Our Services
Turn Your Data into Value
Top manufacturers like P&G and Kale Ceramics rely on KAIZEN’s expertise in AI. Our track record of successful implementations has saved millions in costs.
Our work process
At KAIZEN, we follow a systematic process to deliver optimized AI solutions for our manufacturing clients. Our work takes place across four key phases:
Evaluation
We identify pain points through interviews and a full data inventory and maturity assessment.
Strategy
We determine the quickest ways to deliver value, set up teams, budget, and define success metrics.
Development
Our experts build custom analytics solutions from the ground up.
Deployment
We ensure smooth transition to production, monitoring performance and business impact.
Value
Our ultimate goal is to drive tangible value from data and analytics. We work closely with business stakeholders to define and monitor success metrics that map to financial impact. This includes quantifying the monetary value of increases in revenue, cost savings, improved efficiency, and reduced risks. We also assess less tangible benefits like improved customer satisfaction. With continuous tracking, we ensure data insights are creating real, measurable improvements to your bottom line and strategic objectives. Data becomes a high return asset that gives you a true competitive edge.
Frequently Asked Questions
FAQ for KAIZEN
How can deep learning improve my manufacturing operations?
Deep learning can help with predictive maintenance to reduce downtime, energy optimization to lower costs, inventory optimization to prevent shortages, and predictive quality to reduce scrap. Our AI analyzes sensor data, production parameters, inventory levels, and other data to optimize manufacturing.
What kind of data do you need to implement deep learning?
We need historical operational data from your equipment sensors, process parameters, production output, inventory systems, and other sources. The more relevant data we can collect, the better our deep learning models will perform.
How long does it take to see results from deep learning?
After collecting your data, we can typically have an initial proof of concept model built and tested in 4-6 weeks. Refining the models and integrating predictions into operations takes additional time depending on complexity.
What makes your deep learning solutions unique?
Our team has Over 40 years of combined experience building and deploying deep learning solutions for top manufacturers. We use cutting-edge techniques like convolutional neural networks and recurrent neural networks tailored for industrial use cases.