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. 

In the past decade, the US Food Drug Administration (FDA) and regulatory bodies in other countries have published a number of guidance documents for the industry to encourage the use of statistical methods and sound science.  For example, Process Validation: General Principles and Practices provides a three-stage framework for implementing process validation (PV) using the principles of Statistical Process Control (SPC): Process Design, Process Qualification, and Continued Process Verification (CPV).

The life sciences industry is increasingly embracing the concept and practice of SPC to improve the quality, safety, and cost of pharmaceutical and other medical products.  The progress remains slow.  As a practitioner and consultant in statistical methods, I have seen the challenges facing many organizations.   Here are a few examples of incorrect or ineffective use of control charts. 

1. Retrospective analysis of what went wrong.   Often control charts are used as a tool for root cause analysis.   While the analysis can provide useful insight and lessons learned, not much can be done for things that happened months or years ago.  The right evidence and knowledge were long gone, and the opportunity to understand the true cause and/or make a positive impact was lost.  
2. Updating control limits with every new observation.  This happens when a control chart is made using software.  It is easy to do but is neither correct nor necessary.  If the control limits represent the true inherent variation of the process, they should not change unless for an assignable reason.
3. Poor measurement systems.  The observed variation comes from both the process and the measurements.  If the measurement system itself is inadequate, i.e., too much variation compared to the process variation, a control chart of the process will not produce correct signals. 
4. Used only by specialists.  Process operators and other staff are not trained or involved in generating the charts.   The data and charts are in the computer and visible to only a few selected members.  The rest of the organization are not routinely engaged in understanding process variation.
5. Over-reliance on software and rules.  Software can quickly and reliably compute data and detect special cause variation using the set rules.  But it does not connect observations on the shop floor with the analysis as humans can.  It is a missed opportunity for learning, especially when compounded with a lack of broader involvement of the organization.

Effective use of SPC and other scientific methods requires both resources and expertise.  But it is achievable with careful planning.  If you want to implement or invigorate SPC, I recommend paying attention to the following.

1. Get the organization’s commitment from the top.  Keep in mind that this capability development is also a culture change in most organizations.  It takes time, resources, and long-term commitment to change. Change management is essential. 
2. Develop deep expertise in quality.  SPC cannot be implemented in isolation but is an integral part of quality management.  Ineffective use of SPC is often due to lack of understanding of the fundamental theory in quality.  Ideally the resource should be an internal/external consultant who has expertise in statistics, science, business, as well as the subject matter.  
3. Involve the whole organization.  A few experts are not sufficient if the rest of the organization does not have a quality mindset.  Make sure that everyone (including management) is trained in the basics of quality concepts and tools and understands how they contribute to quality in their daily work.  Wherever possible, develop mechanisms to allow them to use the tools or data and create routine dialogues on quality and process improvement.

I hope that with the concerted effort of the life science industry and regulators, by 2030 when the 100^th anniversary edition of Shewhart’s book is published, we will see much progress in our industry.

ProtoArray was the world’s first commercially available high-density human protein microarray when it was launched in 2004.  While I wish it would continue, I am gratified to see how much ProtoArray products and services have helped researchers advance science and medicine in the past 14 years.

The news brought back a lot of memories.  As a scientist I joined the start-up company, Protometrix, in an empty 19th-century factory building in Guilford, CT in September 2001.  Protometrix was acquired by Invitrogen in April 2004, and we launched the ProtoArray product four times within the first year of the acquisition.  I led the design, manufacturing, and application development of the product and learned many lessons from the experience.

The most important lesson is “understand the customer.”  As technology-based first-in-class product developers, we tend to focus on overcoming technical challenges and let R&D drive the development.   As a scientist and engineer, I could not help but be passionate about the technology.  But it is a common mistake that I still see among some life science companies.   There is a lot of uncertainty in the commercial value of the product because it is new, unproven.  If few customers have used it or would know what performance attributes are important to them, how do we make design trade-offs? 

A better way is to involve our commercial colleagues and customers as early and frequently as possible.  The goal should be commercial proof-of-concept, e.g. is there at least one paying customer?  If we did not have a paying customer in the unfavorable capital market of 2004, I think Protometrix would have a hard time finding a buyer. 

It was on the New Product Introduction teams when I was first exposed to the Agile product development concept back in 2004.  But it was the experience of launching the product 4 times in a year with progressively more features (i.e. proteins), refined designs, added software and services that helped me appreciate the value of understanding the customers and the mechanisms required to operationalize it.

