Let’s go digital, digital! If you don’t do something about digital, you will be left behind or disrupted into extinction – this is a message that energy executives and CXOs often hear, especially in the last year or so from consultants, vendors, industry experts or the IT organization. But why now? It’s not like digital is a brand-new concept. Energy companies have been transforming digitally since the late 1990s, when ERP systems helped lead the digital wave

Today, companies are faced with some compelling new choices, like robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), blockchain and Industrial Internet of Things (IIoT), to name a few. CXOs have the daunting task of deciphering what these buzzwords mean, understanding what is relevant to their business and determining in which technology to invest. It’s important that CXOs have a foundational knowledge of digital transformation because they will rely on digital business to make their numbers. It will be hard for the CXOs to lead digital initiatives if they don’t understand digital clearly. A lack of understanding can result in misdirection of efforts and painful experiences, and ultimately place the organizational transformation goals in jeopardy.

In this blog, we will look at four foundational concepts: digital, digitization, digitalization, and digital transformation. These concepts are often misunderstood, and not just semantically. We will also decrypt the digital alphabet soup by explaining and illustrating some high-level use cases for some of the newer digital technologies (RPA, AI, ML, Blockchain and IIoT).

What does digital really mean?

According to Gartner IT Glossary, digital is the “representation of physical items or activities through binary code. When used as an adjective, it describes the dominant use of the latest digital technologies to improve organizational processes; improve interactions between people, organizations and things; or make new business models possible.” Simply put, it is the way of doing something that creates value by leveraging technology.

What does digitization mean?

According to Gartner’s IT Glossary, digitization is the “process of changing from analog to digital form” – a definition few would disagree with. Digitization refers to creating a digital representation of analog or physical artifacts (e.g., paper documents) into a digital format (0’s and 1’s). The data itself is not changed; it is encoded in a digital format so the information can be used by a computing system for further processing by using technologies to extract information from a scanned document, process automation, etc. While digitization can help provide efficiencies, the core processes and systems still largely mimic the analog process.

What does digitalization mean?

Digitalization is often used interchangeably with digitization, but it really means something very different. Gartner’s IT Glossary defines digitalization as "the use of digital technologies to change a business model and provide new revenue and value-producing opportunities.” It is the process of moving to a digital business. One way to think about digitization vs. digitalization is that digitization deals with the information, while digitalization deals with the process.

What does digital transformation mean?

George Westerman, MIT principal research scientist and author of Leading Digital: Turning Technology Into Business Transformation defines digital transformation as “a radical rethinking of how an organization uses technology, people and processes to fundamentally change business performance.”

In general terms, digital transformation can be thought of as integration of digital technology into all areas of a business resulting in fundamental changes to how businesses operate and how they deliver value to their stakeholders (employees, vendors, customers, etc.) to help the organizations compete effectively in an increasingly digital world. 

In many ways, digital transformation is a misnomer, because digital is not all about technology. Digital transformation is about solving a business problem or developing a new approach where the technology is an enabler and never the driver. It is about how a technology can help a company rethink the way in which it conducts business and change the stakeholders' (customers, vendors, employees) experience, and it’s about adaptation. This sometimes means walking away from longstanding business processes that companies were built upon in favor of relatively new practices that are still being defined. Think Uber, Lyft, Netflix, Airbnb.

Another key point to note with digital transformation is that it is not a one and done exercise; rather, it is a mindset, a paradigm shift that allows the organizations to continually improve and ultimately develop a level of digital maturity in order to keep up with the rapidly evolving technological advances.

Overview of newer digital technologies (RPA, AI, ML, blockchain, IoT) and common applications in the energy industry

Now that we have covered the four foundational D’s (digital, digitization, digitalization, and digital transformation), let’s take a look at some of the newer digital transformation technologies, their benefits and some practical use cases.

Robotic Process Automation (RPA)

In the simplest terms, RPA can be described as a technology that allows for configuration of a computer software or a bot/robot to replicate human actions and tasks, especially those that are repetitive in nature, only done substantially better. The bot never sleeps, is cost effective and is not prone to any errors or distractions. They can log in to any of the applications, open files, move files, scrape external websites, copy and paste data and fill in forms, to name a few of its uses.

