华海的生活， 工作， 学习.

“七步分析法”是麦肯锡公司根据他们做过的大量案例，总结出的一套对商业机遇的分析方法。它是一种在实际运用中，对新创公司及成熟公司都很重要的思维、工作方法。

第一步：确定新创公司的市场在哪里?
这里一是要搞清楚市场是什么?再一个是在市场中的价值链的哪一端?确定自己的市场在哪里，才能比较谁和你竞争，你的机遇在哪里。
第二步：分析影响市场的每一种因素
知道自己的市场定位后，就要分析该市场的抑制、驱动因素。要意识到影响这个市场的环境因素是什么?哪些因素是抑制的，哪些因素是驱动的。此外还要找出哪些因素是长期的?哪些因素是短期的?如果这个抑制因素是长期的，那就要考虑这个市场是否还要不要做?还要考虑这个抑制因素是强还是弱?
第三步：找出市场的需求点
在对市场各种因素进行分析之后，就很容易找出该市场的需求点在哪里，这就 要对市场进行分析，要对市场客户进行分类，了解每一类客户的增长趋势。如中国的房屋消费市场增长很快，但有些房屋消费市场却增长很慢。这就要对哪段价位的 房屋市场增长快，哪段价位的房屋市场增长慢做出分析，哪个阶层的人是在买这一价位的，它的驱动因素在哪里?要在需求分析中把它弄清楚，要了解客户的关键购买因素，即客户来买这件东西时，最关心的头三件事情、头五件事情是什么?
第四步：做市场供应分析
即多少人在为这一市场提供服务，在这一整个的价值链中，所有的人都在为企 业提供服务，因位置不同，很多人是你的合作伙伴而不是竞争对手。如奶制品市场中，有养奶牛的，有做奶产品的，有做奶制品分销的。如公司要做奶制品分销，那 前两个上游企业都是合作伙伴。不仅如此，还要结合对市场需求的分析，找出供应伙伴在供应市场中的优劣势。
第五步：找出新创空间机遇
供应商如何去覆盖市场中的每一块?从 这里能找出一个商机，这就是新创公司必需要做的这一块。这样分析后最大的好处是，在关键购买因素增长极快的情况下，供应商却不能满足它，而新的创业模式正 好能补充它，填补这一空白，这也就是创业机会。这一点对创业公司和大公司是同样适用的，对一些大公司的成功的退出也是适用的。对新创公司来讲，这一点就是 要集中火力攻克的一点，这也是能吸引风险投资商的一点。
第六步；创业模式的细分
知道了市场中需要什么，关键购买因素是什么，以及市场竞争中的优劣势，就 能找出新创公司竞争需要具备的优势是什么，可以根据要做成这一优势所需条件来设计商业模式。对于新创公司来讲，第一步是先把市场占住，需要大量的合作伙 伴，但随着公司的发展，自有的知识产权会越来越多，价值链会越来越长。
第七步：风险投资决策
以上七点做为商业机会的分析，大小公司都可以运用，这第七点就是针对VC(风险投资商)的。VC主要看投资的增值能力，什么时候投，投多少?这要结合VC自身的财务能力、公司的背景、经历。VC投的不光是钱，他是需要考虑各方面的因素的。
文/方 哲

　　企业如何让人才有一个好的环境来创新呢？更重要的还是企业的文化和公司的管理机制。
　　当托马斯·弗里德曼(Thomas L. Friedman)先生还没写《世界是平的》这本书的时候，他在一次座谈会上发表了与此相关的观点。这时有一位先生走到他面前说：“你讲得很有道理，可是我想我可以帮助你补充其中的不足或你没有覆盖到的东西，你可不可以到我们公司来，我们谈一谈。”
　　这位先生就是比尔·盖茨(Bill Gates)。
　　于是弗里德曼到了西雅图，宣布两年后接替比尔·盖茨位置的那个人跟他花了几天的时间交谈。当《世界是平的》这本书出版以后，弗里德曼在一次论坛上谈到，他的30%的观点是比尔·盖茨和另外一个先生提到的。
　　我想比尔·盖茨先生提到的这个观点跟中国有关系。因为全球30%的研究院都在中国，而比尔·盖茨看到中国研究院在世界有名的期刊上发表的论文远超过其他国家的研究院，所以他说全球一体化是一个不可避免的趋势。
　　这本书里谈到：如果你是美国人，你每天早上起床后想到的，就是你的工作是不是被中国人和印度人抢走了。但是，我们要想到的是，随着全球的网络创新和传播创新，在全球任何地方，不管你是什么肤色，只要你是人才，在全球一体化的环境里，凭着你的创新和才能，你就会出人头地！
　　而现在重要的就是软件的创新以及软件技术的发展，通过网络，在各个地方都可以取得公司的资讯，通过软件的帮助，这些取得的资讯可以化作洞察公司发展的知识。这一点就是全球一体化里关于人才和创新非常关键的一点。

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　　创新的挑战
　　当然创新给中国带来非常大的挑战。我们知道华为、中兴等公司，可能做通讯的公司超过100亿美元的营业额，其中一半就要靠外销。再如我们看到的联想和TCL，他们都在往海外走，在创新的条件下，在参与海外竞争的情况下，公司如何应对环境变化也是个不小的难题。
　　到现在为止我在三家公司工作过：第一家是贝尔实验室，做了9年；第二家是摩托罗拉，做了10年；第三家是微软，已经做了3年。这三家公司在创新上都有特色：像贝尔实验室，半导体、移 动通讯都是它发明的；摩托罗拉的研发创新能力也很强，手机、BP机都是它发明的；微软的软件也是很有创新的。我认为企业如果没有持续的创新能力是不能立稳脚跟的。最近贝尔实验室在和朗讯合并，这是非常令人震惊的事情。这说明什么？虽然以前有很好的创新，但创新没有持续，就会在全球化的趋势下遇到很大的挑战。我们公司今年推出的产品是过去几年里花了200亿美元才研发出来的，就是我们每年投入70亿，积累几年后，今年一次推出200亿美元的产品。这里面可以看到一点，创新需要很大的投入、持续的投入，这一点对中小企业的压力是非常大的。当然，这并不表示中小企业没有创新的机会。因为中小企业也在互联网的技术环境里，而软件的技术越来越向互联网技术的软件方面发展，所以这里面产生了一个新的商业模式。大家都知道像google，创造出了免费的服务，但是没有打广告而赚取利益，这个也有另一方面的创新。

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　　人是创新的基础
　　在过去十年中，中国专利数量已经增加了六倍，专利申请量已经排到全球第五，但是我们在创新方面还是要加强。如何创新呢？创新的基础在哪里呢？我认为还是人和知识产权，有时候大家开玩笑说什么是IT？我说IT就是IQ(智商)加上IPR(知识产权)。IQ就是人，企业是靠人的，我记得比尔·盖茨先生上次在美国拜见温家宝总理的时候，温家宝问比尔·盖茨：“你们公司创新的基础在哪里？”比尔·盖茨回答说：“我们创新就是靠人创新。”大家在媒体上看到微软雇用研发人员要通过八到十次筛选才选择一个人。我想人是创新的基础。
　　另外一点，关于知识产权，胡锦涛主席上次去微软时跟媒体说，我们中国要促进知识产权的保护，不仅仅是跟国际接轨，更重要的是保障我们中国的自主创新有很好的发展。我们也看到过去这一段时间，政府已经创造了很好的一系列关于保护知识产权的措施，对于我们软件业来讲，比如说PC预装软件，比如政府带头推行正版，企业推进做正版，营造了一个很好的环境。

