Intralogistics and supply chain under pressure: Why automation is now becoming an existential necessity

Intralogistics at a turning point: Why standstill is more expensive than any investment

Europe's industry is facing a perfect storm. What was long discussed as a purely efficiency-related issue at the operational level of logistics departments has evolved into a strategic question of survival for the boardroom. Intralogistics and supply chain management are not just undergoing change, but are in the midst of a fundamental transformation crisis. Market parameters have shifted: the primary goal is no longer simply to make processes faster or cheaper – it's about maintaining them at all.

The reality is paradoxical: While global markets for intelligent warehouse solutions are exploding and forecasts predict a fourfold increase in market volume by 2034, a large proportion of German companies remain dangerously passive. Current data paints an alarming picture: 63 percent of companies have barely or not at all automated their intralogistics. This is happening despite knowing better, as 94 percent of those who have invested report positive results. The hesitancy is often based on outdated assumptions about costs and complexity, while the opportunity costs of inaction are increasing daily.

Three massive forces are driving this pressure to act: a historic shortage of skilled workers, which is far more dramatic in the logistics sector than the global average; a new wave of technological maturity, which, through AI and autonomous robotics, is often pushing the return on investment (ROI) to less than two years; and a regulatory straitjacket of ESG requirements and the AI ​​Act, which turns manual, opaque supply chains into a liability risk.

This article analyzes the uncomfortable truth behind the numbers: why automation is now the only answer to demographic change, how modern systems don't destroy jobs but enhance them, and why companies only have a short window of a few years left to avoid permanently losing their technological edge—and thus their competitiveness. Those who fail to act now risk not only their profit margins but their very existence.

Those who fail to act will lose the competition – an uncomfortable reality for Europe's industry

Europe's intralogistics and supply chain management are undergoing a structural transformation crisis. What was long considered an efficiency issue has now become a strategic matter of survival for companies. The available data paints a clear picture: German and European companies are modernizing their internal logistics processes dramatically too slowly, while market dynamics and regulatory pressure are accelerating exponentially. At the same time, there is a massive shortage of personnel to perform traditional warehouse work. This combination creates a critical imperative: automate or perish.

The key findings can be summarized succinctly. In Germany, a representative survey of over 2,500 companies shows that 63 percent have not automated their intralogistics at all or only to a limited extent. Only 4 percent have autonomous systems. This stands in direct contrast to economic realities: 94 percent of companies that have already invested in automation report positive results. The return on investment is less than 1.5 years for small-scale solutions and two to three years for larger systems. Nevertheless, most companies hesitate. The paradox is classic – the fear of change is greater than the existential threat of stagnation.

Global markets are responding to this transformation with explosive growth. The market for intelligent warehouse solutions is growing at an average annual rate of 14.22 percent and is projected to quadruple between 2024 and 2034 – from US$26.1 billion to US$98.64 billion. The warehouse robotics market is experiencing similarly dynamic growth. This dynamic is driven by three converging forces: the collapse of the traditional labor market, the digitalization of supply chains, and new regulatory requirements, particularly the European AI Act.

The German dilemma: Automation meets skills shortage

The labor market has fundamentally shifted. In 2014, 40 percent of German companies reported difficulties filling vacancies. By 2025, this figure had risen to 86 percent – ​​a doubling in just eleven years. Germany is thus significantly above the global average of 74 percent and has achieved a leading position internationally in terms of skills shortages. The situation is particularly critical in the logistics sector: 76 percent of logistics companies report an acute shortage of skilled workers, while at the same time job postings have increased by 16 percent.

This is not a cyclical problem that will resolve itself. The shortage is rooted in structural and demographic factors. The baby boomer generation is leaving the workforce faster than young people are entering it, and immigration cannot fill the gap. For intralogistics, this means that companies that do not automate their processes will simply not be able to afford employees in five years. The first symptom is already visible – 25 percent of logistics workers in Germany have left their jobs due to a lack of career prospects.

