Manufacturing quality monitoring

Southern Minnesota Beet Sugar Cooperative (SMBSC) modernized intake quality inspection for beet loads during peak harvest by deploying an AI-powered computer vision system with Tactical Edge AI.The solution replaced manual visual grading and binary inspection with continuous 0–100 impurity scoring to improve consistency, visibility into quality trends, and throughput across receiving operations.

Southern Minnesota Beet Sugar CooperativeAgriculture

AlphaBOLD, a consulting firm, helped manufacturing clients streamline operations through Microsoft Dynamics 365 enhanced with Copilot. By integrating data from supply chain, production, sales, and inventory into a single platform, AlphaBOLD improved real-time decision-making for multiple manufacturers.For a mid-sized OEM, the solution enabled real-time production monitoring, automatic alerts, and data-driven adjustment of shop floor processes, slashing reporting time and downtime. An automotive parts supplier saw smarter inventory management that reduced excess stock and lowered capital costs by over $400K a year.The implementation included IoT integration for shop floor automation, AI-driven forecasting to better match production with demand, and custom human-friendly workflows that accelerated user adoption. Across projects, Dynamics 365 helped companies reduce costly delays and minimize time spent on manual data management.Improvements were also observed in training times (reduced by half), adoption rates (doubled within three months), and overall company responsiveness due to unified access to information. AlphaBOLD tailored the system for each client, moving beyond templates to address specific industry and business workflows.

OEM clientManufacturing

Jabil, a global manufacturing company, faced challenges with traditional, manual quality control processes that were slow, inconsistent, and prone to errors. To address these issues and optimize product quality, Jabil implemented an AI-powered quality control system powered by Microsoft technologies.The solution utilizes Azure AI Vision for automated visual inspection across multiple production lines, enabling real-time and objective defect detection. Automated defect alerts and corrective workflows were set up using Power Automate. The integration of Dynamics 365 provided end-to-end data visibility across production, quality, and management domains.Copilot is leveraged to assist production teams with decision support and troubleshooting in real time. These technologies together reduced manual errors, increased the speed of inspections, and helped to identify root causes of recurring defects.The new system enabled a scalable, reliable, and repeatable quality control standard across Jabil's factories. It also allowed Jabil to shift from reactive to proactive maintenance, supporting continuous improvement and compliance.

JabilManufacturing

This article analyzes the architectures and use cases of leading Manufacturing Execution Systems (MES) including Microsoft Dynamics 365 Supply Chain Management, Siemens Opcenter, and other major platforms enhanced with AI and cloud technologies.

SiemensManufacturing

Schaeffler AG faced significant challenges in harmonizing and extracting insights from factory data distributed across IT and OT systems worldwide. To address this, Schaeffler partnered with Avanade to implement an end-to-end solution using Microsoft Fabric and an AI agent in Azure AI, as part of Microsoft Cloud for Manufacturing. The platform allows Schaeffler employees to access and analyze key production data in natural language, resulting in actionable, near real-time insights covering machine downtime, quality issues, and productivity metrics. The solution leverages standardized data pipelines to unify and contextualize disparate datasets, driving up operational efficiency in over 100 plants. Factory workers and managers now resolve production issues faster and more independently, reducing both troubleshooting time and unplanned downtime. This approach highlights the democratization of advanced analytics and generative AI for factory staff, not just specialist teams, representing a scalable foundation for future innovation across Schaeffler’s global manufacturing footprint.

Schaeffler AGManufacturing

Schaeffler AG sought to enhance quality control and minimize downtime in its advanced manufacturing operations.Traditional manual inspection methods and legacy systems limited defect detection rates and delayed response to problems on the production line.To address these limitations, Schaeffler implemented the Microsoft Factory Operations Agent, powered by Large Language Models, to analyze massive real-time data streams and detect quality issues and equipment anomalies as they occur.The solution enables automated identification and diagnosis of production defects through AI-driven analytics and recommendations, empowering faster resolution and increasing operational efficiency.The result was a significant reduction in downtime, more rapid problem mitigation, improved overall product quality, and greater visibility for factory operators.This case demonstrates large-scale adoption of AI-driven manufacturing analytics for quality control and continuous improvement at scale.

