This document outlines a Lean Six Sigma project to improve productivity and quality at a distribution center. It discusses defining the project goals of predicting order quantities and reducing picking errors. Data on line counts and errors will be collected and analyzed to identify trends and relationships. Improvement ideas like reducing bottlenecks and implementing mistake-proofing are proposed. Dashboards will be developed to monitor ongoing performance and ensure continuous improvement through the DMAIC process of define, measure, analyze, improve, and control.

1. Project 3: Lean Six Sigma & BI
Distribution Center
Productivity and Quality
MGS 8020
Group 2: At:
Adam Griff
Yisi Lu
Matt Tinaglia
12/1/2011

2. Lean Six Sigma
Continuous Improvement Method
D • DEFINE
M • MEASURE
A • ANALYZE
I • IMPROVE
C • CONTROL

3. DEFINE: Executive Summary M
A
• Project Status: I
There is a strong need to be able to predict and track trends in
ordering quantities (lines) month-to-month. There is also the C
need to better control quality via material handler picking errors.
• Current State:
Line counts per material handler are manually counted daily
when time allows. Errors are also tracked only semi-
automatically via a Shipping Discrepancy Report (SDR) relying
on customer feedback.
• Findings/Accomplishments:
Alternatives identified, risks assessed, and important Metrics
defined.
• Next Steps:
Initiate a DMAIC project and begin taking measurements for
process improvement for data analysis.

4. DEFINE: Using Business Intelligence M
A
• Enhance Process Improvement: I
Be able to see data that is updated on a daily basis and see the C
development of long-term trends.
• Enhance Leadership Decision Making:
Up-to-date view on material handler quality and line quantities
to allow for staffing improvements and/or reassignments.

5. DEFINE: LSS Tool M
A
• Process Mapping: To understand the process and I
identify value-added steps and opportunities for C
improvement:
Order Processing
Distributed to material
Order placed Delivery prints off
handlers (MH)
Parts Packaging
Parts packaged and confirmed in SAP
Parts picked from storage areas
by MH
Shipping
Packages brought to Processed with UPS Picked up by carriers for
shipping station software delivery

6. D
MEASURE: Executive Summary
A
• Project Status: I
Begin compiling data that has been collected for daily-monthly line
C
counts and picking errors per material handler from the SDR.
• Current State:
Monthly line counts are unpredictable, there is no established
process for providing feedback to handlers regarding picking
quality.
• Findings/Accomplishments:
Data collected for 5 years of monthly line counts and up to 12
months for material handler picking errors.
• Next Steps:
Begin analyzing the data we have collected to identify trends and
material handlers with high error levels/low quality.

7. D
MEASURE: Using Business Intelligence
A
• Enhance Process Improvement: I
By tracking attribute (picking errors) and variable (line C
counts/order quantity) data to get a well rounded and complete
look at the process and develop basic Control Charts.
• Enhance Leadership Decision Making:
Allowing us to hear the ‘Voice of the Process’ will lead us to
better identify the expected vs. unexpected variation and make
the right decisions on how to reduce that which is unexpected.

8. D
MEASURE: LSS Tool Company Business Performance
Metric Unit KPIs of Service
A
Total lines by
Productivity
• Control Charts: Efficiency/
Accuracy
Month and Year
I
Identify Measurements Error Rates vs.
Line Counts/
C
and Collect data
Quality Working Days/
Months

9. D
ANALYZE: Executive Summary M
• Project Status: I
Data has been collected and compiled for error rates and line counts
per month. Data has been analyzed to look for patterns indicating C
any relationship between these two metrics and any trends in line
counts over longer periods of time.
• Current State:
No data analysis has been done on these metrics previously, only
measurements taken.
• Findings/Accomplishments:
An expected increase in average picking errors along with higher
line counts was not found in the data. Year-to-year trending was
seen with high and low months identified.
• Next Steps:
Form interpretations from data analysis and formulate ways to
improve upon line count trend visibility and reduction of picking
errors.