In some respects, RPA is nothing new. Businesses have been looking to add technology and automation for decades now to increase efficiencies, improve quality and reduce costs. Cost reduction has been and remains a major driver of automation. However, RPA promises much more than simple cost cutting. While prior efforts at automation focused on data processing, RPA focuses on automating the steps between automated processes. The beauty of RPA, and why companies like it so much, is that it enables customers to bring a level of automation to legacy processes without having to rip and replace the legacy systems.

One concern is that employees will lose their jobs due to automation, but this is not the case. RPA takes on the dull, repetitive and time-consuming work and allows employees to focus on other tasks. Organizations that deploy RPA effectively can redeploy their people into high-value-added roles that require judgement, empathy and cognition, things not readily available through machines (well, at least not yet!).

Key benefits of RPA:

• Increased throughput
• Reduced human error
• Improved internal processes
• Decreased costs and deployment times
• Quick and attractive ROI

High-level use cases of RPA in the energy industry:

• Document scanning and management – RPA combined with optical character recognition (OCR) software can be utilized to scan paper documents and convert them into digital formats which can then be written to a database or entered into another system using the bot. This capability can be applied to companies that have a high volume of paper documents (e.g., customer onboarding, invoices, trade confirms, truck tickets, etc.) to streamline the underlying processes including matching and reconciliation of this counterparty-supplied information against its own system(s) of record.
• Joint venture (JV) accounting – RPA can help energy companies automate their JV accounting process, which often entails extensive amounts of data massaging. With the bot’s ability to be operational round-the-clock, companies can expect a significant improvement in its throughput and accuracy. Automating these traditionally manual and repetitive tasks can free up resources to focus on value-added activities and increase employee productivity and morale.
• Month-end close – RPA bots can help energy companies save a tremendous number of manhours by automating and expediting its month-end close processes, from account reconciliation and journal entry to final reporting, while at the same time improving accuracy and auditability and reducing the risk of human error. Automation of this process can allow the functional leads to manage by exception instead and allows accountants and finance departments as a whole to work more efficiently while at the same time lowering risk.
• Trade lifecycle – RPA can support many areas within the trade lifecycle that are traditionally challenging, for example:
  • Nominations and processing of scheduling data
  • Risk and compliance reporting
  • ETRM – ERP reconciliation

Artificial Intelligence (AI)

From Siri to self-driving cars, AI is progressing rapidly and is exceptionally wide in scope. According to Andrew Moore, former dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), this “intelligent” behavior is known as artificial intelligence. For example, Google’s AI DeepMind learned how to play chess at a professional level in just a few hours. Two more examples that are common in day-to-day life are Alexa and Siri.

Key benefits of AI:

• Increased precision, accuracy and speed of information handling
• Translate information into knowledge
• Round-the-clock availability
• Ability to deploy in challenging environments

High-level use cases of AI in the energy industry:

From meeting the demands of a transitioning energy market urgently in need of cleaner and more efficient energy, to improving safety on the forecourts of its service stations, AI is at the top of the agenda at many of the major companies. The most popular use cases in the energy sector seem to be focused on predictive maintenance and deploying AI-capable robots for exploration and production to improve productivity, reduce costs and improve worker safety.

• Improved productivity in exploration and production – Earlier this year, BP invested in Houston-based technology startup Belmont Technology to bolster the company’s AI capabilities, developing a cloud-based geoscience platform nicknamed “Sandy.” Sandy allows BP to interpret geology, geophysics, historic and reservoir project information, and create unique “knowledge-graphs.”
• Predictive maintenance in the power and renewable sectors – The shift in resources toward using more renewable sources is challenging to manage in an integrated grid environment due to its intermittent nature. This is an area where AI can have a huge impact. AI can be used in this situation to predict weather forecasts and the corresponding impact on the supply and demand of power. Access to these predictions allows for smarter and more timely energy storage decisions; either store the excess supply or augment the decreased supply of power due to weather conditions or other constraints in the grid. Over time, as this technology collects droves of data from millions of integrated devices across the grid, coupled with rapid advancement of “deep learning” algorithms, AI will be able to revolutionize the way the power grids are managed, from balancing supply and demand to managing grid congestion and even bolstering grid security.