Schaeffler AGManufacturing

Epiroc, a global Swedish manufacturer of mining and construction equipment, faced challenges ensuring consistency in steel quality across its worldwide facilities. Relying on disparate local systems, Epiroc struggled to leverage massive amounts of operational data, impacting both product quality and process efficiency. By deploying a modern 'AI factory' on Microsoft Azure, the company centralized data collection and utilized Azure Machine Learning, Data Factory, Databricks, and ESML (Enterprise Scale Machine Learning) to build automated predictive models for its heat treatment process. The ESML accelerator and local partner Molnbolaget enabled the rapid deployment (within 60 hours) of an architecture spanning multiple services with secure networking. Epiroc now benefits from improved quality control, reduced waste, best-practice sharing across sites, and support for sustainability initiatives—all powered by Microsoft’s cloud and AI capabilities.

EpirocManufacturing

Microsoft Cloud for Manufacturing promotes intelligent factories using AI, Industrial IoT, and data analytics. Real-time defect detection and operational visibility enhance Overall Equipment Efficiency (OEE). Improved frontline worker connectivity drives productivity and communication. Factories benefit from sustainable practices and adaptive manufacturing processes.

ALTEN, a global engineering and technology consulting firm, partnered with a high-volume manufacturer to implement predictive maintenance powered by Microsoft technologies.The solution utilized AI and machine learning models, hosted on Azure, to monitor and assess the quality of ball bearings in real time.By enabling operators to anticipate quality issues up to an hour before production completes, the system empowered real-time operational adjustments.This approach reduced defective products, minimized manufacturing waste, and extended machinery lifespan, delivering measurable sustainability benefits.Real-time dashboards equipped operators with actionable insights, fostering quick, data-driven decisions and more streamlined workflows.The implementation demonstrates how data-driven insight and AI can transform manufacturing operations, sustainability, and productivity.The article also references a second ALTEN Group case (with VMO and a Taiwanese firm leveraging AWS and GCP), but this instance is centered on the Azure-based predictive maintenance deployment for industrial manufacturing.

Siemens announced a new industrial foundation model (IFM) developed in partnership with Microsoft to revolutionize automation and engineering processes across industries. The foundation model utilizes Siemens' domain-specific expertise and data archives, along with cutting-edge technologies like virtual programmable logic controllers (vPLCs), to optimize production systems and alleviate workforce shortages. Integrated systems with Microsoft's Azure cloud, Industrial Copilot tools, and vPLCs have been deployed in Audi factories to enhance production speed and scalability. Siemens emphasizes the game-changing nature of industrial AI in its ONE Tech Company strategy, further supported by collaborations with Accenture, Nvidia, AWS, and more. Real-world implementation at Audi includes AI-powered optical inspections and the virtualization of the shop floor for flexible production.

SiemensManufacturing

Capgemini, in partnership with NVIDIA, deploys custom agentic AI solutions for manufacturing and other sectors. Leveraging the NVIDIA NIM and AI Enterprise platform, Capgemini’s agentic gallery simplifies building and deploying industry-specific agents, speeding up digital transformation. In manufacturing, smart camera-based process monitoring helps improve shop floor safety and performance. Capgemini is also highlighted for its collaboration with Telenor in building a secure, sovereign AI Factory for Norway, offering companies AI-driven innovation with full data control and sustainability. The solutions demonstrate Capgemini’s expertise in operational efficiency, regulatory compliance, scalable agentic AI, and responsible, sustainable technology adoption.

TelenorManufacturing

ZEISS Digital Innovation won the Microsoft Intelligent Manufacturing Award 2025 for its pioneering solution in precision manufacturing. The implementation integrates AI and IIoT technologies to bring closed-loop, autonomous quality assurance directly into the manufacturing workflow.Their solution combines virtual and physical metrology, enabling real-time quality prediction and instant production process adjustments. This results in machine-integrated process optimization where AI models analyze part and process data, improving quality outcomes before defects occur.By leveraging the Microsoft technology stack, ZEISS enables autonomous manufacturing for complex and traditional industries (such as semiconductors and machining) where efficiency improvements are difficult through conventional means.The solution delivers measurable benefits, including significant productivity gains, cost reductions, and a return on investment within a few months of deployment. It is suitable for brownfield (existing) manufacturing environments and scales well with growing production needs.The project showcases the strategic role of data-driven and autonomous manufacturing in keeping production competitive for European manufacturers. Immediate process adjustments enhance yield, reduce waste, and build long-term operational resilience.

