These prediction models become the basis for most business decisions. Thus machine learning is more than a technological innovation; it will transform the way business is conducted as human decision making is increasingly replaced by algorithmic output. Ant Microloans provides a striking example of what this future will look like.
When Alibaba launched Ant, in , the typical loan given by large banks in China was in the millions of dollars. Banks were reluctant to service companies that lacked any kind of credit history or even adequate documentation of their business activities. As a consequence, tens of millions of businesses in China were having real difficulties securing the money necessary to grow their operations.
At Alibaba, we realized we had the ingredient for creating a high-functioning, scalable, and profitable SME lending business: the huge amount of transaction data generated by the many small businesses using our platform. In , we bundled this lending operation together with Alipay, our very successful payments business, to create Ant Financial Services. We gave the new venture that name to capture the idea that we were empowering all the little but industrious, antlike companies. How is this possible?
When faced with potential borrowers, lending institutions need answer only three basic questions: Should we lend to them, how much should we lend, and at what interest rate? Once sellers on our platforms gave us authorization to analyze their data, we were well positioned to answer those questions. Our algorithms can look at transaction data to assess how well a business is doing, how competitive its offerings are in the market, whether its partners have high credit ratings, and so on.
Ant uses that data to compare good borrowers those who repay on time with bad ones those who do not to isolate traits common in both groups. Those traits are then used to calculate credit scores. All lending institutions do this in some fashion, of course, but at Ant the analysis is done automatically on all borrowers and on all their behavioral data in real time. At the same time, the algorithms that calculate the scores are themselves evolving in real time, improving the quality of decision making with each iteration. The algorithms might, for example, analyze the frequency, length, and type of communications instant messaging, e-mail, or other methods common in China to assess relationship quality.
This work requires both a deep understanding of the business and expertise in machine-learning algorithms. Consider again Ant Financial. If a seller deemed to have poor credit pays back its loan on time or a seller with excellent credit catastrophically defaults, the algorithm clearly needs tweaking. Engineers can quickly and easily check their assumptions. Which parameters should be added or removed? Which kinds of user behavior should be given more weight?
To become a smart business, your firm must enable as many operating decisions as possible to be made by machines fueled by live data rather than by humans supported by their own data analysis. Transforming decision making in this way is a four-step process. Ant was fortunate to have access to plenty of data on potential borrowers to answer the questions inherent in its lending business. For many businesses, the data capture process will be more challenging.
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But live data is essential to creating the feedback loops that are the basis of machine learning. Consider the bike rental business. Start-ups in China have leveraged mobile telephony, the internet of things in the form of smart bike locks , and existing mobile payment and credit systems to datafy the entire rental process.
Renting a bike traditionally involved going to a rental location, leaving a deposit, having someone give you a bike, using the bike, returning it, and then paying for the rental by cash or credit card. Several rival Chinese companies put all of this online by integrating various new technologies with existing ones.
A crucial innovation was the combination of QR codes and electronic locks that cleverly automated the checkout process. By opening the bike-sharing app, a rider can see available bicycles and reserve one nearby. Once the rider arrives at the bicycle, he or she uses the app to scan a QR code on the bicycle. Assuming that the person has money in his or her account and meets the rental criteria, the QR code will open the electronic bike lock. When the bike is returned, closing the lock completes the transaction. The process is simple, intuitive, and usually takes only several seconds.
Datafying the rental process greatly improves the consumer experience. On the basis of live data, companies dispatch trucks to move bikes to where users want them. They can also alert regular users to the availability of bikes nearby. Thanks in large part to these innovations, the cost of bike rentals in China has fallen to just a few cents per hour. Most businesses that seek to be more data-driven typically collect and analyze information in order to create a causal model.
The model then isolates the critical data points from the mass of information available. That is not how smart businesses use data. Instead, they capture all information generated during exchanges and communications with customers and other network members as the business operates and then let the algorithms figure out what data is relevant.
In a smart business, all activities—not just knowledge management and customer relations—are configured using software so that decisions affecting them can be automated. This does not mean that a firm needs to buy or build ERP software or its equivalent to manage its business—quite the opposite. Traditional software makes processes and decision flows more rigid and often becomes a straitjacket.
In contrast, the dominant logic for smart business is reactivity in real time.
