Written by Armin Scheuermann
The factory floor seems deserted. No forklift beeps, no one checks the fill level of the big bag, no operator stands at the mixer. Raw materials are automatically identified, weighed, dosed, and conveyed. Sensors monitor moisture, particle size, and flow rate. Robots palletize finished containers. A control system reports deviations before rejects are produced. The lights could be turned off—not because nothing is happening, but because no one needs to stand directly at the line anymore.
That is the idea behind the Dark Factory: production that runs without a permanent human presence on the shop floor. Such approaches already exist in electronics, robotics, and consumer goods factories. The World Economic Forum calls its collection of examples of industrial pioneers in AI, robotics, digital twins, and advanced analytics the “Global Lighthouse Network.” The network now encompasses more than 220 locations in over 30 countries and across more than 30 industries—not a collection of pure dark factories, but a showcase of the technologies that enable low-labor production.
For process industries, however, the issue presents itself differently than in assembly manufacturing. The chemical, pharmaceutical, food, and mining sectors do not simply produce identical components at high cycle rates. Here, the focus is on powders, granules, liquids, active ingredients, natural raw materials, ore, dust, hygiene, explosion protection, GMP, and cleaning. The Dark Factory therefore does not emerge as a deserted mega-plant, but as a network of autonomous process islands: dosing, conveying, mixing, screening, grinding, drying, filling, packaging, palletizing, testing, cleaning.
On the west coast of Saudi Arabia, Aramco’s Yanbu Refinery demonstrates why the term “lighthouse” may be more apt for the process industry than “dark factory.” The refinery was included in the WEF Global Lighthouse Network because it utilizes Fourth Industrial Revolution technologies on a large scale—AI, advanced analytics, and robotics—to improve operations, economic efficiency, and environmental performance.
This is crucial for the chemical industry: The future does not lie primarily in darkening production halls. It lies in making processes so transparent, stable, and self-monitoring that human intervention becomes less frequent—and, above all, more cost-effective. “If we don’t make our plants smarter, others will soon be producing for us,” was the message, for example, at the user conference of the Association of Process Automation Engineers (NAMUR) in November. Continuous plants offer good prerequisites for this. They are instrumented, automated, and often run for long periods within defined process windows.
Dark Factory elements are created here through Advanced Process Control, soft sensors, digital twins, AI-based anomaly detection, and robotic inspection. Mobile robots or drones are already performing patrols, capturing thermal images, analyzing noises, or detecting leaks. The operator no longer necessarily stands next to the plant but monitors processes from a control room or remote operations center.
For equipment suppliers, this means that apparatus, skids, and components must become “data-capable.” In the future, mixers, dryers, screens, valves, pumps, and conveyor lines will not only perform mechanical functions but also provide condition data, quality indicators, and interfaces to higher-level optimization systems.
How do digitalisation, automation, and smart manufacturing translate into tangible added value for the processing industry? Answers to these questions can be found at the “Pavilion Smart Industry-in-Focus – Powering the Next Industrial Era” in Hall 7A. The joint booth brings together providers of smart solutions and offers companies a convenient way to showcase their products and services within a relevant thematic context.
For trade visitors, the Pavilion is a central hub for discovering new approaches to networked, automated, and data-driven production processes.
Further information: Pavilion Smart Industry-in-Focus
Interested in this topic? Then come to POWTECH TECHNOPHARM and experience this and many other topics live at the trade fair. Secure your ticket here using the following code: PTTP26Insights
In Södertälje, AstraZeneca operates another “Lighthouse”: it is one of the largest pharmaceutical production sites in the world. More than 50 digital solutions, including AI-based digital twins, machine learning, process simulation, and robotics, have significantly improved productivity and reduced development times there. AstraZeneca reports a 56% increase in productivity and a 67% reduction in development times for new products at Södertälje.
Here, too, this is not about a deserted factory. But the example shows where the industry is headed: toward closed, monitored, data-driven, and more highly automated process chains. In the pharmaceutical industry, the “dark factory” is less a matter of lighting than of validatability. Anything that runs autonomously must be documented, auditable, and regulatory-compliant.
Processes in which manual intervention is undesirable anyway are particularly well-suited: aseptic filling, containment, active ingredient handling, continuous manufacturing of solid dosage forms, automated packaging, laboratory automation, and electronic batch documentation. Regulatory developments support this approach: The international guideline on the continuous manufacture of medicinal products, ICH Q13, outlines the framework for the continuous manufacture of active pharmaceutical ingredients and medicinal products; through its Emerging Technology Program, the FDA promotes modern manufacturing technologies such as continuous production, model-based control strategies, and closed aseptic systems with robotics. This represents a core area for the POWTECH-TECHNOPHARM world: powders must be processed in closed systems, be traceable, precisely dosed, and monitored inline. The real challenge is not to replace people. It is to demonstrate quality during the process, rather than verifying it only after the fact.
