For many Small and Medium-sized Enterprises (SMEs) in the UK’s manufacturing sector, the term ‘data analytics’ can sound complex and expensive, seemingly reserved for large corporations with dedicated data science teams.
However, the reality is that valuable, data-informed decisions are well within reach. By focusing on practical applications using accessible tools, manufacturers can make significant improvements to their operations.
Why Practical Data Skills Matter for Manufacturers
In a competitive market, efficiency, cost control, and quality are paramount. Data provides the objective information needed to make targeted improvements in these areas. Instead of relying on intuition alone, data analysis allows you to identify specific patterns, pinpoint the root cause of problems, and measure the impact of any changes you make. It’s not about a complete technological overhaul, it’s about developing the in-house skills to ask the right questions and use data to find the answers.
Three Starting Points for Data Analysis in Manufacturing
Beginning your data journey doesn’t require a massive investment in new software. The most effective starting points often involve solving specific, tangible problems using tools and data you already have. Here are three examples of projects your team can begin.
1. Analysing Material Waste with Spreadsheets
Material scrap and product rework are direct hits to your bottom line. While most manufacturers track this, the data is often underutilised. A designated employee can begin by creating a simple, structured log in a spreadsheet program like Microsoft Excel to record waste by shift, production line, reason, and material type.
By applying basic statistical functions, you can move beyond a simple total and calculate scrap rates as a percentage of production. This data can then be used to create charts that visualise trends over time. This process of using averages and spreads to understand data is a fundamental concept in descriptive statistics. It quickly answers critical questions: Does one shift produce more waste than others? Is a particular material causing more issues? These insights provide a solid foundation for targeted process improvement initiatives.
2. Monitoring Quality Control with Interactive Dashboards
Quality control (QC) data is often collected in logs or separate files, making it difficult for managers to get a clear, real-time overview of performance. This is where data visualisation tools can make a significant difference.
An employee trained in a platform like Microsoft Power BI can connect directly to these data sources. They can then build an interactive dashboard that displays key QC metrics, such as defect rates per product or pass/fail trends over time. This provides managers with a dynamic report they can explore to identify recurring issues. This practical skill is a core component of advanced data visualisation, allowing your team to move from static reports to engaging analytical tools.
3. Improving Procurement by Cleaning Supplier Data
Inconsistent or inaccurate supplier data can cause significant problems, from incorrect orders to an inability to analyse spending effectively. The process of cleaning and preparing data is one of the most critical tasks in analytics.
Using tools like Excel and Power BI, a team member can learn to standardise supplier names, correct data entry errors, and fill in missing information. For instance, they could ensure that “Component Supplier Ltd” and “Comp Supplier” are correctly categorised as the same entity. Creating a clean, reliable supplier dataset is a foundational step that improves the accuracy of all future procurement analysis and reporting. Training programmes can also introduce staff to how AI can assist in suggesting the best ways to manage missing data, improving speed and accuracy.
Building In-House Data Capability
The most sustainable way to leverage these applications is to develop the skills within your current workforce. Your employees already possess invaluable business context providing them with structured data training allows them to apply new skills to the challenges they understand best. A practical course can equip your team with the core competencies needed, from data cleaning and statistical analysis to building dashboards and understanding the fundamentals of SQL and Python.
Investing in Your Team’s Professional Development
If you are a manufacturing SME in the UK, developing these skills is more accessible than you may think. Code Institute has secured funding across the UK to support businesses in upskilling their employees in data analytics.
For eligible companies, subsidies of up to 90% are available for our Professional Certificate in Data Analytics and AI. This is a practical opportunity to invest in your team and your company’s future with minimal financial outlay.
To find out if your business is eligible and to learn more about the programme, please get in touch.
