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Essential Tool for Machine Data Availability and Quality

Updated: Mar 20


Pocket Factory Site Acceptance Test of 3 Machines in a CAN line


Feb 01, 2024 - More case studies at quantis.ai



Case Study Highlights


Machine data quality dicrepancy discovered: testing reveals 89,3 % vs. customers specification of 96 %


Third-party testing confirmed: KPI availability of 100 % vs. customer specification of 100 %


Learn how you can benefit today from a neutral third-party validation delivered in 5 days that can help you reduce your operational costs.







The State of Manufacturing Innovation


Digital Transformation


The beverage bottling industry is embracing digital transformation to optimize its operations and cater to individual needs and specifications.


Amid the unstoppable advance of digital transformation in discrete manufacturing, the beverage bottling industry is focusing on optimizing its operations through digitalization.


The industry is striving to refine and implement its processes according to individual needs and specifications to ensure more efficient and customized production.




New Machines, New Systems


When purchasing new machines and systems, attention is no longer paid purely to hardware compliance with customer specifications, but also to the availability of machine data, which is important in order to be able to make statements about system efficiency later on.


Although machine data is provided by the OEM, it is not always as valuable as the customer would like.



Guaranteed Standard


Most beverage manufacturers use the Weihenstephan Standard as a data basis, which defines how the data is transferred to higher-level IT ecosystems. In the Industry 4.0 sector, communication via OPC UA servers has proven its worth. This architecture is platform-independent and guarantees a high level of security.



Successful Instant Support


Some beverage manufacturers have further developed the Weihenstephan Standard to create their own default. These norms specify which data must be available and readable in the individual machines.


However, it is not just the mere presence of the data, but also its quality and the customer-specific requirements for the data, such as units of measurement or decimal places, that play a significant role. In coordination between the OEM and the customer, the specifications and the framework conditions for testing are presented in a set of rules.


Quantis offers its customers the Site Acceptance Test to support companies in complying with the specified standards for their machine data. The focus here is on checking the machine data provided by the OEM. Testing is carried out at the customer's plant during operation. A test interval of 5 consecutive days during the shift while the machine is in operation has proven to be successful.



Problem


After the commissioning of the new canning line, it turned out that the interaction of the machines in the line in particular, required the data to be checked again.


A customer therefore asked Quantis to test the following machines of a canning line on site:


  • 1 pasteurizer

  • 1 packer

  • 1 palletizer


The following should be examined:


  • Machine data specification: 100% availability of all required data points

  • Does the data supplied, correspond to the parameters requested by the customer? Requirement: validity at least 96%

  • Where are there deviations? Specification: quality at least 96%




Solution


Quantis acts as a third, independent testing body for the Site Acceptance Test. The beverage manufacturer has further developed the Weihenstephan Standard and used it as the basis for the test. The data check is carried out on the basis of this individualized list.


In this case, the OEM was responsible for reading out the data during the 5 days in the individual shifts on site. An edge device was installed and the data transfer from the edge to the cloud was checked. The OEM then read out the data and made it available to Quantis.


All data points required by the customer were tested in the Pure Data application for the abovementioned specifications. The testing was documented in a detailed list for all required data points for which the rules were complied with or violated. This was handed over to the customer.



Results


This test results in a reflection of the data that was actually provided. This allows standardized documentation of the values and serves as a basis for further tests.


The following technical results were achieved for the individual machines:


  • pasteurizer: KPI availability = 100 % KPI quality = 87,08 %

  • packer: KPI availability = 100 % KPI quality = 92,74 %

  • palletizer: KPI availability = 100 % KPI quality = 89,79 %


The packer and pasteurizer have additional KPIs that were not defined in the customer's original list of requirements.


All three machines passed the test in terms of data availability. The required availability was 100%.


In terms of data quality and data parameters, the three machines fell short of the expected result. Here, the OEM must decide together with the customer which data needs to be improved. The Site Acceptance Test report serves as a basis for this.




Quantified Report for Maximum Line Performance


As a fundamental tool for determining the availability and quality of machine data, the plant operator gains a detailed insight into machine processes.


This report enables data-related cleansing, which is essential to reduce the downtime of the canning line with the three machines. In addition, the precise evaluation of the line enables optimization of the line output.


After all, a high data quality of more than 96% is decisive for maximum line performance.



Summary


The Site Acceptance Test acts as an essential tool in the continuous improvement process. It creates a bridge between the customer's expectations and the OEM's technical feasibility with regard to the generated data.


This creates a mutual understanding of what is feasible and what is not acceptable. This foundation forms the starting point for the development of customer-specific standards for machine data.










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