ZEISS Digital InnovationManufacturing

SICK AG, a leading provider of sensor-based automation solutions from Germany, implemented an AI-driven assistant system to transform its manufacturing processes. The solution uses predictive quality analytics and real-time process control to detect and address production defects before they escalate. With deep integration between industrial sensors and AI, the system analyzes production data to create a 'fingerprint' for defective products and proactively intervenes. This enabled SICK AG to drastically reduce failure costs, increase manufacturing yield, and promote sustainable production by reducing material waste. The project was recognized with the Microsoft Intelligent Manufacturing Award (MIMA) 2025 for its disruptive impact on the field.Leveraging sensor technology, SICK AG’s AI system enables continuous monitoring and analysis throughout the production line, allowing early detection of quality issues and facilitating direct process adjustments. The result is improved efficiency, better product quality, and significant cost savings. This innovation showcases the combined strength of data, AI, and process control in next-generation manufacturing operations.

SICK AGManufacturing

Manufacturers leverage Microsoft Fabric to drive quality control, predictive maintenance, and supply chain optimization. Fabric combines OneLake's unified storage, built-in Spark and ML frameworks (including PyTorch and TensorFlow), Azure Data Factory orchestration, and Power BI visualization in a single platform. This ecosystem enables manufacturers to develop, test, and deploy computer vision models for defect detection, predictive analytics for maintenance, and forecasts for supply chain management—all within a unified data landscape. Real-world cases include automated defect recognition, streamlined production, early failure alerts, and optimized inventory planning. The unified approach reduces data silos and accelerates AI adoption on the factory floor, providing rapid prototyping and easy scaling across multiple business areas. Fabric’s built-in orchestration and visualization tools further enhance cross-team collaboration and business leader engagement.Computer vision systems were deployed for automating quality inspections, reducing error and manual labor. Predictive maintenance pipelines redefined asset uptime using Spark analytics and IoT data. Supply chain optimization models integrated disparate sources into real-time, actionable dashboards for logistics and scheduling. Fabric’s architecture supports low-code deployment of complex end-to-end analytics processes, significantly reducing setup time and operational complexity for industrial teams.

Sight Machine addressed a persistent challenge in manufacturing data management—fragmented and inconsistent naming conventions across data sources. The company, which specializes in advanced manufacturing analytics, developed the Factory Namespace Manager leveraging Microsoft Azure AI Foundry's language model catalog. This solution automates the process of mapping disparate manufacturing data schemas into unified, standard corporate namespaces, essentially creating a common data dictionary for the organization. By utilizing AI, Sight Machine has simplified the integration and analysis of vast amounts of production data, empowering manufacturers to more rapidly deploy analytics, optimize processes, and ensure better data governance. Sight Machine's solution demonstrates the growing trend of AI-driven automation in industrial settings and showcases how modern cloud platforms, such as Azure AI Foundry, can add substantial value beyond basic hosting and storage. Through improved data standardization, the company is paving the way for greater operational efficiency in manufacturing enterprises.

Loopr, in partnership with Xenus Technology Limited, leverages Microsoft Fabric, Microsoft Copilot, and Power Automate along with AI Machine Vision technology to transform manufacturing quality control. The solution automates the quality inspection process, detecting defects in real-time and generating actionable manufacturing analytics. By integrating with Microsoft Fabric, manufacturers can consolidate and analyze production data across systems to improve process visibility. The system is tailored for each client, optimizing AI inspection workflows to address specific production challenges.Manufacturers utilizing Loopr's solution benefit from reduced faulty product output, operational efficiencies, and informed quality decision-making. The collaboration drives better product standards and overall operational excellence. The article provides practical insight into the real-world use of Microsoft technology in manufacturing quality management, with measurable improvements cited.The case demonstrates holistic data integration and action-oriented analytics, going beyond traditional visual checks by introducing scalable, AI-powered automation.

