Despite rapid advancement in life science and technology, much is still unknown about disease biology and variation in individual human responses to the pathogens and therapeutic interventions.  Our ability to understand the mechanisms and create effective solutions is still limited relative to the wide, complex problems.  Research and development as well as the manufacturing of vaccines, medicines, and diagnostic tests take much more time and resources than most people realize.  One of the reasons is that these products must meet very high standards of safety and quality, and therefore, require very rigorous development and testing.  Another reason is that the biological processes take time, e.g. time for patients to respond to the treatments, time for cells to grow in production, etc.

While we have limited ability to accelerate the natural processes in biology or control their outcomes, there are significant opportunities to reduce unnecessary failures, defects, delays, and waste in general that we can control.  As a scientist and quality professional, I have worked in R&D, manufacturing, quality, and business improvement in life sciences for over two decades.  No one wants to generate failures, defects, delays, or any type of waste.  Nevertheless, waste occurs and impedes the development and delivery of life science products, impacting the life and well-being of people.  

Waste stifles the innovative potential of life science and technology.  Waste must be reduced.

The first step to reducing waste is to see it. 

Lean practitioners are familiar with the traditional 7 or 8 types of waste that are common in all organizations across different industries.  However, many types of waste, especially in R&D, are not as visible as defects in manufacturing.  They are hidden in plain sight because they appear to produce desired results.  Even negative results are often explained away as expected biological or natural variation.  It is only when we look closely and compare them to the alternatives, do we realize they are full of waste.

Here are a few examples.

1. Design and perform experiments that only marginally improve our existing knowledge or decisions.   Even if successful, the outcome merely confirms what we already knew — we could have made the same decision without it.  The cost benefit analysis should clearly define the incremental knowledge sought before committing time and resources.  

2. Fast-track product development without a thorough characterization of the design space or without proper process and method validation, resulting in high costs, rework, and poor quality downstream in development and/or manufacturing. Quality by Design (QbD) would have been much more effective over the long term.

3. Conduct poorly designed experiments that unknowingly include high variation (noise), leading to failure to detect the change or difference (signal).  Poorly executed experiments can also create noise and lead to similar failures.  

4. Include an unnecessarily large number of runs or replicates when a properly designed experiment can get the same results at a fraction of the time and cost.  Statistically designed experiments can also reduce the likelihood of inconclusive results due to lack of power (i.e. too few replicates). 

5. Use manual procedures to perform tasks when technology is available to automate the job with much less time, cost, and errors.  It is not uncommon to see highly educated, expensive resources perform routine, manual tasks in the laboratories and on the computer.  A few lines of code can turn hours of manual data analysis into instant results.

6. Acquire and/or build complicated solutions when a simple and robust solution exists.  Ambitious scientists/engineers tend to chase cutting-edge solutions without investigating simpler, cheaper, readily available solutions.  Dedicated equipment and sophisticated algorithms cost much more time and resources but may not perform significantly better.

The above are only some examples of hidden waste at the operational level.  Bigger waste can happen at a strategic level (e.g. developing the wrong product and solving the wrong problem) and at an organizational level (e.g. misaligned objectives and broken processes); they must be addressed by the senior executives of the organization.  But everyone can improve productivity by learning to see the waste in what we do every day.

I hope the COVID-19 pandemic heightens the awareness of the value of life sciences and the need for higher productivity.  I am proud to work in this industry, but also feel strongly our duty to continually reduce waste – the opportunity cost is simply too high.

Life science products save not only lives but also our livelihood. 

1. Adaptive mind-set
2. 3-D savviness
3. Partnership skills
4. Agile ways of working
5. A balanced field of vision

It is a well written article with useful insights and actionable recommendations for effective leadership development. However, there is one flaw – presentation of the survey data. Did you notice any issues in the figures?

I can see at least two problems that undermine the credibility and impact of the article.

Inconsistent numbers
The stacked bar charts have four individual groups (C-suite, Top team, Middle managers, and Front line) in addition to the Overall. In Exhibit 1, for example, the percentages of the respondents that strongly agree with the statement “My organization has a clear view of the 2-3 leadership qualities and capabilities that it wants to be excellent at” are 44, 44, 30, and 33%, respectively. Overall, can 44% of them strongly agree? No. But that is the number presented.

It doesn’t take a mathematical genius to know that the overall (or weighted average) has to be within the range of the individual group values, i.e. 30 < overall < 44. Similarly, it is not possible to have an 8% overall “Neither agree or disagree” when the individual groups have 11, 9, 16, and 17%. The same inconsistency pattern happens in Exhibits 4 and 5.

Which numbers are correct?

No mention of sample size
Referring to Exhibit 1, the authors compared the executive responses in the “strongly agree” category (“less than 50 percent”) to those of middle managers and frontline staff (“around 30 percent”), stating there is a drop from the executives to the staff. But can a reader make an independent judgment whether the difference between the two groups really exists? No, because the numbers alone, without a measure of uncertainty, cannot support the conclusion.

We all know that the survey like this only measures a limited number of people, or a sample, from each target group. The resulting percent values are only estimates of the true but unknown values and are subject to sampling errors due to random variation, i.e. a different set of respondents will result in a different percent value.

The errors can be large in such surveys depending on the sample size. For example, if 22 out of 50 people in one group agree with the statement, the true percent value may be somewhere in the range of 30-58% (or 44±14%). If 15 out of 50 agree in another group, its true value may be in the range of 17-43% (or 30±13%). There is a considerable overlap between the two ranges. Therefore, the true proportions of the people who agree with the statement may not be different. In contrast, if the sample size is 100, the data are 44/100 vs. 30/100, the same average proportions as the first example. The ranges where the true values may lie are tighter, 34-54% (44±10%) vs. 21-39% (30±9%). Now it is more likely that the two groups have different proportions of people who agree with the statement.

Not everyone needs to know how to calculate the above ranges or determine the statistical significance of the observed difference. But decision makers who consume data should have a basic awareness of the sample size and its impact on the reliability of the values presented. Drawing conclusions without necessary information could lead to wrong decisions, waste, and failures.

Beyond the obvious errors and omissions discussed above, numerous other errors and biases are common in the design, conduct, analysis, and presentation of surveys or other data. For example, selection bias can lead to samples not representative of the target population being analyzed. Awareness of such errors and biases can help leaders ask the right questions and demand the right data and analysis to support the decisions.

In the Preface of Out of Crisis, Edwards Deming made it clear that “The aim of this book is transformation of the style of American management” and “Anyone in management requires, for transformation, some rudimentary knowledge of science—in particular, something about the nature of variation and about operational definitions.”

Over the three and half decades since Out of Crisis was first published, the world has produced orders of magnitude of more data. The pace is accelerating. However, the ability of management to understand and use data has hardly improved.

The authors of the McKinsey article are correct about 3-D savviness: “To harness the power of data, design, and digital (the three d’s) and to stay on top of the changes, leaders need to build their personal foundational knowledge about what these advanced technologies are and how they create business value.” That foundational knowledge can be measured in one way by their ability to correctly use and interpret stacked bar charts.

Now, more than ever, leaders need the rudimentary knowledge of science.