Essentials of Digital Transformation PDF

2 Essentials of Digital Transformation Industrial digital transformation is the journey that organizations undertake where they integrate business model change, process improvement, and cultural shift, often leveraging a number of digital and emerging technologies. We will refer to industrial digita...

2 Essentials of Digital Transformation Industrial digital transformation is the journey that organizations undertake where they integrate business model change, process improvement, and cultural shift, often leveraging a number of digital and emerging technologies. We will refer to industrial digital transformation often in the context of companies from the commercial or public sector that deal with physical assets, factories, and field operations, generally dealing with business-to-business scenarios, and the transformation involves improvements in these products, equipment, and operations. On the other hand, when the transformation involves software or business enhancements for asset-light or pure tertiary services companies, including business-to-consumer scenarios, then we will refer to it as digital transformation. To include both, we will refer to it simply as transformation. Likewise, our use of the term industrial includes both the commercial and public sectors, in the same way as industrial revolutions have used this term. On the one hand, this book will be a guide for business leaders, Line of Business (LoB) managers, C-suite executives, including the Chief Information Officer (CIO) and Chief Technology Officer (CTO), and digital leaders to help identify opportunities for transformation. On the other hand, this book will provide mid- career professionals in Information Technology (IT) and business with recipes for success in the transformation journey, both in terms of digital technology selection and implementation, to help achieve the associated business outcomes. This book Chapter 2  Essentials of Digital Transformation will prepare technology professionals to influence business decision makers toward industrial digital transformation. In this process, mid-career professionals will achieve significant professional advancement. Exploring industrial digital transformation Industrial digital transformation may often bring a radical rethinking to the use of technology, culture, people, and processes in an enterprise. This can lead to a fundamental change in business performance and outcomes, as well as how the customers perceive the company. Figure 2.1 provides an easy way to look at the transformation. It shows that culture and technology changes go hand in hand with the business process and business model changes. Several books have been written on the broad topic of digital transformation, and some of these will be referenced here. George Westerman and Didier Bonnet wrote a book en titled Leading Digital: Turning Technology into Business Transformation, George Westerman, Didier Bonnet, Andrew McAfee, Harvard Business Review Press, published in 2014: Figure 2.1 – Digital transformation The technologies used to help drive an industrial digital transformation may include one or more from the Internet of Things (IoT), cloud and edge computing, Artificial Intelligence (AI), big data and analytics, blockchain, robotics, drones, 3D printing, Augmented Reality (AR) and Virtual Reality (VR), Robotic Process Automation (RPA), and mobile technologies. New technologies continue to emerge, so this list is not meant to be an exhaustive one. The main goal of these transformations is to gain a competitive advantage, drive new revenues, improve productivity and efficiency, as well as enhance customer and stakeholder engagement. The term technology, or digital technology, in the context of industrial digital transformation is not limited to software or IT only. It may include physical, chemical, or biological/life sciences-related technologies as well. For example, in the context of autonomous vehicles, it can be Light Detection and Ranging (LIDAR) or a more efficient car battery. In an industrial safety context, it can be a sensor or a system for fall detection or a thermal scanning camera for infectious disease detection or prevention. These emerging technologies that often accelerate industrial digital transformation will be covered in detail. Chapter 2  Essentials of Digital Transformation According to the Customer Insights & Analysis group of the International Data Corporation (IDC), the worldwide investment in industrial digital transformation- related initiatives is expected to exceed $6 trillion over the next 4 years (2020–2024): see (https://www.businesswire.com/news/home/20190424005113/en/ Businesses-Spend-1.2-Trillion-Digital-Transformation-Year). Smart manufacturing will account for a large part of this spending. Other sectors, such as finance, retail and logistics management, and transportation, will also undergo a large- scale industrial digital transformation. In April 2020, while delivering the quarterly earnings of Microsoft, their CEO, Satya Nadella, said We’ve seen 2 years’ worth of digital transformation in 2 months. In the next section, we will learn about the business drivers for industrial digital transformation. Identifying the business drivers for industrial digital transformation The power of transformation applies to some or every aspect of an organization. It can generate business value, agility, and resilience. The importance of resilience is shown at the time of local or global crises. This book will focus on driving transformation in industries–in both commercial and public sectors – to accelerate business outcomes, by deploying the digital technologies in combination with transformative planning and shifts in the culture. The different forces that help to shape the industrial digital transformation in an organization are shown in Figure 2.2. The industrial digital transformation often entails a series of big bets or bold steps, to achieve large-scale benefits or competitive advantage. This differentiates transformation from regular generational changes, which are often linear or a series of small and gradual steps. Humans climbed mountains through the historic ages and eventually climbed Mount Everest. However, the same series of incremental improvements could not land a human on the moon. This is probably an extreme example of scaling new heights in the history of humanity. But so is a level 5 autonomous car (see https://www.nhtsa.gov/technology-innovation/ automated-vehicles-safety#topic-road-self-driving) compared to the first car with an internal combustion engine built in 1885 [Germany Patent DRP No. 37435]: Figure 2.2 – Industrial digital transformation forces Chapter 2  Essentials of Digital Transformation Figure 2.2 shows the change agents on the left-hand side, such as the business process and model changes, with support from technological and cultural shifts. This is often forced by traditional competitors or disrupters. The regulatory changes and the expectations of the customers as well as the shareholders change over time. Transformation helps to ensure that productivity, profitability, and social responsibility improve and align with the stakeholders. Business drivers in the commercial sector In the commercial sector, often, the need for industrial digital transformation is driven by two kinds of strategy: Defensive strategy Offensive strategy The defensive strategy of transformation refers to