A manufacturing footprint fit for the future, via advanced analytics

Consumer-goods companies are under pressure to optimize manufacturing for the next normal. Advanced analytics could drive significant impact for their complex footprints and portfolios.

As uncertainty and volatility continue unabated, a set of emerging consumer trends are compelling consumer-packaged-goods (CPG) companies—often typified by highly complex product portfolios and production networks—to review and redesign their manufacturing footprints. With shocks expected to increase in frequency, further exacerbating these challenges, supply chains often lack the resilience needed to respond. Furthermore, consumers are looking for greater customization (adding more SKU complexity), faster delivery, and improved environmental sustainability.


Increasingly, CPG leaders are optimizing their cost lines to fuel growth in competitive markets. The specific issues vary by context: in Australia, the small number of players in the grocery channel results in high demand for operating excellence among CPG partners. By contrast, in China CPG makers are looking to fund investments in customer acquisition, territorial expansion, and digital capabilities.

To relieve these types of pressures, CPG companies have a powerful ally at hand: advanced analytics, which is already helping early adopters achieve major improvements in cost and agility.

Resilience is a postpandemic CPG priority

Particularly since the onset of the COVID-19 pandemic, supply-chain leaders have made resilience a priority. In a recent survey conducted by McKinsey, 98 percent of the respondents whose companies had experienced pandemic-induced footprint challenges said that they have taken action to increase the resilience of their manufacturing footprints (Exhibit 1). Furthermore, 93 percent identified resilience as a priority area to address.

Most supply-chain leaders have acted to boost resilience in their manufacturing footprints.
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Three factors are adding to CPG players’ footprint challenges:

  • high SKU and product complexity, with SKU totals reaching into the tens of thousands as companies introduce products for fast-changing preferences across proliferating market segments
  • diverse product categories, resulting in a need to accommodate multiple technologies
  • varied demand scenarios, which must be considered given the evolving business conditions, consumer preferences, and buying patterns

Traditional approaches and spreadsheets are unable to evaluate these complex factors, handle the large volumes of data, or perform the multivariate regression analyses needed to inform decisions about how to optimize manufacturing footprints.

An analytics solution to CPG footprint complexity

Digital solutions based on advanced analytics can help to improve resilience, reduce costs, increase revenues, release cash, and reduce capital expenses by calculating the impact of multiple footprint scenarios across the business. Moreover, these solutions are often highly customizable yet can be built quickly on top of preexisting code, easing scale-up in complex environments—with the potential for scenario generation and evaluation to be automated as the footprint redesign evolves (Exhibit 2).

Analytics-based optimization models simulate real-world scenarios, yielding more accurate insights for decision-making.
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Optimizing tools can model multiple, interdependent variables and user-defined parameters to simulate the real world as closely as possible, and in near real time as well. Interactive visualizations can illustrate existing and potential future network configurations. The results can then be shown in the form of performance metrics followed by the organization, simplifying decision making by allowing leaders to evaluate each scenario’s fiscal benefits, impact on service levels, and related performance indicators.

A Japan-based CPG company shows the potential of advanced analytics in optimizing a manufacturing footprint. By leveraging detailed SKU-level cost data from each of its factories, and by developing an analytical tool to test potential footprint scenarios, the company was able to determine where to dial down production and where to ramp up. The new, optimized manufacturing footprint is projected to decrease outsourcing by about 50 percent, reduce manufacturing costs by around 8 percent, and shorten lead times by almost 10 percent.

Another example of the opportunities available is a multinational CPG manufacturer operating in more than a dozen countries and with nearly 50 plants. The manufacturer was experiencing flat sales and facing competition from new companies that were rapidly gaining market share. By developing an integrated plan informed by advanced analytics, the company was able to implement a five-year transformation road map that included footprint shifts, remote-sourcing efforts, and the implementation of lean manufacturing methods at high-cost sites. This resulted in the identification of a net positive opportunity valued at more than $1 billion.

Making the most of CPG footprint analytics

For companies to be successful in adopting advanced analytics to help optimize their manufacturing footprints, there are a few key considerations they can keep in mind:

  • Align from the outset. Optimizing for the global organization, rather than focusing on regions, divisions, or business units, is crucial for rigorous scenario development and assessment. That means starting with an enterprise-level view, one that sees value flows from end to end—and not only within the organization itself, but upstream to suppliers and manufacturing partners, as well as downstream to customers, servicers, and end users.
  • Take an expansive view of potential demand shifts. The past two years have illustrated how rapidly demand scenarios can change. Current demand patterns may not remain valid for long.
  • Recognize that imitation isn’t flattering. Competitors’ footprints typically aren’t a good model to build from. Instead, the outcomes of the analysis—backed by comprehensive data sets to mitigate against hidden exposures, risks, and missed opportunities—are more effective in guiding decision making.

Those companies willing to embrace advanced analytics have even more opportunity to use these techniques to solve related challenges involving decision making in complex scenarios—such as new-product introductions designed to support greater customization, healthier lifestyles, and environmental sustainability. They can guide improved decisions in emerging segments, such as electric vehicles in the automotive sector or home-automation devices in electronics. And retailers can make more informed choices about whether to use online marketplaces or brick-and-mortar outlets, and improve reverse logistics and return policies. By gathering information to create a single source of truth, companies can make decisions better—and faster.

For companies that can use advanced analytics effectively as a basis for determining their manufacturing network, there is an opportunity to mitigate emerging volatility and other consumer-led pressures and find new sources of competitive advantage in their manufacturing footprints and portfolios.

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