Despite the best efforts in many areas, the accounting and finance systems currently in use overlook the costs of complexity.
The costs are hidden in the operating statements of a company until the period-end results show the adverse effects.
It is time to recognize that these costs exist, identify them, and develop new metrics in the place of those that are missing.
Accounting systems have come a long way in the past decades. Activity-based costing revealed where costs were being incurred and what was driving them. The blizzard of regulations following the debacles involving Enron, WorldCom, and others led to the passage of the Sarbanes–Oxley Act (in the United States) and many other new regulations. Although these are burdensome, they impose much-needed disciplines on finance and accounting.
In spite of this, one area remains unmeasured, untracked, and unmanaged: costs caused by complexity. I began studying this area in earnest shortly after the dot-com collapse. When an area comes under intense scrutiny, some details are discovered that have thus far gone unnoticed. This is the case with the costs of complexity.
As far back as 2001, Oracle CEO Larry Ellison described a “War on Complexity” in computer software. There were simply too many systems that were not integrated, and others that were very difficult to integrate. This fragmentation of systems caused huge complexity, duplication of effort, and waste (which Ellison’s Oracle Corporation hoped to solve).
Variety Can Add Value—If Managed Properly
On the other hand, there are instances when complexity—properly managed—can be a source of great competitive advantage. Structure, systems, and processes must be carefully designed to minimize transaction cost and complexity. One example of the productive use of complexity is the web retailer Amazon, whose breadth of offering is extensive, thus making it a “one-stop shopping” site for millions. While Amazon’s distribution system is always at risk of being overburdened by complexity, its front end handles the huge variety of goods seamlessly.
Similarly, US sandwich seller Subway assembles sandwiches to order from about thirty containers of meat, cheese, and vegetables, using just a half-dozen varieties of bread. It can make millions of sandwich (and salad) combinations, to customer preferences, with minimal waste. There are many other examples like these two. All depend on the right systemic design to keep complexity from growing out of control, causing waste and inefficiency.
Complexity Costs Are Hidden
When I first began to research why complexity costs remained unmeasured in nearly all companies, I discovered that it was because these costs are, by their nature, hidden in accounting systems. To bring this problem into perspective, let’s consider how complexity occurs and what kinds of waste it causes. It will become apparent why financial systems simply “overlook” complexity’s costs until the end-of-period reporting shows the detrimental effects.
There is no doubt that complexity’s effects are readily apparent in month-end, quarter-end, and year-end results, where they adversely affect both the income statement and the balance sheet. Unfortunately, the only place where they are visible is on the bottom line, or on a few lines of the balance sheet. Even then, there’s no indication of how these costs were incurred, or what might have been done to manage them.
Seeking High Growth in Low Growth Markets
Much of complexity that goes unmeasured and unmanaged is created with the best of intentions, in search of revenue growth. Many wealthy developed countries (the United States, most of Europe, Japan, etc.) are growing very slowly, both in population and in their economies. When companies seek growth in these mature markets, they usually resort to proliferation, which leads to complexity. The gain in revenue is redistributed across a broader range of products and services, with only modest increases in total. The many resulting new products, customers, markets, and suppliers add much more in complexity costs than in profit.
Mergers and acquisitions are another source of complexity. If either of the two combined companies is already burdened with complexity, this will transfer to the merger. If both are thus burdened, real trouble is likely. Simply combining the “DNA” of two companies is a daunting task, as illustrated by the troubled combination of Alcatel (France) and Lucent (United States). There are issues of product and customer overlap, duplications of organization and facility, systems redundancies, and large cultural conflicts that must be sorted out. This is perhaps one of the main reasons why mergers seldom lead to long-term growth in shareholder value.
Less developed countries are typically growing at much higher rates (China, India, Brazil, etc.). Emerging consumer societies and favorable balances of trade fuel their economic growth. There’s a different complexity problem here: most of these countries save more and spend less—both as consumers and as governments. Further, these countries are less familiar to sellers who operate in developed countries, and therefore marketing and operating mistakes are made. These mistakes also lead to proliferation, often due to errors in targeting or serving the desired markets and customers.
Profits Are Proportional to Revenues; Costs Are Proportional to Transactions
Thus, either approach to growth adds to complexity, but for different reasons. Profits are derived from increased revenues, but costs are incurred from increased transactions. Therein lies the root of the problem. A few simple reports can expose the problem. First, calculate sales per customer, per product, per location, etc., and track the trends. They are typically declining, indicating more transactions for less revenue. Next, sort the annual sales, profits, etc., for customers and products, in descending order of value, and compute a cumulative column. Now look at the bottom of the list. There is always page after page of “losers” with few sales and low or negative profits.
