Sections

Managing Upstream Challenges In The Digital Oil And Gas Revolution

Brent Potts

In a recent episode of Gamechangers, Bonnie explored upstream challenges in the oil and gas industry regarding digitization. Her guests were Valerie Jalufka from Accenture, Chris Niven at IDC, and Stephane Lauzon from SAP.

Here’s what they discussed.

Pricing impacts on the industry

The recent fall in oil and gas commodity prices has had a strong impact on upstream companies. It’s impacted cash flow and high-risk project investment. Exploration and production involves finding, recovering, and reducing raw materials. It covers well location, construction, and operation. The recent focus has changed from drilling new wells to managing the efficiency of existing wells.

Understanding upstream helps everyone understand market prices. Exploration and production has been the key focus over the past 18 to 24 months.

Changing commodity prices drive automation and robotics. But many companies back off digitization because of the economic cycles common in the industry. How you move forward is vital to the success of your company. Innovation means you can keep your business moving to provide for the world’s energy needs. At the same time, you need to manage risks in capital spending.

Three trends in $50 oil

There are three trends in $50 oil. The first is the focus on innovation forming from upstream businesses. Smaller independent companies are changing the standard operations expected in the industry. In the shale boom, they bought up land and left industry leaders wondering where they could set production. This focus can make $50 a barrel the long-term goal.

The second trend is a shift to the customer’s perception. Digitalization and connectivity changes how customer engagement happens, and not only at the pump. It’s how we can engage land owners for exploration. Creating a customer focus allows you to better develop the necessary resources.

The third trend is more philosophical: It’s the belief that the only way to remain in the industry is to push through the changes. Oil companies around the world are hunkering down and consolidating their position after the changing commodity prices. Costs and employees have been cut, while production levels have been maintained. Overall, the businesses have become more agile. New reserves must be found to replace production that’s already been used.

Long-term changes

Oil and gas companies are no longer focused on the next few months, but on the next several years as they shift toward long-term planning. They strive to create a stable entity that is well positioned to come out of the downturn as industry leaders for the next decade.

Demand increases as population rises, especially in countries that are improving their living standards, such as China and India. Knowing how to deal with a range of oil prices from $20 to $120 a barrel is vital to success. Surveys show that the top IT initiatives involve digitization, citing the strong benefits of cloud, Big Data, automation, and analytics as their reason for innovating.

Innovation allows smaller and more agile companies to take advantage of changing conditions. They’re gaining more of the market share as large industry leaders take too long to shift. Modernization mixes cloud and master data management with Internet of Things (IoT) platform development, which enables companies to monitor oil field equipment as well as device and data system efficiency. This in turn allows automation.

Past and future digitization

Oil and gas involves a mix of technology lag and innovation. The environment in which oil is produced demands creative approaches to discovery and production. New technology is required to get hydrocarbons from miles below the surface. Industrial robots are expected to comprise one-fifth of today’s price within three years, while offering a 500% increase in capability. Sensors, tools, and capabilities will allow robotic automation. This may include machine vision, speech recognition, force sensing, and advanced mechanics.

Master data management helps define terms and processes across the business. When one Texas oil CEO asked his company how many oil wells they had, different departments offered different numbers based on their perception. A central platform helped unify the company’s approach. Real-time systems use IoT for devices, data systems, equipment and controllers. Automation provides connectivity across the oil field.

Investment in underwater submersible design and advancement is removing the need for tethering. Intelligent drill bits are being developed to work around particular certain geological structures. Intelligence will play into modeling production, PDPs, and similar data to help figure out where to drill. This information helps figure out costs, returns, depth, and other criteria that feed into drilling site locations. Further automation of oil rigs allows for multiple wells from one well pad using drones and robotics.

The oil and gas industry has been digitizing for 15 to 20 years. It’s now seeing an increase in sensors and automation combined with a push for digitization in daily activities. Accounting, data processing, and analytics must be added to the existing capability. Connectivity must be improved to finish the process. Predictive analytics, real-time systems, and an IoT environment will prevent downtime. Overall changes to the industry requires creating a meld of existing and new digital technology.

Learn more about digital transformation in the oil and gas industry.
Comments

About Brent Potts

Brent Potts is the senior director of Industry Marketing at SAP, responsible for the global marketing for the Oil & Gas industry. His specialties include product management, strategy, business development, and product marketing.

Predictive Procurement Gets Real

Marcell Vollmer

The physical and digital worlds have officially collided. In the old days, we’d have the morning paper delivered to our doorsteps and read it on the way to work while sipping coffee we made at home. Today, the news stories we care about are automatically delivered to our mobile devices, and we scan them while enjoying the beverage that was ready and waiting for us at the local coffee shop after we ordered it via mobile app. In years past, we attended events after work to expand our professional networks. Now we link to our peers — and their peers — around the world, online in real time.

Connecting the dots

As a society, we are more connected than ever. Thanks to the Internet of Things (IoT), we can see and be seen like never before. We can learn about the future and use this information to shape it to our advantage.

