盡管科技有了翻天覆地的發(fā)展，但許多公司為送貨卡車規(guī)劃路線的方式仍然和十年前一樣。負責人前一天定好路線，并把打印的路線圖交給司機，或是把它們傳到司機褲子后面口袋放著的手機里。

不過如果司機在路上陷入堵車，他們只能怪運氣不好，導致晚點。如果出現(xiàn)了意外的暴風雪導致道路無法通行，結(jié)果也是一樣。

簡而言之，這些路線太不靈活了。

不過波士頓的初創(chuàng)公司W(wǎng)ise Systems正在讓機器學習來自司機手機中的數(shù)據(jù)，借此解決這個年代久遠的問題。它會綜合考慮駕駛速度、GPS定位以及包括交通路況、天氣、訂單目的地、客戶收貨時間等細節(jié)。

最后生成的就是可以根據(jù)任何狀況隨時調(diào)整的送貨路線。如果這項技術(shù)判定司機會因為道路關(guān)閉等因素無法按計劃抵達，就會調(diào)整全天的日程。如果無法調(diào)整，司機就會在手機上收到明顯的警告，提醒需要加快送貨速度。（紅色說明這不是個好的提醒。）

這項技術(shù)旨在繪制路線，提高司機的工作效率。如此一來，公司可以通過提高司機一趟的送貨量來節(jié)約成本，同時還能通過提高訂單準點率或由客戶偏好的司機運送的概率，從而取悅客戶。

Wise Systems擁有15名員工，他們脫胎于麻省理工學院（MIT）創(chuàng)業(yè)學研究生班的一項任務。起初，公司四位創(chuàng)始人的計劃是做犯罪地圖，但是潛在的客戶表示：“犯罪地圖是個好主意，但是交通狀況是更糟糕的事情?！眮碜月槭±砉W院交通和物流中心（MIT Center for Transportation and Logistics）的團隊指導教授也鼓勵他們往這個方向發(fā)展。2014年，他們意識到這個想法蘊含潛力，于是成立了公司。

從本質(zhì)上說，優(yōu)化配送路徑的問題就是數(shù)學家從1930年開始就試圖解決的巡回售貨員問題（Traveling Salesman Problem）。盡管任務簡單易懂——讓推銷員以最高的效率來往于各城市，在最后返回家中——但目前為止答案仍然懸而未決。推銷的路線有無窮多種可能性，就如送貨一樣。

Wise的機器學習就在此大顯身手。它的算法會從日常數(shù)據(jù)中學習，從而改善技術(shù)提供的行駛路線。Wise的首席技術(shù)官阿里·卡米爾表示：“這項技術(shù)遠遠不只是獲取數(shù)據(jù)并吸收，它還會忘記那些可能出錯的路線?！?

隨著各公司都在努力爭奪那些希望幾乎立刻就收到貨物的消費者，改善送貨路線比以往更加重要。例如，亞馬遜（Amazon）就給食品雜貨和特定的Prime產(chǎn)品提供當日快遞，一至兩個小時內(nèi)就能送到，這需要強大的運算能力和機器學習工具。

不過開發(fā)這樣的系統(tǒng)難度很大。UPS十多年來一直在研發(fā)自己的客戶軟件Orion。據(jù)說有超過500人參與開發(fā)該項技術(shù)，不過十年的努力仍然無法讓它得到完全應用。在曼哈頓，UPS的司機仍然在使用老版的ED系統(tǒng)，因為在復雜的城市環(huán)境下，Orion的表現(xiàn)并不理想。

2016年底，安海斯布希（Anheuser-Busch）同意在西雅圖和圣迭戈的批發(fā)商那里試用軟件，就此成為了Wise System的第一個客戶。六個月后，這家啤酒巨頭在美國更多的批發(fā)商處全面采用了這項技術(shù)——其中包括一個供司機使用的移動應用和一個供管理者使用的網(wǎng)絡工具。今年2月，Wise把這項技術(shù)應用到了所有美國的批發(fā)商處——總共20家，另外還有兩家位于加拿大的安大略和魁北克。

