Analytics, Data Science & AI Service Provider for Manufacturing Sector

Manufacturing

Manufacturers have streamlined their operations using traditional methods so long. They can leverage their own data to further boost productivity and profitability. Data can be put to work by harnessing information from various sources – equipment controls, PLC, industrial internet of things (IIOT),input, output, process parameters etc. through predictive and machine learning algorithms for improving operations and making it more intelligent.

In a venture driving operations excellence through data analytics, Business Brio was entrusted with a project by one of the biggest producers of pig iron and ductile iron pipes. The company produces more than 700K tons of pig iron annually and was on the look out to find a solution that can optimize their bottom line of operations. Business Brio had to study the processes - understand the existing manual and electronic data collection, do diligence on data quality using gage R&R, and then build, validate and refine models using advanced methods of supervised machine learning like DOE and RSM. The client was further enabled with statistical controls for preventive as well as predictive models for uninterrupted operations starting from sinter plant, metal blast furnace to the centrifugal casting.

We partner with you
for transitioning to Industry 4.0

Forecasting

At Business Brio, we have used Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network. We did find Neural network to have better forecasting performance than the classical forecasting algorithms in case of wind energy forecasting for a particular project and won the NASSCOM Analytics Innovation award in 2015 for effectively using the same for business.

Optimization

Methods like Goal Node, Integer Linear Program, Simplex Method and Interior Point Method are used depending on the context and relevance. At Business Brio we use Lindo as a tool for optimization.

Machine Learning

Unsupervised and supervised learning methods like regression, support vector machines (SVM),KNN, K-means, PCA are used to recognize patterns and make data-driven predictions or decision outputs. We extensively use Python and R for applying the algorithms.

Decision Trees

CART, CHAID, Random Forests, mathematical and computational techniques are used to aid the categorization and classification of a given data information. Apart from programming tools, we also use WEKA for decision trees.

Sentiment Analysis

Opinion mining or emotion AI refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information. In past projects, we have heavily used LSE, HSA, text mining for semantic algorithms.

Predictive Maintenance

Maximize uptime of critical assets by leveraging machine log data - either manual or electronic (IoT) - to predict and anticipate failure modes.

Applications

Supply Chain Management

Dynamic optimization with advanced modeling setup points (sales mix, value allocation, procurement mix) to maximize profit per hour of generation.

Ongoing Operations Excellence

Strong control measures through historical data trends and performance thresholds built into dashboards for leading & lagging metrics helps in timely process escalations and contingency.

H&S/Environment Compliance

Sensor based (IOT) real time data analysis with built in workflow and escalation management can save critical downtimes, non-compliance and safety hazards.

Maximizing Yield

Maximize yield or throughput by modelling process data and optimizing working/process parameters based on input parameters and outcome needs.

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Advantages

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