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　　如何以创新留住人才？
　　中国每年有30万软件人才从大学毕业，在印度大概有20万，美国15万，可以说我们在人才创新方面有很好的基础。但使毕业生出来就能到公司里面，这是个很大的挑战。我知道很多公司进新人都要花半年到一年做培训，这个是很大的成本，于是造成一个现象——很多企业都不愿意聘用刚毕业的人，就去挖其它公司的人，但这样它们的成本又会增加。
　　另外，中国软件业每年流失15%～20%的人，这种流失有好处，把大公司的人流出去到中小企业，帮助中小企业发展。但另一方面，人才流失以后，也造成公司成本的损失，年薪加上福利起码是30%的损失，严重一点可能到150%的损失。金钱的损失是小的，但是这个人走了以后把经验带走了这是更大的损失。
　　公司如何留住人才？必须有很好的创新。如果一个公司没有好的创新，没有好的愿景，没有给人才一个成长的环境，自然留不住人。所以，人才创新对于全球化的发展是非常关键的。
　　创新不仅是一个口号而是一个文化，比尔·盖茨每年都征集所有员工的论文。三五页也可以，20页、100页也可以，只要你写出你关于新的发展的想法。他花两个星期什么都不做，专门去读文章，读过每篇文章之后都会做出反馈，然后发到网上给大家看。这个就是我们重视创新的文化。我们把这些文章归纳起来，整理出60个课题，每一个课题有像全球副总裁这样的人带领，从各个事业单位中抽出精英，抽出架构师组成60个课题的工作小组来研究如何做创新。我想大家也看到IBM要求全球员工都提创新设想，筛选出几万个想法，这个也是创新的文化。我想在中国这个环境里面，追求商业成长是主要的，但是也要在创新方面做一些工作。
　　在企业里面如果人才是根本，创新是关键，那么企业如何让人才有一个好的环境来创新呢？我觉得关于软件的工具非常关键，我们公司在过去几年推行企业信息化过程中看到一个问题，大家都讲企业信息化，但是企业信息化不代表一套电子的系统，用SAP、或者是思科，或者是用我们的软件，不是这个意思。我想大家花在软件的购买和培训上的付出是不成比例的，就是说你公司买了很多的软件，但是员工没有办法利用这个软件增加公司的效率。所以，在企业里面除了有人才以外，还要看是否让你的人才有足够的工具去工作，彼此之间协调，把孤岛整合起来。这不仅仅是应用软件，更重要的还是企业的文化和公司的管理机制。
　　最后，我想通过弗里德曼《世界是平的》这本书，我们看到事实上未来世界到底会多好，完全是靠我们大家的想象力与创造力，我觉得创新还是我们最关键的，人才是创新的基本，把这两个放在一起，中国不管在人才方面还是创新方面都应该有一个好的远景。

作者：贾品荣

　　我们一些企业家在某事上一旦取得成功，就认为自己无所不能，无所不往，过分夸大主观作用，这是导致中国企业经营复杂化的深刻思想渊源。

　　中国企业的这种“大企业管理幻觉”，更多来自于决策者深层次的自卑和虚荣，希望通过大小获得公众良好评价，外界不实在的吹捧也加速了这种心态的扩张。

　　复杂化导致削足适履

　　复杂化的思维已经在众多的管理者心中留下了太深的烙印，而复杂化必将导致管理者在管理思维上产生一系列画蛇添足、得不偿失的后果。

　　我们以绩效管理为例来说明这个情况。在西方经济发达国家，绩效管理已成为一门比较成熟的学科，近年来国内也翻译出版了较多的绩效管理专著。一时间国外的理论盛行，战略性的绩效管理、平衡记分卡、360°考评等等方法、工具铺天盖地，使人摸不着头脑。国外的理论、工具固然先进，但是毕竟与中国的国情、企业的实际有很大的距离，不加分析和转换直接采用，有削足适履之感。

　　上海一著名企业不惜重金聘请国外咨询公司，直接采用平衡积分卡方法，为公司制定目标与绩效管理，前后花了半年时间写了一大本几百页，但是由于市场的多变使得目标与计划多变，原来的那套东西只能搁置一边，既浪费人力、财力，也丧失了机会和时间。其中的原因不在平衡积分卡本身，而在于盲目追求先进理论而囫囵吞枣，直接套用。

　　两位商学院教授和一位麦肯锡前顾问组织了一项叫“常青树项目”的开拓性研究，分析了160家公司在1986－1996年的10年期间所采用的200多种管理工具，从中探寻成功企业的管理规律。结果发现，绝大多数管理工具和技巧与企业的业绩没有任何直接的因果关系，最基础的经营要素才是决定企业成败的关键所在。

　　基于研究结果，研究者总结出了企业的“4＋2”成功法则，即在4个首要管理实践──战略、执行、文化和组织结构上要做到个个优秀，而在4个次要管理实践──人才、创新、领导力和兼并合作中选取2项做好，便能长期拥有良好的业绩。

　　关于战略方面：重点突出的战略比制订何种战略都更重要。要注意制订和保持界定明确、沟通充分；关于组织结构方面：精简工作、减少官僚主义是架构组织的关键；关于执行方面：如何执行比执行什么更重要；关于文化方面：让公司的每一个人都感到自己是公司的主人，努力营造一种氛围，从而上下一心共同努力成就了成功的企业。

　　研究者通过对4个次要管理实践的分析，发现领先企业对行业变革的突破性创新孜孜以求；成功企业不仅设法留住人才，而且创造环境培养人才；CEO是否能和员工建立良好的关系、能否及时发现机遇和问题所在，都和公司的命运息息相关。那些在行业中总是领先的公司都无一例外地贯彻了“4＋2”成功法则，只有坚守这一法则，才能确保公司的优秀业绩。

　　现在在各种管理工具充斥的商业世界里，要想找回企业经营之根本，不妨仔细思考“4＋2”法则，明确哪些管理实践是重中之重，哪些管理实践是可以忽略的。

　　记住：理论毕竟是理论，再好的理论如果不结合实际，也只是理论而已。理论的最大价值在于结合实际解决实际问题，而不是让实际适应理论。

　　复杂化导致工具主义

　　绩效管理既是一种思想，也是一种方法和工具。就工具而言，常用的有岗位分析与评价方法、目标制定方法、能力与业绩评价方法等不下上百种工具。时下书店里出售的绩效管理表格、通用模本就包括了大量的工具，很多从事人力资源研究、咨询和实际工作的人经常会拿出一大堆让非专业的人士看得眼花缭乱，但还是不知其为何物的模型、表格，以显示所谓的专业和高深。

　　不是不要工具，而是所有的方案必须让使用者明白理解才是好的方案。曾经有一个企业，其考核表复杂得让员工不知如何计算当月自己的得分并计算出自己的收入，就连人力资源部也觉得难算。

　　在一次长达2小时的会议上，一家设计公司展示一个价值数百万元的标志设计项目方案。与往常一样，报告人使用诸如“范式”一类的术语，并含糊其辞地提到“颜色偏好”。报告充斥了晦涩复杂的概念。CEO坦言说被报告内容搞糊涂了，并询问其他人的意见。同事突然笑了起来，显出释然的样子。他们承认什么也没听懂，但是害怕被人嗤笑而羞于启齿。

　　其实，不应有害怕在众人面前犯傻的想法。有时候最天真的问题最深刻。人们容易陶醉在复杂知识的掌握和使用上，而忽视了更简单易行的方法。

　　记住：工具只是工具而已，最重要的是目标制定得正确与否与思路的恰当与否，否则再好的工具都将失去意义，甚至会使人陷在工具中不能自拔。

　　复杂化导致面面俱到

　　一个企业在某个特定的阶段，存在的问题与重点不会超过5项。管理就是围绕着这些关键点的管理，引导全体员工朝这一方向努力。

　　然而，有的企业其考核表洋洋洒洒，考核的内容总共有30多项，大到利润小到出勤等等。其结果是员工工作没头绪，不知哪个是重要的，最后出现重要工作没做好而诸如出勤做得较好的员工得分反而高的不正常现象，失去了企业以业绩论贡献大小的管理原则。

　　表面上看，我们抓住了所有方面，但面面俱到和复杂化却使得我们偏离了公司目标追求，最后没起到作用。对此，乔治。梅森大学的休。赫克洛教授自有一番见解。“长远来说，在技术手段泛滥的情况下，拥有相对信息优势的不再是拥有大量信息的人，而是掌握有序信息的人；不再是能处理巨量信息的人，而是知晓信息价值的人。”

　　在哈佛商学院的教学中，经常给学生讲述一种很有效的做事方法：80／20法则。即任何工作，如果按价值顺序排列，那么总价值的80%往往来源于20%的项目。简单地说，如果你把所有必须干的工作，按重要程度分为10项的话，那么只要把其中最重要的两项干好，其余的8项工作也就自然能比较顺利地完成了。所以，要把手中的事情处理好，就要抛开那些无足轻重的80%的工作，把自己的时间、精力全部集中在那最有价值的20%的工作中去，这会给你带来意想不到的收获。

　　记住：人生就是不完美的过程，舍弃才是智慧，有所舍弃才能有所收获。当你能够在最恰当的时机舍弃某些东西时，实际上你已经得到了你应该有的东西。

　　简单之美

　　简单是美。简单化的要求就是缩小选择范围，回归到一条正确的道路上来。

　　许多人看微软是一个单一、巨大的企业，事实上微软是由许多小而独立的单位集合在一起。在微软，各个单位各自进行不同的方案，只有在吃中饭的时候，来自不同单位的员工才有机会同桌交换心得。