Few accounting systems calculate a couple of simple, yet important, measures. What is the cost to process a customer order from “end to end”—from receipt of the order until the payment is in the bank? Few, if any, companies know the answer to this question. One US study, performed by Sterling Commerce, calculated it at about $50. Consider the following quick calculation to show how complexity adds cost and waste.
Average companies make about 5% net profit (after tax) on sales revenue. That means they must get $20 of sales to make $1 of net profit. If processing an order costs $50, they must get a $1,000 order to earn the equivalent of what it costs to process the order. If that type of customer orders every week, $50,000 worth of annual sales is barely generating net profit that equals the cost of processing the orders.
This dramatically illustrates how customer orders that are small and frequent can add complexity cost, and yet this cost can remain undetected as a drain on profit. A similar comparison could be made for the cost to process purchase orders, or the expense to set up and maintain documentation for a product or service. Nowhere are these costs gathered—or managed. Most companies have a few departments that perform these functions. Therefore, totaling those departmental expenses and dividing that sum by the total number of orders processed will yield an approximation of how much each order costs to process. Yet, few or no companies do this calculation or consider its impact.
Complexity costs are also insidious because most of them are hidden in “catch-all” accounts such as variances, allowances and deductions, and so forth. Extra effort is needed to reveal the origin of such entries (more on that later). First, let’s consider a simple example of how easily complexity can occur and grow.
A Simple Example: One White Coffee Mug
Imagine a product: a coffee mug offered in one style, color, size, and type of packaging. It is sourced from one supplier, packaged and stocked in one location, and offered for sale to one customer. You can easily compute the “standard cost” of this mug in terms of material, labor, and overhead (an older way), or in terms of material plus cost of acquisition, plus fixed and variable conversion costs (a newer way). If the mug’s total landed cost is $1 and it sells for $2, this yields a 50% gross profit margin.
Because the mug is successful, the company has decided to expand the line to four styles, four colors, two sizes, and two package options. There are now sixty-four different mug variations, which lead to increased complexity in forecasting, buying, controlling, and managing raw materials, inventory, etc. The “standard cost,” however, is still computed in the same way as before, which yields apparently accurate results: cost = $1 (assuming a good job of purchase negotiation), price = $2, and gross profit margin = 50%. But something is wrong. Intuitively, you know that there are complexity costs that the old metrics don’t capture—at least, not assigned to the product line. The true profitability is not the same as before.
Expand the product line again: Purchase from two suppliers, package and stock in three locations, and sell into (just) three different countries. Assuming there are no differences in purchase cost or productivity, the standard cost, price, and gross profit margin remain the same. But now, the combinations and permutations have grown to over a thousand, and the company must take into account different marketing materials, purchasing errors (due to forecast errors and demand volatility), and more. Now the profitability is clearly lower.
On top of all this, there are color mixes and assortments of the product that vary according to market, customer, production plant, distribution center, and country. Consequently the warehouse begins to fill up with products in the wrong colors or styles, wrong package sizes, etc. Something must be done with these oddments, so they are repacked (at a cost variance) and sold at discounts (at a price variance), and new replacements are flown in (at huge freight expense variances) to meet customer service needs. More of the profits disappear into those “catch-all” accounts.
As noted earlier, many of the extra costs reside in accounts like deductions, allowances, premium freight costs, or variances. Complexity creates noticeable increases in overhead and administrative expenses; impacts the reserve available for inventory obsolescence; or incurs additional labor to rework, repack, and remark inventory. Few, if any, of these costs impact the standard cost of sales and the standard gross margin. Thus, the product still appears to be nicely profitable, and the complexity costs remain hidden in undifferentiated accounts—or result in “non-recurring charges,” which, mysteriously, seem to “recur” from time to time. At the end of accounting periods, the true costs hit with full impact, in many cases wiping out all profit.
A Complexity Crisis Calls for Metrics
I call this sequence of events “a complexity crisis.” The finance and accounting metrics, intended to help track the results of the company do so—eventually. Unfortunately, the complexity remains unmanaged and the missing metrics do not reveal the problems until after the fact. Complexity strikes like a robber. The money is gone. Clues to the crime are few, and the perpetrators plead innocence. Only a knowledgeable accountant, with help from supply chain or marketing staff can unearth the clues and track the waste back to its root causes.
