Technology Development Network

Coming May 2022 New Book on Technology Development

    Technology Development

    Lessons from Industrial Chemistry

    and Process Design

 

    by Ron Stites

 

 

 

Ron has recently completed the manuscript for a new book on Technology Development under contract with Walter de Gruyter GmbH. De Gruyter is a German scholarly publishing house specializing in academic literature. The new book should be available May of 2022. The Table of Contents below gives you an idea of the topics covered:

    Chapter 1: Introduction

    Chapter 2: What is Technology and Technology Development?

        Some Common Misconceptions

        Types/Classes of Technology

        B2B Markets

        Consumer Markets

        Government Markets

        Social/Common Good

        Impact on Existing Markets

        The Benefits of Technology Development

    Chapter 3: Some Basics Axioms of Technology Development

        Axiom 1 – Nothing is Completely Predictable

        Axiom 2 – Collaboration can be a Four-Lettered Word

        Axiom 3 – Money is the Final Unit of Measure

        Axiom 4 – Everything is Urgent

        Axiom 5 – Size Matters

        Axiom 6 – The Quest for Knowledge Requires Intuition and Deduction

    Chapter 4: The Science of Technology Development

        Markets First

        Investors/Stakeholders Second

        Go/No-Go Decision Process Third

        Finally, The Technology Development Process

        Planning to Learn

        Designing Your Experiments

            The Intuitive Phase

            The Deductive Phase

            Making Sense of Uncertainty

            Simple Statistics – Population, Sample, Average, and Standard Deviation

            Guarding Against Major Blunders

        Execute Your Plan

        Evaluation and Reporting

        Improve Your Experiments or Wrap Up Your Conclusions

        Valuation Methodology (Present Value/IRR)

        Evidence-Based Decision-Making and Data Science

    Chapter 5: The Art of Technology Development

        The Art of Human Relationships

            With Employees

            With Funders and Potential Funders

            With Customers and Potential Customers

            With Stakeholders

        The Art of Matching Technology with Markets

        The Art of Monetizing the Technology

    Chapter 6: Funding and The Discipline of Technology Development

        How Discipline Impacts Funding and the Growth of Wealth

        The Biz Plan

        Funding in Large Organizations

        Funding in Small Organizations

        Technology Development Startups – A Peculiar Subset of Small Business

        The Funding Process

            Dispelling Some Myths about Raising Capital

            Assessing Capital Needs

            Set Capital Goals

            Detail Capital Goals and Strategy

            Identify Potential Capital Sources

            Guiding Principles for Contacting Potential Investors

            Structure Capital Offering(s)

            Manage the Capital Campaign

    Chapter 7: The Future of Technology Development

        Critical Technology Development Needs

        Capital Sourcing Challenges

        Evaluation of Technology

        Use of Capital

        Scarce Supply of Capital for Small Business

        Accountability

        Patience

    Chapter 8:  Where Do We Go from Here?

        Reducing the influence of academics and Higher Education

        Strengthening review of government Technology Development programs

        Specifically requiring statistically sound review

        Encouraging small, primary investments directly in small businesses

        Graduated Capital Gains Tax

        Capital Gains Tax on Non-profits

        Phasing out Non-profit Status for all Non-profits

        Phasing in Strict Financial Reporting for all Non-profits

        Reforming all of Education to make it eventually Competitive, For-profit

        Requiring Grade-appropriate Probability and Statistics Curricula

        Requiring Appropriate Field Proficiency Probability and Statistics STEM and Professions

        Deregulating Crowdfunding for Small, Technology Development Companies

    Chapter 9: Some Final Words

If you would like information on how to get a copy, contact us at Ron Stites at [email protected]

What is Data Science? Part I

Among the hottest buzzwords in industry today are:

• Data Science
• Big Data
• Data Mining
• Statistics/Data Analytics
• Modeling
• Artificial Intelligence (AI)/Machine Learning

But what do they mean? Are they new, space-age discoveries that only a PhD in Statistics or Computer Science can understand?

Not really. Like so many things, the truth is hidden in a maze of jargon. The concepts are relatively simple and with the help of the personal computer, the calculations are much more manageable than in the past.

This little paper is designed to strip away much of the mystic and get down to the business utility of Data Science.

First – Some Background:

There are some things that have changed. The personal computer, the Internet, the rise of many, cheap methods for capturing data and using large databases have changed the landscape. It is now possible to capture incredibly detailed measurements about most processes, store them retrievably and analyze those data quickly and inexpensively. We have become obsessed with collecting data – mostly because we can. Data Science has recently come on the scene as an answer to the question, “Can we do anything useful with all these data?”

Data Science has been around since the Egyptians first started tracking the times and duration of the Nile flood and a bunch of other miscellaneous data. They first gathered information about the flood, crop practices, crop yields and the movements of the stars and planets. One of the first Data Scientists noticed that when the Star of Isis (Sirius) appeared to rise just before the sun, the Nile floods would start soon. Farmers planned their activities accordingly and the Egyptians flourished even though they lived in the center of a vast, dry wasteland. History indicates that Data Science was around at least by 3285 BCE.

Now this may be a bit of a stretch since the Egyptians added lots of religious and cultic notions around their science. Data Science was clouded in the occult and superstition until the experimental, scientific revolution that began in the 15^th century (or so). Galileo is often cited as the first experimenter that concerned himself with data accuracy and precision in an objective way around 1600 AD. It was the hard sciences of chemistry, physics and astronomy that pushed forward scientific data analysis. Later, botany, genetics and agronomy pushed data analysis into areas where understanding random, experimental “error” became critical.

Over time, it became clear that statistical analysis of experimental data was crucial for understanding the usefulness of the data. Initially this as attributed to unavoidable errors or limitations in measurement technologies. By the early 20^th century, however, it had become clear that all systems displayed probabilistic behavior to a greater or lesser degree. This was especially true with sub-microscopic phenomena (e.g. photons, electrons, etc.) and complex biological phenomena (e.g. mutation/variations, psychological trends and preferences, etc.).

We will return to these probabilistic issues in a later blog, but for now, be aware that the mathematics of data management – especially statistics – was a well-developed field long before we were talking about Big Data. Data Science has borrowed much of its analytical approach from the physical, biological and social sciences.

Data Science is essentially the:

• Collection of data,
• The categorizing of these data,
• The creation of predictive models from the categorized data and
• The application of the predictive models for useful purposes.

The various buzz words tend to focus on some aspect of this process and emphasizes specific tools.

Some of the Buzz Words:

Two closely related terms, Big Data and Data Mining began to emerge around 2000 and by 2010 had become the hot items in IT. The growth of data storage capabilities during the 1990’s made possible accessing terabytes of data. By the early 2000’s developments in parallel processing and large, volatile memory chips had dramatically increased the speed of data searching and handling. It became practical to investigate gigabytes of data simultaneously in fast, volatile memory rather than combing through multiple magnetic or optical discs. It became practical to do rapid statistical analysis on gigabytes of data hunting for potential relationships between data elements and including many simultaneous variables in linear and non-liner models.

