When AI meets IoT! Powering up your data-driven corporation

 

The ever evolving Artificial Intelligence (AI) technology opens many new opportunities to the industrial corporations. Altogether, the Internet of Things (IoT) improves efficiency, scalability and collaboration for these industrial enterprises while saving valuable time and cost.

Companies have begun to deploy sensory technology as a means to augment workplace safety and operational efficiency thereby reducing cost and time previously incurred on unnecessary maintenance.

The deployment of both AI and IoT gives competitive edge which further strengthens the data-driven businesses of the future.

AI & IoT: Potential in the industrial sector
Collaboration and deployment of the two technologies can significantly extend data analytics to unexplored solutions, rather quickly and effortlessly. Predictive modelling technique of both AI and IoT is exploited by data scientists and analysts which lets business to transcend from descriptive (problem identifying) to prescriptive analytics (solution to the problem).

The Chief Technologist at Hewlett-Packard Enterprise Group (HPE); Steve Fearn explained the combined potential AI and IoT that production concerns can truly be resolved by deploying machine learning on different levels of the assembly process.

He also shared example of a video analytics tools that can extract information from the production system and matches it to the video-image on the assembly line which ensured on correspondence of products and customer orders.

Managing the digital conversion
Although digital transformation executed through IoT and AI can systematise older issues and queries while solving new ones, the main challenge is managing this advancement from existing manufacturing systems to those powered by AI and IoT. Overcoming the challenge needs one-time effort by all participating groups engaged in development, deployment and process the system. All of it requires streamlined and systematic approach to identify current situation, outline the challenges, find liable solutions and deliver results that give entities a competitive advantage.

The present scenario
The existing data analytics system allows corporations to amass huge volume of data of their customers, products and corporate operations. That said, there isn’t any common framework to gather and structure data in a silo that can be mutually pooled across different departments of the organisations. However, it raises a number of problems for business entities such as:

 

  • Manufacturing inadequacies
    Production/manufacturing businesses would frequently oversee the opportunities for reducing of production cost due to partial data sharing and analysis.

 

  • Inaccurate design
    When information of customer preferences or requirements isn’t shared across multiple production levels, it’s obvious of the final product to contain errors and features that are unneeded or even lacking essential features that the client actually requested for.

 

  • Overlooked marketing opportunities
    Businesses are likely to rule out the market opportunities by producing less than the team can actually sell. It happens mostly when there’s an inaccuracy in the customer demand and expected data.

 

  • Customer reviews
    Customers often report over delays on certain defects against a product due to lack of shared information. This delay also compromises quality of customer service.

Executing the transformation
Business entities willing to deploy the emerging technologies of IoT and AI also need to look over impact on existing employees. With new technology, industrialised IoT can radically enhance production efficiency but can also result change or loss of employment.

Taking on the challenges
An effective way to improve industrial data process can be through edge analytics which is an improved data collection process and speed of analysis by executing these functions where information is being gathered.

Global accounts manager of Schneider Electric; Eddy Biesemans further explained on big data and edge analytics that deployment of the two would streamline complex calculations taking place in real-time as well as remove issues of cost saving, latency on security and bandwidth usage.

Closed loop production
Closed loop manufacturing has the capability to become the accepted industry standard for production and manufacturing businesses; included in the list is PCI compliant data centre. The aim of framework is extracting all the products data like how they’re used, the context, possible demand fluctuations based on the cost, performance and customer rating.

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