10. D
ANALYZE: Using Business Intelligence M
• Enhance Process Improvement: I
By allowing us to identify the Material Handlers that need C
improvement in picking errors and understand the
relationship(s) that exist between line counts and error rate.
• Enhance Leadership Decision Making:
By allowing us to make appropriate staffing decisions based on
historical trends of line counts, as well as identifying Handlers
for additional training or replacement based on Picking error
rate, all in the context of comparison against ‘World-Class’
Benchmarks.

11. D
ANALYZE: LSS Tools M
• Benchmarking: Using the established Benchmark I
of the ‘best warehouse operators in the US have C
shipping accuracy near or at 99.97%*’
Overall Shipping Accuracy
100.00%
Benchmark 99.97%
99.90%
Accuracy =
1-(Errors / Lines)
99.80%
99.70%
99.60%
99.50%
Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11
*World Class Warehousing and Material Handling, Frazelle, 2001, pg. 55

12. D
ANALYZE: LSS Tools M
• Regression: To describe the linear relationship I
between the independent variable (line count) and C
dependent variable (errors).
Monthly Errors vs. Line Count Low R2
30,000
coefficient of
25,000
determination
value, little
correlation
Monthly Line Count
20,000
R² = 0.003
observed in
15,000
explaining
10,000 variation
5,000
0
0 5 10 15 20 25
Number of Errors per Month

13. D
IMPROVE: Executive Summary M
A
• Project Status:
Brainstorm on improvement methods to better predict line counts
C
and reduce picking errors.
• Current State:
Staffing is adjusted through temporary workers based on perceived
quality and there is no mistake-proofing in place to help with
picking errors.
• Findings/Accomplishments:
Identified ways to reduce bottlenecks and implement some Poka-
yoke’s.
• Next Steps:
Use developed BI system as a Control method for monitoring
future performance.

14. D
IMPROVE: Using Business Intelligence M
A
• Enhance Process Improvement:
By allowing us to identify areas in the process best suited to C
mistake-proofing for prevention and opening up bottlenecks.
• Enhance Leadership Decision Making:
By allowing us to envision and improved Future state and
identify and evaluate the Risks involved in moving toward that
state.

15. D
IMPROVE: LSS Tools M
A
• Reduce Bottlenecks:
C
• Improve material handler productivity
• Cross-training
• Overtime
• Mistake Proofing:
• Prevention- stocking locations in fixed, rounded
quantities; pick by bar-code scanner.
• Detection- Already exists through SDR, but response
time and automation can be improved.

16. D
CONTROL: Executive Summary M
A
• Project Status: I
Introduce controls to monitor the process and error count going
forward.
• Current State:
Line counts and picking errors are monitored in an ad hoc way
as part of other metrics and only when work flow allows.
• Findings/Accomplishments:
Dashboards developed to allow us to monitor the ongoing
processes to manage organizational changes and ensure
continuance.
• Next Steps:
Continuous improvement and monitoring via Dashboards.

17. D
CONTROL: Using Business Intelligence M
A
• Enhance Process Improvement: I
By allowing us to have a concise ongoing monitoring
framework to constantly evaluate the process and see where
any further improvements may be needed and can be made.
• Enhance Leadership Decision Making:
Dashboard monitoring allows process owners to make quick
decisions based on complete information on planning staffing
levels and disciplinary needs for error rates.

18. D
CONTROL: LSS Tools M
A
• Monitoring: The key metrics are monitored via I
visual controls and charting, while implementing
periodic reviews and audits.
• Control Plan: The material handler process is well
documented and simplified as much as possible
through Standard Work.

19. Lean Six Sigma
Dashboards
Total Lines by Month & Year
2007 2008 2009 2010 2011 UCL LCL
30,000
28,000
26,000
24,000
22,000
Lines
20,000
18,000
16,000
14,000
12,000
10,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

20. Lean Six Sigma
Dashboards

21. Lean Six Sigma
Dashboards

22. Thanks!
Questions?