Cafe X and Amazon Go: Auditing a robot-operated store?

By now you've probably heard of the robot-barista - Cafe X.  If not check out this video from Wired, where David Pierce walks us not only through how the robot will make your latte, but why he thinks it better than the human alternative:



Amazing isn't it?

In a presentation I did last year on how these forces of automation could impact auditing & accounting, I noted it's easier to see how technology disrupts someone other than you.

And so it looks like baristas have met their match.

As Pierce notes in the video, the inconvenience of dealing with imperfect people is something that most people want to avoid in the rat-race we live in: who wants the barista to remake your coffee 11 times as he says? ;) 

The Wired article also notes that Cafe X is 'high-quality at a cheaper price': 

"Surprisingly delicious coffee, starting at $2.25—cheaper than you’d find at Sightglass or even Starbucks. Cafe X’s location in the corner of the Metreon may not entice you out of your daily routine."

Amazon Go: Walkthrough Technology 
Amazon has also wowed the "techthusiasts" out there with their cashier-less store concept:



In the FAQ section, Amazon summarizes how this cashier-less store works:

"Our checkout-free shopping experience is made possible by the same types of technologies used in self-driving cars: computer vision, sensor fusion, and deep learning. Our Just Walk Out Technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt."

Although this has the potential to revolutionize retail, Amazon has experienced some setbacks of late. The store can allegedly only handle 20 people at a time. So there maybe some kinks to work out before this goes mainstream.

Obviously, this could have a massive impact on entry level jobs: most of us who were young a while ago relied on these McJobs for spending money and funding our college/university tuition. They also gave students some practical work experience to help land a career accounting profession ;)

But let's save this discussion for a future post.

How would you audit cashier-less stores, like Cafe X or Amazon Go?

The retail industry has been a manual intensive industry that requires cashiers, stock room personnel and the like. Such a process naturally requires policies and procedures (aka internal controls) that ensure that merchandise makes it from the shelf to the cash register and into the customers possession. And there are those anti-theft mechanisms to prevent shoplifting as well. In the industry, "shrinkage", the amount of merchandise that is stolen, robbed, damaged, etc, is estimated by the National Retail Federation to be 1.38% of sales or $45.2 billion for 2015.

Cafe X and Amazon Go offer a glimpse into how automating traditional businesses can alter these fundamental risks that impact the way we go about conducting our financial audits.

With Cafe X, shrinkage is almost eliminated as there is no humans involved in the production process. Once the kiosk is loaded up with cups, coffee, syrup, sugar, milk, etc. the system is essentially fully automated - no manual intervention by baristas or customers.

Amazon Go, on the other hand, uses a whole lot of automation that is watching and analyze every move of the customers (and employees) throughout the store. Consequently, this would not be the store to steal from! And let's not forget Amazon is experimenting with those drones and are we really sure that they are unarmed?


Given this level of automation of the actual business process and controls, could auditors stick to the tried, tested and true retail audit procedures? Or would this enable a more automated approach?

I was directly involved with the recent test-audit of the blockchain involving loyalty points. One of the realities of auditing such exponential technologies is that it makes controls testing a must. For example, for the financial auditor to rely on the digital signatures there needs to be some testing around the wallets to ensure that the signatures are reliable.

Consequently, testing such automated stores would require either a SOC2 or modified SOC report to meet the needs of such a store. For example, the SOC2 would need to have some way of having comfort of how the stock and inventory gets loaded into the store. Likely the auditor would rely on the automated process which the store uses to replenish stock, but it's that hand off between the delivery person (assuming it's still human) that would be the area there is a risk of shrinkage. For example, how does legitimately damaged inventory get accounted for at that point? Whatever process and controls Amazon/Cafe X put in place would need to be tested from a controls perspective.

For the substantive component, I think that's where things get interesting: enter the "embedded audit module". This concept has been around since at least 1989. The idea is that the auditor installs independent software onto the client's system and then transmits it back to the auditor, who uses it as a basis for conducting the necessary audit procedures and tests. The core idea is that the auditor has full control over such a system and the client cannot tamper with the code.

What would be relatively straightforward would be the data capture-component: sales data, stock data, spoilage, etc. would be uploaded from the automated store right into the auditor's system. But this then requires the additional step of verifying the data to independent source documents (e.g. invoices, purchase orders, etc.). In other words, the audit procedure would still require manual intervention as the auditee would need to send this information back to the auditor to complete their audit.

