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Lean, Six Sigma, and the Risks involved in Pharmaceuticals and other Medical Applications

time:2012-7-26 read:1237
Lean, Six Sigma, and the Risks involved in Pharmaceuticals and other Medical Applications

Eric C. Maass, PhD

The set of approaches referred to as Lean grew from methods deployed at Toyota that led to impressive results. In recent years, the Lean methods have been incorporated into the Six Sigma framework to provide a systematic approach for improving processes in terms of important business and customer metrics involving defects, yields, cycle times, inventories and costs.

Many of the valuable Lean approaches align very well with expectations in pharmaceuticals and other medical applications. For example, expectations of discipline and cleanliness in Pharmaceuticals align very well with Lean’s 5S approach. Other Lean approaches help to ensure best practices that are relevant for virtually any enterprise, from financial institutions to high tech industries to pharmaceuticals to utilities and so on.

Six Sigma brings in a step-by-step, methodical approach whereby Lean approaches can be implemented in a structured way. Six Sigma also provides a way to combine Lean’s straightforward approach with some statistical methods as appropriate. Some of these statistical methods are particularly relevant for medical applications in general, and pharmaceuticals in particular, because of the nature of the risks that must be managed.

With statistical methods, we often talk about two types of risks: the producer’s risk (Alpha risk) and the customer or consumer’s risk (Beta risk). In non-medical applications, these risks involve tradeoffs can often be considered in a financial sense: a non-medical company may be willing to take a 5% Alpha risk and a 20% Beta risk; if the customer is impacted due to the Beta or consumer’s risk, the company will handle it, at some financial cost that can be balanced against the other financial risks.
In the world of pharmaceuticals, the consumer’s risk involves much more than just a financial risk. People’s lives can be at stake - literally. People who work in Pharmaceuticals and other health and medical related business and professions know this – it is part of what makes their job so important, and so fulfilling.

The challenge is, how can we ensure that the Lean Six Sigma approach, so effective for processes in general, takes these heightened risks into account? This challenge must be taken into account for any improvements to any process that can affect life-saving and life-enhancing therapies.

A key aspect of Lean is a set of methods to understand and improve cycle times; some relevant Lean concepts include Value Stream Mapping, TAKT time, and Pull System approaches such as Kanban. These methods can be directly applied to virtually any process, including processes involved in pharmaceutical development, manufacturing, and delivery. However, bringing in the challenge of ensuring that these
process improvements will also handle the risks to the customers adds another layer to the approach.

For example, if the straightforward Lean approaches and analyses indicate a certain level of inventory as being appropriate for reducing manufacturing cycle time, the question should arise: while this inventory level might help the company minimize cycle time, does it mitigate risks to the customers? Are there situations that could cause a sudden increase in demand for a certain medication, and do these levels of inventory allow the flexibility to respond effectively, to save lives? Even without a major increase in demand, do these levels of inventory allow the flexibility to respond effectively if there is a glitch in manufacturing, or are we putting our customers at risk that we won’t be able to deliver for a period of time?

A deeper integration of Six Sigma and Lean approaches provides an answer for these situations. The statistical approaches in Six Sigma provide means to quantify risks, and to describe and model risks and contingency plans. Statistical modeling approaches such as Monte Carlo Simulation can supplement the effective Lean approaches. The intial approach would involve Lean approaches. Risks can then be identified through risk management approaches, such as FMEA (Failure Mode and Effects Analysis), FTA (Fault Tree Analysis), and other sophisticated approaches. These risks tend to fall into two categories – those that are present in normal processing (“Common Cause” in SPC terminology; generally, normal variations that can cause defects or product exceeding specification limits), and exceptional circumstances (“Special Cause” in SPC terminology; generally, major – often catastrophic – problems that can conceivably arise and that require contingency planning).

The identified risks can then be quantified, and used with a modeling approach such as Monte Carlo Simulation or Discrete Event Simulation. These modeling approaches provide insight into the nature and magnitude of the risk, and provide a tool for rapid “virtual prototyping” of various alternative approaches for managing the risks, such as by setting inventory levels for key products or key ingredients that provide the business with the confidence they want in cycle time, lead time, and costs, while providing confidence that the customers will be protected against both normal process variation and unusual circumstances.