前瞻：人工智能在仓储情景中的应用

CSCMP的朋友们大家好

今天为大家推荐一篇关于人工智能在仓储场景中的应用，供稿作者来自美国科朗叉车公司副总裁Luke Waltz。作者在文中分别从人工智能应用的背景以及发展现状分别写出了自己的看法，那么我们就开始欣赏全文吧！

前瞻：人工智能在仓储情景中的应用

人工智能近年来的迅猛发展，预示着其将为仓库运作方式带来革命性的变革。但在企业决定在运营实践中引入并实施这一新技术之前，必须要确保已拥有相关数据及所需人才。

对相关企业而言，即时关注并对供应链技术的进步具有敏感性几乎已经成为必须。机器人技术、自动化、数据分析和工业物联网等各种新技术，正在逐步展示出其在提升货物运输，处理，存储和配送效率方面的潜力。这些新技术的不断涌现，使得我们很难确认究竟应把注意力集中在哪一方面。

在这其中一项值得仔细研究的新技术是人工智能(AI)。简单而言， 人工智能是计算机系统发展到一定阶段的产物，即代为执行通常需要人类智能参与的任务（如视觉感知、语音识别、决策和语言翻译）。人工智能出现于1956年，但绝大多数情况下，我们都必须将智能程序明确地输入到计算机中。

近年来，机器学习作为一种典型的人工智能技术，发。机器学习主要是探索如何可以使计算机程序通过对输入数据的学习来提高其输出性能。这些程序可以嵌入在机器中，也可以在服务器或云端操作。亚马逊(Amazon)、谷歌、Facebook、微软(Microsoft)等大型科技公司已经将机器学习融入到他们的产品和服务中，为用户提供：相关度更高的网络搜索内容，更好的图像与语音识别技术以及更智能化的设备。

机器学习与数据分析（收集、转换及数据分析的流程）之间有一些相似之处。两者都需要一个经过清理的、多样化的、大型的数据库才能有效地运作。然而，主要的区别在于，数据分析允许用户从数据中得出结论，进而要求用户采取相应措施来改善其供应链。相比较而言，对于已处于可解决范畴内的问题，机器学习可以基于“训练数据库”自动执行操作（本文后续关于监督学习的部分将对此进行讨论）。基于其允许任务自动执行这一特性，人工智能 — 尤其是机器学习 — 对许多供应链管理人员来说都是一项值得关注的重要技术。对于今天的许多企业来讲，制定并实施供应链相关的人工智能战略，将使其随着技术的逐渐成熟，提升自身的生产力、速度与效率。

划重点啦

然而，大多数供应链专业人士并没有在前文提到的那些技术巨头类型的公司内工作。他们没有大量的数据科学家作为同事，没有足够的研发预算，也没有办法对人工智能在供应链运营中的作用给出标准化的定义。本文的目标就是以这样的公司为对象，明确其需要采取哪些步骤，才可以将人工智能技术应用于供应链中的一个重要场景：仓储运营。

Part

1

人工智能的发展现状

人工智能近期的迅猛发展，得益于以下因素的共同作用。第一，各种设备的互通互连而产生的数据量的增长以及促使日常生活数字化的高级传感器的使用的增长。第二，从移动设备到云计算，各种设备的计算能力也在持续增长。因此，机器学习可以运行在最新的硬件运算设备上，同时获取大批量、多样化及高质量的数据库，进而自动执行各种任务。

案例一：

下面是一个众多消费者将逐渐熟悉的场景。如果你有一个iphone而且每天早晨通勤上下班， 最近一段时间你可能留意到了以下情况：当你坐进汽车的时候，你的手机将自动提示你开车去公司将需要多少时间，根据实时的路况信息给出最佳行车路线的建议。当这一现象第一次发生时，你可能会有这样的疑惑：“手机怎么会知道我要去上班？感觉很酷，但也有一点点恐怖”。

因为内置了机器学习功能，手机可以根据你过去做过的事情来预测你将要什么。如果你换了新工作或者开车去了另外一个目的地，设备会自动调整它的预测，并根据新的目的地发出新的通知。这一应用场景的特别强大之处在于：设备对用户来说越来越有帮助，而用户或软件开发人员不必采取任何行动。