Hitachi, a global leader in manufacturing, introduced an automated quality inspection solution using Robotics and AI tools integrated with the Microsoft ecosystem.Demonstrated at Microsoft Ignite, the solution aims to increase operational efficiency and improve defect detection accuracy in manufacturing environments.AI-powered robotics automate visual inspection tasks that were previously manual, enabling faster throughput and improved product quality.The approach significantly reduces the burden on human inspectors while minimizing the risk of undetected defects.Although detailed metrics were not provided, customer engagements and event demonstrations highlight positive outcomes in efficiency gains.

HitachiManufacturing

Zettl Automotive GmbH, a German automotive manufacturer, faced increasing demands for higher quality assurance due to complex, connected vehicle ecosystems and the integration of Industry 5.0 technologies. To maintain their reputation for premium solutions and meet the needs of OEMs and Tier 1 suppliers, Zettl Automotive implemented AI-powered automated visual quality inspection solutions using Microsoft Azure. Their offering covers every aspect of the manufacturing process, ensuring high precision and preventing costly product recalls or failures.As the automotive sector becomes more software- and connectivity-focused, the number of potential failure points rises. Zettl GmbH's approach integrates automation and artificial intelligence across their QA workflows, reducing human error, accelerating delivery without sacrificing quality, and positioning them at the forefront of industry innovation.With a broad footprint (12 locations, serving 230+ customers, over 400 employees), Zettl Automotive has set new industry benchmarks for quality, providing tailored QA services while maintaining cost efficiency and superior operational excellence.By partnering with leading technology providers and leveraging innovative software, Zettl remains competitive and ready to meet the evolving requirements of Industry 5.0.

Zettl Automotive GmbHAutomotive

Siemens, in partnership with Microsoft, has launched the Siemens Industrial Copilot, a generative AI-powered agent based on Azure OpenAI Service. The Copilot is transforming manufacturing operations at over 100 companies across Europe and the US, including thyssenkrupp Automation Engineering. It automates complex engineering tasks such as sensor configuration and reporting, streamlines workflows, and combats skilled labor shortages. Leveraging deep integration into Siemens engineering software and Xcelerator platform, the Copilot reduces engineering cycle times and enables scale across product lines, with plans for global rollout by thyssenkrupp. The solution supports advanced manufacturing, including battery quality assurance for electric vehicles, and enhances operational resilience and sustainability. Over 120,000 engineers are able to use the Copilot, emphasizing its wide impact and usability in real-world scenarios. The integration with Azure provides security, reliability, and scalability for industrial automation.

thyssenkrupp Automation EngineeringManufacturing

Grasim Industries, a division of Aditya Birla Group in India, needed to improve its fiber quality control as manual procedures were slow, subjective, and labor-intensive.Defect detection depended on lab technicians performing lengthy, inconsistent manual analyses.Mandelbulb Technologies developed and delivered a deep learning vision model using Microsoft Fabric as the unified analytics platform.The solution integrated Fabric Real-Time Analytics, Data Activator, Power BI Embedded, OneLake, and Azure Kubernetes for highly scalable, automated QC.Processing time per batch was reduced from four hours to just one minute and defect detection accuracy reached 98%.Analysis throughput doubled, and Grasim now meets higher quality standards while cutting operational costs.The solution was prototyped and deployed in three weeks.Sustainability ambitions are supported by further leveraging Fabric and Azure for energy-efficient operations.

Grasim IndustriesManufacturing

Körber, a German manufacturing technology company, has transformed pharmaceutical manufacturing by turning its leading PAS-X MES product into a cloud-based SaaS solution. Utilizing Microsoft Cloud for Manufacturing and Azure Kubernetes Service (AKS), Körber enables scalable, compliant, and agile production management for global pharma companies. The implementation leverages Microsoft Defender for Cloud to ensure cybersecurity and integrates Copilot to accelerate on-site information access via natural language queries. Customers gain real-time, validated visibility across their supply chains and can expand seamlessly from startups to enterprise scale. Early results include significantly reduced IT burdens, investment costs, and time to market. Körber’s future plans incorporate further Copilot use for advanced process automation and Power BI for greater analytics, aiming to create even more efficient, data-driven plants.

