Manufacturers are under constant pressure to improve operational performance in four primary areas: Cost, Quality, Availability and Throughput.
The challenge is to overcome rigid, complex and poorly integrated infrastructure and traditional technologies that are costly, “rip and replace” and slow to implement and evolve.
There’s a wave of transformation coming from the IIoT and the new technologies it presents, and it’s having a profound impact on manufacturers around the world.
Solutions to Manufacturing Challenges
Manufacturing executives are turning to the Industrial Internet of Things (IIoT)/Industrie 4.0 to drive this new wave of continuous improvement in 2 key ways:
1) By making the factory:
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a. Connected (IT/OT convergence)
b. Real-time
c. Digitally integrated
d. Predictive
2) By leveraging purpose-built IoT technology that is:
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a. Quick to deploy
b. Quick to value
c. Agile and continuously evolving
d. Delivering modern experiences with web, mobile and augmented reality
Three Stages of Manufacturing Transformation
According to a 2016 LNS Research study, 51% of manufacturers surveyed will be investing in the IIoT in the next 12 months, and 75% are eventually expecting to invest in it. Manufacturers can leverage the IIoT to continuously improve their operational performance and flexibility through digital manufacturing, real-time intelligence and predictive analytics. There are three stages of Manufacturing Transformation: Understand, Advance and Outperform.
Stage 1: Understand
In the first stage of the transformation journey, manufacturers should consider taking these initial steps to get their arms around existing data, and gain real-time access to it:
- Connect diverse and disparate assets, sensors, business systems and external data sources in real time
- Broadcast real-time alerts about assets and performance anomalies
- Simplify data in up-to-the-minute, role-based views of operational performance
- Enhance existing infrastructure with smart sensors and modern technologies
Expected results include improved information quality & reliability, decreased unplanned downtime, increased operator efficiency, improved maintenance efficiency and improved product quality. By taking these steps, a leading transportation manufacturer used real-time Performance Visibility data to reduce unplanned downtime by 10-20%, with plant deployments taking just a matter of weeks.
Stage 2: Advance
Once the data is available in real-time format, manufacturers should then:
- Apply predictive analytics to machine health (to alert when the machine might fail) and quality processes (if a certain parameter is trending downward, it can be quickly corrected)
- Employ intuitive, in-context 3D and augmented reality to guide workers
- Digitally design manufacturing processes & quality plans
- Utilize agile methodologies to rapidly create & continuously evolve manufacturing applications
By implementing these processes, results can include accelerated continuous improvement, increased speed and flexibility, increased workforce efficiency, improved product quality and optimized maintenance processes. For example, a boat builder streamlined their product development and manufacturing processes using these techniques, which enabled them to deliver their product on-time and under budget for the first time ever.
Stage 3: Outperform
In the final stage, the goal is to extend these capabilities to an enterprise level, to include suppliers and:
- Obtain supplier production visibility to gain early status into performance and quality
- Synchronize resources to ensure flawless execution of production
- Implement consistent KPIs and operations-wide performance benchmarking to identify and implement best practices
- Deploy physical-digital closed- loop processes to drive continuous improvement
This will result in improved production processes, improved profitability, reduced unplanned downtime, shortened lead times and improved agility and responsiveness. One Fortune 50 Food and Beverage leader uses real-time visibility to improve productivity by 8-10%.
