AI-Powered Manufacturing Data Analyst Intern
About The Business: Heyside Group is a UK manufacturer specialising in the production of PVC products for a range of commercial applications. The business is committed to continuous improvement and is investing in digital technologies to enhance operational performance, improve efficiency and support future growth. By combining manufacturing expertise with data-driven decision making, Heyside Group is exploring innovative ways to optimise production processes and strengthen its competitive advantage
Overview: As an AI-Powered Manufacturing Data Analyst Intern, you will work with Heyside Group to collect, organise and analyse manufacturing data from across the business. You will use data analytics and AI techniques to identify trends, uncover improvement opportunities and support operational decision-making. The project will involve developing insights, testing ideas using real production data and helping to create practical tools that support continuous improvement across factory operations
Desirable Skills: AI, data analysis and problem-solving skills.
Job Description:
The team at Heyside Group has invested significantly in digitalisation and automation in recent years, including the introduction of automated production equipment, shop floor monitoring capabilities, CIM50 business systems and manufacturing data collection.
The business is continuing its growth journey and is increasingly reliant on data to support operational decision-making. Heyside Group collects growing volumes of information from machine monitoring systems, production records and business systems. However, this information is currently distributed across multiple sources and is not yet being fully utilised to support production planning, machine utilisation optimisation and continuous improvement activities. The business is seeking to establish a stronger data-driven approach to operational improvement and explore the practical application of artificial intelligence within its manufacturing environment.
The internship will focus on consolidating and analysing manufacturing data, evaluating operational improvement opportunities and developing a practical continuous improvement tool that can support future decision-making.
The internship will utilise AI and advanced data analytics techniques to extract insight from manufacturing and operational data, identify process improvement opportunities, evaluate production hypotheses and create a practical continuous improvement tool for ongoing use by the business.
Project Goals and Objectives
SMART
- Collect, clean and consolidate manufacturing data from shop floor monitoring systems, machine data, production records and business systems into a structured dataset within the first half of the 300-hour internship.
- Analyse production performance data to identify a minimum of three measurable improvement opportunities relating to machine utilisation, scheduling, throughput or operational efficiency before completion of the project.
- Test operational hypotheses using AI and data analytics, including production balancing scenarios such as the allocation of larger and smaller products across Presma machines, and quantify potential performance improvements where data allows.
- Develop a continuous improvement dashboard or decision-support tool enabling managers to monitor key production metrics, identify trends and support evidence-based operational decisions.
- Produce a brief final recommendations report and presentation summarising findings, potential business benefits and suggested next steps for future AI adoption and continuous improvement initiatives.
Scope of Work, Methods, and Tools
The intern will:
- Review available data sources including machine monitoring systems, production records, spreadsheet-based information, CIM50 and other relevant business systems.
- Assess data quality, identify gaps and develop a consolidated dataset suitable for analysis.
- Apply data analytics and AI techniques to identify patterns, inefficiencies and potential improvement opportunities.
- Investigate operational questions and production hypotheses, including machine loading, product mix balancing and utilisation trends.
- Develop visual dashboards and reporting tools to support ongoing performance monitoring.
- Engage with managers, engineers and operational staff to validate findings and ensure recommendations are practical.
- Produce documentation and guidance to support future use and development of the outputs.
Potential tools may include
- Power BI
- Microsoft Excel
- Python or similar analytical tools
- AI-assisted data analysis techniques
- Existing manufacturing data platforms and databases used by the business
Timelines and Milestones
Phase Activity
Weeks 1-2: Project initiation, stakeholder engagement, review of available systems and data sources Weeks 3-5 Data collection, cleaning, validation and consolidation
Weeks 6-8: Data analysis, AI modelling and investigation of improvement opportunities
Weeks 9-10:Evaluation of operational hypotheses and development of dashboard/tool
Weeks 11-12: Testing, refinement and stakeholder feedback Final Stage Recommendations report, knowledge transfer and final presentation
Timeline may be delivered full-time or part-time depending on student availability, whilst remaining within the 300-hour internship allocation.
Intern Roles and Responsibilities
The intern will:
- Gather and organise manufacturing and business data.
- Evaluate data quality and perform data cleansing activities.
- Conduct quantitative analysis of production and machine performance.
- Apply AI and analytical techniques to investigate operational improvement opportunities.
- Develop dashboards, reports and visualisations.
- Document findings and recommendations.
- Present progress and final outcomes to project stakeholders.
- Support knowledge transfer to enable continued use of project outputs after completion.
Intern Skills Required
Essential:
- AI, data analysis and problem-solving skills.
- Ability to work with large datasets.
- Good written and verbal communication skills.
- Ability to present technical findings in a clear and practical manner.
Desirable:
- Experience with Power BI or similar dashboarding tools.
- Knowledge of AI, data science or machine learning techniques.
- Experience with Python, SQL or similar analytical tools.
- Interest in manufacturing, engineering or continuous improvement.
- Understanding of production processes and operational performance metrics.
Expected Outcomes and Success Metrics
The project will be considered successful if:
- A consolidated and validated manufacturing dataset is created from the key data sources.
- At least three evidence-based improvement opportunities are identified and presented to the business.
- At least one operational hypothesis is assessed using AI or advanced data analytics techniques.
- A continuous improvement dashboard or decision-support tool is delivered and demonstrated.
- Garreth Brown and the management team confirm that the outputs provide actionable insight to support future operational improvement and decision-making.
Expected Business Benefits
- Improved visibility of production performance.
- Better utilisation of manufacturing data.
- Increased capability for evidence-based decision-making.
- Identification of operational efficiency opportunities.
- Establishment of a foundation for future AI, predictive analytics and data-driven continuous improvement initiatives.