By around 2010 the collection, storage, retrieval, investigation of gigabytes and terabytes of data became known as Big Data. The methods for collecting, storing, searching and retrieving relevant data from existing data became known as Data Mining. When we talk about Data Mining, we are focusing on the challenges of retrieving useful data from a variety of independent sources. Frequently, these sources are “legacy” databases created by old hardware and software. Sometimes these databases have little structure and are of questionable quality. The Data Mining expert deals with all these potential issues when trying to sort through giga- and terabytes of data.

Two other buzz words that are often used interchangeably are Statistics and Data Analytics. They are not the same. Statistics is a body of scientific, mathematical principles and theorems based on various types of data randomness. It first developed from studying games of chance (dice, cards, etc.), but became closely associated with the experimental sciences (especially agronomy, botany and chemistry).

Data Analytics uses Statistics extensively. Data and data trends are studied to see if “statistically valid relationships exist.” The question is, “Are there persistent relationships that cannot be explained simply by random chance?” The hope is that we can find relationships that are persistent enough to be used to predict the future. Ideally, the relationships are so persistent that they could be called “laws” that “cause” the results that we observe. We start by looking at “correlations” and then test them to see how “important” and persistent they are. In the hard sciences we can often run enough controlled experiments that we can start talking about “causality.” This is much less common in business and social sciences where we often are working “in the wild” or with “secondary” data – that is data we have “inherited” and not generated under conditions that we controlled.

Data Analytics is more than just statistical analysis, however. It includes evaluating the completeness and reliability of the data being used for statistical analysis. This has been one of the greatest challenges for Data Analysts. Historical data is never complete or without obvious and suspected errors. Data Analysts must decide what to do with such data without biasing the data set. There is constant tension between deciding which data are “good enough” and which data must be rejected as incomplete, inaccurate or irrelevant. The users of data should also be aware of the “judgements” that Data Analysts must make. Those “judgements” could make the analysis and conclusions biased or irrelevant.

Anyone who thinks that Data Science is purely objective does not understand it at all. The Data Scientist must “prejudge” many aspects. We can only hope that it does not lead to intention or even unintentional prejudice.

The goal of Data Science activities is to “learn” in the sense of building and validating models that allow the measurement of readily observable factors to predict important results. This notion is worth spending time pondering. When we “learn” we are creating “models” in our head that organize distinct observations and relate them to each other. We are hoping that A causes B with enough reliability that when we see A, we can get ready for B.

An example might be using data we have collected about buyer preferences. Assume we have done a survey of customer preferences that included satisfaction/dissatisfaction with how well our product lasted. Now suppose that our competition extended their warranty from ninety days to a year. We might wonder if we needed to extend our warranty program. We would be scrutinizing our data to see if it could be used to make a statistically valid model of how our customers will react to our competitor’s action. If we “knew” that, we might be able to react to minimize damage or even gain an advantage in the “exchange.” If we “knew” our customers were very satisfied with our product’s reliability, we might do something quite unexpected. We might run an ad campaign that emphasized our product longevity and make our competitor’s “need” for a warranty extension a negative in the minds of the consumer.

Modeling is rife with challenges, especially when all data are historical rather than experimental. Experiments are designed to control all factors that seem to be important and to randomly manipulate a few factors to see how results of interest are impacted. Good experimental design can often build a very strong case for causal relationships between a small number of very important factors and results. Historical data is rarely so nice and neat.

Historical data is collected as it happens. It frequently does not follow an experimental design nor is there much hope that all relevant factors were observed. There can be important factors that were not measured that “confound” the data. An example might be price and sales data that were taken during a period when the economy was slipping into a recession. Unless the analysis considered macro-economic factors, it could be useless. And finally, historical data is always plagued by errors and missing data points.

The famous statistician, Dr. George Box, was noted for saying, “All models are wrong, but some models are useful.” Models are useful, numerical approximation of reality if they can reliably predict important factors from easily measured ones. They may be useful for a season and pass away into history or they may indicate very persistent relationships that seem to always be reasonably true. Hence, the users of Models need to be careful to be neither too demanding of accuracy nor too certain of predictions. One of the arts in Modeling is for the Data Scientist to understand the data, the relationships and the data needs well enough to communicate to data users the reliability and limitations of any Model they create.

Pulling it all together – Artificial Intelligence (AI) and Machine Learning:

Perhaps the sexiest concepts in Data Science today are AI and Machine Learning. They are closely related concepts that either focus on the process of “thinking” (AI) or more on the gathering and using the “thinking” (Machine Learning). The concepts bring together much of what we have discussed so far. Let’s start with Machine Learning.

The clearest way to think about Machine Learning is to imagine an example – the Roomba. This amazing device uses Machine Learning to navigate your house and vacuum your floors. How does it do it?

The Roomba has multiple sensors for detecting when it runs into something and multiple pre-programmed actions to try to continue vacuuming. What it does differently from a “dumb machine” is that it remembers bumping into something and ties to map out a “better” performance next time. The Machine Learning part of Roomba is the sophisticated sensing equipment that is tied to a large memory and a fast, powerful processing system able to recall and use gigabytes of data. Roomba can “learn” to avoid obstacles by rote but optimizing the time and efficiency of vacuuming requires a bit more. This is where we tend to talk more about AI.

AI is focused on optimizing the algorithms for doing a task. It requires knowing about the task, guessing at reasonable optimizing actions, attempting many different actions, measuring results and then selecting and refining the decision algorithms that work the best. These algorithms include a decision tree for taking action and a Model to help select optimal solutions.

A simple algorithm might be, “If you can’t go forward, then try going right, repeat until you are going forward again.” A more sophisticated algorithm will look at many factors before just deciding to go right. Current position in the room and a history of what has happened in the past at that position would come into play. Furthermore, the outcomes of previous decisions made at that position would be considered.

AI isn’t very impressive if it is nothing more than remembering not to repeat mistakes. It becomes much more useful when algorithms are developed that successfully navigate “changes” to conditions. That is, when good decisions are made when a chair is arbitrary placed in the room. “Good” decisions generally follow from certain decision patterns. If we “learn” these patterns, incorporate them into the decision algorithm(s) and then modify them with the newest data, we are creating a much more powerful AI application.

Machine Learning and AI can also be illustrated in a data management project. Suppose we wish to use AI to set asking prices for houses we are selling. We would certainly want a large database of asking and selling prices for “relevant” houses. That could mean those in the neighborhood and of similar size but there could be a whole host of other features that could be relevant. Sadly, tastes, budgets, interest rates, consumer confidence and even our own financial goals are not static. If we could gather large chunks of data and measure how they interact to produce quick, profitable sales we might create an AI algorithm that would give us a distinct advantage.

