Benchmarking in the dairy industry: Strengthening performance amid double volatility

| Artigo

The COVID-19 crisis has damaged consumer confidence and increased the focus on essential categories. For consumers, it has expedited the trend toward increasingly embracing lifestyle products, such as healthcare and cleaning items and healthy eating, including fresh foods and dairy. In the United States, sales of most categories purchased in grocery stores have risen significantly over 2019 as people started spending more time at home.

That current reality and the prospect of the next normal will have strong implications for consumer-packaged-goods (CPG) manufacturers, many of which have already started preparing their supply chains to handle more agile operations. The dairy industry, like CPGs, now faces the double volatility of shifts in demand and higher prices for inputs. Dairy consumption was declining even before the crisis: millennial and Generation Z consumers were more assertively seeking out new tastes and healthier options, as well as dairy-free alternatives. In addition, the price of dairy products has steadily increased over the past several years against the backdrop of trade disputes, rising tariffs, and pressure from Chinese exports.

The pandemic has exacerbated these developments, spawning a significant market of “free agents”; more than 30 percent of all consumers have experimented with new products or brands during the crisis. Although availability was the main driver for these experiments, perceptions of health also motivated purchases outside the typical comfort zone. In our studies of key players in the global dairy industry, we find that organizations are responding innovatively—for instance, by introducing more plant-based products in their manufacturing portfolios—and thus accepting additional operational complexities and challenges.

Most companies with complex portfolios of stock-keeping units (SKUs) can achieve excellent labor productivity within plants and minimize losses and waste by maintaining high capacity utilization. This kind of fact-based steering of a complex, flexible, yet reliable supply chain requires a high level of transparency and the granular benchmarking of operations. Best practice calls for continuous benchmarking to enable continual improvement.

New insights from external benchmarking

Availability and price are clearly the two main reasons for consumers to switch products during the crisis. Manufacturing facilities therefore need to maintain and even improve their production costs while ensuring greater flexibility and reliability. To balance the flexibility, reliability, and cost performance of plants, production managers should also make KPIs more transparent and identify opportunities for improvement.

Although CPG companies with broad networks already conduct internal benchmarking as part of their continuous-improvement journey, there are limits to how much this information can improve performance in an effective, sustainable way. Only by comparing their own KPIs with those of their industry peers can CPG companies determine where and how changes will truly eliminate the barriers to excellence in their facilities. For example, one company that produces dairy powder at ten sites ran an internal benchmarking process that identified a gap of 25 percent in conversion costs compared with top-quartile rivals. The company wished to focus on improving the utilization of equipment and reducing overhead costs. Further data from an external-benchmarking database showed a full-potential opportunity of 42 percent compared with the highest-performing quartile of companies. This made it possible to achieve top-quartile performance—with even larger opportunities and new pockets of improvement in the overall effectiveness of equipment and direct-labor productivity (Exhibit 1).


Large-scale external benchmarking can yield other significant insights as well. For instance, one company’s ultrahigh-temperature (UHT) milk category had a gap in total conversion costs of at least 16 percent between top performers and average plants: $4.70 per hundredweight (following the US measure) among median plants but $4.10 per hundredweight in the top quartile. The average conversion costs of the top-performing plants were 29 percent lower than those of a median-level plant.

Capturing the full value of benchmarking

To set up benchmarking, companies can standardize metrics for data collection and identify and categorize the value chains’ specific costs and processes. After completing these preliminary tasks, they can undertake a five-step process, refined from our observations of best practice, to ensure that they capture benchmarking’s full value (Exhibit 2).


1. Process mapping. This step includes evaluating the participants’ financial, operational, and customer-facing profiles, as well as strictly standard definitions and a common approach to detailed data collection. For example, one dairy company, following the best-practice approach to performance benchmarking in its global footprint, initiated kickoff sessions with data-collection coordinators across its whole network. In these sessions, the company clearly communicated its goals and a single methodology for defining KPIs. It also brought regional production and supply-chain managers together with a central coordinator. If the reporting system allows, the central office can coordinate benchmarking by reaching out to specific units, where data have more details and context.

2. Structural-difference analysis. Best practice calls for the creation of a detailed cost-to-serve model (with profiles of production sites and a network-design overview) to capture all the details of particular plants, warehouses, or distribution models. In the dairy company, the differences across networks mainly involved technologies, the scale of outsourced operations, routes to market, and geolocation characteristics.

