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Quest Training

AI & Data Analytics for Quality Management Training Course

Applies AI, machine learning and statistical analytics to quality data to detect anomalies, predict defects and support faster, better quality decisions.

Q4DX · Quality 4.0 & Digital QualityAll Levels#2539ClassroomEnglish , 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

This advanced practical course explores how Artificial Intelligence and Data Analytics can transform quality management from reactive inspection into a data-driven, predictive, and continuously improving function. It focuses on using quality data to identify patterns, detect anomalies, predict defects, and support faster and more reliable quality decisions. Participants will examine practical applications of AI, machine learning, statistical analytics, automation, and real-time data visualization across quality assurance, quality control, process performance, root cause analysis, and continuous improvement. The programme combines analytical methods with realistic quality scenarios, enabling participants to assess data quality, select appropriate analytical techniques, develop predictive quality approaches, and translate analytical findings into measurable operational improvements.

Learning Objectives

  • Analyse the role of AI and Data Analytics in modern quality management.
  • Evaluate quality data sources, structures, accuracy, completeness, and usability.
  • Apply analytical techniques to identify quality trends, patterns, and process variations.
  • Use statistical and predictive analytics to support quality-related decision-making.
  • Identify anomalies and early warning signals within quality and process data.
  • Apply AI and machine learning concepts to defect prediction and quality improvement.
  • Develop data-driven approaches for root cause analysis and problem investigation.
  • Design quality dashboards and performance indicators for management decision-making.
  • Evaluate opportunities for automation across quality inspection, reporting, and monitoring activities.
  • Integrate quality analytics with operational, production, maintenance, and process data.
  • Assess AI-related risks, data governance requirements, and implementation challenges.
  • Develop a practical roadmap for integrating AI and analytics into quality management.

Who Should Attend

Quality managers, quality assurance and quality control professionals, quality engineers, process improvement specialists, operational excellence professionals, and continuous improvement teams. The course is also suitable for operations and production managers, process owners, data analysts, digital transformation professionals, business improvement teams, and managers responsible for quality performance and organizational improvement. It is particularly relevant to organizations seeking to strengthen data-driven quality management, improve defect prevention, enhance process visibility, and use AI to support operational decision-making.

Learning Outcomes

  • Interpret the business and operational value of AI-driven quality management.
  • Assess the readiness and suitability of quality data for advanced analytics.
  • Analyse quality datasets to identify trends, correlations, and process abnormalities.
  • Select appropriate analytical methods for different quality problems.
  • Develop data-driven approaches to monitor critical quality indicators.
  • Identify potential defects and quality risks using predictive techniques.
  • Apply AI concepts to quality inspection, anomaly detection, and defect prevention.
  • Build meaningful quality dashboards and analytical performance indicators.
  • Use analytical evidence to strengthen root cause investigations.
  • Evaluate automation opportunities within quality management processes.
  • Recognize data, governance, transparency, and implementation risks associated with AI.
  • Develop an actionable plan for deploying AI and Data Analytics within quality functions.

Course Outline

Day 1

AI, Data Analytics & the Evolution of Quality Management

  • From traditional quality control to data-driven quality management
  • Role of AI and analytics across the quality lifecycle
  • Quality data ecosystems and key data sources
  • Structured and unstructured quality data
  • Data quality, completeness, consistency, and reliability
  • Quality KPIs and analytical requirements
  • Descriptive, diagnostic, predictive, and prescriptive analytics
  • AI and machine learning applications in quality
  • Quality analytics maturity assessment
  • Identifying high-value AI and analytics opportunities
  • Practical Application: Assessing an organization’s quality data and analytics maturity
Day 2

Quality Data Analysis, Statistical Analytics & Anomaly Detection

  • Data preparation and analytical quality checks
  • Statistical analysis for quality performance
  • Variation, trends, distributions, and process behaviour
  • Correlation and relationship analysis
  • Identifying abnormal quality patterns
  • Outlier and anomaly detection
  • Root cause analysis using data
  • Early warning indicators for quality risks
  • Integrating process and quality datasets
  • Analytical interpretation for operational decisions
  • Practical Application: Analysing a quality dataset to identify trends, anomalies, and potential causes
Day 3

