Contact Us

Blog

Strategy, scenarios, and the global shift in defense power

As the strategic landscape shifts, an economic-scenario approach can help defense organizations grapple with uncertainty.

The art of strategy, in defense as elsewhere, involves understanding possible futures to inform present decisions. Change, volatility, and uncertainty are perennial challenges to the defense strategist and are likely to increase in the coming years. Formulating strategy in these conditions will test planners in the public and private sectors alike.

To succeed, decision makers should look behind the headlines of the day to ask the right questions about what will affect their organization in the future. This requires considering the deeper underlying trends that will reshape the strategic landscape in the years ahead. Foremost among them is the shift in global economic power. Although often commented upon by economists and pundits, many strategists focused on defense issues have not fully internalized this historic shift and its implications.

Here we offer a perspective on how strategists in defense organizations and aerospace and defense companies should approach this challenge. First, we describe how the profound shift in economic power since the end of the Cold War has already reshaped the world’s strategic landscape, including the distribution of global defense spending. The potential evolution of these economic dynamics is fundamental to strategy. Predicting their future is, of course, impossible. Instead, we offer something more modest and practical: a new approach to scenario planning that is rooted in a deep understanding of global economics. Such an understanding reveals the potential for unexpected scale and pace in the shift of defense spending from the United States and its treaty allies to emerging economies.

The strategic landscape reshaped, 1991-2012

The past 20 years saw dramatic changes on the battlefield, even as some features endured, and the beginnings of an equally dramatic shift in economic power. In combination, these movements have altered the strategic landscape, and provide a glimpse into the future.

Continuity and change in military operations

For the world’s defense and security organizations, history certainly did not end with the collapse of the Soviet Union and the cessation of the Cold War in 1991. We have since seen conflict on almost every continent, from the last major tank battle of the 20th century at 73 Easting in the Gulf War to numerous wars, clashes, and insurgencies in Europe, Asia, Africa, and the Americas. Since the attacks of September 11, 2001, attention has focused on the greater Middle East—Afghanistan, Iraq, and, increasingly, the struggles for control and influence in the aftermath of the Arab Spring.

The tempo of military operations has been relentless. Since 1991, for instance, the United States has embarked upon a new military intervention roughly every two years. North Atlantic Treaty Organization (NATO) forces have been at war in Afghanistan for over a decade. South Korean and Japanese forces have deployed for the first time to the Middle East. Meanwhile, the United Nations has launched a new peacekeeping operation every six months. Moreover, the duration of most of these operations has increased to five to ten years.

Innovations in military technology and operations have marked these past two decades of conflict. Precision-guided munitions have evolved and demonstrated their effectiveness in conflicts beginning with the Gulf War and continuing in Kosovo, Afghanistan, and most recently NATO’s Operation Unified Protector in Libya in 2011. Advanced missiles now pose particular threats to capital ships and fixed bases. Unmanned aerial vehicles (or remotely piloted air systems) are now standard components in many militaries’ intelligence, surveillance, and reconnaissance (ISR) tool kits, and they increasingly serve as weapons platforms as well. Harnessing big data and employing advanced analytic techniques are other features of 21st century ISR. Cyberwarfare has moved from theory to practice.

Militaries today confront adversaries who employ a full spectrum of tactics, from conventional to irregular and even criminal (“hybrid war”). Modern navies, for instance, cope with traditional and unconventional foes, including Somali pirates and asymmetrical threats such as terrorist suicide speedboats. Air forces are investing in fifth-generation fighters even as they continue to provide workhorse logistical support for operations in the field. And for foot soldiers, despite the numerous technical advances in communications and equipment, the past decade has been largely spent relearning the lessons of counterinsurgency: “Walk. Stop by, don’t drive by. . . .Situational awareness can only be gained by interacting face-to-face, not separated by ballistic glass or Oakleys.” Plus ça change, plus c’est la même chose.

The power shift begins

Concurrent with these tactical and operational developments, tectonic plates moved at a deeper strategic level. A profound shift in economic power began during this period—one that has already manifested itself in the distribution of traditional “hard” military power among the great powers. This shift will continue to reshape the strategic landscape in the years ahead.

Future historians will likely point to 2007–08 as an inflection point in global history. For the first time in over two centuries—since the start of the Industrial Revolution—the majority of the world’s economic growth took place in the developing world, driven in large part by China, India, and other Asian economies. In addition to favorable demographics and reforms to open emerging economies, increasing urbanization—especially in China—drove much of this growth. Significantly, 2008 was also the first time ever that a majority of people lived in cities. The pace of urbanization is staggering. More than 1.3 million people migrate every week to urban areas. And this historic migration will likely continue unabated for the next two decades, mainly in the emerging economies of Asia, Latin America, and, increasingly, Africa. The global economic crisis that began in 2008 accelerated this shift in economic power from developing to emerging economies, as the BRIC countries (Brazil, Russia, India, and China) weathered the storm well and the developed economies of the United States, Europe, and Japan suffered and remain vulnerable more than four years later. Between 2009 and 2012, China’s economy grew over 30 percent and India’s 22 percent in real terms, whereas Germany’s grew 7.9 percent and the United States’s 7.1 percent. And in 2008, for the first time, a Chinese company led the world in international patent applications.

Taken together, economic and demographic forces drove the most rapid shift in human history in what the McKinsey Global Institute (MGI) calls the “economic center of gravity”—the geographic midpoint of global economic activity (Exhibit 1). MGI projects this movement to continue, at a slightly slower pace, for the next 15 years or so, when, by this measure, most of the regional imbalances ushered in by the Industrial Revolution will have been erased.

Exhibit 1

The ten years from 2000 to 2010 saw the fastest-ever shift in the world’s economic center of gravity.

These economic trends have already started to reshape the global landscape of defense spending. To be sure, a nation’s assessment of its security threats plays a critical role in shaping its defense spending in the near term. That said, a major country’s military power flows in large part from its underlying economic strength over the medium to long term: the faster a country’s economy grows, the more likely its defense spending will increase as well. Despite widely different geopolitical complexities and economic dynamics, the countries with the largest defense budgets, which account for the vast majority of the world’s defense expenditures, have fit this pattern since 1991 (Exhibit 2).

Exhibit 2

Growth in military spending and GDP are correlated over the long term.

Other more subtle shifts in national power were also taking place between 1991 and 2011. R&D expenditures in all major economies nearly doubled in constant 2011 dollar terms, rising from roughly $740 billion in 1991 to $1.5 trillion in 2011. But just as we saw in defense spending, countries’ R&D investments mirrored trends in their overall economic growth. Europe and Japan’s combined share of global R&D expenditures declined by 11 percent in the 20 years after 1991. Meanwhile, China increased its share of global spending to 9 percent from 1 percent during the same period. Developing countries are thus emerging as true competitors to the developed economies not only with regard to their economies’ sheer scale but also their innovation and technical prowess. R&D spending matters: a country’s R&D investments are strongly correlated with the quality of the military’s equipment 25 years later. This suggests that in the future, developing countries will narrow the gap in quality between their military equipment and that of developed countries.

Glimpses of the future

We have seen these dynamics play out around the world since 1991. Sustained operations and the increasing costs of modern weapons platforms have proved too much for many Western governments and their publics as they manage the aftermath of the global economic crisis and structural strains in their aging societies. For example, the United Kingdom’s 2010 Defence and Security Review declared bluntly that its “inherited defense spending plans… [are] completely unaffordable” and that it needs to “confront the legacy of overstretch.” France’s earlier national-security review analyzed the changing strategic context and new priorities and thus, through a different logic, reached similar conclusions. The US Department of Defense (DOD), under Secretary of Defense Robert Gates, launched its own efficiencies campaign, which has accelerated under his successors. The planned reductions in the US defense budget in the next decade are literally on the scale of some countries’ GDPs. Other Western militaries are undergoing similar retrenchments. At the same time, however, China, other East Asian countries, India, Brazil, and other emerging economies have continued to increase not only their defense expenditures in real terms, as shown in Exhibit 3, but also, in many cases, the sophistication of their own defense industries.

 

Exhibit 3

Developing countries have closed the gap in defense spending since the end of the Cold War.

 

Today, the United States remains the world’s preeminent military power in scale, sophistication, battle-tested experience, and global reach. US defense spending in 2011 was more than five times that of the next highest defense spender, China. America’s traditional allies also occupy important positions in the global defense landscape. Japan, France, and the United Kingdom ranked third, fourth, and fifth in defense spending in 2011. The momentum, however, is unmistakable: emerging economies are positioned to displace the other developed economies in the top tier of defense spenders. China’s rise in defense spending is starkest. In 2011 it spent $126 billion, more than twice as much as the countries that are the next largest spenders: Japan, France, the United Kingdom, and Russia. Chinese authorities announced in March 2013 that they plan to increase defense spending another 10.7 percent in 2013.

The direction is clear. Developed economies—and the world’s leading military establishments historically—have experienced relative decline vis-à-vis the major developing economies since 1991. Yet the scale, scope, and pace of change of the shift in economic power cannot be simply extrapolated from the recent past to understand the future. Instead, strategists will require a new approach to manage this era’s particular strategic uncertainties.

An economic-scenario approach

Scenario planning is an established tool for business and defense strategists. Done right, scenario planning accounts for the major variables or drivers that could shape the future, in sufficient depth and vividness to enable strategists to draw out potential implications for the strategy and plans of their organizations. Scenarios are not meant to be predictive or comprehensive, covering all possible futures. Rather, the goals are to define a range of possibilities and identify important continuities among them, as well as significant uncertainties or risks that a strategy should address. Such an analysis, in turn, helps to frame critical questions and generate strategic insights that then shape the strategic decisions of today and adaptation tomorrow.