3. Normalization and translation. Internal benchmarking within a single industry is a huge challenge given the many operational complexities and structural differences involved (for instance, the product mix, packaging formats, SKU complexity, and production steps). Often, performance measurement is not standardized among the production facilities of an industry or even a company. By following a robust methodology for normalizing and translating KPIs in alignment with common definitions or units of measure, an organization ensures that it is comparing like with like. Setting up a normalization model typically requires compiling extensive data sets—preferably including external references—in many dimensions to help draw clear correlations and avoid confusing performance with the structural differences among plants, both inside and outside the network.

4. Benchmarking and driver analysis. By acquiring a detailed perspective on performance drivers and a profile of production sites or the general supply chain, the leaders of a company can maintain a fact-based overview of areas in which it is falling behind and those that reflect best practice. Conversion cost, for example, is a multidimensional KPI driven by a number of interlinked levers with various weightings of impact. Improving one of these levers won’t necessarily improve a company’s financial performance.

Benchmarking shows that the labor productivity of top performers is, on average, 30 percent higher than the median—yet still some 30 percent lower than the best-in-class benchmark. So there is plenty of room for improvement, even for companies that seemingly do well. It may also come as a surprise that 80 percent of the plants with the best costs do not achieve top performance in labor productivity. What’s more, among the key plants around the world that we observed, only four out of nine with the best overall equipment effectiveness reached the top quartile in conversion costs. This finding indicates the complexity of conversion costs: many companies may underestimate the importance of other underlying drivers. Benchmarking can help organizations to grasp the range of cost drivers fully.

5. Estimating gaps and opportunities. When companies look at conversion costs, best practice suggests splitting labor and nonlabor costs into production, maintenance, utilities, depreciation, and factory-overhead buckets to perform a standardized benchmarking. Cost structures vary across industries and even product subcategories. In dairy, the costs of liquid operations are split almost equally among these buckets except for the large nonlabor-costs component. This suggests that in making improvements, the focus should be placed not only on labor productivity but also, and more heavily, on asset efficiency and the cost of utilities and spare parts.

In some industries, improvement initiatives could generate better results by focusing on specific production steps. In the dairy industry, for instance, about half of the conversion costs in ingredients plants are related to the drying process. To control the cost of drying operations, top performers therefore invest heavily in energy-efficient equipment, negotiate better contracts with utilities, and pursue alternative-energy sources.

Although greater efficiency can typically also be achieved through reduced flexibility, that can also decrease the overall network’s ability to generate revenue. A highly flexible plant with no frozen period and close to 100 percent adherence to schedule can still reach the top quartile in financial performance. Around 30 percent of top performers achieve excellence in reliability, with an adherence to schedule of more than 99.4 percent. And most top performers—about 80 percent—are very flexible, with a frozen period of not more than one or two days.

The power of continuous benchmarking

Continuous benchmarking is meant to identify not only gaps in emerging areas of excellence but also additional potential for improvement when peers get better over time. The dairy company established a regular benchmarking process that revealed, in its first year, an opportunity to save 12 to 28 percent of the cost base of each of its plants. Key areas of improvement included capacity utilization, quality, and energy consumption. A second review, after two years, showed that the company had achieved a huge step change in performance and improved conversion costs by an average of 8 percent compared with the results of the previous benchmarking.

Across nearly all plants, the company’s operational metrics, such as the first-time-right rate, also improved on average by 3 percent, in combination with improved reliability for parameters such as adherence to schedule and the frozen period. As peers continued to improve and the company added more plants to its benchmarking effort, it identified an additional opportunity—this one to shave 14 percent off baseline costs—helping to further prioritize improvement initiatives. Without the continuous benchmarking review comparing the company with its evolving peer set, its aspirations may not have reached their full potential.

This is a volatile period of shifting demand and price pressure. Companies in the dairy industry can substantially improve their performance by acting upon the insights of a benchmarking process that draws data from not only their own operations but also industry peers. This approach makes it possible for companies to understand what high performance really means in their own industry and to improve that performance constantly. Companies that systematically and repeatedly implement the five steps of best-practice benchmarking can ensure that they achieve outstanding productivity, maintaining a competitive advantage and maximizing their potential by continually measuring and refining their performance.

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