CSCMP的朋友們大家好

今天為大家推薦一篇關(guān)于人工智能在倉儲場景中的應(yīng)用，供稿作者來自美國科朗叉車公司副總裁Luke Waltz,。作者在文中分別從人工智能應(yīng)用的背景以及發(fā)展現(xiàn)狀分別寫出了自己的看法,，那么我們就開始欣賞全文吧！

前瞻：人工智能在倉儲情景中的應(yīng)用

人工智能近年來的迅猛發(fā)展,，預示著其將為倉庫運作方式帶來革命性的變革,。但在企業(yè)決定在運營實踐中引入并實施這一新技術(shù)之前，必須要確保已擁有相關(guān)數(shù)據(jù)及所需人才,。

對相關(guān)企業(yè)而言,，即時關(guān)注并對供應(yīng)鏈技術(shù)的進步具有敏感性幾乎已經(jīng)成為必須。機器人技術(shù),、自動化,、數(shù)據(jù)分析和工業(yè)物聯(lián)網(wǎng)等各種新技術(shù)，正在逐步展示出其在提升貨物運輸,，處理,，存儲和配送效率方面的潛力。這些新技術(shù)的不斷涌現(xiàn),，使得我們很難確認究竟應(yīng)把注意力集中在哪一方面,。

在這其中一項值得仔細研究的新技術(shù)是人工智能(AI)。簡單而言,， 人工智能是計算機系統(tǒng)發(fā)展到一定階段的產(chǎn)物,，即代為執(zhí)行通常需要人類智能參與的任務(wù)（如視覺感知、語音識別,、決策和語言翻譯）,。人工智能出現(xiàn)于1956年，但絕大多數(shù)情況下,，我們都必須將智能程序明確地輸入到計算機中,。

近年來，機器學習作為一種典型的人工智能技術(shù),，發(fā),。機器學習主要是探索如何可以使計算機程序通過對輸入數(shù)據(jù)的學習來提高其輸出性能。這些程序可以嵌入在機器中，也可以在服務(wù)器或云端操作,。亞馬遜(Amazon),、谷歌、Facebook,、微軟(Microsoft)等大型科技公司已經(jīng)將機器學習融入到他們的產(chǎn)品和服務(wù)中，為用戶提供：相關(guān)度更高的網(wǎng)絡(luò)搜索內(nèi)容,，更好的圖像與語音識別技術(shù)以及更智能化的設(shè)備,。

機器學習與數(shù)據(jù)分析（收集、轉(zhuǎn)換及數(shù)據(jù)分析的流程）之間有一些相似之處,。兩者都需要一個經(jīng)過清理的,、多樣化的、大型的數(shù)據(jù)庫才能有效地運作,。然而,，主要的區(qū)別在于，數(shù)據(jù)分析允許用戶從數(shù)據(jù)中得出結(jié)論,，進而要求用戶采取相應(yīng)措施來改善其供應(yīng)鏈,。相比較而言，對于已處于可解決范疇內(nèi)的問題,，機器學習可以基于“訓練數(shù)據(jù)庫”自動執(zhí)行操作（本文后續(xù)關(guān)于監(jiān)督學習的部分將對此進行討論）,。基于其允許任務(wù)自動執(zhí)行這一特性,，人工智能 — 尤其是機器學習 — 對許多供應(yīng)鏈管理人員來說都是一項值得關(guān)注的重要技術(shù),。對于今天的許多企業(yè)來講，制定并實施供應(yīng)鏈相關(guān)的人工智能戰(zhàn)略,，將使其隨著技術(shù)的逐漸成熟,，提升自身的生產(chǎn)力、速度與效率,。

然而，大多數(shù)供應(yīng)鏈專業(yè)人士并沒有在前文提到的那些技術(shù)巨頭類型的公司內(nèi)工作,。他們沒有大量的數(shù)據(jù)科學家作為同事,，沒有足夠的研發(fā)預算，也沒有辦法對人工智能在供應(yīng)鏈運營中的作用給出標準化的定義,。本文的目標就是以這樣的公司為對象,，明確其需要采取哪些步驟，才可以將人工智能技術(shù)應(yīng)用于供應(yīng)鏈中的一個重要場景：倉儲運營,。

