From Machine Monitoring to Data-Driven Decision Making

Sensors have been an integral part of manufacturing enterprises for the last couple of decades. From machines that could communicate break down to bins that triggered alerts when a particular stock was empty. Modern enterprises have invested millions to enable their plants to communicate.

The emergence of Big Data with its cheap data transfer, storage, and computing availability has added another dimension to this scenario. Today’s manufacturers understand the importance of data and how it can impact the industry which is already working on razor-thin margins.

Applications of Big data

The applications of Big data on IoT are manifold. Sensor data can be captured and analyzed in real-time helping in undertaking predictive maintenance. Sensors can be installed in warehouses to measure stock level and prevent outages, with the advancement in sensitivity of hardware, IoT devices. It can also be used for aspects like temperature control in the cold chain.

But one area where IoT has the most important impact on top-line is how the Internet of Things in conjunction with Big Data can help corporations streamline production and align their operations to market demands. Having said this, it must be noted that key decision-makers in most manufacturing organizations have very little visibility into key aspects of their operations like production scheduling and machine utilization.

The current manual methods of operation hinder data collection resulting in the unavailability of real-time data. By deploying the AWS IoT Enterprise button and enabling it to trigger an AWS Lambda functions, enterprises can easily improve production efficiency.

By analyzing data and visualizing it with a solution like Tableau, helps enterprises track utilization, operational efficiency, and machine status.

Benefits of deploying Big Data

Some benefits of deploying a Big Data and real-time analytics in manufacturing operations are-

• Real-time notifications and alerts triggered by various factors like machine breakdown or falling below the production schedule.
• Integrating machine data to the human-machine interface (HMI). This can enable machine controllers to remotely access their consoles and access data seamlessly. With an operator mounted interface downtime can be reducing.
• By visualizing a shop floor performance on screens and dashboards in the plant employee motivation can be increased and accountability can be set for individuals.
• Prior to the era of Big Data, corporations had to continuously monitor their equipment, without having any insight into the wear and tear that their tools have gone through. By coupling machine monitoring with Big Data, this becomes a thing of the past. Machine Analytics can detect patterns and generate insights helping report faults, status, etc.
• Figuring out the cost of operation especially in a discrete manufacturing setup has always been a complex job. Machine Analytics can generate a detailed report on how much effort was taken to complete a piece of work

Common challenges in deploying IoT analytics

Reliability

With a large amount of data being generated by sensors, it presents a challenge in ensuring that data being captured is without a flaw and for mission-critical data, the amount of time lag should be kept at a minimum

Analytics

IoT sensors are omnipresent, be it homes, retail stores, or manufacturing operations. But transferring One terabyte of data over a traditional network could take significant time. When designing an IoT architecture, enterprises need to plan how to address this requirement.

To Wrap up

Businesses have been leveraging the internet for quite some time. Collaborations within enterprises are now in real-time. It is time that this platform moves beyond corporate headquarters and into the shop floor of enterprises.