20多年來，安海斯布希使用的都是Roadnet，該技術(shù)可以生成當天的配送路線。Roadnet可以幫助建立計劃，設置配送順序，但是在司機上路之后，那些路線不再會發(fā)生變化。

當安海斯布希把Roadnet創(chuàng)立的路徑與司機的實際行駛路徑進行對比之后，一個明顯的問題出現(xiàn)了。公司發(fā)現(xiàn)，司機往往會偏離計劃。這表明司機認為自己比技術(shù)工具更了解實際。當他們多年來每天按照同樣的路線行駛時，很容易出現(xiàn)這樣的情況。它也凸顯出一個問題：司機會積累一些路線相關(guān)的工作經(jīng)驗，但使用技術(shù)很難獲取這種經(jīng)驗。

為了讓系統(tǒng)整合這類經(jīng)驗，Wise Systems會讓司機通過移動應用輸入實時數(shù)據(jù)，例如客戶是否希望由特定司機送貨，某地是否難以停車等。這些共享的說明會通過代碼加入應用，如此一來，算法在未來就能利用這一信息。當一個新司機開始使用現(xiàn)有路線時，這類共享的經(jīng)驗尤其能夠起到幫助。

使用一年后，安海斯布希表示，他們發(fā)現(xiàn)Wise Systems的幾大優(yōu)勢。公司批發(fā)運營主管馬特洛克·羅杰斯表示：“Wise可以學習模式和歷史，這讓它在將來會效率更高?！盬ise可以讓他的團隊看到司機的實時位置，這減少了電話溝通，也不必更新文字狀態(tài)了。

安海斯布希表示，在員工得到培訓，可以恰當使用該工具的城市市場，每站里程數(shù)減少了4%，這意味著燃料的節(jié)省，磨損率的降低，而工作效率的提高也讓司機的收入水漲船高。

另一個好處在于客戶服務的提升。過去，司機錯過送貨時段也不會收到提醒。羅杰斯表示：“如今，Wise會展示可能遲到的15個站點，如果我們要為特定的客戶保證某些送貨時段，還能把它們按優(yōu)先級排列。”

Wise Systems表示，隨著這項技術(shù)的用戶越來越多，可供利用的數(shù)據(jù)越來越多，它還會漸漸改善。想象一下，如果Wise現(xiàn)有用戶群的2,000名司機變成20,000名，他們的集體智慧將達到什么水平。

Wise的卡米爾表示：“在物流業(yè)，司機的薪水是與送貨單數(shù)而不是工作時間掛鉤的。所以他們天生就喜歡我們，因為我們可以幫助他們每天送出更多貨物?！保ㄘ敻恢形木W(wǎng)）

更正：本文之前錯誤地表示安海斯布希在美國的20家零售商和加拿大的另外兩家零售商合作測試Wise Systems。實際上，那些測試是與批發(fā)商合作進行的。

譯者：嚴匡正?

Despite radical advances in technology, many companies still plan routes for their delivery trucks the same way they did a decade ago. Managers create itineraries the day before, and then hand printouts to drivers to follow or add them to the hand-held devices that their drivers carry at their hip.

But when drivers get stuck in traffic jams while on their rounds, they’re simply out of luck and behind schedule. The same thing happens if there’s a surprise snowstorm that makes roads impassable.

In short, the routes are inflexible.

But Wise Systems, a Boston startup, is tackling this age-old problem by pairing machine learning with data it collects from drivers’ mobile phones. It crunches information like the driver’s speed and GPS location with other details including traffic, weather, where the order is being delivered, and when customers are available to receive their orders.

What emerges is a delivery route that can be tweaked on the fly depending on any complications that come up. If the technology determines that a driver will miss a scheduled stop because of road closures, for example, it will adjust the schedule for the entire day. If that’s not possible, the driver will receive alerts on his or her mobile phone as a not-so subtle hint to pick up the pace. (Red is not a good sign.)

The goal is to create routes that allow drivers to work more efficiently. By doing so, companies can save money by increasing the number of deliveries that drivers can make during shifts while also making customers happier by improving the likelihood that orders will arrive on time, or by the driver they prefer.

