Manufacturing Case Study: Using Six Sigma to Reduce Excess Inventory

In 2009, Unison Industries, a worldwide provider of electrical

and mechanical aviation components and systems, had a

problem common to many manufacturers: an excess amount of

inventory. Contributor Melissa Connolly describes how Six

Sigma helped to get the excess inventory under control.

Many manufacturing businesses utilize Enterprise Requirement

Planning (ERP) systems to manage supply and demand management of

raw materials, work in progress (WIP), and finished goods. Unison

Industries (UI), a worldwide provider of electrical and mechanical

aviation components and systems (their products can be found in trainer

planes, jumbo jets, and even spacecraft) was no exception to this.

Early in 2007, UI transitioned to a new ERP system called ORACLE. Shortly following

the transition to ORACLE, the company found that it had amassed a significant amount

of excess inventory.

In January of 2009, a vast quantity of UI inventory was pegged to excess, constraining

working capital, resources, and capacity. Excess inventory per category was 20%

production (which supports products already in production) and 65% engineering (which

supports the development of new products). The 65% of excess engineering inventory

had a dollar value that equated to approximately 20% of the company’s 2009

engineering budget.

Because of the seriousness of excess inventory, the company’s executive team took

immediate action. Two teams were formed to rein in the excess inventory and prevent

future excess growth. The first team focused on the production inventory and the

second on the engineering inventory. Within each of the two teams, responsibilities

were assigned in the areas of excess growth and on hand inventory burn down. At the

time I was the company’s Six Sigma MBB, and was assigned responsibility to stop the

growth of excess engineering inventory.

Getting Started

The Vice President of engineering selected the Six Sigma methodology as the best

approach for attacking this business critical issue. (The Six Sigma methodology had

been engrained within the company after General Electric Engine Services [GEES]

acquired UI in 2002.)

First, excess engineering inventory growth was base-lined. Then this base-line data was

compared with the rate of excess engineering inventory growth during and after the

implementation of Six Sigma methodology. In the end, comparisons of these data sets

were utilized to illustrate the effectiveness of the Six Sigma methodology in reducing the

growth of UI’s excess engineering inventory.

Population for this research project consisted of data from UI’s ERP system, specifically

related to the inventory management modules. Sampling consisted of all incoming P.O.

receipts meeting the following criteria: received from January 31, 2009 to July 31, 2009;

designated as engineering inventory and flagged as excess inventory. Data was

gathered by running an ORACLE report that extracted data from UI’s ERP system. This

data extract was then exported into EXCEL and analyzed.

The measurement instrument was verified with a Gage R&R test. An ORACLE report of

incoming engineering P.O. receipts was run, exported into EXCEL, and a subset of 30

data lines selected. These 30 lines were provided to two project BBs. Each BB served

as an independent inspector, reviewing the data lines and recording a status of excess

or not excess in a Gage R&R template. This process was repeated two times and the

results recorded as seen in Figure 1. An Attribute Gage R&R score of 100% was

achieved. This score exceeded the 90% threshold, thus enabling the research to

proceed with the selected measurement instrument.

Figure 1. Attribute Gage R & R Effectiveness.

After the measurement instrument was validated, numerical data was collected from

UI’s ERP system to quantify

the project’s base-lined

process capability. Analysis

of received engineering

P.O.s from January 31, 2009

to February 28, 2009 was

completed. This analysis

showed a month on month

increase in excess

engineering inventory of

approximately $200,000.

The majority of the excess

growth, over 40%, was due

to received P.O.s. Figure 2,

illustrates the excess growth

trend.

Figure 2. Base-lined Excess Inventory Growth Trend.

Next, a statistically based EXCEL short form was utilized to calculate a base-lined

capability score. Because received P.O.s were the main driver for excess growth, a

received P.O. with destination engineering was selected to be the unit of measure. Total

units for the January 31st – February 28th time period equaled 102,716. A defective unit

was defined as a unit flagged by the ERP system as excess. Total defects for the data

sample was 81,058. Utilizing these parameters the short form calculated a 79% defect

rate, which equated to a short-term capability score of 1.156. Figure 3 is a screenshot of

both the short form utilized and the resulting score.

Figure 3. Base-lined Capability Score.

Following determination of the base-lined capability, the BB team began root cause

analysis to identify the underlying causes for excess inventory growth. The team

leveraged experts from UI’s supply chain team, along with ERP system experts to

populate the fishbone template in Figure 4 below. Items selected on the fishbone as

critical, significant drivers of excess, were broken out into the following Sub-Y’s: P.O.s,

Internal Sales Orders, WIP, Finished Goods, Raw Material, and Obsolete. Each of the

Sub-Y’s was then executed to completion as an individual Six Sigma DMAIC project.

Figure 4. Completed Fishbone Template.

After all related DMAIC

projects were evaluated by

UI’s MBB and categorized as

completed, numerical data

was collected from UI’s ERP

system to quantify the

project’s new base-lined

process capability. Analysis of

received engineering P.O.s

from May 31, 2009 to June

30, 2009 was completed. This

analysis showed a 99%

decrease in excess

engineering inventory growth

as captured in Figure 5

below.

Figure 5. Post Excess Inventory Growth Trend.

