How Leading Manufacturers Use Data and AI to Innovate and Cut Costs

Georgia-Pacific: Keeping the supply chain moving with 15K AI models in the loop

Pulp and Paper / Consumer Products

Maximize equipment efficiency and maintain supply chain continuity during demand surges, without adding new production lines

Pandemic-driven spikes in demand pushed Georgia-Pacific's aging assets and fragmented supply chains to their limits. Unplanned downtime and slow manual adjustments threatened to derail production. The company needed real-time intelligence to keep operations going and products flowing to shelves.

The company deployed SAS Viya on AWS as its enterprise AI and IoT platform. Each day, 85,000 industrial sensors stream 1TB of data into 15,000+ ML models that optimize settings, detect anomalies, and schedule predictive maintenance. Edge-based computer vision adds instant defect detection on the production lines. The system continuously analyzes performance and recommends proactive actions—all in real time.

• Vibration, temperature, and power data from 85,000+ sensors
• Live process metrics, camera feeds, and quality data (1TB/day)
• Historical equipment and defect records for ML model training

• 10% increase in overall equipment effectiveness during peak demand, resulting in more output with no new plants
• 30% reduction in unplanned downtime through predictive alerts
• 5x increase in processed data volume year over year, fueling smarter decisions every day

Eurocell Optimizes Inventory Management with AI, Unlocking $2.31 Million in Cash and Improving Stock Availability

Building-Products Manufacturing & Distribution

Optimize supply-chain decisions with predictive models to improve stock planning and service-level performance

Eurocell faced challenges with managing a highly mixed portfolio of traded, manufactured, and made-to-order SKUs. It was difficult to keep products available without tying up excess working capital. Manual rules couldn't balance product availability with overstock, which resulted in inefficiencies.

The company developed an inventory management system to optimize stock planning and improve service-level performance. By integrating real-time inventory and demand data, the system generates dynamic restocking targets and automatically feeds them into the ERP system. The new system empowered managers to make informed decisions about ordering and stock transfers, ensuring product availability and reducing excess inventory.

• Continuous feeds of branch-level inventory positions and sales orders
• Historical demand data for training AI demand-forecasting models
• ERP integration for automated write-back of AI-generated targets

• $2.31 million in inventory released, freeing up cash tied up in excess stock
• 6.7% increase in product availability
• Most SKUs now classified as "well-stocked"
• $62 higher average transaction value compared to website orders

Marshalls Optimizes Inventory Management with AI, Saving $368,280 Daily and Improving Stock Allocation

Building-Materials Manufacturing

Optimize supply-chain decisions with predictive models to improve stock levels and order-fulfillment priorities

The volatility caused by COVID flipped Marshalls from excess stock to severe shortages. Manual forecasting could not quickly decide which limited stock should go to which customers or set branch-level minimums, risking lost sales and poor service.

The company developed an AI-based inventory management system to optimize stock levels and order-fulfillment priorities. Moving away from manual spreadsheet-based planning, the predictive model forecasts demand, calculates optimal minimum stock levels, and prioritizes order fulfillment. The new system runs overnight and writes targets straight into Marshalls' ERP system, automating and improving efficiency.

• Historical and real-time sales data by SKU and branch
• Current inventory positions across all depots
• Master-data feeds for product attributes and seasonality
• ERP connectivity for bidirectional data sync (forecasts and order-priority write-back)

• 4,000 AI-powered decisions every day, improving allocation accuracy
• 600 customer orders assigned to trucks daily by the AI model
• $368,280 in order value prioritized daily with optimized stock placement, helping Marshalls reduce carrying costs while meeting service goals

Fristads Reduces Inventory by up to 6M with AI-Driven Supply Chain Optimization

Professional Work-Wear Manufacturing

Optimize supply-chain decisions with predictive models to improve supply planning and reduce excess stock

After COVID-era turbulence, Fristads faced challenges as customers cut their own stock, pushing variability back onto the company. Planners lacked visibility into partner stock levels and downstream sales, which forced them to guess demand based on raw historical data. This led to overstock, long delivery times, and an increase in backorders, which negatively impacted working capital and service levels.

The company developed a supply-planning and S&OP platform that generates accurate demand forecasts by analyzing historical data, seasonality, and market trends. It integrates with sales and supply chain data, ensuring alignment across departments and providing a single version of truth for decision-making. The platform flags shifts in demand early, supports multiregional analysis, and helps synchronize inventory, budget, and production decisions.

