On average, large manufacturing plants around the world lose 323 production hours annually due to downtime. This translates to more than $530 000 per hour, totalling more than $172 million 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 might seem like a swear word, especially the popular misconception that it will result in job losses. The reality is far different, as it could empower manufacturers to upskill and reskill plant workers to deliver more strategic functions and move away from being stuck on repetitive tasks.
Flag potential issues
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. Just by doing so can significantly streamline maintenance and even 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 that there is 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 those 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.
Upskilling
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 result in thousands of rands in downtime being saved.
Having the data seamlessly connected and integrated in a database provides the operations planning team with complete visibility on the issues at hand and quickly identify the problematic machine or machines. They can then act on suggestions for alternative routing of the operations. By doing this, any delay to the delivery date to the customer is minimised, and operations ‘downstream’ of the stopped machine are kept operating as close to the production plan as possible.
Supply chain efficiencies
The disruptions experienced by global supply chains in recent years are already pressuring manufacturers to maximise machines’ performance to keep up with demand. Any breakdowns, especially lengthy and complex ones, can have a profound effect on operations and the ability to meet customer demand.
The ability to automate repair call-outs can deliver significant improvement in efficiencies to strengthen time to market and provide a quality customer experience while minimising the potential of significant downtime.
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