Using analytics to address inflation risks and strengthen competitive positioning (2023)


Article (5 pages)

During 2021 and 2022, consumer inflation accelerated in most developed and emerging economies. In the United States, the consumer price index (CPI) rose from 2.6 percent in March 2021 to 8.5 percent in March of this year. In June, the pace reached 9.1 percent, the fastest in 40 years, while producer prices have increased faster still. In the eurozone, consumer inflation reached 8.6 percent in June 2022, its highest-ever level (exhibit).

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Using analytics to address inflation risks and strengthen competitive positioning (1)

Image description:

Two line charts show the year-on-year change, by month, of the consumer price index (CPI) and the producer price index (PPI) in the United States and the eurozone from 2019 to June 2022. Both the CPI and PPI stood under 2 percent in January 2021. Since then, the CPI has risen by almost 10 percent in both the United States and the eurozone. PPI data have climbed even higher: the United States is at 16 percent and the eurozone at 37 percent.

End of image description.

Investors, economists, and forecasting institutions expect inflation to ease, but only gradually. (July measurements were somewhat lower in the United States but higher still in the eurozone.) The return of inflation is linked to the pandemic—the public-health measures taken to contain the spread of the virus and the economic and fiscal measures taken to mitigate the disruption this caused. The Russian invasion of Ukraine is exacerbating the inflationary dynamics.

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Inflation accelerated in an environment of strong consumer demand, supply shortages, production shortfalls, and rising energy prices. The main inflation driver, energy prices, increased in Europe by 38 percent in April and by 45 percent in March. In June, the core inflation rate in the eurozone (inflation excluding energy, food, alcohol, and tobacco) was 4.2 percent, a record level but one that underscores the lopsided composition of the overall rate.

For many companies, a high-inflation environmentis an unstable and insecure one to operate in. Responding to inflation is of paramount importance now, but responses must carefully account for future inflation, impact on the company business model, and the time lag for any response to manifest.

Analytics can be used to improve decision making in a high-inflation environment, with the level of analytics sophistication determined by the business requirements. In sectors where businesses are highly specialized and margins are thin—such as consumer packaged goods—analytics will need to be more precise to aid in developing a nuanced understanding of exposures. On the other hand, high-margin enterprises (software development or luxury goods, for example) can benefit from a more conceptual approach, without building deep analytics.

Inflation forecasting is a separate and complex topic of its own, and in developing inflation responses, most organizations use forecasts and scenarios developed externally. Analytics for decision making, on the other hand, cannot be outsourced. Without resorting to direct inflation forecasting, companies can use a flexible, analytically sophisticated method to help determine how and when to react. The approach includes assessing the extent of exposure and breaking down the types of exposures.

Companies can use a flexible, analytically sophisticated method to help determine how and when to react to high-inflation environments.

Assessing the extent of inflation exposure with simulations and scenarios

Analytics can help companies estimate their exposure to inflation. Mitigation strategies can then be prioritized based on the estimates. To assess exposure, companies can associate drivers of cost—such as commodity prices, foreign-exchange rates, labor costs—to actual costs. The association can be made in detail, potentially down to the subproduct level. A variety of analytical methods can produce simulations and scenarios for the drivers of costs. The estimates should be historically accurate as well as forward looking. The estimates should maintain consistency across factors: for example, the prices of construction commodities such as steel and copper tend to be correlated.

Once companies have assessed their exposure, they can prioritize risk factors with the largest exposure and then overlay and select potential mitigation strategies. Proper exposure assessment requires capabilities for scenario analysis, stochastic simulations, predictive modeling, and well-established, repeatable analytical methods.

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Decomposing exposure: Pure inflation, relative price inflation,and idiosyncratic inflation

Companies are exposed to different types of inflation; analytics can be used to establish the levels of exposure to pure, relative price, and idiosyncratic inflation. 1 Their exposure profiles provide a basis for tailoring mitigating actions to manage business in inflationary conditions. A number of robust methods can be employed to estimate the inflation breakdown repeatedly and accurately. Economists sometimes use a two-step method, for example, first separating out exposures to idiosyncratic inflation and then separating pure inflation from what remains (by estimating the proportion of prices that move equiproportionately).

