Design for Data Analysis for Engineers 1 MPD 575 DFX Cohort 8 November 29, 2007 Developed by: Tjuana Buford Professor: Jonathan Weaver

Design for Data Analysis for Engineers 2 Introduction to DFDA (Design for Data Analysis) Define DFDA for Engineers DFDA and SEF (Systems Engineering Fundamentals) Examples Summary References

Design for Data Analysis for Engineers 3 Introduction to DFDA (Design for Data Analysis) Definitions for Data Analysis:  Data analysis is the process of looking at and summarizing data with the intent to extract useful information and develop conclusions. 1 1  Confirmatory data analysis is based on confirming or falsifying existing hypotheses. 1 1  Exploratory data analysis is based on discovering new features in the data 1 1  Data analysis – a procedure that prepares a data model for implementation as a non-redundant, flexible, and adaptable database 2 2

Design for Data Analysis for Engineers 4 Introduction to DFDA Cont. Design for DFDA relates to the following Design for X Modules:  Design for Testability  Design for Serviceability  Design for Robustness  Design for Reliability  Design for Reuse  Design for Failure

Design for Data Analysis for Engineers 5 Introduction to DFDA (Design for Data Analysis) Define DFDA for Engineers DFDA and SEF (Systems Engineering Fundamentals) Example Summary References

Design for Data Analysis for Engineers 6 Define DFDA for Engineers Testing falls within Systems Engineering Fundamentals, which creates a need for analysis of data. Falling within the generic, right hand side of the SEF “V”, verification using data analysis may include:  Analysis of large or small amounts of data  Varying data formats from multiple sources  Data from multiple engineers within a department  Data from several engineering departments  Report generation  Result retention capability Engineers are required to meet requirements through testing, but who designs the test and analysis?

Design for Data Analysis for Engineers 7 Define DFDA for Engineers Cont. Tests and analysis are designed by Test engineers, R&D engineers, Technical Specialists, Software Engineers, and many more! "Just as a requirement specifies the functional performance to be delivered (not how it is to be designed), a Design Verification Method defines what the test or analysis must deliver, not how the test or analysis is to be designed. “ 3 (emphasis added) 3 How good is your Analysis?

Design for Data Analysis for Engineers 8 Define DFDA for Engineers Cont. Test Engineers who create analysis procedures need to have both engineering and computer science knowledge to be able to move beyond Microsoft Excel as a method of data analysis. Past experience with strictly IT personnel creating or choosing data analysis tools has resulted in tools which do not stand up to engineering requirements, for example:  Allowing final results to be altered  Misinterpretation of engineering theory  Various forms of analysis documentation throughout company  No version control  No means of sharing (process, analysis, results) easily with other engineers  Cost to local engineering departments for design, and maintenance of tools

Design for Data Analysis for Engineers 9 Define DFDA for Engineers Cont. According to Broy (2006) 4, 4 The typical electrical engineer lacks the following:  Do not know enough about software  Do not understand the software processes  Do not understand project management

Design for Data Analysis for Engineers 10 Define DFDA for Engineers Cont.

Design for Data Analysis for Engineers 11 Introduction to DFDA (Design for Data Analysis) Define DFDA for Engineers DFDA and SEF (Systems Engineering Fundamentals) Examples Summary References

Design for Data Analysis for Engineers 12 DFDA and SEF (Systems Engineering Fundamentals) Fabrication/ Verification Subsystem Verification System Verification Customer Satisfaction Integrate Integrate Component Design Ver.Req. Verification Requirements Customer Focused Feedback Verification using data analysis can occur at any level

Design for Data Analysis for Engineers 13 DFDA and SEF (Systems Engineering Fundamentals) Fabrication/ Verification Subsystem Verification System Verification Customer Satisfaction Integrate Integrate Component Design Ver.Req. Verification Requirements Customer Focused Feedback Analysis and test results are recorded in the DVP&R as the Verification plan is executed. (actual results vs. targets)

Design for Data Analysis for Engineers 14 Introduction to DFDA (Design for Data Analysis) Define DFDA for Engineers DFDA and SEF (Systems Engineering Fundamentals) Examples Summary References

Design for Data Analysis for Engineers 15 Example 1: My manager asked me to develop a regression model for some data from a new process, what are the basic steps I should follow? 5 5 Input Data Output Data Process The Data “ On a fundamental level, all computer programs do the same thing ” 6 6

Design for Data Analysis for Engineers 16 Example 1 Cont. The Basic Steps In the form of a Flowchart. Start Select form of model based on current data or results from prior model. Design New Experiment New Data needed to fit model? Collect New Data Fit model using parameter estimation method suggested by data and /or process knowledge Validate model to assess its adequacy New model describes data well? End Yes No Yes No

Design for Data Analysis for Engineers 17 Example 2 I use Excel to analyze data collected from highway driving studies. It takes me 20 hours to analyze approximately 22,000 data points, and create a report. What can I use to speed up this process? *Fictitious Data

Design for Data Analysis for Engineers 18 Example 2 Cont. There are many software packages on the market to analyze data including:  Excel Macros/Data Analysis Toolkit  MATLAB/Simulink  Minitab  Labview  NumPy and SciPy for Python (Freeware) Software choices have to take into account cost, functionality, ability to generate reports, customer service and support. Use trial software offers to help test software packages.

Design for Data Analysis for Engineers 19 Example 2 Cont. Most Engineers that have a multitude of data to analyze will eventually move away from the most basic products to products that make their time more productive. Some issues with software packages:  Excel – Okay for small projects but for large projects, major version changes from Microsoft can kill macros and Excel functions you depend on.  MATLAB/Simulink – Large online community of users, support available online, by phone, and in person (for a fee). Ability to handles large amounts of Data. Each additional toolbox costs more money. Has report generator capability.  Labview – Widely used in the automotive industry.  Python – Customizable to individual needs. Free to use. Growing in popularity.

Design for Data Analysis for Engineers 20 Example 2 Cont. General Comparison of Numerical Analysis Software 7 7

Design for Data Analysis for Engineers 21 Example 2 Cont. Operating System Compatibility 7 7

Design for Data Analysis for Engineers 22 Introduction to DFDA (Design for Data Analysis) Define DFDA for Engineers DFDA and SEF (Systems Engineering Fundamentals) Examples Summary References

Design for Data Analysis for Engineers 23 Summary  Use data collection software to collect data as close as possible to the software format that will be used to analyze the data  Make sure the software package chosen has the functions you need  Software should be able to process data from different sources  The goal is consistent results  Reach out to other engineers and developers  Participate in the data analysis community  Ford Employees - Contact Tjuana Buford (Core Developer) – for access to the Data Processing tool presented in example 3.  The Death tool is used by 400+ engineers within Ford PD

Design for Data Analysis for Engineers 24 Introduction to DFDA (Design for Data Analysis) Define DFDA for Engineers DFDA and SEF (Systems Engineering Fundamentals) Examples Summary References

Design for Data Analysis for Engineers 25 References Ford Motor Company – Systems Engineering Fundamentals Reference Guide (2005) Ford Confidential 4.Broy, M., Pretschner, A., Salzmann, C., and Stauner, T., "Software-Intensive Systems in the Automotive Domain: Challenges for Research and Education," , SAE World Congress, Detroit, Michigan NIST/SEMATECH e-Handbook of Statistical Methods, Bronson, Gary J., “ C for engineers and scientists an introduction to programming ”, West Publishing company, St. Paul, MN DEATH Design developed by Erwin Peters 2002, Ford Motor Company Confidential. Copyright All Rights Reserved