How Industry 4.0 and Digital Technologies Are Transforming Laboratories Our expertise

Getting more businesses on board with Industry 4.0-driven applications continues to be a major hurdle. At present, particularly in Asia, many manufacturers admitted that the barriers to large-scale implementation remain a huge obstacle. Many of them have only managed to achieve limited progress with their initiatives over the past year.

Some of the main challenges faced include:

• Lack of unified leadership making cross-unit coordination difficult within the organization
• Data ownership concerns when choosing third-party vendors for hosting and operationalizing business data
• Lack of courage to launch the radical digitalization plan
• Lack of in-house talent to support the development and deployment of Industry 4.0 initiatives
• Difficulties with integrating data from various sources to enable initial connectivity
• Lack of knowledge about technologies, vendors, and IT outsourcing partners that could help execute the core initiative

For those who have taken the steps forward, they have enjoyed various benefits and return-on-investment in their businesses. For instance, real-time quality control provides data points at every stage of the production process. This will help you check how changing conditions affect the quality of your products and ultimately improve the quality of the final product.

Digital technologies also reduce operating costs and help employees become more efficient in their daily tasks. Instead of operating in “individual silos”, manufacturers can form seamless exchanges and ensure that there is consistent support for high service-parts fill rates, high levels of product uptime with minimal risk, and higher customer service levels.

The most evident is the increased productivity resulting from the ability to predict and prevent downtime, optimize equipment effectiveness, and manage and schedule preventive maintenance requirements ahead of time. As predictive maintenance can identify equipment failure before it occurs, these systems can spot repetitive patterns that precede failures, notify your teams and have them schedule an inspection.

Other business savings may come from the following processes:

• Real-time production monitoring and quality control to reduce waste and rework
• Higher automation to save labor costs and improve throughput
• The use of 3D printers to achieve faster prototyping, reduce the cost of engineering, and accelerate time to market

With the advancement of digitalization and Industry 4.0, the laboratory environment is changing fast. Various processes and structures need to be upscaled for the laboratory to stay relevant. Two key areas within the laboratory space are lab automation and laboratory information management systems (LIMS).

The automated laboratory. New technologies can equip labs with complete sample preparation, analysis processes, and sample transportation to and within the laboratory. These procedures can be mechanized and computerized, while results can be merged, validated, and distributed with little or no human intervention. This leads to higher repeatability, smaller tolerances, and lower costs for labs analysis requirements.

Equipment requirements vary from a single-line, with one preparation machine coupled to one analysis system, to multiple-line solutions where several preparation machines are coupled to multiple analysis systems.

The use of advanced inline process analyzers and instruments can reliably replace or supplement many of the measurements traditionally made in a laboratory. Moving from offline to real-time inline measurements cuts labor costs by eliminating manual sampling and analysis, while critically, improving consistency to the measurement process.

LIMS and the digital ecosystem. Implementing digital transformation and relying on data to build a business ecosystem, help businesses to reduce risk, accelerate innovation, and drive growth. Having an integrated and flexible LIMS deployment in place can significantly increase data transfer and contribute to the broader analytics capabilities of the organization.

There are however various variables that need to be taken into consideration before making any decision to adopt new technologies in the lab. This is where the use of advanced analytics holds the key to extracting enormous value from the lab’s LIMS data.

Using artificial intelligence and machine learning, these advanced tools rapidly process all structured and unstructured LIMS data and integrate it with external enterprise data. These tools generate real-time and relevant insights for lab managers and analysts to proceed with the necessary actions and meaningful recommendations.