• Historical and real-time sales by region/channel to train demand models
• Current inventory positions and SKU attributes for Slim4's multi-echelon calculations
• ERP and BI connections for nightly forecast write-back and side-by-side comparison with sales figures

• Reduced inventory by SEK 60–70 million (≈ €5.3–6.2 M) while maintaining product availability
• Significant reduction in excess stock, freeing up working capital
• Backorders dropped below the 4% target
• Greater cross-department transparency and faster responses to changes in demand

A Manufacturer of Thermal Imaging Equipment: From Quality Blind Spots to Full Visibility with AI

Optical/Thermal-Imaging Equipment Manufacturing

Improve product quality, reduce warranty returns, and streamline distributor collaboration through a unified, AI-driven operations platform

The company faced growing complexity across 120 business units, with limited visibility into product defects. Quality data was scattered across emails and spreadsheets, slowing down improvements and raising warranty costs. A rigid legacy ERP system further blocked agility and strained communication with global distributors.

Brimit partnered with the manufacturer to lead a full-stack digital transformation that involved the following tasks: Ingesting and cleaning 23 GB of historical manufacturing and warranty data from multiple sources, including ERP and MES systems. Developing a time-series forecasting model using historical sales and defect rates to predict seasonal demand fluctuations with a 90% confidence interval. Implementing statistical quality control dashboards using defective parts per million (DPPM) metrics to monitor real-time defect trends. Building a custom defect classification model to identify recurring failure patterns across product lines using labeled warranty claims and service reports. Integrating sales, warranty, and production datasets into a centralized data warehouse to enable cross-functional visibility and reporting.

• The company's Azure data lake, which contained 577 connected tables (production, sales, warranty, distributor data)
• Historical defect data (DPPM) for pattern recognition and root-cause analysis
• Real-time ERP, order, and return streams that feed dashboards and models

• AI-driven analytics to identify high-defect SKUs for significant quality gains
• Forecast-driven production planning with seasonality modeling
• Reduced warranty returns and rework
• Real-time distributor visibility and collaboration via portal
• Faster ERP feature rollouts through DevOps and modular architecture

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Epiroc: Standardizing Steel Quality Across Continents with an AI Factory

Mining & Construction Equipment

Improve consistency in steel treatment across global production sites to reduce defects, rework, and customer returns

Epiroc's global plants collected massive amounts of process data, from furnace temperatures to material properties, but lacked a unified system that could analyze and share insights. This led to uneven steel quality, inefficient energy use, and more and more customer complaints.

Epiroc built a centralized "AI Factory" on Azure using ML, Databricks, and the open-source ESML accelerator. First, the system ingests real-time and historical data—including furnace conditions, steel grades, and quality test results—into a secure, centralized Azure data lake. Then, it auto-generates ML models that predict target steel properties such as density, hardness, and flexibility. Finally, the system feeds optimized heat-treatment parameters back to each plant to facilitate continuous improvement in product quality.

• Real-time and historical furnace data (temperature, cycle time, energy use)
• Steel grade and composition records (3,500+ types)
• Quality-test outcomes for supervised learning
• Data mesh architecture for plant-to-cloud sharing

• Just 60 hours to deploy and launch the entire global AI Factory environment
• 30% reduction in rejections and returns due to consistent steel quality

Hershey: Reducing ingredient waste with AI

Confectionery Manufacturing (Food & Beverage)

Cut ingredient waste and improve batch consistency by predicting product weight in real time, without slowing down the line

To avoid underweight compliance issues, Hershey had to consistently overfill each candy, using more sugar and flour than needed. Manual weight checks were time-consuming and infrequent, meaning operators had little visibility into batch accuracy. This led to excessive ingredient waste, reduced throughput, and limited process control. Hershey needed a real-time system to monitor and correct the filling process.

Hershey developed a temperature-monitoring and weight-prediction system for a 14,000-lb licorice tank with 22 temperature sensors and streamed second-by-second readings into Azure. Using built-in ML, the system predicts the final weight of each batch based on live temperature patterns. Operators receive real-time alerts and build visual dashboards to guide process changes, reducing raw-material waste and removing the need for manual weight checks.

