Plant optimization - Sustainability and resource conservation through optimized processes
Last updated: 17 December 2025
Fine-tuning control systems or redesigning operating procedures can increase throughput. Both technical components and organizational processes are adjusted and continuously monitored so that the system runs permanently at optimal performance.
Through targeted adjustments, e.g., to process parameters or preventive maintenance plans, the efficiency of a systemcan be significantly increased. Plant optimization is not a one-time step but a recurring improvement process: changes are measured and, depending on the results, gradually further optimized.
Objectives of Plant Optimization
The optimization focuses on clear efficiency goals. Improvement measures aim to increase throughput, reduce scrap rates, and minimize downtime. At the same time, it is about reducing energy and material expenditure without impairing product quality. Other goals are increasing system availability and increasing production flexibility.
The improvements achieved not only ensure cost and resource savings but also a higher quality standard and long-term competitive advantage. These goals are usually reflected in key figures such as OEE (Overall Equipment Effectiveness) or energy indicators. In this way, successes become directly measurable, and decreasing failure rates or lower energy consumption per unit produced make progress directly visible.
Areas of Application of Optimized Systems
Optimization processes are applied across industries. Typical areas of application are:
Pharmaceutical and medical technology: Aseptic production lines (e.g. Blow-Fill-Seal (BFS) systems) for the aseptic production of medicines such as inhalation or infusion solutions.
Chemical industry: Highly efficient filling and mixing systems for temperature- or reaction-sensitive chemicals.
Cosmetic industry: Systems for the hygienic production and packaging of sensitive cosmetic products without preservatives.
Food and beverage industry: Lines for commercially sterile foods (e.g., UHT products), where thermal treatment and aseptic filling are combined.
General manufacturing: Industries such as automotive, machinery, or electronics use optimized production lines and robot cells to increase efficiency, quality, and flexibility.
Research and development: Optimization strategies are also used in pilot plants and test lines to perfect new production processes at an early stage and simulate scaling effects.
At the core of all areas is the aim to minimize waste and maximize added value. Especially in regulated industries such as pharmaceutical production, optimization is essential to meet high-quality standards while maintaining efficient operation. But even in less regulated areas, even the smallest efficiency gains can have a noticeable economic impact.
The Most Important Aspects at a Glance
A holistic view of all production phases should have top priority here.
Process analysis: Recording and evaluation of all relevant processes to identify weaknesses and bottlenecks. Data-based analyses form the basis for targeted improvements.
Overall Equipment Effectiveness (OEE): Key figure from availability, performance, and quality, used to quantify efficiency losses. Regular OEE analyses show whether downtimes or performance losses limit productivity.
Maintenance and servicing: Preventive concepts such as Condition Monitoring or Predictive Maintenance prevent malfunctions before they occur. Sensor-based early detection reduces unplanned downtimes and thus permanently increases system availability.
Automation and digitization: Networking of machines via the Industrial Internet of Things (IIoT) and the use of modern control and management systems (e.g., SCADA, PLC) enable precise real-time control and process monitoring. Production data from sensors and controls are used for adaptive process adjustments and early fault diagnosis.
Layout and material flow: Efficient system planning with optimal placement of machines and clearly defined material paths minimizes idle time and transport times. Simulation tools identify bottlenecks already in the planning phase so that they can be constructively eliminated.
Energy and resource management: Optimal parameter settings can massively reduce consumption. Demand-based control and the elimination of idle phases can significantly reduce energy requirements.
Employee training: Targeted training of the operating staff in efficient system operation increases performance and reduces operating errors. Only well-trained personnel can fully exploit all optimization potential.
Quality management: Close coordination with regulatory requirements (e.g., Good Manufacturing Practice, GMP) and internal quality standards ensures that measures to increase efficiency and reduce costs do not impair product quality. Hygiene concepts and validations are also examined for optimization potential. Optimizations take place within validated processes and are evaluated via Change Control; if necessary, revalidation is required.
These aspects usually work together: higher automation increases availability, while an improved layout optimizes material flow. Optimization projects therefore often begin with a detailed actual analysis and end in iterative improvement cycles based on the determined key figures (e.g., OEE). In this way, all levers can be gradually adjusted to an optimum.
An Example of Optimized Production Processes in the Pharmaceutical Industry and Their Enormous Savings Potential
In pharmaceutical production, plant optimization plays a central role. Modern aseptic BFS filling systems (Blow-Fill-Seal, bottelpack) achieve throughputs of up to 33,000 containers per hour, depending on the machine type. Through systematic data analysis – for example, via OEE analyses – typical efficiency losses such as changeover times, downtimes, or suboptimal process parameters can be reliably identified and specifically reduced.
In practice, structured optimization programs in industrial and pharmaceutical processes often achieve efficiency increases of around 10–30% (Source: Fraunhofer IWU – “Energy Efficiency in Production”). Higher values are occasionally reported only for clearly defined sub-steps but are not representative of complete production lines. Likewise, depending on the initial state, manufacturers can achieve significant reductions in scrap, although large improvements beyond individual cases cannot be generalized.
Other industries also show that optimizing process speed, setup times, or material flows enables measurable economic advantages – always in compliance with applicable quality and safety requirements.
The Most Important Methods and Strategies at a Glance
For practical plant optimization, proven methods are combined. Important approaches are:
Lean Management: Eliminates waste and unnecessary processes. The goal is lean production that increases efficiency and quality. Typical methods are 5S, Kanban, and continuous improvements (Kaizen).
Six Sigma (DMAIC): Reduces process variations through data-based analysis. Following the DMAIC cycle (Define, Measure, Analyze, Improve, Control), sources of error are identified and systematically eliminated.
Simulation and modeling: Digital twins and simulation tools enable testing of new production scenarios and parameter changes in advance without interfering with real production.
Automation and Industrial Internet of Things (IIoT): Networked controls, robotics, and the Industrial Internet of Things increase speed and precision. Sensors provide real-time production data, enabling early maintenance detection and adaptive process control.
Process data analysis and Artificial Intelligence (AI): Big data analyses and machine learning identify patterns in production data. This makes hidden bottlenecks visible and optimization potential recognized earlier. These technologies are also gaining importance in BFS technology but are still in the development stage in many areas.
Continuous Improvement Process (CIP) and PDCA Cycle (P=Plan, D=Do, C=Check, A=Act): Cyclical processes of planning, implementing, checking, and adapting ensure that the system is continuously further optimized. The optimization process ensures that all improvements are not random but structured and measurable.
These methods are often combined and adapted to the respective application. In practice, the process usually starts with measuring existing processes, then implements Lean or Six Sigma measures, and finally validates the effect with simulations and key figures. Smart sensors and data analysis (Condition Monitoring) complement the process: for example, through digital twins, the effect of changes can be tested in advance before they are implemented in reality.