KörberPharma

Grupo Bimbo, a global baking leader, implemented Azure IoT across its US facilities (Bimbo Bakeries USA) to address the complexities of managing high-volume, perishable baked goods. Previously reliant on manual data collection for quality control, efficiency, and compliance, the organization sought a real-time analytics solution to improve production speed and agility. After piloting solutions from various providers, Azure IoT proved most adaptable—facilitating seamless integration with existing Oracle systems and diverse bakery machinery. The GBConnected solution combined PLC sensors, Azure IoT, Azure Data Factory, Azure Data Lake, and Power BI to deliver machine-level and operational insights direct to factory teams and management. This approach enabled rapid, data-driven decisions, reduced downtime, and significantly improved Overall Equipment Effectiveness (OEE). Comprehensive change management ensured frontline workers could leverage new insights to enhance continuous improvement, further accelerating Grupo Bimbo's digital transformation agenda.

Grupo BimboConsumer & Food

Intertape Polymer Group, Schaeffler AG, and Bridgestone EMIA have accelerated their industrial digital transformation by implementing Microsoft Fabric's manufacturing data solutions, Azure OpenAI Service, and Copilot-powered tools. These leading manufacturers faced challenges around siloed IT and OT data, hindering their ability to extract actionable insights and drive operational improvements. By unifying data across systems with Fabric and deploying conversational copilots for the factory floor, frontline workers are now empowered to use natural language to perform root-cause analysis, access metrics, resolve issues, and enhance productivity.Partners such as Accenture and Avanade supported the integration and implementation, creating repeatable data patterns that enable scalable innovations. The copilots leverage Azure OpenAI Service and robust guardrails to ensure accurate, safe responses within complex manufacturing environments. Early results include improved production performance, democratized information access, faster resolution of manufacturing disruptions, and enhanced decision-making from top management to the shop floor.These use cases, highlighted during Hannover Messe 2024, demonstrate the value of unifying manufacturing data for analytics and AI, democratizing data access, and accelerating operational agility across global production facilities.

Siemens, in collaboration with Microsoft, has launched the Siemens Industrial Copilot, leveraging Azure OpenAI Service to drive transformation in manufacturing. This AI-powered tool assists developers with coding, debugging, and optimizing industrial processes. Engineers gain access to powerful generative AI capabilities for quick analytics, root-cause assessments, and process corrections, significantly enhancing operational efficiency across industries. This partnership heralds a leap forward in digital manufacturing, enabling advanced automation and AI adoption globally.

SiemensManufacturing

Capgemini, in collaboration with Microsoft, has developed an AI-driven computer vision framework tailored for manufacturing facilities. This innovative solution utilizes Microsoft AI and computer vision technologies, providing an efficient process for defect detection, proactive maintenance, and emission monitoring. The system leverages synthetic data generated through metaverse simulations to train AI models comprehensively, overcoming the challenges posed by a lack of real-world visuals. The framework offers scalability, regulatory compliance, improved product quality and worker safety, as well as cost-effective plug-and-play operations.

Unnamed Manufacturing clientsManufacturing

A leading European industrial products manufacturer, operating over 70 sites worldwide, faced challenges with non-standardized IT across its global footprint. The organization partnered with DXC, Dell Technologies, and Microsoft to create a hyper-converged infrastructure (HCI) stack powered by Microsoft Azure. Sensor and machine data from factory floors are now streamed to Azure SQL Edge for edge analysis and routed via Azure Arc to a managed, centralized Azure SQL instance. This standardized platform enables rapid global data collection, real-time insights, and unified workflows. Applications developed by Luxoft further optimize production, providing visualization dashboards, operator tools, and advanced AI/ML-driven process optimization. End-to-end managed services, compliance, and lifecycle management are provided by DXC and Dell. The transformation empowers company-wide cost reductions, better quality control, increased innovation, and a shift toward data-driven, standardized operations across all manufacturing locations.