• 60 million+ rows of temperature data per 2-month cycle
• Historical batch weights for supervised learning
• Azure cloud for model training, real-time scoring, and dashboard delivery

• $500,000 in ingredient savings per 1% reduction in overfill
• 50% cut in weight variability across batches
• 20x increase in control responsiveness
• Operators now make 240+ process adjustments per day vs. just 12 in the past

Bridgestone: Scaling tire production with AI-driven process control

Tire Manufacturing

Increase output to meet growing OEM demand while preserving quality, efficiency, and delivery performance

With rising demand from European automakers, Bridgestone needed to boost production at one of its plants. But accelerating traditional processes risked bottlenecks, material waste, and inconsistent tire quality. Scaling required smarter, real-time coordination across machines, materials, and outbound logistics.

In 2019, Bridgestone invested $30.5 million to expand its proprietary tire production control system for monitoring curing, material feed, and machine behavior. The system recommends real-time adjustments to press temperatures, sequencing, and timing to optimize every batch of tires produced. A new 10,000 m² warehouse was integrated with the AI platform, enabling the system to sync production flow with outbound delivery schedules and maintain high line speed without bottlenecks.

• Real-time sensor data from curing presses and building machines (e.g., temperature, pressure, tread splicing)
• Quality inspection results: uniformity, balance, X-ray scans for model retraining
• Inventory and shipment data for AI-driven production-to-warehouse alignment

• +15% in annual output (from 6.3M to 7.2M tires/year)
• +180,000-tire warehouse capacity, enabling faster OEM fulfillment
• 3x overall increase in capacity since the start of the AI program, thanks to continuous upgrades

Global pharma manufacturer: AI-driven resource optimization for scalable, cost-efficient analytics

Pharmaceutical Manufacturing

Optimize cloud resource usage across a large Microsoft Fabric analytics environment to reduce costs, prevent downtime, and ensure stable performance during peak hours

Unmonitored workloads, inefficient scheduling, and lack of real-time visibility led to frequent capacity bottlenecks and rising cloud costs. Business-critical reports were sometimes delayed or disrupted due to background jobs overloading the system. Over-provisioning to stay safe drove up spending without solving the core issues.

Brimit introduced a cloud capacity optimization framework that involved the following tasks: Deploying Microsoft Fabric's Capacity Metrics for live usage tracking. Adding surge-protection logic to throttle or reschedule background workloads during production hours. Building predictive models to analyze usage patterns and automatically scale or pause environments based on demand. Implementing real-time alerting and separating dev/test from production to protect business-critical processes.

• Real-time capacity telemetry from Microsoft Fabric (CPU, memory, job queues)
• Historical workload execution logs to train predictive models
• Metadata on job types, user behavior, and demand timing to guide automated scaling decisions

• 30% reduction in cloud analytics capacity costs thanks to smarter, AI-driven resource management
• Zero performance disruptions during peak hours after isolating and protecting production workloads
• Real-time visibility and forecasting tools now guide long-term analytics planning
• Greater control and scalability without sacrificing speed or stability

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Microsoft: Using AI to unify manufacturing operations brings $60M in annual profit

Consumer Electronics & Hardware

Build a connected, predictive system to improve planning accuracy, reduce production errors, and speed up global order commitments across hardware operations

Microsoft manufactures over 42,000 hardware SKUs (Surface, Xbox, HoloLens, and accessories) across 33 global sites. Disconnected factory data and siloed sales, inventory, and promotion systems made order commitments unreliable (just 40% on time within 5 days). This led to frequent production errors, costly waste, and misaligned forecasts.

Microsoft's teams built a "connected-predictive-cognitive" AI platform that integrates machine telemetry from all 33 plants with inventory data, live sales, and promotional calendars. AI models enable predictive maintenance, demand forecasting, and real-time production scheduling. A unified dashboard provides real-time visibility across engineering, logistics, and leadership, allowing every decision-maker to work from the same operational truth.

• Real-time machine telemetry (status, cycle times, sensor signals) from 33 plants
• Inventory levels and movement data
• Sales order history and promotional campaign data
• A cloud-based OT/IT data fusion layer for enterprise-wide modeling

• Order-commitment accuracy improved from 40% (within 5 days) to 95% (within 48 hours)
• $50M saved annually from reduced production errors
• Additional $10M in yearly savings from waste reduction and process optimization
• 15% improvement in demand forecast accuracy

Pharma manufacturer: Real-time manufacturing insights with a modernized business intelligence stack

Pharmaceutical Manufacturing

Modernize business intelligence infrastructure to deliver faster, scalable, and real-time manufacturing insights without disrupting daily plant operations

The company's legacy Qlik system couldn't keep up with rising data volumes from dozens of global plants. Load times stretched to hours, delaying reports and limiting visibility into critical manufacturing and quality data. The system was also difficult to maintain and didn't support modern governance or DevOps workflows.