Automation solutions address precisely this problem. Autonomous mobile robots (AMRs), automated guided vehicles (AGVs), collaborative robots, and modern warehouse management systems make it possible to reduce dependence on human labor while simultaneously increasing productivity. A practical example: In an optimization simulation conducted by an automotive manufacturer, the implementation of an intelligent task assignment algorithm for AMRs reduced the required fleet size by 30 percent while maintaining the same delivery reliability. Specifically, this means that instead of 58 robots for a given scenario, only 42 robots were needed to achieve the same performance.

But it's not just about robots. The second element is data centralization. Modern, cloud-based warehouse management systems (WMS) enable real-time transparency regarding inventory, picking processes, and throughput. Cloud-based systems can be implemented in days, not months, and allow even small and medium-sized enterprises to unlock automation potential. One retailer that implemented AI-powered inventory optimization reduced excess stock by 25 percent and stockouts by 30 percent—while simultaneously lowering warehousing costs.

Level of automation and ROI: An economic necessity, not an option

Technical feasibility isn't the issue – it's economic viability that determines adoption. The available data clearly shows that automation investments become profitable within a reasonable timeframe. For smaller solutions, such as a modern WMS combined with pick-by-light systems, the break-even point is around 1.25 years (€50,000 investment, €40,000 annual savings through personnel costs, error reduction, and space optimization). For medium-sized AMR integrations with 10 to 15 robots, the break-even point is around 2.9 years (€350,000 investment, €120,000 annual savings). Even for larger high-bay automation solutions with artificial intelligence, the break-even point is around 3.2 years.

The key to these profitable business models is the reduction in personnel costs, coupled with a decrease in errors. In traditional warehouses, labor costs account for up to 80 percent of total costs. Automated systems reduce human error to below 1 percent (compared to typically 3-4 percent with manual processing), lower the costs associated with errors, and free up valuable employee capacity for higher-value tasks. Furthermore, intelligent automation enables space savings of up to 80 percent – ​​a significant cost driver in expensive metropolitan areas.

The second economic argument is capacity flexibility. Automated systems can increase operational capacity by up to 53 percent during peak periods and boost inventory turnover by 25 percent without incurring proportional personnel costs or additional space. This is critical for e-commerce companies struggling with extreme demand fluctuations—74 percent of consumers are willing to pay for same-day delivery. Without automation, this level of service speed is impossible.

Global markets are expanding, European companies are losing ground

The market dynamics are clear. The global smart warehouse market is projected to grow at a CAGR of 14.22 percent between 2024 and 2034 – from $26.1 billion to $98.64 billion. The specialized warehouse robotics market shows similar dynamics, with growth rates of 15.6 to 16 percent. For humanoid robots, which are expected to be deployed more widely in warehouses starting around 2025, annual growth rates of 34 to 45 percent are anticipated, potentially driving the market from $1.68 billion (2023) to as much as $74 billion (2032).

Who benefits from this dynamic? Primarily companies that invested early in scalable solutions. Amazon, Tesla, and other tech giants have long since deployed capital-intensive automation solutions. Some German and European hidden champions have also done so, but the broader industry lags significantly behind. This creates a competitive problem: companies that don't automate today will be competing in five years against rivals who have achieved 30-40 percent cost advantages through automated logistics.

The technological architecture: Three pillars, one ecosystem

The most successful implementations are not based on an isolated solution, but on three complementary pillars. The first pillar is physical automation and robotics. This includes autonomous mobile robots, driverless transport systems, collaborative robots (cobots), and state-of-the-art storage and retrieval machines. The advantage of modern systems, especially AMRs, is that they do not require special infrastructure – no magnetic tracks, no predefined routes. They navigate autonomously using sensors and artificial intelligence. This makes them flexible for brownfield scenarios, i.e., the modernization of existing warehouses.

The second pillar is the Internet of Things (IoT) and data connectivity. Sensors on goods, containers, machines, and robots continuously generate data on inventory, movements, conditions, and utilization. These data streams enable the system to react in real time—optimizing picking lists, anticipating bottlenecks, and predicting maintenance needs.

