Does your Six Sigma effort face these challenges:

• Trouble selecting projects or calculating their potential for financial success?
• Difficulties quantifying and forecasting the effects of variation in your new process or design?
• Inability to identify the key factors (CTQs) that drive uncertainty in your process?
• Problems optimizing your processes to improve efficiency and reduce waste?
• Need to maximize the probability of meeting design elements without excessive testing costs?
• Trouble explaining the effects of variation in a clear, concise manner?

If you’re only basing your analysis on hard data—not on the variation inherent in any manufacturing, engineering, or service process—you’re only getting halfway there. Oracle’s Crystal Ball takes you the rest of the way, allowing you to use simulation, modeling, optimization, and forecasting to predict and reduce the effects of variation.

Crystal Ball is a Microsoft® Excel®-based suite of analytical tools that includes Monte Carlo simulation, optimization, capability metrics, and forecasting. The most popular use of Crystal Ball is to define variable inputs as probability distributions and use simulation to view the effects of this variation on one or more outputs. With little effort, you can apply these advanced analytical techniques to your new or existing spreadsheets to help understand and reduce the effects of variation on new or existing designs and processes. (For information on how Crystal Ball is applied in Design for Six Sigma, click here.)

Key features of interest to your application include sensitivity analysis, capability metrics, correlation, and scatter charts. The sensitivity analysis helps you to understand which of the uncertain input variables are most critical and drive the uncertainty in your models. Capability metrics help you understand the quality of your virtual design or process prior to implementation. Correlation lets you link uncertain inputs and account for their positive or negative dependencies. Scatter charts help you to better visualize the correlations between variable inputs and outputs. If historical data does exist, the data fitting feature will compare the data to the distribution algorithms and calculate the best possible fit and parameters for your data.

See our Six Sigma datasheet for details on how Crystal Ball fits into DMAIC structure

To succeed, Six Sigma programs must combine a tight focus with the right people and the best tools—and when it comes to software, the best tools are the ones that streamline your journey to profitability. Crystal Ball is one such tool. Today, Crystal Ball is the tool chosen by more than 85% of the Fortune 500. Companies like Motorola, Honeywell, Seagate, Black & Decker, and Osram Sylvania all rely on Crystal Ball to manage risk, reduce variability, and make more informed business and strategic decisions.

HOW DOES CRYSTAL BALL DIFFER FROM STATISTICAL SOFTWARE TOOLS?

Statistical analysis and data visualization software programs serve a different purpose than Crystal Ball. Statistical tools (e.g., MINITAB, JMP, QI Macros) help to plan experiments, analyze data, and visualize the meaning within the data. Most teams use statistical software to create a mathematical model (often just a transfer function) that represents a system. Crystal Ball works as a complementary tool with these packages and shares only a small number of features, mainly distribution fitting and regression analysis.

Once you have developed a mathematical equation or system using the tools from statistical packages, you then use Crystal Ball to simulate the impact of the variable inputs on the system performance, helping you to rapidly understand and optimize the system.

This virtual testing helps practitioners to refine products and processes and test proposed improvements prior to implementation.

View white papers with more examples on how Crystal Ball compliments statistical packages

LEARN MORE ABOUT CRYSTAL BALL FOR COST ESTIMATION

This page offers links to a growing number of resources, including recorded Web seminars, articles, white papers, case studies, and example models. Additionally, you can view a list of common uses and examples reported directly from customers using Crystal Ball. You can also download a free trial version of Crystal Ball to see how it can help improve your business forecasts and decisions!

TRAINING CLASS: CRYSTAL BALL APPLICATIONS FOR SIX SIGMA

This accelerated one-day course will focus on the use of simulation, risk analysis and stochastic optimization within Six Sigma. In addition to learning to navigate Crystal Ball, you will discover when and where the different Crystal Ball analysis tools fit within the Six Sigma, Design for Six Sigma, and Lean Principles processes. Instructors will use hands-on instruction and real-world case studies to teach students the basics of Monte Carlo simulation, how to gain insights from simulation results, and the best ways to present findings to peers, management, or clients.

