SUCCESS

Slide

A hidden champion manufacturing company for high performance foil had a challenge in the organization of maintenance tasks.

Name of the Person

Position / Company Name

A hidden champion manufacturing
company for high performance foil had
a challenge in the organization of
maintenance tasks.

Smart Maintenance Worker

Initial situation
Profol Greiz GmbH is a hidden champion for high-performance films. Profol invests massively in innovative products such as the Progano® composite material and award-winning food films. The company not only wants to be a leader in its products, but also to make its processes sustainable. In order to benefit from best practice experience, the technical manager decided to work with FactoryPuls.

Project process
FactoryPuls project goal is to optimize the organization of maintenance tasks. An intensive kick-off workshop with the maintenance management team raised awareness of lean management and digitization. With the help of the LEAN method Makigami, the actual and target process was determined.
The target process was documented and coordinated with the management team. To enable the process to be implemented quickly and efficiently, a planning tool was set up and adapted to the special requirements of the technical team.
In order to be able to use the new technology operationally and mobile, the entire technical team was equipped with modern handheld devices and trained in the use of the planning tool.

Result
The optimization of the process and the use of more digital technologies led to an increase in transparency, efficiency and employee satisfaction.
Since the technology can be used company-wide, there are plans to roll out the developed solution in other areas.

A hidden champion manufacturing company for high performance foil had a challenge in the organization of maintenance tasks.

Slide

Hands on exercise to apply 5S in the workshop

5S PROJECT

Initial situation
After the LEAN management workshop with FactoryPuls, our customer - a world class industrial foil producer had the new objective to improve the operational efficiency and the employee satisfaction of the team from the maintenance department. Therefore, they planned to implement a Total Productive Maintenance (TPM) system including tools like Predictive Maintenance and Remote Monitoring. The first step on this journey is to optimise the work organisation and processes.

Project process
To kick-start the project in this direction, FactoryPuls was invited to deliver training workshop for all production-related departments (Production, Maintenance, Logistic and Quality Management).

A 5S training workshop was customized and delivered to the customer. The training included:
- The knowledge foundation based on the 5S method: Sort Out – Straighten – Shine – Standardize - Sustain
- The best practices shared by the FactoryPuls team
- The hands-on exercise to apply the 5S method in real life conditions

To ensure the long-term effectiveness after the training, FactoryPuls had the follow-up actions to support our customer.
- Supporting in setting of a clear goal for every department
- Supporting in defining the organisational structure and responsibilities
- Continuously coaching the participants (team leader) after the 5S training

Result
- The training participants were well-aware of the relevance and the benefits of 5S method.

- A change in the mindset of the participants was triggered. They started to become the advocates for the method.

-The participation during the workshop strengthened the communication and collaboration among all departments, thus increased employee satisfaction.

Hands on exercise to apply 5S in the workshop

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FactoryLens IoT Gateway and the data visualisation displayed on Power Bi dashboard

FactoryLens enables consignation stock for Waygate Technologies .

Automated Warehouse Management

Initial situation
Waygate Technologies is an industrial inspection solutions company and world market leader for nondestructive testing (NDT). At their Ahrensburg facility in Germany, the goal is to minimize the effort for material management so they can focus on their core competency: providing best-in-class X-ray machines for their customers.
Before the project started, the silicon stock was monitored manually. This caused comparatively high inventory costs and created the risk of material shortage and production disruption.

Project Implementation
To help Waygate Technologies automate their material order process, FactoryPuls was invited to implement a digital warehouse solution. A complete FactoryLens IoT toolkit was proposed as a solution. The solution includes:
- FactoryLens IoT Gateway Installation: to capture stock pictures through an integrated camera. In addition, to complying with the GDPR (General Data Protection Regulation) at the workplace, FactoryLens was equipped with a movement sensor that prevents the camera from taking pictures that consist of people.
- IoT Platform + Computer Vision Algorithm: After capturing, the data was stored on Microsoft Azure and analysed through a smart algorithm developed by FactoryPuls.
- Data Visualisation: the data was resulted on a user-friendly individual interface (Microsoft Power BI) to help our customer monitor their silicon stock.

Result
- The FactoryLens IoT Toolkit solution helped our customer monitor their stock data effectively. The warehouse stock now can be monitored digitally and remotely with a mobile application.

- The provided solution can automate the re-ordering process by sending purchase orders to the supplier and enable a consignation stock

FactoryLens enables consignation stock for Waygate Technologies .

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FactoryLens + Vibrationssensor and data visualisation displayed on Power Bi dashboard.

Remote Production Monitoring

Initial situation
Our customer, an international producer for fire protection systems, wants to improve production transparency, maintenance operation as well as monitor effeciency. In order to leverage the full potential of industry 4.0, they plan to implement a production-wide monitoring system.

Project Implementation
We implemented our IoT Toolkit to execute a proof of concept that monitors the run time and performance of their milling machines. This solution includes the following.
- Vibrationssensor :To sense the vibrations of the milling machine
- FactoryLens: For edge computing and data transmission to Azure IoT Cloud.
- IoT Platform: For data storage and advanced analytics
- Data Visualisation: Visualisation in user-friendly dashboard (Microsoft Power BI)

Result
- The FactoryLens IoT Toolkit helped our customer to monitor the machine run time and optimize production by analysing raw data into meaningful insights. For example number of drills per shift.
- The Information is used by the customer to optimize processes and forms the base for further predictive maintainance development.
- In the future, the solution will be rolled out to every production machine in the company.

FactoryLens + Vibrationssensor and data visualisation displayed on Power Bi dashboard.

Slide

FactoryPuls Smart Algorithm automates the quality control of Dräger`s Gas Sensors

Quality Check with Computer Vision

Initial situation
Dräger, a world leader in the fields of medical and safety technology, resolved to implement an in-line automated quality check of the gas sensors, to ensure welding quality. Before the project, the quality testing was destructive, which resulted in high quality costs. With the innovative Computer Vision algorithm we provided, the detection is automated, and greatly cuts down quality costs.

Project Implementation
To find the best solution, we executed several architecture workshops with the experts from Dräger and our Data Scientists. After the architecture was defined, we developed the algorithm in the following agile sprints:

Sprint 1.: Kick-off Workshop : To define attributes for algorithm development.
Sprint 2.: Selection of suitable Computer Vision algorithm to accomplish goals.
Sprint 3.: Label images and set up environment.
Sprint 4. : Programming model script using deep neural networks technology.
Sprint 5. : Evaluate and optimize the model.
Hand over of Computer Vision application to Dräger.

Result
- Successful automated detection of welding quality of gas sensors via Computer Vision Algorithm.
- In-line and Non-destructive quality assurance (QA).
- Faster problem detection.
The next step is it to roll out the algorithm in the production environment.

FactoryPuls Smart Algorithm automates the quality control of Dräger`s Gas Sensors.