Fertigungsindustrie

Hersteller müssen sich weiterentwickeln, um wettbewerbsfähig zu bleiben. Der traditionelle Ansatz, die Kosteneinsparung zu priorisieren, wird dem Fokus auf Wertschöpfung und Resilienz weichen, was eine Vielzahl von Vorteilen bieten kann, wie zum Beispiel verbesserte Produktionserträge und -qualität sowie reduzierte Verschwendung und Ausfallzeiten. Die Lieferkette kann einen großen Beitrag zu diesem Bestreben leisten. Aber Hersteller müssen zuerst herausfinden, wo es Möglichkeiten gibt, Wert zu schöpfen, und in diesen Bereichen nutzen, was wir "erweiterte Intelligenz" nennen—einen Ansatz zur digitalen Transformation, der den Menschen in den Mittelpunkt stellt.

Die zuvor besprochene Entwicklung treibt Unternehmen dazu, ihr Fertigungsnetzwerk umzugestalten. Dabei kann es helfen, eine Simulation-first-Mentalität zu verfolgen, um das Geschäft zukunftssicher zu machen. Digitale Zwillinge sind wesentliche Werkzeuge in diesem Wandel.

Durch die Erstellung eines virtuellen Abbilds eines physischen Objekts, eines Prozesses und/oder einer ganzen Fabrik simulieren digitale Zwillinge die Auswirkungen von Veränderungen, bevor sie umgesetzt werden, sodass Hersteller sicher optimieren und Stresstests durchführen können. Sie tragen auch dazu bei, große Kapitalkostenrisiken zu reduzieren, indem sie die Machbarkeit von Prozessen/Produktionen bestätigen, bevor der Bau oder die Einführung beginnt.

Wie bei vielen anderen Transformationsinitiativen erfordern simulationsgeführte Ingenieur- und Betriebsprozesse eine solide Datenbasis. Im Jahr 2026 werden Hersteller neue Wege finden müssen, um ihre Daten zu verknüpfen und umfassendes Datenmanagement- und Governance-Wissen in ihre Strategien zu integrieren. Diese Grundlage ermöglicht es Unternehmen, sichere, genaue und bedeutungsvolle Abbildungen ihrer physischen Vermögenswerte zu erstellen und Innovationen in einer geschützten Umgebung zu testen.

Wenn Hersteller ins Jahr 2026 gehen, wird sich simulationsgesteuerte Technik und Betrieb von zukunftsorientierten Konzepten zu essenziellen wettbewerbsfähigen Fähigkeiten entwickeln. Digitale Zwillinge dienen nun als Bindeglied, das die strategische Vision mit der praktischen Umsetzung verbindet und Organisationen die Fähigkeit gibt, Störungen vorherzusehen, Produkt- und Prozessinnovationen zu beschleunigen und mit Zuversicht auf Marktschwankungen zu reagieren. Organisationen, die in starke Datenfundamente investieren und digitale Zwillinge als zentrale Unternehmensfähigkeit übernehmen, werden am besten positioniert sein, um eine führende Rolle einzunehmen, indem sie die Art und Weise, wie Entscheidungen getroffen werden, wie Wert geschaffen wird und wie Hersteller in einem sich schnell entwickelnden Markt konkurrieren, neu gestalten.

Why do so many AI pilots fail? Organizations often move faster on experimentation than on building the foundations needed for success. Teams jump into the technology before they’ve aligned on the business problem, which makes it hard to focus on the right opportunities. In many cases, the organization also lacks the digital and data capabilities required to move from a good idea to a solution that can scale. When business and technology teams aren’t working together in a truly integrated way, pilots can lose momentum and never reach the level of impact leaders expect. Strong governance helps, but the bigger opportunity lies in creating a consistent way to identify where AI can create value, testing those ideas effectively, and expanding on what works. 

Organizations that build these capabilities turn AI from a series of experiments into real, repeatable outcomes.

