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Bettered Industry 4.0: Achieving Lean Manufacturing by Predicting Production Waste

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Bettered Industry 4.0: Achieving Lean Manufacturing by Predicting Production Waste

See how to combat uncontrollable waste production with lean manufacturing.

· AI Zone ·
Free Resource

The fourth industrial revolution, or Industry 4.0, dawned upon the enterprise sector faster than any of the prior industrial eras (steam, electricity, and computers). Yet, excess wastes haven’t escaped the balance sheets. The zest to predict industrial waste and keep it to a minimum is one of the promises that industry 4.0 — driven by predictive analytics — holds.

Despite the automation hangover (Industry 3.0), manufacturers are proactively attuning their processes into smarter ecosystems. According to the PWC Global Industry 4.0 Survey, industry 4.0 could potentially bring cost reduction up to 3.6% p.a., amounting to USD 421 billion globally.

Combating Uncontrollable Waste Production With Lean Manufacturing

40% of industrial waste ends up in landfills, which is not only scary for the climate but for the manufacturing facilities that aren’t able to process it as well. Production imperfections in manufacturing units create volumes of production wastes that could have been utilized for better throughput rates. The inability to accurately predict such losses could take a drastic toll overproduction yield, while enterprises are exploring immediate yet effective solutions. GE says a 1% improvement in their global manufacturing productivity could contribute USD 10 trillion to world GDP. Henceforth, the ability to reduce such inconsistencies could revolutionize the industrial preparedness; exactly what Seebo aims to do.

Being able to predict production waste followed by prescribing focused actions to address them is a boon for an industry that has battled the problem for centuries. Therefore, ‘Reduce waste and increase margins’ is the newest slogan in the ecosystem while everyone wants to be a part of the bandwagon. Lean manufacturing is defined as a series of techniques that enhance the performance of manufacturing units by minimizing waste production. Targeting increased productivity and throughput quality with minimal rework, lean manufacturing when assessed by industrial AI, yields remarkable derivations that could strengthen the mission of Industry 4.0.

How Does an Ideal Predictive Waste System Work?

Engineered by industrial AI, production teams are able to prevent excess waste by identifying areas of loss and defining focused actions needed to minimize inefficiencies. By deploying predictive analytics and automated root cause analysis, process failures that cause wastage beyond threshold levels can be accurately speculated.

To start with, the current performance metrics of the system must be analyzed. The predictive system can capture data from different sources such as the PLCs and the historian systems on the production line. The digital twin (used in predictive maintenance) can be utilized to access actionable insights about the waste being produced. Using Machine Learning algorithms, the data can be used to predict excessive waste levels thereby predicting peak levels in production waste and deviations if any in the production that could affect the normal waste production.

Digging deeper to identify the source of issues could be excruciatingly time-consuming and thus, automated root cause analysis is performed. Taking into account the historical data of alike events in the past, the investigation matures to pinpoint the problematic areas on the production line. Ultimately, based on the findings so far, optimal setpoints are determined for control metrics so that the production waste is minimized. An ideal predictive system must be adaptable for risk-proof experimentation. Without having to modify the standard machine settings, the simulation should allow adjusting values in the digital twin. Upon reaching the targeted waste levels and the findings in the root cause analysis, the determined set points can then be applied to the actual production line.

Beyond traditional AI, innovative attempts have been made with process-driven AI to achieve high-quality preventive waste prediction. Take Seebo for example, a leading industrial AI service provider for manufacturing units that have unlatched the power of IoT to feed AI systems with real-time data. Their predictive waste system deploys a visual, code-free modeler to analyze the client’s production line and feed the dynamic data to an AI-powered digital twin; Deployed on the cloud, Seebo wraps process modeling, digital twinning, and process-based machine learning into a collective service thereby addressing production losses

Given the complexity of dependencies of multi time-series data in such a setup, the predictive waste system meticulously captures data from connected products and delivers actionable insights so that untimely losses could be diverted. Besides raising the red flags against the process failures that yield waste, it retraces the root cause and drives continuous improvement in the throughput quality

Conclusion

While the use of IoT in manufacturing, logistics, and transportation could touch USD 40 billion by 2020 (Forbes), leading manufacturers like Nestle, Procter & Gamble, Allnex, etc. are already using predictive waste systems to accelerate their production resilience. Manufacturing waste is nothing but operational loss, and enterprises that use contemporary industrial technologies such as AI and IoT surely have a competitive edge.

Topics:
industry 4.0 ,predictive maintenance ,artificial intelligence ,ai ,waste production ,ai and waste production ,predicting production waste

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