When products simply can’t fail, what role does data play in ensuring quality? 

Nowadays, we are used to getting real-time information from our smartphones at the touch of a button, whether that is our bank balance, live traffic updates or by-the-minute weather forecasts. The ambition is to recreate that experience in the workplace, with access to usable information as close to real time as possible. 

Some products are simply too important to fail. Take the brakes on a plane – coming into land, you would like to think the materials of this vital component were made with precision. So, when products cannot fail, data modernisation is essential.  

“Nowadays, data is powering precision, with sophisticated analytics ensuring utmost quality at all times, with any slight anomaly flagged immediately.”

Poornima Ramaswamy, Chief Transformation Officer of business analytics at Qlik

Nowadays, data is powering precision, with sophisticated analytics ensuring the utmost quality at all times, with any slight anomaly flagged immediately.  

An example of this is demonstrated by the organisation Greene Tweed, which produces high-performance elastomers, thermoplastics, composites, and engineered components. With its precision components used in an array of harsh conditions, including plane braking systems, the company is indeed focused on quality assurance. 

Using sophisticated analytics and a cloud-based approach, it now benefits from a highly statistical view of its quality system running in near real-time. Achieving this took a strategic approach, as part of realising the company’s vision of becoming a data-driven organisation by 2025. So how can other organisations work to achieve this themselves? 

Real-time data modernisation 

Data has the power to transform a huge range of business and operational processes, but companies can come up against issues relating to production. Every time a firm needs to build a new application for products, it may build a product master from scratch, which could be done differently each time, depending on the developer. If there is no consistent set of data for a company’s products, they need to start by creating standard product sets to draw on for sales and manufacturing data. This is the start of the journey to modernise production through data. 

One of the biggest steps in achieving this goal is to move to the cloud. Data should be moved from system applications and products (SAP) and other systems to the cloud to power a responsive system, giving the company insights into everything from work orders, sales orders, and purchase orders, ensuring every end of the enterprise is covered. This means near real-time insight across a host of critical business processes.  

Inspiration for modernisation is driven in part by the expectations now set by consumer technology. Again using the example of Greene Tweed, data in the company’s cloud environment is on average two minutes old and sales data is refreshed every 30 minutes, with plans to reduce this to 20 minutes in the near future. The success of the data analytics program means that the company is about halfway to achieving its 2025 goals, with the next three years earmarked for a focus on implementing a formal data governance program across the whole organisation. 

Hard material gains 

This data-led mindset has also powered the transformation of the manufacturing floor in organisations, with many modern manufacturers adopting a smart factory approach, where myriad devices are all connected via sensors, generating a huge amount of data. 

Smart factories, also spoken of as Industry 4.0, uses AI, cloud computing and connected IoT devices to improve efficiency, optimise production, streamline operating procedures and deliver more sustainable practices. Rich-data analytics is delivering benefits that Deloitte claims help achieve 20% improved asset efficiency, 30% improved product quality, 30% reduced costs, and 10% improved safety and sustainability. 

Capturing all data in the cloud through analytics can make a huge difference in the early detection of anomalies. Early detection of potential issues is hugely important. For example, the ovens used in the company’s manufacturing process at Greene Tweed run at 500oF, and any fluctuation in temperature can compromise the quality of the final product. Equally important, the data from those connected devices enables predictive maintenance; early detection of potential issues with equipment is essential. In short, this approach has allowed the company to ensure product quality and eliminate 15-20% of waste materials. 

By building a robust analytics architecture, organisations can realise the benefits, with cloud technology and analytics combining to deliver continuous actionable intelligence from real-time data to take contextual and timely action.