　　世界500强企业之一的宝洁公司也是这么做的。“全球市场真的需要31种海飞丝洗发水吗？真的需要52种佳洁士牙膏吗？”宝洁公司CEO德克。贾格尔撰文说：“这些年来，我们可真难为消费者了。”贾格尔认识到，宝洁几十年来不断推出改良产品，各种香型或特大号产品，品种太过繁多了。

　　只要找到问题所在，解决办法就变得非常简单。宝洁决定：规范产品配方，减少繁琐交易和优惠措施。从感觉上说，上述这些措施无疑会造成销售额随之下降的结果。但事实并非如此，从宝洁公司年度销售报告中可以看出，仅护发产品一项，虽然品种少了一半，市场占有率却上升了5个百分点。

　　这就是简单之美。

　　老子说，“天下难事，必做于简；天下大事，必做于细。”

　　通用电气公司原CEO杰克。韦尔奇说得好：“有不安定感觉的经理人制造复杂。他们对前途担忧，神经紧张，使用厚厚的计划书和幻灯片，凡是童年以来知道的东西里面都有。真正的领导者不需要杂乱无章的思想。他们必须自信有能力做到头脑清晰、表意准确，确认公司中的每一个人，无论职位高低，都明白企业追求的目标。这一点却不容易做到。你很难相信做到简单是多么艰难，人们又是多么惧怕简单。他们担心，如果他们简单了，别人会认为他们就是头脑简单。当然，事实正好相反。头脑清楚、讲求实际的人最简单。”

中国青年报

　　何卫宁

　　-在市场竞争中，需要懂得进与退的辩证法。什么时机退，什么领域不竞争，要相机行事。有时往往需要以不争去躲避恶性竞争，用不争来获取战略优势。

　　最近，以制造精美个人电脑而著称的美国苹果电脑公司有两个新举动：一个是将公司的名字改为“苹果公司”(删去原名字中的“电脑”二字)，第二个是进军手机制造业，推出一款设计极其精巧的手机iPhone。从苹果公司的这两项惊人的举措看，苹果的新方向似乎是要退出个人电脑市场。

　　主动退出个人电脑市场的不止是
苹果公司。个人电脑业的老前辈美国的IBM公司，早在2005年5月，就把扭亏无望的个人电脑事业部卖给了中国的联想公司。对IBM来说，出卖个人电脑事业部是一个困难的决定，因为个人电脑市场本身就是由IBM一手缔造的。

　　也许有人会说，忙于退出个人电脑市场的公司都是些
恐龙级的怪物。然而，就是当今个人电脑市场里“风头正劲”的戴尔电脑公司，也遇到了麻烦。最近其
股票价格大幅下跌，原因是投资者对戴尔的业绩不满意。

　　是个人电脑市场衰败了吗？其实，个人电脑市场并没有衰败，而是正在发生着深刻的变化，这种变化体现在三个趋势上：

　　其一，是信息处理能力的渗透趋势。个人电脑的信息处理能力越来越强大，反衬出消费者身边其他电器在信息处理能力方面的弱小，于是消费者就愿意花钱去弥补那个差距。例如，新近流行的智能手机不仅能打电话，还能像个人电脑一样上互联网、接收发送电子邮件、听音乐甚至看电视。

　　其二，是个人电脑瘦身的趋势。个人电脑功能越变越强，可是使用人群却越来越庞大，很多人其实只需要电脑一小部分的功能，电脑功能的浪费现象越来越严重。例如，商业公司里的绝大部分个人电脑都被当做打字机和幻灯机来使用，商业公司的董事们总有一天会下决心消灭那个浪费。

　　其三，是消费者的偏爱在改变。以前，消费者满足于低成本个人电脑；现在，消费者越来越挑剔产品是否有创新设计，越来越强调个人电脑是否能与家庭中的其他电器相匹配，越来越要求更好的售后服务。这三个趋势的力量很强大，所有的个人电脑公司都在寻找应对策略。

　　苹果公司目前占世界个人电脑市场份额的5%，想再增加1%都是一件难事。所以，苹果公司的应对策略就是以更快的速度去推进向个人电脑的渗透进程。虽然看上去它的iPhone不像个人电脑，但实际上却是比竞争对手的设计更新颖、包含更多先进技术的“小个人电脑”。请看，苹果公司的调整策略具有极强的辩证思维，其核心思路是不是很像老子曾经说过的：有无相生，前后相随。

　　IBM的应对策略与苹果公司不同，但也有相似之处，那就是去迎接个人电脑瘦身趋势。IBM把个人电脑里的公共软件功能(比如，文字处理、电子表格、会计)向IBM计算中心转移；人们将像使用电能一样，去使用从IBM的计算中心输送过来的软件功能；一个IBM巨型电脑中心能为数十家公司的数十万个员工服务，每个公司的成本都能大幅降低。到时候，商业公司能像省电一样地去节省个人电脑费用。那么，退出个人电脑市场的IBM不再继续驻守对自己来说已经没有战略意义的阵地，这又很类似于老子讲的：曲则全，洼则盈。

　　当今个人电脑市场里的领袖戴尔公司与众不同，它认为自己不需要什么应对策略。1990年它的年收入仅有5亿美元，经 2001年的320亿美元，抵达2006年的560亿美元。被戴尔公司斩杀的知名公司很多，比如，名噪一时的美国康柏公司因抵挡不住戴尔的低价压力而败下阵去。帮助戴尔成功的有效武器是其极为高效的运作模式——在互联网上直销低价格个人电脑。很多公司都想挑战戴尔的直销模式，但是均以失败告终。所以，戴尔公司很少有什么危机感，却总是能让别人怀有危机感。

　　不过，戴尔公司的优势最近被惠普和联想打破。为了扭转局面，戴尔照旧发动新一轮的价格战。大幅的价格下降确实像往常一样能使销售增长，但是这次销售只提高了6%，更令人沮丧的是，公司的运营利润却下跌了48%。这背后的原因并不神秘，以前，消费者对大幅降价敏感；现在，拥有了新偏爱的消费者对大幅降价变得不甚敏感。戴尔公司盲目崇拜低价格战略的效力，漠视了消费者心理的改变。这不正说明戴尔的竞争战略里缺乏的是辩证思维吗？老子曾说，水盛在器皿里太满就会溢出来，所以要适可而止；刀刃磨得太锋利就会折断，很难长久。虽说戴尔公司的刀刃并没有折断，但是被砍的东西却变得不惧怕它那把锋利的刀了。

　　总之，在市场竞争中，需要懂得进与退的辩证法。什么时机退，什么领域不竞争，要相机行事。有时往往需要以不争去躲避恶性竞争，用不争来获取战略优势。

作者：王奇珍

麦肯锡曾这样评价中国企业：成本优势的巨人却是成本管理上的侏儒。特别是人力成本方面，由于管理者与员工长期片面形成的“人本观念”，没有全面理解“以人 为本”的实质，从而进入了“人本陷阱”，造成人工成本呈现失控状态，最终导致企业利润与人工成本的失衡，也成了企业管理者最“头疼的顽疾”。

    其实，成本控制是一门花钱的艺术，而不是节约的艺术。关键是如何将每一分钱花得恰到好处，将企业的每一种资源用到最需要它的地方。如何控制好企业人工成本 呢？我们首先要从企业成本控制入手，把人工成本控制放入企业成本的“大局”之中来分析，如果只以“人工成本”论成本，必将起不到应有的效果。著名经济学家 吴敬琏说，“成本控制是一门花钱的艺术，而不是节约的艺术。以节约为成本控制基本理念的企业只是土财主式的企业，他们除了盘剥工人和在原材料上大打折扣以 外，没有什么过人之处。所以，我们需要学习现代企业应有的成本控制战略以及方法。”

    一、企业总体成本的控制

    第一步：明确战略目标

    方向正确等于成功了一半，成本控制也一样。实际上，企业降低成本的途径必须以提高(或不损坏)其竞争地位为指针。具体地说，如果某项成本措施削弱了企业的 战略地位，就应弃之不用；如果某项成本的增加有助于增加企业的竞争实力，这种成本的增加就是值得鼓励的。如果企业把成本作为战略来看待，那么成本管理就已 经不仅仅是财务部门的事情，更不仅仅是生产部门的事情，它应该是全方位、多角度、突破企业边界的成本管理体系。

    那么，如何确立这个体系呢？就是运用价值链分析手法，分析企业内部、所处行业以及竞争对手的价值链构成状况，从战略角度确定控制成本的基本方向。

    （一）、进行企业内部价值链分析

    对各个部门、各个环节进行了梳理，对每一个环节的成本与收益进行了细化。比如广告需要成本多少？创造价值多少？运输需要成本多少？创造价值多少？除此之外，还对管理部门、销售部门、采购部门等主要部门的成本与效益进行了梳理。

