When Production Fails: A Logistics Problem in Disguise

by Jeffrey Olsen Neal based on

Fabian Kleinke, Jan Grünfelder, Jeffrey Olsen Neal (2026) A Factorio Case Study in Industrial Efficiency, Tech-Report, Hochschule Kaiserslautern, Campus Pirmasens, Germany

This case study is based (read the description here) on a simulated production system built in the game Factorio, developed by Prof. Dr. Martin Wölker. The starting point was an existing factory with an output of 7.7 Utility Science Packs per minute, which was to be optimized toward a target of 32 Utility Science Packs per minute.

The challenge was not only to increase production capacity, but also to consider economic factors such as cost, resource consumption, and environmental impact. Every structural decision within the factory directly influenced the overall performance and evaluation of the system.

At first, the problem seemed straightforward. Increasing output would require increasing capacity, meaning additional machines and higher throughput. This assumption proved to be correct in the initial phase, as reaching the target production rate was only possible by expanding the number of machines.

However, as the system evolved, it became clear that increasing capacity alone was not sufficient to ensure a stable and efficient production process.

From Facility Planning to Operating a Production System

A particularly interesting aspect of this case study was its connection to a project from the previous year. In that earlier project, the focus was on planning the construction of a production facility, including layout design, transport routes, and the integration of technical infrastructure such as IT systems, electricity, and piping.

In contrast, the Factorio case study focused on the operation and optimization of an existing production system. This shift made logistical challenges much more tangible, as problems were no longer theoretical but directly observable within the system.

The First Key Observation: Capacity Alone Is Not Enough

By increasing the number of machines, the factory was technically able to reach the target output of 32 Utility Science Packs per minute. In this sense, the system was capable of delivering the required production performance.

However, the behavior of the system during operation revealed significant inefficiencies.

Materials accumulated on Transport Belts, while downstream Assembling Machines were occasionally under-supplied. At the same time, large quantities of intermediate products were stored in Chests without being processed further.

Picture 1: Congested Transport Belts with accumulated intermediate products

This situation led to multiple issues. Production became unstable, startup times increased significantly, and a substantial amount of resources was tied up in intermediate buffers instead of contributing to value creation.

In a real-world production system, such behavior would be unacceptable, as materials cannot remain idle indefinitely within transport systems. The factory was therefore technically capable of reaching the target output, but it was neither economically efficient nor realistic in its operation.

Controlling Material Flows – From Push to Pull

The decisive improvement was achieved through the implementation of targeted control systems (Steuerungen).

Initially, the factory operated largely according to a push principle, where materials were produced continuously regardless of actual demand. Over time, this approach was replaced by a pull system, in which production is triggered based on actual requirements.

Picture 2: Control system regulating
material flow based on demand

Material flows were actively controlled.

The implemented control systems monitored inventory levels within selected Chests and regulated material supply accordingly. When stock levels dropped below defined thresholds, Inserters and Splitters were activated to request materials from upstream production stages. When buffer levels exceeded defined limits, production was temporarily reduced or stopped.

This transformation led to a demand-driven production system. Machines only produced when their outputs were required, which significantly reduced congestion on Transport Belts, minimized buffer levels, and improved the overall distribution of materials.

As a result, the factory evolved into a stable and coordinated production system with significantly improved efficiency

When Optimization Suddenly Costs Millions

In addition to technical optimization, economic evaluation played a crucial role.

Every change introduced costs. Additional machines increased investment requirements, modules raised both acquisition and energy costs, and larger layouts resulted in higher infrastructure and area-related expenses.

One of the most significant decisions was the complete demolition and reconstruction of the factory. In our case, the existing system was dismantled and rebuilt using Blueprints and Construction Robots. Within the evaluation system provided by Prof. Wölker, this resulted in costs in the range of several million Euros, including reconstruction, resource usage, and environmental impact.

This created a clear trade-off between technical optimization and economic efficiency.

Picture 3: Cost Analyses done using the Cost and Profitability Calculator from Prof. Wölker
Link to theCalculator

Picture 4 Old Factory Layout (left) and New Factory Layout (right) after Teardown and Rebuild

To evaluate the final system, the factory was simulated over an entire Factorio year. This simulation included both the operational phase and the seven in-game days required for demolition and reconstruction.

The system was evaluated using a scoring model that considered production output, resource efficiency, environmental impact, and infrastructure usage. This long-term perspective ensured that the solution was not only functional, but also sustainable and efficient over time.

Picture 5: Production performance of Utility Science Packs per minute over time (60min Interval)

Key Insight of the Case Study

One of the most important insights from this project is that factory performance can be interpreted in different ways.

The initial factory was technically capable of achieving the target production rate of 32 Utility Science Packs per minute once additional machines were introduced.

However, it was not economically efficient and did not reflect realistic production conditions.

Only through the implementation of control systems and the transition to a pull-based approach did the factory become truly efficient from a logistics perspective. Materials were distributed according to demand, buffers were reduced, and system stability improved significantly.

This highlights a fundamental principle of logistics:

A system is only truly efficient when it is not just functional, but also economically and operationally realistic.

Factorio as a Logistics Laboratory

Factorio proved to be an effective environment for exploring logistics concepts in a practical context.

Theoretical ideas such as buffer management, material flow control, and demand-driven production became directly observable within the system. Even small adjustments had visible effects across the entire production chain.

In this sense, Factorio can be understood as a logistics laboratory, where complex systems can be analyzed, tested, and continuously improved.

Teamwork and Collaboration

Beyond the technical and economic aspects of the optimization, the way the team approached the problem also played a significant role.

Since all team members started with a similar level of experience in Factorio, there was no strong initial knowledge imbalance. This created a working environment in which tasks were not strictly assigned from the beginning, but instead evolved over time based on individual strengths and interests.

As the project progressed, team members naturally specialized in different areas. Some focused on the technical implementation of control systems (Steuerungen), while others concentrated on analyzing production flows, evaluating costs, or optimizing the factory layout. This informal specialization allowed the team to work efficiently without requiring rigid role definitions.

At the same time, key decisions were always discussed collectively. This was particularly important in situations with high impact, such as the decision to completely dismantle and rebuild the factory. Although this approach resulted in significant costs within the evaluation system, it also provided the opportunity to fundamentally improve the structure and efficiency of the production system.

Interestingly, the project was characterized by a lack of major conflicts. This was largely due to the clearly defined objective and the fact that most optimization decisions were logically derived from the system’s behavior. Discussions were therefore not driven by opposing views, but by the shared goal of improving the system.

Another important aspect was the iterative nature of the work. Many solutions emerged through testing, observation, and continuous adjustment within the simulation. This required ongoing communication, particularly when implementing control systems that affected multiple parts of the factory.

Overall, the teamwork can be described as collaborative, adaptive, and solution oriented. This reflects a key principle also observed within the factory itself: just as production systems require coordination between components, effective teamwork depends on the alignment of individual contributions within a shared objective.

Conclusion

This case study demonstrated that increasing production capacity alone is not sufficient to create an efficient system.

Only through the targeted control of material flows and the integration of economic considerations could the factory be transformed into a stable and efficient production system.

The key takeaway is clear:

Efficiency in production is not determined by machines alone, but by the logistics behind them.

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Version: 1.4 April 2025, Kontakt: E-Mail Martin Wölker
Pirmasens, Germany, 2018-, ausgelesen am: , Licence CC BY