RECORDED WEB SEMINARS

Using Probabilistic Modeling Techniques and Monte Carlo Simulation To Improve Supply Chain Inventory Forecasting Capability, While Optimizing Product Fill Rates Historically, several organizations spent a tremendous amount of effort and money forecasting the appropriate amount of inventory during peak seasonal demand periods, sometimes forecasting too much inventory, in excess of customer demand, and sometimes under forecasting below the true customer demand. The latter scenario will unfortunately result in a lost sale opportunity and an Out of Stock condition, thus directly impacting sales and margin dollars. The former case typically results in excessive inventory levels of unsold products, along with an excessive dollar tag attached to the inventory carrying costs and a high risk of product obsolescence. This presentation provides a solution to this problem, by demonstrating a methodology that improves product fill rates and reduces inventory simultaneously. Presented by Hani I. Noshi, Senior Consultant & Manager, MBB, with Accenture Recorded May 15, 2008	View recording
Monte Carlo Simulation as Process Control Aid Statistically designed experiments (DOEs) have become an essential tool in many fields of research because they can lead to rapid learning and optimization in less time and with less cost. Learn how Monte Carlo simulations helped uncover a “hidden” process factor and how a process was improved through sequential DOE. Presented by Dirk Jordan, Ph.D., Six Sigma Black Belt at Motorola Recorded July 18, 2007	View recording Download files
Selecting Distribution Models Distribution models are important tools for all statistical tasks, including estimation, prediction, simulation, and communication. This seminar presents essential tools for selecting and applying distribution models. Presented by Andy Sleeper, President of Successful Statistics LLC Recorded March 21, 2007	View recording Download files
Teaching Simulation for Six Sigma Learn how simulation works with Design for Six Sigma (DFSS) to reduce cycle costs, improve cycle time, and increase customer satisfaction while eliminating rework or scrap and reducing end-of-line testing. Presented by Crystal Campbell, Trainer and Product Consultant for Decisioneering and Motorola University Blackbelt Recorded October 5, 2006	View recording Download files

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WHITE PAPERS & ARTICLES

Crystal Ball as a Complimentary Tool to Statistical Packages

Crystal Ball and Minitab: Complementary Tools for Statistical Automation By Andy Sleeper, Successful Statistics LLC	Download
Implementing Design for Six Sigma (DFSS) with Crystal Ball and JMP By Bob Launsby, Launsby Consulting	Download
Using Crystal Ball and MINITAB Together in Six Sigma Projects By Andy Sleeper, Master Black Belt, Successful Statistics LLC	Download

Applications in DFSS

Applying Crystal Ball to Transaction Process Analysis Paul Benson, Certified Master Black Belt, Xerox	Download
Evidence-Based Decisions, Six Sigma and Million Dollar Breakthroughs By M. Daniel Sloan, Evidence-Based Decisions, Inc.	Download
Executive Six Sigma: How to Use the 6ó Profit Strategy And Tools By Daniel Sloan, Sloan Consulting/Quality Health Systems of America, Inc.	Download
How to Build Uncertainties to Make Hospital Budgets More Realistic By Andrew R. Ganti, GE Healthcare	Download
Improving Software Productivity using Lean Six Sigma Methods and Tools Kevin Lehigh, Manager, BearingPoint (formerly Raytheon)	Download
Misclassification Rates in Measurement Systems Analysis (Gauge R&R) By Dr. Phil Rowe, Six Sigma Group Download the spreadsheet model that accompanies the article	Download
Monte Carlo Simulation as Process Control Aid Dirk Jordan, Six Sigma Black Belt, Motorola	Download
Selecting Project Portfolios Using Monte Carlo Simulation and Optimization By Lawrence Goldman and Karl Luce, Crystal Ball (Reprinted from Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices with permission from Prentice Hall Professional)	Download
Selecting Winning Projects By Thomas Pyzdek, Pyzdek Consulting (Six Sigma and Beyond column for Quality Digest, August 2000)	Download
Six Sigma and Process Simulation By Steve Fleming and E. Lowry Manson, SigMax Solutions (Quality Digest, March 2002)	Download
Six Sigma in Internal Audit: How Bean Counters Become Change Agents By Merel Ritsma (Article on Auditing in November/December 2004 issue of Six Sigma Today)	Download
Software Project Management Meets Six Sigma By David L. Hallowell, Six Sigma Advantage, Inc.	Download
The Use of Monte Carlo Simulation in Production Modeling Jerry Hamilton, Lean Six Sigma Coordinator - Black Belt, Lockheed Martin Dallas MFC Jorge Pica, Research Engineer, Lockheed Martin Dallas MFC Sean Elliot, Graduate Student, Florida A&M University Randy Burch, Sr. Manager Production Contracts PAC-3 Program, Lockheed Martin Dallas MFC	Download

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CASE STUDIES

Cycle Time Reduction Misys Healthcare Systems used simulation to validate that a new process for software implementation will result in a 50% cycle time reduction and accelerated revenue recognition.	Download
Product Maintenance Improvement New York Air Brake Uses Crystal Ball to Keep Their Production - Not Their Products - Rolling	Download
Six Sigma Consulting Crystal Ball Provides Quality Insights to Six Sigma Consultant Andell Associates	Download
Six Sigma DMAIC Case Study #1 - Perishable Inventory By Decisioneering, Inc.	Download