Wise Systems, which has 15 employees, grew out of an assignment in a graduate class on entrepreneurship at MIT. At first, the idea of the company’s four founders was to map crime, but potential customers told them “crime was good, but traffic is worse.” One of the teams’ advisors, from the MIT Center for Transportation and Logistics, also nudged them in this direction. In 2014, when they realized the idea had potential, the team incorporated the company.

Optimizing delivery routes has its roots in what’s called the Traveling Salesman Problem, which mathematicians have been trying to solve since 1930. While the task is straightforward––finding most efficient route between cities for salesmen before returning home––it remains unsolved. The possibilities are limitless, much like the possibilities for deliveries.

This is where Wise’s machine learning comes into play. Its algorithms learn from each day’s data so that it can improve the routes the technology provides going forward. “It’s much more than taking the data and feeding it in,” says Wise chief technology officer Ali Kamil. “It’s also unlearning some things that might go wrong.”

Increasing the efficiency of deliveries is now more important than ever for companies as they battle for customers who expect their orders almost immediately. Amazon, for example, offers same-day deliveries of groceries and certain Prime products within one- and two-hour delivery windows, requiring huge computing power and machine learning tools.

But creating such a system is difficult. UPS has been building its own custom software––Orion––for over a decade. Over 500 people reportedly worked on the technology, but after 10 years, it’s still not fully deployed. In Manhattan, UPS drivers still use an old version called ED because Orion doesn’t do well in complex urban environments.

Anheuser-Busch became Wise Systems’ first client when it agreed in late 2016 to test the software with its Seattle and San Diego wholesalers. Six months later, the beer giant rolled out the technology––a mobile app for drivers and a web-based tool for managers––to more of its’ wholesalers across the country. As of this week, Wise has been implemented at all of its U.S. wholesalers—20 in total, plus two others in Ontario and Québec

For over 20 years, Anheuser-Busch used Roadnet, a technology that creates delivery routes up to the day of. Roadnet helps build the plan and set the sequence, but those routes don’t change after drivers get on the road.

Another problem became apparent when Anheuser-Busch compared the routes the Roadnet software created with those that drivers actually took. The company found that drivers often deviated from the plan. It was a sign that drivers thought they knew better than the technology, an easy slip-up when they follow the same route every day for years. It also highlighted the problem of incorporating some of the on-the-job knowledge that drivers had about their routes that technology has difficulty capturing.

To get some of that expertise into its system, Wise Systems lets drivers enter real-time data through its mobile app. Examples include whether a customer prefers to be serviced by a specific driver and whether parking is scarce. These shared notes are added to the app with a code so that the algorithm can take that information into account in the future. This kind of shared knowledge can be especially helpful when a new driver takes over an existing route.

After one year, Anheuser-Busch says it’s noticed several benefits of using Wise Systems. “Wise learns patterns and history, which helps it be more effective in the future,” says Matlock Rogers, director of wholesale operations for Anheuser-Busch. It lets his team see where drivers are in real time, reducing the phone calls and texting otherwise required for updates.

In urban markets where employees are trained and using the tools properly, Anheuser-Busch says it has reduced the miles traveled per stop by 4%, which translates into fuel savings, lower wear and tear on trucks, and, for the driver, improved earnings based on higher productivity.

Another benefit is improved customer service. In the past, drivers wouldn’t be alerted to missing a delivery window. “Now, Wise will show us the last 15 stops might be late and we can prioritize them if we need to hit a specific window for a certain client,” says Rogers.

Wise Systems says that its technology will improve over time as it takes on more clients, which in turn provide its system with more data to crunch. Imagine a network of 2,000 drivers––Wise’s pool now––versus one that taps the collective brainpower of 20,000 drivers.

“In the logistics industry drivers are paid by deliveries made, not time,” says Wise’s Kamil. “Inherently, they love us because we help them make more deliveries in a day.”

Correction: An earlier version of this article mistakenly said that Anheuser-Busch was testing Wise Systems technology with 20 retailers in the U.S. and two others in Canada. In fact, those tests are with wholesalers.