Identify, Analyze and Resolve: Manufacturing Analytics

In Fortune 1000 companies, it is imperative that mechanization with sensors, such as IoT, is the order of the day.  With the introduction of IoTs, the companies can now sense micro second details of the manufacturing process parameters. The introduction of Big Data analytics  has made the collection of sensed data and processing easy. More importantly, it’s created an opportunity to improve manufacturing KPIs like efficiency and quality of outputs. This era of perfection has set the bar high, where now, every manufacturer is looking to increase productivity by decreasing human error.

Despite   Big Data implementation in manufacturing becoming the norm, several manufacturers still use some specific manual and traditional processes to ensure quality is maintained.

In bulk manufacturing industries that produce intricately handled products, like eyewear glasses, where even the most trivial faults like an edge cut on glasses or a spec of a scratch can be detrimental to the consumer, it is crucial to recruit people with an eye for detail to do this tedious job of looking for finesse of every single item in the assembly line.

It is critical for companies to turn to Big Data and apply intelligent machine learning algorithms to eliminate the challenge, where hundreds of products like eyeglasses are being manufactured and are unable to have an accurate, error-free production line in real time.

Read on.

Manufacturing Process Analytics is one of the crucial facets of the deep learning advanced analytics realm where processes are meticulously studied and analyzed to check for any malfunctions or iterations in the final production.

So, what next? Who conducts these procedures of Identification & Analysis leading to resolution? Companies like Intuceo.

Identification: One of the biggest eyewear manufacturers in the world came to Intuceo to intervene and automate their processes. Intuceo was able to see through to the miniscule details of the data available from the processes in place using its predictive model building accelerator BoldVista.

Analysis of the data: Intuceo read the image data and etched out the deviations that were present in the defective and non-defective eyewear across categories. Every aspect of the lens was studied and  modelled automatically by BoldVista. After building the best BoldVista model, it was deployed into pilot production that resulted in the error-free eyeglasses being rolled out in the assembly, minimizing  the defect rate significantly. .

Impact:  More than 99% defect free products were rolled out without human intervention, saving the company millions of dollars in terms of labor costs wastage and more importantly higher customer satisfaction.

With Intuceo, data becomes easy to analyze and thus, help manufacturing companies automate their processes for efficient and error-free production.