工业设计


Design Without Designers
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原文:

Design Without Designers
I will always remember my first introduction to the power of good product design. I was newly arrived at Apple, still learning the ways of business, when I was visited by a member of Apple's Industrial Design team. He showed me a foam mockup of a proposed product. "Wow," I said, "I want one! What is it?" That experience brought home the power of design: I was excited and enthusiastic even before I knew what it was. This type of visceral "wow" response requires creative designers. It is subjective, personal. Uh oh, this is not what engineers like to hear. If you can't put a number to it, it's not important. As a result, there is a trend to eliminate designers. Who needs them when we can simply test our way to success? The excitement of powerful, captivating design is defined as irrelevant. Worse, the nature of design is in danger. Don't believe me? Consider Google. In a well-publicized move, a senior designer at Google recently quit, stating that Google had no interest in or understanding of design. Google, it seems, relies primarily upon test results, not human skill or judgment. Want to know whether a design is effective? Try it out. Google can quickly submit samples to millions of people in well-controlled trials, pitting one design against another, selecting the winner based upon number of clicks, or sales, or whatever objective measure they wish. Which color of blue is best? Test. Item placement? Test. Web page layout? Test. This procedure is hardly unique to Google. Amazon.com has long followed this practice. Years ago I was proudly informed that they no longer have debates about which design is best: they simply test them and use the data to decide. And this, of course, is the approach used by the human-centered iterative design approach: prototype, test, revise. Is this the future of design? Certainly there are many who believe so. This is a hot topic on the talk and seminar circuit. After all, the proponents ask reasonably, who could object to making decisions based upon data?

Two Types of Innovation: Incremental Improvements and New Concepts In design—and almost all innovation, for that matter—there are at least two distinct forms. One is incremental improvement. In the manufacturing of products, companies assume that unit costs will continually decrease through continual, incremental improvements. A steady chain of incremental innovation enhances operations, the sourcing of parts and supply-chain management. The product design is continually tinkered with, adjusting the interface, adding new features, changing small things here and there. New products are announced yearly that are simply small modifications to the existing platform by a different constellation of features. Sometimes features are removed to enable a new, low-cost line. Sometimes features are enhanced or added. In incremental improvement, the basic platform is unchanged. Incremental design and innovation is less glamorous than the development of new concepts and ideas, but it is both far more frequent and far more important. Most of these innovations are small, but most are quite successful. This is what companies call "their cash cow": a product line that requires very little new development cost while being profitable year after year. The second form of design is what is generally taught in design, engineering and MBA courses on "breakthrough product innovation." Here is where new concepts get invented, new products defined, and new businesses formed. This is the fun part of innovation. As a result, it is the arena that most designers and inventors wish to inhabit. But the risks are great: most new innovations fail. Successful innovations can take decades to become accepted. As a result, the people who create the innovation are not necessarily the people who profit from it. In my Apple example, the designers were devising a new conception. In the case of Google and Amazon, the companies are practicing incremental enhancement. They are two different activities. Note that the Apple product, like most new innovations, failed. Why? I return to this example later. Both forms of innovation are necessary. The fight over data-driven design is misleading in that it uses the power of one method to deny the importance of the second. Data-driven design through testing is indeed effective at improving existing products. But where did the idea for the product come from in the first place? From someone's creative mind. Testing is effective at enhancing an idea, but creative designers and inventors are required to come up with the idea.

Why Testing Is Both Essential and Incomplete Data-driven design is "hill-climbing," a well-known algorithm for optimization. Imagine standing in the dark in an unknown, hilly terrain. How do you get to the top of the hill when you can't see? Test the immediate surroundings to determine which direction goes up the most steeply and take a step that way. Repeat until every direction leads to a lower level.

