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Quest Training

IoT & Real-Time Quality Monitoring Training Course

Explores how connected IoT sensors and real-time data help monitor product quality, detect process deviations sooner and speed up corrective response.

Q4DX · Quality 4.0 & Digital QualityAll Levels#2875ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Quality Management & Operational ExcellenceCategories:Quality 4.0 & Digital Quality

Course Overview

The IoT & Real-Time Quality Monitoring Training Course explores how Internet of Things (IoT) technologies can improve quality control, process visibility, and operational decision-making across industrial and manufacturing environments. It focuses on using connected sensors, smart devices, and real-time data collection to monitor product quality, detect process deviations, and reduce the time between identifying a quality issue and taking corrective action. The course covers IoT architectures, sensor technologies, data acquisition, industrial connectivity, and real-time quality monitoring systems. Participants learn how to capture and interpret quality-related data, establish monitoring thresholds, configure alerts, and integrate sensor data with manufacturing execution systems, quality management systems, and industrial dashboards. The programme also examines statistical process control, anomaly detection, data integrity, cybersecurity, and the challenges of deploying connected quality monitoring solutions in operational environments. Through practical exercises and industrial case studies, participants develop monitoring concepts, define quality indicators, analyze real-time process data, and design alert and escalation mechanisms. The course supports organizations in improving defect detection, reducing scrap and rework, strengthening process consistency, and building a data-driven approach to quality assurance and continuous improvement.

Learning Objectives

  • Explain the principles of IoT and its applications in quality monitoring.
  • Identify suitable sensors and connected devices for quality-related measurements.
  • Understand IoT architectures, industrial connectivity, and data acquisition methods.
  • Design real-time monitoring approaches for critical quality parameters.
  • Establish quality thresholds, alerts, and escalation procedures.
  • Integrate IoT data with quality management and manufacturing systems.
  • Apply statistical process control and basic anomaly detection techniques.
  • Evaluate data quality, system reliability, and cybersecurity requirements.
  • Analyze real-time quality information to support corrective and preventive actions.
  • Develop an implementation framework for IoT-enabled quality monitoring.

Who Should Attend

Quality assurance and quality control professionals. Manufacturing and production engineers. Industrial and process engineers. Quality managers and continuous improvement specialists. Automation, instrumentation, and control engineers. Industrial IoT and digital transformation professionals. Maintenance and reliability engineers. Data analysts supporting manufacturing and quality operations. Operations managers and production supervisors. Professionals involved in smart manufacturing and Industry 4.0 initiatives.

Learning Outcomes

  • Explain the role of IoT in modern quality assurance and process control.
  • Identify appropriate sensors and data sources for quality monitoring.
  • Describe the main components of an IoT-enabled monitoring architecture.
  • Define quality parameters, monitoring thresholds, and alert conditions.
  • Interpret real-time sensor readings and quality performance trends.
  • Apply basic statistical process control methods to process data.
  • Recognize abnormal patterns and potential quality deviations.
  • Integrate quality monitoring data with operational systems and dashboards.
  • Establish procedures for alerts, escalation, and corrective action.
  • Develop a practical framework for implementing and evaluating real-time quality monitoring.

Course Outline

Day 1

IoT Fundamentals and Quality Monitoring Applications

  • Introduction to the Internet of Things and its role in industrial quality management.
  • IoT applications in manufacturing, inspection, testing, and process control.
  • Components of an IoT system: sensors, devices, gateways, networks, and platforms.
  • Types of industrial sensors and their quality monitoring applications.
  • Identifying critical-to-quality parameters and measurement requirements.
  • Understanding data collection frequency, accuracy, and measurement limitations.
  • Overview of Industry 4.0 and connected quality systems.
  • Practical Exercise: Select an industrial process and identify the quality parameters, sensors, and data sources required for continuous monitoring.
Day 2

IoT Architecture, Connectivity, and Data Acquisition

  • Designing IoT architectures for industrial quality monitoring.
  • Sensor integration, signal acquisition, and measurement interfaces.
  • Industrial communication protocols and connectivity options.
  • Introduction to MQTT, OPC UA, and other relevant industrial communication technologies.
  • Edge computing versus cloud-based data processing.
  • Data transmission, storage, timestamping, and synchronization.
  • Integrating sensor data with manufacturing and quality management systems.
  • Practical Exercise: Develop a conceptual IoT architecture for a quality monitoring application, showing sensors, gateways, communication protocols, data storage, and monitoring interfaces.
Day 3