Again, a

statistically

based EXCEL

short form was

utilized to

calculate the

new base-lined

capability score.

The same unit

and defects

were utilized as

in the base-lined

analysis.

A received P.O. with destination engineering was selected to be the unit of measure.

Total units for the May 31st – June 30th time period equaled 4,907. Total defects for the

data sample was 1,285. Utilizing these parameters the short form calculated a 26%

defect rate, which equated to a short-term capability score of 2.138. Figure 6 is a

screenshot of both the short form utilized and the resulting post score.

Figure 6. Post Capability Score.

Following project completion, a Chi square test of significance was conducted to test

whether Six Sigma methodology had resulted in a statistically significant reduction in

UI’s excess engineering inventory growth. The test was run with the level of significance

set to 0.05 for the following two data sets: (a) base-lined excess growth due to P.O.s at

project launch and (b) excess growth due to P.O.s following all project improvements.

Table 1 displays the detailed results of the Chi Square analysis. Since the p-value was

< 0.05, it was concluded that Six Sigma had made a significant impact on reducing

excess inventory.

Chi Square Analysis

Group Count N Expected Chi-Sq Base-lined 81058 1E+05 78588.625 77.592 Post 1285 4907 3754.375 1624.189 Totals 82343 1E+05

Chi-Sq 1701.780

Critical Chi-Sq 3.841

p-value 0.000

Results

Specific project results included a 99% decrease in excess engineering inventory

growth and a 25% decrease in total excess engineering inventory. The cash savings

associated with these reductions equated to over 7% of the engineering budget and

were instrumental in ensuring UI engineering met all new product delivery and margin

commitments for the 2009 fiscal year.

This research project supports the claims that the Six Sigma methodology can be

leveraged to solve business critical issues, such as excess inventory. By clearly defining

the problem, strategically aligning key business resources, and following the methodical

Six Sigma approach UI’s excess engineering inventory was significantly reduced.

As expected at project launch, poor physical inventory management was identified as a

root cause of UI’s excess engineering inventory growth. Historically, UI had a reputation

of not carefully managing physical inventory slated for engineering use. Engineering

inventory is expensed upon receipt and thus not carried as a business asset. Because

of these reasons UI had been exempting engineering inventory from annual physical

inventory counts.

A surprising root cause identified to be driving excess engineering inventory growth was

a poorly defined business metric. Inventory that was shared between engineering and

production part numbers was, upon receipt, often classified as production inventory.

This misclassification of inventory was occurring because the receiving team’s

performance was measured as a function of dock to stock time. Because it was much

faster for receiving personnel to process a package of shared inventory as one

classification (engineering or production versus splitting the inventory into stock classes)

the stock almost exclusively went into production inventory. Meanwhile engineering

orders were showing as unfilled and driving more purchases. By the time the excess

production inventory was identified through physical inventory cycle counts and moved

to engineering inventory the extra inventory had already been received.

There are four key findings of this research project that can be leveraged to any

manufacturing business. First, the Six Sigma methodology can be rapidly deployed to

identify root causes, develop corrective actions, and provide sustained improvements

for business critical issues, such as excess inventory growth. Secondly, inventory

accuracy within an ERP system is paramount. An ERP system is a powerful tool that

can ensure on time delivery and low carrying cost. However, when inventory

inaccuracies exist the ERP system becomes a dangerous weapon that will drive excess

inventory.

Next, having the right business metrics is a key cornerstone to a successful inventory

management program. Metrics that focus on only a portion of an end-to-end process

can drive narrowly focused behaviors. Such behaviors propagate unhealthy business

practices that have a detrimental effect on overall business performance. Lastly, the Six

Sigma methodology can be rapidly deployed to identify root causes, develop corrective

actions, and provide sustained improvements for business critical issues, such as

excess inventory growth.

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About the Author: Melissa Connolly

As a member of the Baker Hughes’ CIO office Melissa is responsibility

for the Discovery and Exploration phases of the IT organization’s

Approach to Value Delivery process. In this role, Melissa is transforming

the existing front end IT processes to the next level of performance by

creating a framework for strategic innovation. This framework will provide

the IT organization with innovation processes, methodologies, technologies, training,

and coaching to drive a step change in the solutions and value delivered to Baker

Hughes customers and Baker Hughes enterprise. In addition, Melissa leads a robust

Organizational Effectiveness (OE) program that measures and improves the

effectiveness of IT’s culture, processes, and systems with Lean Six Sigma (LSS),

Business Process Management (BPM), and Program Management (PM)

methodologies.

Melissa has been with Baker Hughes for two years, most recently as the director of the

Baker Hughes Operating System (BHOS). In this role she designed and initiated the

BPE program for Baker Hughes and successfully ensured the global rollout of the

BHOS portal, Baker Hughes’ first instance of BPM.

Melissa has over 15 years of engineering, quality, and leadership experience with

various commercial and U.S. Department of Defense companies, as well as experience

as a Mathematics professor. She earned a Bachelor of Science degree in Aerospace

Engineering and Master’s Degree in Management from Embry Riddle Aeronautical

University where she graduated with honors. In addition, Melissa holds a LSS Master

Black Belt (MBB) certificate from General Electric Aviation where she was previously

employed as a MBB for New Product Development.