1 A Lean Six Sigma Analysis Supported by Discrete Event Simulation for Pecan Production Improvement By Carlos Escobar New Mexico State University May 31, 2015

2 Bio EDUCATION Doctor of Philosophy, PhD Industrial Engineering. New Mexico State University, Las Cruces New Mexico. August Master in Engineering with a specialization in Quality and Productivity Systems. Monterrey Institute of Technology and Higher Education, Ciudad Juarez, Chihuahua. December Bachelor in Industrial Engineering with a specialization in Automated Manufacturing. Technological Institute of Ciudad Juarez. June CERTIFICATIONS Design for Six Sigma Black Belt. February 2012 Design for Six Sigma Green Belt. March 2011 University of Michigan College of Engineering Six Sigma Black Belt Certification. December 2008 Arizona State University PROFESSIONAL DEVELOPMENT Teaching Assistant May 2014 – June 2015 New Mexico State University, Las Cruces New Mexico. Senior Research Engineer Jun 2015 – Current General Motors, Warren Michigan.

3 Goal of the Project Increase daily production rate and the percentage of halves Production volume and percentage of halves can be significantly increased by improving process performance, while maintaining all other factors constant (i.e. headcount, # of machines, labor hours)

4 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

5 Company Overview Stahmanns Inc acre farm located in Southern New Mexico One of the biggest pecan suppliers in the world All pecans are grown and shelled at the farm Packed in 30 pound boxes Bulk pecan meats for sale on the wholesale, industrial and commercial markets

6 Company Overview Products *Pecan market value and demand decrease dramatically when pecan is broken into smaller pieces

7 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

8 Problem description Low percentage of halves (10%) Low production volume (8000lbs/shift)

9 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

10 Six Sigma Overview Six Sigma is a set of strategies, techniques, and tools for process improvement. It is a well defined methodology that is rooted in mathematics and statistics. The objective of Six Sigma quality is to reduce process output variation in which no more than 3.4 defect parts per million (PPM) opportunities are generated. The six sigma methodology is a rigorous approach defined by five steps which are: Define, Measure, Analyze, Improve and Control. DMAIC is an acronym for the five phases that make up the process. Six Sigma has a martial arts convention for naming many of its professional roles. They are described as belts according to their level of expertise.

11 Characteristics of a Six Sigma Project Connected to business priorities Reasonable scope, 3-6 months Clear quantitative measures of success Should have support and approval of the management Problem of major importance without easy solution

12 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

13 Define Six Sigma Template Information Process: shelling process Problem description:  low production volume  low halves ratio Objective:  Increase production up to 12,000lbs per shift  Increase halves percentage up to 40% Time frame: 4 months Team members:  Quality manager, plant manager, production leader, quality inspectors, black belt (my role)

14 Define Shelling Process Variable Analysis Source: Snee and Hoerl 2003  Conveyor speed  Number of operators  Preventive maintenance  Operator’s attention  Machine’s failure

15 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

16 Measure Shelled Pecan Production Phases 1. Harvesting 2. Sorting 3. Shelling  Sanitizing  Cracking  Shelling (machine)  Sorting (visually/manually shell elimination)*** 4. Packing

17 Measure Shelling Process Diagram

18 Measure Production Rate Average of 8000lbs per 8-hour shift

19 Measure Quality Inspection Inspection 1  Lot size 180lbs (container)  Sample size 20lbs  Sampling procedure Sample is collected from the top the container  Sampling rejection parameters

20 Measure Inspection 2  Lot size 90lbs (3 boxes)  Sample size 30lbs (1 box)  Sampling procedure First box of each lot is sampled  Sampling rejection parameters

21 Measure Inspection 3  Lot size 90lbs (3 boxes)  Sample size 1lb  Sampling procedure 150grs are sampled from the top of each box  Sampling rejection parameters

22 Measure Sample Size Ratio Analysis Rework  Rejection rates, Inspection % Inspection % Inspection %

23 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

24 Analyze Sampling Failures Inconsistent sample sizes  Sample might not be representative of the population Not random sampling  Not all the elements within the lot have the same opportunity to be sampled High rejection parameters  High rework level

25 Analyze Discrete Event Simulation (DES) Due to the central limit theorem, rework stations were modeled with a normal distribution with rejection rates of 0.44, 0.17 and 0.41 For every 10,000lbs outcome 6,000lbs were reworked Source: Simio

26 Analyze Rework Analysis Breaks pecans Decrease production volume

27 Analyze Rejection Parameters Analysis According to the United States Department of Agriculture (USDA)

28 Analyze Rejection Parameters Analysis Rejection parameters were not consistent with the national standards set by USDA Analyze – Summary Rework was the main source of low volume production and low percentage of halves

29 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

30 Improve

31 Improve Where: p = Population proportion Z α/2 = represents a level (likelihood) of error (usually 5%) d = minimum absolute size difference we wish to detect (margin of error, half of the confidence interval) N = Population

32 Improve Values: p = Z.05/2 = 1.96 d = 0.01 N = 300 Estimated sample size (n) 18lbs

33 Improve Rejection parameters re-estimation

34 Improve Process redesigned  Decrease conveyor speed using DES model to determine optimal speed Quality inspection redesigned  Only one final inspection  Appropriate sample size  Simple random sampling

35 Improve Improve Summary: 1. Estimated the sample size using sampling design methods 2. Estimated rejection parameters based on the USDA 3. Redesigned of the sorting process by using DES to determine the optimal conveyor speed 4. Redesigned the quality inspection process considering lean manufacturing

36 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

37 Control Daily Reports Generation to Monitor:  Production volume (per shift)  Percentage of halves  Rejection rates

38 Agenda Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions

39 Results and Conclusions Results Shelled pecan production increased up to 12,000lbs per shift Percentage of halves increased up to 45% Conclusions DES helped to understand how rework was affecting overall system performance (production volume) DES helped to accurately determine the optimal conveyor speed DES is a valuable analytical tool for Six Sigma, Design for Six Sigma, and/or Lean Six Sigma projects

40 References  Thompson K Steven, Sampling. Wiley Series in Probability and Statistics. pg  USDA. United States Standards for Grades of Shelled Pecans. Version January 1997  Jeffrey A. Joines and Stephen D. Roberts. Simulation Modeling with SIMIO: A Workbook.