What Sets Breakthrough Strategies Apart

Strategy advice has taken a rather negative tone of late. Consultants and scholars alike seem obsessed with eradicating bias and error in human judgment and decision-making. A virtual cottage industry has emerged to offer advice how to do that, often pushing managers to replace flawed human judgment with big data analytics and various computational tools. Given this abysmal view of human judgment, it’s no wonder that some authors have suggested that algorithms and artificial intelligence (AI) should play a greater role in strategic decisions.

No doubt bias and error are important concerns in strategic decision-making. Yet it seems quite a stretch to suggest that the original strategies developed by people like Apple’s Steve Jobs, Starbucks’ Howard Schultz, or even Walmart’s Sam Walton had much to do with error-free calculations based on big data. Their strategies, like most breakthrough strategies, emerged in settings with remarkably little data to process and little basis for calculation — situations in which the paths to value creation were highly uncertain and evidence was sparse. We are highly skeptical that debiasing decision-making, eradicating errors, or ceding strategy to AI will improve strategizing, let alone lead to breakthrough strategies.

What Do You See?

Composing valuable strategies requires seeing the world in new and unique ways. It requires asking novel questions that prompt fresh insight. Even the most sophisticated, deep-learning-enhanced computers or algorithms simply cannot generate such an outlook.

But where does the uniqueness and novelty so essential to innovative strategic thinking come from? It comes from contrarian, perhaps even “distorted,” perceptions and beliefs about reality and the “facts” that surround us. We think that venture capitalist and PayPal cofounder Peter Thiel gets it roughly right when he asks prospective entrepreneurs to tell him something they believe is true that nobody agrees with them about. If everyone believes the same thing — or if everyone uses the same variables, information, and computational tools — the logical result is computational consistency, shared conclusions, and me-too strategies. Thus, while renowned behavioral economist Daniel Kahneman and his coauthors Andrew M. Rosenfield, Linnea Gandhi, and Tom Blaser argued in a 2016 Harvard Business Review article that it is problematic that professionals “often make decisions that deviate significantly from those of their peers,” it is this seeming pathology that provides the underlying raw material — the essential ingredient — for valuable strategies. In setting strategy, deviation in judgment is a feature, not a bug.

Examples abound. In the mid-1970s, computers were used for large-scale industrial and office applications. A mass-market personal computer was a reality few envisioned to be feasible, and any number of facts, surveys, expert opinions, and data could corroborate that conclusion. Yet despite the evidence and widespread agreement, Steve Jobs, cofounder of Apple, somehow believed otherwise. Similar narratives could be told about Herb Kelleher of Southwest Airlines or Jeff Bezos of Amazon.com. All three entrepreneurs ignored current evidence to pursue a future reality that only they and perhaps a handful of others envisioned.

It is tempting to believe that the right evidence and the right analysis will yield the right strategy. But just as customer surveys seldom lead to breakthrough products that capture the imagination of customers and markets, substantive strategy-making requires that we see well beyond the available data. As Polaroid Corp. founder Edwin H. Land once noted, “every significant invention … must be startling, unexpected, and must come into a world that is not prepared for it.” The story is no different for managers seeking to advance valuable new functional strategies — supply chain solutions, product development ideas, or marketing strategies. Paths to substantive value creation emerge from those capable of envisioning a reality that others simply can’t imagine.

We view the strategist’s task as akin to an inkblot test, where participants are presented with highly ambiguous evidence and signals that afford many possible realities, but offer no single correct answer. With such tests, the very same evidence — an ambiguous picture or set of marks — can be interpreted correctly in many different ways. Indeed, Jobs and the rest of the nascent computer industry all had the same data. But in the words of an old Apple slogan, Jobs did indeed see and “think different.” Valuable strategizing demands this novel perception — an ability to see in ambiguous cues and data what others can’t see. Strategic thinking is fueled by the novelty of our observation, not its consistency. The object of strategic thinking is not to ensure that we all observe the same information and derive the same conclusion. It is precisely the opposite: If your desire is to be a value creator, you must aspire to see what others cannot.

