Reindustrialization moves into a more selective, strategic phase in Europe and the US


  • Reindustrialization[1] moves into the mainstream: 73% of large European and US organizations now have a strategy in place or in development, up from 59% in 2024
  • Planned investment decline from $4.7 trillion in 2025 to nearly $2.5 trillion in 2026 over the next three years, signaling a shift toward more selective, capital‑efficient models
  • Long‑term strategic benefits outweigh short‑term savings, with 86% of organizations prioritizing market access and supply chain resilience in their decisions
  • AI becomes a core enabler of reindustrialization execution, with 87% of organizations planning to invest in AI and other advanced manufacturing technologies to help reduce costs associated with reindustrialization

Mumbai, April 21, 2026 – Reindustrialization has entered a more mature, disciplined phase as organizations seek greater control over dependencies while maintaining economic viability and competitiveness. According to the 2026 edition[2] of the Capgemini Research Institute’s report The resurgence of manufacturing: Reindustrialization strategies in Europe and the US, 2026’nearly three‑quarters of large European and US organizations now have a strategy in place or in development, reflecting a clear shift toward resilience and control‑first operating models. At the same time, planned reindustrialization investment over the next three years has fallen sharply this year, highlighting a more pragmatic and selective approach to capital allocation rather than reduced ambition. Organizations are now recalibrating their manufacturing and supply‑chain footprints to limit critical dependency risks while preserving competitiveness through hybrid domestic, nearshore, and friendshore strategies, increasingly enabled by automation and AI.

The impact of reindustrialization is not uniform across sectors. The shift is most pronounced in manufacturing‑intensive and strategically critical industries, including automotive, electronics, semiconductors and aerospace and defense, where dependency risks, supply‑chain exposure and market access considerations are most acute. These sectors are driving the transition from large‑scale expansion toward more selective, technology‑enabled industrial models.

“Amid heightened geopolitical and economic uncertainty, reindustrialization is entering a more mature phase – one that is clearly focused on resilience, sovereignty, and long‑term competitiveness,” said Michael Schulte CEO of Capgemini Engineering and Member of the Group Executive Board. “Today, the more established reindustrialization strategies are about building regionally balanced, technology‑enabled ecosystems that reduce critical dependency risks, coupled with a pragmatic approach to investment which is driving more flexible, capital-efficient models. With intent now clear, success will depend on delivery – anchoring decisions in long‑term value and building the digital and workforce foundations for durable industrial strength.”

Diverse regional pathways emerge as strategies mature

The 2026 survey shows that organizations are increasingly favoring diverse, hybrid reindustrialization approaches tailored to regional contexts, rather than converging on a single model. This is illustrated notably by the prominence of friendshoring in continental Europe, cited by 64% of organizations marking a clear shift toward allied‑based manufacturing and supply-chains to manage strategic dependencies.

Reshoring activities in the US show an acceleration, with nearly half (48%) of organizations reporting investments, up from 30% in 2025, while a significant 42% continue to invest in nearshoring. Within Europe, nearshoring recedes from 2025 levels (from 55% to 39%), while reshoring rises more modestly (from 34% to 42%), reflecting structural cost pressures and regulatory complexity.

According to the report, as US and EU organizations rebalance from China, they are increasing their presence in India, followed closely by Vietnam, Mexico, and Canada, underscoring a broader reconfiguration of global supply chains around diversified ecosystems. The US is also attracting increased foreign investment, with a large majority (nearly 85%) of EU‑based organizations investing in US manufacturing to benefit from direct market access and navigate trade policy. At the same time, around two‑thirds of organizations (64%) plan to maintain or increase investments in China over the next three years, highlighting a pragmatic, rebalancing of operations and supply chain across industries and markets.

Selective investment and long‑term value take precedence over scale‑driven expansion

According to the report, organizations operating in non‑critical sectors increasingly favor more flexible alternatives in order to decouple access to industrial capacity from asset ownership and greenfield projects. To preserve strategic control while limiting capital intensity, organizations are increasingly turning to models such as multi‑product manufacturing assets, contract manufacturing partnerships and shared infrastructure.

The report further finds that reindustrialization decisions are being evaluated through a more holistic economic lens. A clear majority of organizations say supply‑chain resilience justifies reindustrialization decisions, with strong expectations for revenue growth over the next three years. Nearly eight in ten anticipate economies of scale will lower unit costs over time, underscoring a shift toward long‑term strategic value over short-term savings.

Technology, especially AI, acts as a catalyst

Technology is playing a growing role in sustaining effective reindustrialization execution. The report finds that a large majority (87%) of organizations plan to invest in advanced manufacturing technologies, notably AI, automation, and digital twins, to help offset higher production costs closer to end markets.

AI, including generative and agentic, is seen as essential for boosting efficiency. Execution‑critical use cases are concentrated in areas such as production planning and optimization, supply‑chain risk modeling and location selection, where AI directly supports faster, more informed industrial decision‑making.

However, talent shortages remain a common constraint to scaling reindustrialization for a large majority, particularly in skills related to advanced manufacturing engineering, automation, AI and digital technologies, reinforcing the need to align technology deployment with workforce transformation.

Methodology of the report

From January 2 to February 3, 2026, the Capgemini Research Institute surveyed 1,208 executives employed at organizations with annual revenue exceeding $1 billion (or $500 million for the defense sector), across the US, the UK, and continental Europe (France, Germany, Italy, the Netherlands, the Nordics, and Spain). Surveyed organizations operate across 13 key industries. Executives surveyed are at director level and work across diverse business-, technology-, and manufacturing-related functions. In addition to the survey, The Capgemini Research Institute also interviewed supply-chain and manufacturing executives and experts at large global organizations.