Today, defense scenarios typically start with geopolitical, security, or more operational assumptions or with a statement of guiding objectives (such as “Counter violent extremism, deter and defeat aggression, strengthen international and regional security, and shape the future force”; “US forces must be prepared to fight ‘two and a half’ wars simultaneously”; or “Blue Force must be ready to counter Red Force in country X”). For example, the US DOD’s recent strategy documents—the February 2010 Quadrennial Defense Review Report and the February 2011 National Military Strategy of the United States—briefly review the changing strategic environment but do not offer alternative scenarios. The importance of this era’s economic forces, however, suggests that a different approach, one with a more explicit grounding in these new economic realities, is needed. National-security strategies typically do not explore the full implications of the shift in economic power now under way. The approach outlined here is designed to address this gap. The development of rich, sophisticated scenarios for the future course of the global economy provides its starting point. These scenarios, in turn, enable the mapping of changes in countries’ economic power in relative and absolute terms. The trends most immediately relevant for defense and security organizations—especially levels of defense spending and R&D investment—are then built upon these economic foundations.

We use a set of economic scenarios for the next decade built with McKinsey’s Global Growth Model (GGM) to illustrate this approach. The GGM is a database and modeling tool that supports the construction of detailed economic scenarios down to the country level. Its variables span GDP and more than 100 others, including demographics, education levels, public debt, R&D investments, and urbanization rates.

The baseline economic scenarios employed here are predicated on the assumption that major developed and emerging economies will develop with different growth rates—namely, developing economies will tend to grow at a significantly higher rate than developed economies. We emphasize these different outcomes and groups because, broadly speaking, the challenges faced by countries in each group are similar. Generally, advanced economies have struggled not only with the aftermath of the 2008 downturn but also with meeting their aging populations’ multifaceted challenges. Meanwhile, emerging markets have tried to sustain or accelerate growth through export promotion, the allocation of capital, continued economic reform, urbanization and industrialization, and other tactics.

The relative performance of these different groups of economies thus describes a range of four principal scenarios. The GGM scenarios are bounded by emerging markets’ growth rates (between 3 and 7 percent per year from 2013 to 2022) and advanced economies’ growth at 1 to 3 percent annually. These growth rates define four broad-based scenarios shown in Exhibit 4: Global Growth Renewed, Advanced Economies Rebound, Emerging Economies Lead, and Global Lost Decade.

 

Exhibit 4

Four scenarios describe possible paths for the global economy from 2013 to 2022.

 

Together, these scenarios portray different potential future international landscapes and balances of economic power among the major economies. They make clear that the stakes are extraordinarily high for the health of the entire global economy. While growth continues across all scenarios, the difference in total global real GDP between the most favorable scenario ($100 trillion, under Global Growth Renewed) and the least favorable one ($84.5 trillion, under Global Lost Decade) is a staggering $15 trillion per year in 2022. That is roughly equivalent to the US economy today.

The scenarios also have quite different outcomes for the relative distribution of economic power among countries. Across all scenarios, the US economy remains the world’s largest economy. China’s rapid growth continues, but by 2022, it still does not equal the size of the US economy today. The absolute gap between the two narrows more or less depending on the scenario. The other major developing economies follow a similar pattern; Europe and Japan experience steady growth in the most favorable scenarios and stagnation in those that are less favorable.

Defense-spending scenarios to 2022

The four baseline economic scenarios paint very different possible futures for the world’s major economies—and thus for their self-confidence, internal political stability, and the vision of their roles internationally. Geopolitical relations (for example, strains among past allies over burden sharing or opportunities for new alignments among rising powers) would also vary under the different scenarios.

To understand these implications, we analyzed global defense spending under the economic scenarios. We focus our analysis on the top 15 defense spenders in 2011. These countries account for the lion’s share of global defense spending under any scenario (that is, nearly 85 percent in 2011) for the sake of simplicity. As mentioned, growth in countries’ defense spending is strongly correlated (r-squared equals 0.84) with their underlying economic growth over longer periods of time. Therefore, we use each scenario’s estimate of countries’ real GDP and a country-specific correlation between defense spending and GDP to derive the percentage of each nation’s real GDP devoted to defense spending in 2022 under each scenario.

The likely changes in countries’ relative share of global defense spending between 2011 and 2022 are striking (Exhibit 5). If we divide the 15 countries studied into the United States and its 9 treaty allies, and the emerging BRIC countries and Saudi Arabia, we see that while the US and its allies still account for the majority of defense spending in all scenarios in 2022, their relative advantage is eroded in all scenarios by emerging markets’ higher rates of economic growth. Most dramatically, in the Emerging Economies Lead scenario, the United States and its treaty allies’ relative advantage in global share of defense spending falls by 23 percentage points in a decade, dropping to 55 percent by 2022.

Exhibit 5

A substantial share of global defense spending will shift away from the United States and its traditional allies.

The scenarios likewise suggest dramatic shifts in defense spending in absolute terms. Defense spending in the BRIC countries and Saudi Arabia will increase significantly in all scenarios—from roughly $290 billion in 2011 to between approximately $550 billion and $830 billion by 2022 (in constant 2011 dollars). The fate of the United States and its major treaty allies’ defense spending is mixed, however. When the major developed economies fare well, their combined defense spending increases from a little over $1 trillion in 2011 to more than $1.4 trillion in 2022; when they fare poorly, in the Global Lost Decade and Emerging Economies Lead scenarios, their combined defense spending falls below $1 trillion by 2022.

Exploring these scenarios for individual countries again shows shifts in defense spending away from the developed economies. Consider the top five defense spenders in each scenario (Exhibit 6). The United States and China maintain their one-two positions in total defense spending in all scenarios. In those scenarios in which US economic growth remains sluggish throughout the coming decade, its defense spending falls more than 15 percent in real terms. The United States’s relative advantage in defense spending over China, however, declines significantly from 5.4x in 2011 to between 3.2x and 1.6x in 2022. India enters the ranks of the world’s top five defense spenders in all scenarios (see “A bright future for India’s defense industry?” [PDF–417KB]). Russia and Saudi Arabia round out the top five defense spenders in three of the four scenarios. Only in the Advanced Economies Rebound scenario do France and the United Kingdom—two of the US treaty partners—remain in the top five.

Exhibit 6

The difference between the two biggest defense spenders varies widely by scenario.

Considering total R&D investments across the different countries provides a rough measure of the dynamism of high-technology industries and innovation, and, as noted above, an indication of long-term trends in the quality of the military equipment the country makes (Exhibit 7).

 

Exhibit 7

The combined European, Japanese, and US share of global R&D investment will shrink in any scenario.

 

Our scenarios show overall R&D investments tracking the trends seen in defense spending but at different rates. The United States retains its leadership position but by less than before, as emerging economies gain ground on the United States and its traditional allies. Most important, China’s investments in R&D accelerate in the next ten years; in all scenarios, China more than doubles its share of global R&D investment. In the coming decade, we should expect China to place second only to the United States in total R&D investments, just as it became the world’s second-biggest defense spender in the previous decade. China’s rise in R&D is stunning: in all scenarios, it will increase its share of global R&D from roughly $1 of every $100 in 1991 to $1 of $5 spent in 2022. In doing so, it surpasses both Europe and Japan; their investments grow slowly in all scenarios. That said, Europe and Japan will remain major centers of global R&D into the 2020s because of their relatively robust starting point. Meanwhile, Brazil, India, and Russia also gain share, though at a much slower pace than China, and they will continue to lag behind Europe and Japan by a significant margin in 2022 (for example, Japan’s R&D spending will remain roughly five times India’s across the scenarios).

This economic-scenario approach enables strategists to assess other underlying factors should a particular analysis require it. These include public-debt burdens (for instance, high for most developed economies, with exceptions such as Australia), subtle variations among some countries’ relative performance (for example, South Korea consistently punches above its weight with regard to defense spending and R&D), education levels (for instance, other countries continue to narrow the US lead in average number of years of education per person), and demographic trends.

From using scenarios to framing the right question

Decision makers in ministries of defense and corporate boardrooms require analytic tools to help them manage uncertainties. Without them, they risk falling prey to misguided confidence in a single, clear, but almost certainly erroneous prediction of the future, or they will be forced to rely on their gut. The economic-scenario approach outlined here is one such tool for the strategist’s tool kit.

The economic-scenario approach highlights how the global strategic landscape may change in the coming decade. Such scenarios can help organizations identify the specific potential opportunities, risks, trade-offs, and outcomes that their strategies should consider. From a defense official’s perspective, such questions could include the following:

  • What does it mean for the United States when the defense spending of its traditional treaty allies will continue to decline in relative, and perhaps absolute, terms? What capabilities might these allies be able to deploy in the future? What new security relationships might be needed to manage the shifting balance of defense power? What might be the implications of such shifts for US force structure, overseas basing, and diplomacy?
  • What does it mean for European countries’ role in the world as their relative share of defense power shrinks? Will NATO’s role in the world correspondingly retract? Will NATO’s “out of area” operations become a thing of the past? Will individual European countries have effective expeditionary forces in the 2020s, or will limitations force them to decide among increased dependence on US support (for example, logistical and lift support), increased defense cooperation within Europe, and disengagement from traditional areas of influence such as Africa? What might be the implications of these different scenarios for the future affordability of independent nuclear-deterrence forces in France and the United Kingdom?
  • What does the wide range of possibilities for US defense spending in 2022 mean for Asian countries? How will such uncertainties shape their defense postures and diplomacy toward the United States, and one another?
  • What does it mean for emerging countries that for the next decade the United States will remain the global leader in military spending and R&D investments despite those countries’ rapid growth? How relevant will European powers be in their strategic calculus? What security relationships should they prioritize to cope with the shifting strategic landscape?

From an aerospace and defense executive’s perspective, such questions could include the following:

  • Which markets will matter most for a company’s growth in the next ten years? What should be the relative balance among developed and developing markets in its portfolio?
  • What does the possible emergence of India and Saudi Arabia among the world’s top five defense spenders suggest for a company’s strategic priorities?
  • How should a company manage the diversity of regulations and laws related to technology transfer, intellectual property, and local content provisions as it seeks to expand into specific developing markets? How should it manage its defense and civilian aerospace businesses in such markets in light of other diplomatic and commercial considerations? What innovative joint ventures, mergers, or other collaborations will fuel growth among aerospace and defense companies based in different countries?
  • How should a company leverage the continued robust R&D base in Europe, Japan, and the United States to serve both developed and developing aerospace and defense markets?
  • How will developing countries’ aerospace and defense industries “go global” and compete directly against more established Western players in defense markets around the world in the coming decade? What are the implications for Western companies’ strategies, operations, and costs if many systems are produced for emerging markets?
  • What should be a company’s global manufacturing footprint in light of these trends and uncertainties in the coming decade?
  • What skills and talent will a company need to succeed in the changed global defense landscape?

