1 A Lean Six Sigma Analysis Supported by Discrete Event Simulation for Pecan Production Improvement...

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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.