There are plenty of examples of this in the consumer world—for example, refrigerators that predict when you’re about to run out of milk and automatically order and have it delivered before you even notice, and devices that know you’re on your way home and turn on the lights before you get there.

It’s happening in procurement as well, and transforming the function as we know it. Procurement is complex and involves lots of moving parts, from sourcing and manufacturing to transportation and logistics. It’s an intricate web of systems, processes, and relationships that must be coordinated and managed, both internally and externally, to ensure that goods and services get delivered on budget and on time.

Predicting the future

Over the years, procurement has made great strides, leveraging disruptive forces such as business networks and cloud technologies to evolve from a tactical manual process to a strategic digital one. Paper orders and invoices are all but dead. Electronic payments are taking hold. Buyers and sellers are meeting and collaborating online.

Yet the transformation has only begun. Aided by Big Data and the IoT, procurement is becoming smarter and more predictive than ever.

Data is the lifeblood of any organization. From structured information on production, marketing, sales, HR, finance, facilities, and operations to transaction-level data on suppliers, customers, and partners, it tells the story of a business. For years, companies have been mining data simply to figure out what it all means—essentially, to learn from the past and perform better in the present.

Now they are leveraging advances in technology such as in-memory computing, real-time analytics, and the IoT to create assumptions about what will happen in the future and take actions that drive optimal outcomes.

Eliminating risk

Supply chains are more global than ever, and as a result, fraught with more risk. Many companies are turning to the IoT to anticipate and mitigate this risk before it disrupts their business. Consider the mining industry. Trucks are the critical link to transport raw materials to either further process or sell them on the market. If one of these trucks stands still due to maintenance issues, losses to the company could run into the millions, as they only can sell what they get out of a mine and deliver.

With the help of sensors, companies can continually monitor their fleets and receive notifications on upcoming maintenance needs to prevent breakdowns before they occur. Critical components such as engines and braking systems, for example, can be connected by small IoT sensors that monitor their temperature, hydraulic pressure, container angle, position, and vibrations. The sensors transmit all data to a live dashboard, and if a key parameter such as temperature changes, it will trigger an alert for the radiator. This information is then automatically routed to the procurement system, where a replacement order for radiator hose and radiator cleaner is automatically processed in line with the company’s procedures and policies. Related maintenance service is scheduled with a qualified technician who will arrive as soon as the material arrives and perform the work before a fatal defect of the radiator causes the truck to literally stop in its tracks. Risk avoided.

Delivering value

Supply chains are no doubt complex — and the data within them even more so. But data is the new global currency. And the IoT holds the key to unlocking its value. With the IoT, companies can not only spot patterns and trends in their business but anticipate risk and changes and adapt their businesses to gain advantage.

For more on how data analysis is transforming business, see Living The Live Supply Chain: Why You Need Data Scientists.

The article originally appeared in Spend Matters. It is republished by permission.

Comments

Marcell Vollmer

About Marcell Vollmer

Marcell Vollmer is the Chief Digital Officer for SAP Ariba (SAP). He is responsible for helping customers digitalize their supply chain. Prior to this role, Marcell was the Chief Operating Officer for SAP Ariba, enabling the company to setup a startup within the larger SAP business. He was also the Chief Procurement Officer at SAP SE, where he transformed the global procurement organization towards a strategic, end-to-end driven organization, which runs SAP Ariba and SAP Fieldglass solutions, as well as Concur technologies in the cloud. Marcell has more than 20 years of experience in working in international companies, starting with DHL where he delivered multiple supply chain optimization projects.

Ontario’s ‘Smart Cities’ Talk Digital Innovation

John Graham

“The government no longer has a monopoly on information. Our structures of government are outdated.”

Stephen Goldsmith, former deputy mayor of New York and now director of innovation at Harvard Kennedy School of Government, set the scene bluntly as he sat on a panel at SAP’s 2nd Annual Smart Cities Forum on March 7: “Citizens see the services they can get from the likes of Amazon, and want the same from government.”

Densely populated and growing cities around the world know the demands and challenges they face, but they are also waking up to the opportunities today’s technology brings. Fittingly playing host to the event after being named the world’s 8th-most-digitally innovative city in late February, Toronto unleashed its enterprise solutions services director Fazal Husain to the stage to talk about what the city is doing.

He led with the analogy that cities have to learn to walk before they can run in the digital economy. He explained that unlike corporations and other large organizations, which have a more vertical product line, the city has 44 different and varied services.

To tackle that challenge, the city began looking at it from the perspective of business rather than IT. That shifted the focus to the key business processes the entire enterprise builds on: HR, finance, payroll and procurement.

Once that foundation was set, it started to become a digital reinvention of the organization. Had it not been looked at it this way, and instead 15 disparate technologies were used to run the 44 lines of business, change would have incremental and insubstantial and the reinvention wouldn’t have been initiated. This is what Fazal defines as “the art of learning to walk.”

Thanks to all the work done behind the scenes in Toronto, learning to run as a world-class digital entity became a much more distinct possibility. The city is now looking realistically at web-enabled IoT technologies and working more effectively with community partners using its open data sets.