Asking the right questions is the starting point for any strategy. We are now living through an unprecedented shift in global economic power. No one can predict precisely what this means for our future. Robust economic scenarios, however, can help strategists frame the right questions and trade-offs for their organizations, which is more helpful than aspiring to predict that which defies prediction.

Printed with permission from McKinsey Insights & Publications.

in Blog

Beyond Korean style: Shaping a new growth formula

Beginning in the 1960s, South Korea has set economic-development records with a growth formula that focused on heavy-industry and manufactured exports. GDP has tripled in just the past 20 years, and South Korea became the first nation to go from being a recipient of aid from the Organisation for Economic Co-operation and Development to being a member of its donor committee. South Korea is the leading supplier of LCD screens, memory chips, and mobile phones and is the world’s number-five automaker.

Yet the nation’s GDP growth is increasingly decoupled from the lives of its middle- income citizens. The number of middle-income households—earning 50 to 150 percent of median income—has fallen from 75.4 percent of the population to 67.5 percent since 1990, and more than half of middle-income households are cashflow constrained when the full costs of housing payments are counted. The squeeze contributes to trends that could affect future growth, including a plummeting personal-saving rate and one of the world’s lowest fertility rates. Beyond Korean style: Shaping a new growth formula, a new report from the McKinsey Global Institute (MGI), explores the causes of these economic challenges and makes specific recommendations for combatting them. Among the report’s findings:

  • South Korea’s largest industrial corporations have continued to grow rapidly, but mostly in new global markets; their domestic employment has fallen by 2 percent annually for 15 years, leaving job creation to small and medium-sized enterprises (SMEs) and Korea’s underdeveloped service sector, where wages are just 55 percent of manufacturing pay.
  • Spending on housing and education have soared (exhibit). The median price for a home in South Korea is 7.7 times the median annual income—more than twice the US multiple. South Koreans also pay much more to finance their home purchases because low loan-to-value limits often force homebuyers to seek supplemental, high-interest loans. Spending on private education is extremely high as well (around 9 percent of GDP) because South Koreans believe admission to a top university is the only path to success for their children.

 

Exhibit

Beyond Korean Style - exhibit

 

But South Korea can take specific steps to strengthen its middle-income households, increase domestic demand, and build a more balanced economy. Namely:

  • Reduce housing payments. By switching to mortgages with looser loan-to-value limits, MGI estimates that South Korean homeowners could save $8 billion annually in payments. South Korea can also encourage more investment in rental housing and can consider shared ownership programs.
  • End the education “arms race.” Even though Koreans continue to sacrifice to prepare their children for university, unemployment rates are higher for South Korean college graduates than for graduates of vocational high schools. And when costs are factored in, the net present value of the lifetime earnings of a privately educated college graduate is lower than those for a graduate of vocational high school. To help parents consider alternatives to a university education, MGI suggests higher investment in vocational education and expansion of the Meister school program, in which employers collaborate with schools to create job-relevant curricula. A dual-track system would enable students to continue on to college degrees as they progress in their careers.
  • Build up services and SMEs. South Korean services are dominated by low value-added enterprises, particularly local services (for example, restaurants, real-estate sales, transportation). South Korea can build on the success of sectors that are already globally competitive, such as construction engineering, and help expand sectors such as health care, tourism, and financial services.
  • Create an entrepreneurial SME sector. Most SMEs are very small, and few mid-sized enterprises become large companies. Structural problems are partly to blame, but South Korea also lacks an entrepreneurial tradition and offers limited support for innovative risk-takers. South Korea can work to expand access to capital, increase intellectual-property protections, teach entrepreneurism, and update bankruptcy regulations. These initiatives will take a concerted effort by policy makers, business leaders, and South Korean citizens. But the result could be a new growth formula that complements the current model, reverses the erosion of middle-income households, and builds a sustainable future for all South Koreans.

Printed with permission from McKinsey Global Institute

in Blog / Document translation

Manufacturing the future: The next era of global growth and innovation

The global manufacturing sector has undergone a tumultuous decade:

Large developing economies leaped into the first tier of manufacturing nations, a severe recession choked off demand, and manufacturing employment fell at an accelerated rate in advanced economies. Still, manufacturing remains critically important to both the developing and the advanced world. In the former, it continues to provide a pathway from subsistence agriculture to rising incomes and living standards. In the latter, it remains a vital source of innovation and competitiveness, making outsized contributions to research and development, exports, and productivity growth. But the manufacturing sector has changed—bringing both opportunities and challenges—and neither business leaders nor policymakers can rely on old responses in the new manufacturing environment.

Manufacturing the future:

The next era of global growth and innovation, a major report from the McKinsey Global Institute, presents a clear view of how manufacturing contributes to the global economy today and how it will probably evolve over the coming decade. Our findings include the following points:

  • Manufacturing’s role is changing. The way it contributes to the economy shifts as nations mature: in today’s advanced economies, manufacturing promotes innovation, productivity, and trade more than growth and employment. In these countries, manufacturing also has begun to consume more services and to rely more heavily on them to operate.
  • Manufacturing is not monolithic. It is a diverse sector with five distinct groups of industries, each with specific drivers of success.
  • Manufacturing is entering a dynamic new phase. As a new global consuming class emerges in developing nations, and innovations spark additional demand, global manufacturers will have substantial new opportunities—but in a much more uncertain environment.

Manufacturing’s role is changing:

Globally, manufacturing continues to grow. It now accounts for approximately 16 percent of global GDP and 14 percent of employment. But the manufacturing sector’s relative size in an economy varies with its stage of development. We find that when economies industrialize, manufacturing employment and output both rise rapidly, but once manufacturing’s share of GDP peaks—at 20 to 35 percent of GDP—it falls in an inverted U pattern, along with its share of employment. The reason is that as wages rise, consumers have more money to spend on services, and that sector’s growth accelerates, making it more important than manufacturing as a source of growth and employment.

The sector is also evolving in ways that make the traditional view—that manufacturing and services are completely separate and fundamentally different sectors—outdated. Service inputs (everything from logistics to advertising) make up an increasing amount of manufacturing activity. In the United States, every dollar of manufacturing output requires 19 cents of services. And in some manufacturing industries, more than half of all employees work in service roles, such as R&D engineers and office-support staff.

As advanced economies recover from the Great Recession, hiring in manufacturing may accelerate, and some nations may even raise net exports. Manufacturers will continue to hire workers, both in production and nonproduction roles (such as design and after-sales service). But in the long run, manufacturing’s share of employment will remain under pressure as a result of ongoing productivity improvements, faster growth in services, and the force of global competition, which pushes advanced economies to specialize in activities requiring more skill (Exhibit 1).

Exhibit 1

Manufacturing is not monolithic. No two manufacturing industries are exactly alike; some are more labor- or more knowledge-intensive. Some rely heavily on transportation, while for others, proximity to customers is the critical issue. We have identified five broad manufacturing segments and analyzed how different production factors influence where they build factories, carry out R&D, and go to market.

The largest segment by output (gross value added) includes industries such as autos, chemicals, and pharmaceuticals. These industries depend heavily on global innovation for local markets—they are highly R&D intensive—and also require close proximity to markets. The second-largest segment is regional processing, which includes industries such as printing and food and beverages. The smallest segment, with just 7 percent of global manufacturing value-added, produces labor-intensive tradables (Exhibit 2).

Exhibit 2

Manufacturing is diverse: We identify five broad groups with very different characteristics and requirements

Manufacturing is entering a dynamic new phase. By 2025, a new global consuming class will have emerged, and the majority of consumption will take place in developing economies. This will create rich new market opportunities. Meanwhile, in established markets, demand is fragmenting as customers ask for greater variation and more types of after-sales service. A rich pipeline of innovations in materials and processes—from nanomaterials to 3-D printing to advanced robotics—also promises to create fresh demand and drive further productivity gains across manufacturing industries and geographies.

These opportunities arise in an extremely challenging environment. In some low-cost labor markets, wage rates are rising rapidly. Volatile resource prices, a looming shortage of highly skilled talent, and heightened supply-chain and regulatory risks create an environment that is far more uncertain than it was before the Great Recession.

Manufacturers and policy makers need new approaches and capabilities. Companies must develop a highly detailed understanding of specific emerging markets, as well as the needs of their existing customers. They will also require agile approaches to the development of strategy—using scenario planning rather than point forecasts, for example. And they will have to make big bets on long-range opportunities, such as tapping new markets in developing economies or switching to new materials, but must do so in ways that minimize risk.

A critical challenge for manufacturers will be to approach footprint decisions in a more nuanced way. Labor-intensive industries will almost always follow the path of low wages, but others, with more complex needs, must weigh factors such as access to low-cost transportation, to consumer insights, or to skilled employees. The result could very well be a new kind of global manufacturing company—a networked enterprise that uses “big data” and analytics to respond quickly and decisively to changing conditions and can also pursue long-term opportunities.

For policymakers, supporting manufacturing industries and competing globally means that policy must be grounded in a comprehensive understanding of the diverse industry segments in a national or regional economy, as well as the wider trends affecting them. For example, shapers of energy policy need to consider which segments will be affected by higher or lower energy costs, how great the impact is likely to be, and what magnitude of difference will trigger a location decision. Policymakers should also recognize that their long-term goals for growth, innovation, and exports are best served by supporting critical enablers for manufacturers (such as investing in modern infrastructure) and by helping them forge the connections they will need to access rapidly growing emerging markets.

