Applying data-driven ergonomics in food manufacturing: MOVA MMH at Riverside Bakery
Updated: Aug 28
How MOVA MMH Delivers AI-Driven Manual Handling Risk Assessments in Food Manufacturing, Using HSE Frameworks to Reduce MSD Risks
In January 2026, I had the opportunity to visit Riverside Bakery, part of The Compleat Food Group (TCFG). During this visit, we deployed our MOVA MMH solution to conduct comprehensive manual handling risk assessments in a live food manufacturing environment.
Manual Handling Challenges in Food Manufacturing Environments
Food manufacturing presents a uniquely dynamic and demanding setting for manual handling. Tasks are often repetitive, time-sensitive, and distributed across multiple stages of production. During my visit, I assessed a wide range of activities across key operational areas, including:
Preparation
Fabrication
Cook house
Feeding
Packaging
This multi-area approach allowed me to capture a holistic view of manual handling risks across the end-to-end process.
Quantitative Manual Handling Risk Assessment Using HSE MAC, RAPP, and ART
Using MOVA MMH technology, I conducted detailed and objective ergonomic assessments aligned with established HSE risk assessment frameworks, including:
By combining these frameworks with our AI-driven analysis, I was able to move beyond traditional subjective assessments and generate quantitative risk insights. This enabled a more precise understanding of where and why risks occur within specific tasks and workflows.
AI-Driven Ergonomic Risk Reduction Using the Hierarchy of Controls
A key advantage of the MOVA MMH system is its ability to identify risks and recommend actionable improvements. My AI engine analysed the collected data and suggested a range of mitigations aligned with the hierarchy of controls, including:
Eliminating risks
Substitution
Engineering controls
Administrative controls
This structured approach ensures that recommendations are both practical and aligned with best-practice safety principles.
Using Ergonomic Data to Prioritise Risk and Build Business Cases
The insights generated during the assessment provide significant value to operational and safety stakeholders. By establishing a robust baseline, the data can be used to:
Prioritise high-risk areas for intervention
Support evidence-based decision-making
Build strong business cases for implementing controls
Track improvements over time
This shifts manual handling risk management from a reactive exercise to a proactive, data-led strategy.
Supporting Safer Food Manufacturing with Data-Driven Ergonomics
I am pleased to partner with Riverside Bakery, The Compleat Food Group, to support their ongoing efforts to reduce the risk of musculoskeletal disorders (MSDs) and improve workplace safety across their operations. Collaborations like this highlight the importance of combining domain expertise, advanced technology, and operational insight to drive meaningful and sustainable improvements.
We look forward to continuing our work together—and to helping more organisations unlock the value of data-driven ergonomics.
Explore MOVA MMH Technology: Manual Handling | SpatialCortex
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Frequently Asked Questions (FAQs)
What is a manual handling risk assessment?
A manual handling risk assessment evaluates tasks involving lifting, carrying, pushing, or repetitive movements to identify risks that could lead to musculoskeletal disorders (MSDs).
What are MAC, RAPP, and ART assessments?
MAC (Manual Handling Assessment Charts), RAPP (Risk Assessment of Pushing and Pulling), and ART (Assessment of Repetitive Tasks) are Health & Safety Executive (HSE) frameworks used in the UK to systematically evaluate different types of manual handling risks.
How can companies reduce musculoskeletal disorder risks?
Organisations can reduce MSD risks by first quantifying exposure using objective tools like MOVA MMH, which generates MAC, RAPP, and ART scores from wearable sensor data. Then, they can apply the hierarchy of controls to the highest-risk tasks identified.
SpatialCortex’s MOVA MMH solution is used across the UK to support industries including food manufacturing, logistics, ports, and warehousing in reducing manual handling risks and improving worker safety through AI-driven ergonomic assessments.
