Can your spreadsheets:

• Forecast production given uncertain demand?
• Predict operational bottlenecks before they occur?
• Account for the variability in product design?
• Pinpoint which model inputs are causing the most uncertainty?
• Quantify the risks associated with operations and financial costs?

No? Then you need Crystal Ball®!

Crystal Ball is a Microsoft® Excel®-based suite of analytical tools that includes Monte Carlo simulation, optimization, and forecasting. With little effort, you can apply these advanced analytical techniques to your new or existing engineering and finance spreadsheets to create more accurate predictions and better informed business decisions.

With increased competition, globalization, reduced resources, and staff cutbacks, this is a challenging time for businesses. Crystal Ball is the tool chosen by more than 85% of the Fortune 500. Companies like Caterpillar, Johnson & Johnson, Pitney Bowes, General Electric, Osram Sylvania, and Cargill all rely on Crystal Ball to manage risk and make more informed business and strategic decisions.

Primary Crystal Ball applications include production and inventory forecasting, financial analysis, tolerance analysis, business case risk assessment, and simulation of product design requirements and schedule risks.

Crystal Ball is for anyone who uses spreadsheets and needs to forecast uncertain results. Whether you're working on NPV analysis, operations management, supply chain forecasting, or product life cycle cost analysis, Crystal Ball can help to improve the quality of your spreadsheet-based decisions.

Key features of interest to your industry include sensitivity and tornado analysis, correlation, and historical data fitting. The sensitivity analysis and tornado analysis are two separate methods that help you to understand which of the uncertain inputs drive the uncertainty in your models. Correlation lets you link uncertain inputs and account for their positive or negative dependencies. 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.

LEARN MORE ABOUT CRYSTAL BALL FOR ENGINEERING

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!

RECORDED WEB SEMINARS

Supply Chain Management: The Inventory Question How much inventory is the right amount? Learn how Crystal Ball software can be used to understand supply chain management, inventory choices, and variability. Presented by Jonathan Fleck of Decisioneering, Inc. Recorded January 1, 2007	View recording Download files
Capacity Modeling with Monte Carlo Simulation for Finished Goods Warehouses Learn how a capacity planning model using Monte Carlo simulation and safety factor analysis was created to enable Intel Corporation's warehouse operations to make both tactical and strategic capacity decisions. Presented by Scott J. Edwards, Industrial Engineer, Intel Corp. Recorded August 24, 2006	View recording Download files
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

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WHITE PAPERS & ARTICLES

Basic Techniques for Analyzing and Presentation of Cost Risk Analysis By Randy Lorance & Robert Wendling	Download
Capacity Modeling with Monte Carlo Simulation for Finished Goods Warehouses Scott J. Edwards, Industrial Engineer, Intel Corp. C. Grant Lindsay, Industrial Engineer, Intel Corp.	Download
Cash Flow at Risk – Electronic Chip Production Alan E. Gorlick, Financial Consultant, University of Phoenix	Download
Development of Simulator to Improve Process Efficiency (in Steel Industry) Jong Hag Jeon, Master Black Belt, POSCO	Download
Egypt after the Multi-Fiber Arrangement: Global Apparel and Textile Supply Chains as a Route for Industrial Upgrading By Dan Magder, Financial Analyst, Capital One (Institute for International Economics, Working Paper WP 05-8, August 2005)	Download
Expense and Schedule Uncertainty in Phased Development of New Products By Russ Howell (visit the TTM Solutions, LLC Consultants' Corner listing for more information on the author)	Download
Monte Carlo Simulation as Process Control Aid Dirk Jordan, Six Sigma Black Belt, Motorola	Download
Simulation Modeling to Optimize Stochastic Manufacturing Processes and Resources by a Dynamic Monte Carlo Method By Roberto F. Lu, The Boeing Company, and Guixiu Qiao, National Institute of Standards and Technology	Download

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CASE STUDIES

Analyzing Engineering Projects Analyzing Engineering Projects Using Real Options -- Intelligent Investments in the Face of Uncertainty	Download
Designing Portable Products Motorola Labs Engineers Apply Crystal Ball Pro to Improve the Design of Portable Products	Download
Lifecycle Cost and Benefit Analysis Use of Crystal Ball Pro Provides TRW With Increased Flexibility and Efficiency	Download
Projecting Unit Cost Figures 3M Company's Use of Crystal Ball Improves Unit Cost Estimates	Download
The Timken Company on Real Options in R&D and Manufacturing	Download