另一个场景是自动驾驶汽车。目前路面上行驶的的自动驾驶汽车正在被用来收集数据，用来改进下一代自动驾驶汽车的技术。当人工操作人员直接对车辆进行控制时，相关的数据就会与其他车辆的数据汇集起来并进行对比分析，以确定在何种情况下自动驾驶汽车将切换到由人工驾驶模式。这样的数据收集与分析将使得自动驾驶汽车变得更加智能。

虽然人们很容易被今天人工智能相关的令人兴奋的发展所鼓舞，但了解人工智能的局限性也很重要。在《哈佛商业评论》(Harvard Business Review) 2016年的一篇文章中，《人工智能现阶段的能与不能》，斯坦福人工智能实验室前负责人、跨国科技公司百度的人工智能团队前首席科学家Andrew Ng明确表示，“人工智能将变革许多行业，但它并不具有无所不能的魔力。”

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Ng强调，虽然人工智能已经有很多成功的实施案例，但大多数都是在监督学习的场景下展开应用。在这一模式下，每一个训练输入数据库与正确的输出决策相关联。机器学习算法通过比对这个训练库的信息来根据新的输入数据做出决策。监督学习的一些常见应用包括照片标记、贷款处理与语音识别。在每一个应用案例中，系统都会接收输入信息 — 比如照片标签应用中的图片 — 并基于它从训练数据库中学到的信息做出决定或做出反应。

如果拥有一个足够大的输入数据库，并用对应的人工响应 (或输出) 做以注释 （例如：这幅图片是一张脸），那么就可以构建一个人工智能应用程序，允许计算机系统接收新的输入数据并自行做出决定。这可以使过去不容易自动化的流程变的可以自动运作，最终提升仓库啊的运营效率。而实现这一目的的关键就是辅助做出决策的数据库的大小、质量与多样性的程度。训练输入数据库越大、越多样化，机器学习算法做出的决策就越优化。

Part

2

选择可参照案例

当考虑在供应链中应用人工智能的各种方案时，直接应用相应技术然后确定应用方案或许很有吸引力。但是，如果你首先分析一下公司业务面对的挑战与机遇，然后再选择相匹配的人工智能技术来解决相关问题，这样的流程会有助于你选择更有效率、更适合的应用方案。

图片源自网络，感谢原作者提供。

就仓库及其运作而言，人工智能的应用应该以企业所关注并不断优化的关键性能指标(KPI)为指导（订单准确性、安全性、生产率、履行时间、设施损坏或库存准确性等）。仓库通常已经拥有大量与KPI指标相关的数据，这些都可以被人工智能应用程序用于自动完成任务或做出决策。然而，这些数据由于数据类型的原因并不能直接用于人工智能技术，并且通常分布在不同的仓库管理系统中。因此，在正式应用之前，许多人工智能应用程序需要对不同仓库管理信息系统中的数据进行整合。

下面的3个案例（生产力、设备利用率、效率）说明了人工智能在仓储运营场景中的应用潜力。虽然这些案例可能并不适用于所有仓库，但它们确实展示了企业如何将自己已有的数据整合成可以应用机器学习技术的形式。

图片源自网络，感谢原作者提供。

案例一、生产力

在拣选订单的环节，所有的仓库都存在不同员工的生产力不同这一现象（有效率最高的订单拣选员也有变现一般的员工）。但是相对于使用系统引导进行拣选的仓库而言，员工在生产力方面的差异在不使用系统引导的仓库中表现更为明显。

对于那些不使用系统引导进行拣选的仓库，机器学习提供了一个可以更好推广最高效员工经验的机会，并将系统引导模式引入到所有员工的工作中。如果联系到上文提到的监督学习，最高效员工的拣选列表将作为人工智能应用的输入数据；这些员工在拣选列表中货物的顺序决策即为输出数据（基于条码扫描或其他可获取信息）。除了最短拣选距离这一指标之外，避免拥挤通常是提升生产力的另外一个重要指标。因为最佳拣选员工通常会同时考虑这两个因素，因此上面的输入输出数据库应该已包含这些信息。

