The impact of information technology (IT) on the manufacturing industry has been tremendous in recent times. With an increasing number of machines being connected to the Internet, data are being generated in huge volumes. Advancements in real-time data processing and predictive analytics further help in discovering newer ways of utilising the data to generate insights for effective decision making. Today, The Internet of Things (IoT) and data analytics are the two main technologies that are driving the manufacturing space.
Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, and cloud computing.
Industry 4.0 creates what has been called a smart factory. Within the modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time, and via the Internet of Services, both internal and cross-organizational services are offered and used by participants of the value chain.
Industry 4.0 refers to the convergence and application of nine technologies: advanced robotics; big data and analytics; cloud computing; the industrial internet; horizontal and vertical system integration; simulation; augmented reality; additive manufacturing; and cybersecurity. Companies unlock the full potential of Industry 4.0 by coordinating the implementation of those technologies—for example, by deploying sensors to collect data within a secure cloud environment and applying advanced analytics to gain insights.
In this way, a manufacturer can create an integrated, automated, and optimized production flow across the supply chain, as well as synthesize communications between itself and its suppliers and customers. This end-to-end integration will reduce waiting time and work-in-progress inventory and, ultimately, may even make it possible for manufacturers to offer mass customization at the same price as mass production.
As adoption proceeds, the labor cost advantages of traditional low-cost locations will shrink, motivating manufacturers to bring previously offshored jobs back home. Manufacturers will also benefit from higher demand resulting from the growth of existing markets and the introduction of new products and services.
The profile of the workforce will also change. The critical Industry 4.0 jobs—such as for data managers and scientists, software developers, and analytics experts—require skills that differ fundamentally from those that most industrial workers possess today. Manufacturers will need to take steps to close the skills gap, such as retraining the workforce and tapping the pool of digital talent. Moreover, manufacturers will need to create new jobs to meet the higher demand.
The race is on to adopt Industry 4.0. Companies in the US and Germany have implemented Industry 4.0 at approximately the same pace. The real value is achieved when manufacturers maximize the impact of these advances by combining them in a comprehensive program. Manufacturers need to gain a deeper understanding of how they can apply Industry 4.0 and accelerate the pace of adoption. The winners will approach the race to Industry 4.0 as a series of sprints but manage their program as a marathon.
As costs fall for infrastructural technologies such as computers and the internet, the technologies would—like railroads, electricity, and telephones—become widely available commodities. Once a technology is ubiquitous and available to all—neither scarce nor proprietary—it no longer confers a lasting competitive advantage.
Factors such as commoditization can erode a product’s advantage over time, but companies can create lasting advantage from widely available technology. The powerful effects of technology are visible in economic metrics. Technology matters to a company’s bottom line and it has impact. The use of proprietary metrics such as technology intensity to make the most of technology lies at the heart of creating what we call technology advantage.
Given the rapid emergence of disruptive products and business models and the transformative power of digital technologies on business and society, executives must become masters of the global “technology economy,” capable of detecting the economic impact of rapid technological change and able to respond with speed and foresight. In these articles, we explore the new metrics and consider the new ways that companies need to think in order to navigate the technology economy and approach the many investment decisions in which technology plays a role.
Technology infuses even the measurement of the market economy. The composition of indices such as the Dow Jones Industrial Average (DJIA) and the S&P 500 has changed. Industrial companies are being replaced by tech powerhouses like Apple, Google, and Amazon, whose stocks are valued much higher than those of many long-time industrial members. Apple, with its high market capitalization, accounts for such a large share of the DJIA, for example, that a hiccup in its quarterly earnings moves the entire index. Just 20 or 30 years ago, the performance of Caterpillar or GM (the latter no longer part of the DJIA) could have similarly shaken up the market.
Furthermore, technology permeates companies. Worldwide corporate IT spending—an important barometer of the technology economy that focuses on corporate spending for hardware, software, data centers, networks, and staff, whether “internal” IT or outsourced services—is nearly $6 trillion per year. This amount is what it would cost to give a $500 smartphone and $350 tablet to each of the 7.1 billion people on Earth. If the global technology economy were a country and that spending its GDP, it would rank between the economies of China and Japan and would be more than twice the size of the UK economy. Corporate technology spending grew by a factor of almost 20 from 1980 through 2015, while global GDP barely tripled.
Of course, the $6 trillion figure for corporate IT spending does not include all the money companies spend on technology. It does not account for spending on the sensors, processors, and other technologies embedded in everyday products, including cars, aircraft engines, appliances, and the smart grid; nor does it include spending on robotics, process automation, and mobile technologies. If we include such investments, our technology-spending estimate increases dramatically.
IT spending data is a proxy for the technology economy. This measure of technology spending, which highlights the complexities of looking at technology through an economic lens, is a critical element of a company’s overall digital transformation. Using technology intensity, we can shine a spotlight that reveals the economic impact of this massive amount of technology spending.
In the past, business leaders tended to examine the two metrics in isolation. But that doesn’t give leaders the whole picture. Revenues don’t automatically rise when companies spend more on technology. And it’s not necessarily a bad thing if a company’s technology spending is high relative to operating expenses. However, if leaders compare technology spending simultaneously with revenues and operating expenses, as technology intensity does, several interesting relationships emerge.
