Project Duration: Initially February 1, 2026 – April 30, 2026, extension planned

Working Time: 60% on-site at the Frankfurt-Höchst plant required

Languages: English and German

Project Objectives:

Expansion of the assembly and packaging lines to implement the global concept of Automatic Reason Code Assignment (ARCA) and integration of the data into an eOEE system

Creation of a consistent data model, ensuring the streamability of production data, and implementation of ARCA for the automated and standardized recording of downtime and alarm causes

Creating transparency (alarm ↔ error) through translation matrices

Implementation of the data pipeline: Alarm data → Streaming engine → Server

Integration into the data landscape (Snowflake, OPC-Kepware, Power BI)

Pilot solution for one manufacturer cell with subsequent adaptation to other manufacturers.

Job Description:

Data analysis and preparation: Familiarization with the existing data from the production facilities and development of a comprehensive understanding of the current data structures

Inventory of the data structure (>700 alarms per plant; high alarm and stop frequencies)

Assignment of alarms to units in the OEE tool (Power BI) via translation matrices (alarm → unit → ARC code)

Data migration planning: Preparation and structuring of plant data for transfer to a standardized data infrastructure

Development of a linking tool between OPC and Data Lake interfaces

Programming in the Plant Connectivity (PCo) agent

Export of raw data (CSV, PDF) → server → streaming to Snowflake; Direct query/import to Power BI

Test runs, error analysis, and optimization of the mapping logic

Performance optimization: Identification of potential improvements in plant performance through data-driven analyses

Problem solving: Development of creative solutions for complex data challenges, especially when conventional methods fail, e.g. B.: Reverse engineering of the existing solution for a Mikron assembly machine and evaluation of its transferability to AL5

Stakeholder communication: Proactive communication with plant manufacturers and internal departments to gather information

Project management: Independent planning, prioritization, and execution of work steps

Visualization and presentation of results (Power BI, management updates)

Qualifications and requirements

Experience:

Sound knowledge of industrial and technical systems

Experience with data migration projects and data harmonization

Proven experience in the performance optimization of production plants

Experience with assembly and packaging systems for autoinjectors and injection pens is an advantage

Prior experience with systems from Körber, Krones, Schubert, Mikron, Harro Höfliger (advantageous/desirable)

Prior experience in the pharmaceutical/medtech sector (advantageous/desirable)

Technical skills:

Expertise in data analysis and preparation

Understanding of industrial Automation systems and plant engineering

Experience with data standards and norms

Knowledge of performance monitoring and analysis of production plants

OPC UA, Kepware, OSIsoft PI, streaming engines (e.g., Kafka/MQTT), Snowflake, Power BI

SAP MII/PCo, data lake architectures, eOEE interfaces

Network/OT security, middleware integration, audit trails, hash codes

Programming languages:

XML (configuration files for PCo/MII)

SQL (Snowflake/databases)

Python or C# (data integration, parsing machine-readable alarms)

Structured Text (IEC 61131-3) and ladder logic (PLC)

Methodological Skills:

Strong problem-solving skills and analytical thinking

Ability to work independently

Structured approach to complex tasks

Six Sigma

Methodical approach to identifying performance bottlenecks

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ASAP Staffing Ltd