Automation: Hard Work Pays Off More Later than Laziness Pays Off Now. Invited Talk given at Bio-IT 2017.

1. Automation
Hard Work Pays Off More Later than Laziness Pays Off Now
Bio-IT World 2017 brian.bissett@ieee.org Slide 1
Brian Bissett
Senior Member
Institute of Electrical and Electronics Engineers (IEEE)
Bio-IT World 2017

2. Objectives
At the Conclusion of this Presentation the Audience
will be able to Decide:
1. Is it Worth it to Automate?
1. Valuation of Intangibles makes Decisions Difficult.
2. What Automation Options are Available?
1. Software (Automation, Computational)
2. Hardware (Instrumentation & Sample Handling)
3. Methods of Validation and Verification.
4. Determine the Effectiveness of an Automated
System?
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3. Overview – Types of Automation
▸Software Automation
- Control of Hardware
- Computational - Curve Fitting, Binning, Interpolation, etc.
• COTS
• Custom
- Reporting – Database Uploads, Dissemination, Legal, etc.
▸Hardware Automation
- Liquid Handling
- Material Handling (Powders, etc.)
- Instrumentation
- Packaging
▸Systems Automation = Software + Hardware
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4. Systems Engineering
▸Systems Engineering is the art of getting a group of
perfectly independently functioning systems to
function correctly as a unit to achieve a specific
result.
▸Design Phase -> Standards and Taxonomies
▸Build Phase -> Physical Limitations (Power, Size, etc.)
▸Integration Phase -> Timing and Programmatic
- Clock Skew, Backplane Noise, 60Hz, etc.
▸Test Phase -> Irregular and Intermittent Faults
- Continues throughout System Lifecycle
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Everything Always Works Great by Itself. . . .

5. Pharma’s Big Three Automation
▸Data Management
- Industrial Internet of Things (IIoT) requires more data
collection and storage capabilities.
▸Serialization
- Regulatory Tracking Compliance
- Anti-Counterfeiting
- Recalls -> Lot# Tracking
▸Robotics (Boston Dynamics -> now owned by Google
- Packaging
- 12% of Pharma now using Robotics for Assembly,
Processing and depalletization of incoming materials.
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Data Management, Serialization, and Robotics

6. Requirements and Considerations
▸Project Duration
- How Long are we REALLY going to be doing this?
▸Project Resources
- FTEs and Contractor Resources
▸Propensity for Errors
- Complex (Too hard for average person to do correctly)
- Mundane (Errors due to boredom and sloppiness)
▸Seek to ELIMINATE Current Single Points of Failure
▸Cost to Automate (You can get Money back)
▸Time to Automate (You never get Time back)
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7. Is the Juice Worth the Squeeze?
▸Requirement is > 6 Months
▸Time Investment ~ 10% of Lifecycle (6 Months = 2.5 wks)
▸Requirement is Labor Intensive
▸Requirement is Intolerant of Waste
▸Results subject to wide variation depending on the
Experience and Expertise of the Scientist.
▸Situational -> Necessity to free Scientific Talent for other
Projects?
▸Rework and Reuse Possible for any aspects of the Project?
(For Software, almost always possible)
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Rules of Thumb

8. COTS means not Re-Inventing the Wheel
▸Rule 1: Never Start from Scratch
▸Rule 2: Limit Dependence on Proprietary Resources
- 2a. Single Vendor for Proprietary Resources (Toyota)
• i.e. we use Matlab, Origin, or SciPy for all Computational Work.
- 2b. Small to Mid Size Vendors Offer the best Partnerships
▸Rule 3: Open Source should be the Software of
Choice
- 3a. Large Vendors will not customize
- 3b. Smaller Vendors may go under.
- 3c. Open Source -> Widely Utilized -> Debugged
- 3d. Proprietary Software never Agile.
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9. Automation Programming Languages
▸C++, C, Ada, Fortran, ATS, Rust
▸(2x- 3x) Java, C#, PyPy Python
▸Slow Perl Family (20x to 40x C++) Perl, php,
CPython, Ruby
▸VB.NET, VB Script
▸Java
▸C Family (C, C++, C#)
▸Python
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Popularity
S
P
E
E
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10. Software Automation Open Source Python
▸SciPy - Python-based ecosystem of open-source
software for mathematics, science, and engineering
▸NumPy – the fundamental package for scientific
computing with Python.
▸Matplotlib
- PyLab – provides a Matlab “like” experience to Matplotlib
- PyPlot - Provides a MATLAB-like plotting framework.
▸IPython - interactive computing in multiple
programming languages
- IPython Notebook
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Python Based Numerical Computation Software have Matured