基于这些精准标注的数据，机器学习算法在接收新的订单数据后案最佳原则进行归类。通过这种方式，算法可以复制最有效员工的拣选操作，并提高所有员工的生产力。

图片源自网络，感谢原作者提供。

案例二、设备利用率

某一仓库一天内需要搬运的容器或托盘数量与所需的搬运设备数量之间有一定的关系。在大多数情况下，两者之间是一种线性关系。但是，某些因素（例如操作人员的技能水平或货物的混合存放等）也可能会影响到所需搬运设备的佘亮。

在这种情况下，输入数据就需要包括所有可能影响设备需求的数据（从仓库管理系统中调用的拣选订单清单以及从员工管理系统中获取的操作人员生产力水平等信息）。输出信息包括从升降搬运车管理系统中获得的搬运设备使用率信息。

基于这一精准标注的数据库，机器学习算法将可以接收未来数星期或数月的订单预测信息和现有员工的技能水平信息，进而预估出所需搬运设备的数量。升降搬运车车队经理将在同设备供应商的协商中采纳这些信息作为决策参考，以确保通过短期租赁或新设备购买的方式来确保在某一期限内获取合适数量的搬运设备进行拣选操作。

图片源自网络，感谢原作者提供。

案例三、效率

一个好的货位策略应该是将高需求的SKU尽量集中放在最佳位置但同时又要适当的分散摆放，以降低拥堵程度来提高拣选效率。但由于需求的不断变化以及SKU的数量（某些仓库中可能有数千个SKU），仓库很难仅仅依靠员工来判断SKU的需求量来实现最佳存放。因此一些仓库运营商会使用货位分配软件来帮助确定SKU摆放位置。这些软件会提供操作界面允许客户修改运作规则。当接收到销售历史数据或未来销售预测信息后，软件就会推荐相应的货位策略。但是，负责软件的人员经常会依据自己的经验来修改策略，而这些经验却往往不能反应出拣选操作的真实情况。

在这种情况下，输入数据就是软件所推荐的货位策略。输出数据是最终决定执行的策略。机器学习算法可以和货位分配软件结合，通过对实施最终货位摆放策略的员工的倾向性进行不断的学习，最终实现自动调整。

Part

3

制定策略

明确仓储相关领域可以从人工智能技术获益之后，制定相关的应用策略将非常重要。在其发表于《哈佛商业评论》的文章中，Andrew Ng对高管们应该如何定位公司的人工智能策略提出了一些有益的看法。他写道，制定一个成功战略的关键是“理解在哪里创造价值，什么是很难复制的”。

Ng指出，人工智能研究人员经常发布和分享他们的想法，并公布他们的代码，因此我们可以很便捷地接触到最新理念及进展。相反，“稀缺资源”是数据和人才，而这两点对企业制定人工智能策略获取竞争优势极为关键。在数据源已经被精确连接到了对应的输出信息的情况下，复制一款软件比获得原始数据要简单的多。因此，具有鉴别与获取有价值的数据并有能力根据实际情况修改软件参数以最大化利用这些数据的人员，将是制定人工智能策略过程中关键而具有差异性的组成部分。也就是说，如果一个企业向推进人工智能在仓储场景下的应用，那么它就必须将重点放在提高数据和人才的质量这两方面。

关于数据，要明确的一个关键问题是：哪些数据是你的公司所独有而且可以用来提高与业务相关的KPI？这一点明确之后，就需要提高仓储管理系统中的数据的质量。这一步通常被称为数据管控，来确保供应链运作相关的数据具有一个可以“真实反映客观事实的来源”。

举例来讲。叉车司机的信息可以存储在不同的信息系统中，包括人力资源系统、员工管理系统、仓库管理系统、叉车车队管理系统等。如果司机信息被分别录入以上系统，那么同一员工的姓名及身份号码就可能出现不匹配的情况。比如，一个人可以在WMS中被标识为Jo Smith， #01425; 在LMS系统中为Joanne Smith, #1425; 而在车队管理系统中则只登记为Joanne Smith，同时没有认可身份号码。