The third pillar is artificial intelligence and software control. This is where warehouse management systems (WMS), demand planning algorithms, predictive analytics, and AI-powered optimization engines come into play. They analyze IoT data, make automated decisions (Which goods go where? Which robot performs which task?), learn from experience, and continuously adapt processes. Modern WMS also enables integration with enterprise resource planning (ERP) systems and provides transparency across all process steps.

Companies that invested early in clean data foundations – standardized interfaces, API definitions, cloud-based infrastructure – scale these systems faster and more reliably. They can quickly add new functionalities without destabilizing existing systems.

LTW offers its customers not individual components, but integrated complete solutions. Consulting, planning, mechanical and electrotechnical components, control and automation technology, as well as software and service – everything is networked and precisely coordinated.

In-house production of key components is particularly advantageous. This allows for optimal control of quality, supply chains, and interfaces.

LTW stands for reliability, transparency, and collaborative partnership. Loyalty and honesty are firmly anchored in the company's philosophy – a handshake still means something here.

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The risks: geopolitics, cybersecurity, and regulatory compliance

While automation increases internal efficiency, it simultaneously intensifies external risks to supply chains. 2025 is the year in which global supply chains must shift from efficiency-optimized to risk-optimized.

Geopolitical tensions, trade conflicts, and the sanctions policies of the US and Europe have turned supply chains into strategic vulnerabilities. Cybercriminals and state actors are exploiting this – sabotaging ports, payment systems, and digital warehousing infrastructure. A disruption at a critical node can paralyze global production for days. The answer lies in diversification and redundancy. Approximately half of all companies plan to significantly strengthen their multisourcing strategies. Nearshoring – relocating production closer to consumer markets – is being considered by companies across all sectors to reduce transportation and customs risks.

In parallel, regulatory pressure is intensifying. The European AI Act will be fully enforced from August 2025. This means that AI systems in supply chains, particularly those used for supplier or risk assessment, will be subject to new compliance requirements. Companies will have to maintain technical documentation, disclose training data, monitor risks, and implement human oversight. The fines are substantial – up to €35 million or 7 percent of global annual turnover for serious violations.

In addition, there are national obligations such as the Supply Chain Act in Germany and the Corporate Sustainability Due Diligence Directive in the EU. These require companies to create transparency across their entire supply chain – from Tier 1 to Tier 3 and, in some cases, beyond. Only 16 percent of companies see supply chain management as a strategic priority for ESG compliance, and only 12 percent have aligned their key functions with ESG goals. This creates a significant compliance risk.

Companies must therefore not only automate, but also ensure that their automated systems comply with regulations. This requires clear governance – who decides when the AI ​​algorithm rates a supplier as riskier? How is human oversight implemented? How is data protected?

Humanity remains central: transformation instead of elimination

A common misconception is that automation is displacing humans from logistics. Empirical evidence shows the opposite. Automation is transforming job roles. Monotonous order picking tasks are being taken over by robots, but the demands on employees are increasing: they have to monitor systems, interpret data, troubleshoot errors, calibrate robots, and ensure quality.

The greatest risk to this transformation is not technical, but cultural and organizational. Particularly in Germany, where 76 percent of logistics companies are struggling with a shortage of skilled workers and only 23 percent of logistics employees have received AI training, there is a massive skills gap. 25 percent of logistics workers have already left their jobs because they saw no career development opportunities. This is a vicious cycle: companies invest little in training because they don't invest in automation; they don't invest in automation because they lack trust in their workforce.

The companies that will be successful in the long term are those that understand automation as an enabler for employee development. They invest in training programs, creating transparent career paths from manual worker to robot operator to data analytics specialist. This not only increases employee retention but also the quality of operations. People with genuine process understanding and ownership find problems that algorithms miss.

Retrofit and incremental modernization: The realistic way

A common reason for hesitation is the fear of large investments in greenfield automation. This is understandable, but unnecessary. A realistic approach for most European companies is a retrofit strategy – the gradual modernization of existing systems while they remain operational.

