Project 3: Lean Six Sigma & BI

Distribution Center Productivity and

Quality

MGS 8020Group 2:

Adam GriffYisi Lu

Matt Tinaglia 

12/1/2011

At:

Lean Six SigmaContinuous Improvement Method

•DEFINE

D

•MEASURE

M

•ANALYZE

A

•IMPROVE

I

•CONTROL

C

DEFINE: Executive Summary

• Project Status:There is a strong need to be able to predict and track trends in ordering quantities (lines) month-to-month. There is also the 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.

D

M

A

I

C

DEFINE: Using Business Intelligence

• Enhance Process Improvement:Be able to see data that is updated on a daily basis and see the 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.

D

M

A

I

C

DEFINE: LSS Tool

• Process Mapping: To understand the process and identify value-added steps and opportunities for improvement:

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M

A

I

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ShippingPackages brought to

shipping stationProcessed with UPS

softwarePicked up by carriers

for delivery

Parts Packaging

Parts picked from storage areas Parts packaged and confirmed in SAP by MH

Order Processing

Order placed Delivery prints offDistributed to

material handlers (MH)

MEASURE: Executive Summary

• Project Status:Begin compiling data that has been collected for daily-monthly line 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.

D

M

A

I

C

MEASURE: Using Business Intelligence

• Enhance Process Improvement:By tracking attribute (picking errors) and variable (line 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.

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M

A

I

C

MEASURE: LSS Tool• Control Charts:

Identify Measurements and Collect data

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M

A

I

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Performance of

Service

Business Unit KPIs

Company Metric

Efficiency/ Accuracy

ProductivityTotal lines by

Month and Year

Quality

Error Rates vs. Line Counts/

Working Days/ Months

ANALYZE: Executive Summary

• Project Status:Data has been collected and compiled for error rates and line counts per month. Data has been analyzed to look for patterns indicating 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.

D

M

A

I

C

ANALYZE: Using Business Intelligence

• Enhance Process Improvement:By allowing us to identify the Material Handlers that need 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.

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I

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ANALYZE: LSS Tools

• Benchmarking: Using the established Benchmark of the ‘best warehouse operators in the US have shipping accuracy near or at 99.97%*’

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C

*World Class Warehousing and Material Handling, Frazelle, 2001, pg. 55

Overall Shipping Accuracy

Benchmark 99.97%

99.50%

99.60%

99.70%

99.80%

99.90%

100.00%

Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11

Accuracy =1-(Errors / Lines)

ANALYZE: LSS Tools

• Regression: To describe the linear relationship between the independent variable (line count) and dependent variable (errors).

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M

A

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4 6 8 10 12 14 16 18 20 220

5,000

10,000

15,000

20,000

25,000

30,000

R² = 0.00360352037761535

Monthly Errors vs. Line Count

Number of Errors per Month

Month

ly L

ine C

ount

Low R2 coefficient of determinatio

n value, little

correlation observed in explaining variation

IMPROVE: Executive Summary

• Project Status:Brainstorm on improvement methods to better predict line counts 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.

D

M

A

I

C

IMPROVE: Using Business Intelligence

• Enhance Process Improvement:By allowing us to identify areas in the process best suited to 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.

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M

A

I

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IMPROVE: LSS Tools

• Reduce Bottlenecks:

• 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.

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M

A

I

C

CONTROL: Executive Summary

• Project Status: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.

D

M

A

I

C

CONTROL: Using Business Intelligence

• Enhance Process Improvement: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.

D

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CONTROL: LSS Tools

• Monitoring: The key metrics are monitored via 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.

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Lean Six SigmaDashboards

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec10,000

12,000

14,000

16,000

18,000

20,000

22,000

24,000

26,000

28,000

30,000

Total Lines by Month & Year

2007 2008 2009 2010 2011 UCL LCL

Lin

es

Lean Six SigmaDashboards

Lean Six SigmaDashboards

Thanks!

Questions?