Robert Speziale

The increasing demand for speed-to-market requires that modern pharmaceutical laboratories work faster, more accurately and efficiently than before. Unavoidably, this leads to a number of bottlenecks that threaten the overall laboratory operation and efficiency. 

 The first step to be taken to address these issues is to look for instrumentation and software platforms that can automate common laboratory processes. Automation often offers significant advantages, including: 					

less human intervention (thus fewer laboratory errors)

greater accuracy and faster recording of results.

A significant number of procedures undertaken in pharmaceutical laboratories are less complex, but delicate, requiring extremely careful handling while being highly repetitive. These procedures are suitable for automation, and many companies rightly opt for bespoke technology, but only because the skills of humans can be truly imitated. 

However, instruments can rarely multitask, which renders some actions more suitable for humans to perform. In some cases, pharmaceutical laboratory processes often have high levels of variation, making the development of an automated, robust and reliable process inherently difficult. This is further compounded with the high intrinsic value of the end product, making it necessary for a laboratory to acknowledge and accommodate for a degree of risk during their sample management, R&D and processes.  

It, therefore, seems that automation is not always the best solution for a laboratory to achieve optimum efficiency. Automation will solve many of the efficiency and throughput issues in a modern pharma lab; however, it may not be suitable for overcoming common workflow jams and bottlenecks in the overall process. Could the issues instead be resolved by critically reviewing and changing the processes and methods of working? 

Finding an appropriate solution that takes into account the pharmaceutical laboratory's future strategy can be a particularly difficult and time-consuming process. The ownership of the problem can pass from person to person without the underlying issues ever being resolved.  

While universities teach scientists how to perform the various analytical tasks, scientists are infrequently taught how best to address management issues such as workflow, efficiency and productivity. Furthermore, with scientists being occupied with conducting the experiments and evaluating the results, it is very difficult and time consuming to step back and realize the necessity to change a laboratory workflow or process to address efficiency issues. In most cases, change is initiated from the bottom up, with the manager and staff placing pressure on the director.  

Once the need for change has been identified, the most effective solution is to outsource the process analysis to a specialist company. This removes the burden from the scientists and laboratory manager, and imports specialist external knowledge into the company. The outsourced service provider can then impartially review the internal processes and decide whether automation is the best approach to increase capacity in working methods by reducing common pinch points. 

Where automation may not be the most viable solution to address workflow issues, specialists have started to use models from the engineering and manufacturing industries. 

To date, scientists have been reluctant to exploit these available process models. However, the laboratory industry can benefit from employing the long-standing practices of productivity improvement and process automation employed by innovative process engineering companies. Laboratory workflow can be improved by exploiting the available process models from the manufacturing environment (for example, lean manufacturing or Kanban practices).  

Broadly speaking, manufacturing and engineering processes involve following a logical step-by-step approach to set out a typical workflow evaluation project. 

Initially, current processes are carefully mapped to identify critical successful actions (CSAs) and redundant processes or bottlenecks in the process. The next step is to identify the sources and causes of these bottlenecks. Based on these, laboratory managers and scientists can determine the factors within the process that need adjusting, rearranging or amending to improve and control the bottlenecks. 

Following this, the approaches that merit consideration should be assessed and mapped. Important factors to consider include use of capital, workflow optimization, space requirements and planned future expansion options. Finally, a control strategy should be determined to ensure the CSAs are optimized and future additions to laboratory instrumentation can fit seamlessly and efficiently into the new process. 

It is only natural that some people resist process changes for fear of staff reductions or of stepping out of the established comfort zone. A change in processes should be communicated in a positive way. Problem areas and issues are replaced with new ways to better use staff, giving scientists more challenging and fulfilling roles within the laboratory. 

Additionally, change can improve the working environment while increasing throughput and the quality of results. Furthermore, highly trained staff will then have the opportunity to apply their skills in their trained areas, thereby empowering them and making better use of the resources available. 

By examining the simplest of issues, such as the location of equipment and organizing a staggered timetable for research, blockages and bottlenecks can be removed. The end result can be better staff use and reduced or eliminated waiting times. All this can be accomplished without significant investment in new equipment. In fact, it involves rationalizing the layout, workflow and positioning of key assets in the laboratory.  

General laboratory managers often lack expertise and experience to start modelling the process. It may be possible for an organization to invite a manager from within a different department to re-assess the laboratory processes in-house. The immediate benefit will be that the in-house managers will bring an unbiased viewpoint. Unfortunately, most in-house managers do not have enough time to model the process, create a road map for the future and implement the model.  

Consequently, outsourcing a process automation project to an industry expert is often the best approach towards overcoming the workflow issues. The first phases of the project, namely the development of specifications and user requirements, are paramount. These initial phases are the ones that drive the whole process and if they are not executed properly laboratories may waste valuable time and money before the process has even started. An outsourced company will provide an unbiased viewpoint, and use models and expertise from previous projects to provide a bespoke solution that will achieve a laboratory's objectives, but remain flexible to take into account future change. 

Pharmaceutical laboratory managers can, therefore, implement the specialist experience and knowledge that has taken process engineers years to develop, as an approach to solving the problem. Learning more from other people's experiences provides faster, more efficient and more cost-effective pathways to improving the processes.  

As the workload and sample load increase, and common pharmaceutical laboratory practices change and expand, it is necessary for processes to be critically reviewed and updated in terms of workflow bottlenecks that affect the overall operation and productivity. 

 Laboratory managers and scientists should be aware that changing the processes may generate cost savings, a decrease in the turn around time, decrease in waiting times for results, staff retention and greater accuracy of results from greater efficiencies. But are all lab managers sure that future laboratory automation plans are the optimal and appropriate solutions for their business?  

is vice president of business development at Invetech (UK). 

Articles

The increasing demand for speed-to-market requires that modern pharmaceutical laboratories work faster, more accurately and efficiently than before. Unavoidably, this leads to a number of bottlenecks that threaten the overall laboratory operation and efficiency.