Strategy as Theory

This is not to say that we believe strategic thinking sits outside the realm of logic, science, and experimentation. Quite the contrary: We argue that strategic thinkers engage in an exercise that parallels that of scientists. Like scientists, they start with a significant problem to solve, and then use this problem as a prompt to compose a theory — in this case a theory of value creation. This theory then becomes their unique perspective and point of view about the opportunity they see.

One role of a theory is to shape sight and perception, to enable seeing — often from simple observations — what was previously unnoticed. As Albert Einstein observed, “whether you can observe a thing or not depends on the theory which you use.” Through a novel business theory, you see value in choices, in combinations, and in purchases that others cannot. And most importantly, like theories in science, your theory of value should lead to hypotheses and experiments that help realize opportunities unseen by others.

Of course, whether you are an entrepreneur, a corporate strategist, or a mid-level manager, generating the value you envision typically demands convincing others of the merits of your theory. Convincing others to believe in your envisioned reality over theirs is no small task. In 2009, the founders of Airbnb Inc. pitched their now-famous idea to the venture capitalist Fred Wilson and his firm Union Square Ventures, known for its prescient investments in entrepreneurial growth companies such as Twitter, Tumblr, and Kickstarter. Airbnb needed an infusion of cash, but Wilson and his partners were tremendously skeptical — and with good reason. After all, why would anyone want to stay with strangers while traveling? Why would individuals agree to rent their homes to complete strangers? And how on earth would a small startup — without any experience in the industry — take on large established players and brands in the sophisticated hotel market? Given these concerns, Wilson’s company passed on the investment opportunity. The rest, of course, is history. In 2017, Airbnb claimed more than three million listings in 65,000 cities in 191 countries.

Only with hindsight is it easy to see the value in Airbnb. After all, Wilson’s company’s decision was entirely consistent with the facts at the time. But selling a theory like Airbnb’s takes more than selling facts. It is instead about selling assumptions and logic — convincing those whose resources you need that your assumptions and logic are reasonable and compelling. It is about selling a series of if-then statements. For the Airbnb founders, those entailed convincing investors that if they could solve a number of key problems — including secure payment, efficient matching of those seeking accommodations with those renting homes, and development of a mechanism to generate reputation and trust between the two parties — then the business would thrive. Of course, Airbnb could point to eBay Inc. and Amazon as examples of partial solutions to the trust problem. But fundamentally, the path to gaining others’ support and resources depended on selling their theory through a compelling logical narrative.

Keep in mind that the most valuable theories often face the greatest resistance. In both the world of science and the world of entrepreneurship, stories abound of persistent scientists or entrepreneurs facing consistent rejection — until one day they don’t. Novel theories are consistently resisted. And you too will likely face similar resistance in selling your novel theories. But clarity of assumptions, persuasive logic, and persistence are key to breaking through this resistance.

Testing Theories

Of course, the ultimate test of any theory of value rests on whether the strategic experiments you undertake generate the value anticipated; but fortunately, successful theories do tend to share some common features.

First, valuable theories are novel. As discussed above, they are built around novel beliefs and often try to solve previously unrecognized problems. Think of Uber, Apple, Airbnb, eBay, Amazon, or Walmart. At their origin is some form of contrarian or divergent thinking.

Second, valuable theories are simple and clear. They indicate clearly what problems to solve and experiments to run. They also make it easier to spot solutions others have overlooked. Consider the famous 1979 visit of Steve Jobs to the Xerox PARC research center, where he observed many of the central technologies that today shape personal computers: the graphical user interface, bitmapped graphics, and networking technology. Jobs’ theory of value in personal computers focused on generating seamless and intuitive interactions between a user and a computer. Thus, when he walked into Xerox PARC and found technologies that were languishing there, he instantly recognized that they could solve problems framed by his theory. He later recalled one of the technologies he saw that day as the “best thing I’d ever seen in my life.”