This has become technically feasible. Companies like the KION Group demonstrate that existing warehouses can be highly automated by combining artificial intelligence, virtual simulation, and adaptive robotics, without interrupting operations. The process is iterative: First, the system is analyzed using virtual simulation to identify bottlenecks and optimization potential. Then, robots are deployed at critical points. While these are operational, additional systems are added. This reduces implementation risk and allows companies to see the benefits before fully committing.

A second aspect is prioritization. Not all processes need to be automated simultaneously. Companies should identify weaknesses – Where are the biggest costs incurred? Where is there a staff shortage? Where are error rates high? – and then invest specifically in these areas. A company that only processes 1,000 picks per day doesn't need the same automation infrastructure as one with 100,000 picks daily. Cloud-based, modular solutions make it possible to start with small investments and scale from there.

ESG as a competitive advantage, not a burden

Sustainability is often perceived as a regulatory burden. However, intelligent logistics automation is a massive ESG enabler. Fewer manual transport tasks mean less energy consumption and fewer emissions. Digitized supply chains make it possible to create transparency about suppliers and Tier 2/Tier 3 suppliers – critical for compliance with supply chain law and corporate sustainability due diligence.

Companies that go beyond ESG compliance requirements today secure a competitive advantage. They build trusting relationships with customers, investors, and employees. Automation, coupled with a genuine commitment to sustainability, is a strategic differentiation opportunity – especially in Europe, where consumers and institutional investors take ESG seriously.

The implementation framework: What successful companies do

Successful automation projects follow a clear pattern. First, clear target metrics. Success isn't about having robots, but about achieving specific process goals – increasing throughput by X percent, reducing the error rate to Y percent, or cutting personnel costs by Z percent. Technology is the means, not the end.

Secondly, data foundations before automation. Many errors occur because companies introduce incomplete or corrupted data into automated systems. A data audit is essential first – Is the existing data accurate? Are the process steps documented? Are the interfaces between systems defined? Companies that invest 3-6 months in data cleansing and process documentation will later save ten times that amount in implementation problems.

Third, a start-small mentality. Don't buy the biggest system, but start with a pilot project. Test a small automation solution in one area, learn, iterate, then scale. This reduces the risk of the project and gives teams time to understand the new technology.

Fourth, employee engagement from day one. The secret to successful adoption isn't technical excellence, but rather that teams understand why automation is necessary and how they will benefit from it. Training, transparency, and genuine participation are vital.

Fifth, plan for governance and compliance. Not as an afterthought, but as an integral part of the design. Which data flows where? Who has access? Which AI systems are high-risk and require additional audits? This is complex, but necessary.

The window of opportunity

Europe has a window of about two to three years in which companies can still invest in automation relatively cheaply and without massive competitive setbacks. After 2027-2028, the pressure will become existential – competitors will have 30-40 percent cost advantages, talent will migrate to automation leaders, and new market entries will be automated.

Companies starting today have time to learn, make mistakes, and correct them. They can build their own expertise instead of blindly relying on external integrators. They can develop employees instead of replacing them. They can proactively address regulatory requirements instead of reactively.

Companies that wait will be forced to automate under pressure – expensive, risky, and without time for employee transformation. Some will fail.

An inconvenient truth

The uncomfortable truth is that automation in intralogistics is no longer optional. There is an objective shortage of skilled workers – not fear of job loss, but a physical impossibility. Markets are growing exponentially, and those who aren't part of it are marginalized. Regulation is becoming stricter, and those who aren't compliant will be disciplined. The ROI is positive – the investments pay for themselves in 1-3 years.

The real question is therefore no longer “Should we automate?” but “How quickly can we automate competently without losing our employees and without falling into regulatory traps?”

Successful European companies don't see automation as an attack on their workforce, but as a necessity for their survival – and as an opportunity to transform their employees into higher-value roles. They invest in technology and people simultaneously. They build digital foundations before scaling. They start small, learn quickly, and then scale.

This isn't the modernist promise of automation providers – the truth is more arduous, but more realistic. Companies that take this path will be successful in 2030. Those that wait will not.

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