-
Blog
/
14. August 2023
Success Story: Transparency in production – Galfa plans with insight and flexibility
-
Coating specialist connects three locations with the GANTTPLAN Planning & Scheduling Tool
Planning is at the heart of the manufacturing process at Galfa GmbH & Co. KG. Each month, more than 5,000 coating orders and nearly 40 systems are planned, processing over 60,000 tonnes of customer parts annually. This is made possible by the APS tool GANTTPLAN from DUALIS, which supports centralized planning across three locations and creates the necessary transparency in production. Since the software’s introduction, the company has benefited from improved throughput times, greater adherence to delivery dates, and increased planning accuracy.
Key Highlights
Initial Situation:
- Production orders were planned independently for each plant using Excel.
- The need for networked planning arose due to increasing complexity and cross-plant orders.
- GANTTPLAN was introduced in 2014, alongside the switch to SAP.
Special Features:
- A direct IDOC interface to SAP was required.
- Integrated planning for three plants at different locations.
- Special requirements for applying planning restrictions and target criteria.
- Consideration of machine utilization, set-up optimization and sequencing.
Benefits:
- Immediate transparency of deadlines across all work steps and increased adherence to deadlines.
- Greater planning accuracy and more transparency in production.
- Improved throughput times.
- Increased attractiveness as an employer and employee satisfaction.
Galfa GmbH & Co. KG: Using innovative production planning for more transparency in production

Based in Finsterwalde, Germany, Galfa GmbH & Co. KG is a high-performance coater of metal parts, specializing in functional surfaces for cathodic corrosion protection and thread locking. Their clients span the automotive, construction, hardware, electrical, metal, and mechanical engineering industries. In addition to their headquarters, Galfa operates two other locations in Massen, Germany, and near Kattowitz, Poland.
Before implementing GANTTPLAN, each location independently planned production orders using Excel. As complexity increased and cross-plant orders grew, networked planning became essential. In 2014, Galfa introduced central production planning with GANTTPLAN alongside their switch to SAP. During the evaluation phase, GANTTPLAN was the only solution that met their requirements. Founded in 1958, Galfa now employs 230 skilled workers across all locations.
The introduction of GANTTPLAN: A success story for Galfa GmbH & Co. KG
The requirements for GANTTPLAN and its associated goals were ambitious. The system needed to integrate seamlessly with SAP and support centralized planning across all locations using unified planning data. It also had to deliver highly reliable planning information for customers and provide clear, transparent communication both internally and externally. Additionally, it was essential to include features for simulating and evaluating planning scenarios before scheduling, along with the ability to manually adjust plans when needed.
The overarching objectives were to maximize plant efficiency and minimize throughput times, ultimately improving on-time delivery performance. GANTTPLAN fulfilled all requirements during the theoretical and testing phases, standing out especially for its integrated control center and both manual and networked planning capabilities. The tool then underwent rigorous real-world testing.
Piotr Majchrzak
Head of Supply Chain Management, Galfa GmbH & Co. KGThe concept phase: The key to future planning success
In close collaboration with Galfa’s team, DUALIS developed the basic concept for the planning and scheduling tool. “Investing time in the concept phase is crucial. We were wise to invest more effort here, as it pays off in the long run,” explains Piotr Majchrzak. “Efficient planning also requires clearly defined interfaces,” he adds.
Simultaneously, GANTTPLAN was made compatible with Galfa’s SAP ERP by equipping it with the necessary interface. Key elements in the system configuration phase included:
- Forward scheduling: Each operation starts as early as possible.
- Hard and soft planning restrictions: For example, shift planning and completion dates.
- Machine utilization: Parallel vs. single.
- Set-up and sequence optimization: Static vs. dynamic set-up optimization and planning of production resources and tools.
- Sequence planning: Lot/order grouping.
Well planned – Better manufactured
One major advantage of switching to GANTTPLAN was the immediate transparency of deadlines across all work steps. Even with cross-plant production orders, customers could receive specific information about production deadlines immediately, rather than being told “date to follow.”
Initially, working with the system was challenging for planners, as it required a significant amount of background knowledge about influencing factors and how the software actually works. However, after training and a familiarization phase, the solution was well-received and seen as a relief. Today, two planners, instead of five, manage the entire planning requirement, allowing freed-up resources to be used for other activities.
“GANTTPLAN has significantly increased job attractiveness and employee satisfaction in the planning area,” explains Majchrzak. Additionally, performance is now measurable through traceable key figures (internal and external). All goals – from planning accuracy and improved throughput times to high deadline fulfilment – have been achieved.
The Result: The search for planning transparency led to process efficiency in the networked factory.
DUALIS, Galfa, and other partners are jointly researching AI-supported production optimization
The REPLAKI (REalistic Planning with AI) project, launched in January 2023 and funded by the German Federal Ministry for Economic Affairs and Climate Protection, is researching the challenges of “batch size 1” in the context of volatile and often insufficiently digitized value chains in the automotive sector.
The aim of the project is to analyze the interrelationships of historical process data using artificial intelligence (AI) and machine learning (ML) to increase the prediction accuracy of production plans. The focus is on both a more realistic forecast of delivery dates and process durations and data-supported new parts planning.
Piotr Majchrzak
Head of Supply Chain Management , Galfa GmbH & Co. KG[This article was originally published in 2016 and was updated in August 2023]