Third, particularly valuable theories have broad and general application. They solve not one but a host of problems, and then continue to identify problems to solve. This happened with Apple. Jobs’ theory of seamless interaction between a user and a device has continued to direct Apple’s value-creating efforts, leading to a remarkable succession of devices that have included computers, music players, phones, tablets, and watches. Something similar also happened with The Walt Disney Co. In the 1920s and 1930s, Walt Disney began creating fantasy worlds and fantasy characters through animated film; then, once opportunities for licensing those characters started to emerge, Disney developed a broader theory of value, recognizing that these characters could be replicated and resold in other entertainment businesses, including books, music, character licensing, and later theme parks, hotels, and television. This theory has continued to fuel Disney’s strategic experiments for decades, prompting moves into retail stores, cruise ships, and Broadway shows. More recently, it prompted Disney to purchase Marvel Entertainment LLC and Lucasfilm Ltd. LLC and expand its content into superheroes and science fiction characters.

Getting Strategy Right

The human capacity for calculation is admittedly flawed and error-prone. Strategic decision-makers should do their best to avoid succumbing to any number of biases, including overconfidence, confirmation, and anchoring biases. But the cumulative negative effects of these biases pale in comparison to the capacity for enhanced strategic decision-making that can be provided by a well-crafted theory. Humans in general are endowed with a remarkable capacity to compose theories that facilitate novel perception, experimentation, and value creation. We believe strategic leaders should focus their efforts on positing theories, testing their underlying logic and assumptions, and crafting strategic actions and experiments. It is those activities — rather than computation or the avoidance of biases and errors — that lead to true breakthroughs.

MIT Sloan Management Review

Operators should match their IoT security strategies with their ambitions in the IoT market

Operators should match their IoT security strategies with their ambitions in the IoT market

Operators should match their IoT security strategies with their ambitions in the IoT market

An article by Sherrie Huang and Michele Mackenzie at Analysys Mason.

“It is unlikely that IoT security will generate a significant new revenue stream, but it will be critical in winning new business and differentiating operators’ services from those of competitors.”

We forecast that 6.4 billion IoT connections worldwide will use fixed, mobile and low-power wide-area (LPWA) networks by 2025. As the IoT market grows, so does the security risk. The discussions on how to secure the IoT are increasingly the focus of attention. An end-to-end IoT project consists of multiple, often diverse, devices, various platforms, layers, and interfaces, creating many dimensions to secure. Security has moved up the list of priorities for IoT projects. Telecoms operators, as IoT service providers, need to develop their IoT security capabilities with relevant products and skills to match their IoT strategy and ambition.

Telecoms operators need to develop security offerings to match their IoT proposition

Operators have a strong legacy in securing the connectivity layer with carrier-grade, embedded security solutions. Security requirements such as secure transmission, safe data and user authentication have been fused into the operator networks for decades and cellular networks are generally viewed as secure and reliable.

However, in the IoT market, operators increasingly address components beyond connectivity to capture a larger share of revenue. Moving up the value chain requires more specialist security expertise, which CSPs do not always have. Operators will increasingly need to do the following.

  • Map their security offerings closely to the IoT components of the value chain that they provide, such as the application, device and enabling capabilities like hosting. Providing enhanced security for devices beyond SIM authentication may not be familiar territory.
  • Tailor the security offering for their target verticals. This will require an understanding of the technical component of the offering but also regulatory compliance and business models. Those operating in the EU will need to understand GDPR compliance, for example.

Operators will need to partner or invest to align their security offering with their IoT proposition. Operators that already benefit from an internal cybersecurity unit, like Telefónica and Vodafone, may have the necessary skills to build their own solutions but most will still need to partner for some or all solutions. A few operators, primarily those that have invested heavily in specific sector expertise, may make strategic investments or acquisitions to bolster their offering. Bold moves such as this will send a clear message to the market about their intentions and role in the value chain. Interestingly, this bold approach is not confined to the large global operators such as Telefónica or Singtel. Smaller national and regional-focused operators with strong IoT business units, such as KPN and Tele2, have acquired to strengthen their security credentials.

