More organisations have adopted modern quality systems to move beyond manual and paper-based processes. However, a recent survey found that nearly half of companies that were modernising their quality management practices still had limited ability to use data for proactive quality management.1 More foundational work will be needed if the industry is to harness the power of automation, predictive analytics, and artificial intelligence (AI) for trending and improved decision-making. The first step in this work is to assemble cross-functional teams to analyse processes from different user perspectives and standardise them. This article will focus on two critical areas that are receiving more attention: improving quality control laboratories, processes, and systems and establishing paperless validation management.
Improving QC Laboratory Efficiency
As part of incremental efforts to improve QC lab efficiency over the years, quality leaders at many companies installed different point applications to optimise specific outcomes. Over time, this resulted in a situation where each lab, even those within the same organisation or facility, was using different tools with different logins, making it difficult to find, share, and report on data with colleagues in other divisions. Disconnected workflows left companies ill-prepared to keep pace with evolving business needs. Given the importance of QC to business goals, focusing foundational work in this area alone can significantly impact business results. To develop more agile digital laboratories, QC leaders whose systems have become disconnected will need to reimagine an approach that advances a connected quality ecosystem. This requires thinking beyond specific tools and problems to consider a holistic approach to modernising QC and how it will fit within the organisation’s overall technology modernisation efforts.
Before considering potential technology solutions, quality leaders need to evaluate the problem carefully and from different points of view. Accomplishing this goal will require a cross-functional team whose daily work processes depend on quality data to develop the best approaches for resolving it. The team will eliminate bias by considering processes within a broader cross-functional context. Together, members can consider such questions as whether standards might be used to organise work processes or whether procedures might be combined or improved.
For this challenge, a comprehensive solution based on a single technology platform could streamline sample management and lab investigation processes and decrease inventory expenses.
It could also bring QC and QA processes closer together, streamlining lab operations for faster batch release. However, selecting technology should formally start only after the team has analysed the problem in depth from different perspectives. In this case, the need for connectivity should drive analysis and consideration of potential solutions. QC operations usually include manual processes that depend on paper, spreadsheets, homegrown systems or vendor point solutions. First, the cross-functional team needs to take a step back and evaluate the complete quality ecosystem. The team should ask how multiple business processes could be improved and then map out processes end-to-end to determine what activities or systems can be streamlined. Looking at lab issues holistically is key to more effective overall results.
It’s never too early to think about change management because technology users do not accept will never result in change. Assemble another team across functions to lead the strategy for change management, prioritise efforts, and keep projects on track. Leverage vendor services and consulting firms for expertise that strengthens the initiative. A committee with internal leaders, quality experts, and third-party partners can deliver the right combination of people to drive a successful transformation.
