Pearson on Excellence: Reducing commodity price risk

One hallmark in the era of permanent volatility is fluctuating commodity prices: everything from aluminum (variations up to 30 percent in 2012) to zinc (variations up to 25 percent in 2012). The result is endless headaches for people and departments in virtually every industry: the procurement folks buying materials; the logistics and transportation staff moving it; the finance guys forecasting expenses; and even the sales and marketing staff struggling to pass unanticipated cost increases on to customers.

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One hallmark in the era of permanent volatility is fluctuating commodity prices: everything from aluminum (variations up to 30 percent in 2012) to zinc (variations up to 25 percent in 2012). The result is endless headaches for people and departments in virtually every industry: the procurement folks buying materials; the logistics and transportation staff moving it; the finance guys forecasting expenses; and even the sales and marketing staff struggling to pass unanticipated cost increases on to customers.

Despite the broad use of commodities—and the significant risks that volatility poses—few companies excel at “commodity price risk management.” Of course there are exceptions, notably specialized commodity processors. But in most fields, price risk management is relegated to a few procurement professionals who use supplier relationship management or strategic sourcing tools that aren’t really designed for the task. Following are some technology and process innovations that might do a better job of helping organizations deal with commodity price risk.

Risk analytics. Risk analytics can help companies gauge the effects of commodity price forecasts, control exposure to fluctuating prices, and develop risk-management strategies that align with the organization’s risk appetite. One area with particular relevance is sourcing, where companies can use analytics to:

Develop intelligent segmentation—setting strategic direction for categories and subcategories by identifying, and potentially substituting, materials with lower price volatility.
Discover correlations—understanding the impact of market developments and tying that knowledge to production processes and related byproducts. For example, caustic soda is a byproduct of chloride production.

Understand real costs versus low costs—learning more about what a company specifically needs. For example, an animal feed business may need certain vitamins. However, it isn’t uncommon for a majority of vitamin content to disappear during high-temperature production. Perhaps it would be more cost effective to use a pricier vitamin composition with better resistance to degradation.

Enable deep sourcing—performing in-depth analyses of specific categories to identify new supplier-development, low-cost-country sourcing, and backward integration opportunities.

Hedging optimization. Most firms understand the importance of direct hedging, or using indexed contracts, commodity derivatives, and strategic pre-buys and contract structuring. However, companies might also benefit by optimizing the total hedge position over time—combining similar exposures across different business units or geographical regions (for example, energy exposure in Europe or North America). The key is viewing prices not only at an absolute level, but relative to each other.

Governance model. An innovative risk-management-focused governance model can help cascade an organization’s high-level risk appetite—the amount of budget variation it is willing to tolerate—into specific day-to-day tactics for individual commodities and regions. The model could also help specify accountability for daily risk-management activities and establish a hierarchy for oversight and reporting of positions and risk exposures. Once a governance model is established, a risk policy can be penned that details guidelines for risk analytics, risk reporting, exception management, key performance indicators, and specific roles, job functions and tasks.

Recipe optimization. Recipe optimization can help companies anticipate market changes and risks by focusing more tightly on product characteristics. Such efforts might include rationalizing the bill of materials and/or recipes by choosing low-risk or hedgeable raw materials that reduce the product’s complexity and the variety of required materials. These efforts can help reduce exposure and broaden supplier scope.

Should-cost modeling. Should-cost modeling segments a product into basic cost factors, such as raw materials and transportation costs. Each component can then be broken down into a more-detailed “cost split up.” For example, to calculate the should-cost of a certain process step, that step can be segmented by cost drivers. Companies can monitor the should-costs of a raw material over time and compare those figures with the price fluctuations of the entire product.

Turning risk into opportunity
When it comes to commodity prices, permanent volatility may well be the “new normal.” As a result, companies with sub-par risk-management capabilities are taking a big chance: Pressure on working capital and wide swings in performance will likely increase, and passing price increases through to end consumers could well become more difficult than ever.

The potential upside, however, is significant: According to Accenture research, an organization with a $1 billion materials budget (half of which is tied to volatile commodities) could leverage commodity price risk management to reduce its annual materials costs by up to 10 percent.


About the Author

Mark Pearson
Mark Pearson is the managing director of the Accenture’s Supply Chain Management practice. He has worked in supply chain for more than 20 years and has extensive international experience, particularly in Europe, Asia and Russia. Based in Munich, Mark can be reached at [email protected]

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November 2012 · Supply Chain · All Topics
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