Through the Lens of Lean

Over the past few years, Tunnell Consulting has studied the tactics and results of a number of organizations as they strove to apply Lean and Operational Excellence (OpEx ) principles to improve the efficiency of their quality control (QC) labs. Some were clients and others were cases gleaned from publications  All had a great deal in common in terms of the issues and performance challenges that drove them to seek improvement. While most achieved an acceptable return on investment for their efforts, some achieved extraordinary results. In the case study that follows, we’ll describe how one high-performance organization integrated Lean Lab into a comprehensive transformation of its manufacturing supply chain and achieved outstanding results, which synergistically enabled improvements upstream and downstream in the manufacturing process.

Our case study involves a pharmaceutical company with a large number and variety of products — solids, liquids, aerosols, capsules and tablets. This created significant complexity in the scheduling, queuing, coordinating and conducting of tests. Prior to the Lean improvement project, lab throughput time — the elapsed time between the arrival of a test sample in the lab and the completion of its testing — stood at 15 days. The organization established a goal of 30% reduction in throughput time within three months, and greater reductions over the long term.

In order to realize the full potential of cost savings, it was imperative to increase Right-First-Time (RFT) results.

There was a need to increase  utilization of personnel rather than simply speed up lab operations by throwing more money and people at the problem.

The comprehensive transformation process, of which the Lean Lab initiative was a part, resulted in significant improvements in productivity and cycle time, ultimately powering a 22% reduction in overall conversion cost (see three charts at above, right).

The lab reduced testing turnaround time by more than 50%, simultaneously increasing analyst productivity by more than 25%, and took Right-First-Time from 95% to a predictable performance exceeding 99%, without any increases in the amount of equipment or personnel (see charts at the middle of next page).

Applying Lean to QC lab operations presents some unique challenges. Lean has long been practiced in manufacturing and is relatively well understood there, but its application has lagged in laboratory operations. Many examples have been published describing 5S initiatives in the laboratory, but few case studies exist, showing where manual scheduling has been eliminated in favor of “pull” sample flow.

This fact may stem from the mindset that, due to the technical complexity of testing, the QC lab is somehow “outside” the production process. Organizations that appreciate the complexity of analysis, but at the same time recognize the lab as an interdependent sub-process of the overall product conversion operation, are at a distinct advantage.

In virtually all Lean Lab initiatives, sample scheduling is recognized as a non-value-added activity and, as such, becomes a primary target for improvement. One organization with which we recently worked began its Lean Lab journey with more than a dozen scheduling tools in use. Some were formal parts of Operations Planning and MRP, but many were ad hoc worksheet based work-arounds that evolved in response to highly volatile demand patterns and constant expediting. When the company’s Lean project team quantified the amount of time spent on scheduling alone, they were astounded to find that it constituted more than 13% of the entire lab operation time.

This example clearly showed how a lack of understanding of interdependency can drive waste up and down the manufacturing cycle. In this case, the Operations Planning department did not understand laboratory capacity constraints.  As a result, it was not aware that its decisions, and those of the procurement department, were creating large peaks and valleys in scheduling, which sometimes drove lab work center loading to over 100% and, at other times, left instruments and analysts idle. Operators in the lab, using its dozen scheduling “tools,” felt that they were doing all they could to push samples through the lab.  As evidence, they pointed to an on-time release  exceeding 95%. Hidden from everyone’s view, however, was the fact that the planning department had changed metrics.  The planning team had increased sample release lead time planning values to reflect average lab performance in this chaotic environment, as opposed to setting standards that would optimize downstream manufacturing operations. As a result, even if the lab had an on-time release performance of 100%, overall manufacturing cycle time and work in process would be inflated.

What seems to differentiate high Lean performers from less successful ones is the difference between streamlining and eliminating waste.  Where the less successful aim, simply, to streamline the manual sample scheduling process, high performance organizations strive to eliminate it entirely in favor of true visual cue and “pull” systems.

They recognize that, for visual or “pull” sample management to work, a foundation of Lean prerequisites is needed, each of which contribute to improving throughput speed, capacity and predictability.

The fundamental prerequisite of establishing flow, for example, requires a holistic and coordinated approach inclusive of all Operations functional groups. To be successful, efforts must focus  on establishing mechanisms that balance workload against capacity in a sustainable way. Naturally, creating capacity-balanced workloads in the laboratory requires assuring that there is sufficient capacity to meet peak demand. Where capacity is insufficient, basic Lean waste reduction tactics help increase it by increasing productivity and reducing cycle time. This is synergistic with simultaneous efforts to smooth demand peaks by reducing test cycle time variability and sample demand volatility. Obviously, everything hinges on developing a strong understanding of both internal and external factors affecting productivity and capacity.  Without that, as outlined in the example above, there will be an unclear understanding of lab operations and capacity by functional groups upstream and downstream of the lab.  As a result,  non-value-added activities will increase, and with them, unnecessary costs in the lab and in those functional groups.