Using analytics to address inflation risks and strengthen competitive positioning

Here is an excerpt from an article written by Renzo Comolli, Arvind Govindarajan, Chetan Venkatesh, and Yushan Zhang for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out others, learn more about the firm, and sign up for email alerts, please click here.

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In the new inflationary environment, company leaders can protect their business and gain competitive advantage by deploying analytics-aided strategies.
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).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.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.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 environment is 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.

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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Here is a direct link to the complete article.

Renzo Comolli is a senior knowledge expert in McKinsey’s New York office, where Chetan Venkatesh is an associate partner; Arvind Govindarajan is a partner in the Boston office; and Yushan Zhang is an expert in the Philadelphia office.

This article was edited by Richard Bucci, a senior editor in the New York office.


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