    （二）、进行行业价值链分析

    行业价值链是什么呢？简单地说，企业即存在于某一行业价值链的某个点，这一联系存在于行业内部为消费者提供某种最终产品或服务的相关企业之间。实际上， 上、下游与渠道企业的联接点都能够显著地影响企业成本，如供应商产品的包装能减少企业的搬运费用，而改善价值的纵向联系可以使企业与其上、下游和渠道企业 共同降低成本，提高整体竞争优势。

    （三）、分析竞争对手的价值链

    竞争对手的价值链和本企业价值链在行业价值链中处于平行位置，通过对竞争对手价值链的分析，可以测算出竞争对手的成本。然后，自己企业与之相比较，就找出了与竞争对手在作业活动上的差异，最终就可以确定扬长避短的策略，争取成本优势。

    第一，与对手差距不大的环节，提高起来成本较高，应保持其现有状态；第二，与对手差距较大的环节，加大投入；第三，有可能形成较大优势的环节，要加大投入，争取形成压倒性优势。

    这样，在对企业内外、横向、纵向的价值链进行深入分析的基础上，结合了企业的长远战略诉求，并根据所处产业竞争环境的变化，对价值链进行了适应性的重构。并在此基础上形成一份内容详实的成本控制计划。

    第二步：四步执行法

    执行成本控制计划可分四步走：

    第一，削减

    通常说来，在企业的整体因素基本确定的情况下，企业对成本的控制应该着眼于每项生产经营活动所产生的成本，这既包括企业为生产的产品所付出的作业劳动，同时也包括这一过程所消耗的资源。

    消除生产经营成本的第一个手段就是从减少非增值作业入手。一般情况下，企业的销售为增值作业。而大部分的仓储、搬运、检验以及供、产、销环节的等待与延误等，由于并未增加产出价值，为非增值作业，应减少直至消除。可以通过缩短采购时间和加快交货时间来解决。

    第二，明确各部门的成本任务

    公司最高层领导和财务总监应将企业的整体成本进行了详细的核算，将成本控制的压力分解到每个部门头上。

    在这方面，我们可以学习邯钢“模拟市场核算、倒推单元成本、实行成本否决、全员成本管理”的方法。具体做法就是实行成本倒推，测算出各项费用在每公斤成品 中的最高限额。然后横向分解落实到各部门，纵向分解落实到销售小组和个人，层层签订承包协议，并与奖惩挂钩，使责、权、利统一，最终在整个企业内形成纵横 交错的目标成本管理体系。由于成本控制计划极细，小到一张A4纸都要斤斤计较，为此，公司还应该专门组织一个督察小组，每天对每一个环节进行跟踪检查、记 录和打分。

    第三，精细化管理

    很多优秀的管理者都说过，没有数字进行衡量，就无从谈及节俭和控制。

    伴随着成本控制计划出台的是一份数字清单，包括各工种员工工资、电费、办公用品费、销售费用、油费、样品费等几十项费用。

    我们可以将费用分为可控费用(人事、水电、包装、耗材等)和不可控费用(固定资产折旧、采购、利息、销售费用等)。每星期、每月、每季度都由财务汇总后发 到管理者的手中，超支和异常的数据就用红色特别标识。在每周一的例会和每个月月底的总结会议中，相关部门需要对超支的部分做出解释。

    为了让员工养成成本意识，财务部还需要编写一本工作流程与成本控制手册。该手册从进货、电、水、印刷用品、劳保用品、电话、办公用品、设备和其他易耗品方 面提出控制成本的方法。但是，绝对不会机械地安排资金，有效地激励也是成本控制的好办法，成本控制奖励也可以成为员工工资的一部分。

    第四，成本管理的提前和延伸

    在制订成本控制计划时，还要树立“成本管理提前”的概念。在企业成本结构中，流程的前端与后端的成本比重逐步增加，所以成本管理不应停留在过程的耗费控制 方面，更应着眼于前端产品选择及采购的成本控制、后端的营销和顾客使用成本的控制以及跨组织的成本管理等方面，深入到企业的供应、营销及售后服务部门，超 越企业边界，相互协调地进行成本改进。从产品的选择设计开发开始，就要尽力设计满足目标成本要求、具有竞争力的产品，从源头上控制成本的发生。

    第三步：不可不察的细节

    当然，一个企业的成本控制远非几千字所能描述清楚的，以下是成本控制的一些细节，这也是在实践中可运用的部分技巧。

    细节一，现金折扣激励回款

    如果客户在30天内偿付货款，就给予2%的折扣；60天内付款，就给予1%的折扣；90天内付款，就须全数收取。采取折扣的方式鼓励销售回款。

    细节二，借助应收账款融资

    企业将应收账款出让给贷款者以筹措资金，企业可以在商品发运出去以前向贷款者申请借款。经贷款者同意，即可在商品发运以后将应收账款让售给贷款者。贷款者 根据发票金额，减去现金折扣、佣金以及主要用以冲抵销货退回和销货折扣等扣款后，将余额付给筹资企业。如摩托罗拉中国公司10亿元应收账款曾让中国工商银 行购买，这样的做法也可借鉴使用。

    细节三，年终返利打款激励

    借鉴空调业的操作技巧，生产企业的惯例是利用年终返利政策等来吸引经销商提前打款。至于企业提前打款的时间和奖励额度，一般根据企业产品情况和对资金需求 程度计算成本和收益，有些企业提前打款的时间和奖励额度(按打款额)分别为1～2个月为6‰、2～3个月为12‰、3～6个月为24‰。奖励以现金形式在 年终一次性给予奖励，对提前打款者优先供应货源。这种办法不仅解决了应收账款的问题，还起到融资的作用。

    细节四，延长应付账款期限

    赊账通常被视为现金的来源，因为只要你在一个比较合适的期限内延长付款时间，你就相当于有了一笔无利息的贷款。

    细节五，区分人工工资与人工成本

    两者的区别在于生产效率。杰克?韦尔奇认为：支付更高工资的同时，使人工成本最低是完全有可能的。即使工资在增长，但如果总体生产效率上升幅度大于工资增长，总人工成本相对总产值的比例也下降了。

    细节六，做好淡旺季的人资衔接

    企业一般用淡季裁员的方式来解决人工闲置，并充分利用《劳动法》相关规定中“以完成某项工作为期限”等依据，减少裁员所支付的补偿费用。

    细节七，消除人员重叠

    彻底清查公司各部门间是否职能相互重叠的现象，从而导致无谓的人力成本浪费。将相互重叠的职能整合起来，通过共享作业或服务来降低人力成本。

    细节八，循环取货

    学习上海通用的办法：上海通用的运货车每天早晨从厂家出发，到第一个供应商那里装上准备的原材料，然后到第二家、第三家，依次类推，直到装上所有的材料，然后再返回。这样做的好处是，省去了所有供应商空车返回的浪费，充分节约运输成本。

    细节九，转移库存

    对于那种季节性，特别是持续时间比较短暂的产品，在旺季来临时往往需要有大量的存货以应对骤增的销量，这就会对库存产生极大的压力，同时占用大笔的流动资 金。一个可以借鉴的解决办法就是：要求各经销商在旺季来临前，如果提前两个月提货付款，产品按原出厂价的70%计算；如果提前一个月提货付款，按原出厂价 的85%计算；如果到了旺季来时再提货，就必须按原出厂价的全价付款。这种办法只要折扣收益低于库存成本和资金成本，就有利可图，而且还一同解决了应收账 款的难题，加快了资金周转。

    细节十，不采购多余功能的设备

    如果通过分析，我们可以知道公司计算机在80%的时间里都是做文档处理工作，只有20%的时间才真正使用一台计算机的全部功能。公司可以采购低档次的PC机，为企业节约了大量投资。

    二、人工成本的控制

    人工成本的控制要通过人工成本指标的分析，建立企业人工成本分析的控制体系，即从人工成本的增长状态进行弹性控制，从人工成本的水平状态进行比率控制；同时加强企业人工成本控制的对策，寻求企业人工成本控制有效途径，以保证企业利润和职工收入实现“双赢”。

    （一）、人工成本范围及指标体系

    1、人工成本概念及范围

    人工成本是指企业在一定时期内，在生产、经营和提供劳务活动中因使用劳动力而支付的所有直接费用和间接费用的总和。

    按我国劳动部颁发的(1997)261号文件规定，人工成本范围包括：职工工资总额、社会保险费用、职工福利费用、职工教育经费、劳动保护费用、职工住房费用和其他人工成本支出。其中，职工工资总额是人工成本的主要组成部分。