人工智能的發(fā)展現(xiàn)狀

人工智能近期的迅猛發(fā)展,，得益于以下因素的共同作用。第一，各種設(shè)備的互通互連而產(chǎn)生的數(shù)據(jù)量的增長以及促使日常生活數(shù)字化的高級傳感器的使用的增長,。第二,，從移動設(shè)備到云計算，各種設(shè)備的計算能力也在持續(xù)增長,。因此,，機器學習可以運行在最新的硬件運算設(shè)備上，同時獲取大批量,、多樣化及高質(zhì)量的數(shù)據(jù)庫,，進而自動執(zhí)行各種任務(wù)。

下面是一個眾多消費者將逐漸熟悉的場景,。如果你有一個iphone而且每天早晨通勤上下班,， 最近一段時間你可能留意到了以下情況：當你坐進汽車的時候，你的手機將自動提示你開車去公司將需要多少時間,，根據(jù)實時的路況信息給出最佳行車路線的建議,。當這一現(xiàn)象第一次發(fā)生時，你可能會有這樣的疑惑：“手機怎么會知道我要去上班,？感覺很酷,，但也有一點點恐怖”。

因為內(nèi)置了機器學習功能,，手機可以根據(jù)你過去做過的事情來預測你將要什么,。如果你換了新工作或者開車去了另外一個目的地，設(shè)備會自動調(diào)整它的預測,，并根據(jù)新的目的地發(fā)出新的通知,。這一應(yīng)用場景的特別強大之處在于：設(shè)備對用戶來說越來越有幫助，而用戶或軟件開發(fā)人員不必采取任何行動,。

另一個場景是自動駕駛汽車,。目前路面上行駛的的自動駕駛汽車正在被用來收集數(shù)據(jù)，用來改進下一代自動駕駛汽車的技術(shù),。當人工操作人員直接對車輛進行控制時,，相關(guān)的數(shù)據(jù)就會與其他車輛的數(shù)據(jù)匯集起來并進行對比分析，以確定在何種情況下自動駕駛汽車將切換到由人工駕駛模式,。這樣的數(shù)據(jù)收集與分析將使得自動駕駛汽車變得更加智能,。

雖然人們很容易被今天人工智能相關(guān)的令人興奮的發(fā)展所鼓舞，但了解人工智能的局限性也很重要,。在《哈佛商業(yè)評論》(Harvard Business Review) 2016年的一篇文章中,，《人工智能現(xiàn)階段的能與不能》，斯坦福人工智能實驗室前負責人,、跨國科技公司百度的人工智能團隊前首席科學家Andrew Ng明確表示,，“人工智能將變革許多行業(yè),，但它并不具有無所不能的魔力。”

Ng強調(diào),，雖然人工智能已經(jīng)有很多成功的實施案例，但大多數(shù)都是在監(jiān)督學習的場景下展開應(yīng)用,。在這一模式下,，每一個訓練輸入數(shù)據(jù)庫與正確的輸出決策相關(guān)聯(lián)。機器學習算法通過比對這個訓練庫的信息來根據(jù)新的輸入數(shù)據(jù)做出決策,。監(jiān)督學習的一些常見應(yīng)用包括照片標記,、貸款處理與語音識別。在每一個應(yīng)用案例中,，系統(tǒng)都會接收輸入信息 — 比如照片標簽應(yīng)用中的圖片 — 并基于它從訓練數(shù)據(jù)庫中學到的信息做出決定或做出反應(yīng),。

如果擁有一個足夠大的輸入數(shù)據(jù)庫，并用對應(yīng)的人工響應(yīng) (或輸出) 做以注釋 （例如：這幅圖片是一張臉）,，那么就可以構(gòu)建一個人工智能應(yīng)用程序,，允許計算機系統(tǒng)接收新的輸入數(shù)據(jù)并自行做出決定。這可以使過去不容易自動化的流程變的可以自動運作,，最終提升倉庫啊的運營效率,。而實現(xiàn)這一目的的關(guān)鍵就是輔助做出決策的數(shù)據(jù)庫的大小、質(zhì)量與多樣性的程度,。訓練輸入數(shù)據(jù)庫越大,、越多樣化，機器學習算法做出的決策就越優(yōu)化,。

