Overcome manufacturing challenges through
AI adoption
We live in uncertain times with businesses facing innumerable challenges. Manufacturing companies, in particular, have to manage increasing labor and production costs, changing customer demands, supply chain disruptions, adopting new technology and automation, and compliance with government regulations and industry standards all while trying to stay competitive and ahead of the competition in these uncertain times.
Know more about how Findability Sciences helped Daikin, a leading HVAC Manufacturer in North America, deploy AI and ML solutions to optimize their inventory management, pricing strategy, increase revenue and reduce costs.
Demand Forecasting and Inventory Management with AI
#DemandForecasting&Inventory
AI-powered, sales forecasting dashboard enables a leading manufacturer of residential air conditioning in North America to optimize the inventory for over 2000 SKUs at 250 geographically dispersed locations to reduce incidences of overstocking and understocking at distribution centers.
Presently, Findability Sciences executes over 1 million predictions each month as a part of this project – MAPE (Mean Absolute Percentage Error) being the chief metric for evaluating the model’s effectiveness. The forecasting models from Findability Sciences have helped reduce the MAPE from 30% to 10%.
Key Opportunities in Inventory Management
- Product Revenue and Quantity Forecasting
- Prevent understocking and Overstocking
- Capturing Demand Signals
- Product Price optimization
#FindabilitySolves
Findability.AI
We collect, connect, analyse and learn from structured, unstructured, internal, external and wide data with automated self-learning AI that produces predictions with unmatched accuracy.
Predictive AI
Our flagship, AI-Powered prediction engine for 95% + prediction accuracy
Key Opportunities in Factory Operations
- Predictive Maintenance of Machines
- Resource Requirement Forecasting
- Energy and Commodity Price Prediction
#FindabilitySolves
Findability.AI
With automated self-learning AI, our flagship offering produces predictions with unmatched accuracy and learns from historical data to generate continuously updated results that allow the business to mitigate losses and increase efficient production cycles.
Predictive AI
Our flagship, AI-Powered prediction engine for 95% + prediction accuracy
Key Opportunities in Supply Chain
- Supply chain visibility
- Supply chain risk identification
- Transportation Forecasting
#FindabilitySolves
Findability.AI
We combine elements of Machine Learning, Natural Language Processing and Computer Vision to build an unsupervised, fully autonomous multi-step modelling and prediction process that produces results faster.
Predictive AI
Our flagship, AI-Powered prediction engine for 95% + prediction accuracy
FINDABILITY DRIVES ROI
$22.5 Mil
Incremental gains by 10% stockout reductions.
95%+
Demand forecast accuracy with 1100+ SKUs.
99%+
Raw Material Price Forecasting Accuracy
FINDABILITY IN ACTION
Sales & Inventory Forecasting
AI-powered, sales forecasting dashboard enables a leading manufacturer of residential air conditioning in North America to optimize the inventory for over 2000 SKUs at 250 geographically dispersed locations to reduce incidences of overstocking and understocking at distribution centers.
Predicting raw material prices
Reputed MNC engaged in international trade and financing of commodities used predictive AI from Findability Sciences to predict the prices of aluminum on London Metal Exchange with 99% accuracy
Automate Resource planning
Market-leading provider of premium semiconductor solutions uses Findability Sciences AI to automate resource planning with 45% greater accuracy
Deployment Case Study
Findability Sciences AI helps leading semiconductor client automate resource planning with 45% greater accuracy
AI in Manufacturing Podcast
Anand is the Founder of Findability Sciences. He is a Big Data and Artificial Intelligence technology innovator who believes in driving business impact through strategic AI adoption
AI in Manufacturing Podcast
Listen to episode 2 of the EM360 podcast as Anand Mahurkar, Founder and CEO, Findability Sciences, talks about the relationship between wide data, machine learning, and AI.
AI in Manufacturing Podcast
Listen to episode 3 of EM360 podcast featuring Anand Mahurkar, Founder & CEO, Findability Sciences as he talks about adopting the power of AI for traditional enterprises.
What people say about us
Michael BrookerI had an opportunity to work with the team at Findability Sciences, they have a great understanding of how to bridge the technical necessities and challenges of ML and the business thirst for predictive analytics.
Senior VP and CIO at Synaptics
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Anand Mahurkar awarded the International Achievers’ Award by the Indian Achievers’ Forum
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Findability Sciences Ranks No. 1157 on the 2020 Inc. 5000
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Read MoreF.A.Q’s About Findability Sciences' Solutions for Manufacturing Enterprises
Our AI technology, products and services are industry agnostic. Our core offerings – Findability.AI, Findability.DSL, Findability.Inside and Findability.Accelerate are currently in use by leading manufacturing companies around the world, who have partnered with us to accelerate their digital transformation journeys and realize significant financial, strategic, and capability ROIs.
We are highly experienced in conceptualizing, designing, and implementing AI-powered solutions for
- Inventory optimization
- Demand Sensing/ Sales Planning
- Supply-chain Resilience
- Commodity Trading
- Predictive Maintenance
- Supplier Management
- Customer Service Automation
- Automatic order and quotes processing
The precise nature of use-case would depend upon the client’s business.
Predictive AI projects make extensive use of the organizations internal data – current and historical. Depending upon the use case, data from Production, Transportation, Transactional, POS, CRMs and ERPs may be required. Smart automation projects, such as conversational AI for customer service or AI-powered document processing, require training datasets for unsupervised machine learning. These datasets could be keywords, business workflows, sample documents, or other internal data.
External data may be used to augment the historical data but never fully replace it. Our experience indicates that most organizations have at least a year or more of historical data, even if distributed across multiple silos. Findability Sciences brings the necessary skill-set and technology to unify the various sources of enterprise data. In the highly unlikely scenario of absolutely no historical data, the client would be asked to suggest proxy datasets relevant to the use-case in point.
Findability Sciences was among the first AI companies to leverage Big Data (now Wide Data) for AI, by combining internal, external, structured. and unstructured data for enhancing the accuracies of Use Cases. Through the Findability.DSL offering, we set up joint innovation labs within the client’s ecosystem where AI/ML use cases may be explored, designed, and implemented. Having worked for 10+ years with marquee clients from various industries, we know what does not work.
Our solutions may be implemented as SaaS or licensed for on-prem/ on-cloud or edge-computing deployment.
We follow the industry best practices and have a proprietary methodology – CUPPTM (Collection 🡪 Unification 🡪 Processing 🡪 Presentation) for developing and implementing AI/ML solutions. For further queries, please feel free to reach out to us and we will be happy to take a deep dive into what solutions work best for you.
Working on the data and any feature engineering is a collaboration, but our clients typically see results that they can internalize in 3 to 4 weeks.
The pricing depends on the use case and the offering in use. Findability.AI license costs vary depending upon the volume and frequency of predictions or the estimated number of API calls required for automation. Findability.DSL is an open engagement model, where the activities and use cases evolve, influencing the pricing accordingly.
We typically avoid doing a PoC, but you could use the first 3 months of engagement as a monitoring period for the performance and collaboration.
Our website (Findability.ai) has a lot of information that may be helpful. With NDAs in place, we can also share whitepapers, case-studies etc., but knowing your needs, how quickly you are ready to move with the engagement, what your timeline, budgeting etc., will be helpful and could expedite the delivery of the collateral.
