在接下来的三年中，市场上赢家或输家的公司将取决于他们DAAAB的水平。

Over the next three years, the companies that are winners or losers in their markets will be determined by how well they DAAAB.

什么是DAAAB？

我称之为重塑全球供应链的强大新技术和管理工具的集合。DAAAB代表深度学习、人工智能、算法、分析——所有这些都是最终产生业务结果的数字转换的基本元素。

That is what I call the collection of powerful new technology and management tools reshaping global supply chains. DAAAB stands for Deep Learning, Artificial Intelligence, Algorithms, Analytics—all the essential elements for a digital transformation that will ultimately produce Business Results.

DAAAB代表了当今商业中一些最令人兴奋的工具，因为它们帮助公司为可见性和敏捷性创造了新的机会。公司可以使用DAAAB将其供应链操作与客户、供应商、供应商的供应商以及客户的客户紧密联系起来。DAAAB关注的是数据，但公司需要掌握的是技术和管理技能的结合，才能利用数据的巨大力量来改善业务运营。

DAAAB represents some of the most exciting tools in business today because they help companies create new opportunities for visibility and agility. Companies can use DAAAB to tightly link their supply chain operations to their customers—and their suppliers, and their supplier’s supplier, as well as their customer’s customer. DAAAB is all about the data, but it is the combination of technology and management skills that companies need to master to harness the tremendous power of data to improve business operations.

以下是DAAAB关键元素的一些简单定义:

Here are some straightforward definitions of the key elements of DAAAB:

*深度学习是一种能够(基于神经网络)计算关系的软件。

*人工智能(AI)将商业规则应用到问题上，每天都变得越来越聪明、越来越好。

*分析是关于弄清楚数据意味着什么，以及一个数据点如何影响另一个数据点。

*算法是驱动业务操作的规则或公式集。

*业务结果是在收入、成本和客户满意度方面的实实在在的改善。

*Deep learning is the software that figures out relationships (based on neural networks).

*AI (Artificial Intelligence) applies business rules to problems and gets smarter and better every day.

*Analytics is about figuring out what the data means and how one data point influences another.

*Algorithms are sets of rules or formulas that drive business operations.

*Business results are hard-number improvements in revenue, cost, and customer happiness.

看看今天占主导地位的数字原住民吧。亚马逊(Amazon)、谷歌和Facebook等公司位于其市场的顶端，它们利用DAAAB来创造巨大的股东价值，从中大赚一笔。

Look at today’s dominant digital natives. Companies like Amazon, Google, and Facebook sit at the top of their markets and are making a fortune deploying DAAAB to create huge shareholder value.

然而，很少有企业在积极管理DAAAB。他们才刚刚开始进行深度学习的实验。每个商业领袖(也许不是每个人)都在谈论人工智能和分析。高管们正在使用“算法”这个术语，但他们有开发和部署算法的流程吗?这很值得怀疑。很少有公司真正使用这些技术实现了解决方案并改进了业务结果。许多公司聘请了数据科学家，升级了电脑和网络，并致力于做出更多基于数据的决策。一些公司正在取得切实的进展，但大多数公司正在进行更有限或受控的试验。

Yet few businesses are actively managing DAAAB. They have only begun to experiment with Deep Learning. Every business leader (well, maybe not everyone) is talking about AI and Analytics. Executives are using the term algorithm, but do they have a process for developing and deploying them? That’s doubtful. Few companies have truly implemented a solution using these technologies and improved the results of their business. Many have hired data scientists, upgraded their computers and networks and committed to making more data-based decisions. Some companies are making tangible progress, but most are conducting more limited or controlled experiments.

你和你的公司如何利用DAAAB来获得结果?

How can you and your company use DAAAB to get results?

我有机会与Wes Nichols在Santiago举行的行政领导论坛(Executive Leadership Forum)上共度一段时间。Wes是全球最大的CMO分析软件解决方案MarketShare的联合创始人兼首席执行官。Wes 曾与许多大公司合作，采用数据驱动的决策、分析和人工智能解决方案，从连续创业者转变为独立董事和投资者。他了解DAAAB的所有要素，并帮助企业改善业务。Wes 告诉我:“我一直在与世界各地与DAAAB相关部署的公司合作，几乎所有的公司都低估了它们内部数据的力量，同时高估了自己开发的分析和算法的质量。”

I had a chance to spend some time with Wes Nichols at our recent Executive Leadership Forum in Santiago. Wes is co-founder and CEO of MarketShare, the world’s largest analytics software solution for CMOs. Wes has worked with many of the largest companies to adopt data-driven decision-making, analytics and AI solutions as a serial entrepreneur turned independent board director and investor. He understands all the elements of DAAAB and has helped companies improve their business. Wes told me: “I have been working with companies on DAAAB-related deployments around the world, and virtually all companies are underestimating the power of the data they have in-house, while at the same time overestimating the quality of their home-grown analytics and algorithms.”

我敢打赌你的公司有价值的数据没有被充分利用，你需要的是目前没有的新数据。同时，你没有积极管理或使用DAAAB。下面是一些快速变好的建议:

I bet that your company has valuable data that is not fully exploited and that you need new data that you don’t currently have. And I’ll wager that you are not actively managing or using DAAAB. Here are a few tips on how to get better, quickly:

改善DAAAB建议

清理当前收集的数据，并通过交易或使用传感器/其他数据收集方法获取所需的新数据。

Clean the data that you currently collect and obtain the new data that you need either through trading for it or using sensors/other data collection methods.

使用人工智能来帮助收集、清理和分析数据。

Use AI to help collect, clean, and analyze the data.

建立一个跨职能团队来开发更好地将客户与供应链操作联系起来的算法，并使用分析来改进你的需求链操作(销售、营销、促销等)。

Set up a cross-functional team to develop algorithms that better link the customer to supply chain operations, as well as use analytics to improve your demand chain operations (sales, marketing, promotions, etc.)

制定收入增长和降低成本的目标，并将其纳入绩效计划。

Set targets for revenue growth and cost reduction and build them into performance plans.

现在是提升DAAAB的正确时机。通过确保你的供应链与客户的需求紧密相连，你将击败你的竞争对手。你可能需要对数据科学有深入研究的新人力资源和其他有市场经验的人。不要低估了实现这一目标所需的文化变革的程度。传统的管理者往往依靠直觉，而不是DAAAB提供的洞察力。改变不会很快到来。所以，不要继续等待了，开始使用DAAAB吧！

Now is the right time to ramp up DAAAB. You will beat your competitors by ensuring your supply chain is firmly tied to customer desires. You will likely need new people resources that are deep into data science and others that have market-facing experience. And don’t underestimate the degree of culture change needed to make this happen. Traditional managers often rely on gut feel and not on the insight provided by DAAAB. Change won’t come quickly. So, don’t wait to get started using DAAAB.

大数据时代已经到来，打造数字供应链离不开深度学习、人工智能、算法、分析，时代已经在改变，学生更应该要与时俱进，学习更多供应链大数据分析技能！

“怡亚通杯”首届供应链大数据分析与应用技能大赛已经开启！你还在等什么？

来了！“怡亚通杯” 首届供应链大数据分析与应用技能大赛正式启动