Efficiency vs. Robustness – How Should I Set Up the Production Plan?

Imagine this: Your production schedule looks perfect—but perfect doesn’t always mean optimal. Machines suddenly stop, employees fall sick, deadlines are at risk, and material shortages occur. Sound familiar? Many companies face an apparent dilemma: Should they maximize efficiency in their processes, or prioritize robustness to better handle disruptions?

We often encounter this question in discussions with our customers—and the answer typically lies in balancing both goals. Just like a scale that shouldn’t be weighted on one side, achieving a balanced approach is key. But how exactly can you accomplish this—especially in complex production processes and planning involving traditional backward scheduling and outsourcing?

Efficiency vs. Robustness: Two Sides of the Same Coin

Efficiency aims to optimally utilize resources such as time, machinery, personnel, materials, and costs. Robustness, on the other hand, according to the study report “Robustness in Production Systems” (Stockmann & Winkler, 2020), describes the capability of a production system to maintain stable performance at an acceptable level despite various disruptions and changes, ensuring timely, high-quality, and cost-effective processing of orders. These findings are based on a survey of 199 professionals in managerial or planning roles within production and the broader enterprise. Both goals are important, but their prioritization depends on the company’s specific context.

Key findings from the study:

Efficiency is often considered secondary: Only 4% of respondents view resource efficiency as their top priority.
Robustness is gaining importance: Most medium-sized companies seek increased robustness at least weekly to cope with disruptions such as equipment failures (cited by 19% of respondents) or material shortages (15%).

Balancing the Scale—Even Amid High Complexity

Imagine a scale: efficiency on one side, robustness on the other. Too much efficiency can make the system vulnerable—too much robustness leads to oversized buffers and high costs. The key is to fine-tune the levers—a challenge that grows with the complexity of multi-stage processes involving outsourcing and strict delivery deadlines (On-Time In-Full, OTIF).

Real-world challenges include:

Short-term resource changes: Staff absences, machine breakdowns, or material shortages.
Changes in customer requirements: Adjustments to delivery dates or specifications.
Complexity from outsourcing: Dependencies on external partners increase the risk of delays.

Optional solution: Backward scheduling with robust buffers

In classic backward scheduling—planning “from the delivery date backwards”—efficiency is often the top priority. But even here, it pays off to deliberately build in robustness buffers:

Time buffers: Add buffer time between critical process steps (e.g., before outsourcing).
Capacity buffers: Maintain reserve capacity for machines or staff.
Communication buffers: Align early with suppliers to minimize supply chain risks.

Operational Approaches to Increasing Robustness

A dynamic and resilient production environment starts with smart planning. True excellence is not only reflected in flawless processes, but in the ability to confidently handle unexpected challenges. Below are some effective strategies to take your production planning to the next level (Claus et al., 2021):

1. Planning Stability and Reducing Nervousness
A common issue is so-called planning nervousness—frequent changes to production schedules caused by disruptions.

Solutions:

Rolling planning: Regular updates based on real-time data.
Freezing specific timeframes: Temporarily locking certain periods to stabilize plans.

2. Combining Proactive and Reactive Planning

Proactive measures: Capacity buffers and safety stocks reduce vulnerability to disruptions.
Reactive measures: Rapid plan adjustments, e.g., in response to unexpected machine failures—firefighting mode.

3. Leveraging Modern Technologies
Modern technologies such as simulations and scenario analysis enhance operational robustness. They help identify operational risks early and provide decision-making support that is applicable regardless of specific planning tools.

Systematic Evaluation – Using the Right KPIs

In the study by Stockmann and Winkler, over 90% of respondents consider robustness evaluation important—yet only 24% actually perform it systematically. Successful companies rely on the following KPIs:

OEE (Overall Equipment Effectiveness): Measures equipment availability and reveals bottlenecks.
Lead Times: Indicates how stable the production plan remains despite disruptions.
Schedule Deviations: Tracks the frequency and extent of delivery date changes.

DeepSynergy.AI Production Planning: Automatically Finding the Ideal Balance

How can you determine the optimal balance between efficiency and robustness—without spending months on analysis? With DeepSynergy.AI Production Planning, you get a solution that analyzes your individual planning goals (KPIs), weighs them against each other, and intelligently leverages existing data from ERP and MES systems to:

Simulate scenarios: What happens in the event of machine breakdowns or delivery delays?
Reveal trade-offs: Where does greater efficiency lead to reduced robustness—and vice versa?
Generate actionable recommendations: Which buffers are useful to ensure delivery reliability?

With this innovative software solution, your production planning becomes not only more efficient, but also more resilient to unforeseen challenges. Maximize your resources, minimize risk, and create a stable foundation for long-term success.

Conclusion: Planning is Not a Game of Chance

Efficiency and robustness are not contradictory—they complement each other when planning is data-driven and flexible. The key lies in:

Transparent KPIs to detect risks early.
Intelligent buffers that avoid wasting resources.
Solutions like DeepSynergy.AI ProductionPlanning that automatically optimize the balance.

As the study report emphasizes: “Robustness does not have to contradict efficiency—it can secure it in the long run.”
So don’t tip the scale to one side—use modern solutions to dynamically align it.

Want to test how robust your production plan is? With DeepSynergy.AI ProductionPlanning, we analyze your planning and reveal optimization potential – get in touch now.

Sources: Stockmann, C. & Winkler, H. (2020). Robustheit in Produktionssystemen aus Sicht der industriellen Praxis. BTU Cottbus-Senftenberg. LINK

Claus, T., Herrmann, F., & Manitz, M. (Eds.). (2021). Produktionsplanung und -steuerung: Forschungsansätze, Methoden und Anwendungen. Springer Berlin Heidelberg.

If you have any questions, feel free to contact the author directly at kontakt@deepsynergy.ai.

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