Procter & Gamble (P&G), one of the world's largest consumer products companies, faced high defect rates and significant losses due to damaged diapers during the manufacturing process of Pampers. To address this, P&G launched the Hot Melt Optimization platform, using proprietary IoT sensors on assembly lines and Microsoft Azure's predictive analytics and edge analytics. The system monitors glue temperature and pressure in real time across 11 US manufacturing plants. Machine learning algorithms, supported by an AI rules engine within Azure, identify and correct anomalies before they cause large-scale production issues. This data-driven approach has eliminated 70% of defective diaper output, resulting in seven-figure weekly savings. The project also improved uptime, reduced scrap, and supported higher production capacity. The system was extensively piloted, using an industrial control database and Grafana for monitoring, before rollout across the network. P&G credits collaboration with Microsoft and Rockwell Automation for enabling these reliability, efficiency, and cost improvements.P&G now demonstrates how predictive analytics, IoT, and edge computing can have significant, measurable impacts on consumer goods manufacturing.

Procter & GambleConsumer & Food

HAYAT HOLDING and its KEAS wood-based panel operation built an end-to-end machine learning pipeline to predict MDF panel quality and recommend optimal adhesive usage.Plant sensor and process data are ingested from the production line, batch-aligned features are engineered, and SageMaker models predict multiple quality parameters as well as the minimum glue amount needed to meet quality thresholds.The solution supports near-real-time operator dashboards and edge inference for production use.

HAYAT HOLDINGManufacturing

P&G partnered with Microsoft to deploy AI-powered solutions on Azure, revolutionizing their manufacturing processes at over 100 sites. Predictive analytics and real-time monitoring have improved productivity and quality control.

Procter & GambleConsumer & Food

Japanese manufacturing companies are leading the adoption of Internet of Things (IoT) technologies to optimize production processes and increase operational efficiency. According to the Microsoft IoT Signals report, businesses in Japan are investing in IoT solutions that combine AI, edge computing, and digital twins to address manufacturing challenges, particularly around automation, quality assurance, and productivity. Thoughtful planning has led to projects delivering measurable improvements, such as reduced human error and improved product quality. While the strong presence of legacy systems poses transformation challenges, firms are benefitting from strategic integration of modern technologies to remain competitive. The article details how IoT applications are used to manage industrial automation and monitor production flow, facilitating efficiency gains and stronger market positioning.

Japanese manufacturing companiesManufacturing

GF Casting Solutions, a Swiss leader in lightweight automotive and energy sector components, partnered with Microsoft to optimize its high-pressure die-casting production ramp-up. By integrating Azure Machine Learning with on-site process data, the team sought to minimize waste and maximize product quality early in the process. The solution analyzes extensive manufacturing process data, identifying key parameters that impact final product quality. Production experts use these insights to implement targeted adjustments in the ramp-up phase, leading to significant reductions in scrap rates and waste while maintaining efficiency and flexibility. The collaboration is ongoing, promising continuous improvements in operational sustainability.

GF Casting SolutionsManufacturing

Unilever, a global leader in consumer goods and manufacturing, undertook a deep digital transformation to empower employees and meet evolving consumer demand. Shifting from a project-based mindset to a platform strategy, Unilever used Microsoft Azure as an architectural backbone, enabling real-time data democratization and agility across its supply chain. The company implemented Azure IoT-based digital twins in its factories, enabling automated data collection, advanced analytics, and machine learning for operational improvements such as controlling moisture levels and optimizing production batch processing. Power BI allowed Unilever's employees to create their own reports and reduce false alerts in production lines, leading to increased productivity and reduced manual effort. By introducing Power Apps, employees built and deployed quality assurance tools, which replaced paper-based processes, streamlined factory operations, and contributed to sustainability by saving energy and materials. Microsoft Teams and Microsoft 365 strengthened global connectivity and knowledge sharing among Unilever’s 155,000 employees, further enhancing productivity and rapid decision-making. The digital strategy, developed in partnership with The Marsden Group, delivered 90% fewer actionable production alerts, significant energy savings in factories, and built a data-intelligent organizational culture to support future growth.Unilever democratized access to insights for non-technical staff, built strong collaboration communities for sharing best practices, and created the technological foundation to expand digital twins to dozens of global plants, accelerating value realization.

