Manufacturing
Prevent | Mitigate | Monitor
Now manufacturers can optimize manufacturing processes, reduce operational costs, and improvise how they serve their customers using this data
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Predictive maintenance
No more relying on human-coded thresholds, planned schedules, and alert rules, with AIFA’s Predictive Maintenance technology, predict the subsequent failure of a machine/system accurately before time.
Predictive maintenance also leads to a longer Remaining Useful Life (RUL) of machinery damaging secondary damage. AIFA helps companies avoid machinery failure and the massive costs associated with it.
Price forecasts
Manufacturing companies face the uncertainty of the cost of manufacturing that is often influenced by various factors, with this uncertainty of when to buy materials.
Another uncertainty that manufacturers come across is when to estimate the price of their product when it’s ready to leave the factory.
AIFA can provide accurate price recommendations through artificial learning algorithms that analyze historical and competitive data to always offer competitive prices and profit.
Eliminate downtime
A.I. alerts you early enough to avoid damage-related costs but not too early to waste downtime unnecessarily.
A.I. uses more complex dynamic behavioral patterns of the machinery that raise alarms more accurately.
By preempting a failure with our 100+ artificial intelligence algorithm, systems can continue to function without unnecessary interruptions.
Automated root cause analysis
Pinpoint the source of process-driven losses. AI helps identify the central suspected causes of losses by going through a plethora of multi-source data and eliminating any human bias to show the causes of inefficiencies.
Real-time monitoring leads to long-term cost savings over scheduled maintenance and occasional sample checking. Save costs over scheduled maintenance and sample checking by tracking in real-time.
AIFA’s HR Systems can help you tweak work hours to increase employee productivity and motivation
Quality assurance
Move from defect detection to defect prevention. Quality assurance, in the past, has been a manual job, requiring a highly skilled engineer to ensure quality checks.
Today, AI-led quality assurance solutions combine the best of automation approaches with A.I. and help bring superior results.
AIFA’s Quality assurance eliminates test coverage overlaps, optimizes efforts with more predictable testing.
Decrease assembly-line worker churn rate
AIFA reduces human errors by letting you know if factory workers are showing signs of fatigue.
Our AI-enabled systems will take over observing and analyzing employees’ moods before and after a client call. The H.R. the manager can then decide if the individual needs a break or can continue.
Act in time and prevent losses
AIFA’s proactive prevention mechanism gives precise, clear alerts, giving production teams the timely information that they need. Each alert can be tracked and monitored to weigh the impact of the action taken by the production team.
Proactive alerts
Now, with AIFA, manufacturers can make quick decisions that are backed by data.
Manufacturers can optimize manufacturing processes, reduce operational costs, and improvise how they serve their customers using this data. While machines may not replace humans yet, Artificial Intelligence surely will bring Next-generation optimization for manufacturers.
Eliminate downtime
A.I. alerts you early enough to avoid damage-related costs but not too early to waste downtime unnecessarily.
A.I. uses more complex dynamic behavioral patterns of the machinery that raise alarms more accurately.
By preempting a failure with our 100+ artificial intelligence algorithm, systems can continue to function without unnecessary interruptions.
Scrap Rate Reduction
Manufacturing and suppliers can use AI to analyze historical and current plant data to provide holistic guidance to your production team.
AI can track the relationships between parameters.
And optimal machine so operators can use this to pre-emptively avoid defects and loss in yield.
Proactively correct process behaviour before scrap occurs, improving production KPIs.
Improve Logistics Performance Index
Machine Learning helps gain visibility into the supply chain to determine where future bottlenecks occur.
Much information can be driven to improve productivity out of supply chain, inventory management, manufacturing process, distribution, and fulfillment.
Machine learning can consider various factors that the traditional forecasting model cannot predict.
It looks for patterns, mines deeper into highly complex data, and identifies the potential issues that can be the holdup in the process. ML provides better simulation models of future environments by analyzing complex data sets.
They can also pinpoint the challenging areas that display inefficiencies while projecting the roadblocks or bottlenecks in the future.
Smart Procurement
Artificial Intelligence provides timely analytics and data-driven insights to make better sourcing decision. AI can discover new savings or revenue opportunities by sifting through vast amounts of data.
AI can identify new suppliers or even new markets to enter using vast amounts of external data
AI has can make supplier relationship management more data-informed.
Machine learning applies self-learning to solve defined challenges or improve operational efficiency.
Demand Forecast
AI can help provide more extensive insight into sharing information, implementation, inventory management, planning, and traceability.
Demand forecasting can provide hypotheses for strategic business activities, like planning raw materials, purchasing, inbound logistics, manufacturing and cash flow analysis.
Demand forecasting facilitates business activities, like financial production and planning, risk assessment, and purchasing raw input.
Most importantly, forecast accuracy enables retailers to avoid stock-outs and overstocking. Forecasting can improve production lead times, minimize costs, increase operational efficiencies, and improve the customer experience.
Predictive ETA
When the shipments differ from the scheduled timeline, reliable predictability is the key to making intelligent decisions for remediation.
Predicting potential future disruptions gives you the ability to anticipate and take corrective actions.
Predicting estimated time or arrival gives you realistic arrival times that help you understand schedule changes.
ETA helps you know controllable and known disturbances such as missed ships.
