Focused Data and AI Solutions for the Manufacturing Industry

NEVER-ENDING CHALLENGES OF MANUFACTURERS

Changing demands, rising costs, and data from all directions

Operational inefficiencies

Changing customer preferences and demands affecting everyday production targets, labor management, stock checking, and following manual approach to everything.

Supply-chain disruptions

Unplanned downtime

Environmental concerns

Data trapped within legacy systems

End-to-end manufacturing analytics solutions

Re-designing the wheel with data & AI

Manufacturing automation

Automate any part of your manufacturing - quality control, performance monitoring, inventory, supply chain, or energy usage. Get tasks done faster and more efficiently with fewer resources. Our domain experts can analyze your current workflows, identify bottlenecks, and provide insights to improve automation along with the best course of action.

With best-class resources, we can build advanced analytics and AI models to automate processes and become more proactive. Example: real-time data monitoring to detect defects automatically.

CUSTOMER SPOTLIGHT

Success stories from businesses like you

Improved acceptance rate and real-time QC in pharma manufacturing

15%

Increase in acceptance rate for drug products

12%

Increase in acceptance rate for drug substances

How did a pharma manufacturer improvise their standards to quality 4.0 levels using QC performance analysis?

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Data analysis helped pharma company bring down production costs

55%

Increase in production efficiency

15%

Reduction in night-shift expenses

How did a pharma company achieve cost savings through improved defect tracking, reduced rejections, and optimized manpower costs?

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FAQS

Got questions? We got you covered.

How is data analytics used in manufacturing?

Data analytics helps manufacturing in many ways – from collecting and processing machine data, inventory, and other manufacturing processes, you could plan operations better, remove bottlenecks, and predict and prevent issues ahead. Ex: demand forecasting, inventory analytics, near-zero quality monitoring, etc.

What is JIT and how AI can help that?

JIT (Just-in-time) is a modern inventory process to reduce waste and promote lean manufacturing, aligning inventory with production, maintaining goods only in needed amounts. Data and AI solutions can help with JIT through demand predictability—allowing manufacturers prevent material out-of-stocks.

How is data science used in manufacturing?

Data science in manufacturing is used to amp up the value of data with inventive use cases like predictive maintenance, energy management, quality control, and more. It’s about using historical data, finding hidden trends, and making great improvements, like identifying ideal workload, speeding up assembling works, etc.

CLIENT VOICES

Take our customers word for it

We were really impressed with their service orientation and understanding of our business. Showcasing expertise in data engineering, datakulture was successful in implementing the dashboard, allowing the client to track their KPIs and make data-driven decisions. The team's solid understanding of the client's business and their needs was critical to the project's success.

Biswajit Rath

Head of Analytics, Raymond Limited.

They were flexible and adaptive to the client's changing requirements. The team executed a smooth workflow to ensure the project's success. Their flexibility and adaptiveness were notable in the partnership.

Bhargav Raghavendra

Senior IT Director, Cyient

The team at datakulture are inspiring to work with. Their drive for excellence and the values they share from the top down are impressive. The result is top quality work and a partner you are proud to work with.

Liran Lahav

COO, Q Report

I just wanted to say a big thank you for all the support you've given us. Your expertise in Data and Analytics is really helping us make better data-driven decisions. The way you're assisting us with MS Fabric is fantastic, and we're excited about gradually migrating to it to boost collaboration across our organization.