Machine Learning (ML)

One of the most hyped terms of today is ML, and it's usually heard in conjunction with AI. While related, they are not the same thing. ML is a subset of AI. As the name suggests, ML can be loosely interpreted to mean empowering computer systems with the ability to “learn.” The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions efficiently.

Together, these technologies allow the organizations to improve their overall customer experience by means of automating work processes. ML and AI also boost their employee performance and, most importantly, develop intelligent machines to provide them assistance in their day-to-day functioning.

Key benefits of ML:

• Allows reduction in time spent and efficient utilization of resources.
• Potential to unlock continuous improvement opportunities in large and complex process environments.
• Review and transform large data sets into knowledge and actionable intelligence to facilitate real-time decision making.

High-level use cases of ML in the energy industry:

Machine learning is transforming the way in which oil and gas is discovered and developed, allowing companies to gather large volumes of information in real-time and translate data sets into actionable insights. They now need to view data as an extremely valuable resource, with huge upside for companies with innovative, robust machine learning strategies.

In the ongoing "new norm" low commodity price environment, saving time, reducing costs, boosting efficiencies and improving safety are all crucial outcomes that can be realized from using ML in energy operations.

Given the tightly coupled nature of AI and ML, one must consider these two technologies in conjunction; refer to the previous section for ML relevant use cases.

Blockchain

Blockchain is a powerful peer-to-peer network technology that uses advanced computer science techniques to efficiently enable completely trustworthy interactions between parties, even if they don’t completely trust each other, with no third-party involvement.

In a nutshell, it is a shared electronic ledger that can be accessed and managed by multiple parties and yet is extremely secure (cryptographically encrypted), reliable (built-in disaster recovery as there is no central point of failure), immutable (extremely difficult to alter after created and approved) and provides near real-time updates of data across nodes.

Although blockchain seems to be generating the most buzz in financial services, the networked infrastructure of the energy industry makes it particularly suited for blockchain technology applications. With the rise of the Industrial Internet of Things (IIoT), the entire energy industry may soon find its operations transformed into a vast global network of connected devices, all feeding digital data into blockchain-enabled platforms that can capture and share information in real time.

Key benefits of blockchain:

• Greater transparency
• Increased efficiencies and reduced costs
• Potential to eliminate intermediaries
• Immutability
• Higher resiliency and security

High-level use cases of blockchain in the energy industry:

Key blockchain opportunity areas in the energy industry are vast and extend from land administration, trading and marketing to supply chain, finance and inventory operations. Blockchain is gaining trust at the enterprise level by succeeding at well-defined and practical pilots that have demonstrated the potential to scale into production. Here are a few use cases, platforms and movements that have recently taken shape.

• “Smart contracts” – Energy companies have started piloting blockchain-powered, self-verifying and self-executing agreements that function autonomously when engaging with vendors and engineering, procurement and construction companies. Sophisticated smart contracts can realize cash flow once shipment is confirmed and make payment on feedstock if costs or volumes reach the pre-defined thresholds. The contract automatically executes its terms when conditions are met, reducing human involvement in completing a deal.
• VAKT – This London-based blockchain startup is tackling the global supply chain of big energy companies using distributed ledger technology and is offering a physical trade platform (North Sea-focused, initially) that covers the lifecycle of the trade, from trade entry to final settlement, eliminating reconciliation and paper-based processes. This blockchain-enabled platform promises significant improvements in efficiency and costs which are traditionally paper-based, manual and prone to errors. The platform, backed by companies such as BP, Shell, Chevron, Total, Gunvor and Mercuria and banks such as ING, ABN Amro and Soc Gen, will allow its participants to have access to a single, indisputable source of truth along with the ability to share mutually beneficial information in a safe and secure manner.
• Komgo – This Geneva-based platform, which includes a mix of companies that are involved with the VAKT platform, went live late last year with a couple of initial offerings: a digital letter of credit (LC) and a know-your-customer (KYC) product geared toward eliminating friction in the traditional trade finance process, with the promise of expediting trade finance. The live Komgo platform is now connected to VAKT, which means that banks on Komgo are able to offer blockchain-based financing solutions to VAKT users.
• Oil & Gas Blockchain Consortium – Seven major energy players (Chevron, ConocoPhillips, Equinor, ExxonMobil, Hess, Pioneer Natural Resources and Repsol) came together to launch the Oil & Gas Blockchain Consortium earlier this year and has prioritized use cases in the upstream sector including truck ticketing, ballot sharing, joint-interest billing and digital ownership of seismic data, with the goal of driving industry standards around blockchain technology.

