in public that he supervised the thesis
since it is also a reflection of the standard and competence of the advisor.
Admittedly, two of the three criteria are subjective and can lead to abuse. Only a healthy
system of peer review and established self-discipline by the profession can prevent abuse.
I also understand the current Chinese regulation for Ph.D. degrees requiring four
journal/conference publications. While this is excessive if strictly enforced (footnote: It
is more strict than any system in the world and certainly more than my own
requirements), I understand to some extent the rationale behind it. Until the quality of
Ph.D. advising becomes uniform through out the graduate schools, some form of
quantitative requirement will be necessary to prevent abuse and to insure a minimal
standard.
In short, there is no royal road to research success except focused hard work.
How to do research How to do research? And Related Issues of Ph.D Education
"Can you tell me how to do research?",
"Please tell me what topics should I pick to do my ph.d.thesis? "
are two questions I periodically receive from young researchers and students in China.
There are two difficulties associated with answering these two questions.
1. The first question is open-ended. To answer it honestly and accurately will
require several hours of explanation. In fact, the process could take several
years of face-to-face interaction between an advisor and a ph.d student. This is
really what a ph.d. education involves. It is not the number of courses taken or
the number of papers published. But something about the quality of and taste
in research. I'll have more to say about this point below in connection with
trying to answer this first question here .
2. Behind the second question lie a unintentional misconception by many young
workers about "research" that probably has its origin in the martial arts novels
of Chinese literature (武侠小说). In typical novels of this genre, the
hero/heroine learns powerful martial arts technique through some secret
magical instruction passed down from a master or a long lost instruction book.
As a result, overnight s/he was able to defeat his/her enemy and achieve
success. Thus, many students are constantly looking for that "magical
formula" for research. Another common criticism of Chinese university
education by Westerners is that most students learn to answer questions or
pass exams extremely well, but there is little training in posing questions and
formulating problems which are really the important parts and more than half
of research. Growing up in such environment, it is natural for Chinese
students to seek the magical formula for research success. It is also my
purpose to address this issue here
To give an answer to the first question, you can approach it at three levels. At the first
level, your aim is to be that one scientific person in a century, e.g., the discoverer of the
theory of relativity. I have no experience or advice to give at that level. All I can say is
"good luck". At the next level, you want to be the founder of a research topic or make the
breakthrough on a difficult problem. Again, there is no magic here. For otherwise,
breakthrough will become commonplace. However, I do have one suggestion that worked
well for me and which is generally applicable. This I offer below:
I participated in a panel discussion 8 years ago which involves my answers to several
general questions posed to me at the panel and which still apply today. ( The 1999 IEEE
Conference on Decision and Control panel on "At the Gate of the Millennium – Are we
in control?" Published in IEEE Control Systems Magazine Vol.20, #1, Feb, 2000)
BEGINNING OF QUOTE
1. What are the 5 most notable research results in systems and control theory in the
past 100 years?
The "test of time" and traditions of history rule out mentioning anything
developed in the past 25 years or involving living persons. Furthermore, scientific
discovery often is a matter of standing on the shoulder of others. To single out specific
results do not seem to be fair to others who laid the foundation. Instead, I propose to list
couple of ideas that seems to me have influenced the development of our field in a major
way
1. The fundamental role of probability and stochastic process in system work.
2. The concept of what constitutes a solution to a problem, namely, that which can
be reduced to another routinely solved problem such as numerical integration
3. The notion of dynamics and feedback in all their ramifications
The first item represents how knowledge from outside the field influenced our research
while the third states what specific concepts our field contributed to other fields. The
second item deals with how practices in science and mathematics are changed by
technology. These notions are generic and have parallel in other topics and fields.
2. What are future research milestones (for the next 5-10 years) which will have a
most significant impact on the field? Comment on what we should be doing now
and in the near future to make such accomplishments possible. Also discuss your
thoughts on:
i.Education issues. How should we be training our students to meet the future
challenges?
ii.Technology issues. What we see coming up that will change the control
landscape.
iii.Computational tools.
Scientific crystal balling has a notorious record in the past. The dust heap of past
predictions is filled with gross miscalculations and estimations by noted scientists with
the best of intentions. Let me try to approach the question "what's next in control systems
in the 21st century?" in a somewhat different way. During my travel and lectures, I am
often asked by young scientist/engineers starting out in their careers on what are
profitable avenues of research to pursue. One is often tempted to point to ones own
current research topic, which by definition must be the most interesting things to do.
However, this is selfish and dangerous advice. My considered advice, which I myself
have followed, is this:
"GO FIND A REAL WORLD PROBLEM THAT A GROUP OF PEOPLE IS
EAGER TO SOLVE, THAT HAPPENS TO INTEREST YOU FOR WHATEVER
REASON, AND THAT YOU DON'T KNOW MUCH ABOUT.