    2、人工成本的指标体系

    常用的人工成本分析指标有三类：人工成本总量指标、人工成本结构指标、比率型指标。

    人工成本总量指标反映的是企业人工成本的总量水平。人工成本结构指标是指人工成本各组成项目占人工成本总额的比例，它可反映人工成本投入构成的情况与合理 性。人工成本分析比率型指标是进行企业人工成本分析控制常用的指标，是一组能够将人工成本与经济效益联系起来的相对数。

    （二）、企业人工成本的控制体系

    1、人工成本弹性控制思路

    企业人工成本的弹性控制体系是考察人工成本的增长状态，即从动态的角度通过对人均人工成本变动幅度分别与人均增加值、人均销售收入、人均总成本变动幅度的比值——即弹性的控制，把人工成本水平的提高控制在经济效益和投入产出水平所能允许的范围之内。

    2、人工成本水平状态控制思路

    企业人工成本的比率控制体系是从水平状态考察人工成本，即从分配水平的角度控制人工成本，旨在使企业在分配方面更好地兼顾个人、企业、国家三者的利益关 系，保证企业的持续、稳定发展。人工成本的水平状态主要是从人工成本的比率指标来考察的，以行业平均的劳动分配率、人事费用率、人工成本占总成本比重这三 个比率指标为参照，衡量企业与行业对应比率指标的偏差程度。显然，企业的这三项比率指标应当低于行业平均水平，且这三项比率指标都不能为负值，所以，计算 出的综合偏差率应大于0且小于等于1，符合这一条件的企业是人工成本比率控制较好的企业。反之则认为该企业在人工成本的比率方面失控了。

    （三）、企业人工成本管理的对策

    1、提高对加强人工成本管理的认识

    人工成本管理仍然是企业管理中的一个薄弱环节。提高对加强人工成本管理的认识问题，首先是从战略上，认识到它是关系企业多方位市场竞争中生死存亡的重要战 略因素；其次是从分配的角度，认识到它是正确处理企业、职工二者利益的重要经济杠杆，它是调节劳动者这个利益主体的经济行为，从而调节劳动力资源的配置， 形成企业的激励和动力机制的经济因素；第三是从管理上，认识到它是关系人才资源开发，关系企业经济效益的提高，关系到对活劳动消耗进行监督、投放的重要工 作。

    2、精减人员、合理定岗定编，控制劳动力的投入

    精减人员、合理定岗定编是加强用人管理的基础，也是节约活劳动、降低人工成本的基础工作。若企业冗员太多，必然造成人工成本投入不合理和人工成本的无效益增长，职工收入水平反而难以提高。

    3、加强人工成本的比率控制

    目前在比率控制方面存在着在低水平的基础上收入过分向个人倾斜的问题。例如，有的企业劳动分配率、人事费用率和人工成本占总成本比重都高于行业平均水平， 主要在于企业所创造的增加值中绝大部分用在了人工费用，而用于扩大再生产的积累所剩无几，明显存在收入过分向该企业职工倾斜的问题。

    加强比率控制的措施主要表现在：(1)控制标准的细化，细化到行业内各种不同类型企业，建立行业内各种类型企业的人工成本分析与控制体系，即以各类型企业 平均的劳动分配率、人事费用率、人工成本占总成本比重这三个比率指标为参照，来考察其所属企业与之对应比率指标的偏差率，从水平状态考察企业人工成本的比 率控制情况；(2)加强宏观调控，在政策上对比率控制采取相应的措施。对于比率控制较好的企业，在保持人工成本合理比例的基础上，允许适度地提高工资总 额，在增加职工收入、调动劳动者积极性的同时，保证利润目标的实现，提高企业的经济效益。这样，使企业人工成本的比率指标始终保持在有竞争力的水平之上， 既有利于人力资源的开发、利用，又能形成良性的经营循环。对于比率失控的企业，必须使其将过高的人工成本比率指标降下来，调整好人工成本与增加值、销售收 入、总成本的比例关系。加加强对工资总额的控制，建立工资的控制体系，将人工成本控制指标纳入对经营者目标责任制的考核内容，对人工成本双向失控的企业要 追究企业领导人的责任等等。

    4、加强人工成本的弹性控制

    加强弹性控制，保持人均人工成本增长低于人均增加值及人均销售收入的增长幅度，使人工成本与产出效益保持合理比例，这是人工成本控制的核心问题，也是人工 成本控制的最关键的预警线。人工成本是一种消耗要素，这种消耗的必要性必然是它为企业带来产出的大小，从企业资本经营的角度考察，人工成本决策的首要依据 是经济效益的高低，人工成本支出的阂限值必然是收益＞成本。

    为加强人工成本的弹性控制，在措施上可从以下四方面着手：

    （1）、建立企业人工成本弹性分析与控制体系，总结经验与教训，不断提高人工成本管理水平。弹性控制有效的企业要总结经验，在继续巩固已有成效的基础上， 通过人工成本的控制寻求进一步改进企业经营管理的途径。弹性控制好的企业，说明其对人工成本的投入带来了相应的产出效益，是增收增效的人工成本，既提高了 职工收入，调动了劳动者的积极性，又使企业的整体经济效益有了提高，有利于企业的生存和发展。人工成本弹性失控的企业要进行因素分析，寻找失控的具体原因 进行改进。有的企业在人均人工成本增长的同时，人均增加值、人均销售收入、人均总成本也有所增长，但增长幅度却低于人均人工成本的增长幅度。说明在对人工 成本投入的过程中，并未带来经济效益的同步增长，也就是人工成本的相对投入量过高，这就需要企业在今后的生产经营过程中，结合人工成本的弹性控制体系，采 取相应措施，进一步减少无效的人工成本消耗，以利于企业的生存和发展。

    （2）、从生产经营上找途径，增加产出，即增加值与销售收入的增长。例如开拓市场，扩大销售额，扩大高增加值产品的生产；加大科技投入，调整产品结构，采取有利的产品组合战略，增加产品中的科技含量等。

    （3）、加速转变经济增长方式，加强集约型经营，不断提高生产技术水平，提高劳动生产率，降低单位产品的人工消耗，降低产品的物耗成本，降低总成本，在少增加投入或不增加投入的基础上提高企业产销总量，增加利润总额。

    （4）、严格限制、减少无效消耗人工成本支出，减少冗员、堵塞漏洞，最大限度降低人力资源的无效损耗。

    5、发挥工资激励作用，规范人工成本结构

    在人工成本结构中，工资是最有激励作用的因素，也是构成人工成本的主要部分。可见，工资总额水平的控制以及各类人员工资水平合理拉开档次，充分体现按劳分配、效率优先的原则，是当前人工成本控制的关键性环节。

The goal of this paper is to provide an overview of technologies that can be applied to knowledge management and to assess their actual or potential contribution to the basic processes of knowledge creation and sharing within organizations. The aim is to identify trends and new developments that seem to be significant and to relate them to technology research in the field, rather than to provide a comprehensive review of available products.

Knowledge management (see, for example, Davenport and Prusak¹) is the name given to the set of systematic and disciplined actions that an organization can take to obtain the greatest value from the knowledge available to it. “Knowledge” in this context includes both the experience and understanding of the people in the organization and the information artifacts, such as documents and reports, available within the organization and in the world outside. Effective knowledge management typically requires an appropriate combination of organizational, social, and managerial initiatives along with, in many cases, deployment of appropriate technology. It is the technology and its applicability that is the focus of this paper.

To structure the discussion of technologies, it is helpful to classify the technologies by reference to the notions of tacit and explicit knowledge introduced by Polanyi in the 1950s^2,3 and used by Nonaka^4,5 to formulate a theory of organizational learning that focuses on the conversion of knowledge between tacit and explicit forms. Tacit knowledge is what the knower knows, which is derived from experience and embodies beliefs and values. Tacit knowledge is actionable knowledge, and therefore the most valuable. Furthermore, tacit knowledge is the most important basis for the generation of new knowledge, that is, according to Nonaka: “the key to knowledge creation lies in the mobilization and conversion of tacit knowledge.”⁵ Explicit knowledge is represented by some artifact, such as a document or a video, which has typically been created with the goal of communicating with another person. Both forms of knowledge are important for organizational effectiveness.⁶

These ideas lead us to focus on the processes by which knowledge is transformed between its tacit and explicit forms, as shown in Figure 1.⁵ Organizational learning takes place as individuals participate in these processes, since by doing so their knowledge is shared, articulated, and made available to others. Creation of new knowledge takes place through the processes of combination and internalization. As shown in Figure 1, the processes by which knowledge is transformed within and between forms usable by people are