protecting the business from competitors and disrupters. Most car manufacturers started manufacturing electric vehicles as a defensive strategy. According to Moody’s, traditional US car manufacturers lose $7,000 to $10,000 per electric vehicle. The major reason why car manufacturers continue to invest in electric vehicles is that this market is expected to grow by almost 20% in the next decade. With breakthrough innovations expected in battery and related technologies, the cost of production is expected to go down. While most automobile manufacturers pursued a defensive strategy, Tesla is an example of using an offensive strategy, where it is trying to disrupt the rest of the industry. A large part of both outlook and forecasts in the automobile industry today has been driven by Tesla, which was founded in 2003 and is newer than most US and global auto giants. Today, it reduces some of its losses by charging a price premium by differentiating itself based on becoming a status symbol and offering driver-assisted technologies. Tesla is a good example of industrial digital transformation at work in the automotive industry. The Tesla Semi is targeted to disrupt the trucking industry next. Tesla’s approach is to futureproof their cars with the necessary hardware that will make the cars increasingly autonomous in the near future with Over-the-Air (OTA) updates. This will increase Tesla’s market valuation as well as the value of Tesla cars for the current owners. While Tesla, being a newer company, is free of cumbersome legacy processes, there are areas where Tesla can transform internally. The transformation at Tesla is effectively implemented across its entire value chain, with the integration of its products, services, and operations. Tesla is an example of a connected car that allows the creation of the digital twin of a car. The digital twin is a virtual representation of a physical object or a system that can be used to improve the performance and efficiency of the physical counterpart. Tesla uses the digital twin of the car to provide new services with OTA updates to the software. We will learn more about the role of the digital twin and the somewhat related concept of the digital thread in the next chapter. A digital thread is often used in the industrial manufacturing sector to improve the product quality and the throughput across the entire life cycle of the product. Chapter 2  Essentials of Digital Transformation Business drivers in the public sector Government Digital Service leaped into public consciousness in 2013 during the implementation of the Affordable Care Act (ACA), also known as Obamacare. For a variety of reasons, the development of the federal healthcare exchange – that is, the frontend websites and the backend databases and processes known as HealthCare. gov, started late and development failed miserably. See https://www.gao.gov/ assets/670/668834.pdf. Health and Human Services (HHS) had used the same process that the government has used for many years to develop and deliver solutions and had achieved roughly the same results that government technology projects had achieved for decades. A team within an agency within HHS developed a set of requirements, published a Request for Proposal (RFP), accepted bids, selected a vendor, and then waited for delivery of a product, which turned out not to meet the requirements, and, in fact, failed to deliver the required capabilities for a successful launch of the new healthcare marketplace. Faced with the failure of the administration’s signature legislation, the Obama administration did something different than past administrations and project leaders: they put out a call to the private sector for help. A group of engineers led by Mikey Dickerson worked around the clock for months to repair and modernize HealthCare. gov. In a moment of clarity that comes all too infrequently, members of the team and others within the government recognized that HealthCare.gov was but one example of a larger problem with the way that the public sector builds and buys technology solutions. See https://money.cnn.com/2017/01/17/technology/ us-digital-service-mikey-dickerson/index.html. Many of the leaders of the HealthCare.gov rescue effort, including Dickerson, became the core of the United States Digital Service (USDS), part of the executive office of the president reporting to the then US CTO, Todd Park, the USDS, 18F at GSA, and other digital services teams were created due to a general recognition within the federal government that technology projects took too long, cost too much, failed too often, and, even when considered successful, rarely met the needs of the public that they were designed to serve. The US government spent close to $75.6 billion on various IT projects in 2014 (see https://www.brookings.edu/blog/techtank/2015/08/25/doomed- challenges-and-solutions-to-government-it-projects/) across all federal agencies, including the department of defense, large cabinet-level departments, such as the departments of labor, transportation, and agriculture, medium-sized agencies, such as the environmental protection agency, and small agencies, including the small business administration and the nuclear regulatory commission. In addition, according to the Standish Group, of the over 3,000 IT projects with labor costs that exceeded $10 million that the government executed between 2003 and 2012, only 6.4% of projects were considered successful and over 41% were complete failures – that is, they had to be scrapped and restarted. This problem was not limited to the federal government or the development of new solutions; it plagued state and local governments as well. Government inefficiencies are often blamed for these failures, but the true cause is both more complex and more understandable. It is simply impossible to specify all the functionality of a large software system and anticipate all the complexities before the Chapter 2  Essentials of Digital Transformation development process has begun. Nor is it reasonable to expect that in a time when the technology life cycle is frequently less than 2 years that the technical design and needs of users can be fully specified years in advance. Simply put, it was clear after decades of large- scale failures that the longstanding practice of spending years gathering requirements, months or years selecting a vendor, and then years waiting for a big- bang delivery of a solution that had been developed in seclusion, wasn’t working and possibly had never worked. In addition to the fact that the traditional government project development process didn’t deliver consistently working software that met the requirements initially specified by the project team, the traditional model rarely delivered software that met the needs of end users. Government software was frequently developed with a small number of stakeholders in mind. Stakeholders in the government are generally the individuals who sponsor or fund a solution, but they rarely represent the bulk of users of a system. For example, if the design of a new timecard system is stakeholder- centered, it would be designed to streamline the process for the handful of individuals in accounting who manage the back-office processes. In contrast, a user-centered design would focus on the needs of the large majority of users who interact with the system. Another example of the power of user-centered design is the US Environmental Protection Agency (EPA)’s eManifest