The trend toward autonomous processes is also evident in the food industry. For example, Unilever operates a plant in Tianjin, China, which was also recognized by the WEF as a particularly advanced factory. The company calls this “lights-out production”—that is, 24/7 production with minimal operator intervention. According to Unilever, the automation of routine work has nearly doubled labor productivity. For the food industry, this is a “lighthouse”: The Dark Factory rarely begins with the first process step here. It starts where products are consistently handled, packaged, inspected, stored, and transported. Filling, labeling, packaging, palletizing, high-bay warehousing, quality control, and intralogistics are significantly easier to automate than dealing with fluctuating natural raw materials.
As a WEF Lighthouse, Danone Opole also demonstrates how digitalization, robotics, and AI can influence costs, efficiency, and energy and water consumption. For Opole, Danone cites, among other things, 19% lower production costs, 12% higher efficiency, 50% fewer greenhouse gas emissions, and 23% lower energy consumption since 2019. Yet milk processing is particularly challenging. Powders and bulk materials do not always behave the same way. Moisture, fat content, temperature, particle size distribution, or caking affect flowability and dosing behavior. Added to this are challenges related to switching from allergenic to non-allergenic products, hygiene, foreign object detection, and cleaning.
That’s why, in the food industry, the packaging line is the first to go dark—not necessarily the mixer. But that’s where the need arises for solutions like those showcased at POWTECH-TECHNOPHARM: hygienic design, automated dosing, inline-compatible sensors, gentle conveying, reliable dust removal, CIP-compatible systems, and digitally documented cleaning.
In mining, the beacon isn’t in a factory hall, but in a mine. The WEF describes autonomous trucks, drones, AI-supported maintenance, and data-driven optimization as key innovations in the industry. Automation is intended to increase safety and efficiency by removing people from hazardous areas. This is already evident at Rio Tinto. With AutoHaul, the company operates an autonomous heavy-haul rail network in Western Australia. Trains, trucks, and drilling rigs are increasingly being automated or monitored from remote control centers. Thus, autonomy in mining is initially shifting toward extraction and transport—that is, to areas where distance, danger, and 24/7 operation offer the greatest leverage.
For exhibitors and visitors at POWTECH-TECHNOPHARM, mining becomes interesting at the interface with processing. After all, what is extracted and transported autonomously must subsequently be just as reliably crushed, classified, conveyed, dosed, dried, or separated. Crushing, grinding, classifying, conveying, dosing, drying, and separating are classic process engineering tasks. As open-pit or underground mining becomes more autonomous, processing must not remain analog. Material flows must become measurable, controllable, and maintainable with foresight.
The examples from the WEF Lighthouses show: The Dark Factory in the process industry does not emerge from a single AI platform. It arises from robust, often unspectacular technical building blocks. The first is powder and bulk material handling: automatic bag emptying, big-bag stations, closed conveying, gravimetric dosing, low-dust transfers, containment, and digital material tracking.
The second is inline analytics. Moisture, particle size, density, flow rate, temperature, pressure, vibration, torque, or optical characteristics must be continuously monitored. Only when the process knows its own status can it become autonomous.
The third is automated cleaning. In the pharmaceutical and food industries, low-labor operation is often determined not during production, but during product changeovers. CIP, SIP, validated dry cleaning, allergen management, and documented cleaning sequences are becoming key technologies.
The fourth is intralogistics: AMR, AGV, palletizing, robotic picking, automated warehouses, and end-to-end material flows. Often, lights-out operations do not begin at the reactor, but between the production line, warehouse, and shipping.
The fifth is data integration. Sensors alone are not enough. Data must be contextualized, semantically described, and made usable for MES, control systems, digital twins, AI models, and maintenance strategies.
The Dark Factory is no longer a science-fiction concept for the chemical, pharmaceutical, food, and mining industries. But it looks different than it does in electronics manufacturing. It is not a chemical park that suddenly goes dark, nor is it a fully automated powder mill with no exceptions. It is a maturity model. Humans are not disappearing. They are shifting roles: away from routine operation, manual control, and hazardous rounds—toward monitoring, optimization, data interpretation, maintenance, and exception management.
Whether the next stage of industrial autonomy succeeds depends on sensor technology, dosing, conveying, cleaning, packaging, intralogistics, safety, and data models. The Dark Factory doesn’t begin when the lights go out; it begins when powders, processes, and data reliably communicate with one another.