• Socialization (tacit to tacit): Socialization includes the shared formation and communication of tacit knowledge between people, e.g., in meetings. Knowledge sharing is often done without ever producing explicit knowledge and, to be most effective, should take place between people who have a common culture and can work together effectively (see Davenport and Prusak,¹ p. 96). Thus tacit knowledge sharing is connected to ideas of communities and collaboration. A typical activity in which tacit knowledge sharing can take place is a team meeting during which experiences are described and discussed.
• Externalization (tacit to explicit): By its nature, tacit knowledge is difficult to convert into explicit knowledge. Through conceptualization, elicitation, and ultimately articulation, typically in collaboration with others, some proportion of a person's tacit knowledge may be captured in explicit form. Typical activities in which the conversion takes place are in dialog among team members, in responding to questions, or through the elicitation of stories.
• Combination: (explicit to explicit): Explicit knowledge can be shared in meetings, via documents, e-mails, etc., or through education and training. The use of technology to manage and search collections of explicit knowledge is well established. However, there is a further opportunity to foster knowledge creation, namely to enrich the collected information in some way, such as by reconfiguring it, so that it is more usable. An example is to use text classification to assign documents automatically to a subject schema. A typical activity here might be to put a document into a shared database.
• Internalization (explicit to tacit): In order to act on information, individuals have to understand and internalize it, which involves creating their own tacit knowledge. By reading documents, they can to some extent re-experience what others previously learned. By reading documents from many sources, they have the opportunity to create new knowledge by combining their existing tacit knowledge with the knowledge of others. However, this process is becoming more challenging because individuals have to deal with ever-larger amounts of information. A typical activity would be to read and study documents from a number of different databases.

Figure 1

These processes do not occur in isolation, but work together in different combinations in typical business situations. For example, knowledge creation results from interaction of persons and tacit and explicit knowledge. Through interaction with others, tacit knowledge is externalized and shared.⁷ Although individuals, such as employees, for example, experience each of these processes from a knowledge management and therefore an organizational perspective, the greatest value occurs from their combination since, as already noted, new knowledge is thereby created, disseminated, and internalized by other employees who can therefore act on it and thus form new experiences and tacit knowledge that can in turn be shared with others and so on.⁷ Since all the processes of Figure 1 are important, it seems likely that knowledge management solutions should support all of them, although we must recognize that the balance between them in a particular organization will depend on the knowledge management strategy used.⁸

Table 1 shows some examples of technologies that may be applied to facilitate the knowledge conversion processes of Figure 1. These technologies and others are discussed in this paper. The individual technologies are not in themselves knowledge management solutions. Instead, when brought to market they are typically embedded in a smaller number of solutions packages, each of which is designed to be adaptable to solve a range of business problems. Examples are portals, collaboration software, and distance learning software. Each of these can and does include several different technologies.

The approach to the technology of knowledge management in this paper emphasizes human knowledge. Sometimes in computer science “knowledge management” is interpreted to mean the acquisition and use of knowledge by computers, but that is not the meaning used here. In any case, automatic extraction of deep knowledge (i.e., in a form that captures the majority of the meaning) from documents is an elusive goal. Today the level of automatic extraction is deemed to be rather shallow because only a subset of the meaning, sometimes a very limited one, can be captured, ranging from recognition of entities such as proper names or noun phrases to automatic extraction of ontological relations of various kinds (e.g., References 9 and 10), and there is no system that can reason (in the sense of deducing something new from what it already knows) over the extracted knowledge in a way that even approaches the capabilities of a human. As an example of the current state of the art in applications for extracting knowledge automatically, Figure 2 shows a system¹¹ for analyzing reports of appellate court decisions to find the precedents they may affect. Court opinions are analyzed to find language that refers to other cases that the opinion may modify or invalidate. The candidate cases are retrieved from a database of law reports and are presented to an analyst for final judgment. The results are used to enrich the database with appropriate cross-references. Here the approach is that a template defines the fragment of knowledge to be sought, and the system tries to fill it by extracting information from the text. However, the candidate pieces of extracted knowledge must still be presented to a human for review and final decision, so that the value of the system is in increasing the productivity of the human analysts. For the foreseeable future, knowledge management in business will be about human knowledge in its various forms.

Figure 2

The use of technology in knowledge management is not new, and considerable experience has been built up by the early pioneers. Even before the availability of solutions such as Lotus Notes**¹² on which many contemporary knowledge management solutions are based, companies were deploying intranets, such as EPRINET,¹³ based on early generations of networking and computer technology that improved access to knowledge “on line.” Collaboration and knowledge sharing solutions also arose from the development of on-line conferencing and forums¹⁴ using mainframe computer technology. Today, of course, intranets and the Internet are ubiquitous, and we are rapidly approaching the situation where all the written information needed by a person to do his or her job is available on line. However, that is not to say that it can be used effectively with the tools currently available.

It is important to note that knowledge management problems can typically not be solved by the deployment of a technology solution alone. The greatest difficulty in knowledge management identified by the respondents in a survey¹⁵ was “changing people's behavior,” and the current biggest impediment to knowledge transfer was “culture.” Overcoming technological limitations was much less important. The role of technology is often to overcome barriers of time or space that otherwise would be the limiting factors. For example, a research organization divided among several laboratories in different countries needs a system that scientists with common interests can use to exchange information with each other without traveling, whereas a document management system can ensure that valuable explicit knowledge is preserved so that it can be consulted in the future. Two caveats must be stated at this point. First is the point made by Ackerman¹⁶ that in many respects the state of the art is such that many of the social aspects of work important in knowledge management cannot currently be addressed by technology. Ackerman refers to this situation as a “social technical gap.” Second, the coupling between behavior and technology is two-way: the introduction of technology may influence the way individuals work. People can and do adapt their way of working to take advantage of new tools as they become available, and this adaptation can produce new and more effective communication within teams (e.g., the effect of introducing solutions based on Lotus Notes on process teams in a paper mill described by Robinson et al.¹⁷ or the adaptations made by people in a customer support organization studied by Orlikowski¹⁸ after Notes was introduced).

Other surveys of technology for knowledge management can be found in the book, Working Knowledge by Davenport and Prusak,¹ and in a paper by Jackson.¹⁹ Prospects for using artificial intelligence (AI) techniques in knowledge management have been discussed recently by Smith and Farquhar.²⁰

In the following sections of this paper the technologies that support the processes of Figure 1 are described in more detail and illustrated with examples drawn largely from current research projects.

Tacit to tacit

The most typical way in which tacit knowledge is built and shared is in face-to-face meetings and shared experiences, often informal, in which information technology (IT) plays a minimal role. However, an increasing proportion of meetings and other interpersonal interactions use on-line tools known as groupware. These tools are used either to supplement conventional meetings, or in some cases to replace them. To what extent can these tools facilitate formulation and transfer of tacit knowledge?

Groupware. Groupware is a fairly broad category of application software that helps individuals to work together in groups or teams. Groupware can to some extent support all four of the facets of knowledge transformation. To examine the role of groupware in socialization we focus on two important aspects: shared experiences and trust.

Shared experiences are an important basis for the formation and sharing of tacit knowledge. Groupware provides a synthetic environment, often called a virtual space, within which participants can share certain kinds of experience; for example, they can conduct meetings, listen to presentations, have discussions, and share documents relevant to some task. Indeed, if a geographically dispersed team never meets face to face, the importance of shared experiences in virtual spaces is proportionally enhanced. An example of current groupware is Lotus Notes,¹² which facilitates the sharing of documents and discussions and allows various applications for sharing information and conducting asynchronous discussions to be built. Groupware might be thought to mainly facilitate the combination process, i.e., sharing of explicit knowledge. However, the selection and discussion of the explicit knowledge to some degree constitutes a shared experience.

A richer kind of shared experience can be provided by applications that support real-time on-line meetings—a more recent category of groupware. On-line meetings can include video and text-based conferencing, as well as synchronous communication and chat. Text-based chat is believed to be capable of supporting a group of people in knowledge sharing in a conversational mode.²¹ Commercial products of this type include Lotus Sametime** and Microsoft NetMeeting**. These products integrate both instant messaging and on-line meeting capabilities. Instant messaging is found to have properties between those of the personal meeting and the telephone: it is less intrusive than interrupting a person with a question but more effective than the telephone in broadcasting a query to a group and leaving it to be answered later.

In work on the Babble system,²² chat was evaluated by at least some users as being “… much more like conversation,” which is promising for the kind of dialog in which tacit knowledge might be formed and made explicit. However, not all on-line meeting systems have the properties of face-to-face meetings. For example, the videoconferencing system studied by Fish et al.²³ was judged by its users to be more like a video telephone than like a face-to-face meeting. Currently, rather than replacing face-to-face meetings, many on-line meetings are found to complement existing collaboration systems and the well-established phone conference and are therefore probably more suited to the exchange of explicit rather than tacit knowledge. On-line meetings extend phone conferences by allowing application screens to be viewed by the participants or by providing a shared whiteboard. An extension is for part of the meeting to take place in virtual reality with the participants represented by avatars.²⁴ One research direction is to integrate on-line meetings with classic groupware-like applications that support document sharing and asynchronous discussion. An example is the IBM-Boeing TeamSpace project,²⁵ which helps to manage both the artifacts of a project and the processes followed by the team. On-line meetings are recorded as artifacts and can be replayed within TeamSpace, thus allowing even individuals who were not present in the original meeting to share some aspects of the experience.

