010 The special challenge of measuring industrial company risk main image_Original

The special challenge of measuring industrial company risk

By Claude Généreux, Eric Lamarre, and Thomas-Olivier Leautier

Off-the-shelf tools from other sectors won’t work. What will?

Executives are suddenly preoccupied with risk management. The number of distressed companies is rising, credit rating downgrades are ubiquitous, and equity markets are volatile. Add recent and highly visible accounting scandals to the mix, and it is no surprise that many CEOs rank risk management as one of their top priorities.1

Industrial companies grappling with risk face a particular problem. Though many have long been aware of the need to better manage risk, scant few have advanced very far along the learning curve. Few tools tailored to industrial companies’ specific needs exist. Naturally, many industrial executives turn to approaches borrowed from other sectors, such as financial companies, to calculate sensitivities to individual risks. This can be a costly mistake.

For example, the conventional wisdom that derivatives are dangerous and must be checked carefully would suggest that an industrial company should take inventory of its derivatives exposure. Yet this would be of limited use. While the derivatives portfolio of some financial firms often constitute an important element in overall risk exposure, for most industrial companies they represent only about 10 percent of total risk.

Likewise, a comprehensive audit of a company’s exposure to risks from fluctuating commodity prices, exchange rates, interest rates, fire hazard, and production loss offers senior managers little substantive help. These audits typically produce just a long list of risks, individually quantified. Knowing that a company could, for instance, lose $300 million if commodity prices were to move to their lowest historical level is of little value if such a dramatic event is unlikely, as it frequently is. Moreover, such calculations ignore correlations with other risks, such as foreign exchange or demand swings. Nor do they shed any light on the potential impact on a company’s long-term growth strategy and financial stability.

From our work with industrial companies we have developed insights into some of the most important distinctions between off-the-shelf approaches to risk management and the unique needs of industrial companies. For starters, industrial companies need a more sophisticated approach to measuring risk, which will help them to spot underlying risks, identify what they are already spending to mitigate risk in uncoordinated programs, and frame the key tradeoffs between managing risk and its cost. A clearer picture of risk can also better prepare CEOs to time acquisitions, add capacity, and make operations more stable and productive. They also often uncover new opportunities to benefit from risk, including cross-commodity arbitrage and systems optimization.

We have found that solid risk measurement efforts undertaken by large industrial companies have at times uncovered between $30 million and $50 million per year, mostly by eliminating unnecessary hedging practices, managing the balance of debt to equity, and improving contract practices with customers and suppliers. BHP Billiton, a diversified resources company, eliminated its hedging program after an exercise in measuring corporate risk indicated that the reduction in volatility from the program was negligible and not worth the hedging cost (Exhibit 1).

In our experience the industrial companies that manage their risk most successfully follow four principles. They apply industry-specific tools to develop a precise picture of company risk. They uncover and aggregate often-widespread and hidden sources of risks. They identify underlying sources of risk volatility. And they account for the intricate complexity of both supplier and customer contracts.

Apply the right tool for the industry

The key to measuring risk in any industry is how much value a company’s assets can lose if markets shift while a company liquidates its positions. In the financial sector, analysts using value-at-risk (VaR) calculations typically assume that investments can be sold or closed in fewer than five days. This is not an unreasonable assumption in some sectors, though even among the financial companies where VaR is most common there are notable exceptions, such as reinsurance companies where risk exposure can span 15 to 30 years.

Obviously, a VaR approach is a poor fit for industrial companies where, for example, smelters, power plants, and other industrial assets cannot be closed so quickly. Closures are carefully planned, and require months of lead-time. Similarly, sales contracts cannot be renegotiated solely because the price of the underlying commodity has shifted; industrial companies must deliver what they promise, even at a loss.

The more relevant measure is how much cash a company can lose over months, years, or even decades if assets are long-lived. This measure, a company’s cash-flow at risk (CFaR), is a far more significant measure of the true risk to shareholders. It also is often an order of magnitude larger than five-day VaR, reason enough for shareholders and corporate officers to take note. For example, in 2000 many energy companies were reporting five-day VaR of about $30 million to $40 million, ridiculously small compared to the hundreds of millions of dollars of profits (and the billions of dollars of market capitalization) that shareholders have since lost.

To understand his company’s full risk exposure over time, the CFO of one energy company classified all of its risk positions by degree of liquidity—from forward-looking standard power contracts in a very liquid market to sales commitments that could only be sourced in the spot market. The risk measurement group then computed a five-day value-at-risk for the liquid portfolio, and quarterly cash-flow-at-risk on the illiquid portfolio. The latter was much larger than the former, even though the volumes involved were comparable. The result was a much clearer picture of the company’s exposure to risk. The CFO and senior management team then designed a cost-effective hedging program for the summer, when most of the cash-flow-at-risk was concentrated.