But what if the terrain has many hills? How would you know whether you are on the highest? Answer: you can't know. This is called the "local maximum" problem: you can't tell if you are on highest hill (a global maximum) or just at the top of a small one. When a computer does hill climbing on a mathematical space, it tries to avoid the problem of local maxima by initiating climbs from numerous, different parts of the space being explored, selecting the highest of the separate attempts. This doesn't guarantee the very highest peak, but it can avoid being stuck on a low-ranking one. This strategy is seldom available to a designer: it is difficult enough to come up with a single starting point, let alone multiple, different ones. So, refinement through testing in the world of design is usually only capable of reaching the local maximum. Is there a far better solution (that is, is there a different hill which yields far superior results)? Testing will never tell us. Here is where creative people come in. Breakthroughs occur when a person restructures the problem, thereby recognizing that one is exploring the wrong space. This is the creative side of design and invention. Incremental enhancements will not get us there.

Barriers to Great Innovation Dramatic new innovation has some fundamental characteristics that make it inappropriate for judgment through testing. People resist novelty. Behavior tends to be conservative. New technologies and new methods of doing things usually take decades to be accepted - sometimes multiple decades. But the testing methods all assume that one can make a change, try it out, and immediately determine if it is better than what is currently available. There is no known way to tell if a radical new idea will eventually be successful. Here is where great leadership and courage is required. History tells us of many people who persevered for long periods in the face of repeated rejection before their idea was accepted, often to the point that after success, people could not imagine how they got along without it before. History also tells us of many people who persevered yet never were able to succeed. It is proper to be skeptical of radical new ideas. In the early years of an idea, it might not be accepted because the technology isn't ready, or because there is a lot more optimization still to be done, or because the audience isn't ready. Or because it is a bad idea. It is difficult to determine which of those reasons dominates. The task only becomes easy in hindsight, long after it becomes established. These long periods between formation and initial implementation of a novel idea and its eventual determination of success or failure in the marketplace is what defeats those who wish to use evidence as a decision criterion for following a new direction.

Even if a superior way of doing something has been found, the automated test process will probably reject it, not because the idea is inferior, but because it cannot wait decades for the answer. Those who look only at test results will miss the large payoff. Of course there are sound business reasons why ignoring potentially superior approaches might be a wise decision. After all, if the audience is not ready for the new approach, it would initially fail in the marketplace. That is true, in the short run. But to prosper in the future, the best approach would be to develop and commercialize the new idea to get marketplace experience, to begin the optimization process, and to develop the customer base. At the same time one is preparing the company for the day when the method takes off. Sure, keep doing the old, but get ready for the new. If the company fails to recognize the newly emerging method, its competitors will take over. Quite often these competitors will be a startup that existing companies ignored because what they were doing was not well accepted, and in any event did not appear to challenge the existing business: see "The innovator's dilemma." Gestural, multi-touch interfaces for screen-driven devices and computer games are good examples. Are these a brilliant new innovation? Brilliant? Yes. New? Absolutely not. Multi-touch devices were in research labs for almost three decades before the first successful mass-produced products. I saw gestures demonstrated over two decades ago. New ideas take considerable time to reach success in the marketplace. If an idea is commercialized too soon, the result is usually failure (and a large loss of money). This is precisely what the Apple designer of my opening paragraph had done. What I was shown was a portable computer designed for schoolchildren with a form factor unlike anything I had ever seen before. It was wonderful, and even to my normally critical eye, it looked like a perfect fit for the purpose and audience. Alas, the product got caught in a political fight between warring Apple divisions. Although it was eventually released into the marketplace, the fight crippled its integrity and it was badly executed, badly supported, and badly marketed. The resistance of a company to new innovations is well founded. It is expensive to develop a new product line with unknown profitability. Moreover, existing product divisions will be concerned that the new product will disrupt existing sales (this is called "cannibalization"). These fears are often correct. This is a classic case of what is good for the company being bad for an existing division, which means bad for the promotion and reward opportunities for the existing division. Is it a wonder companies resist? The data clearly show that although a few new innovations are dramatically successful, most fail, often at great expense. It is no wonder that companies are hesitant - resistant - to innovation no matter what their press releases and annual reports claim. To be conservative is to be sensible.

