Manufacturing

Experience the transformative power of AI in manufacturing, unlocking unparalleled efficiency and accuracy in every aspect of your operation. Revolutionise your supply chain management with intelligent forecasting, streamline predictive maintenance to minimise downtime, and enhance workforce planning through skills forecasting. Embrace the future of manufacturing and gain a competitive edge by harnessing the cutting-edge benefits of AI, optimising performance and driving growth across your entire organisation.

Case Study

We applied AI models for creating and updating standards in manufacturing for Enginuity, an NFP dedicated to supporting employers, training providers and individuals in the UK Engineering sector.

Artificial Intelligence for Manufacturing

Skills Forecasting

Past experience working with Engineering and Manufacturing data from various sources has helped us make predictions about future trends and workforce skillsets, which can be used to inform decisions regarding hiring, training, production and inventory management. By utilising data-driven software, manufacturers have access to more accurate information than ever before, allowing them to make decisions based on evidence-based insights and preemptively address potential issues before they arise. This helps them to remain competitive in an ever-evolving market, positioning them to succeed in the long term.

Predictive Maintenance

AI technology has revolutionised the way we monitor machines and predict when maintenance is necessary. By analysing data from various sensors and other sources, AI can improve the accuracy of predictions, helping to prevent machine breakdowns and reduce downtime. This, in turn, can save companies time and money in the long run. AI can also detect patterns in data that are not immediately obvious to the human eye, allowing for more strategic and proactive decisions about maintenance.

Supply Chain Management

AI can be used to optimise supply chain operations, leveraging the power of data to make smarter decisions. By analysing data from suppliers, production facilities, and transportation networks, AI can help identify bottlenecks and inefficiencies, resulting in faster delivery times, cost savings, and improved customer satisfaction. AI can also be used to predict demand and forecast potential problems, ensuring optimal utilisation of resources and helping to drive business growth. With its ability to quickly process large amounts of data, AI can be an invaluable asset in the effort to maximise the efficiency and effectiveness of supply chain operations.

Methodology

How we turn your idea into your creation.

Define the project objectives and scope

Begin by identifying the specific challenges your manufacturing company faces and the goals you want to achieve through the AI project. Determine the key performance indicators (KPIs) that will be used to measure the success of the project. Establish a clear project scope, including a timeline, budget, and resource requirements.

Collect and preprocess data

Gather relevant data from various sources, such as production processes, equipment performance, inventory levels, and supply chain information. Clean, preprocess, and consolidate the data to ensure its accuracy, completeness, and consistency. The quality of your data plays a crucial role in the effectiveness of your AI solution, so invest time in preparing it properly.

Develop, test, and deploy the AI solution

We design and develop the AI solution, leveraging machine learning algorithms and models tailored to address your manufacturing company's challenges and objectives. Train and fine-tune the models using the prepared data. Rigorously test the AI solution to ensure its performance meets the established KPIs, and iteratively refine it based on feedback and testing results. Once the AI solution meets the desired criteria, deploy it to your manufacturing environment and monitor its performance to ensure its effectiveness and alignment with your business goals.

Evaluate and refine the AI solution

After deployment, continuously evaluate the AI solution's performance by comparing its results against the pre-defined KPIs. Collect feedback from users, stakeholders, and customers to identify any areas for improvement. Use this feedback and performance data to further refine and optimise the AI solution, making adjustments as necessary to enhance its effectiveness and ensure it continues to meet your manufacturing company's objectives. Establish a regular review process to ensure ongoing alignment with your business goals and changing market conditions.