The first step is to build a model of how humans currently make decisions and find ways to replicate the simpler elements of that process using software—which is not always easy, given that many human decisions are built on common sense or even subconscious neurological activity. The growth of Taobao, the domestic retailing website of Alibaba Group, is driven by continuous softwaring of the retailing process.
One of the first major software tools built on Taobao was an instant message tool called Wangwang, through which buyers and sellers can talk to each other easily. Using the tool, the sellers greet buyers, introduce products, negotiate prices, and so on, just as people do in a traditional retail shop. Alibaba also developed a set of software tools that help sellers design and launch a variety of sophisticated online shop fronts.
Once online shops are up and running, sellers can access other software products to issue coupons, offer discounts, run loyalty programs, and conduct other customer relationship activities, all of which are coordinated with one another. Because most software today is run online as a service, an important advantage of softwaring a business activity is that live data can be collected naturally as part of the business process, building the foundation for the application of machine-learning technologies.
In ecosystems with many interconnected players, business decisions require complex coordination. Its transaction systems need to work with discount offers and loyalty programs, as well as feed into our logistics network. New software had to be broadly interoperable with all other software on the platform to be of any value. So in , Taobao began developing APIs for use by independent software suppliers.
Getting the technical infrastructure right is just the beginning. Additionally, figuring out the right incentive structures to persuade companies to share the data they have is an important and ongoing challenge. Much more work is needed. But the direction is very clear: The more data flows across the network, the smarter the business becomes, and the more value the ecosystem creates.
Once a business has all its operations online, it will experience a deluge of data. To assimilate, interpret, and use the data to its advantage, the firm must create models and algorithms that make explicit the underlying product logic or market dynamics that the business is trying to optimize. This is a huge creative undertaking that requires many new skills, hence the enormous demand for data scientists and economists. What defines companies that have achieved operational excellence in the shale space and are thriving as a result? Below we discuss eight characteristics common to winning businesses.
See Exhibit 2. Shale development is not about maximizing absolute production; rather, it is about maximizing profitable production. Hence, the aim is to optimize development capital and costs per barrel. Developing a play that has lower estimated recoverable reserves—but has the necessary roads, pipeline infrastructure, water-delivery options, and so forth—can often be more profitable than pursuing a play that has significantly higher reserves but lacks the necessary infrastructure. In the shale arena, once you know that reserves are present, success is more about spending money wisely across many wells.
In fact, companies must orient themselves toward viewing every decision they make through the lens of return. Some of the most successful players, for example, build well pads and facilities assuming a ten-year life cycle for their wells. Other companies construct more robust, more sustainable and more expensive facilities assuming a year life cycle. Given that a shale well may see 60 to 75 percent of its total lifetime production in the first ten years of operation, however, the question must be asked: is the extra money worth it? Players focused on the return on every invested dollar often say no.
More important, they know to do the math to inform their decision rather than simply assuming that the year well and related facilities will be worth the additional investment. Companies can take quite different paths to optimizing well IRR. Exhibit 3 highlights two top-quartile players operating in the Delaware Basin that employ two very different operating models. This is a must-have for any company that hopes to thrive in this business.
The value of being return driven, even down to the individual-well level, is tangible to shareholders. For EOG, however, this was by design: the company sought to optimize well returns and was willing to spend more to do so. How can a company tell if it is sufficiently return driven?
A prominent red flag—a sign that a company has fallen off track—is development decision making that is based primarily, or solely, on geology. Another is infrastructure-related delays in the execution of a well, which would indicate that infrastructure constraints were not sufficiently considered and addressed up front. For these companies, there is essentially an assembly line: bottom-hole locations are identified, permits are secured, pads are constructed, and wells are drilled and completed in quick succession.
This allows the company to efficiently develop each area before moving on to the next. The operator must ensure that the number of wells it drills and puts on production is consistent with those goals; this alignment can be operationalized by determining the required cycle time for each step in the development cycle and configuring the steps to ensure that there is a continuous flow of activity through the factory.
During the execution phase of the cycle, this model enables a repeatable, predictable flow of pads through execution, which optimizes resources and can lead to huge gains in efficiency. An operator running ten rigs in the Permian Basin that has a top-quartile cycle time for a four-well pad—a spud-to-POP time of days—can meet its production targets with one fewer rig than an operator whose cycle time is average approximately days.