選擇可參照案例

當考慮在供應(yīng)鏈中應(yīng)用人工智能的各種方案時,，直接應(yīng)用相應(yīng)技術(shù)然后確定應(yīng)用方案或許很有吸引力。但是,，如果你首先分析一下公司業(yè)務(wù)面對的挑戰(zhàn)與機遇,，然后再選擇相匹配的人工智能技術(shù)來解決相關(guān)問題，這樣的流程會有助于你選擇更有效率,、更適合的應(yīng)用方案,。

圖片源自網(wǎng)絡(luò)，感謝原作者提供,。

就倉庫及其運作而言,，人工智能的應(yīng)用應(yīng)該以企業(yè)所關(guān)注并不斷優(yōu)化的關(guān)鍵性能指標(KPI)為指導（訂單準確性、安全性,、生產(chǎn)率、履行時間,、設(shè)施損壞或庫存準確性等）,。倉庫通常已經(jīng)擁有大量與KPI指標相關(guān)的數(shù)據(jù)，這些都可以被人工智能應(yīng)用程序用于自動完成任務(wù)或做出決策。然而,，這些數(shù)據(jù)由于數(shù)據(jù)類型的原因并不能直接用于人工智能技術(shù),，并且通常分布在不同的倉庫管理系統(tǒng)中。因此,，在正式應(yīng)用之前,，許多人工智能應(yīng)用程序需要對不同倉庫管理信息系統(tǒng)中的數(shù)據(jù)進行整合。

下面的3個案例（生產(chǎn)力,、設(shè)備利用率,、效率）說明了人工智能在倉儲運營場景中的應(yīng)用潛力。雖然這些案例可能并不適用于所有倉庫,，但它們確實展示了企業(yè)如何將自己已有的數(shù)據(jù)整合成可以應(yīng)用機器學習技術(shù)的形式,。

圖片源自網(wǎng)絡(luò)，感謝原作者提供,。

圖片源自網(wǎng)絡(luò),，感謝原作者提供,。

圖片源自網(wǎng)絡(luò),，感謝原作者提供,。

制定策略

明確倉儲相關(guān)領(lǐng)域可以從人工智能技術(shù)獲益之后，制定相關(guān)的應(yīng)用策略將非常重要,。在其發(fā)表于《哈佛商業(yè)評論》的文章中,，Andrew Ng對高管們應(yīng)該如何定位公司的人工智能策略提出了一些有益的看法。他寫道,，制定一個成功戰(zhàn)略的關(guān)鍵是“理解在哪里創(chuàng)造價值,，什么是很難復制的”。

Ng指出,，人工智能研究人員經(jīng)常發(fā)布和分享他們的想法,，并公布他們的代碼,，因此我們可以很便捷地接觸到最新理念及進展,。相反，“稀缺資源”是數(shù)據(jù)和人才,，而這兩點對企業(yè)制定人工智能策略獲取競爭優(yōu)勢極為關(guān)鍵,。在數(shù)據(jù)源已經(jīng)被精確連接到了對應(yīng)的輸出信息的情況下,，復制一款軟件比獲得原始數(shù)據(jù)要簡單的多。因此,，具有鑒別與獲取有價值的數(shù)據(jù)并有能力根據(jù)實際情況修改軟件參數(shù)以最大化利用這些數(shù)據(jù)的人員,，將是制定人工智能策略過程中關(guān)鍵而具有差異性的組成部分。也就是說,，如果一個企業(yè)向推進人工智能在倉儲場景下的應(yīng)用,，那么它就必須將重點放在提高數(shù)據(jù)和人才的質(zhì)量這兩方面。

關(guān)于數(shù)據(jù),，要明確的一個關(guān)鍵問題是：哪些數(shù)據(jù)是你的公司所獨有而且可以用來提高與業(yè)務(wù)相關(guān)的KPI,？這一點明確之后，就需要提高倉儲管理系統(tǒng)中的數(shù)據(jù)的質(zhì)量,。這一步通常被稱為數(shù)據(jù)管控,，來確保供應(yīng)鏈運作相關(guān)的數(shù)據(jù)具有一個可以“真實反映客觀事實的來源”。

