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  • Publicatie 1 August 2021

 

 

Think about a scenario in which your customers send you a schedule that is automatically pulled in by a system and understood, and then the system chalks out a production plan in the back end. This production plan is communicated at the factory level on the digital workstations. Deviations to the defined planned are highlighted and logged.

The process parameters for the parts in the schedule are drawn from the database, and software bots monitor 24/7 to ensure the process parameters are followed. If there is any downtime, the respective people immediately receive a message on their phones informing them about the downtime event and urging them to take the necessary actions.

Source Forbes

1. The fourth Industrial revolution

And finally, at the end of the process, a store of finished goods is built, defining your inventory in real time. Now think about if all the different kinds of reports and log sheets were to be generated by the system automatically and the necessary data pushed into your ERP in real time. 

Think of an event in which you are travelling and unable to visit your factory, but you can still walk right onto your shop floor and find out every small detail on your mobile phone.

Think of an event in which you are travelling and unable to visit your factory, but you can still walk right onto your shop floor and find out every small detail on your mobile phone.

We are in an era of the Fourth Industrial Revolution, where computers are connected to a platform to facilitate communication with one another to eventually accelerate decisions without any human interference. With a combination of a concrete software platform, internet of things (IoT) and internet of services (IOS) smart factories are a reality today.

Asset Management Systems: See The Line of Sight-Deepening the subject

2. Components of Industry 4.0

Industry 4.0 includes IoT gateways, cyber-physical systems, factory digitalization, digital twins, IOS, condition-based monitoring, enterprise resource planning (ERP) and manufacturing execution system (MES) automation, robotic process automation (RPA), cybersecurity infrastructure, virtual and augmented reality, artificial intelligence (AI), and neural networks and machine learning. 

An overview of business targets would comprise an increase in revenue, a decrease in operating costs and an improvement in asset efficiency. Few bottlenecks we face are planned, and unplanned downtimes, quality rework and process wastage lack awareness, accountability and manual interventions.

Based on case studies across our customer base, the expected outcomes of Industry 4.0 are reductions in downtime (35%-40%), improvements in production (15%-20%), improvements in quality (35%-40%), improvements in overall productivity (65%-70%) and improvements in asset utilization (35%-40%).

Other expected outcomes include improvements in power utilization, overall cost reduction, avoidance of unnecessary capital expenses and reduction in wastage, among others.

 

3. Essential Features To Begin The Digital Journey

• Digital Twin: A digital twin is the first process toward Industry 4.0. With the help of a digital twin, one can reproduce the entire shop floor experience on a digital playground, exposing every intricate detail and flow of information. This helps identify bottlenecks and problems in the process. 

• Preventive And Predictive Maintenance: Regular maintenance plans employed by factories may not always be enough because various external variables may not be factored in. The machines’ operating conditions, offsets and environments form a critical part in maintenance.

• Machine And Part History: It is important to understand the history of the machine and part being manufactured. With historic data, trends can be generated, which gives the opportunity to explore the scope of improvement and refine the processes.

• Operator Mapping: Performance of the operators can be highlighted and indexed. Following safety protocols and cross operator analysis can help identify the asset and conditions where an operator performs at their peak efficiency. 

• Machine Health And Tool Life: Machine health and tool life are extremely critical. One must continue to monitor and derive the health of the asset procured and evaluate performance at various intervals. Tool consumables and determining the tool life are effective in not just optimizing the process, but also ensuring an efficient cost utilization for every part made. This gives good visibility on the ROI made.

 

• Real-Time Efficiency And OEE Analysis: Monitoring efficiency and overall equipment effectiveness (OEE) reports on a postmortem basis does not help mitigate the problems occurring during the shift. If the same is monitored in real time, it helps in taking immediate corrective actions, thereby saving the rest of the shift.

• Downtime Analysis: It is important to understand and analyse different reasons for downtime. This can be further studied in-depth so that redundancies can be identified and processes can be optimized to reduce the overall downtime.

Asset Management Systems: See The Line of Sight-Deepening the subject

 

 

4. Conclusion

With the use of AI and machine learning, original equipment manufacturers (OEMs) are progressing toward smarter machines that access more data when the factories are connected. The machines are capable of communicating with each other and passing instructions down the line, which can lead to automated manufacturing processes that are much more efficient.

 

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