Two key priorities for both governments and businesses are education and the development of skills. Companies have to build their R&D capabilities, as well as expertise in data analytics and product design. They will need qualified, computer-savvy factory workers and agile managers for complex global supply chains. In addition to supporting ongoing efforts to improve public education—particularly the teaching of math and analytical skills—policy makers must work with industry and educational institutions to ensure that skills learned in school fit the needs of employers.

in Blog / Document translation

Urban World: A new iPad app for exploring an unprecedented wave of urbanization

The growth of cities in emerging markets is driving the most significant economic transformation in history. The McKinsey Global Institute’s new iPad app, Urban World, offers a sense of how economic power will move as this urban expansion takes place. The app offers previously unavailable data from a proprietary MGI database of more than 2,600 cities around the world.

The app also places urbanization in a historical context, using a view from space of the global nighttime distribution of light as a proxy for the global distribution of economic activity. Users can visualize the world’s shifting center of economic gravity during the past two millennia, to 2025. The app serves a purpose similar to a 16th-century map—a rough but helpful tool to help navigate the evolving urban world.

Corporate strategists, urban planners, economic historians, and geography buffs alike can use the app’s interactive map to compare individual cities on their GDP, population, and income levels in 2010 and one scenario for 2025.

The growth of some urban markets can exceed that of entire nations, which is why cities matter for strategy. Companies can use the data to compare the economic growth from different cities. For example, Vienna had roughly the same GDP in 2010 as Istanbul. By 2025, the scenario presented in the app shows that the GDP of Istanbul will be comparable to the entire country of Austria. Auckland (New Zealand) had roughly the same GDP in 2010 as New Delhi but, by 2025, the GDP of New Delhi will be almost as great as the GDP of the entire country of New Zealand.

MGI research finds that the annual consumption of citizens in emerging markets will reach $30 trillion by 2025, and the GDP of these markets will exceed that of developed countries. Business leaders recognize that emerging markets hold the key to long-term success—but that they also pose daunting challenges. Many companies’ financial results reflect that ambivalence. In 2010, 100 of the world’s largest companies headquartered in developed economies derived just 17 percent of revenue from emerging markets—despite the fact that those markets account for 36 percent of global GDP. Companies that neglect these new markets risk missing out as much as 70 percent of global GDP growth between now and 2025.

iPad screenshot

Urban World: A new iPad app - image3

in Blog / Document translation

The big-data revolution in US health care: Accelerating value and innovation

Big data could transform the health-care sector, but the industry must undergo fundamental changes before stakeholders can capture its full value.

A big-data revolution is under way in health care. Start with the vastly increased supply of information. Over the last decade, pharmaceutical companies have been aggregating years of research and development data into medical databases, while payors and providers have digitized their patient records. Meanwhile, the US federal government and other public stakeholders have been opening their vast stores of health-care knowledge, including data from clinical trials and information on patients covered under public insurance programs. In parallel, recent technical advances have made it easier to collect and analyze information from multiple sources—a major benefit in health care, since data for a single patient may come from various payors, hospitals, laboratories, and physician offices.

Fiscal concerns, perhaps more than any other factor, are driving the demand for big-data applications. After more than 20 years of steady increases, health-care expenses now represent 17.6 percent of GDP—nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.1 To discourage overutilization, many payors have shifted from fee-for-service compensation, which rewards physicians for treatment volume, to risk-sharing arrangements that prioritize outcomes. Under the new schemes, when treatments deliver the desired results, provider compensation may be less than before. Payors are also entering similar agreements with pharmaceutical companies and basing reimbursement on a drug’s ability to improve patient health. In this new environment, health-care stakeholders have greater incentives to compile and exchange information.

While health-care costs may be paramount in big data’s rise, clinical trends also play a role. Physicians have traditionally used their judgment when making treatment decisions, but in the last few years there has been a move toward evidence-based medicine, which involves systematically reviewing clinical data and making treatment decisions based on the best available information. Aggregating individual data sets into big-data algorithms often provides the most robust evidence, since nuances in subpopulations (such as the presence of patients with gluten allergies) may be so rare that they are not readily apparent in small samples.

Although the health-care industry has lagged behind sectors like retail and banking in the use of big data—partly because of concerns about patient confidentiality—it could soon catch up. First movers in the data sphere are already achieving positive results, which is prompting other stakeholders to take action, lest they be left behind. These developments are encouraging, but they also raise an important question: is the health-care industry prepared to capture big data’s full potential, or are there roadblocks that will hamper its use? (In a related video, McKinsey director Nicolaus Henke explains how analytics is transforming the practice of medicine.)

A new value framework

Health-care stakeholders are well versed in capturing value and have developed many levers to assist with this goal. But traditional tools do not always take complete advantage of the insights that big data can provide. Unit-price discounts, for instance, are based primarily on contracting and negotiating leverage. And like most other well-established health-care value levers, they focus solely on reducing costs rather than improving patient outcomes. Although these tools will continue to play an important role, stakeholders will only benefit from big data if they take a more holistic, patient-centered approach to value, one that focuses equally on health-care spending and treatment outcomes. We have created five pathways to assist them in redefining value and identifying tools that are appropriate for the new era. They focus on the following concepts:

  • Right living. Patients must be encouraged to play an active role in their own health by making the right choices about diet, exercise, preventive care, and other lifestyle factors.
  • Right care. Patients must receive the most timely, appropriate treatment available. In addition to relying heavily on protocols, right care requires a coordinated approach, with all caregivers having access to the same information and working toward the same goal to avoid duplication of effort and suboptimal treatment strategies.
  • Right provider. Any professionals who treat patients must have strong performance records and be capable of achieving the best outcomes. They should also be selected based on their skill sets and abilities rather than their job titles. For instance, nurses or physicians’ assistants may perform many tasks that do not require a doctor.
  • Right value. Providers and payors should continually look for ways to improve value while preserving or improving health-care quality. For example, they could develop a system in which provider reimbursement is tied to patient outcomes or undertake programs designed to eliminate wasteful spending.
  • Right innovation. Stakeholders must focus on identifying new therapies and approaches to health-care delivery. They should also try to improve the innovation engines themselves—for instance, by advancing medicine and boosting R&D productivity.

The value pathways evolve as new data become available, fostering a feedback loop. The concept of right care, for instance, could change if new data suggest that the standard protocol for a particular disease does not produce optimal results. And a change in one pathway could spur changes in others, since they are interdependent. An investigation into right value, for example, could reveal that patients are most likely to suffer costly complications after an appendectomy if their physician performs few of these operations. This finding could influence opinions not only about value but about the right provider to perform an appendectomy.

The pathways in action

Some health-care leaders have already captured value from big data by focusing on the concepts outlined in our pathways or have set the groundwork for doing so. Consider a few examples:

  • Kaiser Permanente has fully implemented a new computer system, HealthConnect, to ensure data exchange across all medical facilities and promote the use of electronic health records. The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.
  • Blue Shield of California, in partnership with NantHealth, is improving health-care delivery and patient outcomes by developing an integrated technology system that will allow doctors, hospitals, and health plans to deliver evidence-based care that is more coordinated and personalized. This will help improve performance in a number of areas, including prevention and care coordination.
  • AstraZeneca established a four-year partnership with WellPoint’s data and analytics subsidiary, HealthCore, to conduct real-world studies to determine the most effective and economical treatments for some chronic illnesses and common diseases. AstraZeneca will use HealthCore data, together with its own clinical-trial data, to guide R&D investment decisions. The company is also in talks with payors about providing coverage for drugs already on the market, again using HealthCore data as evidence.

During a recent scan of the industry, we found that interest in big data is not confined to traditional players. Since 2010, more than 200 new businesses have developed innovative health-care applications. About 40 percent of these were aimed at direct health interventions or predictive capabilities. That’s a powerful new frontier for health-data applications, which historically focused more on data management and retrospective data analysis (exhibit).

Exhibit

Many innovative US health-care data applications move beyond retroactive reporting to interventions and predictive capabilities.

Some devices take patient monitoring to a new level. For instance, Asthmapolis has created a GPS-enabled tracker that records inhaler usage by asthmatics. The information is ported to a central database and used to identify individual, group, and population-based trends. The data are then merged with Centers for Disease Control and Prevention information about known asthma catalysts (such as high pollen counts in the Northeast or volcanic fog in Hawaii). Together, the information helps physicians develop personalized treatment plans and spot prevention opportunities.

Another company, Ginger.io, offers a mobile application in which patients with select conditions agree, in conjunction with their providers, to be tracked through their mobile phones and assisted with behavioral-health therapies. The app records data about calls, texts, geographic location, and even physical movements. Patients also respond to surveys delivered over their smartphones. The Ginger.io application integrates patient data with public research on behavioral health from the National Institutes of Health and other sources. The insights obtained can be revealing—for instance, a lack of movement or other activity could signal that a patient feels physically unwell, and irregular sleep patterns (revealed through late-night calls or texts) may signal that an anxiety attack is imminent.

Improvement at scale: What is the potential?

To determine the opportunity of the new value pathways, we evaluated a range of health-care initiatives and assessed their potential impact as total annual cost savings, holding outcomes constant, using a 2011 baseline. If these early successes were scaled up to create systemwide impact, we estimate that the pathways could account for $300 billion to $450 billion in reduced health-care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health-care costs.

Even a few simple interventions can have an enormous impact when scaled up. In the “right living” pathway, for instance, we estimate that aspirin use by those at risk for coronary heart disease, combined with early cholesterol screening and smoking cessation, could reduce the total cost of their care by more than $30 billion. While these actions have been encouraged for some time, big data now enables faster identification of high-risk patients, more effective interventions, and closer monitoring.

Our estimate of $300 billion to $450 billion in reduced health-care spending could be conservative, as many insights and innovations are still ahead. We have yet to fully understand subpopulation efficacy of cancer therapies and the predictive indicators of relapse, for example, and we believe the big-data revolution will uncover many new learning opportunities in these areas.

A few caveats

Although we are optimistic about big data’s potential to transform health care, some structural issues may pose obstacles. The move away from fee-for-service care—already well under way—must continue. Similarly, traditional medical-management techniques must change, since they pit payors and providers against each other, framing benefit plans with respect to what is and is not covered rather than what is and is not most effective. And all stakeholders must recognize the value of big data and be willing to act on its insights, a fundamental mind-set shift for many and one that may prove difficult to achieve. Patients will not benefit from research on exercise, for example, if they persist in their sedentary lifestyles. And physicians may not improve patient outcomes if they refuse to follow treatment protocols based on big data and instead rely solely on their own judgment.

