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The fourth industrial revolution, known as Industry 4.0, has tendency to push the boundaries of science and technology. This is especially true for the manufacturing industry. One of the biggest challenges facing the manufacturing industry today is how to make intelligent systems for production with "self-aware", "self-predict and "self-maintain" abilities. Predictive manufacturing systems (PMS) are new intelligent systems that provide these abilities in the production, processes and machines. The PMS combines different technologies and techniques: statistics, data mining, modelling and artificial intelligence methods. These technologies and techniques are used to convert data into information and make predictions about the observed system.

The world is talking about the Industry 4.0 or the fourth industrial revolution, that is, the current trend of higher level of automation, digitalization and data exchange in manufacturing technologies.

This page will provide an overview of the various challenges, existing solutions and benefits of PMS, with a focus on success factors in Industry 4.0. Keywords: Big Data; Data mining; Industry 4.0; Predictive analytics; Predictive Manufacturing Systems (PMS)

Predictive Analytics in Manufacturing

With the onset of Industry 4.0, manufacturers are readily switching to sensors, connected equipment, and operations, which provide better data than manual data collection and provide it in real time. Software and connected devices reduce labour and magnify the power of predictive analytics, leading to accurate and better results.

Enhance manufacturing execution systems:

Indeed, the absolute transformation of raw materials into finished products is much more challenging than what most manufacturers recognize. Raw materials, machinery/components, supply costs have a direct relation with profits. Subsequently, these prime factors fluctuate due to reasons like availability of materials, seasonality, shipping locations, global demand and much more.

46.4% of manufacturers bear the rise in raw materials costs as a primary challenge. The sudden increase in raw material prices stretches the margins and compels many manufacturers to revise pricing. Using predictive analytics, organisations can analyse the data and foresee major shifts in raw material costs. This knowledge will enable them to predict volatility and revise their plans. Additionally, apart from material costs, predictive analytics can improve manufacturing execution in the following ways:

Benefits of Predictive Analytics in Manufacturing:

Predictive analytics can assist manufacturers in several ways to optimize their processes and productivity. Here are the benefits:

• Improved efficiency: Predictive analytics can help manufacturers identify areas for improvement and optimize processes, leading to increased efficiency.
• Cost savings: Predictive analytics can help manufacturers reduce costs and improve profitability by helping them make better decisions.
• Increased production: By providing accurate predictions on future production levels, manufacturers can allocate resources effectively and increase production.
• Improved quality: Predictive analytics can identify quality issues in real-time and support manufacturers in improving product quality and reducing defects.
• Reduced downtime: Scheduling maintenance of machinery and equipment based on predictive analytics’ prediction of when equipment is likely to fail or err; can augment production and cost effectivity by reducing downtime.
• Better decision making: Valuable insights enable data-driven decisions and respond to changing market conditions. Predictive analytics allows manufacturers to weigh the pros and cons well and drive better decisions.
• Increased customer satisfaction: By providing a slew of benefits such as improved product quality, reduced defects, product availability, and much more, predictive analytics increases customer satisfaction.

Businesses must be in continuous improvement and development in order to maintain their competitiveness today. In the Manufacturing sector, the customer's primary requirement is to get the organization's status, like their turnover, how many workers are there in real-time, and support clients' needs. Today's customers see the working status of their organization along with their production. Here the customer needs a year-to-year trend analysis to get the insights related to sales revenue, their monthly growth, year to year comparison before concluding. Therefore Manufacturing Data Analytics Platform is necessary to compete in the manufacturing industry.

With anaplatform Support, you can build accurate ML models in real-time for Manufacturing Analytics solutions. To better understand the customer for getting the organization's information and their turnover, product status, and labor costing through the model. We can see the production details on every hour of the day with their labors status; by applying the model, the user will easily understand them. Here data sources will generate from the production-based companies.

Why anaplatform ?

Please contact us and request us to give you a demo of our machine learning model. Here you will get insights related to total production labor cost and manpower. Our model will provide you with an accurate prediction of the manufacturing industry with their production analysis. By applying Machine Learning models, the users will get an accurate model for getting their organization's status and turnovers their productions with narrative addition.

• repair maintenance policy, preventive maintenance
• policy,
• predictive maintenance policy
• total productive (efficient) maintenance policy.

Manufacturing Analytics Dashboard

Benefits of the Manufacturing Analytics Dashboard.

• Dashboards show the organization does the total production and their sales revenue and active machines status. So here we can see the organization turnover of 1.9 Million, their manpower status, their labor cost, and customer return of their products.
• The total stock of the products in the organization, here users will also get to know about the products rejected production vs. sales.
• Here maximum % is for damaged products in both cases sales and production. Here, users will also see the production based on shift timings. Where users will get the predicted result for industry turnover and production on shift timing.