The world has changed – and complexity has emerged as one of the defining issues of our time.
For the past two decades, the pursuit of growth has created massive complexity in processes, product and service portfolios, and organizations, adding costs that companies can ill afford.
Good complexity adds value. Bad complexity destroys value. While this may sound simple, it is difficult to spot the bad complexity in practice because the cost of complexity is often “hidden” from view; since it is difficult to spot, we find that companies almost always have too much, which means they carry a lot of bad, value-destroying complexity. As complexity has continued to grow, its impact has swelled to the point that it is now the number one determinant of cost-competitiveness for the majority of companies and industries. But complexity isn’t just a cost issue; it impacts the performance of a company’s processes, and more broadly, the effectiveness of the organization itself. Overall, complexity is now the dominant driver of “health” for the majority of organizations – and addressing the complexity issue is now the new frontier.
But how did things get this way? In short, the old approaches for managing cost and performance in organizations are no longer sufficient. Companies now need new approaches and perspectives for managing in the world of complexity.
Over the last two hundred years the world has seen some significant transitions in industry, each with its own dynamic. In the pre-industrial age, before the spread of factories, steam power, electric utilities, and fossil fuels, energy was generally limited to muscle power and efficiency was driven by the strength or speed of the individual working unit, whether man or beast, and this only varied within a narrow range from person to person, or from animal to animal.
Costs, therefore, were largely variable, meaning that those costs were proportional to volume of work. With rare exception, larger companies did not have significant cost advantages over smaller ones, as there were no significant economies of scale to be had–i.e., ten horses could only do ten times the work of one horse. (See Figure 1)
This all fundamentally changed at the beginning of the industrial age. The steam engine, electricity, and fossil fuels provided significant sources of power. Combined with the advent of the factory, mass production, and interchangeable parts, this created significant opportunities for economies of scale. Larger volumes meant more repetitive tasks, which meant greater worker productivity. More power meant larger machines, and larger machines meant greater efficiency. This was certainly the experience of Samuel Insull, who built Chicago Edison into the America’s first major integrated utility. He launched a virtuous cycle of lower prices, increasing customers, larger generators, greater efficiencies, and even lower costs and prices.
While the pre-industrial age was all about variable costs, the industrial age is a story dominated by fixed costs. Significant fixed-cost investment was required to build ever larger factories and machines, but greater volumes meant greater ability to spread those costs (i.e. fixed cost leverage). Cost no longer rose proportionately with volume. Rather, with economies of scale larger companies had significant cost advantages over smaller ones, and in America companies such as US Steel, Standard Oil, Westinghouse, and GE raced to be the biggest. This was the story of the industrial age, and also of the building of America and the growth of the West.
Complexity has since changed this, and the story of the current “post-industrial” age is now dominated by a third category of costs: complexity costs. What is different today from the industrial age is the significantly greater amount of variety available to consumers (just think of automobiles, toothpaste, entertainment), and the corresponding greater level of product, process, and organizational complexity taken on by companies to deliver that variety.
While economies of scale are real, they now come at the price of complexity costs. The Result: we find that a company’s cost competitiveness comes down to where it falls on the balance between traditional economies of scale and the geometric (i.e. exponential) growth in complexity costs.
In fact, complexity is best understood as the opposite of scale. In the drive for scale, companies seek bigger volumes, but the complexity they take on to get there breaks up that scale into smaller portions. As a result, in the post-industrial age we regularly see larger companies struggle to be as profitable as much smaller companies. Bigger is no longer necessarily better.
Too Big to Scale?
Looking to financial services as an example, we find that many of the largest banks fail to gain scale benefits–here we mean too big to scale, not too big to fail. Have financial service companies achieved efficiency with growth by reaching their goals of increased profits and higher margins?
It may be a surprise that in spite of the financial crisis the Top 100 bank holding companies have grown 175% over the past decade. Some of this growth has been the result of consolidation but new product and service creation has also fueled growth. Many of these banks tripled their asset size leading a traditionalist to expect economies of scale and efficiency. However, the level of complexity increased as well, causing operational efficiency to suffer.
While one could argue that the financial crisis created a very complex environment, it was unmanageable growth that got us to that point. Many financial companies didn’t recognize the complexity they were creating.
In our study of the Top-100 bank holding companies, we found larger banks were not necessarily more efficient and profitable than their smaller peers. Using the banks’ efficiency ratio (non-interest expense over net interest income + total non-interest income, essentially what a bank must spend to make one dollar) as a measure of operational productivity, we found there is no positive correlation between the sizes (assets) of the banks and the efficiency ratios–bigger is not necessarily better. In fact, the opposite is true; seven out of the top 10 largest banks in the U.S. have worse efficiency than the average bank.
Of course, many macro- and micro-economic factors come into play to determine efficiency. Comparing one particular bank’s efficiency ratio to another may not represent an industry trend. However, comparing banks to themselves over a period of ten years, and looking at the relationship between their asset changes and their change in efficiency (see Figure 2), shows that less than 30% of the top banks we studied were able to improve their efficiency while maintaining healthy growth (banks above the zero line). When we overlay profitability, the percentage is reduced to 11% (the underlined banks above the zero line).