Privacy issues will continue to be a major concern. Although new computer programs can readily remove names and other personal information from records being transported into large databases, stakeholders across the industry must be vigilant and watch for potential problems as more information becomes public.

Finally, health care will need to learn from other data-driven revolutions. All too often, players have taken advantage of data transparency by pursuing objectives that create value only for themselves, and this could also occur in the health-care sector. For instance, owners of MRI machines, looking to amortize fixed costs across more patients, might choose to use big data only to identify underserved patients and disease areas. If they convincingly market their services, patients may receive unnecessary MRIs—a situation that would increase costs without necessarily improving outcomes.

Reprinted with permission from McKinsey Global Institute

in Blog

Leadership and the art of plate spinning

Senior executives will better balance people and priorities by embracing the paradoxes of organizational life.

I often ask business leaders three simple questions. What are your company’s ten most exciting value-creation opportunities? Who are your ten best people? How many of your ten best people are working on your ten most exciting opportunities? It’s a rough and ready exercise, to be sure. But the answer to the last question—typically, no more than six—is usually expressed with ill-disguised frustration that demonstrates how difficult it is for senior executives to achieve organizational alignment.

What makes this problem particularly challenging is a number of paradoxes, many of them rooted in the eccentricity and unpredictability of human behavior, about how organizations really tick. Appealing as it is to believe that the workplace is economically rational, in reality it is not. As my colleague Scott Keller and I explained in our 2011 book, Beyond Performance,1 a decade’s worth of data derived from more than 700 companies strongly suggests that the rational way to achieve superior performance—focusing on its financial and operational manifestations by pursuing multiple short-term revenue-generating initiatives and meeting tough individual targets—may not be the most effective one.

Rather, our research shows that the most successful organizations, over the long term, consistently focus on “enabling” things (leadership, purpose, employee motivation) whose immediate benefits aren’t always clear. These healthy organizations, as we call them, are internally aligned around a clear vision and strategy; can execute to a high quality thanks to strong capabilities, management processes, and employee motivation; and renew themselves more effectively than their rivals do. In short, health today drives performance tomorrow.

Many CEOs instinctively understand the paradox of performance and health, though few have expressed or acted upon it better than John Mackey, founder and CEO of Whole Foods. “We have not achieved our tremendous increase in shareholder value,” he once observed, “by making shareholder value the only purpose of our business.”

In this article, I want to focus on three other paradoxes that, in my experience, are both particularly striking and quite difficult to reconcile. The first is that change comes about more easily and more quickly in organizations that keep some things stable. The second is that organizations are more likely to succeed if they simultaneously control and empower their employees. And the third is that business cultures that rightly encourage consistency (say, in the quality of services and products) must also allow for the sort of variability—and even failure—that goes with innovation and experimentation.

Coming to grips with these paradoxes will be invaluable for executives trying to keep their people and priorities in balance at a time when cultural and leadership change sometimes seems an existential imperative. Just as a circus performer deftly spins plates or bowls to keep them moving and upright, so must senior executives constantly intervene to encourage the sorts of behavior that align an organization with its top priorities.

Change and stability

Organizational change, obviously, is often imperative in response to emerging customer demands, new regulations, and fresh competitive threats. But constant or sudden change is unsettling and destabilizing for companies and individuals alike. Just as human beings tend to freeze when confronted with too many new things in their lives—a divorce, a house move, and a change of job, for example—so will organizations overwhelmed by change resist and frustrate transformation-minded chief executives set on radically overturning the established order. Burning platforms grab attention but do little to motivate creativity. Paradoxically, therefore, change leaders should try to promote a sense of stability at their company’s core and, where possible, make changes seem relatively small, incremental, or even peripheral, while cumulatively achieving the transformation needed to drive high performance.

A large universal bank provides a case in point. Given the tumult in the financial-services sector in recent years, it needs to change, and change profoundly. But the cry of “let’s change everything” will be counterproductive in an organization where staff members are mostly hard-working, committed people operating processes that involve millions of transactions per hour.

One large automotive company I’m familiar with, buffeted by three different owners and five different CEOs in the last decade, has recently embraced this paradox with a new management model dubbed “balance,” a word loaded with meaning in the automotive industry because of its association with reducing drag and increasing speed. The simple idea behind the model is that any changes to a company’s systems, structures, and processes should always be introduced in a consistent way, typically quarterly, as part of an explicit change package. If a proposed change isn’t ready in time for one of these regular releases, it is either deferred to the next one or abandoned.

Previously, leaders of each of the company’s functions had been inclined to introduce, on their own, changes they thought might generate value—for example, finance would launch a program to make costs variable, HR would announce an initiative to shake up performance management and compensation, and manufacturing would bring in new vendor-management systems. Hapless middle managers found themselves in a blizzard of change that made it difficult to focus on the organization’s top priorities. Now, before change programs are rolled out more broadly, all of them are integrated, and the resulting complexities are addressed at the top of the organization.

In this way, the company’s underlying operating model has remained more stable than it would otherwise have been, and more stable than it used to be when changes were announced in an uncoordinated fashion. Managers now understand and accept the rhythm of change, while managers and employees alike have gained new confidence that the different elements in the releases will be complementary and coherent.

The result is that a well-intentioned but disjointed flow of unending change has been converted into a well-structured one. Moreover, after years of lagging behind industry peers, the company has shortened its product-development cycles and increased the quality of its products. And it is running much more smoothly, with 20 percent fewer managers.

Control and empowerment

All organizations need at least a thread of control to link those who own the business to those charged with implementing its objectives. Companies that neglect mechanisms that enforce discipline, common standards, or compliance with external regulation do so at their peril. The share price of one global energy group, for example, collapsed when it came to light that poor oversight had allowed internal analysts to develop metrics based on optimistic assumptions and to overstate the company’s oil and gas reserves substantially.

Yet excessive control, paradoxically, tends to drive dysfunctional behavior, to undermine people’s sense of purpose, and to harm motivation by hemming employees into a corporate straitjacket. The trick for the CEO-cum-plate-spinner is to get the balance right in light of shifting corporate and market contexts. In general, a company will probably need more control when it must actually change direction and more empowerment when it is set on the new course.

The story of how a major global technology company recovered from a crisis four years ago is instructive. Forced to write off more than $2 billion of unmanageable contracts—and facing insolvency—a new management team took drastic and decisive action to strip out costs, renegotiate old agreements, change established practices, and impose stringent new controls. The company (with more than 100,000 employees) was saved but in the process found that it had lost the ability to deliver on its top priority: creative new ideas that would fuel organic growth. That’s because an unintended consequence of the much-needed cost reductions had been the emergence of a “parent–child” relationship between the company’s top team and middle managers. These managers had become so used to being told what to do, and had been given so little room to maneuver, that they eventually lost the ability to experiment. The “tree” of top management had grown so large that nothing could grow in its shade.

This company’s solution was to introduce an “envelope” leadership approach, which first involved defining a set of borders. Employees could not go beyond them, but within them there was almost complete freedom to innovate and grow. Other businesses have attempted to copy the envelope idea but few have had the success of this global technology company, whose approach had real teeth. The flame of empowerment was fanned by first identifying some 600 leaders with the best capabilities and then rotating them around different businesses, with a mandate to shake things up. Meanwhile, the company’s purpose, vision, mission, and values were all rewritten and drilled into leaders. Its “signature processes” (five core ones, where it aspired to be truly differentiated) were fundamentally reimagined. And evaluation and reward mechanisms were dramatically tightened to reward stars and actively manage people who seemed to be struggling. As the company added a greater degree of empowerment to the stricter controls—plates both balanced and spinning—its performance improved. Sales are growing again, and fresh energy is palpable throughout the organization.

Consistency and variability

Producing high-quality products and delivering them to customers on time and with the same level of consistency at every point in the value chain is critical to success in most industries. Variability is wasteful and time consuming, not to mention potentially alienating for customers. Most organizations therefore applaud behavior that attacks and eliminates it.

Yet in human terms, consistency too often hardens into rigid mind-sets characterized by fear of personal and organizational failure. It’s been shown that we feel the pain of failure twice as much as we do the joy of success, so employees naturally tend to protect themselves and their teams, behavior that can inadvertently hamper innovation and calculated risk taking. After all, mistakes—from Edison’s countless failed filaments to 3M’s accidental creation of the adhesive behind Post-it notes—can sometimes be the mother of invention; as they say in the mountains, “If you’re not falling, you’re not skiing.”

It’s hard to think of a sector where it’s more important to get the balance between consistency and variability right than it is in pharmaceuticals. Lives are at stake, and the development and launch costs of a new compound often run to billions of dollars. Faced with the approaching expiration of many licenses, one of the world’s biggest pharma companies found that its tradition of reliability and consistency had become a limiting mind-set. Although it desperately needed to make new discoveries, a status quo bias took hold of the organization, which froze around a complex bundle of assurance, governance, and controls. Fear of failure and an obsession with getting these things right produced defensive 100-page PowerPoint presentations in abundance, but little meaningful product-development progress.

Behavioral problems didn’t help, of course. An excessive “telling” rather than “listening” culture had degenerated into bullying; some senior executives literally shouted at their underlings. On one notorious “away day,” a number of exercises revolved around cage fighting, a sport (dubbed “human cockfighting”) that combines boxing, wrestling, and martial arts. The signal this sent from the top was that the culture really was dog eat dog.

Things came to a head when two scientists, frustrated by the time needed to get approvals, left to set up their own successful research business and were openly lauded by colleagues for breaking free of a stifling bureaucracy and dictatorial culture. The morale of those left behind suffered further, and energy drained out of the organization.

The solution the company devised combined building “slack” (additional people) into its resourcing—a bold move in austere times—with a direct attack on negative behavior. The worst offenders were removed, and it was made clear that cage-fighting attitudes were unacceptable.

Steps were taken to bump up the innovation rate by investing in smart people, but the top team went further. It set out fundamentally to alter what it called the organization’s “social architecture” by building worldwide communities of scientists and encouraging exchanges between them across geographical boundaries and industry disciplines.