We would use Machine Learning to go out and constantly get the “relevant data” and update our algorithms based on results we achieved in the marketplace. We would try many different optimizing algorithms to see which ones gave us the best result. Then we would have to try to find decision trees to integrate with our Models that effectively respond to the many changing features of the real estate business. Once we did that and verified that it produced improved decision making, we would be well on our way to having an AI based real estate business.

This all sounds easy enough until you look into the details. It is one thing to allow Roomba to stumble and fumble around learning how to vacuum a room, but a whole different matter to allow a new AI system to replace a real estate “expert” on setting house prices.

The history of AI and Machine Learning is full of spectacular failures. Nevertheless, there are many, less spectacular successes. We will talk about some of the failures and successes in future blogs. Like with any technology deployment, there are constraints and limitations that must be recognized and considered. We will talk about some of those in the next blog: What is Data Science? Part II

 

Stites & Associates, LLC (SALLC), is a technology development group founded by Ron Stites in 1996. The scientists and engineers at SALLC strive to help clients succeed at technology development and deployment by using Evidence-Based Decision-Making. Feel free to contact Ron Stites via email at [email protected].

 

Inside/Outside Sales for Laboratories

All technical service businesses must create sales. To succeed all such organizations need an effective way to:

• Find Prospects
• Qualify Prospects
• Convert the Prospect to a Customer by Closing the Sale
• Deliver the Service
• Retain the Customer
• Create Follow-on Work

In many ways Laboratories are no different from General Consulting, Engineering, Design and similar firms. Though the details may differ, the processes must be planned, coordinated and well executed.

The job of finding and qualifying Prospects is generally the purview of an “outside sales force.” This might be a single person or a whole organization of people working on finding and qualifying Prospects. This group is armed with general information about the Laboratory and the type of person or organization that will likely make a profitable Customer.

The general information about the Laboratory would be things like:

• The Types of Tests Performed
• The Types of Samples Tested
• The General “Turnaround Time” (how long things take to get work done)
• The Options Available for Report/Data Delivery
• The General Prices that Customers can Expect
• Who to Contact and How to do Business with the Laboratory

The “Outside Sales Person” should also know something about whom they should to try to reach. There are many potential Prospects, but many of them will not make good Customers. Some of the reasons might be:

• The Prospect does not have a Need that the Laboratory can meet such as:
  • Wrong Tests
  • Wrong Sample Types
  • Wrong Turnaround Requirements (generally too short)
• The Prospect cannot Pay for the Testing
• The Prospect (in this case the Person or Group) cannot make the Decision to Use the Laboratory

These are “Suspects” and not real “Prospects.” Time spent trying to “sell” to Suspects is not only wasted, it can be detrimental to the organization. Determining who is a Prospect versus who is a Suspect is called the “Qualifying Process.” This might sound simple enough, but it turns out to be far more complex than it appears. Often the Prospect is unsure about their needs, their ability to pay and even if they are able to make a buying decision. Furthermore, what a technical service organization is able to do is never quite as black and white as one might think. A good Outside Sales Person recognizes that things are not exactly black and white and can effectively close the gaps between needs and service offering. That is why it is a Qualification Process and not just a rote decision point. Those persons who do this successfully over the long haul are extremely valuable to the Laboratory. Those who do it poorly can destroy a Laboratory or any technical service organization.

The Qualification Process is rarely done in a vacuum. With rare exceptions, an “Inside Sales Person” needs to be engaged in closing the deal. Because technical services cannot be inventoried, matching Laboratory service offering and capacity with Customer needs is never simple. The Laboratory is much like a commercial airplane. Once the door closes and the airplane leaves the gate, the empty seat can never be sold. It is forever gone. In a technical service business, today’s idle “seats” on analytical instruments or unused “hours” for an engineer or designer are gone at the end of the day, never to be sold. They have “expired.” Hence, just like the airlines, technical service providers try to “oversell” their resources so that they have a low probability of having idle resources. This tenuous balance is maintained by Inside Sales Persons (often called “Project Managers”) who try to balance near term service requests with actual short term capacity.

The airlines are aided in this balancing act by very sophisticated passenger management software. Often the strange and fluctuating pricing structures that on-line buyers see are designed to balance service requests and actual capacity. This all works reasonably well until weather, mechanical failures or criminal action (real or imagined) throws the whole system into chaos. Then the system collapses for hours or days, with the most complex hub and spoke systems suffering the greatest disruption. The result is that there is much dissatisfaction with commercial airlines, especially the large carriers.

For Laboratories, the Inside Sale Personnel or Project Managers are aided in their balancing act by a computerized Laboratory Information Management System (LIMS). The effectiveness of these systems vary widely. These systems are generally fairly good at monitoring what is happening, but are generally much weaker at predicting even near term future capacity. They are often incapable of monitoring even near term capacity and demand. Furthermore, competitive conditions rarely allow a Laboratory (or other sellers of technical services) to use pricing structures to significantly affect short term demand. Hence, Inside Sales Personnel or Project Managers are often in a reactive rather than predictive mode.

It is often the case that Customer experience and retention is highly dependent on the skills and diligence of the Inside Sales Personnel or Project Managers. Frequently, “key Customers” are assigned to the most experienced and effective of these Projects Managers to improve retention of these Customers. Furthermore, effective coordination between Outside and Inside Sales Personnel set the stage for long-term Customer satisfaction. If deals are closed that are doomed to failure, the whole system can be disrupted and even “Key Customers” can become dissatisfied.

Designing effective Outside and Inside Sales operations, training personnel effectively, providing effective management tools and monitoring the performance of this difficult process is a key success factor for any technical service business. Furthermore, competitive pressures will require that the processes be constantly improved. It is one of the key differentiators in any technical service business – especially those that deliver more or less undifferentiated, standardized services.

 

 Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking.  For more information see: www.tek-dev.net.

Finance for the Research Manager II

Last month we introduced the idea of a discounted Net Present Value calculation as one of the most effective ways to evaluate multiple opportunities.  We pointed out that general business risk requires even the most certain of revenue streams to be discounted at around 10% to 15% before an investment of dollars today could balance the risk of the promise of future dollars.  We also pointed out that this discount rate grows very rapidly where there are significant technical risks. 

We summarized risks more or less as:

In general, the job of the Research Manager is to reduce risk and push down the level of discounting needed to justify the funding of projects.  The Research Manager is usually working with questionable or unknown technologies and is trying to assess and improve the probability of success.  The nature of the question may be completely open or quite narrow.  The organization may have a problem for which there is no known or even suspected solution.  Alternatively, the problem may have multiple solutions and the issue may be simply finding the best solution.  To make matters more complex, the Research Manager often has a mix of projects ranging from Moderate Risk to Blue Sky and needs to assign resources in the most effective way.

