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https://www.ah.nl/labs

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https://www.emerce.nl/nieuws/boodschappen-bestellen-vliegtuig-transavia-ah-testen-appie-fly

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I'd like to talk about fast.

On the left, we have the build-measure-learn loop. And on the right we have the common build-build-build.

Think about "fast" in the context of these two approaches. With build-measure-learn, we are optimizing for learning. We move around the loop as quickly as possible. It might look slower to someone on the outside, but we are learning more quickly. With build-build-build, we optimize for output. We ship as quickly as possible. Sometimes output is the right tactic, but sometimes it leads to this:

One winner in five tries. Not good! Time for a silver bullet (joke).

The problem is, we tend not to kill the features. So we have to 1) maintain the complexity, and 2) work around the complexity when adding additional functionality. Poor Ops! They're often left running all of this under-used functionality, and somehow have a problem getting more headcount. Anyway.

If we aren't careful, we also miss good opportunities:

Note how we are able to pursue alternative paths with build-measure-learn. We kill some ideas, test new ideas, and amplify what works. With build-build-build, we're about executing to a pre-determined plan. The last thing stakeholders want, in this case, is to flip the script.

There's a legacy here.

With project work, someone comes to you and asks for software that does X,Y, and Z. For fixed bid work, you get paid cold hard cash when you kick-off the project, hit some milestones, and deliver the project. Perhaps you also agree to host/maintain the software, in which case you collect money for a service contract.

Now, there may be plenty of learning involved in this type of work. This is no knock on project work. With fixed bid, you are primarily concerned with controlling costs, reducing risk, and delivering something that meets the customer's requirements. We often try to replicate this scheme in product development:

This makes logical sense. There are some difficulties, however. Here are a couple:

1. Most of the time, teams aren't paid cold hard "internal" cash when they deliver. They don't enjoy the profits. And they don't get to charge for a service contract (to maintain what they build)

2. If asked to deliver a feature, they miss the opportunity to follow alternative paths to achieve the same goal. They're on the hook for "the feature".

3. By giving an estimate, they've limited their ability to iterate on the feature to achieve a positive business outcome. Most likely, the org will have scheduled follow-up work to start immediately after the project is "done".

4. You run the risk of incentivizing larger "batches" (due to the transactional / startup costs of projects in comparison to dedicated teams).

Broadly, this means we fall into a build-build-build mode (see big batch below) vs. a build-measure-learn mode (see smaller batch below):

Note how we start delivering value earlier using smaller batches. We're "positive" far earlier. Now imagine this across multiple initiatives. It really makes difference.

I see many organizations interpret incremental delivery like this:

We lay out all the "stories", add up all the "points", and finish the project in little increments. The increments are good. They provide an integration point and reality check and decrease delivery risk.

Why do we finish and walk away? In many cases, the organization is persuaded that benefits realization is a "long term" thing, and that there is no possible way to measure/learn prior to done. The attitude is basically build-build-build ..........................measure-learn:

In this model, fast is all about getting to done. We'll play our bets (fast), and then wait until the business value materializes (eventually).

In a build-measure-learn approach, things might look a bit like this:

The team is tasked with moving some user/customer/business metric. They work to move that metric (with a short grace-period at the start). They're free to try different options provided they keep producing results. Ideally they target a specific customer/user cohort, and also use a control.

Yes...this takes better tooling, testing, monitoring, etc.

And yes, this is scary for the business.

What are they going to build?

What will we tell our customers?

Can't we just test it with customers, and just move on?

Can't we make sure customers like it before we ship it?

But results are results. This is an option. You might be able to work this way. Try to figure it out, even with a small experiment.

So when people talk about going "faster" or "just executing", make sure to ask some questions:

1. What does faster mean for you?

2. What are you trying to optimize for?

3. Are you willing to go slower in the future, to go faster right now?

4. How has "going fast" worked out? How has it helped the business? Has it hurt? What has been the impact of the added complexity?

5. Would you be willing to go slower and build less, if it led to superior business results? Can we build less.

So that's some basic thoughts on fast.

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I'd like to talk about fast. On the left, we have the build-measure-learn loop. And on the right we have the common build-build-build. Think about "fast" in the context of these two approaches. With build-measure-learn, we are optimizing for learning. We move around the loop as quickly as possible.

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Opnieuw een technisch proefproject bij Albert Heijn: de supermarkt experimenteert met slimme deursloten. Die maken het mogelijk boodschappen te bezorgen terwijl klanten niet thuis zijn. Bezoekers van het hoofdkantoor van Ahold Delhaize kunnen de slimme sloten vanaf deze week uitproberen; zij stappen daar in de schoenen van bezorger en klant om te ervaren hoe een dergelijk afleverproces in zijn werk gaat.

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Burberry Regent Street Store Interior Tour

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Christopher Bailey Introduces Burberry 121 Regent Street, London

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As more and more retailers become omni-channel in their approach, Flock takes a look at some of the best omni-channel offering on the British retail scene.