Industrial Internet of Things (IIoT) or Industry 4.0

The Industrial Internet of Things (IIoT) is a part of the Internet of Things (IoT) and refers to the extension and use of IoT devices in industrial sectors and applications, where in many devices, sensors and software components within the network are connected via internet and are synchronized to enable intelligent operations by collecting and analyzing data to improve and optimize human-enabled tasks for transformational business outcomes. IIoT can be used to model the physical world and provide answers to even the toughest operational questions. When you add digital twins into the mix, IIoT becomes even more powerful.

The expansion of IIoT comes with its own set of risks and challenges, especially from the standpoint of cybersecurity. The existing cybersecurity measures are inadequate and the fragmented nature of the hardware devices further poses a huge challenge for security solutions to mitigate cyber risks. This is driving the desire to develop security frameworks that are software based or device agnostic.

Key benefits of IIoT:

• Real-time to near real-time visibility
• Increased efficiency
• Decreased safety risk
• Potential for additional revenue streams

High-level use cases of IIoT in the energy industry:

IIoT has been fueling digital transitions for the energy industry since the beginning of this century. As an asset-heavy, data-rich and supply-chain intensive industry, the ever-increasing adoption of IIoT has opened new frontiers to bring significant efficiency improvements, safety standards and reduction in capital and operating expenses. Let’s take a look at some examples.

• Predictive knowledge and action – By installing sensors on existing machinery, O&G companies can gain unprecedented access to a drove of data describing system health in real-time, allowing not only a decline in system failures, but also a decrease in on-site accidents. For example, if a machine monitors a temperature which exceeds the upper control limit, an alarm activates. Traditionally, an operator would react to the alarm. Analytics make it possible to predict when the event will happen and to take steps in advance of it. This means less downtime and maintenance in the long run.
• Remote monitoring – In today’s environment of increasingly shifting human capital demands, IIoT can help companies monitor and respond remotely. For example, installing smart sensors in a pipeline to gather “live” data can allow companies to monitor pressures and flows and identify if any leaks are present. This can also help reduce the number of incidents, improve regulatory compliance and reduce hydrocarbon losses due to timely detection of potential issues.
• Smart metering –The most common use case for utilities IIoT is automatic meter reading (AMR) via smart meters. Metering system operators can collect data automatically, without visiting the physical meter in the field to collect usage data and run analytics to gain business intelligence. For example, this can allow companies to keep track of inventory of crude oil or refined products and enables them to plan future business transactions more accurately. This can also minimize travel and exposure to potentially dangerous work for personnel.

In summary

Digital transformation is no longer a backup singer. Digital transformation has moved to center stage to become part of the main act. Digital technologies are evolving at an exponential rate and are poised to disrupt the energy industry in a similar way to what the music industry, newspaper industry and many others have gone through. Deep digital changes will result in a shift of value creation. The competencies required to deliver the newer kind of value creation will evolve and those late to make the shift will fall behind as new entrants will be able to develop those competencies needed to win in the digital era. Exciting times ahead.

At Veritas, we provide technology solutions to our clients across the digital transformation space. Stay in touch as we will dive deeper into the concepts introduced here in future blogs. We plan to explore the digital transformation journey, the potential pitfalls and opportunities to get the most out of your digital transformation goals and how to get started. Please email Shirin Vakil, director, at shirin.vakil@veritas.com with questions, feedback or suggestions on future topics.