Brimit helped re-architect the BI environment by migrating to Microsoft Fabric. Qlik logic was translated into semantic models for Power BI, and data processing was moved to Databricks for better scalability. CI/CD pipelines and Git-based version control enabled governance and improved reliability. Day-to-day reporting wasn't disrupted, as both stacks ran in parallel until the migration was completed.

• Real-time and historical manufacturing datasets (5M+ records per load)
• Gold-tier quality and operations data standardized for reporting
• Git-tracked semantic models and DevOps metadata for automated deployment and testing

• Data load time reduced from 4–6 hours to just 16 minutes (over 90% faster)
• 5M+ records processed in under 1 minute, enabling near real-time dashboards
• No downtime during migration, uninterrupted access to reports
• Lower BI maintenance costs and improved agility with DevOps-driven deployment

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Dutch door manufacturer: Automating blueprint analysis for faster, smarter operations

Interior Solutions Manufacturing

Speed up planning and improve accuracy by automating blueprint analysis, thus freeing up engineers from manual, repetitive tasks

Engineers spent days manually reviewing architectural drawings to identify doors, rooms, and layout details needed for production and installation. The process was slow and error-prone, and it tied up skilled staff with non-technical work. As order volumes increased, the company required a faster and more scalable method to convert blueprints into structured production data.

Brimit developed a computer-vision-powered "blueprint intelligence" system using custom-trained CV models. The system scans architectural plans, detects doors and room layouts, and automatically converts the results into production, installation, and logistics data. The output integrates directly into the company's planning software, providing instant insights.

• Thousands of labeled architectural drawings used to train CV models
• High-resolution blueprint images processed at runtime
• A metadata pipeline transforms visual data into structured outputs for planning and logistics systems

• Blueprint review time reduced from minutes to seconds per project
• Engineers now focus on high-value planning and oversight
• Real-time production and logistics forecasting with improved accuracy
• Fewer errors and misallocations of materials and labor ensured more accurate and timely deliveries

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AI-powered document analysis for paperwork-heavy manufacturing operations

Manufacturing

Give employees fast, accurate access to insights by automating document analysis and enabling natural-language queries across structured and unstructured data

The company relied on a mix of ERP data and a high volume of unstructured documents, from contracts and technical reports to scanned PDFs, to make daily decisions. But searching, extracting, and combining that information was time-consuming and error-prone, and it had to be done manually. Managers struggled with incomplete views, outdated files, and bottlenecks in reporting.

Brimit built a data integration and search system that combines structured ERP records with unstructured documents and makes them searchable via natural-language search. The system uses retrieval-augmented generation (RAG) and text to SQL to deliver context-aware answers instantly. No more SQL or manual digging. Smart update tracking ensures only new content is processed, keeping every piece of information current without manual work.

• Unstructured documents: contracts, PDFs, multi-page reports (PDF, EDI, image) used for automated data extraction
• Structured ERP and business-system tables (customer, order, finance)
• Metadata mapping to preserve hierarchy and link documents to live data
• An exception-handling workflow integrated with AP users for continuous feedback and bot tuning

• 70% faster data preparation through automated extraction, matching, and context handling
• Employees get complete, reliable answers without having to dig through folders or write SQL
• Cross-document queries and live business insights are now accessible in plain language
• Better decision-making thanks to integrated, always-up-to-date document intelligence

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Pharma Manufacturing: AI speeds up multilingual regulatory review, saves $11M in launch risk

Pharmaceutical Manufacturing and R&D

Meet a high-stakes regulatory submission deadline across multiple regions by automating clinical document analysis in multiple languages

The company received thousands of pages of late-stage clinical trial reports—in French, German, and Japanese—just weeks before a multi-region drug submission. Manual translation and review would have taken over 2 months, risking launch delays and up to $11 million in lost revenue. The regulatory team needed a faster, more scalable way to extract key insights, ensure consistency, and prepare a compliant dossier.

The team deployed the AI for Multilingual Document Comprehension tool to process more than 2,000 pages of clinical documentation. The AI automatically extracted efficacy and safety data, translated and compared language versions, flagged inconsistencies, and generated source-linked English summaries. Regulatory reviewers used the AI output to resolve discrepancies and complete the submission on time without compromising compliance.

