Industries
Olakai for Manufacturing
Measure AI ROI, govern risk, and prove value across supply chains, production floors, and factory operations.
AI Is Modernizing the Factory Floor. Now Prove the ROI.
Manufacturers are investing aggressively in AI. Predictive maintenance, supply chain optimization, and quality inspection systems are already deployed across leading factories. The technology works. What most manufacturers lack is a way to prove it across the entire operation.
The complexity of manufacturing makes this gap especially painful. A modern factory runs dozens of AI models simultaneously: sensor analytics monitoring thousands of data points, optimization algorithms coordinating global supply chains, and safety systems influencing real-time decisions. Each generates its own data and degrades in its own way. Meanwhile, EPA, OSHA, and ISO compliance requirements increasingly intersect with AI-driven decisions. Without unified measurement and governance, manufacturers can’t connect AI performance to operational outcomes or prove compliance when regulators ask.
By the Numbers
92% of manufacturers say smart manufacturing drives competitiveness — but most are still stuck in pilot.
The gap between AI ambition and measurable results is where ROI goes to die.
Example KPIs for Common Agentic AI Workflows
Manufacturers deploy AI from the supply chain to the production floor. Olakai gives you the KPIs to prove each workflow is delivering value, meeting safety standards, and reducing operational risk.
Example 1: Supply Chain Optimization · Operations
Supply chain inefficiencies waste resources and delay production. This workflow optimizes delivery routes considering traffic, weather, and capacity constraints, coordinates multi-stop logistics, evaluates suppliers by cost and reliability, identifies alternative suppliers proactively, and tracks sustainability metrics. For manufacturers managing complex global supply networks, optimization compounds across every node in the chain.
Key KPIs to measure with Olakai
Logistics Cost per Unit Total transportation and warehousing cost per unit shipped.
On-Time Delivery Rate Percentage of shipments arriving within the promised window.
Supplier Reliability Score Composite score of quality, lead time, and delivery consistency per supplier.
Example 2: Maintenance Scheduling · Operations
Equipment failures cause unplanned downtime that disrupts production schedules and damages revenue. This workflow monitors equipment health from thousands of sensors in real-time, predicts failures before they occur, optimizes maintenance windows to minimize production disruption, and coordinates maintenance crews. For facilities where a single hour of unplanned downtime costs hundreds of thousands of dollars, predictive maintenance is the highest-impact AI investment.
Key KPIs to measure with Olakai
Overall Equipment Effectiveness Composite of availability, performance rate, and quality yield.
Unplanned Downtime Hours Total hours of unexpected equipment outages per month.
Prediction Accuracy Rate Percentage of AI-predicted failures that match actual failure events.
Example 3: Invoice Processing · Finance
Manufacturing finance teams process enormous volumes of invoices from hundreds of suppliers, material providers, contractors, and service vendors. This workflow automatically extracts invoice data, matches to purchase orders, validates amounts and terms, routes for approval, and updates the ERP system. For manufacturers managing thousands of vendor relationships, automation reduces payment delays and eliminates the manual errors that trigger costly invoice disputes.
Key KPIs to measure with Olakai
Cost per Invoice All-in processing cost including labor, exceptions, and system costs.
Processing Cycle Time Days from invoice receipt to payment approval.
Exception Rate Percentage of invoices requiring manual intervention.
Example 4: Lead Qualification · Sales
Manufacturing sales teams manage complex B2B relationships where a single account can represent millions in annual revenue. This workflow scores incoming leads and RFP opportunities based on company size, industry fit, project scope, and engagement signals, routing the highest-potential accounts to senior sales engineers while automating early-stage nurture for developing relationships. In a sector where sales cycles span months and technical evaluations are rigorous, focusing on the right opportunities early is decisive.
Key KPIs to measure with Olakai
Lead-to-Opportunity Conversion Percentage of AI-qualified leads that advance to active opportunities.
Average Deal Size Revenue per converted lead.
Sales Cycle Length Days from lead creation to closed deal.
Example 5: Compliance Monitoring · Legal
Manufacturers navigate a dense regulatory landscape spanning EPA environmental standards, OSHA safety requirements, ISO quality certifications, and industry-specific regulations that vary by geography. This workflow continuously monitors regulatory sources, assesses the impact of new requirements on operations, identifies necessary policy and process updates, and notifies compliance teams with clear action items. For manufacturers operating across multiple jurisdictions, proactive monitoring prevents costly violations and production shutdowns.
Key KPIs to measure with Olakai
Regulatory Response Time Days from new regulation published to compliance action initiated.
Violations Avoided Potential violations identified and remediated before regulatory action.
Audit Readiness Score Percentage of compliance documentation current and complete.
Why Measurement Changes Everything
From Supply Chain Chaos to Measured Efficiency
When you track logistics cost per unit and supplier reliability scores, every AI optimization has a price tag. Your operations team stops debating whether supply chain AI works and starts proving how much it saves.
From Reactive Maintenance to Predictive Confidence
Instead of hoping predictive models prevent failures, you measure OEE improvements, downtime reduction, and prediction accuracy. Every maintenance decision becomes a data point that proves AI is protecting production uptime.
From Invoice Friction to Financial Clarity
Measure cost per invoice, cycle time, and exception rates across your entire vendor base. Prove that automation is capturing early payment discounts and eliminating the manual errors that strain supplier relationships.
From Pipeline Guessing to Sales Precision
Track conversion rates and deal sizes for AI-qualified leads versus manual qualification. Prove to the business that your scoring model is routing the right opportunities to the right engineers at the right time.
From Regulatory Lag to Proactive Compliance
Track how quickly your organization adapts to new EPA, OSHA, and ISO requirements. Measure response time and violations avoided rather than waiting for the next audit to find out where you stand.
From Shadow AI Risk to Controlled Adoption
Replace “block everything” with “measure and govern.” See which unsanctioned AI tools employees are using on the factory floor and in engineering, quantify the risk, and bring high-value usage into the fold with proper oversight.