AI, Machine Learning & Predictive Quality

  • AI and machine learning concepts for quality professionals
  • Supervised and unsupervised learning applications
  • Defect prediction and classification
  • Predictive quality models
  • Failure and nonconformance prediction
  • Intelligent inspection and automated quality assessment
  • Pattern recognition and anomaly detection
  • Predictive process monitoring
  • Model performance and analytical reliability
  • Translating AI outputs into quality decisions
  • Practical Application: Developing a predictive quality use case for defect or risk prediction
Day 4

Quality Dashboards, Automation & Integrated Analytics

  • Designing effective quality analytics dashboards
  • Real-time quality monitoring
  • KPI selection and analytical storytelling
  • Automated quality reporting
  • Workflow automation for quality processes
  • AI-supported inspection and quality verification
  • Integrating quality, production, maintenance, and operational data
  • Digital nonconformance and corrective action analytics
  • Using analytics to improve CAPA effectiveness
  • Management reporting and decision support
  • Practical Application: Designing an AI-enabled quality dashboard and automated reporting workflow
Day 5

AI Governance, Implementation & Quality Analytics Strategy

  • AI governance within quality management
  • Data governance, security, privacy, and integrity
  • Managing analytical bias and model limitations
  • Validating AI-generated quality insights
  • Technology and capability requirements
  • Building the business case for AI and analytics
  • Implementation risks and organizational barriers
  • Prioritizing AI use cases and investment opportunities
  • Quality analytics performance measurement
  • Scaling successful AI applications across the organization
  • ورشة ختامية: Developing an integrated AI and Data Analytics roadmap for quality management

Upcoming Dates

Selected date

Amsterdam · 12 – 16 Apr 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
BakuScheduled
2 – 6 Nov 2026€4,800—
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LondonScheduled
2 – 6 Nov 2026€5,600—
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BangkokScheduled
9 – 13 Nov 2026€6,100—
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BarcelonaScheduled
16 – 20 Nov 2026€5,400—
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MadinahScheduled
16 – 20 Nov 2026€4,200—
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BeirutScheduled
22 – 26 Nov 2026€4,000€3,600until 2 Nov
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BerlinScheduled
30 Nov – 4 Dec 2026€5,800€5,220until 9 Nov
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CairoScheduled
6 – 10 Dec 2026€3,300€2,970until 16 Nov
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MadridScheduled
7 – 11 Dec 2026€5,400€4,860until 16 Nov
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Cape TownScheduled
14 – 18 Dec 2026€5,100€4,590until 23 Nov
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CasablancaScheduled
14 – 18 Dec 2026€4,300€3,870until 23 Nov
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DammamScheduled
20 – 24 Dec 2026€4,200€3,780until 30 Nov
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DohaScheduled
27 – 31 Dec 2026€4,400€3,960until 7 Dec
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MakkahScheduled
28 Dec 2026 – 1 Jan 2027€4,200€3,780until 7 Dec
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DubaiScheduled
3 – 7 Jan 2027€4,400€3,960until 14 Dec
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HamburgScheduled
18 – 22 Jan 2027€5,800€5,220until 28 Dec
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ParisScheduled
18 – 22 Jan 2027€5,700€5,130until 28 Dec
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IstanbulScheduled
25 – 29 Jan 2027€4,700€4,230until 4 Jan
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JeddahScheduled
31 Jan – 4 Feb 2027€4,200€3,780until 11 Jan
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KigaliScheduled
1 – 5 Feb 2027€5,100€4,590until 11 Jan
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ViennaScheduled
1 – 5 Feb 2027€5,700€5,130until 11 Jan
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Kuala LumpurScheduled
8 – 12 Feb 2027€4,400€3,960until 18 Jan
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LondonScheduled
15 – 19 Feb 2027€5,600€5,040until 25 Jan
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AmmanScheduled
21 – 25 Feb 2027€3,100€2,790until 1 Feb
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ManamaScheduled
22 – 26 Feb 2027€4,700€4,230until 1 Feb
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MarrakeshScheduled
1 – 5 Mar 2027€4,300€3,870until 8 Feb
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MuscatScheduled
8 – 12 Mar 2027€5,300€4,770until 15 Feb
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OnlineScheduled
14 – 18 Mar 2027€1,700€1,530until 22 Feb
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ErbilScheduled
14 – 18 Mar 2027€3,100€2,790until 22 Feb
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SalalahScheduled
15 – 19 Mar 2027€5,300€4,770until 22 Feb
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AmsterdamScheduled
12 – 16 Apr 2027€5,400€4,860until 22 Mar
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Available in: Istanbul · Dubai · London · Doha · Amsterdam

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