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EXAMPLE MODELS

Gasoline Supply Chain Detail: In this example, we determine the optimum amount of gasoline to transport between different levels of a gasoline supply chain. Our objective is to minimize the total cost, which includes transportation costs and inventory holding costs at various points in the supply chain. We also want to minimize stockouts at various retail outlets. The complexity of the problem arises from the fact that we have stochastic production at the refinery level and stochastic demand at the retail outlet level.	Download For: Crystal Ball & OptQuest Level: Beginner-moderate
Inventory System Detail: The two basic inventory decisions that managers face are: How much additional inventory to order or produce, and when to order or produce it Although it is possible to consider these two decisions separately, they are so closely related that a simultaneous solution is usually necessary. The objective here is to minimize total inventory costs while deciding the optimal order quantity and reorder point. Includes optimizations setting file. Download the model Tutorial	Download For: Crystal Ball Level: Simple
Inventory System Detail: The two basic inventory decisions that managers face are: How much additional inventory to order or produce, and when to order or produce it Although it is possible to consider these two decisions separately, they are so closely related that a simultaneous solution is usually necessary. The objective here is to minimize total inventory costs while deciding the optimal order quantity and reorder point. Includes optimizations setting file. Download the model Tutorial	Download For: Crystal Ball Level: Simple
Product Mix Detail: Classic optimization example. The problem is to determine how many pounds of each food product to produce to maximize gross profit without running out of meat ingredients or casing during the manufacturing run. Includes optimizations setting file.	Download For: Crystal Ball & OptQuest Level: Simple
Machine Reliability and Maintenance From: James Evans and David Olson, from their latest textbook: INTRODUCTION TO SIMULATION AND RISK ANALYSIS, 2/E, Copyright 2001. Detail: This model is an example of an operations management application, where the owners of a plant are attempting to determine the most cost-effective way in which to maintain the operation of machines that constantly need bearings replaced. The purpose of the model is to determine whether a proposed maintenance process would save money over the current process given the uncertainty of the bearing reliability. Electronically reproduced by permission of Pearson Education, Inc., Upper Saddle River, New Jersey.	Download For: Crystal Ball Level: Simple
Simulation of Waiting Lines From: Pierre J. Ehrlich, Empresas de São Paulo da Fundação Getulio Vargas (EAESP-FGV), São Paulo, Brazil, ehrlich@fgvsp.br Detail: This download contains two models and a Word description. These two simple waiting line (dynamic) models measure the percentage of customers that will be kept waiting for service given random arrival and service times. Model L1S1 describes a single waiting line and angle server, and model L1S3 describes a single waiting line with three servers. You'll need to change a couple of the options settings to run these models.	Download For: Crystal Ball Level: Simple

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COMMON USES & EXAMPLES

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

• Analysis of data for elimination of defects, optimisation of process, optimisation of resources
• Assessment of risk factors and impact on potential financial returns for various business cases
  
• Capacity / manufacturing volume modeling
• Capital request analysis
• Characterization of assembly equipment
• Creating financial forecasts for New Business Development projects
• Demand, sales and pricing forecasts
• Design and optimize product tolerance and reliability
• Design For Six Sigma applications
• Dimensional variation analysis (stackups)
• Equipment performance analysis
• Estimate market penetration of new product
• Estimate the profit or loss of a multi-piece product in mass production
• Estimating potential exposures of tolerances and non conformance
• Estimating schedule, financial and business risks
• Estimating software cost and scheduling
• Evaluate implications of operational uncertainty for financial budgets and forecasts
• Evaluate quotes, bids, and strategic options
• Forecasting production capability and identifying opportunities for improvement
• Global production allocation analysis
• Lumber production simulation / forest growth modeling
• Manufacturing constraint analysis
• Margin and budget forecasting
• Measure profitability in separate operations in plant
• Modeling expected service levels
• Monthly performance measurement analysis
• New product development (analysis of total cost of ownership)
• NPV analyses
• Predicting future prices of raw materials
• Process control
• Process improvement and optimisation. Lean/6sigma/SPC...etc.
• Process optimization and financial modeling
• Product life cycle cost analysis
• Production and inventory forecasting
• Sales forecasting
• Simulating manufacturing processes
• Simulating the effect of various management plans and scenarios and assess the operating risks
• Supply chain analyses
• Teaching, coaching, modeling on Six Sigma projects
• Technology selection
• Value-at-Risk, FX simulation, Real options analysis
• Warranty cost prediction
• What-if type analysis on new product development projects and business cases
  

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