• Multilingual clinical trial reports in PDF and Word formats (French, German, and Japanese)
• Previously translated documents and approved terminology to tune the AI
• Labeled examples of translation inconsistencies and key clinical markers from multilingual documents

• Full document processing completed in under 48 hours instead of 2+ months manually
• Regulatory submission delivered on time, avoiding a projected $11M in lost revenue
• $800,000 saved on rush translation costs
• Detected critical inconsistencies that human reviewers missed, improving accuracy and regulatory trust

Morton Salt Cuts Order Processing Time by 95% with AI-Powered Document Automation

Salt Production / Basic Materials Manufacturing

Automate documentation, training, and internal information search to improve operational efficiency

Morton Salt faced high labor costs and slow order processing due to manual data entry for complex, multi-line-item purchase orders (POs). Traditional OCR could not handle the varied document layouts, leading to key-stroke errors and delays in order fulfillment and inventory planning. A team of seven specialists was manually re-keying POs, resulting in inefficiencies and errors.

The team developed a document processing software that automates the capture, validation, and integration of PO data. The system used ML models trained on historical POs to automatically extract data from complex, multi-line item tables, without relying on templates. The AI engine was integrated with the existing ERP system using a third-party RPA platform to automatically extract data from complex, multi-line-item tables. This streamlined order processing and significantly reduced the manual workload. Once extracted, the clean, validated data could be integrated directly into the ERP system via an API, allowing staff to focus only on reviewing flagged exceptions.

• Historical and incoming PDF/image POs for AI model training and continuous learning
• Field-level feedback from users to improve extraction accuracy
• Secure API/EDI link to ERP for data posting

• 95% less time spent per document; processing that once took several minutes is now completed in seconds
• Manual workload slashed, freeing up the team's time for higher-value tasks

Siemens: Scaling employee support with an AI-powered HR assistant

Industrial Manufacturing

Make HR information instantly accessible to a global, multilingual workforce, reducing HR workload and improving employee experience

Siemens' HR teams were overwhelmed by repeated questions from 280,000 employees worldwide. Workers had to dig through portals or wait for email replies, and the existing German-only legacy tool wasn't scalable. Siemens needed a smarter, multilingual, always-on solution to cut support volume and boost satisfaction.

Siemens and IBM co-developed a virtual HR assistant. The solution runs across the HR intranet, handling over 200 topics. It answers questions in five languages and connects to live HR systems to provide personalized information (e.g., vacation balances). A self-serve admin panel lets HR teams add or update content without IT, enabling fast scaling.

• Siemens' entire HR knowledge base: policies, FAQs, and process docs
• Live employee data from HR systems to provide tailored responses

• Over 1 million HR queries handled monthly, greatly deflecting a number of routine tickets
• Significant time and resource savings for HR teams
• Faster, self-service answers improve employee experience

Unilever: Reinventing hiring and HR support with AI

Consumer Goods Manufacturing

Transform early-career hiring and global HR support using AI to improve speed, consistency, and employee satisfaction at scale

Unilever receives over 1.8 million job applications annually to hire ~30,000 people. This process took months, demanded tens of thousands of recruiter hours, and left many candidates without feedback. Meanwhile, HR teams were overwhelmed by routine employee requests across dozens of countries.

The company rebuilt its talent and employee experience pipeline by optimizing recruitment and implementing a virtual HR assistant.

1. AI recruiting workflow

• Game-based assessments measure candidates' logic, risk appetite, and reasoning skills
• AI analyzes video interviews using natural language processing and behavioral signals (facial expressions, tone, etc.)
• Top candidates attend human-led Discovery Days for final selection

2. AI-powered HR assistant (built on Microsoft Bot Framework):

• Available in 36 countries and supports 32 languages
• Provides personalized HR, IT, and workplace support tailored to employee location and role
• Deflects high volumes of routine queries, freeing up HR teams to focus on strategic initiatives

• Historical data on top-performing hires for model training
• Millions of candidate records and video interviews for scoring refinement
• Internal HR documents, live HRIS data, FAQs, and schedules

• 70,000 recruiter hours saved per year through automation
• Time to hire cut by 75%, from 4 months to ~4 weeks
• All candidates receive personalized feedback, improving brand perception

Western Digital: Cutting IT ticket volume with an AI help desk bot

Data-Storage Manufacturing

Deliver 24/7 IT support at scale, reduce ticket load, and enable instant resolution of routine tech issues through an AI-powered virtual assistant

Western Digital's global IT help desk was overwhelmed by ~60,000 tickets per month, mostly for routine tasks like password resets and account unlocks. Employees had to wait days for resolution, while around-the-clock support across time zones remained a challenge. The CIO aimed to shift toward a "zero help-desk" model to increase efficiency and responsiveness.

