Powering Manufacturing and Global Supply Chains
With our domain knowledge and cutting-edge techniques, we empower businesses to harness the full potential of their data, enabling them to drive operational efficiency, improve decision-making, and achieve optimal performance throughout their industry and supply chain operations.
Powering Manufacturing and Global Supply Chains
With their domain knowledge and cutting-edge techniques, we empower businesses to harness the full potential of their data, enabling them to drive operational efficiency, improve decision-making, and achieve optimal performance throughout their industry and supply chain operations.
Case Studies
Our data scientists possess specialised expertise in remote sensing, anomaly detection, and data analysis tailored for process optimisation within manufacturing and global supply chains.
Project: Our project focused on leveraging digital solutions to extract real-time insights from industrial control systems in hydroelectric power plants, ultimately leading to significant cost reductions. We implemented standardised processes for cleaning, validating, and standardising incoming data, including the implementation of “data unit tests” to ensure data quality.
Outcome: Through the utilisation of the Python programming language on the Amazon Web Services (AWS) cloud infrastructure, we successfully achieved real-time validation and monitoring of data from 6 turbines. This enabled us to efficiently identify and predict potential instrument failures, resulting in improved operational efficiency and substantial cost savings for the power plant.
Project: Our project centred around improving production efficiency and detecting anomalies in press cycles for Barry Callebaut Group, a renowned manufacturer of chocolate and cocoa products. Leveraging sensor data, we aimed to identify inefficiencies in production lines and detect anomalies in press cycles.
Outcome: We developed a Python-based, machine learning solution deployed on the Data Bricks platform which enabled Barry Callebaut Group to optimise their production processes and achieve higher operational effectiveness in their chocolate and cocoa manufacturing operations. Leveraged advanced analytics to enhance their production efficiency, reduce costs, and empower factory operators to quickly detect and resolve issues in press cycles.
Project: Our project focused on developing a Python-based model that accurately estimates the size of pineapples using images captured directly from plants in the field. Additionally, we aimed to apply this model to internally developed correlations to predict the sizes of pineapples from pictures.
Outcome: Our deep-learning model, trained with hundreds of annotated pineapple images, achieved an impressive accuracy rate of up to 90%, providing valuable insights into improving size estimation. The remaining errors were identified, and potential solutions were proposed for further enhancement.
Types of Projects We Deliver
We recruit and support data scientists to deliver real-world data related projects for host organisations. They are are supervised by experienced Data Scientists who oversee delivery and ensure high performance.
Data & Software Engineering
AI & Machine Learning
Analytics & Dashboards
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