Agile Digital Transformation Solution for the Manufacturing and Production Industry

Step 1: Target analysis

In the first part, the target state and goals were determined. Why do the participants hope to benefit from a comprehensive service solution? What should the service portfolio during the machine life cycle look like? 
To this end, the participants were asked to evaluate the services during the machine life cycle from start-up, through operation and troubleshooting, to optimization, and finally recycling of the systems according to three categories: 

• Improve
• Offer anew
• Not planned

The participants made it clear that significant improvements are being sought in the first four phases. In particular, the increased digitalization of existing services is to be further expanded to allow a greater focus on environmental protection. None of the participants sees the listed services as irrelevant: either concrete ideas already exist, or implementation is planned.
The aforementioned service goals were then prioritized once again, and requirements were defined more precisely. In the process, three main common goals were identified: 

1) Implement dynamic service protocols and digital signature
2) Offer remote support including VPN tunnels
3) Enable data integration, monitoring, and data analysis

Step 2: Actual state analysis

In the second step, the participants described their current situation. Here, the following pain points emerged: 

•  Acceptance of customer’s cloud-based service solutions (data protection) 
•  Added value of EquipmentCloud^® as a solution compared to ERP or existing systems
• Coupling of systems with each other

Step 3: Deep dive digital transformation solution

Based on the insights gained, the relevant functions of EquipmentCloud^® were explored live and in more detail in the demo:

1.   Implement dynamic service protocols and digital signature

Solution nr. 1: Workflows module

• Efficient and transparent process design through milestones and digital checklists
• Monitor all relevant workflows 24/7
• Clear documentation
• Guarantee adherence to schedules

Solution nr. 2: Maintenance module

• Digital maintenance manager
• Plan and carry out cyclical and preventive maintenance
• Ensure warranty
• Highlight service portfolio

2.    Offer remote support including VPN tunnels

Solution: Remote Assistance 

• Live support via interactive video conference with smartphone, tablet, or data glasses between service technicians on site and remote experts
• Fast reaction time, avoidance of downtimes, efficient work through direct communication

In order to cover all aspects of remote support in future, including VPN tunnels, expansion of the IIoT service solution with a device management module that includes a VPN service is already planned. This should enable the establishment of a secure connection to the end customer's machine network via a VPN (virtual private network) and thus a significantly greater service depth for the manufacturer who can then solve problems remotely down to field and control level. The design has already been presented as a sneak preview. The additional potential this creates for machine manufacturers and their IT and service departments is described in the two linked blog articles.

3.     Enable data integration, monitoring, and data analysis

Solution: Monitoring and data integration via IoT gateway

• Intelligent monitoring of machine and plant performance 
• Systematic evaluation of key figures, alarms, process data, trends
• Database supports the implementation of preventive maintenance and machine learning models

In addition to monitoring, the topic of data science was also discussed and thus the further processing of the collected machine data. This discussion illustrated the range of services offered by data science, i.e., which data analysis tasks must be considered.  And how does the project flow from the target definition to the feasibility check to the implementation in productive use? Kontron AIS has created a proven workflow that profitably combines the interaction of various tools including EquipmentCloud^® with machine learning services. 

In addition, a monitoring API was created for data scientists. The Python module was developed as a REST API wrapper to make the further processing of machine data with Python usable as a programming language in external AI tools. This serves as a basis for implementing predictive maintenance and use cases based on predictions made by AI.

This was followed by the presentation of the latest innovations such as the mobile version of EquipmentCloud® and the dashboard, which functions as a personal digital workplace. 

Existing challenges were taken as examples to show the workshop participants many practical solutions for digitizing services and improving service quality for their individual challenges. To expand your own service portfolio across the entire machine life cycle, the digital transformation solution EquipmentCloud^® is the right platform for offering the proactive service of the future. 

Discover the advantages yourself with the practical test. Two machine manufacturers explain what proactive service means to them and how they have implemented this in practice. Find out more from the recorded webinar: Successful customer projects with RAMPF Production Systems & InnoLas Solutions.