Some of the limitations of groupware for tacit knowledge formation and sharing have been highlighted by recent work on the closely related issue of the degree of trust established among the participants.²⁶ It was found that videoconferencing (at high resolution—not Internet video) was almost as good as face-to-face meetings, whereas audio conferencing was less effective and text chat least so. These results suggest that a new generation of videoconferencing might be helpful in the socialization process, at least in so far as it facilitates the building of trust. But even current groupware products have features that are found to be helpful in this regard. In particular, access control, which is a feature of most commercial products, enables access to the discussions to be restricted to the team members if appropriate, which has been shown²² to encourage frankness and build trust.

Another approach to tacit knowledge sharing is for a system to find persons with common interests, who are candidates to join a community. In Foner's Yenta System,²⁷ the similarity of the documents used by people allowed the system to infer that their interests were similar. Location of other people with similar interests is a function that can be added to personalization systems, the goal of which is to route incoming information to individuals interested in it. There are obvious privacy problems to overcome.

Expertise location. Suppose one's goal is not to find someone with common interests but to get advice from an expert who is willing to share his or her knowledge. Expertise location systems have the goal of suggesting the names of persons who have knowledge in a particular area. In their simplest form, such systems are search engines for individuals, but they are only as good as the evidence that they use to infer expertise. Some possible sources of such evidence are shown in Table 2.

The problem with using an explicit profile is that persons may not be motivated to keep it up to date, since to them it is just another form to fill in. Thus it is preferable to gather information automatically, if possible, from existing sources. For example, a person's resume or a list of the project teams that he or she has worked on may exist in a company database. Another automatic approach is to infer expertise from the contents of documents with which a person's name is associated. For example, authorship (creation or editing) of a document presumably indicates some familiarity with the subjects it discusses, whereas activities such as reading indicate some interest in the subject matter. Two approaches to using document evidence for expertise location suggest themselves: either the documents can be classified according to some schema, thus classifying their authors; or when a user submits a query to the expertise location system, it searches the documents, transforms the query to a list of authors (suitably weighted), and returns the list as the result of the expertise search.

The current state of the art is to use the first three sources of evidence listed in Table 2: explicit profiles, evidence mined from existing databases, and evidence inferred from association of persons and documents. For example, the Lotus Discovery Server** product contains a facility whereby an individual's expertise is determined using these techniques,²⁸ while it and the Tacit Knowledge Systems KnowledgeMail** product²⁹ analyze the e-mail a person writes to form a profile of his or her expertise. Given the properties of on-line discussions, discussed below, it is reasonable to suppose that a fourth source of evidence could be the content of the questions answered by a person in such a system, with the added advantage that such a person is already willing to be helpful. This example is a simple case of the social interaction dimension in expertise location which, as found in empirical studies (e.g., Reference 30), is an important factor but is not yet reflected in available applications, perhaps because of the difficulty of capturing aspects such as the expert's communication skills, in order to rate how useful he or she is likely to be.

Tacit to explicit

According to Nonaka, the conversion of tacit to explicit knowledge (externalization) involves forming a shared mental model, then articulating through dialog. Collaboration systems and other groupware (for example, specialized brainstorming applications³¹) can support this kind of interaction to some extent.

On-line discussion databases are another potential tool to capture tacit knowledge and to apply it to immediate problems. We have already noted that team members may share knowledge in groupware applications. To be most effective for externalization, the discussion should be such as to allow the formulation and sharing of metaphors and analogies, which probably requires a fairly informal and even freewheeling style. This style is more likely to be found in chat and other real-time interactions within teams.

Newsgroups and similar forums are open to all, unlike typical team discussions, and share some of the same characteristics in that questions can be posed and answered, but differ in that the participants are typically strangers. Nevertheless, it is found that many people who participate in newsgroups are willing to offer advice and assistance, presumably driven by a mixture of motivations including altruism, a wish to be seen as an expert, and the thanks and positive feedback contributed by the people they have helped.

Within organizations, few of the problems experienced on Internet newsgroups are found, such as flaming, personal abuse, and irrelevant postings. IBM's experience in this regard is described by Foulger.¹⁴ Figure 3 shows a typical exchange in an internal company forum, rendered here using a standard newsgroup browsing application. It illustrates how open discussion groups are used to contribute knowledge in response to a request for help. Note both the speed of response and the fact that the answerer has made other contributions previously. The archive of the forum becomes a repository of useful knowledge. Clearly the question answerer in this case has made a number of contributions and could be considered to be an expert. Although the exchange is superficially one of purely explicit knowledge, the expert must first make a judgment as to the nature of the problem and then as to the most likely solution, both of which bring his or her tacit knowledge into play. Once the knowledge is made explicit, persons with similar problems can find the solution by consulting the archive. A quantitative study³² of this phenomenon in the IBM system showed that the great majority of interchanges were of this question-and- answer pattern, and that even though a large fraction of questions were answered by just a few persons, an equal proportion were answered by persons who only answered one or two questions. Thus the conferencing facility enabled knowledge to be elicited from the broad community as well as from a few experts.

Figure 3

Explicit to explicit

There can be little doubt that the phase of knowledge transformation best supported by IT is combination, because it deals with explicit knowledge. We can distinguish the challenges of knowledge management from those of information management by bearing in mind that in knowledge management the conversion of explicit knowledge from and to tacit knowledge is always involved. This leads us to emphasize new factors as challenges that technology may be able to address.

Capturing knowledge. Once tacit knowledge has been conceptualized and articulated, thus converting it to explicit knowledge, capturing it in a persistent form as a report, an e-mail, a presentation, or a Web page makes it available to the rest of the organization. Technology already contributes to knowledge capture through the ubiquitous use of word processing, which generates electronic documents that are easy to share via the Web, e-mail, or a document management system. Capturing explicit knowledge in this way makes it available to a wider audience, and “improving knowledge capture” is a goal of many knowledge management projects. One issue in improving knowledge capture is that individuals may not be motivated to use the available tools to capture their knowledge. Technology may help by improving their motivation or by reducing the barriers to generating shareable electronic documents.

One way to motivate people to capture knowledge is to reward them for doing so. If rewards are to be linked to quality rather than quantity, some way to measure the quality of the output is needed. Quality in the abstract is extremely difficult to assess, since it depends on the potential use to which the document is to be put. For example, a document that explains basic concepts clearly would be useful for a novice but useless to someone who is already an expert. If we focus on usefulness as a measure of quality, and if we substitute “use” for “usefulness,” then we have something that IT systems can measure. In fact, portal infrastructures that mediate access to documents can easily accumulate metrics of document use, and hence can estimate usefulness and quality. The next generation of products will include such features.²⁸

Another measure of quality is the number of times a document has been cited, as in the scholarly literature, or the number of times it has been hyperlinked to, as on the Internet. A citation or hyperlink is evidence that the author of the citing or linking document thought that the target document is valuable. The most valuable or authoritative documents can be detected in Internet applications by analyzing the links between Web pages, thus measuring the cumulative effects of numerous value judgments (e.g., see References 33 and 34). The numeric quality estimate that can be derived is useful in information retrieval, where it can be used to boost the position of high-quality documents in the search results list. This method has been applied to citation analysis in scientific papers by the ResearchIndex search engine^35,36 and to Web search by the Google search engine.³⁷

Citation analysis of this kind detects quality assessments made in the course of authoring documents. Quality judgments by experts are another way to capture their knowledge. There are, of course, many deployed solutions in which documents undergo a quality review through a refereeing process, often facilitated by a workflow application. In this case, the quality judgment acts as a gate, and documents judged to be of low quality are not distributed. However, technology also makes it feasible to record judgments as annotations of existing documents.³⁸ Here, the association of an annotation with a document is recorded in some infrastructure, such as a special annotation server that the user's browser accesses to find annotations of the Web page being viewed. Numeric data stored in databases can also be annotated³⁹ to record various interpretations, judgments, or cautions. Annotations may also support collaboration around documents,⁴⁰ although, as in other applications where the underlying documents may be altered, the annotation system needs to be robust in the face of changes.