Successful experiments, to be sure, were more highly valued than failures, but both had their place in the company’s culture. An emphasis in communications on “our wealth of ideas” promoted the simple notion that wealth (economic progress) arises from ideas (experimentation and innovation) and showed how carefully crafted language can help drive change. The result was an increase in the pipeline of products and, over time, a resumption of profit growth.

Embracing the paradoxes described in this article can be uncomfortable: it’s counterintuitive to stimulate change by grounding it in sources of reassuring stability or to focus on boundaries and control when a company wants to stir up new ideas. Yet the act of trying to reconcile these tensions helps leaders keep their eyes on all their spinning plates and identify when interventions are needed to keep the organization lined up with its top priorities. Last, this approach makes it possible to avoid the frustration of many executives I’ve encountered, who pick an extreme: either they try to stifle complex behavior by building powerful and rigid top-down structures, or they express puzzlement and disappointment when looser, more laissez-faire styles of management expose the messy realities of human endeavor. Far more centered and high performing, in my experience, are those leaders who welcome the inconvenient contradictions of organizational life.

Printed with permission from McKinsey Quarterly.

in Blog / Document translation

Big data: What’s your plan?

Many companies don’t have one. Here’s how to get started.

 

The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt. The tally of successful case studies continues to build, reinforcing broader research suggesting that when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition.1 The promised land of new data-driven businesses, greater transparency into how operations actually work, better predictions, and faster testing is alluring indeed.

But that doesn’t make it any easier to get from here to there. The required investment, measured both in money and management commitment, can be large. CIOs stress the need to remake data architectures and applications totally. Outside vendors hawk the power of black-box models to crunch through unstructured data in search of cause-and-effect relationships. Business managers scratch their heads—while insisting that they must know, upfront, the payoff from the spending and from the potentially disruptive organizational changes.

The answer, simply put, is to develop a plan. Literally. It may sound obvious, but in our experience, the missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value. The power of a plan is that it provides a common language allowing senior executives, technology professionals, data scientists, and managers to discuss where the greatest returns will come from and, more important, to select the two or three places to get started.

There’s a compelling parallel here with the management history around strategic planning. Forty years ago, only a few companies developed well-thought-out strategic plans. Some of those pioneers achieved impressive results, and before long a wide range of organizations had harnessed the new planning tools and frameworks emerging at that time. Today, hardly any company sets off without some kind of strategic plan. We believe that most executives will soon see developing a data-and-analytics plan as the essential first step on their journey to harnessing big data.

The essence of a good strategic plan is that it highlights the critical decisions, or trade-offs, a company must make and defines the initiatives it must prioritize: for example, which businesses will get the most capital, whether to emphasize higher margins or faster growth, and which capabilities are needed to ensure strong performance. In these early days of big-data and analytics planning, companies should address analogous issues: choosing the internal and external data they will integrate; selecting, from a long list of potential analytic models and tools, the ones that will best support their business goals; and building the organizational capabilities needed to exploit this potential.

Successfully grappling with these planning trade-offs requires a cross-cutting strategic dialogue at the top of a company to establish investment priorities; to balance speed, cost, and acceptance; and to create the conditions for frontline engagement. A plan that addresses these critical issues is more likely to deliver tangible business results and can be a source of confidence for senior executives.

What’s in a plan?

Any successful plan will focus on three core elements.

Data

A game plan for assembling and integrating data is essential. Companies are buried in information that’s frequently siloed horizontally across business units or vertically by function. Critical data may reside in legacy IT systems that have taken hold in areas such as customer service, pricing, and supply chains. Complicating matters is a new twist: critical information often resides outside companies, in unstructured forms such as social-network conversations.

Making this information a useful and long-lived asset will often require a large investment in new data capabilities. Plans may highlight a need for the massive reorganization of data architectures over time: sifting through tangled repositories (separating transactions from analytical reports), creating unambiguous golden-source data,2 and implementing data-governance standards that systematically maintain accuracy. In the short term, a lighter solution may be possible for some companies: outsourcing the problem to data specialists who use cloud-based software to unify enough data to attack initial analytics opportunities.

Analytic models

Integrating data alone does not generate value. Advanced analytic models are needed to enable data-driven optimization (for example, of employee schedules or shipping networks) or predictions (for instance, about flight delays or what customers will want or do given their buying histories or Web-site behavior). A plan must identify where models will create additional business value, who will need to use them, and how to avoid inconsistencies and unnecessary proliferation as models are scaled up across the enterprise.

As with fresh data sources, companies eventually will want to link these models together to solve broader optimization problems across functions and business units. Indeed, the plan may require analytics “factories” to assemble a range of models from the growing list of variables and then to implement systems that keep track of both. And even though models can be dazzlingly robust, it’s important to resist the temptation of analytic perfection: too many variables will create complexity while making the models harder to apply and maintain.

Tools

The output of modeling may be strikingly rich, but it’s valuable only if managers and, in many cases, frontline employees understand and use it. Output that’s too complex can be overwhelming or even mistrusted. What’s needed are intuitive tools that integrate data into day-to-day processes and translate modeling outputs into tangible business actions: for instance, a clear interface for scheduling employees, fine-grained cross-selling suggestions for call-center agents, or a way for marketing managers to make real-time decisions on discounts. Many companies fail to complete this step in their thinking and planning—only to find that managers and operational employees do not use the new models, whose effectiveness predictably falls.

There’s also a critical enabler needed to animate the push toward data, models, and tools: organizational capabilities. Much as some strategic plans fail to deliver because organizations lack the skills to implement them, so too big-data plans can disappoint when organizations lack the right people and capabilities. Companies need a road map for assembling a talent pool of the right size and mix. And the best plans will go further, outlining how the organization can nurture data scientists, analytic modelers, and frontline staff who will thrive (and strive for better business outcomes) in the new data- and tool-rich environment.

By assembling these building blocks, companies can formulate an integrated big-data plan similar to what’s summarized in the exhibit. Of course, the details of plans—analytic approaches, decision-support tools, and sources of business value—will vary by industry. However, it’s important to note an important structural similarity across industries: most companies will need to plan for major data-integration campaigns. The reason is that many of the highest-value models and tools (such as those shown on the right of the exhibit) increasingly will be built using an extraordinary range of data sources (such as all or most of those shown on the left). Typically, these sources will include internal data from customers (or patients), transactions, and operations, as well as external information from partners along the value chain and Web sites—plus, going forward, from sensors embedded in physical objects.

Exhibit

A successful data plan will focus on three core elements.

To build a model that optimizes treatment and hospitalization regimes, a company in the health-care industry might need to integrate a wide range of patient and demographic information, data on drug efficacy, input from medical devices, and cost data from hospitals. A transportation company might combine real-time pricing information, GPS and weather data, and measures of employee labor productivity to predict which shipping routes, vessels, and cargo mixes will yield the greatest returns.

Three key planning challenges

Every plan will need to address some common challenges. In our experience, they require attention from the senior corporate leadership and are likely to sound familiar: establishing investment priorities, balancing speed and cost, and ensuring acceptance by the front line. All of these are part and parcel of many strategic plans, too. But there are important differences in plans for big data and advanced analytics.

1. Matching investment priorities with business strategy

As companies develop their big-data plans, a common dilemma is how to integrate their “stovepipes” of data across, say, transactions, operations, and customer interactions. Integrating all of this information can provide powerful insights, but the cost of a new data architecture and of developing the many possible models and tools can be immense—and that calls for choices. Planners at one low-cost, high-volume retailer opted for models using store-sales data to predict inventory and labor costs to keep prices low. By contrast, a high-end, high-service retailer selected models requiring bigger investments and aggregated customer data to expand loyalty programs, nudge customers to higher-margin products, and tailor services to them.

That, in a microcosm, is the investment-prioritization challenge: both approaches sound smart and were, in fact, well-suited to the business needs of the companies in question. It’s easy to imagine these alternatives catching the eye of other retailers. In a world of scarce resources, how to choose between these (or other) possibilities?

There’s no substitute for serious engagement by the senior team in establishing such priorities. At one consumer-goods company, the CIO has created heat maps of potential sources of value creation across a range of investments throughout the company’s full business system—in big data, modeling, training, and more. The map gives senior leaders a solid fact base that informs debate and supports smart trade-offs. The result of these discussions isn’t a full plan but is certainly a promising start on one.

Or consider how a large bank formed a team consisting of the CIO, the CMO, and business-unit heads to solve a marketing problem. Bankers were dissatisfied with the results of direct-marketing campaigns—costs were running high, and the uptake of the new offerings was disappointing. The heart of the problem, the bankers discovered, was a siloed marketing approach. Individual business units were sending multiple offers across the bank’s entire base of customers, regardless of their financial profile or preferences. Those more likely to need investment services were getting offers on a range of deposit products, and vice versa.

The senior team decided that solving the problem would require pooling data in a cross-enterprise warehouse with data on income levels, product histories, risk profiles, and more. This central database allows the bank to optimize its marketing campaigns by targeting individuals with products and services they are more likely to want, thus raising the hit rate and profitability of the campaigns. A robust planning process often is needed to highlight investment opportunities like these and to stimulate the top-management engagement they deserve given their magnitude.

2. Balancing speed, cost, and acceptance

A natural impulse for executives who “own” a company’s data and analytics strategy is to shift rapidly into action mode. Once some investment priorities are established, it’s not hard to find software and analytics vendors who have developed applications and algorithmic models to address them. These packages (covering pricing, inventory management, labor scheduling, and more) can be cost-effective and easier and faster to install than internally built, tailored models. But they often lack the qualities of a killer app—one that’s built on real business cases and can energize managers. Sector- and company-specific business factors are powerful enablers (or enemies) of successful data efforts. That’s why it’s crucial to give planning a second dimension, which seeks to balance the need for affordability and speed with business realities (including easy-to-miss risks and organizational sensitivities).

