This is a slideset of a webinar held by Dr. Dirk Ortloff from Process Relations. The webinar elaborated on the challenges and solutions addressing the problem of effectively managing the growing amounts of digital process development data. He introduced approaches that address the “heap” challenge. It has been explained how new software tools and methodologies can be leveraged to convert raw data from diverse source into usable information and especially how to recreate the context the data is generated in.
The metrology capabilities of today’s high-tech R&D generate an increasing amount of digital data. Subsequently process engineers are flooded with this - partly structured but mostly unstructured - data. To organize, manage and evaluate this data requires a major effort. Engineers spent a significant portion (20% – 35%) of their time just administering this data rather than evaluating it.
Using the tools and methodologies introduced in this webinar results in structured and context aware information which can reduce the amount of repeated experiments and speed up developments.
The recording of the webinar can be downloaded from: http://www.process-relations.com/english/services/publications-mainmenu-90/webinars/323-free-webinar-high-tech-rad-drowning-in-data-but-starving-for-information
2. Agenda
Brief Company introduction
Definitions
Situation Today / Problem
Requirements
Converting the data and using the information
Summary
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4. Process Relations’ Mission
…is to enable you to…
Expedite your R&D with flexible Software solutions
Next generation recipe and DoE management
Virtual manufacturing data management
Automated data collection from various sources
Experimental data management, analysis and extraction
We deliver the easy to use, unique, world class Process
Development Execution System (PDES)
and consulting services.
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5. History and Key Milestones
2000 2004 2008 2010
Bosch XperiDesk Entering
Project Launched new
markets
2002 2007 2009 2011
CK Process First First
Project Relations XperiDesk Marque
Founded Customers Customer
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7. Definitions
Data: symbols
Data is raw. It simply exists and has no
significance beyond its existence (in and
of itself). It can exist in any form, usable
or not. It does not have meaning of itself.
In computer parlance, a spreadsheet
generally starts out by holding data.
Information: data that are processed to be useful; provides
answers to "who", "what", "where", and "when" questions.
Information is Data that has been given meaning by way of
relational connection.
Knowledge: application of Data and Information; answers "how"
questions. Knowledge is the appropriate collection of information,
such that it's intent is to be useful.
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Process Relations GmbH Following the DIKW model: http://www.systems-thinking.org/dikw/dikw.htm
8. High-Tech R&D – Yesterday
Decisions
Evaluation
Knowledge
Behavior (Interpretation)
Engineers
Dependencies / Patterns
work time
Relations spent
Data and Parameters
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10. Some citations
“Because most organizations seem to be
drowning in data but starving for
information, there is a growing need for
enterprise manufacturing intelligence
software”
ARC Group
“Nevertheless, several studies in the past
five years point to significant ROI for
improved access to information. ROI figures
range from 38%to over 600%, depending on
whether the new information or content
management system is an incremental
improvement over an existing one or is an
entirely new system replacing previously
manual processes.”
IDC
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12. The challenges
25% development projects reach the market
of those 66% fail their original expectations
20% of projects take too long and miss their market
window
35% of companies experience runaway projects
40% of R&D experiments are repeated
CONFIDENTIAL Source: IDC “Accelerating Science-Led Innovation for Slide 12
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Competitive Advantage” Feb. 2012
13. Few problems in information management?
Excel files on file servers or desktops contain
important data and are not sufficiently searchable
Result files are distributed / duplicated in different
versions on different systems
Link between the data is not sufficiently visible
(only in file system structure)
Only one-dimensional sorting / searching criteria
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14. Typical development challenges
“Once we had a result picture …”
What was the exact context of that experiment? Which
results were achieved, what images made?
Which was the latest data set?
XY left and his lab book was unreadable to anyone but
him
How long does it take your engineers to recover
development data with context from 18 months ago?
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15. Cost increase in R&D efforts
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16. What it boils down to:
“Fact: 80 percent of the
digitized information in
a typical company is in
the form of unstructured
data such as
documents, e-mail, and
images. “1
“Fact: The amount of unstructured content in a typical
business grows by 50 percent every year. “1
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18. Current situation
Distributed, untraceable and
undiscoverable R&D results
Limited formalized data available
that is not interlinked
Sometimes usage of old or
retired data
Incomplete documentation
Lack of access to results and timeline of former projects
Access and transfer protection difficult
Unmanaged data ↑risk + ↑costs of projects
„Great ideas get lost in the sea of incomplete documentation“
(W. Wong – Editor Electronic Design Journal)
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19. Semiconductor R&D spending
45.000.000.000,00 US$ / year
(in 2007)
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20. Wasted for manual data management & search
4.500.000.000,00 US$ / year
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21. Meet Mr. Lumberjack …
Lumberjack is feverishly trying to
fell a tree
Using a dull saw
Therefore going no where
Bystander points out the facts
Reply: too busy sawing to
sharpen my saw
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22. Requirements
Repository of former and current R&D and
manufacturing data / information / knowledge
Full audit trail for all changes & complete history
Possibilities to manage the lifecycle of every item
Easy access and multi-dimensional retrieval possibilities
Low effort documentation approach to relieve engineers
from tedious tasks
Access protection on per-item level
Defined way to document R&D work
Centralized, platform independent, structured and
comprehensive data repository for structured &
unstructured data
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24. Comprehensive Capturing of Developed IP
All data is absorbed in the centralized
database (enterprise information platform)
Nothing is forgotten; history is kept in
versions compliance fullfillment
Everything is available to anybody with
authorized access (blackboxing possible)
All information is searchable
Instantaneous, formalized results
Only relevant data retrieved
Extensive relationships maintained
between the stored data full context
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25. – Covering the Complete Development Cycle
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26. – Covering the Complete Development Cycle
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27. – Filling with experimental data
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28. – Management of Experiment information
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29. – Management of Experiment information
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30. – Management of file data
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36. Time and Cost Savings
Reduction in the development cycle time by
enabling the use of simulation by every process engineers
reducing the development WIP
Reduction in the number of learning cycles by
Avoiding re-learning
Reducing the number of “failed” experiments
Improving knowledge extraction efficiency from the
experimental data
Increasing predictability of cycle time by time-lined history
Seamless, compliant documentation through full audit trail
Creation of process engineer knowledge rather than
management of data
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37. Process Development – Yesterday
Decisions
Evaluation
Knowledge
Behavior (Interpretation)
Engineers
Dependencies / Patterns
work time
Relations spent
Data and Parameters
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38. Process Development – Today
Engineers
Decisions work time
spent
Evaluation
Knowledge
Behavior (Interpretation)
Dependencies / Patterns
Relations
Data and Parameters
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39. What means converting data into information?
It means
Comprehensively collected
Collaboratively collected
Formalized data
Infrastructure knows about the physical quantities of values
Easy selectively sharable data
Information is even better than better data
Multidimensional access and search
Graphical assessment and navigation possibilities
Applying the principles in process development means
Risks ↓, WIP ↓, Costs ↓, Efficiency ↑, Moral ↑
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40. Information Governance principles
Process Relations GmbH applied to
Emil-Figge-Straße 76-80
44227 Dortmund, Germany
Process Development
T: +49 231-9742-5970
F: +49 231-9742-5972
info@process-relations.com
www.process-relations.com