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Minimising downtime in manufacturing plants

Heman Kassan, director of Technodyn. (Image source: Technodyn)

Heman Kassan, director of Technodyn, offers his insights on minimising production downtime to significantly reduce income loss

On average, large manufacturing plants around the world lose 323 production hours annually due to downtime. This translates to more than US$530,000 per hour, totalling more than US$172mn per year. Hardly surprising that manufacturers are pushing themselves to become more efficient at a time when profit margins are continually being challenged by disruptions.

According to the IFS Manufacturing Outlook Executive Summary, improved efficiencies translate to incorporating more automation on the production line. In turn, this sees an increased reliance on the machines that form part of the manufacturing process. Of course, in developing countries across Africa, automation is a somewhat controversial subject, especially due to the popular misconception that it will result in job losses. However, it has the potential to empower manufacturers to upskill and reskill plant workers to deliver more strategic functions and move away from being stuck on repetitive tasks.

Fundamentally, every part in the manufacturing process becomes integral. If any machine breaks down, disruption ensues which directly impacts delivery dates, profit margins, and the reputation of the manufacturer. One of the ways to further optimise the environment, is by equipping the manufacturing with the technology and the means to automate the process of calling a repair technician should the worst happen. This can significantly streamline maintenance and enable the manufacturer to proactively flag potential maintenance issues.

Linking the chain

This does not have to be a complex undertaking. By connecting the production machines to an Enterprise Asset Management (EAM) system, a repair technician of the correct level of competency can be notified of an issue on the production line as soon as it happens. This mitigates the risk of prolonged downtime of a machine and significant production delays occurring.

For its part, the EAM must have sight of where the repair technicians are and be able to identify the ones trained to the right competency levels to repair the specific type of machine. Artificial intelligence and machine learning are important allies in this regard, as it injects the environment with a level of knowledge previously not possible.

This also provides manufacturers with critical insights into identifying problematic machines on the supply line and ensuring they have the right number of technicians available to service those units. This is where upskilling becomes vital as employees must be trained to have the right skills to work on automated production lines.

An example of such an intelligent environment can be found in IFS Cloud. Its single database and open architecture are designed to know where the technicians are and if they have the required skills to repair. It links the monitoring of the machine with data stored on the technicians’ records. Then, using a scheduling engine, it finds the nearest available to reduce the downtime of the stopped machine.

Making sense of data

At its core, an intelligent solution must be able to provide the EAM system with an integrated source of information across the manufacturing environment. This enables the organisation to link the notification of a stopped machine with the production plan to look for alternate machines that might be available to take the load while the stopped machine is repaired.

Additionally, a scheduling engine in the enterprise resource planning (ERP) part of the database can ‘talk’ to the EAM part to alert the right technicians and have them dispatched without any manual intervention. The time saved to automate this previously manually-driven process can noticeably save on costs.

Having the data seamlessly connected and integrated in a database circles back to complete visibility on the issues at hand and quickly identifying the problems. They can then act on suggestions for alternative routing of the operations, ultimately minimising downtime

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