To understand the costs of omitting this step, consider the experience of one bank trying to improve the performance of its small-business underwriting. Hoping to move quickly, the analytics group built a model on the fly, without a planning process involving the key stakeholders who fully understood the business forces at play. This model tested well on paper but didn’t work well in practice, and the company ran up losses using it. The leadership decided to start over, enlisting business-unit heads to help with the second effort. A revamped model, built on a more complete data set and with an architecture reflecting differences among various customer segments, had better predictive abilities and ultimately reduced the losses. The lesson: big-data planning is at least as much a management challenge as a technical one, and there’s no shortcut in the hard work of getting business players and data scientists together to figure things out.

At a shipping company, the critical question was how to balance potential gains from new data and analytic models against business risks. Senior managers were comfortable with existing operations-oriented models, but there was pushback when data strategists proposed a range of new models related to customer behavior, pricing, and scheduling. A particular concern was whether costly new data approaches would interrupt well-oiled scheduling operations. Data managers met these concerns by pursuing a prototype (which used a smaller data set and rudimentary spreadsheet analysis) in one region. Sometimes, “walk before you can run” tactics like these are necessary to achieve the right balance, and they can be an explicit part of the plan.

At a health insurer, a key challenge was assuaging concerns among internal stakeholders. A black-box model designed to identify chronic-disease patients with an above-average risk of hospitalization was highly accurate when tested on historical data. However, the company’s clinical directors questioned the ability of an opaque analytic model to select which patients should receive costly preventative-treatment regimes. In the end, the insurer opted for a simpler, more transparent data and analytic approach that improved on current practices but sacrificed some accuracy, with the likely result that a wider array of patients could qualify for treatment. Airing such tensions and trade-offs early in data planning can save time and avoid costly dead ends.

Finally, some planning efforts require balancing the desire to keep costs down (through uniformity) with the need for a mix of data and modeling approaches that reflect business realities. Consider retailing, where players have unique customer bases, ways of setting prices to optimize sales and margins, and daily sales patterns and inventory requirements. One retailer, for instance, has quickly and inexpensively put in place a standard next-product-to-buy model3 for its Web site. But to develop a more sophisticated model to predict regional and seasonal buying patterns and optimize supply-chain operations, the retailer has had to gather unstructured consumer data from social media, to choose among internal-operations data, and to customize prediction algorithms by product and store concept. A balanced big-data plan embraces the need for such mixed approaches.

3. Ensuring a focus on frontline engagement and capabilities

Even after making a considerable investment in a new pricing tool, one airline found that the productivity of its revenue-management analysts was still below expectations. The problem? The tool was too complex to be useful. A different problem arose at a health insurer: doctors rejected a Web application designed to nudge them toward more cost-effective treatments. The doctors said they would use it only if it offered, for certain illnesses, treatment options they considered important for maintaining the trust of patients.

Problems like these arise when companies neglect a third element of big-data planning: engaging the organization. As we said when describing the basic elements of a big-data plan, the process starts with the creation of analytic models that frontline managers can understand. The models should be linked to easy-to-use decision-support tools—call them killer tools—and to processes that let managers apply their own experience and judgment to the outputs of models. While a few analytic approaches (such as basic sales forecasting) are automatic and require limited frontline engagement, the lion’s share will fail without strong managerial support.

The aforementioned airline redesigned the software interface of its pricing tool to include only 10 to 15 rule-driven archetypes covering the competitive and capacity-utilization situations on major routes. Similarly, at a retailer, a red flag alerts merchandise buyers when a competitor’s Internet site prices goods below the retailer’s levels and allows the buyers to decide on a response. At another retailer, managers now have tablet displays predicting the number of store clerks needed each hour of the day given historical sales data, the weather outlook, and planned special promotions.

But planning for the creation of such worker-friendly tools is just the beginning. It’s also important to focus on the new organizational skills needed for effective implementation. Far too many companies believe that 95 percent of their data and analytics investments should be in data and modeling. But unless they develop the skills and training of frontline managers, many of whom don’t have strong analytics backgrounds, those investments won’t deliver. A good rule of thumb for planning purposes is a 50–50 ratio of data and modeling to training.

Part of that investment may go toward installing “bimodal” managers who both understand the business well and have a sufficient knowledge of how to use data and tools to make better, more analytics-infused decisions. Where this skill set exists, managers will of course want to draw on it. Companies may also have to create incentives that pull key business players with analytic strengths into data-leadership roles and then encourage the cross-pollination of ideas among departments. One parcel-freight company found pockets of analytical talent trapped in siloed units and united these employees in a centralized hub that contracts out its services across the organization.

When a plan is in place, execution becomes easier: integrating data, initiating pilot projects, and creating new tools and training efforts occur in the context of a clear vision for driving business value—a vision that’s unlikely to run into funding problems or organizational opposition. Over time, of course, the initial plan will get adjusted. Indeed, one key benefit of big data and analytics is that you can learn things about your business that you simply could not see before.

Here, too, there may be a parallel with strategic planning, which over time has morphed in many organizations from a formal, annual, “by the book” process into a more dynamic one that takes place continually and involves a broader set of constituents.4 Data and analytics plans are also too important to be left on a shelf. But that’s tomorrow’s problem; right now, such plans aren’t even being created. The sooner executives change that, the more likely they are to make data a real source of competitive advantage for their organizations.

Reprinted with permission McKinsey Global.

in Blog / Document translation

China’s e-tail revolution

Almost overnight, China has become the world’s second-largest e-tail market, with estimates as high as $210 billion for revenues in 2012 and a compound annual growth rate of 120 percent since 2003. The country’s retail sector already is among the most wired anywhere—e-tailing commanded about 5 to 6 percent of total retail sales in 2012, compared with 5 percent in the United States—while it is distinctly different from that of other countries. Only a small portion of Chinese e-tailing takes place directly between consumers and retailers, whether online pure plays or brick-and-mortar businesses on retailers’ own Web sites. Instead, most occurs on digital marketplaces. What’s more, Chinese e-tailing is not just replacing traditional retail transactions but also stimulating consumption that would not otherwise take place. Finally, e-tailing may catalyze a “leapfrog” move by the broader retail sector, putting it on a fast track to a more digital future. These are among the key findings of China’s e-tail revolution: Online shopping as a catalyst for growth, a new report by the McKinsey Global Institute.

Structural differences

Some 90 percent of Chinese electronic retailing occurs on virtual marketplaces—sprawling e-commerce platforms where manufacturers, large and small retailers, and individuals offer products and services to consumers through online storefronts on megasites analogous to eBay or Amazon Marketplace.1 The megasites include PaiPai, Taobao, and Tmall, which in turn are owned by bigger e-commerce groups. A large and growing network of third-party service providers offers sellers marketing and site-design services, payment fulfillment, delivery and logistics, customer service, and IT support.

By contrast, in the United States, Europe, and Japan, the dominant model involves brick-and-mortar retailers (such as Best Buy, Carrefour, Darty, Dixons, and Wal-mart) or pure-play online merchants (such as Amazon), which run their own sites and handle the details of commerce. Developed markets have major specialized retail chains in the e-commerce arena. In China, such independent merchants account for only 10 percent of e-tailing sales. Although still in the early stages of growth, China’s e-tail ecosystem is profitable, logging margins of around 8 to 10 percent of earnings before interest, taxes, and amortization—slightly higher than those of average physical retailers.

Powering consumption

This unique e-tailing engine is enabling China’s shift from an investment-oriented society to one that’s more consumption driven. E-tailing, our research indicates, is not simply a replacement channel for purchases that otherwise would have taken place offline. Instead, it appears to be spurring incremental consumption, particularly in less developed regions. By analyzing consumption patterns in 266 Chinese cities accounting for over 70 percent of online retail sales, we found that a dollar of online consumption replaces roughly 60 cents of sales in offline stores and generates around 40 cents of incremental consumption (Exhibit 1). It’s important to note that the data sets behind this analysis don’t cover the full market. Our approximations do, however, provide a preliminary picture of what’s occurring in China and permit a rough calculation of the extent to which e-tailing may be boosting consumption there. (These estimates suggest that the channel may have added 2 percent of incremental value to private consumption in 2011 and could generate 4 to 7 percent in incremental consumption by 2020.)

Exhibit 1

China e-tailing - Exh1

E-tailing’s impact is more pronounced in China’s underdeveloped small and midsize cities. We found that while incomes in these urban areas are lower, their online shoppers spend almost as much money online as do people in some larger, more prosperous cities—and also spend a larger portion of their disposable income online (Exhibit 2). For these shoppers, the utility of online purchasing may be higher, since they now have access to products and brands previously not available to them, in locations where many retailers have yet to establish beachheads.

Exhibit 2

China e-tailing - Exh2

Further boosting online purchases is the fact that e-tailing has cut consumer prices: depending on the category, they are, on average, 6 to 16 percent lower online than in China’s stores.2 Apparel, household products, and recreation and education are the categories where price discounts are greatest. They are also the three largest online retail segments (Exhibit 3).

Exhibit 3

China e-tailing - Exh3

The leapfrog effect

China’s retailing industry, coming of age in an era of digital disruption, will probably follow a trajectory different from that of retail sectors in other markets. In developed nations, the industry typically followed a three-stage path. It began with the rise of regionally dominant players. This field then consolidated into a smaller number of national leaders. Eventually, online players challenged them, and the industry became multichannel. Some brick-and-mortar players (Tesco and Wal-Mart Stores, for instance) have embraced a multichannel strategy, while others (such as Borders in the United States and Jessops and Woolworths in the United Kingdom) have been driven from the market.

China differs from these developed markets, however, because a crop of national leaders has yet to emerge in traditional retailing. Building stores across China’s considerable geography, with its many smaller cities, takes both time and high levels of investment. As a result, China’s largest brick-and-mortar retailers have captured a smaller share of the country’s overall retail market than have major players in the United States and elsewhere: the top five retailers by category hold less than 20 percent of the market—much lower than US levels of 24 to 60 percent in comparable categories.