UnileverManufacturing

Jabil, a global manufacturing solutions provider, sought to reduce overhead costs and improve the quality of electronic parts inspections on their production lines. To support this goal and accelerate adoption, Jabil’s leadership undertook an AI-driven strategy emphasizing cultural transformation alongside technology investment.Jabil leveraged Microsoft Azure AI and guidance from the AI Business School to launch a non-technical, business-led approach, focusing on empowering employees and building trust in AI-enabled processes.Automated AI inspections replaced repetitive, manual quality checks, freeing up staff for higher-value work and advancing overall factory productivity.Key to the strategy’s success was transparent communication by leadership about AI’s role in the organization, building an inclusive culture where employees trusted and adopted new AI solutions quickly.The initiative led to quantifiable reductions in operational overhead, increased defect detection accuracy, and faster onboarding of AI technology.

JabilManufacturing

Cummins, a global automotive and power technology manufacturer, is transforming its 90+ worldwide factories to create a 'factory of the future.' In response to evolving market demands, including the transition to electric power systems and the need for smarter products, Cummins launched an internal digital accelerator to drive technological innovation across its operations and workforce.The company is deploying a ‘technology raft’ including industrial Internet of Things (IoT), AI, cobots (collaborative robots), and analytics software to modernize manufacturing and ensure worker safety. Virtual and augmented reality tools are being piloted for next-generation workforce training. The initiative aims to improve automation, quality, and efficiency, while also making manufacturing roles more attractive to a younger, tech-savvy workforce.Ergonomics analytics, smart sensors, and human-robot collaboration remove workflow bottlenecks and reduce safety incidents. Workforce development focuses on upskilling both existing staff and recruiting new talent, with partnerships and community outreach to fuel the industrial talent pipeline.This large-scale project creates new benchmarks for digitally enabled, safe, and efficient manufacturing in the automotive industry.

CumminsAutomotive

Outokumpu, a global leader in stainless steel based in Finland, initiated a multi-year digital transformation project at its Tornio facility, aiming for full-scale digital manufacturing. The Outokumpu Digital Platform (ODP) was built in partnership with Microsoft and based on the Microsoft Azure cloud. The platform supports AI-powered process optimization, predictive maintenance, and quality control. In just the first six months, concrete solutions were implemented that are expected to yield significant improvements in production reliability and quality. This digitalization effort demonstrates a holistic approach, including a focus on data-driven decision making and empowering employees amidst change management. The upgraded platform aims to reduce lead times, increase production capacity, and set a benchmark for future innovation both at Tornio and internationally across Outokumpu's other operations.

OutokumpuManufacturing

Piramal Glass, a global glass packaging manufacturer, implemented a digital transformation of its manufacturing operations using Microsoft Azure IoT Hub and Azure Cognitive Services. The new solution, called Real-Time Manufacturing Insights (RTMI), was deployed across 46 production lines in plants across India, Sri Lanka, and the United States. The platform acquired sensor data at every stage of production, enabling real-time visibility into manufacturing operations. This allowed the company to analyze production line losses, monitor quality parameters, and provide actionable insights to plant personnel via dashboards and real-time alerts. The project was implemented by Microsoft partner Precimetrix and included edge gateway solutions, cloud data aggregation, and custom reporting capabilities. As a result, Piramal Glass achieved enhanced manufacturing efficiency, democratized access to production data, and significant cost savings. This digital transformation placed Piramal Glass at the forefront of Industry 4.0 adoption in the glass manufacturing sector.

Piramal GlassManufacturing

At Hannover Messe, ABB demonstrated AI-driven industrial solutions, integrated with Microsoft Azure. The use cases ranged from productivity enhancers to safety systems across manufacturing disciplines. The collaboration also focused on eco-efficiency and showcased food processing advancements by Bühler AG.