A critical hurdle facing manufacturing today is graduating from small-scale AI experimentation to deploying digital intelligence at industrial scale. The goal here is to evolve from passive, reactive monitoring to more proactive modes:

• AI-informed: Humans are entirely responsible for the decisions and actions but use AI to provide data and insights.

• AI-assisted: While humans are the ultimate decision makers, AI offers informed suggestions and can even handle some individual tasks within a process.

• AI-autonomous: Minimal human intervention is needed. The AI tool can largely operate independently and carry out tasks on its own.

The top three capabilities cited as helping manufacturing organizations get beyond pilots and productivity to unlock broader impact from AI:

In 2026, manufacturers must double down on their commitment to this journey. This includes establishing the necessary governance, strategy, rigor, and discipline to move beyond the POC phase; implementing data quality frameworks to increase the reliability of data used to train models; and identifying and prioritizing use cases based on concrete business outcomes. In doing so, manufacturers can build the capability maturity and momentum needed to scale AI impact across the enterprise.

According to a recent study, the manufacturing industry may need as many as 3.8 million additional employees by 2033. While this growth brings opportunity, it also poses certain challenges. 

As some of the industry’s most experienced workers retire, finding ways to pass on the decades of often undocumented, institutional knowledge from the shop floor will become an urgent need. But maybe even more concerning is the difficulty many manufacturers are encountering when trying to fill vacant roles. The HR and finance software company Workday reports that 75% of industry leaders cite attracting and retaining talent as their biggest business challenge. In 2026, manufacturers will need to both capture the know-how of departing team members while also finding ways to entice the younger, more tech-savvy generations to careers in the industry.

What are the main barriers preventing your organization from realizing value from AI, from a workforce perspective?

One way to address both issues is to invest in digitization. Institutional knowledge (SOPs, manuals, troubleshooting guides) can be captured from more tenured employees and delivered to less experienced workers through modern interfaces like AI copilots and wearables. This solves both the knowledge transfer deficit and the need to make manufacturing an attractive career path to workers who are more comfortable with modern technology’s user experience.

But technology alone isn’t enough. Many manufacturing roles are permanently shifting away from manual execution and toward process orchestration and exception management. This requires a cultural transformation, which is often much harder than technology transformations. Our partnerships with HR and operations leadership have shown time and again that defining a future-focused manufacturing workforce strategy can help develop the next generation of talent and empower them to perform even more effectively than their predecessors.

As supply chains transform and IT/OT systems converge, the digital attack surface expands dramatically, making cybersecurity a CEO-level priority. For manufacturers, risks now extend far beyond data theft, encompassing operational downtime, loss of product integrity, safety incidents, regulatory scrutiny, and even environmental and human impact.

A compliance-oriented approach is no longer enough. Adaptive cyber resilience requires intelligence-driven visibility across the converged IT/OT environment, continuous monitoring of cyber-physical threats, dynamic isolation capabilities, and rehearsal of recovery scenarios. AI and machine learning accelerate detection and prediction, but resilience ultimately relies on validated response playbooks, engineering collaboration, and the ability to sustain safe operations during an incident. Emerging technologies such as digital twins can augment this by modeling failure modes and practicing recovery before a real disruption occurs.

Manufacturers are increasingly adopting scenario-based resilience engineering, OT incident simulations, and unified IT/OT security operations to prepare for high-impact events. For example, leading organizations now run ransomware tabletop exercises focused on programmable logic controller (PLC) manipulation, unauthorized logic changes, or sensor spoofing to understand how quickly they can restore production and maintain safety. Others are deploying anomaly detection and adaptive network segmentation that can isolate compromised production cells without halting an entire facility.

How is your organization using AI and other emerging technologies to improve risk management and resilience?

In 2026, manufacturers need to make this crucial shift by utilizing tools to assess operational risk, deploy consistent security policies across the converged IT/OT stack, and establish real-time monitoring and visualization of both cyber and physical system behavior. With adaptive, proactive resilience strategies, organizations can build scenario-based BCP/disaster recovery plans, strengthen safety and reliability, and transform security from a mandatory cost into a strategic competitive advantage.