Where I think the audit innovation would be is exploring how video footage can act as a substitute for physical/direct observation by the auditor. That is, could the auditor install a video camera in the automated store as a part of the EAM that would then act as actual independent audit evidence of the actual sale or purchase? For example, in the Cafe X example the auditor could actually use the footage and the visual software to count the cups sold that day and reconcile that to the sales data transmitted back from the EAM for the day?

Although one can argue such transactions are not material and therefore such procedures are overkill.

However, I think now is the right time to conduct experiments and test audits to see whether we can reinvent the classic audit to meet the technology of today. In a future post, we will explore what this means broadly for jobs and more specifically how this could impact the profession.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

How would you explain BigData to a business professional? (Updated)

Most people are familiar with the 4 Vs definition of Big Data: Volume, Variety, Velocity and Veracity. (And if you are not here is an infographic courtesy of IBM:)

I have written about the Big Data in the past, specifically, on its implication on financial audits (here, here, and here) as well as privacy. However, I was meeting with people recently and were discussing big data and I found that business professional understood what it was divorced from it operational implications. This is problematic as the potential for big data is lost if we don't understand how big data has changed the underlying analytical technique.

But first we must look at the value perspective: how is big data different from the business intelligence techniques that business have used for decades?

From a value perspective, big data analytics and business intelligence (BI) ultimately have the same value proposition: mining the data to find trends, correlations and other patterns to identify new products and services or improve existing offerings and services.

However, what Big Data really is about is that previous analytical technique that was limited due to technological constraints no longer exists. What I am saying is that big data is more about how we can do analysis differently instead of the actual data itself. To me big data is a trend in analytical technique where the volume, variety, or velocity is no longer an issue in performing analysis. In other words - to flip the official definition into an operational statement - the size, shape (e.g. unstructured or structured), speed - is no longer an impediment to your analytical technique of choice.

And this is where you, as a TechBiz Pro, need to weigh the merits of walking them through the technological advances in the NoSQL realm. That is, how did we go from the rows & columns world of BI to the open world of Big Data?  Google is a good place to start. It is pretty good illustration of big data techniques in action: using Google we get extract information from the giant mass of data we know as the Internet (volume), within seconds (velocity) and regardless if it's video, image or text (variety). However, Internet companies found that the existing SQL technologies inadequate for the task and so they went into the world of NoSQL technologies such as Hadoop (Yahoo), Cassandra (Facebook), and Google's BigTable/MapReduce. The details aren't really important but the importance lies in the fact that these companies had to invent tools to deal with the world of big data.

And this leads to how it is has disrupted the conventional BI thinking when it comes to analysis.

From a statistical perspective, you no longer have to sample the data and extrapolate to the larger population. You can just load up the entire populations, apply your statistical modeling imagination to it and identify the correlations that are there.  Chris Anderson, of Wired, noted that this is a seismic change in nothing less than the scientific method itself. In a way what he is saying is that now that you can put your arms around all the data you no longer really need a model. He did get a lot of heat for saying this, but he penned the following to explain his point:

"The big target here isn't advertising, though. It's science. The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years.

But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. Consider physics: Newtonian models were crude approximations of the truth (wrong at the atomic level, but still useful). A hundred years ago, statistically based quantum mechanics offered a better picture — but quantum mechanics is yet another model, and as such it, too, is flawed, no doubt a caricature of a more complex underlying reality. The reason physics has drifted into theoretical speculation about n-dimensional grand unified models over the past few decades (the "beautiful story" phase of a discipline starved of data) is that we don't know how to run the experiments that would falsify the hypotheses — the energies are too high, the accelerators too expensive, and so on."

Science aside the observation that Chris Anderson makes has big implications for business decision making. Advances in big data technologies can enable the deployment of statistical techniques that were previously not feasible and can yield insights without having to bother with model development. Statisticians and data scientists can play with the data and find something that works through trial and error. From financial audit perspective, this has tremendous implications - once we figure out the data extraction challenge. And that's where veracity comes in, which is the topic of a future blogpost.

But to close on a more practical level, companies such as Tesco are leveraging big data analytics to improve their bottom. An example, courtesy of Paul Miller from the Cloud of Data blog/podcast site, is how Tesco extracted the following insight: “[a] 16 degree sunny Saturday in late April will cause a spike. Exactly the same figures a couple of weeks later will not, as people have had their first BBQ of the season”. In terms of overall benefits to the company, he notes “Big Data projects deliver huge returns at Tesco; improving promotions to ensure 30% fewer gaps on shelves, predicting the weather and behaviour to deliver £6million less food wastage in the summer, £50million less stock in warehouses, optimising store operations to give £30million less wastage.”