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ML, RPA Use Cases to Explain How Does Artificial Intelligence Expedite Industry 4.0 – Analytics Insight

ML, RPA Use Cases to Explain How Does Artificial Intelligence Expedite Industry 4.0

Disruptive technologies including Artificial Intelligence could greatly benefit Industries to unlock productivity, engagement, and hardware in collaboration with intelligent automation, predictive machine learning and RPA. Analytics Insights collates 10 use cases that explain how does Artificial Intelligence expedite Industry 4.0-

AI-Collaborative Robots
AI-powered autonomous robots capable of learning various tasks are programmed to detect and avoid obstacles, perform rule-based tasks letting them work along with human workers.
Collaborative robots (cobots) function as an extra set of hands to help manufacturers work on factory assembly lines and tasks that require heavy lifting. Automotive factories use cobots to lift heavy car parts, holding them in place and retrieving items from large warehouses.
Error Detection Capabilities
Manufacturers can use automated visual inspection tools to predict errors even before they occur. For instance, a truck manufacturer may receive supplies of bolts and nuts from two separate sets of suppliers. Take an instance if one supplier accidentally delivers a faulty batch of nuts and bolts, the truck manufacturer will know exactly which truck has those faulty components. Thus, this will make quality checks more seamless, and easier to recall only those trucks which have faulty components.
RPA handles High-value repetitive Tasks
Robotic Process Automation (RPA) is capable of handling rule-based, high-volume, repetitious tasks, automating functions like sales order maintenance, invoice processing critical to manufacturing processes allowing enterprises save time and resources by removing non-value-add activities.
 Machine Learning & Demand Prediction
AI systems that use machine learning algorithms to detect customer buying customer and give this insight to manufacturers for demand planning and potential capacity expansion.
For example, PCA or Principal Component Analysis is an unsupervised learning algorithm that identifies behavioural patterns to make decisions with minimal human intervention helping manufacturers ensure that they are producing high-demand inventory before the stores need it.
Inspection and Maintenance with Digital Twins
Industries use digital twin technology (a digital replica of a living or non-living physical entity) to understand the complex working of complicated machinery.
In the manufacturing domain, digital twin receives information about its physical counterpart through the IoT enabled smart sensors. Using AI and IoT, digital twin helps to deliver intelligent insights letting industries to monitor machinery throughout its lifecycle, be attentive to critical alerts, like predictive inspection and maintenance.
Quality Checks
Manufacturing units into the production of microchips and circuit sheets use machine vision technology to collect process critical images from cameras attached to robots. These high-resolution cameras are used to monitor defects which are identified immediately and a response is automatically configured, sent and managed through a cloud-based information processing system.
Predictive Maintenance to Improve Safety
If machinery and equipment are not maintained timely, they may lead to loss of valuable time and money, and long waiting times causing extensive wear and tear which can potentially expose workers to safety hazards.
Heavy equipment users, manufacturing plants, railroads are increasingly turning their attention to AI-based predictive maintenance models to anticipate future servicing needs.
Inventory Management
Manufacturing companies are increasingly relying on AI systems to better manage their inventory needs. Deep leading predictive algorithms can keep a track of inventories and send alerts when they reach minimum store level and need to be replenished. For instance, a food manufacturing company may use an ingredient which has a short shelf-life. AI models can predict the inventory levels and the time taken for procurement, and how the delay in availability may affect production.

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Kamalika Some

Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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