The solution for this is evident: to devise and implement the “missing metrics.” Many of these are easy to create; some are already in use. In other instances they will require whole new initiatives. If new metrics were in place and tracked regularly, such losses would be found much sooner. Then corrective actions could be started sooner as well. Major public accounting companies could help by sanctioning such metrics, to provide some uniformity. Unfortunately, thus far, they have been unresponsive to those needs.
Typical Missing Metrics
Sales per product stock keeping unit (SKU);
Sales per product category;
Sales per customer;
Sales per location;
Sales per employee (hourly, including full-time equivalent, salaried, and total);
Gross profit per product SKU;
Gross profit per product category;
Gross profit per customer;
Gross profit per location.
Purchases per vendor;
Purchases per commodity type;
Production (output value) per person-hour (or equivalent measure of labor input);
Total number of SKUs by division or business unit and company total;
Number of SKUs added and dropped during the last time period (quarterly, semiannually or annually).
Cost to process a customer order (end to end);
Cost to process a purchase order (end to end);
Cost to set up and maintain a product SKU;
Cost to serve by customer (including freight, handling, and order processing costs).
Within “catchall accounts” like Deductions and Allowances, Variances, and Writeoffs for Obsolescence, add subcategories to segregate entries by major customers (or groups), products (or categories), and locations (divisions).
Expenses per product line or category;
Expenses per customer, and/or by customer type/category;
Expenses per location;
Percentage of sales per product line or category;
Percentage of sales per customer, and customer type or category.
Plus a Totally New Metric—The Complexity Factor (CF)
Obviously, there is a common overall purpose among these metrics. Remember that the objective of new metrics is to reveal where the costs of complexity are hiding and are wasting time and money. Choose among those, or devise your own that measure similar complexity-related outcomes. Finally, an overall Complexity Factor can be calculated by means of the following formula (where “locations” are meaningful facilities and “countries” are places where legal entities exist):
(No. of suppliers + No. of customers + No. of employees)
× (No. of FG SKUs) × (No. of markets served) ×
(No. of locations) × (No. of countries)Total annual sales revenue (in the company currency of choice)
The resultant number provides a “benchmark,” called a Complexity Factor (CF), for the business (or subunit) whose data were used to calculate it. Obviously, a CF can be calculated for each business unit, division, geographical unit, etc. and for the entire company. It can also be customized to a company’s specific situation to make it more relevant, varying the terms in the ideal context.
While this may seem like a large number of new metrics, the data to compile them should already exist. Different parts of the business should use and manage CFs based on various metrics that are relevant to their activities. Not all parts need use all metrics.
What Gets Measured, Gets Managed; What Doesn’t, Doesn’t
The mere presence of metrics doesn’t mean management will do anything different. On the other hand, the absence of metrics virtually assures that nothing will be done. The old line “What gets measured, gets managed,” is true. The opposite, “If you can’t—or don’t—measure it, you can’t—or don’t—manage it,” is also likely to be true.
Measurement alone doesn’t solve any problems. It merely points to the effects of those problems. To manage them requires a series of steps. First, use Pareto’s Principle (the 80-20 rule). Sort products and customers in descending order of annual revenues and profits, and carefully analyze the bottom of the list. Most of these are “losers” with a few strategically important “potential winners” scattered about. Getting rid of the losers is imperative.
Upgrading some “losers” into “winners” (top 20%) is possible, but for most, it is impractical. In the middle group careful analysis can help in upgrading potential winners and downgrading imminent losers. There are newly devised, powerful tools and techniques to help in sorting, selection, and optimization of complex instances, but describing these goes beyond the scope of this discussion. (See More Info for Sixth Sense and Emcien.)
The Time for New Metrics Is Now
Now is the time for accounting and finance organizations around the globe to recognize the huge cost of complexity and how poorly managed it is. It is also time to introduce new metrics that track down, quantify, and help to manage the rampant complexity that plagues so many companies.
The waste of time and money due to “missing metrics” and the failure to track and manage complexity are immense. Correcting these problems is not always easy, but the starting point is as simple as the two basic rules of problem solving: First, identify the problem; second, solve the problem. Once the “missing metrics” have been devised and implemented, it’s time to take action and put them into use. The results will be surprising in terms of both speed and magnitude, and that is a very rewarding outcome for all stakeholders.