plus10 Success Stories


GMP & FDA compliant optimization tools

Fully automated production lines e.g. for pharmaceutical primary & secondary packaging as well as stand-alone machines like injection molding machines are commonly used in pharmaceutical and medical equipment production for instance for self-injection pens or rapid tests for Covid-19.

Output, Ramp-up or cost-efficiency challenges

A typical efficiency of such kind of production equipment is 55 – 85 % OEE (Overall Equipment Effectiveness) facing a long ramp-up period between several months up to two years. In addition GMP/FDA requirements must be fullfilled at all times.

Artificial intelligence (AI) tools from a regulatory affairs point of view (GMP/GAMP, FDA)

All digital solutions that are used in such kind of critical environment must fulfill GMP (Good Manufacturing Practice) standards to the full extent to guarantee a validated production process including data-integrity. Our optimization tools are designed to respect those requirements by all means. Therefore we consider a risk based approach of EU-GMP Annex 11, the practical GAMP 5 guideline as well as computerized system validation (CSV) procedures.

Shannon® for Pharma & MedTech

Shannon®, the operational shopfloor assistance, provides situational problem-solving suggestions for the employees at an automated production line. The tool thus forms a continuously operating optimization control loop based on high-frequency PLC data.

Shannon® makes expert knowledge explicitly available to the entire staff in a mobile app. At the same time it can contribute to GMP/FDA-compliant documentation such as batch records, line clearance checklists and situational standard operating procedures (SOP).

Benefits of Shannon® for Pharma & MedTech

  • 5 – 15 % increase of OEE in 24/7 operations mode due to reducing breakdowns and corresponding downtimes (MTTR = Mean time to repair)
  • Increasing cost efficiency by reducing unit costs and breakdown-related scrap
  • 10 – 30 % shortening the ramp-up and relocation phase in the global production network
  • -30 % of variation of trouble-shooting duration in between different shifts
  • GMP/FDA-compliant documentation of all suggestions and action
Success Story: Shannon® at SCHOTT

Increasing manufacturing line efficiency with Shannon®


Significant increase and stabilization of the productivity of a fully automated syringe production line running 24/7


for generating a high frequency and synchronized data infrastructure taking all PLC into account

for situational recommendations of trouble-shooting actions based on detected root-causes

  • Identification of technical influencing factors regarding individual scrap rate and modelling of counter-actions
  • Technical setup, initialization and brief instruction how to interact with Shannon® done by plus10 onboarding team
  • Productivity already increased during pilot test weeks by 6 % points and reduction of mean variation of productivity during 24/7 operations
  • Technical expert knowledge for problem-solving in case of breakdowns situationally available 24/7 and expandable
  • Quantitative transparency in technical line behaviour, errors, and solutions during normal operations supports conceptional optimizations of new and existing manufacturing lines
polymer syringe
©SCHOTT AG, SCHOTT TOPPAC® polymer syringes

The continuously learning tool Shannon® for the allocation of errors to their technical root causes and the situational generation of solution proposals has already brought us approx. 5 % more productivity during the pilot phase on a fully automated syringe production line. In addition, the spread of productivity across all shifts was reduced by more than 30 %. The situationally transmitted solution proposals thus show a clear effect".


Michael Feldhaus

Pharmaceutical Systems/Director Technology - SCHOTT Schweiz AG

Success Story: Shannon® at Haselmeier

Data-driven production optimization of automated assembly lines for medical self-injection pens revealing up to 10 % OEE increase

A range of self injection pens
© Haselmeier GmbH

Identification and quantification of root causes of availability and performance loss in a fast-moving and fully automated assembly line producing self-injection pens (Initial OEE: 70 % – 80 %, cycletime below 5 seconds).


for high frequency and synchronized data infrastructure taking several Schneider Electric PacDrive PLC into account

for automated root-cause analysis of all losses for each short-stop or disturbance during operation

  • Transparency of line behaviour and allocation of loss reasons to each individual downtime or short-stop
  • Quantified and prioritized action list for technical optimization of the assembly line
  • Trend analysis of mean-time-to-repair and mean-time-between-failure for each individual root-cause
Portrait of a man

Our aim was to identify the technical potentials to significantly reduce manufacturing costs. Therefore, the results are of great importance to us, as they detect and objectively quantify root causes of all losses.


Matthias Meissner

COO Haselmeier GmbH

Darwin for Pharma & MedTech

Stand-alone machines e.g. injection moulding machines often perform differently over time at the same or at different factories.

Darwin, the Machine Performance Finder, offers several intelligent functions to identify concrete optimization potentials of an automated production line during operation. In this way, it significantly increases the performance level of the machines.

The Machine Benchmark learns a virtual ideal process on the basis of control data from several machines, compares this with the processes of the real machines and provides a target value for the fastest possible production process. Based on this, Darwin continuously generates technical recommendations for reducing the cycle time.

This also provides maintenance with an early warning system at the function or component level to prevent unplanned machine failures and to always operate each machine at its optimum.

Benefits of Darwin for Pharma & MedTech

  • Increase in output per time per machine by reducing the cycle time respecting the validated process in terms of GMP/FDA
  • Reduction of throughput times
  • Identification of performance potential regarding cycle time
  • Early warning system before breakdowns happen

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