Artifical Intelligence and Data Analytics

BCS.AI helps leading manufacturers rapidly integrate data from enterprise systems, operational sources, sensor networks, and external providers to power machine learning models that generate predictive insights.

Solving the unsolvable for the industries

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    Health Care

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    Smart Cities

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AI and Machine Learning requirements

Machine learning has a lot of untapped potential in various business aspects which can be utilized to uncover interesting insights and make processes better. We at BCS.AI will take your firm on a journey of this continuous improvement using state of the art algorithms and position your firm with the required competitive advantage. We have a multi-national team of data scientists, data architects and software engineers, which will help your firm to overcome every challenge.

BCS.AI helps leading manufacturers rapidly integrate data from enterprise systems, operational sources, sensor networks, and external providers to power machine learning models that generate predictive insights.

Our services
Charting an effective path forward starts by reviewing your current situation and the path that got you here. We'll discuss the tools you're using, the machine learning models (if any,) that have been built in the past, existing applications of AI, organizational roles, and much more. An AI Architect will also engage with your application developers, solutions architects, and other IT people to get an understanding of your information security requirements and a model's path to production
You may already have a business problem in mind, but if not that’s OK too. Our unique Customer-Facing Data Scientists (CFDS) will help you frame the problem, acquire the data you need, and provide training and other support to help you solve the problem by using DataRobot. At the conclusion of the POC, our team will help you calculate the expected Return on Investment (ROI) based on existing models and business processes to help you gain executive approval to move forward.
The AI Success Plan is the foundation of our approach to helping you achieve success with AI. We’ll help you develop a vision, agree on additional use cases, set milestones and commitments for both organizations, and more. We'll also identify any enablement, execution, training, and advisory assistance that you may want or need to overcome AI creation and adoption challenges.
We'll help you focus on high-value and relatively easy business problems first, then tackle the more challenging ones. Our Platform Delivery Engineer will help you get models into production and show your ML Operations/IT team how to maintain them over time. We'll be your partner every step of the way.
Your AI Success Manager will schedule ongoing meetings to discuss how we're doing, inquire about things we can do better, and brainstorm ways we can further increase the value you're getting from DataRobot software and solutions. They’ll also deliver value-added enablement sessions to cover product updates and industry developments, provide additional training, and give you additional opportunities to ask questions in support of the AI culture you've built within your organization.

Machine learning is a powerful resource from which many parts of organizations can benefit. Broad dissemination of the approach is a way to improve analytics and organizational performance.

Mission is to enable, empower and engage the organization to better use and embedded machine learning.

Enable meant providing the infrastructure to efficiently use and embed machine learning such as the servers, software, and data connectivity.

Empower involved identification of the best set of machine learning tools and training analysts and data scientists to use those tools. R, Python are preferred programming languages.

Engage meant motivating internal clients to use the tools by demonstrating and socializing the benefits through several proofs of concept, advancing code sharing/examples and consulting.

One of the most common benefits cited by companies is increased productivity and effectiveness in creating analytical models. Creating models is a core activity for machine learning. It involves such activities as feature (variable) selection and engineering, data preparation, selection of algorithms, and evaluation and comparison of results. We perform these activities with little need for intervention by a data scientist. The result is both substantially greater productivity and more effective models.

Revolutionizing manufacturing with Enterprise AI
Production Planning and Optimization

Pricing and Quotation Optimization

Aggregate sourcing data into a unified federated image to perform pricing analytics and visualization. Build optimal price estimates for raw materials based on advanced machine learning analysis of previous pricing and expected consumption.

Demand Forecasting and Stocking

Inventory Optimization

a. Reduce inventory holding costs, improve cash flow and supply chain visibility, and increase the productivity of inventory analysts. Inventory Optimization applies advanced machine learning to analyze variability in demand, supplier delivery times, quality issues, and product-line disruptions to build real-time recommendations and monitoring, so users can set optimization by confidence level and receive real-time notifications and root-cause analysis.

b. Access a comprehensive view of global inventory levels across individual lines and factories and entire supply networks. Perform scenario planning and root cause analysis, optimize inventory levels, and manage suppliers comprehensively.

Profitable BoM

Maintain accurate bill of materials (BOM) pricing and componentry for highly complex products at each stage of engineering, delivery, and after-market. Calculate profitability for design, as-built, and added components for aftermarket stages.

Predictive Maintenance to reduce Maintenance Cost

Aggregate petabyte-scale data from sensors, devices, enterprise systems, and operational systems to generate accurate predictions of asset failure. Predictive Maintenance provides planners and operators with comprehensive insight into asset risk, enabling them to maintain higher levels of asset availability, deliver services-based differentiation, and reduce maintenance costs.

Processing Conditions/ Operational Excellence/ Yield Optimization

Improve throughput and product quality by quickly detecting and mitigating emergent process issues. Apply advanced machine learning techniques to predict downstream product yield issues and pinpoint problematic process steps.

Quality Management