The company launched a digital IT assistant built with Moveworks and deployed inside Microsoft Teams. Uses natural language understanding (NLU) to interpret employee requests. Automatically handles tasks like password resets, account unlocks, hardware requests, and ticket updates. Sends targeted IT alerts and promotes digital tool adoption.

• Historical IT tickets and articles in internal knowledge base
• Secure links to identity and access systems (consolidated 65 legacy login systems into one)
• Ongoing chat logs and AI-readiness heatmaps

• Up to 33% reduction in ticket volume (from ~60,000 to ~40,000/month)
• Around 3,000 account-access issues resolved monthly
• > 90% faster resolution time, from days to minutes or seconds
• Equivalent productivity of 25 full-time support agents now automated

Black & Company: Streamlining invoice processing with AI-powered automation

Industrial Distribution / Manufacturing Supplies

Automate invoice processing to reduce manual workload, eliminate delays, and scale operations efficiently

Black & Co. relied on a fully manual, paper-based accounts payable (AP) process. Staff had to match thousands of printed invoices to receipts using a 30-year-old ERP system with minimal automation. The growing volume of vendor invoices led to delays, errors, and strain on the AP team.

The company delivered an AP automation platform that was integrated with the legacy ERP. Used OCR and machine learning to extract data from invoices, classify vendors, and match invoice details (quantities, prices, SKUs) to purchase orders. Automated routing for approvals and payments. Reduced staff involvement to handling exceptions only.

• Historical invoice and payment records
• Vendor and purchase order master data
• ERP integration data

• 50% faster invoice processing
• 25% fewer late payments
• 80% of invoices processed end to end without manual input
• 15% increase in early-payment discounts captured, improving cash management

Global Coatings Manufacturer: Centralizing and accelerating accounts payable with AI and automation

Paints & Coatings Manufacturing

Automate and centralize invoice processing to improve speed, accuracy, and cross-regional visibility

The company's accounts payable (AP) team was processing over 350,000 invoices per year manually across global sites. This caused delays, frequent duplicates, inconsistent data formats, and poor visibility, straining vendor relationships and slowing financial decision-making.

The company delivered an automation suite to centralize and accelerate invoice processing across all global sites. A centralized invoice-processing hub that routes all incoming invoices through a single digital workflow, regardless of format or origin. TruCap+ (IDP) to extract and validate invoice data from varied formats. A chatbot (RPA) to automate routine AP tasks. Real-time dashboards for monitoring invoice status and compliance.

• Historical invoice data across global regions (PDFs, scans, EDI, etc.)
• Vendor and payment master data
• Internal approval rules and financial workflows

• 99.7% processing accuracy through intelligent data capture
• Invoice cycle time reduced from 5 days to 3 days
• Centralized visibility across global AP operations, improving compliance and vendor trust

Stant Streamlines AP Process with RPA, Cutting Invoice Backlog from Weeks to a Couple of Days

Automotive Components Manufacturing

Automate documentation, training, and internal information search to modernize the accounts payable (AP) shared-services operation

The company manually matched supplier invoices with purchase orders and delivery receipts. This led to backlogs stretching up to 3 weeks, causing delays in reporting and increasing the risk of late supplier payments. Additionally, the team spent a significant amount of time and effort reconciling multiple financial systems and validating receipt data.

The company deployed a robotic process automation platform to automate the following processes: Invoice matching and data entry. Creation of manual invoices, data validation, and GL entry coding. Error record filing and automated routing of exceptions through an approval workflow, allowing the AP team to focus on higher-value tasks like supplier management.

• Digital copies of incoming supplier invoices (PDF, EDI, image) used for automated data extraction
• Financial reporting and ERP systems, enabling bots to post validated data and update metrics
• An exception-handling workflow integrated with AP users for continuous feedback and bot tuning

• 80% straight-through processing (STP) of invoices
• Zero data-entry errors recorded
• 94% of targeted supplier invoices automated
• Invoice backlog reduced from 3 weeks to 4 days