Real-Time Quality Monitoring and Process Analytics

  • Defining quality indicators and real-time monitoring requirements.
  • Establishing control limits, thresholds, and alert conditions.
  • Developing dashboards for quality status and process performance.
  • Applying Statistical Process Control (SPC) to monitored data.
  • Recognizing process variation, abnormal patterns, and emerging defects.
  • Introduction to anomaly detection and trend analysis.
  • Using real-time data to support faster quality investigations.
  • Practical Exercise: Analyze a sample real-time quality dataset, identify deviations from established limits, and recommend appropriate responses.
Day 4

Alerts, System Integration, and Quality Response

  • Designing automated alerts for quality deviations and equipment conditions.
  • Establishing escalation procedures and response responsibilities.
  • Integrating IoT monitoring with Quality Management Systems (QMS) and Manufacturing Execution Systems (MES).
  • Linking quality deviations with nonconformance records and corrective actions.
  • Improving traceability through time-stamped data and process records.
  • Evaluating data reliability, sensor calibration, and measurement consistency.
  • Addressing cybersecurity, access control, and operational continuity.
  • Practical Exercise: Design an alert and escalation workflow for a quality deviation, including notification rules, responsible personnel, investigation steps, and corrective action records.
Day 5

Implementation, Performance Evaluation, and Continuous Improvement

  • Assessing organizational readiness for IoT-enabled quality monitoring.
  • Selecting pilot applications based on quality risks and operational value.
  • Defining implementation scope, resources, responsibilities, and milestones.
  • Evaluating system performance, data accuracy, and monitoring reliability.
  • Measuring quality outcomes through defect rates, scrap, rework, and response time.
  • Estimating implementation costs and expected operational benefits.
  • Establishing governance, maintenance, and continuous improvement arrangements.
  • Practical Exercise: Develop an implementation roadmap for an IoT quality monitoring solution, including pilot scope, required resources, quality indicators, expected benefits, and performance review arrangements.

Upcoming Dates

Selected date

Cape Town · 15 – 19 Mar 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
HamburgScheduled
2 – 6 Nov 2026€5,800—
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IstanbulScheduled
9 – 13 Nov 2026€4,700—
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JeddahScheduled
15 – 19 Nov 2026€4,200—
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KigaliScheduled
23 – 27 Nov 2026€5,100€4,590until 2 Nov
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Kuala LumpurScheduled
30 Nov – 4 Dec 2026€4,400€3,960until 9 Nov
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LondonScheduled
7 – 11 Dec 2026€5,600€5,040until 16 Nov
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ManamaScheduled
14 – 18 Dec 2026€4,700€4,230until 23 Nov
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MarrakeshScheduled
14 – 18 Dec 2026€4,300€3,870until 23 Nov
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MuscatScheduled
21 – 25 Dec 2026€5,300€4,770until 30 Nov
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OnlineScheduled
27 – 31 Dec 2026€1,700€1,530until 7 Dec
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SalalahScheduled
4 – 8 Jan 2027€5,300€4,770until 14 Dec
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Sharm El-SheikhScheduled
11 – 15 Jan 2027€4,300€3,870until 21 Dec
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AbujaScheduled
18 – 22 Jan 2027€5,100€4,590until 28 Dec
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AccraScheduled
25 – 29 Jan 2027€5,100€4,590until 4 Jan
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AmsterdamScheduled
25 – 29 Jan 2027€5,400€4,860until 4 Jan
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BakuScheduled
1 – 5 Feb 2027€4,800€4,320until 11 Jan
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BangkokScheduled
8 – 12 Feb 2027€6,100€5,490until 18 Jan
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BarcelonaScheduled
15 – 19 Feb 2027€5,400€4,860until 25 Jan
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BeirutScheduled
21 – 25 Feb 2027€4,000€3,600until 1 Feb
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BerlinScheduled
1 – 5 Mar 2027€5,800€5,220until 8 Feb
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CairoScheduled
7 – 11 Mar 2027€3,300€2,970until 15 Feb
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CasablancaScheduled
15 – 19 Mar 2027€4,300€3,870until 22 Feb
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Cape TownScheduled
15 – 19 Mar 2027€5,100€4,590until 22 Feb
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DammamScheduled
21 – 25 Mar 2027€4,200€3,780until 1 Mar
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DohaScheduled
28 Mar – 1 Apr 2027€4,400€3,960until 8 Mar
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DubaiScheduled
4 – 8 Apr 2027€4,400€3,960until 15 Mar
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HamburgScheduled
19 – 23 Apr 2027€5,800€5,220until 29 Mar
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IstanbulScheduled
26 – 30 Apr 2027€4,700€4,230until 5 Apr
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JeddahScheduled
2 – 6 May 2027€4,200€3,780until 12 Apr
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KigaliScheduled
3 – 7 May 2027€5,100€4,590until 12 Apr
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Available in: Istanbul · Dubai · London · Doha · Amsterdam

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