11. Software Automation Proprietary
SPREADSHEET BASED + MACRO LANGUAGE
▸Excel - Proprietary
▸OriginLab – Proprietary (Excel on Steroids)
▸Igor Pro – Proprietary (Similar to OriginLab)
MATRIX BASED + PROGRAMMING LANGUAGE
▸Matlab – Proprietary (Matrix Based)
▸Mathematica – Proprietary (Matrix Hybrid)
▸SAS – Proprietary High Level Matrix Programming
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12. Software Automation Open Source
SPREADSHEET BASED + MACRO LANGUAGE
▸QtiPlot – Open Source Alternative Origin & Igor Pro
▸Extrema
▸LabPlot and Grace – Documentation Limited.
MATRIX BASED + PROGRAMMING LANGUAGE
▸R – Open Source Alternative to SAS
▸Scilab - Open Source Alternative to Matlab
▸GNU Octave - Open Source Alternative to Matlab
▸SageMath - Open Source Conglomeration
- NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R
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13. Testing Mathematical Software
NIST Provides Diverse Sample Data Sets Tested over a
Broad Range for:
▸Analysis of Variance
▸Linear Regression
▸Nonlinear Regression
▸Univariate Summary Statistic calculations
▸Has the Software Chosen or Developed been
evaluated using the NIST Data Sets?
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Evaluation of Software That “Solves” Difficult Problems

14. Regression Testing
▸With Software Patches and Enhancements:
- New Faults Emerge.
- Old Faults May Re-Emerge.
▸Software Fixes Often Lost Through Poor Revision
Control Practices.
▸Regression Testing Verifies Previously Developed
Software Still Performs Correctly After Changes.
▸Regression Testing Done by Automation Tools.
▸Database Application Regression Testing Tools remain
Immature and Underdeveloped.
▸Any Change in a Third Party Component (Black Box)
May Introduce Errors.
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15. Configuration Management
1. What was Changed.
2. When it was Changed.
3. Who Changed it.
4. Why it was Changed.
5. Items Dependent or Adjacent to Items Changed.
▸Not Uncommon for One Fix to Break Something Else.
▸Must Track not only Changes, but Faults, and the
Changes that Caused them.
▸Stored in a CMDB.
Bio-IT World 2017 brian.bissett@ieee.org Slide 15

16. Hardware Engineering - Instrumentation
▸Hardware Engineering is the Most Difficult Facet of
Automation.
▸Does an Instrument Exist with a USB or Serial Port
interface that can Control the functions needed by
software? Yes -> Buy It.
▸If an Instrument can perform a Measurement by
Hand, then it CAN be modified to perform the
same Measurement by Software Triggers.
- Software Controlled Relay Boards can “Push” switches.
- Digital and Analog I/O Boards can:
• Capture Data from an Instrument
• Send Data (Inputs) to the Instrument
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17. Hardware Engineering – Good Questions
▸Has the Vendor ever dealt with:
1. Clock Skew
2. Jitter
3. Noise
4. Cross Talk
5. Ground Loops
6. Dead Lock
7. Race Conditions
8. Asynchronous Systems
9. Building Interfaces
Bio-IT World 2017 brian.bissett@ieee.org Slide 17

18. Hardware Engineering - Material Handling
▸Where “Material” is any substance that is not a
liquid that needs to handled in an assay.
▸Powders (Static Cling and Clumping)
▸Tars and “Gummy” Compounds (Separation Issues)
▸Consider Investment in a Proprietary Proven System.
▸Tars and Gummy Compounds can often be dissolved
in hard core solvents, measured, and have the
solvent blown down leaving an accurate amount of
compound.
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19. Hardware Engineering - Liquids
▸Sample Delivery – Look for CV’s less than (<) 5.0%
▸Lowest Volume Range: Picoliter, Nanoliter, Microliter
▸Contact or Non-Contact of Sample to Medium?
- Non Contact Aspiration of Air Kills CV’s -> Air is
Compressible, Liquid is not.
▸Liquid Viscosity Range?
▸Is Heat Degradation Possible?
▸Same Liquid Always Dispensed?
- Yes -> Cartridge based Systems a Consideration.
- No -> Develop a Cleaning Control (No Cross Contamination)
▸How Valuable is the Sample to be Dispensed?
- Are “Dead Volumes” Acceptable?
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20. Hardware Engineering – Liquid Dispensing
Micro
Pumps
▸Diaphragm
▸Gear
▸Peristaltic
Syringe (Piston)
Valve Dispensing
Nano
Syringe (Piston)
Piezoelectric
Acoustic
Valve Dispensing
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Engineering Considerations by Dispensation Volume
Pico
Piezoelectric
Acoustic
Pressure Ejection

21. Tracking Material Methods RFID or Barcode?
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Tracking the Finished Product
BARCODES RFID
Less Expensive than RFID Tags Read at Greater Distance (300 ft.)
Smaller and Lighter than RFID Tags No Line of Sight Limitation
Ubiquitous Technology Read/Write Devices
Labor Intensive High Security Levels Possible
Easily Reproduced or Forged Large Data Capabilities Track History Dates
More Easily Damaged than RFID Tags Faster Reads (40 Simultaneous Reads)
Line of Sight Required to Scan More Expensive than Barcodes
Must be Exposed on Outside of
Product
Interference from Multiple Tags or Readers
Responding at the Same Time.
Different Machine to Required to Read and
Write to RFID Chips.