system. This is a voluntary fee-based system successfully deployed by the EPA in 2018, but it didn’t always seem certain that the project would be successful. In 2015, as the project was floundering and under increasing scrutiny, the EPA’s new CTO, Greg Godbout, led a relaunch of the program. One of the first things he learned was that the project team had never talked to a single end user. He arranged for the team to go on a listening tour. During this tour, the project team learned that the solution they were proposing to develop wasn’t what the users needed. They were trying to solve the wrong problem. Talking to users before writing code allowed the project team to reset early when the costs were low, rather than after the project had been completed when the cost of changing course would have been a complete reboot costing millions of dollars. The key idea of user-centered design means that the transition to digital services moves the government closer to the public, allowing the government to develop solutions that more closely match the needs of its constituents. As mentioned earlier in this chapter, traditional development processes don’t just hamper the delivery of new capabilities to the public; they also put existing service delivery at risk. The COVID-19 crisis has exposed this vulnerability to millions of out- of-work Americans who were unable to file for unemployment benefits in a timely manner as states were unable to scale up the systems that process unemployment claims due to antiquated architectures and reliance on obsolete hardware. During the early days of state lockdowns, individuals attempting to file claims reported system crashes, unavailable websites, and hours-long hold times or busy signals as many state systems required individuals call to complete their claims that had been started online. Many of these systems reside on mainframes and are written in obsolete languages such as COBOL and do not follow the best practices used in coding today, as messy spaghetti code was written to preserve then-precious processing power and comments were non-existent. Chapter 2  Essentials of Digital Transformation iMportant note The outbreak of COVID-19 tested the limits of many older government IT systems and highlighted the need for modernization of legacy systems. Many of these legacy systems were written in COBOL, a language that hasn’t been taught at most universities since the 1970s. You can read about how keeping these systems running has created a need for COBOL programmers, much like the Y2K bug did in the late 1990s: https://nymag.com/intelligencer/2020/04/ what-is-cobol-what-does-it-have-to-do-with-the- coronavirus.html In addition to the need to be able to implement technology to support government policy, to deliver new capabilities to the public, and to ensure the reliability of existing services, crises such as 9/11, the COVID-19 pandemic, hurricanes, earthquakes, and wildfires all demonstrate the need for the government to be fast and nimble. COVID-19 required a combination of fixed sensors and contact tracing applications to control the spread. Governments must be able to expand the capabilities of existing systems and deploy new, previously unanticipated solutions in order to respond to crises. Ironically, these same crises demonstrate that the government can be nimble. With a state of emergency declared with the outbreak of COVID-19 and procurement rules suspended, one federal agency hired contractors and redeployed a government loan program application over a weekend, while a state agency engaged a firm to provide call center software, a ticketing system, and agents to augment their unemployment system over another weekend. With a sense of urgency and without the constraints of a highly regimented procurement system, governments can move fast and serve constituents better. As the case became clear and heroic successes such as HealthCare.gov were demonstrated, individuals and teams at all levels of government around the world began to explore institutionalizing digital services across government. Technology drivers for transformation In this book, we will be exposed to a variety of technologies related to such industrial- scale transformation. No specific technology is a solution for all areas of Industry 4.0, but must be paired with an appropriate problem statement, along with an understanding of its limitations. In addition, there are several technologies that are in what may be considered the hype phase of their maturity cycle and it remains to be seen whether these will be viable in the future. A practitioner is well-served by taking an objective stance to these technologies versus climbing onto the hype bandwagon. A specific example relevant today is that of blockchain. Blockchain was conceived as a solution for anonymous, untrusting parties to transact with each other and avoid the double spending problem (see Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, available at www.bitcoin.org). Blockchain can be viewed as a solution looking to solve a problem in the industrial setting. In order to be seen as a viable solution, the problem must satisfy the core premise underlying Bitcoin – namely, transactions across anonymous untrusting parties. In addition, a large number of industrial use cases involve rates of transactions that are much more suited to traditional databases versus a distributed ledger. Chapter 2  Essentials of Digital Transformation Hype Cycle for Blockchain Technologies (July 2019) from Gartner shows a 5 to 10-year timeframe before blockchain becomes mainstream and has a transformational impact across the industries (see https://www.gartner.com/en/newsroom/press- releases/2019-09-12-gartner-2019-hype-cycle-for-blockchain- business-shows). We advise that you vet the blockchain application closely to ensure it is the best fit for your unique scenarios. In the hype cycle, from an industrial perspective, most of the top contenders, such as the use of blockchain in transportation and logistics, blockchain in supply chain and smart contracts, and blockchain in insurance, highlight the likelihood of blockchain-led transformation in the supply chain and distribution space within the next decade. However, the use case of a company called Colu based around the digital currencies for cities, to encourage citizens to spend their money locally, has not been as successful as they might have hoped (see https://www.wired.com/ story/whats-blockchain-good-for-not-much/ for more details). Figure 2.3 – Key technical components for digital transformation. The pyramid denotes distilling information and increasing data intelligence as we move from the bottom toward the top We will now walk through some of the components illustrated in the diagram: Sensing: Before we can talk about digitization, it is imperative to ensure that you have a solid foundation for sensing and collecting data across the enterprise. Data collection can scale from sensors in the process flow – potentially augmented by IoT devices at the edge, which collect and aggregate data, to external factors related to logistics and demand sensing. Another aspect of sensing is machine vision systems and their associated algorithms, which analyze the image data Chapter 2  Essentials of Digital Transformation via edge computing and send summarized data to the data aggregation systems. Such sensor data has to be analyzed in the broader context of the enterprise data, which may originate in