Before we go any further we should discuss the “Aversion for Loss.”  We have been talking about Risk in terms of assessing potential Gain.  One thing that we must make clear is that Gain and Loss are not symmetrical in the human mind.  We do not perceive the probability of Loss as simply 1 minus the probability of Gain.  Even though this might be mathematically so, we do not perceive it that way, and for good reason.  Failure is almost always punished more severely that success is rewarded – especially in the eyes of the one being punished or rewarded.  We are all very averse to Loss and Failure – far more so that our desire for gain.

The Research Manager should always keep this in mind.  No project will be funded by Executive Management that has a significant chance of destroying the Company.  Very few projects will be funded by Executive Management that has a significant risk of being detrimental to the careers of the Executive Managers.  Furthermore, no responsible Management Team would knowingly risk serious injury or loss of life.  Hence, a significant part of the Research Manager’s success will be based on the ability to ferret out the consequences of a “failure” and make sure that those consequences are not catastrophic for the Company, Management, Employees, Owners or other Stakeholders.  A catastrophic failure is one that can lead to all kinds of secondary consequential losses, among them destruction of property, loss of customer base or, heaven forbid, injury or loss of life. 

Having said the above, it must be admitted that no project is without some risk and sometimes even unknown risk.  A key to avoiding legal repercussions is using “reasonable care” and applying “professional judgement.”  To avoid ethical issues one should seek to avoid Moral Hazard.  In no case should others be unwittingly subjected to risks disproportionate to their potential gain.  When there is doubt, full disclosure of the risks should be made to interested parties so that they may participate in balancing of risks and benefits from their perspective.

Now back to the Research Manager’s dilemma.  In practice, the Research Manager quite often has a number of tasks to choose from.  There’s the mundane library study on which brand of catalyst to buy and then there’s that “Blue Sky” project of developing the means to impress any desired catalytic activity on silicon dioxide nano-wafers using electron beam imaging.   The former isn’t much fun and the rewards may be unspectacular but very useful compared to the investment of resources.  Now that “Blue Sky” project is really tempting.  Company and personal rewards might be astronomical.  It might win a Nobel Prize!  Nevertheless, it might be nearly impossible to do.  Clearly the Research Manager needs to do that library study first and think long and hard about putting resources into that "Blue Sky" project.

Of course, none of the real choices are that clear cut.  There are many possible projects and a plethora of resources to manage.  There is also the matter of time.  Some issues MUST be done very soon while others can wait.  The Research Manager needs to budget resources and set priorities based on importance to stakeholders and urgency.  The bulk of the Research Budget will be spent on Risk Level 3 issues that are fairly urgent.  Here possible solutions seem to exist and reasonable planning can be done to progress toward answering specific questions on specific potential solutions.  By driving risk factors from 35% to even 15% there can be a huge change in the value of a technology.  The technology can quickly go from one of interest to one of immediate application.

Let’s put some numbers to this.  Let’s suppose that we have a project on the books that amounts to spending $2 million on a plant change that might save us $450 thousand per year over the next ten years.  At first blush this seems pretty reasonable – a “payout” in 4.5 years.  But let’s also suppose that we aren’t very certain of our potential savings.  In fact, suppose this is a pretty new technology that we don’t have much experience with and we discount it pretty heavily.  Say we even consider it high risk at a 35% discount rate.  We would put the PV of these savings at about $1.2 million.  We wouldn’t be wanting to spend $2 million to get a PV of $1.2 million unless there was some other driver that is impossible to quantitate (e.g. fixes a known and significant safety hazard).  Let’s suppose, however, that we thought we could do a few experiments in our pilot plant to prove out this idea.  If we could drive the risk down to around 15% our PV would jump to around $2.3 million.  In fact, we could spend up to $300K on evaluating the project and, if successful, we would still be happy.  Hence, we could easily justify a $300K research project.  Or could we?

Would we actually do the project?  Well there was a big “if” in there.  How sure are we that we can close the gap?  If we were discounting at 35%, we may have some serious concerns that are not easily resolved.  Maybe $300K is unlikely to actually solve the problem.  We have to convince ourselves that risking dollars today has a good chance of successfully changing the risk/reward dynamic.  Furthermore, we have to assess the consequences of investing in this project especially compared to other projects.  If $300K is a lot for us to put at risk, we might shelve this project, especially if we had some better cost versus benefit options.  On the other hand, if this is the best project opportunity we have and we’ve already sunk a bunch of cost in our R&D budget, we might jump at the chance to prove this out and implement a significant process improvement.

This style of thinking needs to be applied to all the options available to the R&D Group and priorities set.  The Research Manager needs to set a master plan/budget at least annually and provide for scheduled review of progress and adjustment.  Usually a quarterly review is adequate for most Research efforts.

Rarely can this be effectively done in a vacuum.  Research workers and Executives need to participate so that the R&D Group is taking effective action guided by Company priorities and a realistic view of resources.  It does no good to work on that which matters not, nor is it prudent to promise results that are completely impossible.  Furthermore, the issue of urgency should be overtly discussed.  Here broad knowledge of the history of the Company and plans for the coming year can be helpful.  A certain fraction of resources should be reserved for the urgent projects that overtake the company.  Some will come from the regular course of doing business.  Others will come from changes in operations that will generate startup and new problem solving activities that often engage the R&D Group.  Anticipating these will improve the overall effectiveness of the R&D Group.

The many options should be categorized as follows:

In general Risk Level 1 (“Low Risk”) projects are often found in the Low Importance/High Urgency quadrant.  The returns are not very high, but they are pretty certain and the Company needs to benefit from those returns soon for them to have good value.  Nevertheless, these projects cannot squeeze out all other projects just because of their urgency.  Only something like 25% of R&D resources should be budgeted for these projects so that projects with High Importance are not ignored.

Risk Level 3 (“Blue Sky”) projects are often found in the High Importance/Low Urgency quadrant.  The returns could be great.  In fact, they may represent the future of the company, but they may take a long time to develop and prove those returns.  They cannot be ignored forever, but they cannot squeeze out all, more urgent projects.  Again, budgeting something like 25% of the R&D resources on “Blue Sky” would be great.  Often, however, companies just cannot quite get up to that level of commitment.  It must not be 0%.  If it is, the company is dying.

Level 2 Risk (“Moderate Risk”) projects may or may not be urgent.  They tend to have high importance but questions about their risk can reduce their value and urgency.  By reducing risk, R&D can make them the “Do Now” project.  Here is where the bulk of decision making and R&D effort should be spent (50% and more). 

To this point we have focused on financial risks and rewards.  Is that all there is to it?  Can this be applied to the National Science Foundation, the National Institute of Health, the National Renewal Energy Laboratory and etc.?  The answer is a qualified “yes.” 

When it comes to comparing alternatives with similar non-monetary benefits the answer is “heck yes.”  Spending more money than is needed to achieve an outcome is never justified. 