Despite the significant effort and investment required, it is unlikely that security will generate a significant new revenue stream in its own right. However, it will be critical in winning new business and potentially differentiating the operator’s service from that of competitors.

Figure 1: The operator’s role in IoT security must match the operator’s role in the IoT value chain

Analysys Mason chart: operators and IoT security

Security can help operators differentiate and strengthen their LPWA service

By 2025, more than half of the total wide-area IoT connections globally will be on LPWA networks. This will bring new and different challenges. Many of the devices connected to an LPWA network will be low-power devices with limited computing power, factors that will restrict security options.

Operators using 3GPP standards (NB-IoT and LTE-M) have a clear opportunity in the early phase of LPWA market development to differentiate their offering from the proprietary networks by marketing the inherent secure nature of their networks. Operators have been slow to promote the value of embedded standardised security in their cellular networks (although the real value of security may have only recently come to the fore). Providing additional security layers by design from the outset of the project will likely add some additional upfront costs but will reduce the overall costs of delivering security for the lifecycle of the project. For example, building in extra layers to secure and update the connected devices.

Operators could position security as a core differentiator to their LPWA proposition and:

  • promote the embedded security attributes of the network and the SIM and market the capabilities of their connectivity and device management platforms in detecting anomalies and mitigating the consequences – for example quarantining devices, OTA updates etc.
  • develop new capabilities internally or through partnerships to provide additional, value-added security layers that are cost effective for LPWA solutions – for example, some operators are exploring the idea of offering additional security in the SIM
  • ensure that their security offering addresses each component of the value chain where they provide solutions and leverage professional services and cybersecurity business units to advise on and implement security solutions.

IoT security could be an important differentiator

Selling IoT security solutions will not necessarily generate substantial revenue for operators. Security at the connectivity layer is embedded but should be a feature that could bolster the value of the connectivity offering. Security for other components of the value chain will be a premium service but is unlikely to generate significant revenue. However, IoT security will be a critical factor in winning IoT business with the potential to differentiate the operator’s service from its competitors.

The post Operators should match their IoT security strategies with their ambitions in the IoT market appeared first on IoT Business News.

IoT Business News

Five Management Strategies for Getting the Most from AI

Fueled by the buzz around powerful applications of artificial intelligence (AI), many business leaders are contemplating whether to introduce AI into their organizations. While practitioners and academics have outlined some of the strategic challenges of implementing AI, many executives are still seeking good models for how to generate competitive advantage from its application.

To find out more about what contributes to successful AI adoption, we helped lead a survey by the McKinsey Global Institute of 3,000 C-level executives across 10 countries and 14 sectors. From that research, we identified five fundamental strategies for how to get the most out of AI’s potential.

1. Plan to Grow, Not Just Cut

Executives should approach AI as an instrument to expand their businesses — creating new products or services, increasing productivity, or winning more market share — as much as a tool to cut costs. Companies with less experience in AI tend to focus on its ability to help cut costs, but the more that companies use and become familiar with AI, the more potential for growth they see in it.

Retailing executives in our survey, for example, mentioned cost cutting as often as increasing market share or market growth as their main objectives for implementing AI. But the subset of retailers who have adopted AI at scale — meaning, they deploy AI across technology groups, use AI in the most core parts of their value chains, and have the full support of their executive leadership — cited AI’s potential for business growth twice as often as its potential for cutting costs.

This same subset of retailers, the early AI adopters, reported that insight-based selling — using AI to review shoppers’ habits and suggest personalized promotions and tailored displays — increased sales by 1% to 5% in traditional stores. And they reported that personalization and AI-enabled dynamic pricing lifted online sales as much as 30%.

