AI Quality Inspection Intern

About The Business: MGS Technical Plastics is an established contract manufacturer specialising in plastic injection moulding and associated assembly services. The business supports customers across a range of sectors including automotive, defence, consumer products, hygiene and washroom, electronic housings, and industrial applications. The company has invested in digital technologies including Windman ERP, InTouch machine monitoring, digital work instructions, traceability systems and performance management processes, creating a strong foundation for future digital transformation initiatives.

Overview: The internship will focus on assessing readiness for AI-enabled quality inspection automation by analysing current inspection processes, evaluating technology options and developing a practical business case for future implementation.

Desirable Skills: Strong analytical and problem-solving skills. Understanding of manufacturing, engineering or quality-management environments. Experience with data analysis and process-improvement techniques.

Job Description:

The internship will explore how AI-enabled machine vision and automated inspection technologies could be applied within MGS Technical Plastics to improve quality assurance processes, reduce inspection workload and support more efficient manufacturing operations.

The project will assess the practical use of AI technologies for defect detection, quality verification and inspection automation within a plastic injection moulding environment.

Project Goals and Objectives

• Map and document the current quality inspection process across relevant production and quality areas within the first quarter of the 300-hour internship, identifying key inspection activities, bottlenecks and resource requirements.
• Analyse quality and defect data to establish a baseline understanding of inspection activity, quality performance and recurring defect trends before the midpoint of the internship.
• Quantify the current inspection workload by measuring time, labour input and operational impact associated with manual inspection activities, producing an evidence-based baseline for future comparison.
• Research and evaluate a minimum of three AI-enabled machine vision or automated inspection solutions suitable for plastic injection moulding applications and the MGS Technical Plastics manufacturing environment.
• Assess the technical and commercial feasibility of inspection automation opportunities and produce a business case outlining expected benefits, costs, risks and implementation considerations before completion of the project.
• Deliver a final recommendations report and presentation providing a prioritised roadmap and proposed pilot project for future quality inspection automation activity.

Scope of Work, Methods, and Tools

The intern will:

• Review current quality inspection processes, procedures and quality-gate activities.
• Observe inspection workflows and engage with production, quality and engineering personnel.
• Collect and analyse quality, defect and inspection performance data.
• Measure inspection workload, resource allocation and operational impact.
• Research machine vision, AI-enabled inspection and automated quality-control technologies.
• Evaluate suitable suppliers, systems and solution providers. 
• Compare technology options against operational requirements and business objectives.
• Develop a feasibility assessment and business case for future implementation. Produce documentation, recommendations and supporting project outputs.

Potential tools may include:

• Microsoft Excel Power BI AI-assisted data analysis tools
• Process-mapping software Machine vision supplier information and technical documentation
• Existing ERP, MES and quality management data used by the business

The intern will:

• Gather and organise operational, inspection and quality data.
• Conduct process-mapping and workflow-analysis activities.
• Engage with quality, production and engineering stakeholders.
• Analyse quality performance and defect trends.
• Investigate AI-enabled machine vision and automated inspection technologies. Review and compare supplier offerings and solution options.
• Develop feasibility assessments and business case information.
• Document findings, recommendations and project outputs. 
• Present progress updates and final recommendations to stakeholders.
• Support knowledge transfer to enable future progression of the project.

Intern Roles and Responsibilities

The intern will:

• Gather and organise operational, inspection and quality data.
• Conduct process-mapping and workflow-analysis activities.
• Engage with quality, production and engineering stakeholders.
• Analyse quality performance and defect trends.
• Investigate AI-enabled machine vision and automated inspection technologies.
• Review and compare supplier offerings and solution options.
• Develop feasibility assessments and business case information.
• Document findings, recommendations and project outputs.
• Present progress updates and final recommendations to stakeholders.
• Support knowledge transfer to enable future progression of the project.

Intern Skills Required

Essential

• Strong analytical and problem-solving skills.
• Knowledge of Artificial Intelligence, machine vision or automation technologies.
• Ability to gather, organise and interpret data.
• Good written and verbal communication skills.
•  Ability to present technical information in a clear and practical manner.
• Competent use of Microsoft Excel and other data-analysis tools.

Desirable

• Understanding of manufacturing, engineering or quality-management environments.
• Experience with data analysis and process-improvement techniques.
• Interest in Industry 4.0 and digital transformation.
• Experience with Power BI, Python or similar analytical tools.

Expected Outcomes and Success Metrics

The project will be considered successful if:

• A documented quality inspection process map is completed for the agreed production areas.
• A baseline assessment of inspection workload, quality performance and defect trends is produced.
• At least three suitable AI-enabled machine vision or automated inspection solutions are researched and evaluated.
• A feasibility assessment and investment-ready business case are delivered.
• A recommended pilot project and implementation roadmap are presented to the management team.
• The leadership team confirms that the outputs provide sufficient evidence to support future decision-making regarding inspection automation and AI adoption.
• Expected Business Benefits Improved understanding of current inspection activities, costs and operational bottlenecks.
• Greater visibility of opportunities to reduce manual inspection effort.
• Identification of practical AI and machine vision applications relevant to the business.
• Evidence-based recommendations to support future technology investment decisions.
• Reduced risk when exploring quality inspection automation technologies.
• A structured roadmap for future AI-enabled quality-improvement initiatives.
• Stronger foundations for improving productivity, inspection consistency and operational efficiency.
• Project outcome MGS Technical Plastics will have a clear understanding of where automated inspection could deliver the greatest value, supported by a practical business case and recommendations for future investment and potential funding opportunities.