ABBManufacturing

Uddeholm, a historic Swedish steel manufacturer, partnered with CGI to enhance sustainability and profitability using artificial intelligence on Microsoft Azure. By analyzing vast amounts of production data, the company developed AI and machine learning models to predict and prevent the formation of cracks in steel. This enabled earlier detection and improved process efficiency, reducing material waste and energy consumption. Uddeholm established a data governance model and built a central AI-IT infrastructure hub to support ongoing and future data-driven initiatives. Additional AI projects have helped the company with predictive maintenance and real-time detection of leaks, contributing to operational efficiency and reduced costs. The transformation puts data-driven decision-making at the heart of Uddeholm’s operations, aligning business growth with ambitious sustainability objectives. The initiative showcases how collaborative efforts and enabling technology can support industry leaders tackling key environmental and economic challenges.The project featured a comprehensive approach including high-quality data gathering, interdisciplinary information sharing, and dedicated organizational structures such as an AI-IT hub. The partnership continues to identify new areas for AI applications within production and supply chains.

UddeholmManufacturing

HMD Global, a smartphone manufacturing company, faced the challenge of scaling production and business operations rapidly while controlling IT and staffing costs.The company upgraded from an existing SAP environment to RISE with SAP on Microsoft Azure, engaging Fujitsu for strategic cloud consulting and implementation support.This adaptive platform provided system-to-system integration with suppliers and industry partners and supported automation of key business processes, enabling HMD Global to ramp up production capacity quickly.The RISE with SAP solution, running on Azure, enabled the manufacturer to continuously improve efficiency, maintain staff and cost levels, and embed sustainability into core operations.The result is improved emission and waste management, faster decision-making, and a higher pace of business innovation, securing HMD Global's growth trajectory and regulatory compliance.

HMD GlobalManufacturing

FRÄNKISCHE Industrial Pipes transformed its automotive manufacturing operations by implementing a connected factory platform powered by Azure IoT Hub and Azure Data Factory. The new system collects production and machine data across six global sites, linking all steps from raw material procurement to finished goods. Machine and batch data, quality inspections, and camera-based AI checks are now centralized, providing real-time traceability and digital mapping of the product lifecycle. The solution also includes edge devices for data capture, QR code tracking, and AI-enabled camera inspections to automate error detection and bolster quality assurance. The platform is scalable, supports integration with legacy systems, and sets a foundation for future predictive maintenance initiatives. Full visibility is provided through an online portal, supporting both operational teams and customer collaboration.

FRÄNKISCHE Industrial PipesAutomotive

Sage Automotive Interiors, a global leader in automotive interior fabrics, faced challenges with product quality and the inefficiency of manual defect detection. Seeking to minimize margin loss and optimize operations, Sage collaborated with Microsoft partner Mariner to implement Spyglass Visual Inspection, a deep learning and IoT solution powered by Microsoft Azure. The initiative began with a proof of concept, where images from existing camera systems were used to train vision models. Results surpassed expectations, prompting Sage to invest in a multi-year, multi-site deployment. The new system leverages several Azure services including IoT Hub, Blob Storage, SQL Database, IoT Edge, and Container Registry to automate real-time defect detection. This hybrid cloud-edge architecture improved graders' productivity, enabled faster production speeds, and provided granular mapping of defects across fabric rolls. The solution also paved the way for further process improvements, such as integrating root-cause analysis for proactive quality management. As a result, Sage has optimized its manufacturing process, reduced manual workloads, and increased both quality and throughput in its global plants.

Sage Automotive InteriorsAutomotive

BrickRed Systems LLC developed and provides a Vision AI solution leveraging Microsoft Azure to automate and optimize manufacturing defect detection. The solution is designed to reduce quality bottlenecks, minimize production scrap, and improve efficiency on manufacturing lines.Manufacturers receive a tailored consultation and evaluation of their existing defect detection process. The accelerator delivers advanced computer vision and machine learning, built on Azure's scalable cloud platform, to identify flaws in real time.A five-day assessment analyzes defects, quantifies their impact, and presents an ROI-focused solution proposal. Clients gain automated quality inspection, customized recommendations, and a detailed implementation roadmap.With data-driven actionable insights and automation, manufacturing operations become more efficient and consistent, decreasing manual labor and enabling faster, more accurate defect feedback loops.This initiative directly addresses quality control and operational cost challenges prevalent in industrial and automotive manufacturing environments in Canada and the US, making processes more reliable and reducing time to resolution.

BrickRed Systems LLCManufacturing