The Future Automated data-driven processes will slowly make more and more inroads into the space now occupied by human designers. New approaches to computer-generated creativity such as genetic algorithms, knowledge-intensive systems, and others will start taking over the creative aspect of design. This is happening in many other fields, whether it be medical diagnosis or engineering design. We will get more design without designers, but primarily of the enhancement, refinement, and optimization of existing concepts. Even where new creative artificial systems are developed, whether by neural networks, genetic algorithms, or some yet undiscovered method, any new concept will still face the hurdle of overcoming the slow adoption rate of people and of overcoming the complex psychological, social, and political needs of people. To do this, we need creative designers, creative business people, and risk takers willing to push the boundaries. New ideas will be resisted. Great innovations will come at the cost of multiple great failures. Design without designers? Those who dislike the ambiguity and uncertainty of human judgments, with its uncertain track record and contradictory statements will try to abolish the human element in favor of the certainty that numbers and data appear to offer. But those who want the big gains that creative judgment can produce will follow their own judgment. The first case will bring about the small, continual improvements that have contributed greatly to the increased productivity and lowering of costs of our technologies. The second case will be rewarded with great failures and occasional great success. But those great successes will transform the world.

译文 不需要设计师的设计
唐·诺曼

我永远也不会忘记我第一次向人们介绍优秀产品设计的魅力的经历, 那时候 我刚刚到苹果公司,还在逐渐的学习工作上的事务。有一个苹果工业设计小组的 成员来我这里,向我展示了一个即将推出的产品的泡沫模型, “喔!”我说, “这 是什么?我也想要个!” 那次经历让我体验到了设计的原始力量: 当我还不知道他具体是什么之前我

就已经兴奋不已,充满热情了。这种发自肺腑的回应离不开很有创意的设计师。 这种想法很主观,也很有个人感情色彩。哦,不过工程师们可不愿意听到这些。 如果你不能提供和它有关的数据,它就没什么了不起。这样的结果是有一种不再 需要设计师的趋势。当我们可以简单的测试我们的成功之路时,谁还需要设计师 呢?令人充满激情兴奋无比的设计被看得无足轻重。 更严重的是设计的初衷也岌 岌可危了。 不相信吧?看看谷歌。最近谷歌的一位高级设计师有一次在公开场合宣称, 他们对设计不感兴趣也不懂设计。据说,谷歌依靠最原始的测试结果而不是人类 技巧和判断。怎么知道一个设计是否成功呢?测试一下就可以了。谷歌会迅速地 把样品发送给对照试验中数以万计的用户,与其他的设计做个对比,然后选出优 胜者。他们可以靠点击量,销售量以及其他任何他们想要采用的客观依据。什么 颜色的制服最好?测试一下;哪种项目布置最合理?测试一下;哪种网页排版最 好呢?测试一下。 这可不是谷歌的专利,亚马逊早就也这么做了。几年前我很荣幸的被告知它 们不再为哪个设计最好而争论不休了, 他们会测试一下然后用数据来决定。 当然, 这个也是以人为本的迭代设计法采用的途径:原型,试验和修改。 这是设计的未来吗?有很多人会真么认为。 这是一个人们谈论和研究交流的 热门话题,毕竟,支持者也有理有据:谁不想靠数据来做决定?
两种类型的创新: 两种类型的创新:不断改善和全新的概念

