ProcessMiner, an AI-driven autonomous process optimization solutions company, is bringing revolutionary changes in the manufacturing field, supporting their client’s Industry 4.0 initiatives.
Atlanta, Georgia --News Direct-- Prodigy Press Wire
In addition, their adaptive and self-learning autonomous optimization AI platform demonstrates the impact that the practical application of advanced data science can have on reducing raw material consumption and waste while improving quality and productivity.
The manufacturing industry is amid an unprecedented transformation, thanks to the relentless progress of smarter and more efficient technologies. The shift, aptly dubbed Industry 4.0, is revolutionizing how products are made. At its core, Industry 4.0 is about integrating cutting-edge technologies into the manufacturing process to create smarter, more efficient, and self-sufficient factories.
By doing so, it promises to unleash a new era of productivity and efficiency that will power the economy for years to come. So buckle up and get ready for the ride because Industry 4.0 is set to take us on a thrilling journey toward a brighter and more prosperous future.
The Industry 4.0 potential benefits, including increased efficiency, productivity, and profitability, make it a worthwhile endeavor. However, while the promise of benefits is significant, several challenges remain associated with its implementation—integration with legacy systems, workforce training, hiring specialized skill sets, and capital costs.
Given the benefits and challenges, many manufacturers have concluded they must embrace Industry 4.0 and transition to newer technologies to support existing processes.
So the big question is, are there solutions available today that mitigate the aforementioned challenges while, at the same time, help manufacturers to pragmatically progress along the Industry 4.0 maturity curve and implement new technologies to optimize their production process?
One such solution is the ProcessMiner AI platform. By integrating the power of domain expertise with advanced IT, OT technologies, AI and Data Science, ProcessMiner can unlock the value of hidden insights in client historical and in-line process data. With its self-learning AI prediction models and proprietary algorithms, the platform runs 7 X 24 X 365, constantly monitoring production line settings and deviations to detect issues and conditions that can impact quality, increase waste, inefficiently use raw materials and negatively impact overall operating efficiency. In addition to predicting end-of-production inefficient outcomes, the platform recommends and can autonomously execute corrective action to prevent these outcomes.
Karim Pourak, the CEO and co-founder of ProcessMiner, stated that many of their clients have voiced frustration over previous unsuccessful attempts by other tech vendors to execute intricate data analytic projects that generate measurable value, delivered on time and within budget.
“We've discovered the primary obstacles are usually prolonged consulting engagements, significant upfront expenses, difficulty recruiting top talent, and challenges in achieving sustainable and measurable value,” said Pourak. “Currently, the industry has numerous analytics and optimization tools that support tactical systems monitoring, reporting, and alerting, but they are predominantly reactive in nature. These projects succeed in monitoring and reporting what happened and why, but they provide limited insight on what will happen. Regrettably, most of these projects fall short in the most challenging aspect of closing the production process loop, which is preventing bad things from occurring.”
In contrast, The ProcessMiner cloud-based platform can be put into production with little to no capital expense and is interoperable with existing technology and infrastructure already deployed at Mills and Plants.
Since ProcessMiner is a turn-key solution, clients do not need to hire architectural consultants, data scientists, modelers, or analytics experts. Systems can be implemented and in production in a few weeks, optimizing the production process. In most use cases, documented results demonstrate the ability of manufacturers to generate hundreds of thousands of dollars in savings annually per production line.
ProcessMiner has proven to consistently help its clients improve their products' quality with their unique predictive and prescriptive platform. Through this process, manufacturers can detect problems before they occur and take corrective actions before products are produced.
By reducing the number of defective products and making more efficient use of raw materials, manufacturers can reduce the cost of production, improve customer satisfaction and support meeting corporate sustainability goals.
Specifically, in the Pulp and Paper industry, working with leading manufacturers, ProcessMiner has delivered the benefits of autonomous control resulting in up to a 25% reduction in chemistry consumption, increased adherence to quality targets by as much as 98%, reduced wood fiber consumption 2% and contributed to additional sustainability goals reducing carbon emissions by 50 tons per year.
In the plastic injection molding industry, they have been effective in helping clients reduce defects and scrap rates by double-digit percentages in the first year of implementation.
Overall, leveraging detailed data is crucial for manufacturers to remain competitive in an increasingly complex and dynamic market. The effective and timely use of historical and in-line process data is one of the keys to unlocking the door to sustainable competitive advantage. It can provide valuable insight into every aspect of the production process.
By analyzing and understanding the implications of this data and acting immediately, manufacturers can quickly identify inefficiencies, reduce waste, increase productivity, improve quality control and directly impact the ongoing cost of goods.
Name: Rob Berman
Release ID: 582587
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