When it comes to placing an absolute monetary value on a social benefit, the issue becomes much foggier.  Although it is done, it probably isn’t that useful to try to put a dollar value on saving 20,000 lives per year.  Most of the time those data are not very reliable anyway.  It seems to me that this approach just encourages voodoo statistics.  Probably a more useful approach is to be honest about actual costs and probable beneficial outcomes and let stakeholders decide for themselves in an open, democratic forum.  Some will find $200 million spent on a new vaccine for Zika with a small chance of success still a useful investment.  Others would much rather see $200 million spent on habitat studies in Africa with a small chance of reversing the decline of the African cheetah.  It would be very difficult to calculate the best use of that $200 million.  Nevertheless, in an open, vibrant and capital effective society, there would be a much better chance that BOTH programs might be funded to achieve these very diverse social benefits.  It is Stalin-like central planning that is more apt to fund high risk missile programs when people are starving.

So to wrap up, the Research Manager should plan the work, budget the work and tie the work to the goals of the organization.  These should be actual, formalized, written plans/budgets that are developed with participation from relevant stakeholders.  Where practical, goals should be translated into financial measures.  We will get more into the specifics of that in later blogs.

 

Ron Stites holds a BS in Chemistry and an MBA in Finance and Accounting.  Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking.  For more information see: www.tek-dev.net.

Finance for the Research Manager I

 

Today we begin a series on financial management tools for the Research Manager.  As we have said in the past, most research, like any other business endeavor, ought to be focused on acceptable financial return.  This ignores for the time being “basic research” that is intended to expand the horizons of knowledge often for the sake of knowing.  The “value” of basic research is a topic that cannot be dealt with fully here.  Although I would contend that there should still be a value proposition, much of that value could be non-monetary and not amenable to the kind of analysis we are going to talk about.  I don’t want to get sucked into a philosophical discussion, so I am willing to, for practical purposes, allow the discussion to be limited to “applied research” or “development” if that would put the reader at ease.

The basic dilemma of the Research Manager is balancing current costs with potential future benefits.  We begin by introducing the idea of the Time Value of Money.  We will extend the basic concept to include Risk.  Simply stated, money in hand today is of greater value than money promised in the future.  There are two reasons for this.  The first is that money in hand can be invested in very low risk investments that will grow at a reliable rate.  Although these rates are at historic lows, it is still the case that $10,000 put in a Certificate of Deposit (CD) will be worth more than $10,000 in a few years.  Hence, $10,000 promised in three years is not worth $10,000 today.  In other words, a future payment must be discounted for comparison to current dollars.

We can calculate this discount rate by using the idea of compound interest.  If the money we hold in our hand can be invested at a government guaranteed rate of 2% compounded annually, $10,000 today would grow to $10,612 in three years.  This is shown in the simple table below:

 

Naturally, if someone offered us less than $10,612 in three years for an investment of $10,000 today we would laugh at them.  We would want at least that much money or more, and the “more” would depend on how reliable we thought the deal might be.

It is the “more” that begins to put into the equation the notion of Risk.  If a reliable real estate agent were making us this offer on a deal to help finance a house that he/she thought they could flip in three years at a profit, we might consider a payout of $15,000 in three years.  This would be the same as collecting 14.47% interest on our money.  If, on the other hand, a convicted con artist were making us an offer on a certain bridge in London, we would want a lot more than $15,000.  In fact, there might not be any amount of money we would consider “worth the risk.”  When we think like this we are creating a Time/Risk Value of Money.  We want a higher payout based on how long we have to wait and how risky we think the deal might be.

We have been talking about a single final payment to this point.  We discount that payment by the compound interest factor, (1+i)ⁿ, where ‘i’ is the interest rate and ‘n’ is the year.  This same idea can be applied to multiple payments by adjusting ‘n’ for the appropriate year.  This can be applied to payments or costs that are the same from year to year and to those that vary from year to year.  Of course we don’t have to do this my hand.  Excel and other spreadsheets have functions that can do this automatically.

Now let’s think about how our Research Manager might apply this thinking.  Let’s start with a very simple scenario.  Let’s suppose that we are thinking about our HVAC system in our Research Lab.  Right now we are spending $10,000 per month on heating and cooling.  Now suppose that an HVAC salesman proposes that we spend $125,000 on a new system that will save us 20% on our utilities.  Let us also suppose that the system will last about 10 years before requiring a major overhaul.  Should the Research Manager support the idea of putting in a new system? 

The first thing that we should notice is that we have several payments and not just one.  We actually have to consider the savings in years 1, 2, 3, etc.  The easiest way to do this is to set up a table in Excel that shows all the costs and benefits.  We can then discount each payment or savings and add them up.  Our analysis might look something like:

 

Here we discount at 15%, 25% and 35%.  Notice that this was done in a single step in the highlighted lower left hand corner by using the PV function of Excel.  Also notice that the Present Value at a 15% discount is pretty close to our required investment.  We could also apply a built in function in Excel called IRR (“Internal Rate of Return”).  If we set it up correctly we will get an exact calculation of the discount rate where the investment matches the cash flows.  Notice that it is just slightly less than 15%.

So…should we do it?  Should we spend $125K on a new HVAC system?  Well, maybe not.  A 14% IRR is kind of at the bottom of required return on these kinds of investments (more on this later).  Furthermore, the whole project runs 10 years.  A lot can happen – some good and some bad.  Utility prices might go up.  That would probably make this a better deal.  The HVAC might breakdown in year 7 and require $20K in repairs.  We might kick ourselves for having tied up so much money in the new system.  Of course, the salesman might be fibbing about the savings.  And finally, we might have a better way to spend that $125K today.  If there is another project with similar risk that pays out at 25%, we would want to do that first.  All these factors need to be considered.

This approach helps a lot, but we still need to apply a lot of judgement.  We can see that this technique can help us choose between options that have similar risks.  Nevertheless, we need some absolute guidance.  What discount rates should we use?  What is the minimum IRR for different types of investments?  We can only give very general guidance.

First of all we need to recognize that the discount rate we choose is based on both the Cost of Capital and Risk.  Today the Cost of Capital is theoretically low, but, practically speaking, is actually fairly high.  In theory interest rates are low.  Hence, borrowed money should be cheap.  Well, for most folks, it isn’t.  In fact, some would argue that for Small Businesses today, credit exists only in very limited ways.  It can be used to fund sales, buy real estate and purchase of some machinery, but rates are 8% to 18% depending on the use.  Business Credit has been tight for years.  Much of the credit in the world has been absorbed by governments trying to fund their economies or destroy their enemies.  The US Government is a major player in both the domestic and world credit market.