对于跨系统整合数据的机器学习应用案例来说，数据必须是干净的。具有良好数据管控能力的企业可以将其中某一系统定义为存有主要数据的系统，并在需要时通过应用程序编程接口（API）将这一数据导入其他任意系统中。

如果需要整合来源于多个系统的数据，那接下来要面对的挑战就是数据集成。也就是说，要确保所有来源于不同仓储运作相关的系统中的数据可以被整合成一种可以用来机器学习的形式。这就需要与供应商紧密合作，以了解对方的运营能力以及整合来自车队管理、员工管理、仓库管理、企业资源管理等不同系统的数据的潜力。这就为支持数据分析以及客户定制化的人工智能应用奠定了数字化基础。在技术上具有挑战性，但许多系统中嵌入的API接口简化了这一任务。

一个更大的挑战可能来自于人才领域。在你的公司中有多少人专职进行管控、集成于抓取正在创建的数据信息？如果答案是“还不够”，那么你就要考虑设置一个高管级别的职位，致力于在董事会层面来积极推动以公司数据资产为来源来建立企业竞争优势。

这种高级别的助推策略，可以从确定公司如何在这一领域构建能力开始。对大多数公司来讲，也可以通过内部员工和外部顾问的组合来实现。甚至有一些众筹的机器学习平台（例如Kaggle和Experfy）可以协助你将你在数据方面要面对的挑战与世界各地的专家之间建立起联系。因为今天你所获得的数据可能会对未来的机器学习应用产生深远影响，因此建立数据能力是一个优先需要考虑的事项。许多大型企业已经在内部成立了专门部门来引导人工智能及数据分析方面的工作，这一需求也使得这一领域的专业人才变的炙手可热。

Part

4

感想总结

虽然供应链经理需要评估各种技术以及指导以科技为基础的革新，但人工智能不应因此被忽略。但它也不应该被视作可以瞬间完成供应链变革的万灵药。相反地，人工智能应该被定义为一个可以提升与企业成功密切相关的KPI指标的工具。使用这一工具并不需要成为人工智能领域的专家，但必须确保你的企业满足了前文所提到的三个基本要求：确定与提升企业绩效相关的高价值应用案例；创立可以整合这些高价值数据的数字基础设施；开始建立一个由内部与外部专家组成的专业团队。

参考文献：

Notes:

1. Tanya Lewis, 'A Brief History of Artificial Intelligence,' LiveScience (December 4, 2014)

2. Christina Mercer, 'Tech giants investing in artificial intelligence,' TechWorld (February 8, 2018)

3. Andrew Ng, 'What Artificial Intelligence Can and Can't Do Right Now,' Harvard Business Review (November 9, 2016)

4. Mike Faden, 'Using AI to Solve Complex Global Supply Chain Management Challenges,' American Express online (undated)

以上的文章已经全部结束，如果您有更好的想法或者建议，您可以在下方留言区与会员或专家们进行探讨沟通。

英

文

原

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以下是英文原文

如果中文翻译不准确，请指出哦。

原汁原味读着更过瘾

Paving the way for AI in the warehouse

By Luke Waltz | From the Quarter 1 2018 issue 

Recent developments in artificial intelligence (AI) are set to revolutionize the way warehouses operate. But before companies jump into implementations, they must make sure they have the data and talent they need.

Staying abreast of changes in supply chain technology has become almost a full-time job. From robotics and automation to data analytics and the industrial Internet of Things, new technologies are emerging that have the potential to further improve how goods are shipped, handled, stored, and delivered. With all of these technologies competing for our attention, it can be difficult to know where to focus.

One new technology that does deserve a close look is artificial intelligence (AI). In the simplest terms, AI is the development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and language translation. AI has been around since 1956,1 but humans typically have had to explicitly program intelligence into computers. 

