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IoT Applications for Analyzing Manufacturing Industries

IoT Applications for Analyzing Manufacturing Industries

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What are the challenges in Manufacturing Industries?

Technical faults in machinery will decrease turbines' productivity, disrupt deliverables, and ultimately decrease credibility. Several turbines are available for a single farm, so it isn't easy to manage them manually because we have several components for each turbine. If one major component stops functioning and service is not provided on time, it will affect production. So we need to take care of each component, alerts generated by them, and when was last service done. Therefore IOT ( Internet of Things) in Manufacturing Industries is necessary.

Top 5 Use Cases of IoT in Manufacturing

 The following use-cases are :

Remote monitoring of production 

By embedding sensors, production equipment can be remotely monitored for its production rate of inputs (raw materials), labor, and time, among other factors. Their analysis will then provide actionable insight into the optimization's input-output ratio.

Quality control

Automating production with robots or other related technologies is to improve product consistency. Using thermal imaging to automate quality control inspection is one way to ensure that is the case.

Asset tracking

Asset management practices are being adopted by more and more manufacturing companies. Making sound decisions requires accessing real-time asset information made possible by IoT technology and creating native web and mobile apps for iOS or Android.

Logistics management

Regarding maintenance costs and fuel consumption, logistics managers effectively utilize IoT. They improve the efficiency of their tasks, including drivers' diagnostics, smart deliveries, and monitoring fuel costs.

 

Solution Offered 

By using our IoT devices and AI-based models, we will analyze Manufacturing Industries.
  • Our historical data will determine production in each and predict the incoming production year.
  • Thus the customers can see when the last service, like generator bearing replacement, took place and the days spent after the last service.
  • This will also help to determine the total expenses on each turbine. If those particular expenses reach the threshold still, productivity is growing. This means we need to be attentive to other components. There might be chances for damage to some other major parts.
  • Using historical data, we need to forecast the failure of components to provide service in advance through the AI model and IoT sensors used.

Description of the Deployed Dashboard

I've included for you the benefits of our production analytics dashboard.
  • Production by Month: This graph will predict the production through turbines in each month.
  • Production by Time: This graph will represent the maximum production and achieved target.
  • Farms Service and Production Analysis: This graph will represent, after replacing every turbine's components, what will affect the production of every turbine. This also tells if a turbine is crossing the threshold value of that particular replacement component.
  • Expenses on farms: This graph will represent each turbine's expenses. If it exceeds the targeted value, we must consider the other components.

End Customer Value

Main Value Addition: Based on a variable description of the business KPIs. Description: It will help the customer know about turbine performance at different times and dates and monthly production status. Through this, customers will reduce technician service visits and increase the main bearing's life. Identifying turbines with high/low risk of grease problems, saving turbine material cost, increasing Production Avoiding Id extended damage, and reducing fines.

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