In today’s markets, low business risk decisions like we illustrated above generally require around 15% as the minimum cut off on any discussion.  Even when we know costs and savings very well, we still have the Cost of Capital plus general business Risk.  Even if one is sure that the new HVAC system will save money, it is never clear if we will still be using the Research Lab as we have planned.  We may decide to expand, contract, change the use or even move our operations.  Hence, even the most certain of business decisions have generally required about 15% as a minimum discount rate for many years.

When the decision to be made involves new methods or procedures, the appropriate discount rate quickly increase to 25%.  This would apply to well know processes that are being applied to projected conditions.  In other words, if we are expanding our business to try to capture new sales, we need to increase our discount rate.  We may know precisely how to make more widgets, but now our plan includes assumptions about the behavior of others.  We are expecting widgets to stay a hot commodity.  We are assuming that our competition won’t destroy the widget market, nor will widgets suddenly turn out to be declared “unsafe at any speed.” 

When new technologies are involved, the discount rate can go to 35% and beyond.  We are now entering the Venture Capital world of high risk and high reward.  Rules of thumb like “double your money in 2 to 3 years” translates to IRR’s of 26% to 41%.  This is the realm of where the Research Manager can give his/her greatest value to the organization.  Certainly a good manager can weigh the merits of fairly simple decisions like buying a new HVAC system, but the real worth of the Research Manager comes in the area of actually impacting the Risk of a technical project.  Few projects will ever be funded that require 35% return on investment or higher.  The real job of the Research Manager is getting the risk down to more manageable levels.  When that happens, new ideas suddenly become hot commodities.  We will talk more about this process in subsequent blogs.

 

Ron Stites holds a BS in Chemistry and an MBA in Finance and Accounting.  Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking.  For more information see: www.tek-dev.net.

Bringing it All Together for Effective Laboratory Management

This is the last in a series of five articles describing the challenge of managing laboratories effectively.  Today we focus on how all this gets pulled together.  As a review, other topics are linked below:

• The Nature of the Laboratory
  
• Human Resource Management in the Laboratory
• Business Management of a Laboratory
• Technical Management of a Laboratory – Maintaining Quality

Effective laboratory management begins at the Executive level.  The Executive(s) of the Company must identify the crucial
roles to be fulfilled by the laboratory.  Specifically, will this be an R&D role, a production role or some kind of mix?  Then they must hire appropriate technical leadership.  This is where things can go very awry.  Too often there is a mismatch between the talent hired and the task at hand.  Hiring talented researchers into an environment that is really a commercial production or a quality control role will not work out well.  Likewise, hiring dedicated technicians and expecting them to create new technology will not work.  In both cases good people will be frustrated because they don’t have the proper mindset, training or skills.

The role of the Executive does not end with good technical management.  It must be complimented by good business management.  This can include organizational, motivational, legal and even financial skills depending on the specifics of the task(s) at hand.  Often the necessary technical skills are specialized enough that it is not possible to cover both technical and business skills in the same person.  When this happens, the Executive must build a functioning team.  This can be very challenging and there isn’t one formula that works nor one solution that is likely to last indefinitely.  Differing personalities will come into conflict and changing needs and roles will always tend to upset the applecart.  Hence, the Executive will have to carefully pick individuals, artfully establish organizational roles and responsibilities and consistently monitor how the entire process is working.

One indispensable technique for monitoring the effectiveness and health of the laboratory organization is the Executive keeping in close contact with Human Resource, Quality Assurance and Health and Safety personnel.  Often growing dysfunction first shows up in deteriorating quality performance.  This can be quickly followed by HR complaints.  If left unresolved, the dysfunction can start showing up in safety violations – both imagined and real.  The growing conflicts and issues are often just symptoms of a general breakdown in communication and cooperation that cannot be resolved without addressing underlying personality conflicts, confusion over roles and responsibilities or change in mission that has made current solution obsolete.

It is almost impossible to hit upon the correct organization or team of personnel with the first try.  Even the best of scoping, planning, organizing and hiring will usually miss the mark in significant ways.  There are just too many complex factors to think that they can all be chosen correctly.  It is also extremely unlikely that any solution will stable indefinitely.  The culture of an organization must change as it goes from concept, to start-up, to rapid growth, to maturity and possibly into a period of decline.  It is commensurate on the Executive to understand the match between culture and organizational challenges and manage effectively through these natural stages.

And finally, it is important for all the players in the organization to recognize the dynamics that we have been talking about these many months.  Clearly the Executive must understand them in order to make good decisions.  Nevertheless, if all the players understand these dynamics the health of the organization will be much improved.  It is much easier to deal with certain weaknesses and frustrations if all parties recognize the potential sources of these conflicts.  It is much more likely that issues can be dealt with in objective and cooperative ways.  It has been my experience that frequently individuals who are the source of conflict can be helpful in resolving the conflict if they view the conflict as arising from a mismatch of skills or a misunderstanding of goals rather than “bad” performance. 

This is not to ignore the existence of incompetent, highly flawed or even dangerous people.  They do exist and must be dealt with effectively.  They are, however, the exception and not the rule.  The Executive(s) can go a long way toward creating an effective organization that naturally adjusts to challenges by communicating organizational goals to players in open and effective communication.  This is especially necessary when these goals have or are about to change.  It is usually quite counterproductive to keep players “in the dark” about what is going on or about to change in their world.

 

Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking.  For more information see: www.tek-dev.net.

Managing Laboratories – Technical Management – Maintaining Quality

This is the fourth in a series of five articles describing the challenge of managing laboratories effectively.  Today we focus on the technical side of management with special emphasis on maintaining quality results.  As a review other topics are linked below:

• The Nature of the Laboratory
• Human Resource Management in the Laboratory
• Business Management of a Laboratory
• Technical Management of a Laboratory – Maintaining Quality
• Bringing it All Together for Effective Management (Future)

Technical Management/Quality:

Many approach technical and quality management as somehow separable.  They are not.  The selection, validation and application of testing technology to achieve reliable results is inseparable from managing laboratory quality.  This can be seen in any of a number of quality management guides, but none are clearer than ISO 17025.  In the Introduction we read that the standard supersedes ISO/IEC Guide 25 which:

....contained all of the requirements that testing and calibration laboratories have to meet if they wish to demonstrate that they operate a management system, are technically competent, and are able to generate technically valid results....

In the Scope, ISO 17025 states:

1.1    This International Standard specifies the general requirements for the competence to carry out tests and/or calibrations, including sampling.  It cover testing and calibration performed using standard methods, non-standard methods, and laboratory-developed methods.

1.2    This International Standard is applicable to all organizations performing tests and/or calibrations.  These include, for example, first-, second- and their-party laboratories, and laboratories where testing and/or calibration forms part of inspection and product certification. 

It is actually hard to imagine a testing or research laboratory that does not fall under this very broad international scope.  Hence, a review of ISO 17025 guidelines is indispensable in establishing, correcting and improving technical and quality management systems in a laboratory.  Managers of all types engaged in laboratory work (front line to highest executives) are highly encouraged to obtain, read, understand and follow this extensive standard regardless of the specific field of technology.  It is virtually impossible to have consistently reliable testing results unless the recommendations of ISO 17025 are followed.  In this blog we will review just of few of these key elements.