舉例來講,。叉車司機的信息可以存儲在不同的信息系統(tǒng)中,，包括人力資源系統(tǒng)、員工管理系統(tǒng),、倉庫管理系統(tǒng),、叉車車隊管理系統(tǒng)等。如果司機信息被分別錄入以上系統(tǒng),，那么同一員工的姓名及身份號碼就可能出現(xiàn)不匹配的情況,。比如，一個人可以在WMS中被標識為Jo Smith,， #01425; 在LMS系統(tǒng)中為Joanne Smith, #1425; 而在車隊管理系統(tǒng)中則只登記為Joanne Smith,，同時沒有認可身份號碼。

對于跨系統(tǒng)整合數(shù)據(jù)的機器學習應(yīng)用案例來說,，數(shù)據(jù)必須是干凈的,。具有良好數(shù)據(jù)管控能力的企業(yè)可以將其中某一系統(tǒng)定義為存有主要數(shù)據(jù)的系統(tǒng)，并在需要時通過應(yīng)用程序編程接口（API）將這一數(shù)據(jù)導入其他任意系統(tǒng)中,。

如果需要整合來源于多個系統(tǒng)的數(shù)據(jù),，那接下來要面對的挑戰(zhàn)就是數(shù)據(jù)集成。也就是說,，要確保所有來源于不同倉儲運作相關(guān)的系統(tǒng)中的數(shù)據(jù)可以被整合成一種可以用來機器學習的形式,。這就需要與供應(yīng)商緊密合作,，以了解對方的運營能力以及整合來自車隊管理、員工管理,、倉庫管理,、企業(yè)資源管理等不同系統(tǒng)的數(shù)據(jù)的潛力。這就為支持數(shù)據(jù)分析以及客戶定制化的人工智能應(yīng)用奠定了數(shù)字化基礎(chǔ),。在技術(shù)上具有挑戰(zhàn)性,，但許多系統(tǒng)中嵌入的API接口簡化了這一任務(wù)。

一個更大的挑戰(zhàn)可能來自于人才領(lǐng)域,。在你的公司中有多少人專職進行管控,、集成于抓取正在創(chuàng)建的數(shù)據(jù)信息？如果答案是“還不夠”,，那么你就要考慮設(shè)置一個高管級別的職位,，致力于在董事會層面來積極推動以公司數(shù)據(jù)資產(chǎn)為來源來建立企業(yè)競爭優(yōu)勢。

這種高級別的助推策略,，可以從確定公司如何在這一領(lǐng)域構(gòu)建能力開始,。對大多數(shù)公司來講，也可以通過內(nèi)部員工和外部顧問的組合來實現(xiàn),。甚至有一些眾籌的機器學習平臺（例如Kaggle和Experfy）可以協(xié)助你將你在數(shù)據(jù)方面要面對的挑戰(zhàn)與世界各地的專家之間建立起聯(lián)系,。因為今天你所獲得的數(shù)據(jù)可能會對未來的機器學習應(yīng)用產(chǎn)生深遠影響，因此建立數(shù)據(jù)能力是一個優(yōu)先需要考慮的事項,。許多大型企業(yè)已經(jīng)在內(nèi)部成立了專門部門來引導人工智能及數(shù)據(jù)分析方面的工作,，這一需求也使得這一領(lǐng)域的專業(yè)人才變的炙手可熱。

感想總結(jié)

雖然供應(yīng)鏈經(jīng)理需要評估各種技術(shù)以及指導以科技為基礎(chǔ)的革新,，但人工智能不應(yīng)因此被忽略,。但它也不應(yīng)該被視作可以瞬間完成供應(yīng)鏈變革的萬靈藥。相反地,，人工智能應(yīng)該被定義為一個可以提升與企業(yè)成功密切相關(guān)的KPI指標的工具,。使用這一工具并不需要成為人工智能領(lǐng)域的專家，但必須確保你的企業(yè)滿足了前文所提到的三個基本要求：確定與提升企業(yè)績效相關(guān)的高價值應(yīng)用案例,；創(chuàng)立可以整合這些高價值數(shù)據(jù)的數(shù)字基礎(chǔ)設(shè)施,；開始建立一個由內(nèi)部與外部專家組成的專業(yè)團隊。

參考文獻：

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)

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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.

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.

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.

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.

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.