Although the most common way to capture knowledge by far is to write a document, technology has made the use of other forms of media feasible. Digital audio and video recordings are now easily made, and an expert may find that speaking to a camera or microphone is easier or more convenient than writing, particularly if the video is of a presentation that has to be made in the ordinary course of business, or if the audio recording can be made in an otherwise unproductive free moment. It is also now relatively easy to distribute audio and video over networks. However, nontext digital media have the disadvantage of being more difficult to search and to browse than text documents and, hence, are less usable as materials in a repository of knowledge. Browsing of video has been improved by summarization techniques that automatically produce a gallery of extracted still images, each of which represents a significant passage in the video.⁴¹ If the video is of someone giving a presentation, images of the speaker alone will not convey as much as a summary that includes images of any visual aids, such as slides or charts, that accompany the narrative. Several systems that key a recording of a presentation to the slides have been described.^42-44

Although video searching systems have been built that use image searching⁴⁵ of extracted frames,^46,47 they are hampered by the difficulty of composing a semantically meaningful image query. A more fruitful approach to searching is to extract text from the multimedia object, if possible. Although in some cases the video may contain text (on images of text slides), in most cases the challenge is to convert speech to text.

Speech recognition. Improvements in the accuracy of automatic speech recognition (ASR) hold out the promise of usable speaker-independent recognition with unconstrained vocabulary in the foreseeable future. Figure 4 shows progress with time in a number of standardized speech recognition tasks. Word error rates were reported in the Speech Recognition Workshop conferences of the National Institute of Standards and Technology. The accuracy varies with the difficulty of the task. The resource management task involves reading speech with a 1000-word vocabulary. Broadcast news uses recordings with an approximately 20K word vocabulary, whereas the CallHome and switchboard are telephone (lower speech quality) recognition tasks with unconstrained vocabulary. In all cases the accuracy shows steady improvement with time.

Figure 4

Accuracy for speech recorded under controlled conditions is already acceptable, but the error rate for poor quality recordings (for example, from the telephone) is still high enough to cause problems for applications unless the vocabulary is constrained. However, the trends depicted in Figure 4 show that future improvements can reasonably be expected and will lead to new ways to capture knowledge.

Although perfect or near-perfect transcription produces a text transcript that can be browsed like any other piece of text, ways to make an imperfect transcript usable as a browsing aid are being investigated.^48,49 In this work even an imperfect transcript supports browsing because certain words and phrases, which are judged to be significant and for which the estimated accuracy of ASR is high, are highlighted. Such techniques can be used to make the replay of audio more usable even where the transcript as a whole is unreadable because of the density of errors. The highlights can be used to find the passage of interest.

Search. The most important technology for the manipulation of explicit knowledge helps people with the most basic task of all: finding it. Since the trend in most organizations is for essentially all documents to become available in electronic form on line, the challenge of on-line access has been transformed into the challenge of finding the materials relevant for some task. Furthermore, the total amount of potentially relevant information, including what is on the Internet and company intranets and what is available from commercial on-line publishers, continues to grow rapidly. Thus text search, which only 10 years ago was a tool primarily used by librarians to search bibliographic databases, has become an everyday application used by almost everyone. Not surprisingly, the new uses of text search have motivated new work on the technology.

Another driving factor in the use of on-line explicit knowledge is the diversity of sources from which it is available. It is not uncommon for users to have to look in several databases or Web sites for potentially relevant information. Since there is little standardization, users have to cope with different user interfaces, different search language conventions, and different result list presentations. Portals—described in another paper in this issue⁵⁰—are a popular approach to reducing the complexity of the user's task. The key aspect that allows a portal to do this is that it maintains its own meta-data about the information to which it gives access. In the current state of the art, the meta-data may be quite simple, consisting of a list of sources and a search index formed from the content of the sources. Even this simple function provides great value because it relieves the user of the need to visit all the sources to find out whether they contain relevant information. The user is therefore made more productive, and the quality of his or her work is improved. Most portal systems use a single search index, which requires that the documents in the domain of interest have to be retrieved by “spidering” or “crawling” at indexing time. The alternative, using distributed search as in, for example, the Harvest project,⁵¹ has not proved to be popular for knowledge management applications, perhaps because advances in hardware have made it cheaper to build a central index. Recent developments in peer-to-peer applications, such as Gnutella⁵² and the collaboration application Groove,⁵³ have promoted a new interest in distributed search, which may lead to new advances.

The index that is built by a text search engine consists of a list of the words that occur in the indexed documents, along with a data structure (the inverted file) that allows the documents in which the words occurred to be determined efficiently at search time.⁵⁴ Users can therefore use query words that they expect to occur in the documents. The problem is that not all the documents will use the same words to refer to the same concept and, therefore, not all the documents that discuss the concept will be retrieved. In a world of information overload this situation is not usually a problem, but for applications where it is important to have high recall, an alternative approach can be used in which documents are assigned meta-data that describe the concepts they discuss in a controlled vocabulary. This is a classical approach used in bibliographic databases. However, where searches are being done by untrained end users rather than librarians, the evidence is that searching with natural language gives better results than does searching with a controlled vocabulary.⁵⁵

The most common problem in a search is that a query retrieves many documents that are irrelevant to the user's needs, known as the problem of search precision (a measure of accuracy). Precision is of paramount importance in a world of “info-glut.” However, results from TREC (Text REtrieval Conference)⁵⁶ indicate that the accuracy of natural language search engine technology has reached a plateau in recent years. What are the prospects of improvements to the search function that will benefit knowledge management systems? Two areas of potential improvement can be identified: increased knowledge of the user and of the context of his or her information need, and improved knowledge of the domain being searched.

The notion that increased knowledge of the user can be beneficial comes from the realization that in almost all search systems today the only information about the user's information need that is available to the system is the query. The most common query submitted to Web-based search services is two words, and the average query length is only about 2.3 words.⁵⁷ Obviously, this amount is not much information. A challenging research area is to gather better information about the context of a search and to build search engines that can use this information to good advantage.

The goal of gathering and using more information about the domain being searched is one that is well-established, but progress so far has been limited. It is common to use a thesaurus—a kind of simple domain model—as an adjunct to a search, although this is more common in systems designed for specialists. Expansion of a query with synonyms is known to improve the recall in a text search, but expansion is only effective in well-defined domains where the ambiguity of words, and the validity of term relationships, is not an issue. To improve precision in broad-domain searching by reducing the ambiguity of ordinary words using thesauri or other structures such as ontologies has been a goal of much research, with many negative results (e.g., Reference 58). Recently, however, some encouraging findings have been obtained.⁵⁴ Using WordNet⁵⁹ (a large manually built thesaurus that is widely available), combined with automatically built data structures encoding co-occurrence and head-modifier relations, Mandala et al.⁶⁰ showed significant improvements in average precision, a measure of accuracy, as shown in Figure 5. The results were obtained using TREC data, from queries derived from the search topics using the title field, the title and description fields, or all the fields in the topic. Woods et al.⁶¹ also reported improvements by using a different approach to encoding knowledge of the domain, in this case a semantic network that integrated syntactic, semantic, and morphological relationships.

Figure 5

Taxonomies and document classification. Knowledge of a domain can also be encoded as a “knowledge map,” or “taxonomy,” i.e., a hierarchically organized set of categories. The relationships within the hierarchy can be of different kinds, depending on the application, and a typical taxonomy includes several different kinds of relations. The value of a taxonomy is twofold. First, it allows a user to navigate to documents of interest without doing a search (in practice, a combination of the two strategies is often used if it is available). Second, a knowledge map allows documents to be put in a context, which helps users to assess their applicability to the task in hand. The most familiar example of a taxonomy is Yahoo!,⁶² but there are many examples of specialized taxonomies used at other sites and in company intranet applications.

Manually assigning documents to the categories in a taxonomy requires significant effort and cost, but in recent years automatic document classification has advanced to the point where the accuracy of the best-performing algorithms exceeds 85 percent (F₁ measure) on good quality data.⁶³ This degree of accuracy is adequate for many applications and is in fact comparable to what can be achieved by manual classifiers in a well-organized operation,⁶⁴ although the accuracy of automatic classification over different types of data varies quite widely.⁶⁵ An attractive feature of the current generation of automatic classifiers is their inclusion of machine-learning algorithms that train themselves from example data, whereas the previous generation required construction of a complex description of the category in the form, for example, of an elaborate query. Selecting documents as training examples is a simpler task.

Automatic classification, although simple in concept, is capable of surprisingly refined distinctions, given enough training data. For example, it has been known for some time (see the brief review in Kukich⁶⁶) that automatic essay marking systems can assign grades to student essays with an accuracy and consistency only slightly worse than human graders, and recently it has been shown that a document classifier can perform well in this application.⁶⁷