Toronto isn’t the only Ontario city making its name for itself as a smart city. Mississauga, an 800,000-strong city that’s grown rapidly in recent years as part of Toronto’s urban sprawl, was represented by CIO Shawn Slack at Smart Cities Forum. The city has enjoyed its own fair share of international attention and recognition as a burgeoning smart city, and for good reason.

Dealing with the pressures of GTA-wide traffic that could see up to six million people travelling through and around the city every day, Mississauga is well into the implementation of an advanced traffic management system, a network of 750 traffic light sensors linked to a data analysis dashboard.

Then there is the cloud-based, city-wide network of LED streetlights kitted out with radios and sensors that determine when they should be brighter or dimmer and send alerts when they need replacing. If that’s not impressive enough, how about a sensor network in the waterways that detects threats from heavy rainfall or pipe leaks, sending automated alerts to make life a lot easier for emergency services?

Not to be outdone, Joyce Evans, deputy city treasurer and director of revenue for the City of Kitchener, and Alex Ahkoon, manager of ERP business transformation, made the case for Kitchener’s emergence as a smart city.

Privileged to be home to world-class tech leaders and incubators in a region that is often referred to as “Silicon Valley North,” the city is using that to its advantage. Networks of smart LED streetlights, smart utilities services, and free public Wi-Fi are set to become the norm, and city staff members are working with local innovators at the famed Communitech Hub to take things even further.

It’s amazing to see the civic innovation taking shape across southern Ontario. As a resident of Toronto, I can feel that I’m right in among something special, and Smart Cities Forum only made that feeling stronger.

Read about another Canadian smart city initiative: Mississauga: Canada’s Rapidly Growing Smart City.

Comments

John Graham

About John Graham

John Graham is president of SAP Canada. Driving growth across SAP’s industry-leading cloud, mobile, and database solutions, he is helping more than 9,500 Canadian customers in 25 industries become best-run businesses.

How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


Comments

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

Tags:

In An Agile Environment, Revenue Models Are Flexible Too

Todd Wasserman

In 2012, Dollar Shave Club burst on the scene with a cheeky viral video that won praise for its creativity and marketing acumen. Less heralded at the time was the startup’s pricing model, which swapped traditional retail for subscriptions.

For as low as $1 a month (for five two-bladed cartridges), consumers got a package in the mail that saved them a trip to the pharmacy or grocery store. Dollar Shave Club received the ultimate vindication for the idea in 2016 when Unilever purchased the company for $1 billion.

As that example shows, new technology creates the possibility for new pricing models that can disrupt existing industries. The same phenomenon has occurred in software, in which the cloud and Web-based interfaces have ushered in Software as a Service (SaaS), which charges users on a monthly basis, like a utility, instead of the typical purchase-and-later-upgrade model.

Pricing, in other words, is a variable that can be used to disrupt industries. Other options include usage-based pricing and freemium.

Products as services, services as products

There are basically two ways that businesses can use pricing to disrupt the status quo: Turn products into services and turn services into products. Dollar Shave Club and SaaS are two examples of turning products into services.

Others include Amazon’s Dash, a bare-bones Internet of Things device that lets consumers reorder items ranging from Campbell’s Soup to Play-Doh. Another example is Rent the Runway, which rents high-end fashion items for a weekend rather than selling the items. Trunk Club offers a twist on this by sending items picked out by a stylist to users every month. Users pay for what they want and send back the rest.

The other option is productizing a service. Restaurant franchising is based on this model. While the restaurant offers food service to consumers, for entrepreneurs the franchise offers guidance and brand equity that can be condensed into a product format. For instance, a global HR firm called Littler has productized its offerings with Littler CaseSmart-Charges, which is designed for in-house attorneys and features software, project management tools, and access to flextime attorneys.

As that example shows, technology offers opportunities to try new revenue models. Another example is APIs, which have become a large source of revenue for companies. The monetization of APIs is often viewed as a side business that encompasses a wholly different pricing model that’s often engineered to create huge user bases with volume discounts.

Not a new idea

Though technology has opened up new vistas for businesses seeking alternate pricing models, Rajkumar Venkatesan, a marketing professor at University of Virginia’s Darden School of Business, points out that this isn’t necessarily a new idea. For instance, King Gillette made his fortune in the early part of the 20th Century by realizing that a cheap shaving device would pave the way for a recurring revenue stream via replacement razor blades.

“The new variation was the Keurig,” said Venkatesan, referring to the coffee machine that relies on replaceable cartridges. “It has started becoming more prevalent in the last 10 years, but the fundamental model has been there.” For businesses, this can be an attractive model not only for the recurring revenue but also for the ability to cross-sell new goods to existing customers, Venkatesan said.

Another benefit to a subscription model is that it can also supply first-party data that companies can use to better understand and market to their customers. Some believe that Dollar Shave Club’s close relationship with its young male user base was one reason for Unilever’s purchase, for instance. In such a cut-throat market, such relationships can fetch a high price.

To learn more about how you can monetize disruption, watch this video overview of the new SAP Hybris Revenue Cloud.

Comments