在设计和几乎所有改革中,其实都至少有两种不同的类型。第一种是持续改进现 有产品, 在产品制造业中企业认为通过不断地改善和优化单位成本也会持续的降 低。不断改善的带来稳定的利益链条又强化了操作,资源部门和产业链管理。产 品的设计并没有停止, 改变一下外表, 增加一些新的功能, 不时的做些小的改动。 新的产品都是对现有平台很小的改动,每年都宣称有了与众不同的特征。有时候 一些功能被去掉以用来支持一条新的,低成本的生产线,有时候很多功能又被组 合或被添加上。产品不断地改善,但基础的平台一直没有改变。持续的设计和改 进可没有开发新概念或新理念那样的引人瞩目,但是它们很常见也很重要。很多 这样的创新都是小规模的, 但大多数都很成功。 这就是企业们所说的 “摇钱树” : 一条只需要很小改进的生产线,但是却可以年复一年的有利可图。 第二种类型的设计就是在设计,工程和 MBA 课程中经常谈论到的“有突破性的 创新设计”。这里提出了全新的概念,新颖的产品定义和新型的商业模式,而且 这些正是设计的乐趣所在。因此,这也是大多数的设计师和发明家乐意为之的地 方。但是风险也很大:绝大多数的新发明都以失败告终。那些成功的设计发明往 往需要数十年才得到了人们的认可, 这样的后果就是发明者不一定就是以它们获 利的人。 在我刚才提到的苹果公司的例子中,设计者正在开发一种新概念产品。在谷 歌和亚马逊的例子中,这些公司在不断地实践着不断的优化。它们是两种不同的 行为,看看苹果的产品,像大多数的新发明设计一样失败了。为什么呢?我一会 儿再回到这个案例中。 这两种设计都是很有必要的。对数据主导型设计的激烈争论误导了人们,我 们用前者的力量否定了后者的重要性。 通过测试数据主导型设计对改进现有的产 品很有效果。但是新产品最初的观念有从何而来?一些人创造性的想法。测试可 以高效的优化一个想法,但是创造性的设计者和发明家却需要有自己的想法。
为什么说测试既很有必要又不太完美

数据主导型的设计就是“爬山策略”,我们熟知的一种追求最优化的算法。 假设你在黑夜里站在一个连绵起伏的山坡上,你什么也看不到,你怎么知道你就 站在山坡的最高处?检验一下你周围的环境,判断哪个方向最陡峭,然后向这个 方向迈进。这样不断的重复而知道每个方向就找到了最低的地方。 但是如果山坡上有很多的山峰又该怎么做呢?你怎么知道你是否已经在最 高的地方了?答案是你会不知道。这就是所谓的“局部最大值”问题:你不能区 分你是在最高处呢还是只在一个小山坡的最高点。 当计算机在数学空间里攀登时, 它可以通过无数次的尝试来探索不同的空间 以避免局部最大化的难题。虽然这不能保证可以找到真正的最高点,但至少可以 避免掉入低层次的行列中。对设计师来说这种战略几乎毫无用处。解决一个单一 的起点就够困难了,更不用说错综复杂的问题了。通过测试了改良设计通常能够 达到局部的最大利益。还有更好的解决办法吗(就是说,有没有受益大于测试结 果的情况)?测试不能告诉我们。 这时候就得靠有创意的人了,他对问题的重新组合,于是就决定去看似错误 的地方探索一下,新的突破就是这样产生的。这正是设计发明创造性的一面,不 断地改良和完善不能让我们拥有这样的效果。
伟大发明的障碍

