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IE 361 Lennox Project Statistical Analysis of Run Test Data By: Adli Shah Adnan, Josh Lamm, Peter Schulte, Yusuf Yigit.

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Presentation on theme: "IE 361 Lennox Project Statistical Analysis of Run Test Data By: Adli Shah Adnan, Josh Lamm, Peter Schulte, Yusuf Yigit."— Presentation transcript:

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2 IE 361 Lennox Project Statistical Analysis of Run Test Data By: Adli Shah Adnan, Josh Lamm, Peter Schulte, Yusuf Yigit

3 Project Contents and Outline 1. About Lennox - Lennox International - Branch : Lennox Marshalltown - Client Information 2. Introduction to Run Test - Run Test - Category : Compressors and Fan Motors 3. Problem and Solution Methods 4. Solution Statement 5. Group Recommendations

4 About Lennox

5 Prologue : Lennox International Inc. Lennox International was founded by Dave Lennox in Its operates through three segments: Residential Heating & Cooling, Commercial Heating & Cooling and Refrigeration. o Company name : Lennox International Incorporated o Headquarters :2140 Lake Park Boulevard, Richardson, Texas, United States o CEO : Todd M. Bluedorn o Products : HVAC (Heating, ventilation and air conditioning) equipment o Employees : ~ 9,700 o NYSE : LII Stock Price : ~ $86.67

6 Prologue : Lennox Industries Branch : Lennox Marshalltown o Factory Location: 200 S 12th Marshalltown, IA o Plant Area: 996,000 sq.ft. o Employees: 1,000 (approx.) Products: Manufacturing residential heating and cooling products: gas furnaces, split-system condensing units, split- system heat pumps History: Original location of Lennox Furnace Company Incorporated in 1904

7 Prologue : Client Information Client Contact Quality Engineer, Chuck Strobbe o o Quality Engineer, Jason Kern o o

8 Introduction to Run Test Lennox

9 Run Test Run Test is a station that is located at each manufacturing assembly line. The station is to test the functionality and safety of their products As part of Lennox’s quality goals, they measure the First Pass Yield (FPY) of this process. Our client indicates that there are errors in the test procedure. o Two class error  False Positive  False Negative o Tolerance are heavily biased against false positive False Negatives are the target of this study. Run Test Station

10 Category : Compressor and Fan Motor Compressor and Fan motors are tested for Amp draw. Amp draw are their way of testing motors for safety and quality assurance. Each assembly number has different amp draws that will need to meet their requirement. Both motors frequently fails by being outside of the parameters but will pass with subsequent retest. Fan Motors Compressor Motors

11 Problem and Solution Method

12 Identify The Problem Test parameters (set by monitoring functioning properties with response history) Parameters are not up-to-date for certain assembly models  New suppliers - different range of tolerance Re-analysis of model parameters is necessary due to altering of model parts that adjust their amp draw values Auto generated data (Fail - Red Column) (Pass - Green Column)

13 Problem Statement & Analysis Determining causes of error for First Pass Yield (FPY) This statistic is measured in parts per million that were passed on the first test

14 Data Collection Sample of the data Amp measurements are automated

15 Methods Outline 1.Pareto analysis (Top 80% of false negatives) 2.Creating histograms and control charts 3.Data Analysis (searching for patterns in the data) 4.Cp and Cpk analyses 5.Recalibration and new parameter calculations

16 Pareto Analysis

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18 Histograms and Control Charts

19 Control Charts & Data Analysis

20 Cp and Cpk Analyses Cp and Cpk Example Old parameter value =(D2-E2)/(6*C2) =(D2-E2)/(7*C2) =(D2-E2)/(8*C2) =MIN((D2- B2)/(3*C2),(B2- E2)/(3*C2)) =MIN((D2- B2)/(3.5*C2),(B2- E2)/(3.5*C2)) =MIN((D2- B2)/(4*C2),(B2- E2)/(4*C2))

21 Solution Statement

22 New Tolerance Calculations (Solution) The new parameters through our process of analysis should provide minimal retesting of units that prove to be false-negatives

23 Discussion In this example, the parameters prove to be very similar to what the model was previously set at. Other models may show that parameter changes are necessary based on past test data Overall, this should provide fewer false-negatives while still maintaining heavy bias against false- positives Sensitivity analysis of Cp and Cpk values can be conducted to aid in the decision making of tolerance setting for the managers and quality engineers

24 Group Recommendations Due to this providing only a temporary solution we recommend... New quality assurance program More reliable testing equipment Widen tolerance Automate parameter adjusting Organize worker instruction to remove over-retesting, improper testing, and time waste

25 Conclusion The new parameters have been provided so that false-negatives may be minimized for the time being and FPY may be higher Considerations of a new-lasting quality assurance process is recommended Results will not be recognized during the span of this course

26 Thank you for listening For questions, feedback or comments Please contact Adli Adnan - Josh Lamm - Peter Schulte - Yusuf Yigit -


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