With increased regulation, volatile market economics, global expansion, multiple service delivery channels, and internal control risk, banking has become more complex as an industry. Many companies believe that you have to grow to survive in this type of environment. To some extent that may be true, but managing your complexity during growth is the key to achieving this goal. Otherwise, you risk ending up like the other 70% that lost efficiency.
In addition to the connection between assets and efficiency, consider the impact on profitability. Figure 3 illustrates banks with better efficiency tend to also have higher Return on Assets (ROA). In the upper right quadrant, banks like Capital One, Discover, US Bank, USAA and Wells Fargo have been able to grow while maintaining good complexity and scale, as well as being more productive with their assets. The banks in the bottom left demonstrate the opposite–poor efficiency leads to lower ROA. Aggressive expansions or high growth without planning for and aggressively managing operational complexity results in unwanted costs and hidden risks which can easily move a bank from a position of strength in the upper right quadrant to the lower left within two to three years.
Looking beyond efficiency and ROA measures, what are some symptoms of out-of-control complexity: internal fraud, negative public image, poor asset quality, increased litigation, high employee turnover? This sounds very familiar and evident, for instance, in the case of Bank of America.
Bank of America, the 2nd largest US bank, expanded substantially during the 2000s thanks to a series of aggressive acquisitions adding a multitude of widely dispersed locations and new service areas (FleetBoston, MBNA, US Trust, ABN AMRO North America, Countrywide and Merrill Lynch), and not only has its efficiency suffered, but also its reputation. In the latest reputation survey1 of 60 major companies among 17,000 respondents, Bank of America suffered the biggest hit, dropping from 55th to 58th. This is also evident through its high customer and employee turnover. The bank’s executive turnover rate since 2005 following its expansions has been a remarkably high 13% annually.
Taking a closer look at the bank’s income and expenses reveals more symptoms of its missteps that led to more complexity than it could manage. From 2006 to 2011 Bank of America’s assets increased 1.5 times, but its efficiency ratio deteriorated from 50.4% to 67.4%. This is a result of both the disappointing revenue growth and the disproportionate associated costs (its revenue grew by 5% while its non-interest expenses expanded 18% annually).
The most obvious sign of BofA’s organizational complexity is that the company’s work force and associated expenses almost doubled after the acquisitions to $37B, and have stayed at that elevated level despite drops in revenue in the last two years. Litigation and other expenses associated with Countrywide-related lawsuits have increased almost $5B. And the company had more than $15B in goodwill impairment in the last two years associated with its 2005 acquisition of the MBNA credit card and mortgage business.
In contrast to Bank of America who operates in all aspects of banking around the world, Capital One, the 14th largest US bank, expanded with a focus on supporting and growing its core credit card business in the United States and its efficiency has improved.
Beginning in 2004, Capital One set out to mitigate some of the risks associated with the funding of its credit card business by obtaining low-interest, stable deposits from retail banking expansion with a series of bank acquisitions – Hibernia (2005), North Fork (2006) and Chevy Chase (2009). It then divested the insurance and mortgage businesses that came with those acquisitions, to focus on consumer banking. And Capital One has continued this focused acquisition strategy with its 2012 acquisitions of HSBC’s North American credit card business and ING Direct. Although time will be the ultimate judge, it appears that Capital One is achieving the objective of expansion without exponential growth in complexity costs.
Five Key Lessons
• Lesson #1: Bigger is no longer necessarily better. Gone are the easier times when revenue growth meant more profits. Companies need to consider what it means for them now that larger organizations are no longer necessarily more advantaged than smaller ones. The basis of competition has changed.
• Lesson #2: Complexity breaks up your scale. Today, greater volume usually comes at the price of greater complexity (a greater variety of resources and systems executing a greater number of processes to deliver a wider range of goods and services across more channels to a larger number of markets). Unfortunately, given their levels of complexity, many large organizations actually have little scale.
• Lesson #3: Growing complexity faster than volume often means declining profitability. Since complexity breaks up scale, companies need to examine how they are driving their growth, and chart their profit trajectory. Too often, in the quest for volume companies take on much complexity, which erodes and often outweighs the benefits of that volume. Most insidious, the cost of complexity is often not recognized.
• Lesson #4: Complexity can be good and bad, but too much complexity is bad, and companies almost always have too much. Why do companies take on too much complexity? One reason: the benefits of complexity are local while the cost of complexity is very distributed. At the point of adding complexity (whether a new product, system, report, etc.) the benefits of doing so appear very real, but there is usually a very incomplete view of the real overall cost throughout the company to support the additional complexity.
• Lesson #5: Doing more things is easier, but doing what you do better is more profitable. Broadly, these are the two ways to grow: to do more things or to do what you do better. The first leads to more complexity and greater complexity costs, but the second can bring true scale and therefore greater profitability–and ultimately only profitable growth is truly sustainable.
Andrei Perumal is co-author of Waging War on Complexity Costs (McGraw-Hill) and managing partner of Wilson Perumal & Company, an international strategy consulting firm. Kelly Jones is senior advisor, financial services, at Wilson Perumal & Company and Ann Bryan is a manager at Wilson Perumal & Company. They can be reached at
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1.Survey conducted by Harris Interactive, February 2012