Management System:

One of the most important features of ISO 17025 is recognition that there must be a management system in place.  It does not do to hire the “best and the brightest,” buy them “very expensive toys” and turn them loose.  Nothing good will ever come of that.  The data generated, even if valid, will have no credibility and for that reason will have no value in the marketplace.  Quite to the contrary, a management systems must be deliberately design and maintained that touches all facets of the laboratory.  This includes systems that:

• Provide a planned and functioning quality management organization,
• Provide for written quality standards of performance – including Standard Operation Procedures (SOP’s),
• Provide for the management of all pertinent records – including results and documents supporting those results,
• Provide a stated quality policy that can supersede and critique specific procedures,
• Provide methods for identifying and maintaining quality objectives and
• Provide means for management review of performance.

Unless an organization has the systems in place to implement these basic functions, quality performance will eventually suffer with dramatic consequences.  That “system” includes a human resource element.  A management system cannot function if the managers are do not have the proper training, experience and commitment.  Furthermore, the management system must be designed such that it naturally hires, trains and utilizes appropriate technical personnel in the testing processes.

Document Control:

ISO 17025 relies heavily on documentation to ensure that quality systems are in place and operational.  This grates on many technical personnel.  Unfortunately, failure to document almost always results in later confusion and doubt.  This destroys the value of any test result.  Even more insidious is the failure to plan document control and retrieval.  Much too often, good initial documentation is frustrated by the inability to retrieve the data and the supporting information needed to verify and validate those data.  Without the ability to retrieve critical information, the value of testing efforts evaporates.

A simple “record and keep everything for X years” is not a document control plan.  Quite to the contrary, in the case of document control, less is often better.  Aggressive destruction of redundant and irrelevant records quite often helps focus attention and efforts onto the critical few rather than the irrelevant many.   Retention of key data elements “forever” may be necessary to preserve the value of some experimental efforts.

Corrective Action:

Corrective Action is another key tenant of ISO 17025.  It appears in many ways.  Clearly when procedures fail, there needs to be a recognition that the system has failed and corrective action should be taken.  Most organization formalize procedures around failures that are likely and maybe even anticipated.  The danger here is that these “failures” can become “routine” and not considered failures at all, but just the way business is done.  When “corrective action” becomes routine, it is no longer “corrective.”  It has become a part of the standard way of doing business.  In this case, Corrective Action must go deeper and investigate the entire quality system.  It is at this point that Corrective Action becomes a management review to ferret out where policies or even managers themselves have gone awry. 

Selection and Validation of Methods:

ISO 17025 specifically acknowledges a common error in testing – using the wrong method.  Applying inappropriate measurement technique on a sample happens far too often.  The results of the measurement may be horribly biased and that bias may not be at all obvious.  Careful selection of sampling, sub-sampling and measurement techniques are all crucial for valid results.  Horror stories in this area abound.  Hence, the Standard requires that SOP’s clearly state the appropriate scope for sampling, analysis and data reporting.  This selection process cannot be left up to chance, but must be an overt part of the management system so that even accidental mismatches are quickly identified and corrected.

The Standard goes on to say that all methods must be validated for the types of samples being processed.  Where practical “standard methods” should be used.  These are methods that have been validated by multiple organizations and documented as being appropriate for specific sample types.  The Standard describes some of the techniques used for validating non-standard method.  The technique used “shall be as extensive as is necessary to meet the needs of the given application or field of application.”  This is a tall order in many cases and can rarely be done without some equivocation.  The limitations of the validation should be disclosed to data users.

Statistical Controls:

ISO 17025 relies heavily on statistical validity of data.  Data that are devoid of any estimates of precision and accuracy are of little value.  In all cases, data should have associated estimates of uncertainty.  Where this can be done in a statistically valid fashion the actual uncertainty should be reported.  Where this is not practical, comments on the probable error and potential error should be given.  The degree of rigor in estimating error should be appropriate for the use of the data.  The management system should be capable of ensuring that the precision and accuracy of the results are appropriate for the use of the data and that, where this may be questionable, the data user is appropriately notified.

These same statistical control principles must be applied to all equipment, standards and reagents.  Calibration procedures and checks should be documented in SOP’s and the acquiring, use and retiring of standards and reagents be appropriately controlled.

Sampling:

Unlike many standards, ISO 17025 overtly address the appropriateness of sampling.  This is one of the Standard’s most significant contributions to quality management.  Inappropriate sampling usually results in bias and often contributes to imprecision.  Too often the laboratory ignores or is unaware of the sampling challenge.  This is especially problematic when the laboratory must take a sub-sample in the laboratory to do an analysis.  If unaware of innate inhomogeneity or instability, the laboratory can inadvertently ruin an otherwise valid assay.  The Standard strongly suggests cooperation between the data user, field personnel and the laboratory.

Traceability:

The Standard emphasize in several places the need to be able to trace calibration materials to the International System of Units (SI).  This includes masses, lengths, volumes, temperatures, pressures, frequencies, voltages, energies, etc.  Pertinent units must be clearly and accurately reported in all results and calculations.  Traceability means that all important calibration standards and materials are certified as to their value and can be traced back to SI, usually by the manufacturer of the standard or material.  Where this is not practical, the laboratory must make its own disclosure on how a calibration standard or material was prepared by using SI traceable calibration standards or materials.  The Standard specifically recognizes the potential for inappropriate calibrations that can be difficult to discover.

Appropriate Data Reporting:

The Standard specifically acknowledges that some test results will be deficient in some ways, but may still be useable for the intended purpose.  The Standard requires that all deficiencies be acknowledge to data users so that they may be the final judge on the utility of the data.  The Standard encourages “full disclosure” regardless of the source of the deficiency. 

Conclusions:

ISO 17025 is a very detailed listing of quality management principles that should be used by all laboratories.  We have listed most of the general principles.  Managers should be familiar with and embracing of these principles.  They are a very good start toward consistently reliable and relevant laboratory data.

Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking.  For more information see: www.tek-dev.net.

New eBook Available

The Science, Art and Discipline of Technology Development by Ron Stites is now available on Amazon.com in Kindle format.

The purpose of the book is to:

1. Raise awareness of the need for validation of claims,
2. Give some of the “science” on how to evaluate claims,
3. Give some guidance on the “art” of managing evaluation and development processes and
4. Give some guidance on how to build an organization that can create and sustain a culture where critical evaluation and development of new ideas is an innate discipline.