激动人心的新发明往往有一些基本的特点让它们不适应由测试所做出的判 断。人们往往也不太喜欢猎奇,行动也很保守。新的科技发明和方法往往经过数 十年或者更长才逐渐被人们认可接受。 但是测试的法子都是假设某个东西很有前 途值得一试,并来判断它是否比正在使用的更好。我们没有现成的方法判断一个 十分新奇的想法会获得成功,这就需要出色的领导和鼓励。历史告诉我们很多在 他们的想法被认可以前面临长期不断的抨击的人们获得成功以后就是这样, 没有 它以前,人们不知道是怎么如何度过的。历史同样也告诉我们还有很多人坚持不 懈最终也没有成功。对疯狂想法的怀疑是可以理解的。在一个想法的最初阶段它 没有被人们接受很可能是因为技术还不太成熟, 还需要很多的改善优化也可能是 因为消费者还没有准备好。或者说它本来就是个坏主意,很难确定这是哪种原因 决定的。在它实现很长时间以后,这才会变得可以预见。 一个想法最初形成实施到最终在商业上的成败之间的漫长时间被当做是战胜 那些想把其作为展开新方向研究判断标准的人的武器。 即使做某件事比较好的方 法已经找到了,自动测试程序也会拒绝它,不是因为它不好,而是我们不能为了 这个答案等数十年。那些只看测试结果的人们将会失去丰厚的回报。当然,这也 有为什么忽略潜在的新做法也可能是很明智的决定的合理商业因素。 毕竟如果受 众还没有为新的产品做好准备, 它在市场上肯定会失败。 从短期看, 这是正确的。 但是为了长期的繁荣发展,最好的做法是开发和商业化新的想法去获得市场经 验,在开始优化过程和发展客户群。与此同时,还有一些在为公司做准备等待时 机成熟。就是这样,做着旧的产品但是在为新产品做准备。如果一个公司没能够 掌握不断涌现的新方法,那么它的竞争者就会接管过去。这些公司往往成为新兴 的公司,现有的公司没有注意到它们,因为它们所做的东西还没有被广泛接受, 它们无论如何也不会对现有的业务构成危险。明白了创造者所处的窘境了吧。 电脑游戏所用的触摸屏和手势控制屏就是个很的案例。这是个很优秀的新发 明吧?当然!新的?绝对不是。触摸设置在首次作为成功大批量生产的产品之前 在实验室里已经研究了 30 多年了。手势控制的示范我 20 多年前就见过了。新的 想法需要相当长的时间才可以在市场上获得成功。如果太早的商业化,其通常以

失败告终(还会有巨大的经济损失)。 这就是我开头时说的那个苹果设计师做的事情。 他向我展示的是一个专门为 学龄儿童设计的电脑,有一些我从未见过的特征。它真的很好,虽然我的眼光很 挑剔。它很看起来很符合它的设计意图和受众。在阿拉斯加,这个产品在引起了 决策层的激烈争论。尽管它最终被决定投放市场,但论战影响了它的整体运作, 没有好好执行,没有得到很好的支持,也没有很好的被市场化。 一个企业对全新的创新的抵制很容易建立, 为一个不知道利润如何的产品建 立一条新的生产线代价很大。还有,由于新产品会打乱现有的产品销售,现有的 产品部门也会受到影响,这就是所谓的“品牌替换” 。这些担心是正确的,这 是一个很典型的情况,对公司有益的产品会不利于现有的部门,这也意味着不利 于现有产品的提升和投资回报的机会。 这是不是很纠结的企业坚守?数据很清楚 的告诉我们只有极少的创新确实很成功,大多数都失败了,付出了高昂的代价。 无论是它们没有什么压力还是年度报告危言耸听,很多公司还是犹犹豫豫,抵制 创新,这并不奇怪。保守点往往是很明智的。
未来

自动数据驱动的设计过程会慢慢得为设计师控制的领域带来越来越多的危害。 新 的计算机控制创造力的趋势,例如遗传算法,集成化系统,以及其他的未知的方 法, 将取代设计中的创新形态。 这发生很多领域, 不论是医疗诊断还是工程设计。 我们将会有更多的不需要设计师的设计,但这主要是巩固,改进和优化现有 的概念。即使新的创新人工系统很发达,无论是通过神经网络还是基因算法或者 其他未知的方法, 任何新概念都必须面对克服人们缓慢的适应性和人们复杂的心 理,社会,实际需求的障碍。为了解决这样的问题,我们需要有创意的设计师, 追求创新的商业人士和愿意去冲破藩篱的冒险者。新的理念会被拒绝,伟大的创 新的代价就是伟大的失败。 没有设计师的设计,那些人不喜欢人类判断的含糊性和不确定性,他们试图 用测试的踪迹不定的记录和相对的稳定性来废除与确定性相关的人类因素, 而由 数字和数据来提供。 但是那些认为可以自己可以做出创造性判断的人们想获得更 大的收获,他们听从自己的判断。前者可以带来细微的,持续的改进,这可以使 我们提高生产效率和降低技术成本;后者会带来惨烈的失败和偶然的巨大成功, 但是这些伟大的成功将改变我们的世界。



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