The Sweet Spot of Diversity on Productivity in Apparel Manufacturing

Transcription

1 The Sweet Spot of Diversity on Productivity in Apparel Manufacturing Dr. Sriram Narayanan, Michigan State University Ed Terris, Peckham Inc. Amy Rose Seabold, Peckham Inc.

2 Session Agenda Peckham Introduction Team Introduction Work Accomplished Future Work Impact 2

3 Presenting Team Dr. Sriram Narayanan, Associate Professor with the Department of Supply Chain Management Michigan State University Ed Terris, Vice President of Manufacturing Peckham Inc. Amy Rose Seabold, Manufacturing Administrative Specialist Peckham Inc. 3

4 Peckham Inc.- An Introduction Peckham is a nonprofit vocational rehabilitation organization. Our mission is to provide a wide range of opportunities to maximize human potential for persons with disabilities who are striving for greater independence and self-sufficiency. 4

5 Peckham Inc.- An Introduction 2015 Elite Award Winner for Best and Brightest Medium Sized Company Winner for 9 consecutive years ( ) #11 National Best Medium Workplaces Winner for Flexibility and Giving Back 5

6 Peckham Inc.- An Introduction 6

7 Peckham Inc.- An Introduction Peckham Manufacturing consists of 154,000 square feet of LEED-Silver Certified manufacturing space and 775+ production Team Members. Currently producing 32+ different garments Government and commercial contracts Over 2,100,000 pieces cut in 2016 Over 2,600,000 Automotive items assembled 7

8 Non-Presenting Team Instrumental in Results ~2 years of ongoing research projects with MSU Supply Chain Management and Computer Engineering Kalyanmoy Deb Professor & Koenig Endowed Chair, Electrical and Computer Engineering Additional Support from Michigan State University: Tharo Soun Computer Engineering Intern 8

9 Primary Disability Profiles in Manufacturing Psychiatric 44% Physical 31% Learning 7% Autism Spectrum 2% Developmental 7% Substance Abuse 5% Deaf or Hard of Hearing 2% Traumatic Brain Injury 1% Visually Impaired 1% *Percentage for fiscal year

10 Project Philosophy Productivity Quality Inclusive Manufacturing Diversity, a Key Element to Embrace in the Operations 10

11 Project Stages Qualitative engagement Early Data Collection and Simulation Results Upstream and Floor Improvement Working on Second Round Results Second Round Data Collection 11

12 Early Work Early conversation(s) to understand the ground level issues in the factory flow Starting with several one-on-one meetings with Ed Terris and Amy Seabold Multiple focus groups with volunteers across several levels of employees, sewers, and product development personnel 12

13 Early Work People Process Technology Individual: Behavior, Open-mindedness, variety of skillsets Group: Diversity Skill, ability and cultural; accessing the tribal knowledge Steadiness of output as compared to high level of skills Communication of expectations from supervisor Frequency of supervisor meeting When is the production team involved in the process? What is the role of time studies? And their implementation? Role of Self-inspection Sequencing and understanding how sequence influences productivity Insights on role of video training and other initiatives The role of investments in technology Jigs and Fixtures 13

14 Assistive Technology Using innovative tools to adapt work for a wide range of clients Specialized trimming machines Jigs, fixtures 14

15 Comprehensive Approach Deriving insights from data Historical Data Collection Simulations for Predictive Modeling Deductive Predictive 15

16 Analytical Simulation Collected data on 6504 shorts Precedence, flow diagram and planned worker assignments Conducted physical time studies to get actual productivity estimates Simulated the lines for 100*100 instances Collected Archival time study data for about 1400 instances Skill level Languages (Dis)ability Calculated the mean and standard deviations of times from the current time study data (Allows possibility of prediction) 16

17 Analytical Simulation Model the line using simulations with the following objectives 1. Maximize the pieces made 2. Maximize the diversity of different kinds of languages and disabilities 17

18 Flow Chart

19 Quantifying Diversity The total diversity is calculated using a (Blau) index Disability(x) = 1 σ9 2 i=0 P Dis i Count of disability number i P DIS i = total number of different kinds of disabilities Diversity(x) = 1 σ 22 2 i=1 P(Div) i Count of diversity number i P(DIS) i = total number of different kinds of diversities Index restricts the diversity values between indicates least diversity, and a 1 indicates maximum diversity 19