In China, the combined effects of the complexities of store expansion and a distinctive model of e-tailing could lead to a different retail dynamic: as e-tailing continues to grow, China’s industry may leapfrog the second (national) stage, passing directly from the regional to the multichannel one. In fact, China’s online ecosystem of marketplaces and agile support services has grown rapidly precisely because it can exploit the inefficiencies and higher costs of China’s existing retail market. Already, the major online companies Alibaba (which owns marketplaces such as Taobao) and 360buy.com (focusing on sales of electronics) have established a prominent national role, ranking among China’s top ten retailers.

Coming next

The view forward may be more impressive. We estimate that by 2020, as 15 to 20 percent annual growth rates (before inflation) continue, e-tailing could generate $420 billion to $650 billion in sales, and China’s market will equal that of the United States, Japan, the United Kingdom, Germany, and France combined today.3Patterns of future change are coming into focus.

Retail modernization

E-tailing will continue to transform the retail sector. As competition among e-tailers has lowered prices, it has also both increased the size of the consumer market and created efficiencies in the important adjacent markets that support e-commerce—logistics, supply chains, IT services, and digital marketing. This efficiency edge should force brick-and-mortar retailers to modernize and pave the way to a more efficient coordination of supply and demand across the Chinese economy.

One cloud hanging over the e-tailing scene is a growing talent shortage resulting from heady growth. Eventually, it could raise labor costs and hamper expansion plans unless e-tailers significantly improve their labor productivity, which at best matches that of physical retailers. The good news is that if the online ecosystem learns from developed markets, e-tailing’s productivity should rise as high as two to four times that of offline retailers.

Meanwhile, China’s store-based retailers, and the manufacturers that supply them, will need to place some new bets—soon. Many have yet to fully embrace multichannel strategies, focusing instead on the sizable growth and consolidation opportunities still available in their brick-and-mortar businesses. They’ll have to decide whether to join existing e-tail marketplaces or establish their own online storefronts and whether to own parts of the value chain (such as distribution and IT) or use third-party suppliers.

To what extent will e-tailers bypass virtual marketplaces?

As the e-tail ecosystem diversifies and matures, merchants that today use digital marketplaces may find it tempting to pursue growth by operating independently. To do so, these companies must go beyond current strategies, which depend chiefly on products and prices, where competition already is fierce. Instead, to build a strong online brand, e-tailers will need to dedicate management resources and investments to creating an attractive package of value propositions—superior customer service, fast and reliable delivery, a better shopping experience, or more targeted marketing. That will require a new level of capabilities and, perhaps, partnerships with experienced players outside China.

Consumer companies: Threats and opportunities

Since marketplaces hold the leading share of China’s e-tailing market, they are a natural place for consumer-products manufacturers to focus when they enter China—or grow outside its leading cities. Marketplace ecosystems provide a business infrastructure to reach customers at a reasonable cost. That infrastructure is particularly valuable for new entrants, which may find it an economical way of testing a market’s temperature. Uniqlo, for one, used a combination of marketplaces and service providers when it started its online apparel business in China in 2009.

At the same time, however, e-tailing innovation is creating more competition. New entrants have sprung up on the major e-tail marketplaces (known as Taobrands on the Taobao marketplace) to sell lines such as apparel and cosmetics directly to consumers. With products sourced straight from workshops and OEM factories, and sales stimulated by targeted marketing campaigns, these immensely popular companies offer good quality and attractive prices.

Meanwhile, China’s model and innovations are spilling beyond its borders. Other emerging economies are developing e-tailing markets that could follow China’s business model—and potentially achieve similar growth rates. China’s new marketplace sellers are expanding internationally, leveraging their direct access to Chinese workshops and OEM factories. Global consumer-goods players should be ready to face competition from Chinese small and midsize enterprises and microbusinesses selling directly through marketplaces in emerging economies.

China may have largely sat out the 19th-century Industrial Revolution, but as the explosion of its new consuming class continues to reshape 21st-century economic life, e-tailing and the Internet revolution have important roles to play. E-tailing is boosting the Chinese consumer’s propensity to spend. The distinctive course charted by the country’s e-tailers is having an impact on merchants, consumer-product companies, and value-chain partners. And it’s widening the field of opportunities for players both in and outside China. With continued robust growth, changes in industry business patterns that are already under way will only grow in importance.

Download Executive Summary 

Download Full Report

Printed with permission from McKinsey Global.

in Blog / Document translation

Learning from Japan’s early electric-vehicle buyers

About one-third of early buyers in Japan say their next car may not be an electric vehicle. Companies should heed their complaints.

If electric vehicles (EVs) are to develop from a niche into a mass market, car makers should learn from early adopters who say they may not buy one again. Our recent research on such consumers in Japan finds that about one-third of them fall into this category. These buyers said they were “seduced” by low energy costs, attractive subsidies, and a good test drive. But they were less well informed about EVs than were environmentally conscious “green enthusiasts” (who love EV technology for its low energy costs and comfortable driving experience) and became less enthusiastic about their purchase when they faced issues such as higher electric bills and locating places to charge their cars. To lock in the reluctant buyers, EV makers should adopt retention and education programs to avoid negative market feedback that could “poison the well” for new buyers. We also found that although early adopters weren’t concerned about price, nonbuyers were. Until prices drop to the point where the level of mass-market uptake stimulates infrastructure development, manufacturers must learn how to build customer loyalty to broaden the market for EVs.

Additional Findings:

  1. Approximately one-third of early adopters in Japan may not buy an electric vehicle next time.
  2. 58% of electric-vehicle buyers with 58% satisfaction rating said they would not choose an electric vehicle for their next car purchase.

Adopted from McKinsey Quarterly.

in Blog / Document translation

Understanding Asia’s conglomerates

Asia’s conglomerates

Asia’s conglomerates

Conglomerates are shaping the competitive landscape in Asia. Would-be rivals must understand them to compete with them.

Conglomerates may be out of favor in much of the developed world, but don’t tell that to senior executives who contend with fast-growing conglomerates in major Asian markets, where this business model remains a competitive force.

McKinsey research finds that over the past decade, the largest conglomerates in China and India expanded their revenues by more than 20 percent a year, while conglomerates in South Korea exceeded 10 percent annual revenue growth (see sidebar, “About the research”). These companies diversified at a blistering pace, making an average of one new business entry every 18 months. The nature of those moves was striking: nearly half of the companies favored businesses that were completely unrelated to the parent companies’ operations.

Of course, only time will tell if Asian conglomerates’ “step out” approach to diversification will endure as the region’s economies mature. Nor is it clear how much shareholder value will be created—and sustained—by these companies’ growth. Nonetheless, a closer look at its characteristics and at the aggressive, M&A-fueled strategies that sustain it offers insights for senior executives seeking growth in Asian markets and gives potential entrants a useful glimpse into the evolving nature of competition there.

Big and growing

Over the past decade, conglomerates in South Korea accounted for about 80 percent of the largest 50 companies by revenues. In India, the figure is a whopping 90 percent. Meanwhile, China’s conglomerates (excluding state-owned enterprises) represented about 40 percent of its largest 50 companies in 2010, up from less than 20 percent a decade before. All these companies generated strong topline growth: an average of 23 percent a year over the past decade for conglomerates in China and India, and 11 percent for those in South Korea. Such growth is remarkable considering the large size of the companies involved—an average of $3 billion in revenues a decade ago and $12 billion in 2011.

Stepping out

When we looked more closely to determine the sources of this revenue growth, we found a strong connection with new business entry. The average rate of revenue growth for companies that entered at least one new business over the period we studied was 25 percent a year—more than two times higher than the revenue growth of companies that didn’t.

Also notable were the strategic motivations behind the new business entries. Fully 49 percent were step-out moves into businesses completely unrelated to the parent companies’ existing activities—for example, a South Korean chemical company acquiring a life insurer or a Chinese mining group’s expansion into the media industry. The remaining half were about equally split between two kinds of moves: category expansions into adjacent businesses and value-chain expansions that positioned the parent company up- or downstream from its existing business.

Large returns, large risks

Although step-out moves were the most common form of new business entry we observed, they were far from the most successful. Just 22 percent of those we studied had a beneficial impact on revenue growth, profits, and market share relative to those of competitors. In fact, our findings almost certainly understate the difficulties involved in diversifying into entirely new businesses, since companies rarely publicize the full financial and organizational implications of unsuccessful moves. Nonetheless, when step-out moves were successful, they delivered strong results—$3.8 billion in additional revenues, on average.

Regardless of how related the new business was to the existing one, the most common paths to success were M&A, joint ventures, and technology partnerships. Together, these accounted for three-quarters of the successful moves we studied.

Outlook and implications

Given the rapidly changing business climate in much of Asia, we believe senior executives in other regions should approach these findings judiciously. Certainly, not all Asian companies will follow the path of the conglomerates we examined. For example, state-owned companies and companies in markets with strong traditions of board governance (such as Malaysia) might find it difficult to convince skeptical boards of the need for bold step-out moves. Furthermore, if governance structures in Asia continue to evolve toward the shareholder-driven models prevalent in Europe and the United States—away from family-ownership or -control models that can introduce major shareholders’ personal interests into the equation—the growth patterns will probably change.

Nonetheless, there are equally valid reasons to believe that Asian conglomerates’ push for growth through aggressive diversification could continue for some time. For starters, many Asian conglomerates have ready access to capital, as well as aggressive growth ambitions that cause them to view strong local reputations and relationships as platforms for stretching into new areas. They seem to be particularly attracted to nascent industries, such as green energy, where dominant global leaders have yet to emerge. Local market dynamics also play a role. Ambitious conglomerates in smaller Asian economies, for example, may seek growth in new geographies given the relatively limited opportunities at home.

High growth aspirations intersect with a singular feature of emerging Asian economic life: the extraordinary need for infrastructure, since conglomerates are often involved with it. Finally, they can offer up-and-coming managers broader career-development opportunities, boosting their attractiveness to local talent in a region characterized by tight talent markets. Potential competitors will be well served by developing a better understanding of these and other sources of the conglomerates’ advantage.

The bottom line: business leaders in Asia are building large, fast-growing companies around the conglomerate business model. Understanding the drivers of that growth may give competitors and emulators alike a firmer footing in a volatile business environment.

in Blog / Document translation
Page 19 of 33« First...10...1718192021...30...Last »