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Real cases where AI empowers your decisions
Narrative: A multinational food company implemented machine learning to refine its demand forecasts, while several industry studies demonstrate the transformative impact of AI on inventory management.
Challenge: Companies face constant stock-outs that result in lost sales, product obsolescence due to excess inventory, forecasting errors that affect planning, and overworked planning teams.
Benefit: 30% reduction in lost sales due to stock-outs, 30% less product obsolescence, 20% less forecast errors, up to 50% less planning team workload, and decreased demand errors by 30-50% with logistics costs reduced by 10-40%.
Narrative: A cement manufacturer saved more than $1 million in six months with sensors and predictive AI, while an automotive company identified problems in more than 100 machines before buying them, avoiding $112,000 in repairs.
Challenge: Unplanned failures cause costly production downtime, reactive maintenance generates high costs, and lack of visibility into the actual condition of assets prevents efficient planning.
Benefit: ROI of 57 times the investment, reduction of up to 50% of unplanned downtime, maintenance costs reduced by 10-40%, and significant improvement in overall productivity.
Narrative: A parcel delivery company developed a route optimization system using big data and advanced algorithms, implementing it on 55,000 routes to generate massive savings in fuel and operations.
Challenge: Inefficient routes result in high fuel costs, long delivery times, increased vehicle wear and tear, and a negative environmental impact from unnecessary emissions.
Benefit: Annual savings of 10 million gallons of fuel, reduction of 100 million miles traveled per year, savings of $300-400 million annually in operating costs, and reduction of 100,000 tons of CO₂ emissions.
Narrative: A European coffee retail chain applied AI to optimize its product mix and inventory, while large retail chains implemented AI analytics to predict and avoid stock outs.
Challenge: Mismatches between purchases and sales create costly surpluses, stock-outs cause lost sales, and manual inventory management consumes excessive staff resources.
Benefit: 15% reduction in stock levels, 5% increase in staff productivity, 25% reduction in inventory costs, and 30% decrease in stockouts.
Narrative: An auto parts company adopted an AI-powered business intelligence system that monitors its sales, inventory and supply chain KPIs in real time, achieving millions in savings.
Challenge: Delayed reporting prevents timely reactions to critical deviations, lack of real-time visibility generates avoidable losses, and decision making is based on outdated information.
Benefit: Annual savings of $5 million, 95% inventory accuracy, 30% reduction in order processing time, 20% decrease in transportation costs, and 50% greater likelihood of meeting targets on time.
Narrative: A global food manufacturer used production analytics AI to identify slow points on its lines, while a technology company helped an automotive supplier double production by identifying manual bottlenecks.
Challenge: Hidden bottlenecks limit production capacity, repetitive delays affect overall efficiency, and lack of visibility prevents resource scheduling from being optimized.
Benefit: 5% increase in output, recovery of more than $0.5 million per week in productivity, elimination of repetitive delays, and doubling of production on specific lines without investment in assets.
Narrative: A major restaurant chain implemented AI for intelligent staff scheduling, while a customer service center used AI to forecast volumes and optimally assign agents.
Challenge: Manual shift assignment generates costly overtime, peak demand creates staff overloads, and inefficient distribution causes idle time and cost overruns.
Benefit: 25% reduction in overtime and idle time combined, 50% cut in overtime costs, full peak demand coverage, and freeing up management time for strategic tasks.
Narrative: An automotive company incorporated vision and AI systems into its assembly lines to detect more defects than human inspectors, while a financial services company applies AI to keep error rates below 0.1%.
Challenge: Quality errors generate rework costs, manual inspections have consistency and coverage limitations, and undetected defects affect customer satisfaction.
Benefit: Up to 90% increase in defect detection rates, significant reduction in rework required, 24/7 fatigue-free inspections, 99% identification of defects vs. 80% manual, and 20% reduction in false positives.
Narrative: Financial organizations implement AI to review transactions in real time, while a global bank developed a check verification system that automatically identifies fraud.
Challenge: Manual audits are slow and error-prone, compliance is resource-intensive, and late detection of non-compliance results in costly penalties.
Benefit: 60% reduction in documentation errors, 50% fewer incidences of non-compliance, $1.2 billion in avoided penalties, $20 million annual fraud prevention, 170% ROI, and 67% less manual audit effort.
Narrative: A consumer goods company implemented digital twins of its factories to test decisions before executing them in 8 plants, while an automotive company introduced digital twins in stamping presses to optimize operations.
Challenge: Strategic decisions carry significant risks, lack of simulation can lead to miscalibrated investments, and operational adjustments without prior testing can cause costly disruptions.
Benefit: 65% less unplanned downtime, 20% energy savings, 15% less scrap, $52 million annually in net savings, 25% reduction in unplanned downtime, and 20% increase in overall equipment efficiency.
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Narrative: A multinational food company implemented machine learning to refine its demand forecasts, while several industry studies demonstrate the transformative impact of AI on inventory management.
Challenge: Companies face constant stock-outs that result in lost sales, product obsolescence due to excess inventory, forecasting errors that affect planning, and overworked planning teams.
Benefit: 30% reduction in lost sales due to stock-outs, 30% less product obsolescence, 20% less forecast errors, up to 50% less planning team workload, and decreased demand errors by 30-50% with logistics costs reduced by 10-40%.