Taylor 教授是在麦肯锡做过两年后才进入学术界的，在斯坦福大学拿到博士学位后，主要在哥伦比亚大学任教过六年，2007年的时候来到伯克利：
B.S., Industrial Engineering, Stanford University, 1994
McKinsey & Company, Business Analyst, 1994 – 1996
Ph.D., Management Science & Engineering, Stanford University, 2000
Stanford University, Graduate School of Business, Global Supply Chain Management Forum, Director of Research, 2000 – 2001
Columbia University, Graduate School of Business, Associate/Assistant Professor, 2001 – 2007
University of California, Berkeley, Haas School of Business, Associate Professor, 2007 – present
我听一位二年级的同学说，他们以前跟 Taylor 教授聊过，问他为什么在麦肯锡做的好好的，后来要去学术界了，Taylor 教授回答说在麦肯锡做事，做完一个项目就结束了，满足不了他追根问底的求知欲（intellectual curiosity），于是他想去学术界把 operations 的问题弄明白和清楚。
第一节 Operations 课就让我震了一下，因为 Taylor 教授实在是我见过最井井有条（ organized）的人。光看他写的课程大纲，别的课的大纲一般只是粗略的告诉学生每节课要上什么内容，他这大纲很仔细的告诉每节课前你要准备什么，课后你要看什么，哪节课你要带计算器，哪节课你要带电脑…… 连期末考试的地点，由于不是在商学院的楼里考试，他都给你画好地图！他把一切细节都安排好了，让人有一种特别好的享受VIP服务的感觉，呵呵。结论是：Taylor 教授真不愧是做 Operations 的！
Taylor 教授人也长得精神帅气（听说很多女生迷他，哈哈），每次都精神抖擞的西装革履、正装衬衫领带一丝不苟的来上课，他人又非常聪明，每次 Operations 课堂讨论气氛都很活跃…… 上他的课真是享受。
也难怪他无论在 Columbia 还是 Berkeley 教书都能拿 teaching 的奖：
Earl F. Cheit Award for Excellence in Teaching (Full-Time MBA Program), 2009
Earl F. Cheit Award for Excellence in Teaching (Full-Time MBA Program), Honorable Mention, 2008
Columbia Business School Dean’s Award for Teaching Excellence in a Core Course, 2003
Part I Physics and Economics of Production and Service Processes
Process Analysis:
Little’s Law: inventory = flow rate x flow time.
Bottleneck determines system capacity.
Batch production: economies of scale in production (setup costs) vs. inventory costs [EOQ model].
Variability in Processes
Variability leads to congestion and delay even when have more capacity than average demand.
Queuing models of service systems.
Part II Supply Chain Management
Critical-fractile Approach (Newsvendor Model)
Value of delaying final production decisions by postponement (delayed differentiation) or using reactive capacity.
If can’t adjust supply (hotel rooms, airplane seats), adjust demand through revenue management: booking limits on discounted fares, overbooking.
System performance is determined by the limiting resource (system capacity is the bottleneck capacity)
Bottleneck capacity determines system capacity.
Bottleneck may shift… when relieve bottleneck, or when product mix changes.
Failure of one resource threatens overall system.
Process choices should be integrated, consistent, self-reinforcing
Power of consistent, self-reinforcing decisions.
Risk in cherry-picking elements of successful operational models without considering interrelationships.
Uncertainty and variability are painful…
Performance degrades rapidly as utilization approaches 100%.
Performance degrades with increased variability.
Demand uncertainty makes managing supply chains challenging. Information distortion grows as move up supply chain away from end customer [Bullwhip Effect].
…but proper actions mitigate this pain
Build in slack/safety capacity.
Eliminate the variability you can, schedule demand you can control, and accommodate the rest.
Make intelligent gambles using critical-fractile approach.
Powerful “pooling efficiencies” are obtained by taking advantage of “statistical economies of scale”.