2. Invest in Both Technical and Managerial Talent Capabilities

In our survey, executives gave several reasons for not adopting AI. The largest share (30%) said they were uncertain of its business case. Another 21% cited the scarcity of AI-related human capabilities — and these same executives were 50% more likely to also say that AI presented an uncertain business case, suggesting that human capabilities are critically important to capture the returns from AI in new organizations.

The talent question is challenging for many organizations on two grounds. First is the need for new talent: When debating how AI may affect labor markets by automating parts of old jobs, companies have paid less attention to how AI is likely to require new technical job categories such as “DevOps Engineers” and “Next-Gen Machine-Learning Engineers.” Second is the need for managerial attention: Good return on AI will be captured only when the technology is embedded in business and workflow processes — a job that typically is complex and requires management from the highest-level leaders.

Regarding technical jobs, AI promises to be a great source of employment — but also of headaches. Filling new technical positions is expensive and time-consuming because we have not been turning out enough skilled professionals to keep up with the demand. In the United States, for instance, there were approximately 150 million workers in 2016, but only 235,000 data scientists. To circumvent the issue, companies should be using multiple paths for talent acquisition. Organizations that have been best at adopting AI are better at anticipating needs, starting with a few hires during pilots and then scaling their recruitment process just before they move from piloting to full-scale development.

The management of AI technology also involves new leadership skills, including those required to implement modern processes embedded with AI. Companies that are successfully embracing AI are committed to transformation programs, with top management embracing the change and cross- functional management teams ready to redefine their processes and activities.

3. Be Open to Revising Your Strategic Goals

In the age of digital disruption, incumbent organizations often “play defense” and protect existing business lines by cutting costs, boosting automation, or improving customer service. Often, though, they would be better off playing offense by pioneering new products and business models. We saw this with the Schibsted Media Group of Oslo, Norway, which moved its entire newspaper classified business to a free online marketplace, opening up a new revenue stream that now generates more than 80% of the group’s earnings.

Similarly, companies committed to adopting AI need to make sure their strategies are transformational and should make AI central to revising their corporate strategies. There can be a clear strategic payoff in fully embracing the use of AI: In our data, we found that for 12 of the 15 sectors studied, companies that use AI at scale and go on the offensive report profit margins of 5 points higher than others — 18% versus 13%.

4. Rely on a Solid Digital Foundation

AI works best when it has real-time access to large amounts of high-quality data and is integrated into automated work processes. Thus, AI is not a shortcut to creating digital foundations, but a powerful extension of them instead.

Our analyses back this up. At the McKinsey Global Institute, we built a comprehensive measure of the status of digitization intensity in an enterprise. The measure looks at digital assets, including an organization’s computers, robots, digitally connected systems, and other information communication and technology (ICT) assets. It also looks at how digital assets are used, such as for digital payments, digital marketing, back-office operations, and customer relations, and at the human resources devoted to using digital.

We found that companies that are able to show a statistically significant impact from AI not only have a strong digital intensity but also have a strong AI intensity. Overall, AI intensity is relatively uncommon: Less than 5% of companies report that they are using AI as an enterprise-wide solution. Most of these are digital-native companies. But those that score high on both AI and digital dimensions report a much larger and statistically significant impact of AI on their profit development than companies with high scores in AI alone. Our conclusion: Leapfrogging digitization to adopt AI does not seem a good idea.

5. Help Nurture the Creation of AI Ecosystems

Leveraging network effects, which were so important to building global digital centers like Silicon Valley, appear to be just as important to budding AI hubs. A critical mass of researchers, developers, financiers, and customers can create a fertile, self-sustaining ecosystem in which innovation and entrepreneurialism can thrive.

Business leaders can nurture the development of AI ecosystems in their communities by encouraging supportive government policies. Thoughtful incentives to attract investment and talent are helpful — for example, tax breaks for AI entrepreneur immigrants and special tech visa quotas. Funding for leading-edge science programs is also important, including grants to universities, the creation of government laboratories, and joint research initiatives with the private sector. Our global review finds that AI investment is concentrated geographically: In 2016, the United States absorbed around 66% of external investment (defined as venture capital, private equity, and merger and acquisition activity). China was second, at 17%, but growing fast. In Europe, London was the leading city.