enterprise resource planning (ERP) and other manufacturing or maintenance systems. Data aggregation: Rather than keeping this data in silos, we must aggregate it in a common location – which can range from an internal data lake hosted on- premises to Storage as a Service (STaaS) hosted by an off-premises cloud services provider. Typically, the latter also offers additional services to entice customers to move to their platforms. Data aggregation is critical to enable everyone involved in the enterprise to get a single version of the truth. Connectivity and integration across various segments of the enterprise are key to enable this capability. Analytics: This comprises a suite of methodologies to operate on the aggregated data. Statistical analysis: This is fundamental for all smart manufacturing efforts. Initial use cases revolved around statistical process control (SPC), first proposed by Walter Shewhart in 1924 with the invention of the control chart (see Walter Shewhart, Economic Control of Quality of Manufactured Product, American Society for Quality Control, 1931), which found widespread use during World War II – leading to the six sigma methodology. Shewhart greatly influenced W. E. Deming, who created the now-famous funnel experiment resulting in a greater reliance on statistical process control that initially hindered the application of modern control systems methodologies to industrial problems. In 1951, Box and Wilson introduced response surface methodology, which led to the development of the design of experiments. This was the first attempt to systematically develop input-output models of industrial processes in order to drive the process to the optimal operating point. In addition, statistical analysis is widely used in inventory management across the industrial supply chain. AI: This is a broad field covering traditional rule-based systems, statistical machine learning, and recently deep learning. We will refer you to Figure 2.4, which shows the relationships between these various methods: Figure 2.4 – Different fields under AI and the approximate timeframe they gained in popularity Chapter 2  Essentials of Digital Transformation Some examples of specific techniques that have been popularized across these fields are as follows. Traditional AI: rule-based systems and fuzzy logic inferencing (Zadeh, L.A. (1965), Fuzzy Sets, Information and Control. 8 (3): 338– 353); Statistical machine learning: tree-based classifiers, such as random forests (Breiman L (2001), Random Forests, Machine Learning, 45 (1): 5–32), and support vector machines (Cortes, C. and Vapnik, V. N. (1995), Support-vector networks, Machine Learning. 20 (3): 273–297); Deep Learning: convolutional neural networks for image processing (Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998), Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86 (11): 2278-2324); and deep reinforcement learning (Arulkumaran, K., Deisenroth, M. P., Brundage, M. and Bharath, A. A. (2017), Deep Reinforcement Learning: A Brief Survey, IEEE Signal Processing Magazine, 34 (6): 26–38). You must take care to select the correct methodology for the task – as is often the case, the simplest methodology leads to the most robust and sustainable solution. Later chapters in the book will provide some examples of what is applicable in specific scenarios. Optimization and simulation: Optimization and simulation are critical tools for implementing any kind of decision system. Such systems can function either in an automated mode – for example, scheduling systems – or can be used to guide a human to make decisions by simulating and optimizing various scenarios (that is, the user asks what if xyz were to occur and the system will simulate that condition and optimize system performance to give the answer). Visualization and dashboards: As data moves through the analytic engines, there is still a need to visualize it from time to time. In order for a person to make sense of the data, there is a need for the analytics to distill the raw information across all sources to a few key metrics that will be meaningful to the user. As AI applications proliferate, the user would need to get less and less involved in mundane decision making and would only need to respond in situations where the autonomous decision system is unable to make a decision or to course correct an erroneous decision. As such, the metrics should reflect not only the overall health of the industrial system (be it a manufacturing plant, or the entire supply chain), but also relevant metrics to track the robustness of the AI models. You will also hear the term big data associated with machine learning. There is definitely an intersection between the two – however, big data focuses more on the data storage infrastructure, the platform for executing analysis of this data in an efficient manner, and computationally scalable algorithms for feature extraction (or dimensionality reduction) in order to make this large volume of data amenable to machine learning algorithms. One of the most successful big data platforms is Hadoop, with its distributed filesystem and the capability to efficiently implement MapReduce algorithms that provide the highly scalable processing of large volumes of data. While these digital technologies are adopted, it is important to keep the security and safety of people and property in mind. As the physical world gets connected to the network due to IoT, the cybersecurity considerations become of paramount importance. As the digital twins are created and stored, they could be targets of security breaches, to get access to restricted data or operating details that are otherwise not public Chapter 2  Essentials of Digital Transformation information. It is important to ensure that the digital twins of power plants or nuclear plants and other critical infrastructure do not fall into the wrong hands. In the next section, we will learn about the historical evolution of large-scale industrial transformations and how it leads to industrial digital transformation. The evolution of industrial transformation Changes in the global landscape seen during the healthcare and economic crisis in the first half of 2020 due to COVID-19 (https://www.cdph.ca.gov/Programs/ CID/DCDC/Pages/Immunization/ncov2019.aspx) have highlighted the need to develop a deeper understanding and preparedness for transformation, not only for the government at different levels (federal, state, and local), but the commercial sector as well, across the board, be it a family-run business or a global enterprise. This book will cover some of the major crises the world has experienced in the last several decades and the lessons learned from each of them. This would allow us to see concrete examples where the industry has successfully identified an opportunity for transformation. Let’s look at some examples of past crises and the lessons learned in the following table: Chapter 2  Essentials of Digital Transformation Progressive companies take the approach that no crisis should go to waste. As a result, looking at major crises is important to study their impact on the industrial landscape over time. This provides valuable insights into how to identify future opportunities for industrial digital transformation. We will look at several examples of such innovations and transformations. What do crises teach us in terms of transformation opportunities? Let’s look at all the innovations seen in the short term due to