Care must be taken to ensure that the factory model, once established, continues to run smoothly. If there is a structural bottleneck in the line—such as too few completion crews to keep up with the rigs, leading to a buildup of inventory—the problem must be addressed and the line rebalanced to ensure efficient, steady-state performance.
The same holds if execution is not repeatable and predictable. To be sure, there will always be instances when a tool is dropped down the hole or a water-delivery mishap delays completions. But these types of disruptions should be few and far between. Red flags indicating that the factory model is faltering include frequent hiccups in scheduling especially problematic is excessive standby time, during which rigs or crews are idle or underutilized , little or no visibility into the next 12 to 24 months of the execution schedule, and ongoing troubleshooting of the schedule and reallocation of resources.
Top-quartile operators within a basin derive a median 15 percent of their global production from that basin; bottom-quartile players derive only 2 percent. This focus helps these players more quickly identify and develop expertise in the most important determinants of competitive advantage within that basin. Additionally, the drivers of advantage can vary considerably among basins, given underlying differences in the basins themselves. How much do basins differ? Geologically, the differences are quite stark. This can force players to completely rethink fracturing operations, including decisions on long-distance hauling of produced water versus on-site systems for water treatment and reuse which become more economical in an environment where transportation and disposal costs are high.
Successfully navigating the differences among basins, and the challenges those differences pose, requires more than ad hoc tactics.
It requires comprehensive and integrated basin-specific strategies. Although many basin-specific strategies, and the knowledge gleaned from implementing them, are localized, many can also be used across other basins. The goal for multibasin players is to quickly, before wasting much time and money, identify what can and cannot be translated from one basin to another. Best-in-class players punch out wells across a shale basin in quick succession and with excellent repeatability, largely driven by the fact that the companies are essentially drilling the same well over and over again.
Standardization of well design and execution practices allows good operators to perfect their work and become great. Top performers then optimize these strategies and tactics to become best in class. They also strive for, and attain, consistent execution, enabled by their use of such things as standardized but customized to each rig field checklists that go beyond standard operating procedures to simplify various activities. These checklists can offer additional guidance as well, such as directions for drillers on how to drill through problematic formations. Successful companies in this arena are typically run with a strong business, rather than science, orientation.
This means that for engineers and scientists who aspire to constantly work on unique or complex design challenges, a highly efficient shale company might not necessarily be the most satisfying place to work.
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It should be noted that, properly managed, the pursuit of standardization should not restrict innovation. Rather, it should control and focus innovation while allowing the company to maintain strong, reliable performance in the field. Nimble shale operators have well-structured programs that foster controlled experimentation by internal teams that is aimed at delivering concrete business improvement through innovation.
One company, for example, encourages its completion engineers to experiment with different proppant-loading ratios in pursuit of an effective, scalable result. Once a concept is proven, the company incorporates it into its standards. The results of these efforts can be substantial. Red flags here include intensive design processes—for example, the practice of starting from scratch on well design and the need for multiple weeks or months of design work or rework , decision making, and approvals to develop the final design.
Well designs should be about 90 percent standardized, with only relatively small tweaks needed at individual drill locations. Other red flags include ongoing ad hoc adjustments to well designs and execution practices, resulting in reduced learning-curve benefits and inconsistent, expensive execution. Best-in-class players in the shale arena are obsessed with controlling costs.
Penny-pinching is not only a virtue in this business but also an absolute requirement for making money, especially in the current environment. Players that manage costs best know their data inside out and up and down the organization, from the president of the company to the rig foreman in the field, and individuals are accountable for the costs they influence. Critical to being able to properly manage costs and identify potential hot spots is access to real-time cost data and a commitment to capturing and reporting it weekly, if not daily.
This does not, it should be stressed, necessitate a fancy, expensive IT tool. In fact, some very successful companies collect important data through smartphone photos of signed invoices taken on the pad; the pictures are forwarded to data-entry personnel at headquarters, allowing nearly immediate data capture and tracking. Successful players also document changes in contractor scope and materials—and any additional costs associated with such changes—in real time and demand hard and fast day payment terms so that line items can be verified as soon as is reasonable.
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In the end, effective cost control is about capturing the data and having everyone in the company looking at it—and being held accountable for it—each and every day. The prize for getting it right can be significant. A company operating in the Permian Basin, for example, embarked on a focused, yearlong, cross-functional cost-improvement program and managed to maintain its per-well investment return despite a 25 percent drop in oil prices.