Governments have other important tools to foster AI ecosystems. They can act as lead customers, ensuring that regulations are AI-friendly. And they can make more data available, both by opening up their own data and by establishing standards that make data readily available, while still protecting individuals’ privacy.

These AI ecosystems not only create high-skill, high-paying jobs but also, critically, produce knowledge and innovation spillovers. Indeed, our survey suggests that leaders in AI innovation — the United States and China — also lead in AI adoption. Employees become entrepreneurs, AI-savvy workers move from company to company, and innovative products can be developed for and deployed in local markets.

MIT Sloan Management Review

Inmarsat: IoT ‘essential’ to digital transformation strategies

IoT now essential for enterprise digital transformation - Inmarsat

For businesses aiming for digital transformation, the IoT is now the leading technology to deploy, according to research from global mobile satellite communications company Inmarsat.

As well as being the leading technology, adoption and integration of IoT is the number one priority for 92 percent of organizations to deliver on their digital transformation ambitions. Respondents place IoT way ahead of other technologies as a priority, including machine learning and robotics, cited by 38 percent and 35 percent, respectively. In fourth place is 3D printing, taking 31 percent of the vote.

The findings have been released as part of The Inmarsat Research Programme report, The Future of IoT in Enterprise 2017, conducted on Inmarsat’s behalf by research company Vanson Bourne. The survey polled 500 senior respondents, divided evenly across the agritech, energy production, transportation, and mining sectors, from organizations with more than 1,000 employees in EMEA, the Americas and Asia-Pacific.

According to Inmarsat’s survey, IoT is streets ahead because almost all of the respondents (97 percent) are experiencing, or expect to experience, significant benefits from their IoT deployments. The benefits will come, they believe, from improved service delivery capabilities (47 percent), better health and safety (46 percent), and greater productivity (45 percent).

Inevitably, security concerns persist. Almost half (47 percent) of respondents believe they will need to change their approach to security, while 45 percent believe there is a lack of skills in the industry to deal with the security challenges that IoT presents. Twenty-nine percent, meanwhile, worry that connectivity issues threaten to derail their IoT deployments before they have even begun.

Read more: How IoT and digital transformation open up new opportunities

“Collaboration is key”

Paul Gudonis, president of Inmarsat Enterprise, commented: “The development and deployment of IoT is a new phenomenon spreading over every industry, in every part of the world and this research has confirmed that IoT is the leading technology in digital transformation, taking a steady lead over other forms of innovation.

“IoT acts as the eyes and ears of organizations and its value comes from how the data it collects is used to improve effectiveness across an organization. As such, it is unsurprising that so many organizations are deploying IoT to propel their digital transformation initiatives,” he continued.

However, this is not to imply that challenges are absent, he added. The research points to clear concerns – namely, security, skills, and connectivity. “The increasing interconnectivity of devices, teamed with a heightened cyber-security landscape and a short supply of relevant skills, brings an array of issues. To overcome these challenges, collaboration is key,” he said.

“Developing new technology is complex and draws on many different types of skills. Reliable network infrastructure providers, that can operate anywhere in the world, need to work closely with end-user businesses to make sure they understand their operational needs.”

Read more: Honeywell’s IoT Connected Aircraft takes flight

The post Inmarsat: IoT ‘essential’ to digital transformation strategies appeared first on Internet of Business.

Internet of Business

3 Strategies For Driving Digitalization Across Your Business

Business and IT leaders are always looking for new ways to serve and delight customers. With the emergence of Internet of Things (IoT) devices and data – along with associated technologies such as cloud computing, predictive analytics, artificial intelligence, and machine learning – companies can achieve these goals through digitalization.