Many organizations now embrace various Agile frameworks to accelerate product development.  They should be aware that these and other similar frameworks are off-the-shelf solutions to overcome structural barriers that slow us down in understanding customers.  Implementing such frameworks is neither necessary nor sufficient for innovators.  What is necessary is the unwavering focus on improving our understanding of the customer needs in the context of their business or life.  Under competent management, the right structure for the innovator, whatever it is, will emerge eventually and continue to evolve with the customers.

I will continue my journey on understanding the customers in the new year.  I hope you join me.

Who doesn’t? But how?

For over a decade, I have used many management methodologies for business improvement, e.g. Lean, Six Sigma, Enterprise Process Management, Change Management, etc. One of the biggest lessons I learned is that no matter what methodologies we use, ultimately, sustainable improvement is built on one foundation: understand, develop, and enable people.

Anytime when the people component is lacking in a change initiative or operating model, it will inevitably fail.

It is not a new concept, and no one seems to disagree with the premise. Yet few put enough emphasis on people in everyday practice. The people and culture piece often gets the least amount of attention on a Balanced Scorecard — if it is used at all. Businesses need to achieve financial goals, satisfy customers, and improve capabilities. No doubt. Guess who make these happen: it’s their people.

Many organizations start to pay attention to people only after they begin a change initiative or when there is an attrition problem. Even then the task is often delegated to Human Resources or other specialists, and the resources disappear as soon as the initiative officially ends or when the symptom is gone.

But change is constant. The need to develop and enable people never ends, and it is the professional responsibility of the managers.

Nowadays, every organization tries to be agile and embrace change, including digital transformation. But are their people willing, prepared, and ready? The outcome is predictable: those who succeed have nurtured the right culture and people from the start.

People familiar with the Lean concepts know the seven types of waste and the benefits of relentless elimination of such waste. Lean practitioners are trained to see them in everyday activities and act on them. There is the eighth type – unused human potential, which is the biggest but least visible or recognized waste. Reducing or eliminating this type of waste is not the responsibility of a process improvement or HR specialist but management. Unfortunately, many managers (if not the majority of) do not proactively develop and enable their people. They are only trained or expected to handle performance issues when things go wrong.

Not realizing people’s creative and productive potential is a huge missed opportunity for both the organization and employees. But it doesn’t have to be.

I encourage every manager to ask one question:
What have I done today to develop my people or improve the environment to enable them to accomplish more? How about in the last week, in the last month?

I want to leave you with one of my favorite quotes from Peter Drucker.

Entrepreneurs innovate. Innovation is the specific instruments of entrepreneurship. It is the act that endows resources with a new capacity to create wealth. Innovation, indeed, creates resources. There is no such thing as a “resource” until man finds a use for something in nature and thus endows it with economic value. Until then, every plant is a weed and every mineral just another rock.

Typically, OpEx professionals stress how process design, management, and continuous improvement help reduce variation and waste and increase quality and productivity. Scientists often see the drawbacks — processes being rigid, overly prescriptive, and unnecessarily complex, creating bureaucracy and limiting people’s creativity.

Understanding Innovation Work

As W Edwards Deming said, “A bad system will beat a good person every time.” A poorly designed or implemented process certainly can stifle innovation. A better question is “How can we design and implement processes that enable creativity and innovation?” The emphasis here is “enable” not “control.” It is not an easy task, and I have been involved in making a few bad processes in my career.

From my experience, the first step to answering this “how” question is to understand deeply, first-hand, how the creative and innovative work is done. This cannot be accomplished by studying tasks and making flowcharts as we have done in manual work analysis for a century. Creative work does not follow a linear sequence of steps, visible and repeated as in a value stream. There is no value object that we can follow as it is being created. Instead, we have to get to know the people involved and learn how they work, individually and collectively, to generate results. Go Gemba, as we say in Lean. Only then can we start designing processes that enable them to innovate.

A Perspective from Apple

It is instructive to quote a few statements from Tim Cook in an interview with Bloomberg Businessweek 16 months after becoming CEO of Apple. When asked about the enormous pressure to continue to create breakthroughs, Cook responded:

“Creativity and innovation are something you can’t flowchart out. Some things you can, and we do, and we’re very disciplined in those areas. But creativity isn’t one of those.”

Maybe Apple’s secret of innovation includes its understanding of where processes are needed and where they are not.

The following also resonates strongly with me.

“Creativity is not a process, right? It’s people who care enough to keep thinking about something until they find the simplest way to do it. They keep thinking about something until they find the best way to do it. It’s caring enough to call the person who works over in this other area, because you think the two of you can do something fantastic that hasn’t been thought of before. It’s providing an environment where that feeds off each other and grows.”

How well do you understand what drives creativity and innovation in your organization?