Matching strategies to inflation types

Depending on exposure and inflation decomposition, companies can use analytics to size and prioritize the various risk factors for inflation. Strategies can then be selected according to their efficacy in addressing the risk factors that lead to the greatest company exposure. Among others, potential strategies include hedging to reduce price volatility, vertical integration up the value chain, and pass-through pricing. The following discussions of hedging and pass-through pricing demonstrate how strategy choices depend on company position and inflation decomposition.

Volatility reduction through hedging: A response to relative price changes

Companies have long used hedging strategies to mitigate and manage the risk of price fluctuations for their businesses. To manage hedging risks, companies can involve different parts of the organization, actively managing inventory price risks and commercial contracts, building analytics capabilities such as scenario testing, and developing governance and policies for oversight. The hedging option will require the strongest analytical capabilities, including forecasting and optimization. It can often mitigate the risks of price fluctuations for feedstock, which mostly relate to relative price changes. For example, a chemical company executed a financial hedge to lock in natural gas prices, resulting in a significant reduction in risk from rising prices.

This lever comes with risk and requires careful consideration. The danger is that organizations can lock themselves into higher prices or significant margin calls. External early-warning signals (such as a steel price threshold) should be in place and periodically refreshed.

Pass-through pricing: A response to pure inflation

Dynamic pricing levers are an alternative to cost reduction levers. Companies can respond to increases in input costs and price volatility by adopting a dynamic pricing strategy. They can often derive more value from pricing by setting the right price, optimizing discounts and rebates, and managing margin leakage. This option requires strong analytical capabilities, including sophisticated market segmentation and assessment of pricing impact.

The dynamic pricing response to input cost increases and volatility is a strategy that is relevant for pure inflation. However, a partial price pass-through may be a component of an optimal response to all types of inflation. A manufacturing company, for example, managed granular price increases across thousands of products through customer pattern analysis and ended up with hardly a complaint; by contrast, a packaging company that had not prepared its sales force for price changes experienced double-digit market share losses.


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Analytics, implementation, and continuous testing: An example sequence

The implementation of strategies to improve a company’s posture can follow an analytics-based sequence of steps. Here is an example sequence:

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  1. Analytically or qualitatively decompose inflation and evaluate the composition and extent of the company’s exposure—its inflation fingerprint—according to the relevant inflation factors.
  2. Compose the inflation mitigation strategy: using a cross-functional approach, select the levers to be applied according to the company’s position.
  3. Test potential levers analytically against scenarios, incorporating variable demand, labor costs, commodity prices, energy prices, interest rates, and supply delays. For a risk–reward analysis, evaluate levers with financial and nonfinancial metrics (such as EBITDA and volatility fluctuations, respectively); then prioritize strategies and optimize decision making under uncertainty.
  4. When testing strategies against critical scenarios (such as supply chain concentrations or exposure to a foreign-exchange pair), connect actions to early-warning signals (such as a threshold for price increases).
  5. Estimate the benefits and costs of actions under different scenarios. (Some steps will not be effective in reality—a contract change will not alter the effects of a country-wide ban on exports of an affected product, for example).
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Accelerating your company’s analytics journey

Companies will begin with different levels of analytics sophistication. Some have had experience in implementing analytics solutions while others have not (see sidebar, “Analytics, implementation, and continuous testing: An example sequence”). For the application of inflation analytics, a few guideposts will help companies get up to speed:

  1. Start small. Start with a single product, business unit, or geography. Most of these analyses are accretive and need not be completed together.
  2. Start simple. Break down the income statement to drivers of margin. “Stress” these drivers with your view of inflation impact. Not all drivers will be affected by inflation, so they will not need complex analytics solutions.
  3. Start now. Experience shows that capabilities, data, and know-how are always works in progress. The benefits of starting now always outweigh impact with better resources.


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Last Updated: 12/11/2022

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