What is service reliability in manufacturing?
Data and reporting systems are critical to manufacturing operations with many companies on tight deadlines and production targets.
The systems used by many manufacturers should help identify the exact moment something goes wrong with their manufacturing process - for example, if a machine goes down.
Reliability tools would help support systems that allow manufacturers to cross-check against other activities in the plant and find a correlation.
The internet of things (IoT) can help determine downtime and refers to machines that have network connectivity, allowing them to send and receive data.
The data from the machines and hardware can be used to streamline processes and remove inefficiencies from the process and help improve SRE.
An industrial IoT application combined with a strong predictive maintenance system can significantly improve the effectiveness of equipment by 10 times.
The challenges of SRE within manufacturing
Manufacturing downtime creates a large decrease in profits. When production is reduced or completely stops it means that no revenue is generated. This puts strain on relationships and contracts with who you might have commitments. The stress and anxiety this causes during downtime have an impact on business and people’s health, as the input and output of production are disrupted.
Studies show that accidents at work increase by 12 times during production highlighting the need to avoid manufacturing downtime.
Expenses, losses, and safety costs
Emergency service and maintenance
Identifying errors, inefficiencies, and problems within the manufacturing process
Manually checking hardware and machines
Making older systems coexist with modern digital tools
What if your manufacturing services go down?
Downtime brings about a loss of customer trust and negatively impacts productivity and key performance indicators (KPIs).
Manufacturing downtime is defined as a period of time during which a manufacturer’s output is stopped. This includes planned downtime (e.g. for scheduled maintenance), as well as unplanned downtime due to equipment failure or other events.
According to stats, 46% of unplanned downtime is a result of hardware failure or malfunction and 40% is a result of software malfunction. An outage that lasts for four hours costs over 3 million dollars in financial losses.
Downtime costs businesses an average of $260,000 per hour - which only reflects a loss of productivity. Liability costs due to workplace injuries associated with asset maintenance mean that the true figure is likely higher.
Why manufacturers should focus on scaling reliable services
A report by Deloitte shows that manufacturing downtime costs the industry around 50 billion dollars annually. The industry must evolve in order to combat losses by embracing digital transformation, digital solutions, and predictive maintenance.
Reliable systems, AI, AR, and the Internet of Things (IoT) play an essential role in ensuring equipment is reliably, as well as helping identify faults early, and solving problems quickly in any location.
The future of manufacturing is digital, so it’s important for businesses to care about reliability and improving their systems.
How our tools improve SRE within manufacturing services
Different teams and departments will think about reliability differently and how it impacts product lines - Reliably provides the tools needed to reduce risk and provide secure, reliable, resilient, and always available services to improve your culture of reliability.
Predictive maintenance systems that are used to assess unplanned machine downtimes provide massive volumes of real-time data that helps in improving reliability but ensuring those systems are okay is critical. This data can be sent to SRE tools such as reliably.
With Reliably you can
Use an SLO oriented approach to reliability in financial services
Give a live view of system health through scoring
Enable cultural shifts that promote and increase reliability
Define what a reliable system means to a team, and keep track of it
Proactively track weaknesses with Chaos Engineering
- How does IoT help you determine downtime types and priorities?
- IoT (internet of things) is a system used in manufacturing to receive data from hardware such as its machines and mechanisms that provides transfer data over a network without the need for manual inspection. The data provided by sensors is used to train machine learning models of predictive maintenance. This enables manufacturers to reduce downtime caused by unnecessary maintenance.
- What is predictive maintenance in manufacturing?
- Predictive maintenance requires analytics of operational data from machines which provide useful insight into potential problems. Operators can then predict a machine's maintenance schedule requirements based on the patterns of data they see. The goal is to avoid potential problems and use a proactive method of identifying maintenance requirements in the factory rather than condition-based maintenance, which involves carrying out manual maintenance using a fixed schedule or a reactive, ad hoc approach to combat issues that have suddenly arisen.
- How does Reliably help my predictive maintenance efforts?
- Data from your systems can be sent to Reliably and then stress-tested using chaos experiments as well as providing you with intelligent suggestions based on your SLOs and scorecards.
- Can I try Reliably for free?
- Yes, sign up for free (no credit card required) and get started with defining and observing your reliability. You can cancel any time and you won't be charged anything.