High-Tech R&D -- Drowning in data but starving for information
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Dortmund, 9 th of January 2013 Webinar: High-Tech R&D: Drowning in data but starving for information
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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
Transcript of High-Tech R&D -- Drowning in data but starving for information
Dortmund, 9th of January 2013
Webinar:High-Tech R&D: Drowning in data but starving for information
CONFIDENTIAL
Process Relations GmbH
Slide 2
Agenda
Brief Company introduction
Definitions
Situation Today / Problem
Requirements
Converting the data and using the information
Summary
Dortmund, 9th of January 2013
About Process Relations GmbH
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Process Relations GmbH
Slide 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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Process Relations GmbH
Slide 5
History and Key Milestones
2000
2002
2004
2007
2008
2009
Bosch
Project
CK
Project
XperiDesk
Launched
Process
Relations
Founded
First
XperiDesk
Customers
2010Entering
new
markets
First
Marque
Customer
2011
Dortmund, 9th of January 2013
Definitions
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Process Relations GmbH
Slide 7
Definitions
Data: symbolsData 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.
Following the DIKW model: http://www.systems-thinking.org/dikw/dikw.htm
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Slide 8
High-Tech R&D – Yesterday
Data and Parameters
Dependencies / Patterns
Knowledge
Relations
Behavior (Interpretation)
Evaluation
Decisions
Engineers
work time
spent
Dortmund, 9th of January 2013
Situation Today / Problem
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Process Relations GmbH
Slide 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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Slide 11Reused from: http://blog.mindjet.com/wp-content/uploads/2011/11/Drowing-
in-Data-Infographic.jpg
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Slide 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
Source: IDC “Accelerating Science-Led Innovation for
Competitive Advantage” Feb. 2012
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Slide 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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Slide 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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Slide 15
Cost increase in R&D efforts
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Slide 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
1: Oracle: Information Management – Get control of your Information
Picture is property of: www.yakidoo.com
Dortmund, 9th of January 2013
Current Situation and
Requirements
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Slide 18
Current situation
Distributed, untraceable and undiscoverable R&D results
Limited formalized data availablethat is not interlinked
Sometimes usage of old orretired 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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Slide 19
45.000.000.000,00 US$ / year(in 2007)
Semiconductor R&D spending
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Slide 20
4.500.000.000,00 US$ / year
Wasted for manual data management & search
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Slide 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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Slide 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
Dortmund, 9th of January 2013
Converting the data and using
the information
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Slide 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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Slide 25
– Covering the Complete Development Cycle
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Slide 26
– Covering the Complete Development Cycle
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Slide 27
– Filling with experimental data
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Slide 28
– Management of Experiment information
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Slide 29
– Management of Experiment information
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Slide 30
– Management of file data
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Slide 31
– versatile searching capabilities
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Slide 32
– versatile searching capabilities
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Slide 33
– Export tables to JMP, Excel, …
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Slide 34
– and analyze in JMP, Excel, …
Dortmund, 9th of January 2013
Summary
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Slide 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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Slide 37
Process Development – Yesterday
Data and Parameters
Dependencies / Patterns
Knowledge
Relations
Behavior (Interpretation)
Evaluation
Decisions
Engineers
work time
spent
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Slide 38
Process Development – Today
Data and Parameters
Dependencies / Patterns
Knowledge
Relations
Behavior (Interpretation)
Evaluation
DecisionsEngineers
work time
spent
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Slide 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 ↑
Process Relations GmbH
Emil-Figge-Straße 76-80
44227 Dortmund, Germany
T: +49 231-9742-5970
F: +49 231-9742-5972
www.process-relations.com
Information Governance principles
applied to
Process Development
