Case Study: Lean Six Sigma Improves Order Quality
at Ingersoll Rand Security Technologies
April 2011: Published in IDQ Newsletter Vol 7 Issue 2
Steven C. Bell and Michael A. Orzen
Editor’s note: This article is an excerpt from the book Lean IT - Enabling and Sustaining Your Lean Transformation. It has been modified to fit the format of a stand-alone article. The book is listed on the IAIDQ Bibliography page at http://bibliography.iaidq.org.
Introduction
- Define the issue
- Measure and quantify the impact of the issue
- Analyze the issue: determine root cause
- Improve the process
- Control the process
Ingersoll Rand (IR) is a US$13 billion global diversified industrial company, driven by employees who are proud to offer products and solutions that people use every day to create a positive impact in their world. Driven by a 100-year-old tradition of technological innovation, IR enables companies and their customers to create progress. The company’s Security Technologies sector provides state-of-the-art security solutions for residential and commercial markets.
This case study describes the challenges of entering complex order information into a business system with the goal of achieving 100% accuracy. It describes the problem-solving process undertaken and the tools utilized to dramatically improve the process for manufacturing industrial doors and related hardware.
Problem Description
There are an unlimited number of product configurations and order possibilities that may be required to meet our customers’ needs. The placement and fulfillment of a customer’s order require the entry of the customer requirements into the manufacturing and business information system. Critical parameters that must be accurate include size, shape, material, color, finish, and key locations for locks, closers, and other associated hardware, and a correctly calculated price for the configured item. Customers often provide product specification information in their own format, which typically does not match the format of our system, creating the potential for introducing additional errors during data translation and entry.
Orders are received by the Sales Support area via fax, e-mail, and traditional mail. The first step in the order placement and fulfillment process is to understand what the customer is trying to order. This is not easy, since there are myriad ways to describe what is actually desired. Order entry associates must also determine if what the customer orders is truly what is necessary to meet fire, safety, and weather-related requirements. This verification step requires an understanding of the customer’s specific application, as well as regulations in locations worldwide.
Once the order has been clarified and reviewed, it must be entered into our information system. A product configurator has been developed to enable us to meet many of our stock offerings; however, it is virtually impossible to predefine all likely combinations.
When a configuration isn’t available, the order entry personnel must then key in the order manually. This tedious process requires a great deal of product and application knowledge; order entry personnel must understand the products and how they are constructed. They must be familiar with specific applications, specific door construction techniques, and specific hardware locations and requirements. This manual process creates a fairly high risk of error.
Many consequences arise from this error-prone process, usually in the form of delays, errors, and rework. The high occurrence of defects via manual order entry can cause disruption within the order entry function, in manufacturing, and also lead to dissatisfied customers. The many variations in the ordering process were the enemy. Understanding what causes the variation and dealing with root cause issues comprised the focus of this problem-solving effort.
IR took on the task of improving the order entry process, utilizing the DMAIC approach and Six Sigma tools.
The DMAIC Approach
The DMAIC approach consists of the following steps:
- Define the issue
- Measure and quantify the impact of the issue
- Analyze the issue: determine root cause
- Improve the process
- Control the process
Define the Issue
IR needed to deal with quality issues (defects) by improving the overall order entry accuracy and reducing variations.
Measure and Analyze
With the aid of an outside consultant, the group established a metric for counting total defects. The process was mapped, and defects were recorded at key internal process points.
Understanding where defects occurred in the process was vital to solving and addressing the issue. Key tools included process maps, supplier input-process-output- customer analysis (SIPOC), operational definitions, and quantitative methods. Statistical process control was used to trend errors and highlight special causes of variation. Probability analysis was also applied to predict total defects by type. Fishbone diagrams were used to analyze potential defect causes.
Data collection showed that one of the key process variables was the influence of operator knowledge and skills. Several experienced operators had developed a “knack” of entering, checking, and providing defect-free orders.
Improve
Once this knack was discovered, it was captured, standardized, documented, and spread throughout the department through a mentoring program. Creating standard work enabled the process to initially drop defects by 50 percent within the first six months of the project. Key tools utilized were control charts, which were used to identify patterns and trends over time, and Pareto analysis.
Process specialists were also engaged to determine where additional information systems improvements could be made. These individuals made many suggestions on ways to mistake-proof the order entry software. By engaging the operators in a cross-functional team, specific intelligence was built into the software, making it easier to enter the order correctly the first time.
Early involvement by the IT organization led to a collaborative relationship between IT and the operators and enabled rapid and simple enhancements to existing software. Look-up tables and search functions were added to the system, which helped order entry operators find key information much easier and faster. IT was also able to add enhancements to mistake-proof key error sources in the entry process.
As a result of these efforts, defects were reduced dramatically. The process itself is approaching a 70-percent reduction in defects, moving from 0.5 sigma to 3.53 sigma. Customer satisfaction has also improved tremendously.
Control
A plan was put in place to help sustain the gains. Improvements in quality, stabilization, and consistency enabled the effective use of traditional Lean tools for continuous improvement efforts now underway.
Lessons Learned
Early involvement of IT resources with the inclusion of operators and support personnel in a combined team effort was instrumental to our overall success.
Understanding the process, involving those who work the process, and seeking outside advice were vital to making the process better.
Having the discipline to adhere to the DMAIC method generated significant gains, and delivered value to our customers.
Author’s note: this case study was contributed by Rajesh Solanki (Director of Lean Six Sigma), William Kemerer (Lean Six Sigma Manager, Master Black Belt), and Brent White (Master Black Belt), all with Ingersoll Rand Security Technologies.
Excerpt from Lean IT - Enabling and Sustaining Your Lean Transformation, Copyright © 2010 by Steven C. Bell and Michael A. Orzen. Lean IT has been honored with the 2011 Shingo Prize for Operational Excellence, Research and Professional Publications Award.
For a free download of Chapter 1 please visit http://www.steadyimprovement.com/publications.
For a copy of the presentation “Lean IT and Data Quality” presented to the San Francisco chapter of the Data Management Association (http://www.sfdama.org) in December 2010 please send a request to info [at] steadyimprovement [dot] com
About the Authors
About the Author
Steven C. Bell, CFPIM brings over twenty years' experience in finance, operations management and information systems. He is the author of "Lean Enterprise Systems: Using IT for Continuous Improvement" which was nominated for the 2007 Shingo Prize Research Award.
About the Author
Michael A. Orzen, CMA, CFPIM, PMP delivers a unique blend of Lean, Six Sigma, IT, project management, and operations management. With a BA from Stanford University in economics and an MBA from the University of Oregon, Mike has been consulting, coaching and teaching for over 20 years.