the COVID-19 crisis. Figure 2.5 describes the voluntary use of smartphone location technology to track the risk of infecting others. Smartphone data has been utilized to track adherence to social distancing guidelines (see https://www.washingtonpost.com/ technology/2020/03/24/social-distancing-maps-cellphone- location/): Figure 2.5 – Using smartphones to track the spread of COVID-19 Chapter 2  Essentials of Digital Transformation Another is the 3D printing of masks and ventilators to speed up the production of critically required materials and equipment. In the same vein, the medical devices manufacturing company Medtronic and Intel are working together to add IoT features, such as remote management capability for PB980 ventilators (see http://newsroom. medtronic.com/news-releases/news-release-details/medtronic- provides-ventilator-progress-update). This allows the clinicians to control and adjust the settings of the ventilator remotely. As a result, they do not have to go to the Intensive Care Unit (ICU), thus staying away from patients. This reduces the healthcare worker and clinician’s exposure to patients recovering from COVID-19. In this context, Medtronic has also open sourced its ventilator design (see https:// www.medtronic.com/us-en/e/open-files.html?cmpid=vanity_url_ medtronic_com_openventilator_Corp_US_Covid19_FY20) to allow others to collaborate and speed up the manufacturing and contribute improvements to this critical device. The use of blockchain technology for tracking the integrity of the ventilators is another such use of emerging technology (see https://www.industryweek.com/ technology-and-iiot/article/21127623/getting-ventilators-to- the-people-is-a-problem-built-for-blockchain). General Electric (GE) healthcare has also deployed IoT-based, remote patient data monitoring technology that allows the clinicians to fight the battle against the critical COVID-19. This solution would allow the monitoring of critical patients across the health system (see https://www.businesswire.com/news/home/20200415005370/en/GE- Healthcare-Deploys-Remote-Patient-Data-Monitoring). These are great examples of industrial digital transformation driven by a crisis, where industrial companies acted fast. However, many of these innovations will continue after the crisis and drive transformations in other areas. Other examples include the use of drones for spraying virus disinfectants and the delivery of medicines to rural areas. The 9/11 crisis in the US led to several transformations in the aviation industry. Many new technologies emerged to make airports and passenger screening much stricter. The regulatory landscape changed as well. The Travel Security Administration (TSA) was created in the US on November 19, 2001 (see https://www.tsa.gov/about/ tsa-mission). Companies such as Clear started in 2004 and transformed the airport experience for frequent airport passengers by using biometrics for identification. Clear saw this opportunity and was much ahead of the TSA PreCheck system that started in October 2011. These are good examples to show that national and global crises often accelerate emerging technology and create industrial digital transformation opportunities for companies or government agencies that capitalize on them. Can the proactive readiness of the company avert a crisis or help them overcome the crisis quickly? In recent times, the Boeing 737 MAX aircraft (see https://boeing. mediaroom.com/2019-04-05-Statement-from-Boeing-CEO-Dennis- Muilenburg-We-Own-Safety-737-MAX-Software-Production-and- Process-Update) has been a subject of much controversy. This crisis led to the loss of lives in aircraft crashes. Unfortunately, Boeing has also been impacted by the ripple effect of the airlines, which saw over a 90% reduction in travel in the US after the COVID-19 crisis. We will discuss how large enterprises have to be on the lookout for disruptive forces, whether internal or external to the company. The ability to transform rapidly in light of a crisis or threat of disruption is critical in this age. Chapter 2  Essentials of Digital Transformation Is industrial digital transformation only about survival in the industry? Interestingly, the book titled Digital Transformation: Survive and Thrive in an Era of Mass Extinction, Tom Siebel, RosettaBooks, ties it to the concept of mass extinction. We have seen not only civilizations that have perished due to a crisis, but also large global companies. One example is Lehman Brothers, which collapsed in 2008 soon after the financial crisis. Can digital transformation help with risk management to prevent the reoccurrence of the fall of large companies? On the other hand, the example of the rise of Netflix and the fall of Blockbuster shows that Netflix disrupted the industry, leveraging the technology of video streaming. In recent times, many companies have looked for opportunities to disrupt themselves before the competition does. As a result, companies have invested resources to stay ahead of the curve: Need for disruption from within: Utility companies such as Exelon moving toward renewable sources (solar and wind) is an example of disruption from within. Probably, Intuit is a good example of going digital using cloud technology. They acquired the company Turbo Tax for $7.1 billion, to get a good share of the tax market in the case of individuals as well as the small and medium company sector. Hence, transformation initiatives may include both organic changes as well as Mergers and Acquisitions (M&As). Fear of getting disrupted: An example is General Electric (GE), where IBM and other technology companies were trying to offer predictive maintenance services to industrial customers, such as to goods train operators. GE Transportation sold locomotives to these companies along with the highly profitable service contracts. We will look at the historical evolution of large-scale transformations. The industrial revolution can be defined as the process of change from the current state of society and economy to the next advanced state, powered by technology. These revolutions have created monumental changes to humans in the last few hundred years. That is why it is important to understand these revolutions, before discussing any kind of transformation going forward. The world changed for the better after each of these revolutions, as we will see in the following sections. There were massive disruptions in each phase. Each phase also introduced some challenges that can be seen as opportunities to solve in the future, such as the high density of populations in cities and additional constraints on natural resources. The four waves are as follows: The first industrial revolution: The first industrial revolution originated in the 18th century in Britain and then spread to the other parts of the world. The second industrial revolution: Following the first industrial revolution, almost a century later, the world went through the second industrial revolution. The third industrial revolution: The third industrial revolution laid the foundation of the internet and many technologies that are mainstream today. The fourth industrial revolution, or Industry 4.0: The fourth industrial revolution started in the early 2010s and we are still experiencing it, as this book is being written. Industrial digital transformation is one of the biggest opportunities for the 2020s. This book has been written to help companies capitalize on the transformations in their respective