One type of AI called machine learning, which has become prominent in recent years, explores ways to enable computer programs to improve their output based on learning from data inputs. These programs can be embedded in machines, or they can operate on servers or in the cloud. Large technology companies such as Amazon, Google, Facebook, Microsoft, and others are already incorporating machine learning into their offerings,2 creating more intuitive Web searches, better image and voice recognition, and smarter devices.

There are some similarities between machine learning and data analytics, or the processes used to collect, transform, and analyze data. Both require a clean, diverse, and large data set to function effectively. The primary difference, however, is that data analytics allows users to draw conclusions from data but requires them to take the action to improve their supply chain. For the right types of problems, machine learning can automate the actions based on a 'training data set,' described in the discussion of supervised learning later in this article. For many supply chain executives, AI—and particularly machine learningis an important technology to consider because it allows tasks to be automated. Organizations that begin today to develop AI strategies that are relevant to the supply chain will be positioned to increase productivity, speed, and efficiency as the technology matures.

Yet most supply chain professionals don't work at companies like the technology giants mentioned earlier. They don't have hundreds of data scientists on staff, and they do not have large research and development budgets. Nor can they look to a standard definition of the role of AI in the supply chain. The goal of this article is to highlight what steps these companies can take to enable AI in an important part of the supply chain: the warehouse.

Part

1

The current state of AI

AI is growing rapidly today because of the convergence of several factors. First is the rise in the amount of data being generated through increased connectivity and the advanced sensors that enable more aspects of our lives to be digitized. Second is the continued rise in computing power in everything from mobile devices to the cloud. As a result, machine-learning applications that are running on the latest computing hardware and have access to large, diverse, and high-quality data sets can now automate a wide range of tasks.

Here's an example that will be familiar to many consumers. If you have an iPhone and commute to work every morning, you may have noticed recently that when you get in the car in the morning, your phone, without prompting, issues a notification telling you how long it will take you to drive to work and the best route to take based on traffic conditions. The first time this happened, you may have thought, 'How did my phone know I was going to work? That's cool—and a little creepy.'

The phone knows because it has machine learning embedded in the device, allowing it to predict what you are going to do based on what you have done in the past. If you change jobs and start driving to a different destination, the device adjusts its predictions and gives you a notification based on your new destination. What's especially powerful about this example is that the device is getting more useful to the user without the user or the software developer having to take any action.

Another example is self-driving cars. The current generation of self-driving cars on the road today is being used to collect data that will lead to improvements in the next generation of autonomous vehicles. Whenever human operators override the vehicle controls, that data is pooled with data from other vehicles and analyzed to determine why the override was necessary. All vehicles become smarter based on that experience.

While it's easy to get swept up in the exciting developments associated with AI today, it's also important to understand its limitations. In a 2016 article in Harvard Business Review, 'What Artificial Intelligence Can and Can't Do Right Now,' Andrew Ng, former head of the Stanford Artificial Intelligence Laboratory and former chief scientist of multinational tech company Baidu's AI team, states clearly, 'AI will transform many industries. But it's not magic.'3

Ng stresses that while there are a wide variety of use cases for AI, most applications use a type of machine learning called supervised learning. In supervised learning, a training input data set is associated with the correct output decision. The machine-learning algorithm uses this training set to make decisions based on new input data. Some common applications for supervised learning are photo tagging, loan processing, and speech recognition. In each case, the system receives inputs—in the case of photo tagging, pictures—and makes decisions or responses based on what it has learned from its training data set.

Given a sufficiently large data set of inputs that is annotated with the appropriate human response (or output)—for example, this picture is a face—it's possible to build an AI application that allows a computer system to receive new input data and make decisions on its own. This allows processes to be automated that couldn't easily be automated in the past and, ultimately, will enable warehouses to operate with greater effectiveness. The key to unlocking the potential benefits of supervised learning is the size, quality, and diversity of the data set used to make decisions. The larger and more diverse the training input data set, the better the decisions that will be made by the machine-learning algorithm.

Part

2

Choosing a use case

As you consider opportunities to apply AI in the supply chain, it may be tempting to start with the technology and seek out an application. However, more useful applications are likely to emerge if you evaluate the business drivers that represent the greatest challenges or opportunities for your company, and then apply an appropriate understanding of AI technology's capabilities to those issues.

