Transparency for warp knitting efficiency


Trade disputes, supply chain bottlenecks and increased competition are causing enormous cost pressures for producers around the world, including warp knitting companies, and only those who use their resources to optimally can survive, says leading machine builder Karl Mayer.

The technology subsidiary of the German company KM.ON has developed the k.management dashboard to enable manufacturers of warp knitted textiles to fully exploit their potential.

The smart tool uses real-time data from warp knitting machines connected to the KM.ON Cloud via k.ey to provide insight into key figures anytime, anywhere. The handy dashboard was launched in 2018. This was followed by continuous optimizations in cooperation with pilot customers, including Filesan.

Up to 15 employees of the Turkish warp-knit fabric manufacturer – primarily designed for agriculture – used the k.management offering for production planning and quality control purposes, while CEO Bilgin Türkoğlu was also constantly connected to production events.

“I saved the link I needed at the time in my Google bookmarks. The dashboard was constantly running in the background. I checked the numbers almost every hour,” explains the managing director, who until there had to call the production manager for more details every time the machine stopped longer.Although the dashboard is a useful tool for him, there is still room for improvement based on his feedback .

Input from Filesan and all other pilot customers led to a first upgrade in March 2021 and further optimizations followed in October 2021. The latest generation dashboard now offers even more transparency in production with new functions, and therefore provides the basis for highly efficient management of all resources and processes.

“The machine situation remains unchanged, but customers can act proactively and quickly, for example by optimizing employee scheduling”, explains Marcel Wenzel, Product Owner at KM.ON. “No one has to regularly check the situation on the machines anymore.”

Detect problems at a glance

To ensure highly efficient production, manufacturers of warp knitted textiles must avoid unnecessary and costly downtime. Upcoming steps must be planned in advance and real-time information must be provided quickly. This is precisely what the k.management dashboard offers.

The tool provides a live overview of all machines available anywhere, highlights issues like machine downtime using easy-to-understand symbols and also suggests the cause. Rapid detection enables effective response, enabling customers to optimize delivery reliability, machine utilization and machine downtime, while maximizing throughput.

Sort by activity pressure

To maximize machine utilization and thus cost-effectiveness and delivery reliability, a special sorting function has also been integrated into the k.management dashboard. The feature displays upcoming scheduled machine downtimes and any unscheduled downtime. Planned service interruptions occur, for example, due to beam changes and are sorted according to urgency.

Unscheduled downtimes are triggered by things like wire breakage and are immediately recognizable by the machines being ordered and grouped according to their status.

Display of machine condition history over an observation period as a prerequisite for process optimization and design of an efficient process during shift change.  ©Karl Mayer

By providing information about the upcoming measurement at an early stage, resources can be used optimally: It is always clear which employee has to attend to which machine and when, which means that production runs as efficiently as possible.

Looking to the past to effectively plan for the future

Another challenge for warp knitting mills is the pressure to optimize their processes. Production processes must be extremely lean and cost-effective. The k.management dashboard supports this through a feature that displays the status history – running, stopped, offline – of each machine to date. During an examination, the maximum period is three months. Based on the timeline, the added value contribution of each machine can be analyzed in detail and compared with other machines to optimize important work processes.

In addition, shift changes can be facilitated. As part of this process, answers to key questions about what happened on the last shift are handed in along with the tasks. Functionalities are being prepared for this, which relate to the frequency and type of production fault, the cause and time of a machine failure, the duration until the resumption of machine operation or the causes differences in performance between machines.

www.karlmayer.com

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