industry sectors (see https://trailhead. salesforce.com/en/content/learn/modules/learn-about-the- Chapter 2  Essentials of Digital Transformation fourth-industrial-revolution/meet-the-three-industrial- revolutions): Figure 2.6 – The history of industrial revolutions Figure 2.6 represents the history of industrial revolutions. Next, we will look at the details. The first industrial revolution The first industrial revolution had many features– namely, technological, socioeconomic, and cultural. Its origin is tied to the rapid mechanization in the textile industry in Britain at the time (see https://www.economist.com/leaders/2012/04/21/ the-third-industrial-revolution). The final outcome of this revolution was the mass production of manufactured goods. There were 27 inventions, as shown in the following table, that were made during this period that are considered as breakthroughs or transformations that moved the world forward: Chapter 2  Essentials of Digital Transformation (See https://interestingengineering.com/27-inventions-of-the- industrial-revolution-that-changed-the-world). The technological advancements during this period consisted of the following: Iron and steel as the basic raw materials for manufacturing Energy sources, such as coal and petroleum, and motive power, such as the steam engine, internal combustion engine, and electricity Machines such as the power loom and the spinning jenny, which helped to amplify human energy, resulting in large-scale production Organizations such as the factory system that advocated the division of labor and the specialization of roles Transportation and communication means, such as the steam engine, steam ships, the automobile, telegraph, and the radio Science applied to the industrial sector The following illustration dates back to the first industrial revolution: Figure 2.7 – The first industrial revolution (Source: http://brewminate.com/the- market-revolution-in-early-america/, License: CC BY-SA-NC) Chapter 2  Essentials of Digital Transformation The preceding description of the first wave of the industrial revolution highlights that it consists of a series of transformations that together propelled society and the economy forward over a period of time. Figure 2.7 depicts the industrial and societal landscape in that period, which is important to help us understand how this series of industrial revolutions accelerate the change to lead us to our current landscape. This book will explore and highlight how well-orchestrated industrial digital transformation opportunities lead the world forward. The second industrial revolution The second industrial revolution (1870–1914) saw large-scale electrification and the buildout of railroad infrastructure. The use of electricity dramatically changed the lifestyle and profession of people. In the 1870s, the first commercial electric generators were used. Great Britain built the first power station around 1881. In the early 1900s, these power stations started powering whole towns or parts of larger cities. Alexander Graham Bell invented the telephone in 1876. Soon after, in 1879, Thomas Edison and Joseph Swan designed the light bulb for home use. This period also saw the creation of the first electric railroad in Germany, as well as electric streetcars replacing horse-drawn carriages in major European cities. The first radio waves were sent across the Atlantic Ocean in 1901 and were credited to Guglielmo Marconi. The Wright brothers invented the first airplane in 1903. The motion picture, which is the foundation of the modern film industry, also started at this time: Figure 2.8 – The second industrial revolution (Source: https://en.wikipedia.org/ wiki/ File:Ford_assembly_line_-_1913.jpg, License: CC BY-SA) Large-scale socio-economic changes took place around this time in North America as well. By 1913, the US overtook Great Britain, France, and Germany combined in industrial productivity. The US accounted for one-third of the world’s production. This helped to improve the economic status of the middle class, leading to increased purchasing power. This led to rapid urbanization and about 11 million Americans moved from rural and agricultural professions to city-based living between 1870 and 1920. By the end of this period, there were more city dwellers than those living on farms. This period also saw large-scale immigration to the Americas. Chapter 2  Essentials of Digital Transformation Overall, it shows that the second industrial revolution changed society from agrarian to primarily urban. This period saw the rise of technical skills and laid the foundation for the pursuit of prosperity based on an individual’s capabilities. Figure 2.8 shows the concept of the assembly line in the factories. Even current day manufacturing uses assembly lines, after a few generations of automation added to them. This highlights that transformation is not just about rip-and-replace, but rather perfecting concepts that work well. The third industrial revolution The third industrial revolution, or the computing and digital revolution, started in the 1950s. The key invention was the transistor. The transistor emerged at the Bell Laboratories in Murray Hill, New Jersey, which was the research arm of American Telephone and Telegraph (AT&T). The invention of the transistor was accredited to three scientists, namely, William Shockley, John Bardeen, and Walter Brattain. The third industrial revolution saw the large-scale transition from analog to digital technologies. The semiconductor industry paved the way to mainframe and personal computing and eventually to the internet. This was the beginning of the information age. Electronic appliances and gadgets invaded households in this period. The fourth industrial revolution – Industry 4.0 The fourth industrial revolution started around the 2010s. The term Industry 4.0 was coined in 2011 by the German government. In this phase, the focus of companies shifts from pure manufacturing to the delivery of services and outcomes around the product. Servitization is the key feature and point of differentiation. This term was first used by Sandr Vandermerwe and Juan Rada in 1988 when they wrote the article Servitization of Business: Adding Value by Adding Services, in the European Management Journal (see https://www.sciencedirect.com/journal/european-management- journal/vol/6/issue/4). Servitization helps to transform a company from having a focus on product manufacturing and sales to the delivery of results to the customer. According to the company Salesforce, The Fourth Industrial Revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. As a result, the advances in AI, robotics, IoT, 3D printing, quantum computing, genetic engineering, Global Positioning System (GPS) and related technologies fused together to achieve outcomes unseen in the past. Today, voice-activated systems facilitate the conversation between a human and car navigation system to recommend the optimal route when traveling (see https:// www.salesforce.com/blog/2018/12/what-is-the-fourth-industrial- revolution-4IR.html?). What is the relationship between the four industrial waves or the revolutions and this book, Industrial Digital Transformation? We are in the second decade of the fourth wave of the industrial revolution. The authors of this book strongly believe that the year 2020 will help to shape this decade in the form of industrial digital transformations across the board – the public and the commercial