In relation to the warehouse, AI applications should be guided by the key performance indicators (KPIs) a particular organization is trying to optimize, such as order accuracy, safety, productivity, fulfillment time, facility damage, or inventory accuracy. Warehouses typically already have a wealth of data that is related to their KPIs and could be used by an AI application to automate tasks or decisions. However, this data typically is in a form that is not conducive to using AI techniques, and it often is spread across various warehouse systems. As a result, many AI applications will likely require information to be aggregated across various information systems in the warehouse before it can be used.

The following examples illustrate the potential for AI in the warehouse. Each of them is focused on a KPI: productivity, equipment utilization, or efficiency. While the examples may not be applicable to every warehouse, they do show how companies can take available data and fit that data into a form in which machine-learning techniques can be applied.

Productivity. When it comes to picking orders, all warehouses experience a range of productivity, from their highest-performing order pickers to their average performers. However, those warehouses that do not use system-directed picking often experience a greater range of productivity than warehouses that do use it.

For those warehouses that do not use system-directed picking, machine learning offers an opportunity to leverage the experience of their most productive order pickers and move toward a system-directed solution for all order pickers. If you think in terms of the supervised learning described above, the input data for the AI application would be the pick lists of the selected operators with the highest productivity, and the output data would be the sequence in which they picked the products on those lists. The output data would be based on bar-code scans or other available information. In addition to shortest overall travel distance, avoiding congestion can often be a significant factor in maximizing picking productivity. Since the best order pickers probably consider both of these factors in their pick sequences, the data sets should contain this information.

With this properly annotated data set, a machine-learning algorithm could receive new orders and sort them in the best order to be picked. In this way, the algorithm can replicate the choices that the most productive order pickers are making and enable all order pickers to improve their productivity.

Equipment utilization. There is a relationship between the number of cases or pallets a particular warehouse needs to move in a day and the amount of material handling equipment required to support that goal. In most cases this is estimated as a linear relationship. However, there may be additional factors that contribute to the amount of equipment needed, such as the skill level of the operators and the mix of stock-keeping units (SKU).

In this case, the input would be all available data that could impact equipment requirements, including the detailed order list of what needs to be shipped from the warehouse management system (WMS) and the productivity level of the operators from the labor management system (LMS). The output data would be the material handling equipment utilization data from the lift truck fleet management system.

With this properly annotated data set, a machine-learning algorithm could receive a forecast of orders for the coming weeks or months together with data about the current skill level of the operators, and then provide an estimate of the material handling equipment needed. The lift truck fleet manager would then be in a good position to work with the equipment provider to ensure that the required equipment will be available through short-term rentals or new equipment purchases.

Efficiency. A good slotting strategy seeks to optimize the location of high-velocity SKUs while also spreading them out enough across the pickface to minimize congestion and improve picking efficiency. But with demand changing constantly and the number of SKUs in some warehouses in the thousands, it can be difficult and time-consuming for a human to keep SKUs in the optimum locations based on their velocity. Some warehouse operators use slotting software products that assist in keeping the SKUs slotted in the optimum positions. These slotting products typically provide an interface that allows the user to include operating rules for the warehouse. When given past sales history or a forecast of expected future sales, the slotting products can then provide a recommended slotting strategy. However, it is common for the people in charge of slotting to make adjustments to the slotting strategy based on their own knowledge of the warehouse that is not reflected in the operating rules.

In this case, the input data would be the initial slotting strategy as recommended by the slotting product. The output data would be the final slotting strategy as executed. A machine-learning algorithm could be incorporated into a slotting product, which could then learn over time the preferences of the person implementing the final slotting strategy and make these adjustments automatically.

Part

3

Developing a strategy

After identifying a warehouse-related area that could benefit from AI, it's important to set a strategy that will prepare your company for implementing the application. In his Harvard Business Review article, Andrew Ng makes some helpful observations about how executives should think about their AI strategy. The key to developing a successful strategy, he writes, is 'understanding where value is created and what's hard to copy.'