20 Data Sanitation - HIPAA To comply with HIPAA, MSU s Institutional Review Board Requirements, and out of respect for our Team Members and their privacy, all data used was blindly coded/de-identified. Languages (Grouped) Language # used English 1 Spanish 2 Vietnamese 3 Nepali 4 Disabilities (Grouped) Broad category # used Mental Illness 1 Learning Disability 2 Physical Disability 3 PTSD 4 Original group contains 10 different disability categories & 22 different language categories. 20

21 Results From Analytical Study (Shorts) Workers with physical disability Short-term disabilities Workers with Mental disability Population of only able workers (Benchmark case) Mix population of able and disabled workers Collection of All disabilities only Workers with Cognitive impairment Results come from cutting 10% of outliers and drawing sample from a mix of able and disabled employees 21

22 Frontier Productivity, Diversity Trade-off Diversity (Combined Languages and Disability) Productivity (Piece Rate) Inflection point * This frontier comes from cutting 10% of outliers and drawing sample from a mix of able and disabled employees 22

23 Frontier Productivity, Diversity Trade-off 0.8 Inflection Point Diversity (Only Languages) Productivity * This frontier does not consider disability related diversity 23

24 Follow-up Detailed Data Collection Spent a year setting up the data collection Monthly aggregation of productivity numbers for multiple garments 6 Garments About 3 years ( ) of data collected for each garment Productivity, goals, quality, disruptions, team size and diversity 24

25 Elements Collected Productivity/Size Average Daily Production Labor Hours per part Monthly Goal achievement Set Goal FTE equivalent Team members total Total Production Quality Outputs First time quality; Scrap Percent Defective Total Rejects Operator and material defects Inputs Inspected Quality team members Leaders/Trainers Diversity Skill diversity Number of individuals in each skill type Diversity by languages and culture Diversity by (dis)ability 25

26 Correlational Results Productivity is positively correlated to quality 26

27 Correlational Results Teams that achieve goals are more likely producing higher first time quality and lesser defects 27

28 Correlational Results Loose Goals are likely associated with more rejects 28

29 Correlational Results Goals Diversity Labor hours per part Goal achievement (actual/target) * Set goal (per unit part) 0.781* Diversity abilities (Number) * 0.247* Total ability types (Primary) 0.515* * Diversity ability types (Primary) 0.229* * Average sewer level * Diversity (Skill Level) Diversity (Language) * Number of Unique languages 0.453* * Productivity is uncorrelated to, or negatively correlated to diversity! 29

30 Correlational Results Goals Diversity Labor hours per part Goal achievement (actual/target) * Set goal (per unit part) 0.781* Diversity abilities (Number) * 0.247* Total ability types (Primary) 0.515* * Diversity ability types (Primary) 0.229* * Average sewer level * Diversity (Skill Level) Diversity (Language) * Number of Unique languages 0.453* * Diversity is less correlated to goal achievements 30

31 Correlational Results Goals Diversity Labor hours per part Goal achievement (actual/target) * Set goal (per unit part) 0.781* Diversity abilities (Number) * 0.247* Total ability types (Primary) 0.515* * Diversity ability types (Primary) 0.229* * Average sewer level * Diversity (Skill Level) Diversity (Language) * Number of Unique languages 0.453* * Diversity is positively correlated to set goals (per unit part) 31

32 Correlational Results First time quality is positively related to diversity 32

33 Correlational Results Scrap is somewhat negatively related to diversity 33

34 Correlational Results Percent defective negatively related to diversity 34

35 Correlational Results Total and operator rejects are positively correlated to diversity 35

36 Regression Analysis: Productivity Labor Hrs Percent Inspected Positive Line Leaders Negative Line Trainers Positive Diversity - Ability None Diversity - Language None Average Sewer Level Positive Fraction No (Dis)ability Negative Total Observations 138 Inspection is negatively related to productivity 36

37 Regression Analysis: Productivity Labor Hrs Percent Inspected Positive Line Leaders Negative Line Trainers Positive Diversity - Ability None Diversity - Language None Average Sewer Level Positive Fraction No (Dis)ability Negative Total Observations 138 Leaders have a positive effect on productivity 37

38 Regression Analysis: Productivity Labor Hrs Percent Inspected Positive Line Leaders Negative Line Trainers Positive Diversity - Ability None Diversity - Language None Average Sewer Level Positive Fraction No (Dis)ability Negative Total Observations 138 Diversity has no significant effect on productivity (Both ability and Languages) 38

39 Regression Analysis: Productivity Labor Hrs Percent Inspected Positive Line Leaders Negative Line Trainers Positive Diversity - Ability None Diversity - Language None Average Sewer Level Positive Fraction No (Dis)ability Negative Total Observations 138 More experienced sewers increase labor hours per unit 39

