Ever since its first introduction by the Japanese automotive industry in the 1980s, lean manufacturing has been successfully adopted by many companies. For more than 10 years BSM have been global leaders in the provision of “Real Lean”. Now, BSM are implementing “Real Lean” in the generics Life Science industry. This effort is not without its difficulties, providing many unique challenges and customization of the lean process.
When designing lab solutions, Analysts, Lab Managers, Supervisors and Approvers are all important stakeholders. The solution will be designed so that these stakeholders can carry out their tasks as efficiently and obstruction-free as possible. However, it shouldn’t be forgotten that the lab Planner is also a critical stakeholder, and planning of the workload, both for the lab as a whole and for individual analysts, is the first step to ensuring a levelled workload and flow through the lab.
The effort to make the work and processes visible, in a work environment, is called visual management. In general, there are a couple of key items for any successful application of visual management.
Structured Problem Solving has been one of the foundations of Lean transformation, and of almost any high performing company over the past 50 years. However, many labs reject Structured Problem Solving techniques outright, or use them as a ‘box – ticking’ exercise to satisfy management that they are adhering to the latest directive. Why is it, when successful organisations pride themselves on a culture of continuous improvement and problem-solving, that in Labs, it is often the missing link to true transformative improvements…?
Pharmaceutical testing laboratories face many challenges including high volatility in incoming workloads, non-optimized analyst roles and undefined testing sequences. These issues are often ‘managed’ by dedicating resources to specific tasks and creating subject-matter experts in an attempt to improve performance and reduce errors. More recently there has been a move towards dedicated reviewers, where analysts are “promoted” off the bench into full-time review roles.
To an outsider (and often even the insiders) laboratories can seem like a workplace hovering on the brink of chaos. The lab is constantly bombarded with hot requests for this lot or a special test for that project. Investigations, vacations, changes in product, adjustments in mix, FDA inspections, equipment issues and narrowly specialized analysts can often add to this sense of chaos. Usually it is difficult to see how work flows in the lab, if in fact it does flow. It can also be next to impossible to identify what is “normal” behavior. One of the critical steps in creating a Lean Lab is separating the routine (or in some cases, the most routine) from the non-routine or non-predictable.
Slow down....so that you can speed up. Sounds like something Yoda would say. Component sub-optimization for increased system performance.
Deployment of 5S in a laboratory setting is a time consuming effort and one which in itself delivers little in terms of productivity gains. So why bother…?
While it might sound like some sort of fad diet, “lean” in the context of business improvement refers to a specific methodology that originated in the Japanese motor industry toward the end of the 1980s. Over the decades, this lean philosophy has been successfully adopted by many companies across a broad spectrum of industries and, more recently, lean thinking has filtered into laboratories. The focus of a lean laboratory is to test samples in the most efficient way possible in terms of cost, or speed, or both. Although most of the key principles of lean apply in labs, the specific challenges facing laboratories require significant adaptation of standard lean tools.
Day to day operations of individual departments in life science companies rely on many decisions made outside of each department’s own remit. When embarking on a Lean strategy, the pillars of operational excellence (Levelling and Flow) can be supported by increasing awareness of how each department functions and explaining constraints.