sector. Hence, industrial digital transformation will help to unleash the real power of the fourth industrial revolution to the world at large. Chapter 2  Essentials of Digital Transformation Despite the large-scale development of our civilization in the last 300 years, the benefits have not reached the 7 billion people of the earth in an equitable manner. As a result, the United Nations has set 17 Sustainability Development Goals (SDGs) to help transform the world by 2030 (see https://www.un.org/development/desa/ disabilities/envision2030.html): For more details on DARPA Ocean of Things, see https://gcn.com/ articles/2020/01/03/darpa-ocean-of-things.aspx. The preceding list of UN goals showcases some fundamental challenges to solve using industrial digital transformation, which will have a profound impact on the world. When a private sector company creates a complete solution or part of the technology toward a solution, then it is very likely to be deployed and adopted. This helps to build the business case for the transformation and reduces the investment risk. The revenue for such transformative solutions may come from the governmental agencies or the end consumers and the beneficiaries. Very often, such transformational initiatives will drive successful public-private partnerships. Chapter 2  Essentials of Digital Transformation The impact of industrial digital transformation on business The internet, web applications, and the easy availability and low cost of massive amounts of computing power and storage have revolutionized the way that businesses operate and, in the process, have reset the competitive landscape. In some cases, industrial digital transformation is a competitive advantage, but in other cases, it is simply the minimum effort required to stay in business. For many organizations, digital transformation is a do-or-die proposition. Industrial digital transformation can serve one or more of three purposes for business: Improve internal processes, thereby reducing costs and increasing competitiveness. Streamline the delivery of existing solutions within an existing business model to reduce costs or improve customer service. Transform a business completely, resulting in new products and business models. A true digital transformation is a disruptive innovation that fundamentally changes the user experience. This new experience, if delivered properly, will delight the customer and provide the business with insights into how to better serve that customer in the future. It can also enhance the customer support processes, leading to lower support costs and new insights about customers. Industrial digital transformation is not simply the automation of existing processes using new technology, but rather the re-engineering of existing processes and products to deliver fundamentally different solutions. A simple example of internal process improvement is the routing of a document for review. When that document is routed on paper, it would move to each individual reviewer in sequence. Once that document is digitized, it could continue to route to each reviewer sequentially. However, if the process were redesigned, it might be routed to all the reviewers except the final approver, concurrently shaving days or weeks off the review process. At the product level, industrial digital transformation allows the creation of entirely new products that could not exist before digital solutions existed, disrupting entire markets. For example, the ridesharing applications Lyft and Uber would not exist if not for the digital disruption of business models. Before the advent of the smartphone and sophisticated algorithms that can rapidly match riders and drivers and manage pricing to keep supply and demand evenly matched, these car-sharing services could not have existed. They have disrupted both the taxi and car rental markets. Digital transformation matters to businesses because virtually all businesses are being disrupted. New entrants are arriving with lower costs and new approaches to the existing business or with new business models that cannibalize their business. Incumbents must transform their culture, processes, and technologies to compete and thrive in this changing landscape. Chapter 2  Essentials of Digital Transformation Quantifying business outcomes and shareholder value The decision process in large public or private organizations is often driven by strategic goals or value of investments to its stakeholders while making the organization stronger and sustainable. As a result, any new initiatives beyond the incremental efforts to preserve the business goes through a business case of Return on Investment (ROI) analysis. As a result, it is important to understand the key benefits of the industrial digital transformation to the business. The desired outcomes of digital transformations are often as follows: New digital revenues Productivity gains Corporate social responsibility New digital revenues In this scenario, transformation is used to drive new lines of business or new digital revenues for an existing business. A good example is the servitization of a product. In this model, the company tries to wrap the physical product with services that bring recurring revenues – for example, buying the scheduled maintenance service when buying a car. This prevents the service revenue from going to the after-market parts and third-party service providers. More complex examples include a jet engine provider selling thrust by the hour for an aircraft or the power by the hour model. To build the business case for this type of outcome of industrial digital transformation, the proposed investment is weighed against the possible new revenues (see https://knowledge. wharton.upenn.edu/article/power-by-the-hour-can-paying-only- for-performance-redefine-how-products-are-sold-and-serviced/). Productivity gains In this scenario, the primary goal of industrial digital transformation is to improve the bottom line and drive efficiency. Let’s take the example of a wind turbine owner or the operator. The cost of servicing a certain type of wind turbine that includes an oil change and servicing the bearings of the wind turbine is about $8,000 per event. In order to prevent overly frequent servicing, which would result in higher routine maintenance costs and not servicing when it is due, leading to expensive damages to the wind turbine, the company decides to go to Condition-Based Maintenance (CBM). They add sensors to monitor the viscosity and particulate levels in the oil. This allows the company to come up with the optimal frequency of servicing by monitoring wind turbine remotely. This is a good case study for productivity gains through the use of industrial digital transformation. Social responsibility Often, both private and public sector companies look at transformative ways to fulfill their corporate citizenship goals. The business case for these may consist of tangible and intangible benefits. For instance, an airline may set stringent goals for carbon offset and look for transformative changes to accomplish that. In the next section, we will look at the different phases of the industrial digital transformation journey. Chapter 2  Essentials of Digital Transformation The phases of the digital transformation journey Let’s look at an example of a phased approach to digital transformation using the example of the automotive industry. In recent decades, we have experienced phases such as going from