Across a range of industries, companies with high technology intensity have high gross margins. For instance, in the insurance sector, top-performing companies enjoy gross margins that are more than three times the margins of average performers and technology intensities that are more than 50% higher. In banking and financial services, companies with the highest gross margins have technology intensities and margins that are roughly double those of average performers. This industry has seen extremely high levels of automation over the past five years—including technology systems that streamline processes, and advances in artificial intelligence that allow robots to answer clients’ questions and, eventually, to execute trades. Michael Rogers, the president of State Street, estimated in Bloomberg Markets that by 2020, automation will have replaced one in five of the company’s workers. Within a decade, 1.8 million employees in US and European banks could be out of jobs.
We see not just a connection between technology intensity and gross margins but also a strong correlation. That is, technology intensity and gross margins tend to rise and decline together. This effect was seen before and after the recent world economic crash. In the run-up to the Great Recession that started in 2007, companies were investing more and more heavily in technology relative to revenues and operating expenses, and gross margins were rising. That trend accelerated through 2008 and until 2009, when companies belatedly realized the magnitude of what had happened and began to cut technology investment dramatically. After that, technology intensity dropped precipitously along with gross margins.
Along with the technology intensity metric, companies can add other measures to their management dashboard, such as income per dollar of technology spending. We define income as revenues minus operating expenses.
For example, the energy industry produces the highest income per dollar of technology spending ($24.24). At the other end of the spectrum, the software publishing and internet services industry produces the lowest ($0.98). In both total technology spending and the technology spending required just to “keep the lights on,” we saw a similar rise until 2008, followed by a plunge in income per dollar of technology spending during the market collapse. Afterward, we saw what might be called the failure of recovery as a result of sluggish growth. Income per dollar of technology spending in 2014 and 2015 has basically flatlined, reaching only precrash levels.
Another measure that companies can use to connect the dots between the business and the IT function is the IT cost of goods. For example, in the US, the IT cost per day of a hotel bed is $2.50, and for a hospital bed, it is $65. The IT cost of a car is $323.
More than such individual measures, however, companies require different measures at different points in time. It is not enough simply to measure whether a project is on time and on budget. When companies are in the early stages of building new IT systems, leaders need progress measures to tell them whether a project is on track. For example, a bank may invest in automation and artificial intelligence in order to process loans better, cheaper, and faster. It needs metrics to understand how these projects are progressing.
Later on, a company may need deployment measures that determine whether the original business case is still valid. For example, while the bank is building its new system, it might shift a lot of work to the Philippines, cutting the cost of loan processing in half. With the new system, however, the context may change and the original plan may no longer make sense.
Once a company has implemented a project, it needs realization measures that can discern whether the project has yielded the intended results. These microeconomic metrics aren’t the only way to look at the impact of technology spending, of course. Technology matters in a host of macroeconomic measures. In short, technology matters both to companies and to the larger economy.
Top performers are different from average companies. Many top performers achieve higher margins by spending their technology dollars more efficiently and with greater focus than average companies.
Consider the case of a global financial services company that for years had prided itself on its low levels of technology spending. However, the company’s gross margins were the lowest in its industry. Incidentally, its peers with higher margins had higher technology intensities. The company turned things around by rebalancing its technology spending and increasing automation. It invested hundreds of millions of dollars in technology, funded by the lower operating expenses and greater revenues it gained through automation. Now, compared with its peers, it is the only company whose gross margins are increasing faster than its change in technology spending relative to revenues.
To support this kind of digital transformation, executives must define metrics such as technology intensity as key performance indicators for the organization and benchmark their performance relative to that of competitors and companies in adjacent industries. They must then incorporate new metrics into monthly management reports and dashboards and review the role and purpose of technology investments in the light of these measures.
For their part, CIOs can embed key performance indicators into the business on the basis of metrics such as those outlined in this article, conducting regular reviews and supporting efforts to optimize performance. Executives should develop even more sophisticated metrics that truly measure the disruptions that technology fuels. Adopting best practices in these areas will enable a new generation of executive-level technology economists not only to measure what really matters to company performance but also to thrive in the technology economy.
Currently, most machines are embedded with low-level logical processors that demand a considerable amount of human intervention in making logical and reasonable decisions. Cognitive manufacturing is an evolutionary step in which machines would be able to detect changes in the manufacturing process and know-how to respond in real time to the constantly changing manufacturing scenario with minimal human intervention. The objective of this research service is to give a detailed account of cognitive computing and its application in manufacturing and to understand the competitive landscape.
This research service will also look into how cognitive technologies would be the next step in the evolution of smart manufacturing and how this would benefit companies by helping them make smarter decisions on the factory floor. The study also throws light on what the market participants are currently doing in this space and provides proven use cases pertaining to how the market is likely to transform the manufacturing arena in the coming years, which could be helpful to the current set of participants in the market that are looking at understanding what their competitors are doing in the space. It can also serve as a useful point of reference for all other market participants that are not aware of the immense benefits of cognitive computing or are in the verge of making a move in the market.
1. What are the evolving technologies that summarize the complex cognitive ecosystem in manufacturing?
2. What is the value addition of cognitive technologies in manufacturing?
3. What are the key trends that will shape the evolution of a cognitive factory?
4. What are cognitive solution providers and adopters doing to enhance manufacturing?
5. What are specific use cases illustrating the application of cognitive technologies in manufacturing?
6. Where are the opportunities in this market and what are the strategies that manufacturers can adopt to accelerate growth?
7. How will the adoption of cognitive affect manufacturing, human resources and economies?