Four strategies to increase agility in manufacturing
1. Share information in real-time
People need information and they need it now. This could be about health and safety measures, new workplace practices, changing strategies and everything else that was impacted by the pandemic. The less accessible the information is, the more disorganised things become. Sharing information in real-time allows everyone to have the answers they need on demand allows for better communication.
2. Review data quality
Clean data is the initial point for scaling downstream operations. When data is missing, incorrect or out-of-date, it means unnecessary rework and manual review. By using automation to continuously review data and proactively act on inconsistencies enables all of the downstream processes that interact with that data to transition quickly and efficiently.
3. Identify information bottlenecks
The pandemic accelerated existing information bottlenecks in organisations and created several new ones. Analysing how internal communication works and how information flows through an organisation - identifies where these bottlenecks are and suggests how they can be resolved. Better access to information helps accelerate and improve decision-making.
4. Trigger a process to fix an issue
By automatically triggering an automated process to track and manage an issue allows teams to coordinate all of the required activities around issue resolution and provide visibility into roadblocks or delays in getting to a solution. When a new issue is recognised, it can be quickly and automatically assigned to the right owner based on area of expertise, business function or priority, which avoids multiple handoffs and back-and-forths to find the right owner. Upon being assigned, an automated process can keep both the submitter and escalation paths informed on the current status and expected resolution date.
Predictive Monitoring for Continuous Operations Management
Unplanned downtime and poor maintenance procedure can cost companies a lot of time and money.
For companies looking to set specific targets for cost efficiency, production output and quality control, the ability to predict how certain variables affect machines can aid success in reaching these targets.
Monitoring and remediation are important steps to optimize production, by adopting Predictive Monitoring, organizations can receive the ideal support to keep operations running efficiently, and ongoing maintenance to keep machines running at their best.
Predictive Monitoring is an AI driven method of production analysis. It uses metrics such as temperature and vibrations to determine when machines are working outside of their optimum conditions.
Around 98% of organizations report that a single hour of downtime can cost them over US$100,000, which highlights a significant cost implication that can be avoided with AI driven analytics.
TwinThread applications work with Predictive Monitoring to provide a network of data, which ensure machines work within their optimum conditions, for the best output.
Providing the Digital Tools
According to PwC’s ‘Digital Factories 2020’ report, “manufacturers’ adoption of machine learning and analytics to improve predictive maintenance will increase by 38% by 2022.” One of the main reasons for this, given by 98% of respondents, is to gain more efficiency through investment into digital factory solutions.
By learning what potential issues may occur if machines are not working to the correct standard, Predictive Monitoring systems work with TwinThread’s Predictive Asset Reliability application, which conducts an “automated root cause analysis” enabling the operator to analyze how the machine has fallen from its optimum conditions. Ultimately, any issues will be addressed much faster when predictive technology uses data to monitor variables.
“TwinThread’s Predictive Operations Center is making a big difference to our process engineers, giving them real-time feedback on the stability of our production,” said Domenic Verte, Manufacturing Application Manager at Toray.