The findings are validated through extensive secondary research, with information incorporated up to 26th March 2026; subsequent changes may not be reflected.

Definitions of key terms used in the report:

  • Friendshoring: Relocating part of the manufacturing/production/supplier base/service providers to countries that are geopolitical or trade allies of the organization’s home country
  • Nearshoring: Relocating part of the manufacturing/production/supplier base/service providers to a nearby or neighboring country.
  • Reshoring: Bringing part of the manufacturing/production/supplier base/service providers back to the home country.

About Capgemini

Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of over 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion.

Make it real | www.capgemini.com

About the Capgemini Research Institute

The Capgemini Research Institute is Capgemini’s in-house think-tank on all things digital. The Institute publishes research on the impact of digital technologies on large traditional businesses. The team draws on the worldwide network of Capgemini experts and works closely with academic and technology partners. The Institute has dedicated research centers in India, Singapore, the United Kingdom and the United States. It was ranked #1 in the world for the quality of its research by independent analysts for six consecutive times – an industry first.

Visit us at https://www.capgemini.com/researchinstitute/

[1] Reindustrialization as defined in the report: reconfiguration of global supply chains and manufacturing capacity, often with the aim of bringing them closer to domestic markets.

[2] The global survey was conducted between January and February 2026. The findings are validated through extensive secondary research, with information incorporated up to 26th March 2026; subsequent changes may not be reflected.





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Elasticsearch Filters – Table of Content

What is Elasticsearch Filters

The bucket is the collection of documents which matches with associated filters. Every bucket is associated with a filter. In elasticsearch filter aggregation defines multi buckets. Filters can also be provided as an array of filters. When it receives requests which form in the form of buckets. They are filtered and those filtered buckets returned in the same order as in request. Its field is also provided as a filter array. Parameters are added in response with which the documents do not match the given filters. Those documents returns to the other bucket or in the same bucket named 

Even other parameters are also used to set key for those documents to give value other than default. When the process of collecting data starts. Documents are separated and formed into buckets. Each bucket flows through filters. While the process is going on the documents which are away from parameters of the given filter are identified. Those identified files are separated and transferred into other buckets or in the same as default. To avoid them from default, new parameters are formed to create keys for them then they are formed into the new bucket. The filters which we used frequently are caught by elasticsearch automatically.

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Why Elasticsearch Filters

It stores the documents in the form of JSON each of them relate to one another. This index makes the documents searchable in real time and also helps the users during searching. It is good at full text search. It is also the platform for real time search.

It is known for its time sensitive use, it works fast with rapid results. By using it users can store, search and analyse the data in huge volume and in real time. With this we get rapid results because instead of searching text directly it searches index. It processes and gives back the data as a response in the form of JSON. Its power lies in the tasks distributed, searched and indexed across the cluster. The Cluster part which helps to store data is known as node. It allows users to make copies of the index that process is called replica.

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How to use Elasticsearch Filters

Generally we need various assistants and applications for searching, storing, filtering, classifying, etc. But, do you ever think that there is a single application which does all those things for us with high speed? Yes, they are named as elasticsearch filters. To use it first we have to submit our text to elasticsearch then it receives our text. Then the text was stored into buckets. Buckets are the collection of documents. When the process is going on these buckets goes through filters which are given for filtering them.

While that process the documents which do not meet the parameters of that filter were identified. Those identified documents are separated from the bucket. Those documents are transferred to other buckets or in the same bucket as default. New parameters are created for those other documents to avoid them from being defaults. Then when we search for the particular topic then our text will be found within seconds. Those text is saved as index instead of saved as text. Because the index helps us a lot in exact results. And also in a short period of time. It filters and searches the exact result for us. Which saves us a lot of time.

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Benefits of Elasticsearch Filters:
  • Used for application search, which rely heavily on search for access and reporting of time.
  • Used for website search, which stores heavy text. Found useful for accurate searches. Steadily gaining place in the search domain sphere.
  • Used for Enterprise search, which allows search that includes documents search. Blog search, people search, etc. It replaced many search solutions of popular websites. We can gain great success in company intranet.
  • Logging and log analytics, which also provides operational insights to drive actions. Used for ingesting and analyzing data in real time.
  • Used for infrastructure metrics and container monitoring, many companies used it for various metrics to analyze. Which also includes gathering data, parameters which vary for different cases.
  • Used for security analytics, which access logs. Also concerns system security. In real time.
  • Used for business analytics, works like a good tool for business analytics. It includes learning the curve for implementing this product. Which is felt as a good feature by many organizations. It also allows non technical users, for creating visualization and performs analytical functions.
  • It has rebutted distributed architecture which helped a lot in solving queries. And data processing which is easy to maintain.

Drawbacks of Elasticsearch Filters:
  • It has the ability of searching when there is only the text presented only in data.
  • The syntaxes for queries made simpler and it has auto sharding.
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  • When we came to pricing it felt good at free trial. But there is a significant jump suddenly into other levels of paid services.
  • Difficult architecture to optimize. And also easier to understand its bottlenecks.
  • The encryption which we need is at rest. It has a penalty for performance when using the linked documents.
  • Sometimes to deal with it you need database knowledge.

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Conclusion

Finally, companies found a great application for their maintenance. Which helps the organizations a lot in many necessary works. They are like searching, storing, filtering, and organizing into the index. The index is the best feature maintained by it. Because generally search engines save the text as the data presents. But instead it saves the data in the index. Which helps a lot while searching it gave accurate results. With in low time which also saves a lot of time. The requests made by customers and the result it gave as feedback is in the form of JSON. However, its special features gain its position in the market and even holds it in future as the best and useful application for the development of organizations.

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