MAKE A COMMITMENT TO SOLVE IT BUT NOT A COMMITMENT
TO USE TOOLS WITH WHICH YOU HAPPEN TO BE FAMILIAR"
Such an approach has several immediate advantages. First, if you are successful
then you have some free built-in Public Relations. Unsolicited testimonial by others is the
best kind of publicity for your work. Second, most probably you have discovered
something new or have found a new application of existing knowledge. In either case,
you can try to generalize such discovery later into a fruitful research area for which you
will be credited with its founding. Third, in a new problem area there is generally less
legacy literature you will have to learn and reference. Fourth, a new problem area is like a
newly discovered mine. For the same effort you can pick up more nuggets lying near the
surface than digging deep in a well-worked out mineshaft. By the same reasoning, the
probability of serendipity at work is also by definition higher in a new area. Lastly, even
if you are unsuccessful in solving the original problem, you will have at the very
minimum learned something new which will increase the chance of your success in
future tries.
My own personal experiences whether it was differential games, manufacturing
automation, perturbation analysis in discrete event simulation, or ordinal optimization
reinforces the above belief. Above all, faith in the ability of the future generation of
scientists and engineers makes me an optimist in saying, "the best is yet to be, you ain't
seen nothing yet". It is fine to make predictions and to look forward, but there is no need
to get too obsessed with divining the future.
As far as technology and computational issues are concerned, I believe I have
already given my answer in the recent op-ed piece published in the June '99 issue of the
IEEE Control System Magazine "The No Free Lunch Theorem and the Human-Machine
Interface". I shall simply add by repeating what I said at my Bellamn Award acceptance
"the subject of control which is based on mathematics, enabled by computers, is about to
have a new birth of freedom under computational intelligence".
END OF QUOTE.
I now wish to elaborate on the last sentence in the above advice about making a
commitment. In trying to solve a problem, one is always tempted to use tools with which
one is familiar. This is very natural. Our training in school for the most part deals with
tools. Exercises associated with such learning are always designed to fit these tools. As a
result when we tackle a problem outside the textbook, our first instinct is to reach for
these tools.
But as engineers who ultimately must solve real world problems or as academic
engineers who aspire to pioneer breakthrough research, such an approach is often not the
best way. Basically, you are "a solution in search of a problem to fit". Too often, we bend
the problem to fit the solution.
Instead, it has been my experience that we should take the problem as it presents itself
and not form any pre-conceived idea on how to solve it. Confronting a problem on its
terms promotes looking at it in the simplest terms since we have nothing but common
sense to guide us. New ideas and knowledge, the bloodline of research, are often
discovered this way. Such ideas in addition will be practically relevant and significant
since they are discovered in the process of solving a real world problem as opposed to
extending or generalizing a given mathematical formula.
By the same token, when one derives a formula via analysis, it is useful to ask the
question: "how is this formula useful to an engineer/user? ". A user is not particularly
interested or impressed by the elegance of your derivation but more interested in how the
formula will help him; the relative importance of various parameters in the formula; any
critical threshold which affects performance, etc. A formula will only be used if the user
can understand its significance
In commerce, the adage is that to be successful, you must serve your customers well. As
an academic in an engineering department or a researcher in an industrial company, we
should always remember who are our ultimate customers and make a commitment to
serve them well.
Finally, a purely theoretical paper no matter how elegant will be thoroughly understood
and appreciated by only a few people. But everyone can appreciate the significance of a
real world application. For academic engineers, if you want your work widely admired
and used, you must adapt your theory to practice and not the other way around.
Lastly, most of the published research by most of us belong to the third level which I
denote as incremental research within an established framework. These are still important
works. These efforts provide continuation and transfer of knowledge from one generation
to another, extension, addition and clarification our knowledge base, and above all
justification for our existence. To do this kind of research, you must learn the prior
literature by consulting reviews, tutorials, and related published papers. However, you
should be warned that even if you read the papers on the day they are published, you are
at least 18 month out of date due to the delays in reviewing and publishing. This is where
your advisor comes in and the reason to go to conferences. An advisor, expert in a given
area, is aware of the latest results in the area long before they are published. Other
researchers are eager to sent new and unpublished results to these experts/authorities for
comments and to establish priority. And to maintain his/her expertise, such advisor keeps
up with the literature. If you are students of such as advisor, you benefit accordingly.
Secondly, conference proceedings are faster form of publication than archival journals.
Also the real benefit of going to a conference is the chance to informally gather the latest
information on topics of research. When you are working on the cutting edge of a
research topic, you must spend effort to keep up communications with others interested in
the same topic. "closing the door in order to built a car (冈门造车)" is not a
recommended way to do research which knows no national boundaries. The existence of
the Internet ameliorated the problem of information gathering and transmission for the
individual to some extent. Students in developing countries and developed countries are
more on a even playing field. However, there is nothing to replace the personal attention
of an expert advisors and mentor.