AI researchers, Ng points out, publish and share ideas frequently and open-source their code so there is ready access to the latest thinking. Instead, the 'scarce resources' that allow an organization to develop an AI strategy that delivers competitive advantage are data and talent. It is much easier to replicate software than to get access to data sources, especially data sources that have been annotated with the correct output. So, people who have the expertise to identify and acquire high-value data, and to customize software in order to get the value from that data, become the truly differentiating component of an AI strategy. In other words, as they pave the way for artificial intelligence in the warehouse, organizations should focus on improving the quality of their data and talent.

The key question to address regarding data is, what data that is unique to your company can be used to improve the KPIs that are most important to the business? Once that has been determined, it is important to take steps to improve the quality of the data that is in your warehouse information systems. Commonly referred to as data governance, this is important for ensuring that there is 'one source of truth' for the data elements that you use to run your supply chain.

For example, forklift operator information can be stored in multiple systems in a warehouse, including the human resource system, LMS, WMS, and forklift fleet management system. If all of this data has been keyed in separately, it is possible that the names and identification numbers for the same employee might not match across systems. For instance, an individual could be identified as Jo Smith, #01425 in the WMS; Joanne Smith, #1425 in the LMS; and Joanne Smith, with no ID number in the fleet management system.

For those machine-learning use cases that are aggregating data across multiple systems, it is imperative that the operator data be clean. Organizations with good data governance would recognize one of these systems as having the master data records and would have an API (application programming interface) that exports this identical data into any other systems where it is needed.

If you have selected a use case that requires aggregating data from multiple systems, the next challenge will be integration; that is, ensuring that data from the various systems that run the warehouse can be combined into a form that can be used for machine learning. It is important to work with your providers to understand their capabilities and the potential for combining data from the various systems, such as fleet management, labor management, warehouse management, and enterprise resource planning (ERP) systems. This lays the foundation for a digital infrastructure that supports data analytics and artificial intelligence initiatives customized to your business. This can be technically challenging, but the APIs designed into many systems simplify this task.

A bigger challenge may be in the area of talent. How many people in your organization are dedicated to governing, integrating, and capturing value from the data that is being created? If the answer is 'not enough,' then you should recruit an executive sponsor—someone who sits at the board level and can be an effective advocate for building competitive advantage from the company's data assets.

This high level of advocacy can then be leveraged to begin the process of determining how your company wants to build capability in this area. For most companies, this will probably be accomplished through a mix of internal staff and external consultants. There are even crowdsourced machine-learning platforms, such as Kaggle or Experfy, that can be used to connect you and your data challenge with experts across the world. Building your data capabilities is an important priority because today's data has the potential to teach tomorrow's machine-learning applications. Many larger organizations have already begun building internal teams to guide their AI and data analytics efforts,4 and there is significant competition for specialists in this area.

Part

4

Final thoughts

While supply chain managers have myriad technologies to evaluate and technology-based changes to navigate, artificial intelligence should not be ignored. Neither should it be viewed as a panacea that will magically transform the supply chain. Instead, AI should be viewed as a tool capable of driving improvements in the KPIs that are critical to the success of your organization. It isn't necessary to become an AI expert to leverage this tool, but you do need to make sure your organization has in place the three fundamental requirements discussed above: define high-value use cases that are important for driving improvements to your business, create a digital infrastructure that enables high-quality data to be aggregated from multiple systems, and begin to build a team of data experts both inside and outside of your organization.

Notes:

1. Tanya Lewis, 'A Brief History of Artificial Intelligence,' LiveScience (December 4, 2014)

2. Christina Mercer, 'Tech giants investing in artificial intelligence,' TechWorld (February 8, 2018)

3. Andrew Ng, 'What Artificial Intelligence Can and Can't Do Right Now,' Harvard Business Review (November 9, 2016)

4. Mike Faden, 'Using AI to Solve Complex Global Supply Chain Management Challenges,' American Express online (undated)

Luke Waltz is Vice President of engineering for Crown Equipment Corporation.

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