22. Hardware Engineering
▸Industrial Internet of Things (IIoT)
- Strategic Enabler to improve manufacturing performance by
connecting previously stranded data from smart sensors,
equipment, and other assets with advanced applications and
predictive analytics.
- Edge Devices or Intelligent Gateways that collect, aggregate,
filter, and relay data close to industrial processes to improve
manufacturing process quality and production yields.
- Remote monitoring and IIoT are becoming industry standards
for packaging machinery.
- In two years 76% of manufacturers will increase their use of
smart devices and embedded intelligent systems to enable
manufacturing equipment to collect and exchange data.
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23. Hardware Engineering
▸Will a Systems Engineer perform a financial analysis
(independent of the vendor) to determine the
justified degree of automation based upon the
return on investment?
▸Is the System Engineer willing to commit to
throughput and quality performance specifications?
▸Will a competitive bid process (“Lowest Cost
Technically Acceptable”) be conducted to ensure
equipment and contractor installation is done at fair
market values?
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Things to Consider Before Committing

24. Hardware Engineering Contractors
▸Does the Vendor take complete responsibility for your project
from concept through commissioning?
▸Layout Development
▸Operations Strategy
▸Equipment Selection and Procurement
▸Detailed Line Design and Automation
▸Offsite Modularization and Staging
▸Factory Acceptance Testing
▸Site Preparation and Installation
▸Site Acceptance Testing
▸Operator and Maintenance Training
▸Commissioning
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Are these items in your Statement of Work? SOW = What, not How

25. Testing and Verification
▸Accuracy in pipetting is defined as the relationship
between the volume that is set and the volume
that is actually delivered
▸Precision is a measure of repeatability. It is
expressed as a non-dimensional coefficient of
variation (CV). The CV is the standard deviation of a
number of events (dispenses) divided by the mean
value of those events.
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Parameters of any Automated System

26. Testing and Verification
▸The Problem with many Common Test Molecules is
that they are easy to screen.
▸They often have low molecular weight, good
solubility, and favorable ionization constants.
▸Often Common Commercial Drugs in the Market
Place are used which follow Lipinski’s Rule of 5.
▸The “Low Hanging Fruit” of Drug Discovery has
already been picked, which makes robust assay
performance more critical.
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The Problem with Test Cases is the Solutions are often Obvious

27. Validation Characteristics
▸Specificity
▸Linearity
▸Accuracy
▸Repeatability
▸Reproducibility
▸Robustness (Sensitivity Analysis)
▸Dynamic Range
▸Detection Limit, LoD
- Quantitation Limit, LoQ; Limit of Blank, LoB
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Not all are Applicable for every Type of Screen

28. Measuring Effectiveness of Systems
▸OEE factors: Availability, Performance, and Quality.
▸Identifies the Percentage of Planned Production
Time that is Truly Productive.
▸A Score of 100% means you are manufacturing only
Good Parts, as Fast as Possible, with no Stop Time.
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Overall Equipment Effectiveness (OEE).
A P Q OEE

29. Production Metrics
▸Availability = Run Time / Planned Production Time
- Where: Run Time = Planned Production Time − Stop Time
▸Performance = Net Run Time/Run Time. It is
calculated as: Performance = (Ideal Cycle Time × Total
Count) / Run Time
▸Quality = Good Count / Total Count
- Where: Good Count = Total Count − Reject Count
▸Consider Dashboarding and Control Charting
▸Are CMDB Changes Associated with Metric Changes?
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Tried and True Production Metrics Great for Dashboarding

30. Biases – We All Have Them
Excel was First Released in 1985 but still lacks:
1. The ability to Generate a Data Set to a Regression line.
2. Plots only Data, not Equations.
3. No Percentage Change Function (Really?!!!)
4. No Linear Interpolation Formula.
5. No Drag and Drop GUI Development for Custom Tabs.
6. Must Login to See Help Files for Products?
7. Object Model and Auto Complete have Holes
(ActiveSheet Example)
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Avoid Microsoft – Lack of Commitment to Improving Products

31. Summary
▸Open Source has Come a Long Way.
▸Pick Automation Products Carefully. (Especially
Software for which Transition is Difficult)
▸For Hardware, COTS is 99% preferable to Internally
Developed Solutions.
▸Contract Engineering Requires a Solid Well Thought
out Statement of Work (SOW).
▸Testing, Verification, and Validation Choices are
neither Obvious or Trivial but Critical to Success.
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32. Selected Publications
▸Automated Data Analysis with Excel
- Softcover: 442 Pages
- Chapman & Hall (June 2007)
- Second Edition Coming in Early 2018
- ISBN: 1-58488-885-7
▸Practical Pharmaceutical Laboratory Automation
- Hardcover: 464 pages
- Publisher: CRC Press (May 2003)
- ISBN: 0849318149
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Shameless Self Aggrandizing Promotion