Speaking about advisors and mentors, there are different philosophies on the role played
by a Ph.D thesis advisor. One extreme school of thought (rather prevalent in China but
rare in leading universities elsewhere) views the role of an advisor simply as a
grader/examiner. Students are supposed to be self-sufficient (自生自灭). The advisor
merely makes sure that all rules are observed, requirements satisfied and the student's
thesis meet certain minimal standards. This way, a person can supervise ten, twenty, or
even fifty Ph.D. students. This makes life easy for the advisor but is bad for the scientific
standards of the profession. It also tends to hide the incompetence of the advisor. At the
other extreme, the role of an advisor is more akin to that between a kung-fu master and
disciple – it is a very serious commitment and responsibility on the part of the advisor
lasting over many years and sometimes over a lifetime. In such cases, taking on a Ph.D.
student is a time consuming task for at least five years. The advisor works closely with
the student and tries to convey and teach many things that are not in the textbooks but
important to the student in his/her career and life development. An advisor working in
this Socrates' teaching mode can handle at most five or six Ph.D. students simultaneously
when he is working full time. My own philosophy is more inclined towards this old
fashioned school of thought since I believe except for geniuses, first rate scholars are
taught and created this way.
As to the standard used in a Ph.D. thesis, I have three:
a) Portion of the thesis must be accepted for publication in a leading journal (not
just any SCI listed journal) of the field. Top scholars of a field usually agree on
what set of journals are leading in a given field. This requirement is not only an
independent check of the contribution of the thesis but also serve to publicize the
product of the university to the world.
b) The advisor should learn something new from the student thesis.
c) The advisor should not be ashamed to admit in public that he supervised the thesis
since it is also a reflection of the standard and competence of the advisor.
Admittedly, two of the three criteria are subjective and can lead to abuse. Only a healthy
system of peer review and established self-discipline by the profession can prevent abuse.
I also understand the current Chinese regulation for Ph.D. degrees requiring four
journal/conference publications. While this is excessive if strictly enforced (footnote: It
is more strict than any system in the world and certainly more than my own
requirements), I understand to some extent the rationale behind it. Until the quality of
Ph.D. advising becomes uniform through out the graduate schools, some form of
quantitative requirement will be necessary to prevent abuse and to insure a minimal
standard.
In short, there is no royal road to research success except focused hard work.
星期三, 十月 31, 2007
项武义的Kepler梦想--jasonbwu的博客 - 使用 Google 工具栏进行发送
项武义的Kepler梦想--jasonbwu的博客
数学界很多人都知道项武义，倒不是因为他的数学有多强，而是因为一个古老的问题--Kepler猜想，这也让项武义成了数学界的一个笑柄。
这个项老头子早年跟哥哥项武忠一道研究拓扑。老了就从Berkeley回到香港科大养老。可他又偏偏闲不住，精力还特充沛，于是开始在国内到处宣传他的教学改革。听他的演讲，还真是豪情万丈，"Klein的Erlangen纲领只不过是个小小的idea。"，"分形几何那都是糊弄人的东西，是伪科学。"不明就里的学生还真对这个大块头的老头子佩服不已。直让人怀疑他是不是立志做数学界的"方舟子"，"王海"。
项老头子是个聪明人，因为他知道在德国说"Erlangen纲领是个trivial的idea"可是要挨鸡蛋的，他也知道在国外搞教改肯定没人理他。可怜了他只能在国内到处跑，凡是有关教改的大小会议一概不会错过。还出版了一套基础数学讲义丛书，可惜其中有不少"项氏风格"的废话。
也许觉得还不够出风头，项老头子不知咋的，开始对Kepler装球问题感起了兴趣。1992年到1993年，他在几个数学杂志上发表了几篇号称完全证明Kepler猜想的文章， 可惜不久就被著名数学家J.H.Conway, Toth, Hale等人指出其中存在着明显的漏洞。项老头子一听火了，在Math Intelligencer杂志上与Hales开始了针锋相对的论战。项还到处赠送自己关于Kepler猜想的证明，许多系实在推脱不了，只得收下两本，不禁令人想起图书馆里看到过的好多"民科"自费出版的歌德巴赫猜想的证明。糊涂的项老不是把精力放在检查和修补自己的证明上面，而是在国内有如民科般的行事，多次在国内的讲座上介绍他关于Kepler猜想的错误证明。
数学是来不得半点虚假的，Hales在1997年借助计算机，完全证明了Kepler猜想，得到公认，文章即将发表在Annals of Math上，Hales本人也在ICCM上作了45分钟报告。宣告项的Kepler梦想彻底破碎。
项武义的老哥项武忠曾被向来直言的丘批评过学问二流，所以当哥两听说丘田反目，当即成了田刚的铁杆拥趸。这不，项武义被北大请去开"基础数学讲座"，宣扬"项氏改革"，可惜听众寥寥。近日眼见田被丘批的难以招架，忙把自己的讲座变成了"批丘大会"，这下引来学生无数，项老头子的口才又有了用武之地，好不风光。
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