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Qu Honglun/China News Service/VCG via Getty Images

Pujiang Pu is a smiley, medium-built man in his mid-forties with stylish glasses, a bling gold watch, and a red JD lanyard around his neck. Along with many of the 150,000 employees of JD.com - a city-size e-commerce store sometimes referred to as the Amazon of China - he lives in a free dorm near one of the company's 500 gigantic warehouses. The warehouse I visit is in Jiading, 30km north-west of Shanghai's city centre. Hundreds of people work here, and at 100,000 square metres in size it sits on a JD complex so big it would take at least 45 minutes to walk from one end to the other.

I am allowed here as part of a rare, highly supervised press visit, and warehouse manager Pu is our tour guide. I am not shown everything, but enough to impress - or, as some analysts believe, to show that JD is a kind of company Amazon ultimately wants to become.

JD wasn't always that big. It started out as a small brick and mortar store in Beijing, founded in 1998 by Richard Liu. Then in 2004, Liu moved it online and JD.com, short for Jingdong, was born. Fast-forward to today, and the firm is worth more than $55 billion. In February, logistics magazine DC Velocity called it "the biggest company you may not know all that much about". Not for much longer though - JD is so growing so fast at home in China and expanding so rapidly into other markets such as Thailand, Indonesia and Vietnam and most recently Europe, that even the most devout Amazonians will soon sit up and notice.

Qilai Shen/In Pictures via Getty Images

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The main reason Pu stays in a dorm on site, and away from his family, is to ensure he can meet key performance indicators set by the firm. Sometimes, especially during JD.com's annual shopping event, he has to work late into the night.

But the future of these dorms is uncertain. Many traditional warehouse jobs like stacking shelves and packing boxes at JD are likely to go to robots in the coming years, as the company starts to automate everything that can sensibly be automated. The tech giant is now busy retraining some staff to take on new roles that machines can't yet do. Pu's warehouses have some of the firm's most advanced robotics - and he gets really excited talking about the autonomous forklift trucks and delivery drones.

These drones have been in the news a lot lately. Remember when Amazon's boss Jeff Bezos made claims that his firm would soon drop parcels off at your doorstep? Well, that was in 2013 - and, some small-scale trials aside, it's still not happening. But it's very much happening at JD - since March 2016, its drones have been delivering products across China, having clocked over 300,000 minutes of flight time. "Today we have over 200 people working on our drone programme," says Zheng Cui, director of the firm's drone R&D centre in Xi'an.

The drones come in various shapes and sizes, but the quickest ones can fly up to 100km/h and have a range of 100km. So far though, the furthest delivery has been 15km and that drone flew much slower than 100km/h - but you have to start somewhere. What the drones can't do yet, JD does with its 65,000 van drivers and couriers.

The drone efforts haven't gone unnoticed though, and other companies are keen to replicate JD's air delivery success. Cui says more and more firms are getting in touch to buy their drones. "We've just got an order for 1,000 at the beginning of this year," he adds.

Those drones are still fairly small, but JD is busy developing larger ones that can carry up to five tonnes. "They'll transfer inventory from one warehouse to another," says Cui. "Within three years we're looking at having a couple of thousand," he says - and they will take off right from existing small airports near the company's warehouses.

It's not just the drones that make the Chinese behemoth different from Western e-commerce stores, though. Robots at JD are everywhere. In the warehouse I visit, machines stack tens of thousands of products on 24-metre-high shelves. Over the road from where I am, another fully automated warehouse can pack and ship 200,000 products a day. Robots are not alone yet, though: the fully automated warehouse has four human helpers.

10 Chinese unicorns you haven't heard of yet, but will soon enough

Automation, growth, scale - the mega but still relatively unknown giant seems unlikely to slow down. Its revenues are growing 40 per cent a year, up to $55.7 billion in 2017. The company's spokespeople tell us proudly the firm is the third largest "internet company" in the world by revenue after Google and Amazon, but ahead of companies like Facebook, eBay and Alibaba, its biggest rival.

It has major backers such as Tencent - the largest internet company in China by market cap and the owner of WeChat. Other investors are Walmart, which has a ten per cent stake, and even Google, which last month announced it was investing $550 million into JD to help it expand further outside China.

And the e-commerce giant is busy doing just that. In January, it opened its first European office, in Paris. It now aims to open another one in Milan, and is actively partnering with Spanish and other European brands - especially luxury ones. In 2017, Chinese made up 32 per cent of the worldwide luxury market.

JD's response: last October, it launched Toplife, a platform for luxury buyers that can benefit from same-day deliveries and premium services, such as ultra-clean and secure warehouses with special air filters. Over just a few months, Toplife has already signed up 20 brands, including Saint Laurent and Alexander McQueen. Amazon beware.

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Inside JD.com, the giant Chinese firm that could eat Amazon alive

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Pujiang Pu is a smiley, medium-built man in his mid-forties with stylish glasses, a bling gold watch, and a red JD lanyard around his neck. Along with many of the 150,000 employees of JD.com - a city-size e-commerce store sometimes referred to as the Amazon of China - he lives in a free dorm near one of the company's 500 gigantic warehouses.