In today’s digital world, advances in technology have entirely changed the landscape of manufacturing, leading to manufacturers to a smart manufacturing environment. These advances in smart factories result in relentless voluminous data collection of the production system. This is where big data and analytics technologies come in, further advancing the smart manufacturing processes by coalescing the system and assisting organizations to utilize the data they gleaned.
These days, data is no longer directed to humans only, instead, it can also be shared between machines in a reliable manner to accomplish higher degrees of efficiency and automation. Big data analytics are leveraged to process the data collected and perform advanced capabilities, including the analysis of non-conformance reports, improved security, predictive and preventative maintenance, plant load optimization, amplified supply chain management, financial risk analysis, and operational monitoring, among others, in order to advance overall equipment effectiveness.
Here we have accumulated the top 5 Big Data Applications in smart manufacturing everyone should know about in 2020.
Smart manufacturing consists of the use of IoT devices in the assembly line to infuse manufacturing intelligence in the product’s life cycle as well as enhance the efficiency and productivity of manufacturing operations. However, the use of these devices generates a huge amount of data in the form of both structured and unstructured. Thus to analyze and create meaningful insights, predictive analytics help manufacturers in keeping their assembly line machines by amassing raw data from the sensors and evaluating it to identify machine failures. Predictive analytics also assist in determining usage patterns and forecast possible future outcomes.
While smart factories use machines, smart sensors and robotic platforms, they also produce data for monitoring, maintenance, and the basic management of the production line. However, much of the data collected from these systems remain in information silos within the factory. Hence, to break down that information silos and harness the potential of context data, leveraging big data and AI services on the cloud enables the right data to arrive at the right place at the right time. Using big data also empowers manufacturers to adopt data-driven strategies to attain a competitive edge.
In today’s highly competitive environment, poor maintenance strategies can lead to a decline of a factory’s overall productive capacity and can bring them at stake. Recent studies illustrate that unplanned downtime is costing industrial manufacturers an estimated US$50 billion every year. Predictive maintenance here intends to shatter these issues by giving companies the ability to improve the value of their manufacturing equipment while averting unplanned downtime and lowering planned downtime. Moreover, by making use of sophisticated sensor technology, manufacturers can glean and analyze operational data in real-time for machinery and consumer products that can help envision potential future failures, reducing downtimes and related maintenance costs.
Supply Chain Management
Big data has a huge impact on all supply chain activities as it resolves a range of pain points at strategic, operational, and tactical levels, from improving delivery times to finding ways to lower the communication gap between manufacturers and suppliers. Using big data can also enable smart manufacturers to gain end-to-end visibility so they can aware of where their items are at all times. They can also get high-quality decision support that can be crucial in the adverse scenario. Big data analytics also helps companies to decide how to manage new items that are new to their business or inventory.
While manufacturing firms have already benefited from increasingly powerful tools for demand planning and logistics management using big data analytics, tracking the performance of manufacturing production across the supply chain still requires effective solutions. There is a need to leverage a daily flow of data from production lines to underline any discrepancies and opportunities in real-time. Thus, by leveraging big data analytics to review previous loads, customer data, and changes to major projects can assist companies to optimize their plant loading.