Organizations now have access to unparalleled amounts of data across all areas of the business and can converge operational and information technologies to make products and processes smarter.

But success in this endeavor requires three strategies:

  1. A customer-centric view that delivers a 360-degree customer experience throughout the engagement cycle
  1. A distributed manufacturing process that leverages technologies such as automation, robotics, and 3D printing to serve a segment of one
  1. A network economy of assets, manufacturing, logistics, partners, and people

360-degree customer experience

Customer expectations are driving a need for new business models. Companies can no longer simply sell products. They must take care of their customers throughout the entire engagement cycle.

My son and I recently saw a display of vinyl records. My son asked, “What are they?” I explained that records were popular before CDs. My son asked, “What are CDs?” He has grown up in an era of subscription-based streaming music and no longer has to bother with physical items.

In fact, there are more and more subscription-based, pay-as-you-go, and consumption-based models across industries. To support these models, companies need to design, deliver, track, and maintain products and services differently. Among other things, they need predictive maintenance to keep products up and running, or else they won’t satisfy customers and make money.

Likewise, understanding the customer environment can help predict what customers want. If you operate a retail store, for example, is it in a rural or urban neighborhood? Is it close to a school or an aging population? Are there local special events? What’s the weather forecast? Combining traditional point-of-sale (POS) data with information from IoT sensors and smart products can improve sales forecasts and make sure the right products are in the right place at the right time.

This all needs to be backed up with omnichannel logistics. My son recently went online to order sneakers. Within five minutes he had designed sneakers with his team colors and his number on the side. He clicked “place order,” then discovered that they would take six weeks to deliver. The company lost the sale. There’s no point in enabling ordering that takes minutes if you can’t deliver on the customer promise. Companies must now be able to deliver on the same day or even within the same hour, not only to the retail store but also to the customer’s door. They must be able to deliver both full truckloads to one location and single items to many locations.

Distributed manufacturing process

A desire to get closer to customers through individualized products and services, combined with pressures from new geopolitical realities, is driving companies to rethink their outsourced manufacturing strategies and consider local distributed manufacturing. Similarly, the need for agile manufacturing to produce a lot size of one is driving companies to move from continuous mass production to configurable production cells.

What’s more, to make manufacturing more efficient and economical, manufacturers are increasing their use of automation and robotics. They’re also using 3D printing to reduce inventory carrying costs and enable more rapid prototyping and customization.

As assets become smarter and more connected, companies can design smarter manufacturing processes and create smarter products. They can gain better visibility into equipment performance to improve usage and uptime. And they can better configure and automate manufacturing processes to improve business agility.

Network economy

When it comes to digitalization, the notion that “the whole is greater than the sum of its parts” couldn’t be truer. From cross-department collaboration in functions like sales and operations planning (S&OP), to cross-company collaboration in design, sourcing, and manufacturing, to “collaboration” among smart products and assets, digitalized functions must work together.

Now, cross-industry business models are emerging to leverage data from smart assets to drive processes across sectors. For example, sensors on tractors can measure soil moisture to trigger an order to a chemical plant for an appropriate formulation of fertilizer. Sensors in trucks can likewise drive preventive maintenance, rerouting of shipments, insurance claims, and the redesign of vehicle parts.

The explosion of connected things and data enables the transformation of processes, products, and business models – and is driving the need for truly digitalized business. Embracing 360-degree customer experience, distributed manufacturing, and a network economy are three strategies that will get you there.

Want to learn more about driving digitalization across your business? Join us for the can’t-miss conference, SAP Leonardo Live, July 11 and 12 at the Kap Europa Congress Center in Frankfurt, Germany. The event will bring together a vibrant global community of up to 1,500 IoT, manufacturing, supply chain, R&D, and operations decision makers, influencers, analysts, and media. Learn firsthand from more than 50 SAP customer showcases how to connect IoT and core business processes to achieve digital transformation.

This article originally appeared on the Huffington Post.  

Internet of Things – Digitalist Magazine