Reference no: EM133174421

ERP Project Proposal

To work on the session, you have to send in a project proposal (company, product, short explanation of your choice of company). After the project proposal has been confirmed, you will receive the access data for processing the project in the ERP system Microsoft Dynamics Nav.
IoT

In the case studies section, you can freely choose between working on case study (IoT) or working on both case studies 2 (Mobile ERP) and 3 (Data Connectivity).

Task of the Case Study "loT"

Implement the loT architecture described in the company situation using a Raspberry PI simulator and the Microsoft Azure cloud solution, which is free of charge for students. Proceed as follows:
1. Connect the simulated Raspberry PI to an loT Gateway in Microsoft Azure. k
2. Then transfer data from the Raspberry PI to the Azure Cloud.
3. Then read the data via StreamAnalytics and store the read data as messages in the service bus.
4. Finally, define a program in LogicApp that monitors the values in the service bus and, if a limit value is exceeded, triggers a maintenance order in the ERP system via web service.

Requirements
• Write a video documentation in which you explain your loT architecture (RASP Code, Azure Portal, Navision)
• The video documentation should have a maximum duration of 10 minutes
• In the Azure portal, your login name should be visible in the video documentation in the top right-hand corner at all times:
The evaluation criteria for your performance are:
• Level of detail of your documentation (description of the components, showing the data flow, interpretation of the message content.)
• Functionality of your elaboration (e.g. are sensor values transferred via the Azure Cloud and service orders created in ERP Microsoft NAV)

Case Study loT
The task force "ERP" is in its weekly meeting. The production manager Mr. Walz arrives late, annoyed: "I had to repair another machine and replace parts because it overheated. That always costs a lot of time, during which we should actually continue to produce. Our customers are waiting for delivery."

Lea immediately sees an opportunity to see how the potential of loT in conjunction with the ERP system can help here: "If we read out the machine data with sensors, we could determine if and when the machine is running too hot. We could use this information for predictive maintenance and incorporate the downtimes into the ERP planning run. This would prevent the machine from breaking down on the one hand and further optimize our production planning on the other.

"We could also use the technical infrastructure for connecting the machine sensors and transmitting the data later on to automatically record, for example, the number of pieces and rejects of the machine as well as production times. This would then also eliminate the need for manual feedback of orders on paper".

Mr Walz is skeptical about this: "If we experiment with our machine now, we'll be throwing a lot of money in the wind - not to mention the time that goes into trying it out!

The head of IT, Ms Kehler, on the other hand, likes the idea: "Gee, that could be a huge opportunity in the long term! When I went on holiday last summer, I built an automatic irrigation system for my plants. I simply used a Raspberry PI - an inexpensive mini-computer - and equipped it with a moisture sensor. In the Raspberry I stored a small program code which retrieved the data from the sensor and stored it directly in a cloud database. This way I had the possibility to view and monitor the values at any time, even when I was on the road, so that I could call my neighbor to water the plants if necessary. Because the monitoring of the values was too much work for me, I finally programmed a rule in the cloud environment that my neighbor is automatically informed by e-mail if it became too dry and the values fell below a certain limit. In any case, my plants have survived!"

"And if we proceed just like Mrs. Koehler?", says Mr. Pfestorf. "Lea, you first test this with a Raspberry PI simulator - so we don't have to pay for the hardware. You then make your results available to us. The Raspberry PI is designed to read sensor data and report this data to a cloud system such as Microsoft Azure. From there, a maintenance order is to be created in our ERP system if the temperature of our machine exceeds a certain limit. On this basis, we can then still decide how we can implement this on the large machine".