gas to electric cars to the use of driver-assisted technologies on its way to the different levels of autonomous driving. This journey continues and next, we may witness unmanned taxis and possibly flying taxis in the future. In this example of autonomous cars, it is important to think through the breadth of the impact. As autonomous cars become mainstream, it has an impact on how roads, traffic signs, and even cities and airports are designed. Likewise, it may have a profound economic impact not only on the automotive industry, but also on the utility providers, due to electric vehicles, and finally, on employment via the automobile and the trucking industry. Finally, auto insurance and the department of motor vehicles would have to adapt to this change as well. Hence, a technology-led digital transformation of the automobile has a profound socio-economic and political impact. The change management and phased approach applies not only to the technical aspect of the transformation, but also to the change of the business landscape as well as the societal impact. Although this book will cover several examples of what an industrial digital transformation looks like, we will give you some ideas into how to discover the correct opportunity. A lot can be gained by examining the normal business cycle that an industry goes through in order to manufacture goods or provide a service. We will specifically look at an example where a company is going through a new product introduction. The stages typically involved are illustrated in Figure 2.9: Figure 2.9 – Generic steps involved in a new product introduction Industrial digitization can play a crucial role in each of these stages. We will briefly look at some examples here: Concept: This is the initial ideation stage that helps define the requirements for a new product. Digitization can help here by providing machine learning solutions that combine unstructured data to look for key customer trends. Several suppliers offer platforms – for example, analyzing social media messages to gauge positive or negative sentiments related to features in existing products. In addition, given the lead times to move from concept to production ramp, it is in the company’s interest to forecast product feature sets that will be of interest when the product is in general availability, as well as the expected sales volumes. Machine learning and data mining can provide significant benefits in this area. Design: Digitization can help in the design process by allowing greater collaboration between designers. Collaborative tools that allow designers across the globe to work together on a common platform – in fact, even being able to share and edit drawings concurrently – goes a long way in speeding up the Chapter 2  Essentials of Digital Transformation design process. In addition, digitization provides the means to reuse components already in use by a company and limits later headaches on raw material SKU management, as well as adds to the economies of scale to keep costs down. Prototype and validation: Rapid prototyping is key to evaluating the design for fitness and functionality, and to make any final revisions before the product is released to manufacturing. Additive manufacturing can play a key role in the rapid prototyping of mechanical parts. For electronics, there are special companies that specialize in small-batch orders with a quick turnaround to get samples back to the customer quickly. These companies leverage computer- integrated manufacturing to quickly reconfigure tooling between customer orders. Customer trials, compliance and regulatory testing: By being able to send prototype samples to customers, the manufacturer can get rapid feedback on new product features. As Steve Jobs once said, “People do not know what they want until you show it to them” (see Isaacson, W. Steve Jobs: The Exclusive Biography. New York: Simon & Schuster, 2011). Rapid prototyping provides such an avenue. Customer trials using prototypes can be sped up by employing technologies such as digital twins. These can be employed to conduct tests under extreme environmental conditions, which would help guide the design to meet regulatory requirements. Manufacturing: Although this is called out as a single item here, this encompasses several areas, each with its own sets of challenges and opportunities for digitization. Manufacturing comprises not just the factory or network of factories, but the entire supply chain network. This area alone is teeming with digitization opportunities, some of which we will cover later in the book. You can find several publications and videos related to the use of augmented reality, control rooms, machine learning/AI, detailed real-time simulation models (see, for example, demonstrations from GE on their models of aircraft engines – also referred to as a digital twin), and autonomous planning and scheduling. This is perhaps because manufacturing and the processes involved are relatively well understood and you can control the sensors and metrologies around these versus, for example, by applying natural language processing and sentiment analysis to unstructured data to determine new features that may entice customers during the concept phase. The connected products and operations provide opportunities to improve customer support operations and drive efficiencies. Lastly, you should keep in mind that the aim of digital transformation is to enable the following: faster time to market with a cheaper cost per unit; managing and reducing the environmental footprint; and reducing risk to production by enabling digitization of the supply chain and the workforce. The aim of a commercial enterprise is to maximize profit, revenue, and market share and digitization technologies implemented correctly provide opportunities for visibility, efficiency, and agility. How will industrial digital transformation impact the future of work? This will be a key driver from the perspectives of those who will be responsible for driving the strategy, as well as the execution of the transformation. The growth of automation and the use of ubiquitous AI will profoundly change how we work. The lights out data center is one good example of that. Likewise, cobots, or collaborative robots, where humans work alongside robots in factory settings, are another indicator of the future of work. Chapter 2  Essentials of Digital Transformation The explosion in the use of remote conferencing working technologies, such as Zoom and Webex, in the first quarter of 2020 during the COVID-19 crisis is another example of the changing nature of work that is possible in extreme scenarios. Telemedicine grew as well in this period and the regulatory landscape was relaxed (see https:// www.hhs.gov/ hipaa/for-professionals/special-topics/emergency- preparedness/notification-enforcement-discretion-telehealth/ index.html). The gig economy, or shared economy, has been possible due to transformations in related industries. As we move to autonomous vehicles, such as autonomous trucks, will it disrupt the profession of truck drivers? On a related note, over the last decade, we have seen atlas maps move to apps. Apps powered by maps and geolocation information have really transformed the transportation industry. Truck drivers can look for the fastest route and avoid restrictions for commercial vechicles, such as a bridge with weight restrictions or a highway overpass with vehicle height restrictions, via such apps.