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EXAMPLE MODELS

Catapult Model (for Design of Experiments) From: John J. O'Neill, Jr., Compass Quality Management, jjoneill@compuserve.com (more contact information can be found on John O'Neill's Consultants' Corner Listing). Detail: The Catapult model provides a simple, yet practical example of how Monte Carlo simulation can be used to predict the capability of a design as a function of the "X's." Users can vary Spring Constant, Pull Distance, Mass, and Launch Angle to help understand the benefits of simulation for Design of Experiments. The model can be used as a stand-alone demonstration of Monte Carlo Simulation, or can be used to support a presentation/lecture on tolerance analysis.	Download For: Crystal Ball Level: Simple
Critical Path / Time to Market Analysis Detail: This model analyzes the process, project schedule, or time it would take to get a product to market, with the goal of understanding how uncertainty affects project completion. At the bottom of the Model worksheet, a diagram depicts the flow pattern of the tasks. In terms of Six Sigma, the defect may be defined as the difference between the actual project completion time and the minimum project completion time. The results of this model indicate the likelihood that any particular task will be on the critical path, and the model can then be used to evaluate which pivotal tasks should be addressed to improve the results for the entire project.	Download For: Crystal Ball Level: Moderate
Cycle Time for Insurance Policy Underwriting From: This model is discussed in the textbook Lean Six Sigma Statistics, written by Dr. Alastair Muir, and is available from Dr. Muir's Web site. Detail: The entire process of insuring a risk is called underwriting. The usual practice is to have a large number of insurance agents contacting customers, while the preparation of the policies themselves is undertaken by a centralized underwriting team who evaluated the applications, priced the risk, and wrote the policies. This model follows the entire process and shows how a simple process can be simulated. The results show the range of expected cycle times and identify the key process steps influencing the wide range of cycle times.	Download For: Crystal Ball Level: Moderate
Financial Impact of Process Risk - Medical Claim Payments From: This model is discussed in the textbook Lean Six Sigma Statistics, written by Dr. Alastair Muir, and is available from Dr. Muir's Web site. Detail: We are on the project team directed towards reducing the variation in time for processing medical claim payments. As part of the Measure phase, we must assess the financial risk associated with the existing process. We have surveyed the customers and know that errors causing delays in payment are a common complaint and require a great deal of time on the part of the business to identify and correct. The belief from management is that the error rate is relatively low, on the order of 1-2 percent. Our job is to estimate the span of the problem in financial terms to baseline the process and get buy-in from management. A Monte Carlo simulation of the process is conducted using Crystal Ball. The results show the range of expected cycle times and financial impact on the customer. They also identify the key process steps influencing the wide range of cycle times and financial impact.	Download For: Crystal Ball Level: Moderate
The "Hidden" Factory (Lean Speed and Six Sigma Quality) Detail: This model demonstrates how Lean Speed and Six Sigma Quality are associated. As you will see, reducing “defects” (the target of Six Sigma) alone or reducing “lead time” (the target of Lean Principles) alone will offer some gains. However, you can only achieve lowest cost by simultaneously improving both speed and quality.	Download For: Crystal Ball Level: Moderate
Risk in Setting Quality Goals From: Florin Bocirnea, MS Applied Statistics, Sr. Quality Engineer, florin@rochester.rr.com Application for setting appropriate suppliers and commodity Defective Parts Per Million (DPPM) goals. When reviewing supplier raw data (parts received and defects), engineers can find it difficult to set quality performance goals such as DPPM for individual suppliers or a set of suppliers within a particular commodity (Commodity DPPM goals). Using historical data, the author developed this model to identify individual suppliers and a particular purchasing commodity potential DPPM.	Download For: Crystal Ball Level: Simple
Using Simulation with Design of Experiments Detail: This example model studies how three variables (factors) in an injection molding process affect part length (response) and demonstrates the application of combining simulation with a designed experiment. The model uses linear regression to determine the impact of each of the factors on the response and displays the results of a designed experiment in a table on the Model worksheet. The three factors are defined as assumptions with appropriate probability distributions, and simulation of the model results in a distribution of the response and the likelihood of defective parts. You can adjust the controllable factors to minimize the production of defective parts. NOTE: This model uses macros, so choose to accept macros when you open the file in Excel.	Download For: Crystal Ball Level: Moderate

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COMMON USES & EXAMPLES

The following examples were provided by our customers and represent only some of the potential Six Sigma and DFSS applications for Crystal Ball.

• Analysis of contingency provisions for construction project capital estimates
• Analyze data gathered during Six Sigma project work. Drive efficiencies and process improvements in Supply Chain
• Business process simulations
• Capacity planning
• Cost estimating
• Estimating potential exposures of tolerances and non conformance
• Forecasting demand vs. supply
• Preventive maintenance activity optimization
• Process studies
• Process optimization and financial modeling
• Project planning and forecasting
• Project selection based on constraints
• Queuing process analysis
• Reducing Manufacturing Supply Chain problems such as lead-time reduction and inventory control
• Reliability analysis
• Resource allocation and inventory optimization
• Simulating changes in existing processes
• Simulating manufacturing processes
• Simulating Process Improvement solutions
• Six Sigma Analysis
• Statistical tolerance analysis

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TEXTBOOKS

The Complete Idiot's Guide to Lean Six Sigma

An Introduction to Six Sigma & Process Improvement

Lean Six Sigma Statistics

Profit Signals - How Evidence-Based Decisions Drive Six Sigma Breakthroughs

Six Sigma and Minitab: A complete toolbox guide for all Six Sigma practitioners, 2e

Six Sigma Distribution Modeling

Six Sigma En Action

Six Sigma for Marketing Processes

The Six Sigma Handbook, Revised and Expanded: The Complete Guide for Greenbelts, Blackbelts, and Managers at All Levels

Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices

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