The book is organized as follows:

1. The Science of Technology Development – This is the technical stuff.  Read at least the first few pages on how we know.  The statistical stuff is for those who want to know more.  Much of it can be skipped without losing too much of the general drift.
2. The Art of Technology Development – This is for those who are trying to build an organization that have the capabilities to do evaluation of technology.  It would also be good for those who are evaluating organizations for their ability to make good decisions about new ideas
3. The Discipline of Technology Development – This is the Dark Side of managing.  It presumes that many people are only interested in themselves and that they are at enmity with the organization.  It is a call to resolute commitment toward building an organizational culture that demands truthfulness, objectivity, engagement and hard work.  It is only through culture that a knowledge driven organization can sustain growth.
4. Conclusions – This is a call to both personal and corporate action.

A preview of the eBook is available at Amazon.  Just follow the link:

http://www.amazon.com/dp/B00WTMVP60

 

 

SALLC Publishing eBook

The eBook, The Science, Art and Discipline of Technology Development, by Ron Stites, is scheduled for publication on Amazon.com on or about May 4, 2015.  The eBook is in final review now.  For information on the contents of the book go to www.tek-dev.net and follow the link, “Check Out Our New Book Launch.”

Business Management of a Laboratory – Sales/Marketing/Customer Service

Some may think it strange to deal with Business Management of a Laboratory before dealing with technical issues – especially for a “captive lab.”  We do this for couple of reasons.  First of all, business issues are everywhere, even in academic labs.  All organizations require resources to function and labs can be especially expensive.  Hence, in a general way, business management skills are needed in all labs.

This question actually goes quite a bit deeper than the general statement above.  For commercial labs, it turns out that business issues are quite a bit MORE important than technical issues.  In most commercial labs the technologies are specified and begin to approach a commodity.  That is, everyone must do exactly the same test and produce the same results.  Differentiation is most often found in areas that are actually general business management.  These include customer service, reliability, convenience and cost.  Even in the highly touted area of data management, it is often these business issues that dominate this discussion rather than the technology behind the delivery system.

For “captive labs,” technology may rival business issues for importance, but even there issues like identifying and satisfying internal customer needs in cost effective ways can dominate all discussions.  We will deal with a handful of business issues surrounding the commercial and internal laboratory.  We will deal with:

• Sales/Marketing/Customer Service
• Revenue/Productivity
• Cost and Pricing
• Profitability
• Sustainability

This is done deliberately to make the point:

“The purpose of business is to create a customer....”  Peter Drucker

First we should probably note that in the lab business, customers are always going away.  That is the nature of all service businesses, but the customer cycle in the lab business can be surprisingly short.  Long-term contracts are fairly rare.  The project is done and service relationship ended or at least suspended.  Hence, for a lab, instead of saying “...create a customer,” we could easily say, “...re-create a customer....”  This seems intuitive for the for-profit, commercial lab, but what about a “captive lab?”  If we include the internal customer we quickly get the point.  The “captive lab” that does not create and re-create the internal customer soon finds itself without support and shrinking or closed.  Any laboratory manager who thinks that they really have a “captive internal client” will soon find that internal relationships are quickly strained to the breaking point.  Hence, every lab has as its fundamental mission finding, engaging, servicing and retaining the customer. 

Every laboratory manager needs to fully understand the needs of the client when it comes to:

• Analytical techniques needed
• Precision and accuracy needed
• Turnaround needed
• Reporting formats needed
• Sample archival/retrieval needed
• Data archival/retrieval needed

In addition to these hard specifications, there are two other client related talents that every successful laboratory manager needs:

• The ability to anticipate client needs and
• The ability to design and maintain good client relations

The first of these is important in commercial labs just from a profitability perspective.  It can be a financial disaster to miss a real emerging customer need and allow the competition to take away the very volatile client relationship.  Nevertheless, this skill is even MORE important for the captive lab.  There is a presumption with the clients of the captive lab that the laboratory manager is paying attention to changing organizational needs.  It is expected that when a need become critical that the laboratory manager has thought about it and has pro-actively made plans to handle the need is some reasonable fashion. 

This could be something as simple has having found the proper sub-contractor, but it may be as complex as having found or developed new methods.  Many laboratory managers of captive labs lose all their goodwill with internal clients by having failed to anticipate a crisis.  On the other hand, those who anticipate needs are and are ready to “bail-out” their peers with a quick fix to their latest problem often find undying support from unusual sectors of their company.

The ability to control client relations effectively is also a key skill for laboratory managers – especially in captive labs.  Often in commercial labs the client interaction is “top-of-the-mind” enough to warrant careful organizational design.  This can go so far as to consciously design formal lines of communication between external clients, sales and an internal group, often called something like “project management” or “client service.”  Commercial labs almost instinctively recoil from the notion of letting a bench chemist or technical talk directly to a client.  The results are so bad and so fast that this is rarely attempted in the commercial world.

For the internal or captive lab the situation is much different.  It is rare that the relationship between the internal client and the lab is given much thought.  Furthermore, lab personnel and the internal client(s) often work in close proximity.  It is assumed that “we are all one company” and when conflict arises between a bench chemist and an engineer everyone is shocked.  Unless these relationships are managed the organization will be paralyzed by screaming matches over “who shot John.”  Even if the organization fails to manage this interaction the laboratory manager MUST manage it.  I have seen more than one laboratory manager lose their job over conflicts with internal clients that, at the root, were conflicts between lower level individuals inside the organization.

It is always best for the laboratory manager to work with HR to formalize lines of communication in order to minimize interaction between bench chemists and technicians and other hominoids.  Laboratory personnel are notorious for lacking basic personal interaction skills.  Throw them into a tense situation with a hot-headed junior engineer and you will have an HR case study.  Unfortunately, many organizations refuse to deal with this issue directly.  They not only fail to recognize the problem, they often feel compelled to keep lab head counts low and try to force low level personnel to “get along.”

When this is the case, the laboratory manager MUST make interpersonal skills among his or her laboratory staff a high priority.  He or she MUST take action to keep lab cretins away from internal clients.  Sometimes this can be done by clever scheduling, coaching or “performance improvement plans.”  This would be how most HR programs would approach the problem.  Unfortunately, such programs are rarely successful.  More often than not, the only solution is removal of the offending personnel from the lab organization.  It is almost immaterial who actually “shot John.”  The lab is considered the “supplier” and is presumed “guilty” of poor “client service” in all but the most extreme situations. 

The shrewd lab manager works diligently at resolving these issues ahead of time by deliberately and systematically culling out lab personnel who have interpersonal problems.  Furthermore, he or she makes interpersonal skills more important than technical skills in the hiring (or internal transfer) process.  The clever lab manager reduces the risk of HR fires by keeping the amount of fuel on hand at a minimum.

In the next blog we will deal with the issue of Revenue/Productivity. 

 

Stites & Associates, LLC, is a group of technical professionals who work with clients to improve laboratory performance and evaluate and improve technology by applying good management judgment based on objective evidence and sound scientific thinking.  For more information see: www.tek-dev.net or email us at [email protected] or [email protected].