40 Regression Analysis: Productivity Labor Hrs Percent Inspected Positive Line Leaders Negative Line Trainers Positive Diversity - Ability None Diversity - Language None Average Sewer Level Positive Fraction No (Dis)ability Negative Total Observations 138 Fraction of employees with no (dis)ability increase productivity 40

41 Regression Analysis Goal achievement Percent inspected reduces goal achievement 41

42 Regression Analysis Goal achievement Leaders help in goal achievement 42

43 Regression Analysis Goal achievement Diversity has no impact on goal achievement 43

44 Regression Analysis Goal achievement Average sewer level improves goal achievements 44

45 Regression Analysis Goal achievement Different ability types positively related to goal achievement 45

46 Regression Analysis Quality FTQ Line Rejects Operator Defect Percent Inspected None Positive Positive Line Leaders None Positive Positive Diversity - Ability None Positive Positive Diversity - Language Positive Negative Negative Average Sewer Level Positive Negative Negative Fraction No (Dis)Ability None None None Productivity (LHP) Negative None Positive FTE Per day None Positive Positive Total Ability types Negative None None Total Observations Inspection and line leaders increase Rejects and Operator defects Does not mean we don t use them! 46

47 Regression Analysis Quality FTQ Line Rejects Operator Defect Percent Inspected None Positive Positive Line Leaders None Positive Positive Diversity - Ability None Positive Positive Diversity - Language Positive Negative Negative Average Sewer Level Positive Negative Negative Fraction No (Dis)Ability None None None Productivity (LHP) Negative None Positive FTE Per day None Positive Positive Total Ability types Negative None None Total Observations Diversity in ability and language operate in different ways 47

48 Regression Analysis Quality FTQ Line Rejects Operator Defect Percent Inspected None Positive Positive Line Leaders None Positive Positive Diversity - Ability None Positive Positive Diversity - Language Positive Negative Negative Average Sewer Level Positive Negative Negative Fraction No (Dis)Ability None None None Productivity (LHP) Negative None Positive FTE Per day None Positive Positive Total Ability types Negative None None Total Observations Increasing sewer level in lines impacts productivity negatively but quality positively 48

49 Overall Summary Productivity is positively related to quality A mix of disability does not substantially impact productivity Analytical results suggest a sweet spot effect of diversity Diversity acts in multi-faceted ways Analysis shows that the impact on quality can be positive Increase in sewer levels helps improve quality, and goal achievement, but not labor hours per unit 49

50 Future Work Product Development Support Environment Diversity Physical Environment Manufacturing Process 50

51 Ongoing Communication and Engagement: A Key Ingredient Communication from early on in the study Open philosophy Multiple presentations to supervisors and employees Discussions with VSSes (Vocational Service Specialists) 51

52 Productivity and FTC trends Greenhouse Manufacturing Productivity 111% 111% 108% 109% 114.4% 113.3% 92% 62% 106% 77% 93% 65% 96% 60% 99% 82% 95% 73% 107% 107% 110% 104% 101% 91% 83% 84% 84% 86% 106% 107% 105% 106% 109.0% 99% 98% 97% 102% 106.0% 94% 94% Weighted AVG Average Weighted AVG $$ OCT15 NOV15 DEC15 JAN16 FEB16 MAR16 APR16 MAY16 JUN16 JUL16 AUG16 SEP16 OCT16 NOV16 DEC16 JAN17 FEB17 52

53 Productivity and FTC trends Charlotte Manufacturing Productivity 83% 84% 75% 88% 86% 90% 100% 99% 93% 104% 83% 88% 94% 97% 91% 87% 87% 96% 97% 94% 88% 89% 66% Weighted AVG Weighted AVG $$ OCT15 NOV15 DEC15 JAN16 FEB16 MAR16 APR16 MAY16 JUN16 JUL16 AUG16 SEP16 OCT16 NOV16 DEC16 JAN17 FEB17 53

54 Acknowledgements Peckham Manufacturing Ed Terris, Vice President of Manufacturing Amy Seabold Demmer Center for Business Transformation Students: MBA, SCM Doctoral Students and Other Graduate Students who are currently compiling, or compiled the previous data 54

55 Q & A Special Thank You: Peckham Foundation 55

56 Surveys Time to Give Your Feedback! We are going Green this year! To access the survey, log into the app, click on the session The survey link will be located at the top of the session description Thank you for participating! 56