Uncover and aggregate risk

Risk aggregation is a fundamental aspect of risk management. Without it, companies lack a clear view of their exposures and tend to design inferior hedging strategies. Typically, industrial companies’ risks are spread widely across business units (Exhibit 2). Companies in sectors such as finance and energy have been aggregating their risks into fewer centers (including a treasury or central trading unit), but most industrial companies are still in the early stages of this practice.


As one metals and mining company researched its risk profile, its risk management team tracked commodities and currency throughout the organization. To its surprise, the team found that different groups within the organization were hedging externally what they should have hedged internally. For example, one group bought US dollars to protect its earnings in euros, while another group bought euros to protect its earnings in US dollars. Eliminating that practice alone saved several million per year in unnecessary hedging costs.

Identify underlying sources of volatility

To model risk for most financial instruments, such as stocks or interest rate swaps, a statistical analysis of historical price data provides a sound estimate of individual risk volatility and relationships between risks. For industrial companies, though, modeling risk is more complex. Statistical and historical analyses must be supplemented with a thorough analysis of the specific industry and market (Exhibit 3).


To correctly estimate volatility, it is important to understand the nature of the relationship between supply and demand in each market. For example, in some energy markets such as the US midwest, California, and Scandinavia, power prices become much more volatile as prices rise, and the smallest demand swing can lead to significant price variations. In other markets, such as in Germany, volatility is much lower as prices increase, and even large swings in demand produce only modest price differences. Sources of volatility also vary from market to market. Thermal plants predominate in both the US midwest and Germany, so supply is relatively stable, and demand is largely driven by weather. By contrast, hydroelectric power is more common in Scandinavia and California, where year-to-year fluctuations in rainfall and snowmelt can dramatically influence supply and cause significant price volatility.

Historical analysis alone can be particularly misleading. Consider that gas demand and prices have historically peaked in the winter in the United States, while power demand and prices have peaked in the summer. Gas warms houses and offices in the winter, while electric power cools them in the summer. However, over the past five years, the massive development of gas-fired generation has altered this relationship, loosening historical correlations. Now in the summer, as power demand and prices increase, demand for gas also increases—along with gas prices. The precise correlation varies from market to market and depends on the share and regional characteristics of gas-fired units.

To model the volatility of commodities, the savvy industrial risk analyst will combine an understanding of structural market drivers with a probability-based view rooted in history. Geographic market differences and basis risks must also be considered.

Account for the intricacies of contracts

Much of an industrial company’s risk stems from the structure of its contracts and the flexibility of its assets. Modeling risk requires a tailored approach.

First, most basic materials contracts allow customers to modify the volume purchased based on their need at the actual time of consumption, as opposed to strictly defined quantities based on prior estimates of need. For example, a purchaser of aluminum can sheet metal will buy more if a hot summer increases demand for soda. Similarly, residential customers use more electricity as summer temperatures rise.

This swing in volume can be extremely costly if not properly priced. For example, in 1996 and 1997, many energy companies entered into contracts with cities in the US midwest to provide them with power to meet all their needs, based on average consumption and average prices. During the summers of 1998 and 1999, high temperatures caused demand to skyrocket. The resulting high prices caught many sellers unprepared and cost them hundreds of millions of dollars.

To respond to contract complexity, one chemical company CFO launched a joint sales/risk measurement task force to review the company’s contract portfolio. During the first week, the team made rough classifications and singled out the most complex contracts, combining insights from account managers and risk analysts to define the unique risk features of each contract in terms of fixed elements and options. The risk analysts then modeled the contracts, simulating their value under a wide range of scenarios for variables such as price, customer behavior, and competitor behavior.

At the end of the exercise, the team systematically classified and assigned a value to all options granted to customers. In some cases, the company realized that it had given away valuable options, an oversight that was corrected in subsequent negotiations.

Faced with the imperative to manage risk, industrial companies need first to equip themselves with the appropriate tools and methods tailored to measure industrial companies’ specific risk profiles. Risk models and metrics such as value-at-risk, borrowed from other sectors, will help little and can produce risks of their own.

About the author(s)

Claude Généreux is a director and Eric Lamarre a principal in McKinsey’s Montreal office; Thomas-Olivier Leautier is an associate principal in the Washington, DC, office.

This article was first published in the Winter 2003 issue of McKinsey on Finance. Visit McKinsey’s corporate finance site to view the full issue.