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A Lean Six Sigma Analysis Supported by Discrete Event Simulation for Pecan
Production Improvement
By
Carlos Escobar
New Mexico State University
May 31, 2015
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Bio
EDUCATION Doctor of Philosophy, PhD Industrial Engineering.
New Mexico State University, Las Cruces New Mexico. August 2015. Master in Engineering with a specialization in Quality and Productivity Systems.
Monterrey Institute of Technology and Higher Education, Ciudad Juarez, Chihuahua. December 2005. Bachelor in Industrial Engineering with a specialization in Automated Manufacturing.
Technological Institute of Ciudad Juarez. June 2001.
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.
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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)
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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Company Overview
Stahmanns Inc.
3200 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
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Company Overview
Products
*Pecan market value and demand decrease dramatically when pecan is broken into smaller pieces
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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Problem description
Low percentage of halves (10%)
Low production volume (8000lbs/shift)
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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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.
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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
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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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)
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Define
Shelling Process Variable Analysis
Source: Snee and Hoerl 2003
Conveyor speed Number of operators Preventive maintenance
Operator’s attention Machine’s failure
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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Measure
Shelled Pecan Production Phases
1. Harvesting
2. Sorting
3. Shelling Sanitizing Cracking Shelling (machine) Sorting (visually/manually shell elimination)***
4. Packing
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Measure
Shelling Process Diagram
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Measure
Production Rate Average of 8000lbs per 8-hour shift
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Measure
Quality Inspection Inspection 1
Lot size 180lbs (container)
Sample size 20lbs
Sampling procedure Sample is collected from the top the container
Sampling rejection parametersMAXIMO PERMITIDO PARA ACEPTAR CANASTAS
CANASTAS CASCARAS NUEZ ROJA
NUEZ NEGRA
NUEZ RASPADA
SIN POLVO
NUEZ RASPADA
CON POLVO
FANCY MITADES 3 3 3 5 3EXTRA LARGE PEDAZO 3 3 3 5 3LARGA PEDAZO 4 4 4 5 3
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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
MAXIMO PERMITIDO PARA ACEPTAR CAJAS
CAJAS CASCARAS NUEZ ROJA
NUEZ NEGRA
NUEZ RASPADA
SIN POLVO
NUEZ RASPADA
CON POLVO
FANCY MITADES 1 1 1 3 1EXTRA LARGE PEDAZO 1 1 1 3 1LARGA PEDAZO 2 2 2 3 1
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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
MAXIMO PERMITIDO PARA ACEPTAR CAJAS
CAJAS CASCARAS NUEZ ROJA
NUEZ NEGRA
NUEZ RASPADA
SIN POLVO
NUEZ RASPADA
CON POLVO
FANCY MITADES 1 1 1 1 1EXTRA LARGE PEDAZO 1 1 1 1 1LARGA PEDAZO 1 1 1 1 1
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Measure
Sample Size Ratio Analysis
Rework Rejection rates,
Inspection 1 - 44% Inspection 2 - 17% Inspection 3 - 41%
Inspection Lot size Sample size Sample size ratio1 180 20 11%2 90 30 33%3 90 1 1%
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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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
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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
Source: Simio
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Analyze
Rework Analysis Breaks pecans Decrease production volume
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Analyze
Rejection Parameters Analysis According to the United States Department of
Agriculture (USDA)
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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
MAXIMO PERMITIDO PARA ACEPTAR CANASTAS
CANASTAS CASCARAS NUEZ ROJA
NUEZ NEGRA
NUEZ RASPADA
SIN POLVO
NUEZ RASPADA
CON POLVO
FANCY MITADES 3 3 3 5 3EXTRA LARGE PEDAZO 3 3 3 5 3LARGA PEDAZO 4 4 4 5 3
MAXIMO PERMITIDO PARA ACEPTAR CAJAS
CAJAS CASCARAS NUEZ ROJA
NUEZ NEGRA
NUEZ RASPADA
SIN POLVO
NUEZ RASPADA
CON POLVO
FANCY MITADES 1 1 1 3 1EXTRA LARGE PEDAZO 1 1 1 3 1LARGA PEDAZO 2 2 2 3 1
MAXIMO PERMITIDO PARA ACEPTAR CAJAS
CAJAS CASCARAS NUEZ ROJA
NUEZ NEGRA
NUEZ RASPADA
SIN POLVO
NUEZ RASPADA
CON POLVO
FANCY MITADES 1 1 1 1 1EXTRA LARGE PEDAZO 1 1 1 1 1LARGA PEDAZO 1 1 1 1 1
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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Improve
Sample Size Estimation
(This formula provides us with the minimum sample size needed to detect significant differences)
Sample size for estimating a proportion
Finite population correction factor
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Improve
Where:
p = Population proportion
Zα/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
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Improve
Values:
p = 0.0005
Z.05/2 = 1.96
d = 0.01
N = 300
Estimated sample size (n)
18lbs
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Improve
Rejection parameters re-estimation
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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
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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
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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Control
Daily Reports Generation to Monitor: Production volume (per shift) Percentage of halves Rejection rates
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Agenda
Company Overview Problem Description Six Sigma Overview Define Measure Analyze Improve Control Results and Conclusions
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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
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References
Thompson K Steven, Sampling. Wiley Series in Probability and Statistics. pg 39-50. 2002
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.