- About Inplenion
- Consulting
- Solutions
- Inplenion Solutions
- Oracle
- Hyperion Financial Management
- Hyperion Strategic Finance
- Hyperion Planning
- Hyperion Performance Scorecard
- Oracle BI
- Hyperion Essbase
- Oracle Crystal Ball
- Hyperion Financial Close Management
- Hyperion Disclosure management
- Essbase Analytics Link
- Hyperion Data Relationship Manager
- Hyperion Financial Data Management

- CXO Cockpit
- Bissantz

- References
- Login

Crystal Ball Examples available in the software one can download from Oracle. Those examples are not a limitation of the product, rather some guidelines to demonstrate to which extend Crystal Ball can be used in a variety of situations.

Subject |
Description |
Industry or Department |

Project Cost Estimation | any industry with project cost (construction, …) | |

Crystal Ball simulates the uncertainty in each cost line item and helps you to determine the probability of over-running the original single-point estimate. | ||

Project Investment simulation | ||

You are a potential purchaser of the Futura Apartments complex. You have researched the situation and created a model worksheet to help you make a knowledgeable decision. Because there is some uncertainty surrounding the number of units you can rent each month and the monthly expenses, you need Crystal Ball to simulate your potential profit or loss per month. This knowledge will help you to determine whether or not this complex is worth purchasing. | Real Estate | |

Provider Selection | ||

You are faced with an important decision: which monthly cell phone plan to subscribe to. You have selected two plans, one from Cellular World and one from Freedom Cell, but each offers different benefits. This model is helpful as a basic example of defining assumptions, defining forecasts, and using sensitivity analysis. | Procurement | |

Product to Market (6 Sigma) | ||

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. | Marketing | |

Discounted Cashflow | ||

Your pharmaceutical company is very interested in acquiring AllergyGone, a potential new anti-allergy drug with no known side effects. You have been asked to produce a Discounted Cash Flow (DCF) analysis of AllergyGone over a five-year period to determine if this product is worth acquiring. Because of the uncertainty in the product pricing, demands, and costs, your company has decided to use Crystal Ball to simulate the Net Present Value (NPV) and Internal Rate of Return (IRR) prior to negotiations. Crystal Ball can help you determine a bottom-line negotiation price and the model variables that drive the variability in the NPV and IRR forecasts. | Investment target acquisitions | |

Manufacturing (Six Sigma) | ||

This example represents an application of Design for Six Sigma (DFSS) practices by a pump manufacturer. As the pump manufacturer, you want to design a pump for a liquid packaging system that draws processed fluids from a vat into jars at a consistent rate. You want to meet the specified flow rate target and limits while maximizing performance and minimizing costs. Design for Six Sigma (DFSS) and other design improvement methodologies are intended to improve product quality at the start of product development. DFSS techniques can be used with Crystal Ball to simulate various design options for a fluid pump in a food production setting. You will see how simulation works with DFSS to:(1) reduce product development cycle costs, (2) improve product development cycle time, and (3) increase customer satisfaction while eliminating rework or scrap and reducing end-of-line testi | Production Processes | |

Maintenance | ||

"When drilling in certain types of terrain, the performance of a drill bit erodes with time because of wear. Eventually, the bit must be replaced as the costs exceed the value of the hole being drilled. The problem is to determine the optimum replacement policy; that is, the drilling cycle, T hours,between replacements. In this model, you will determine the optimal replacement time and maximize profits while still accounting for the uncertainties inherent in the drilling process." | Operations | |

Supply Chain | ||

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. | Supply chain | |

Minimizing cost | ||

Your company wants to bid on an environmental remediation project for a community with a contaminated aquifer. The community's goal is to reduce the overall risk to below a 1 in 10,000 cancer risk with 95% certainty using one of three treatment methods. The task is complicated by uncertainties in remediation costs, population statistics, and contaminant cancer potency. Your goal is to win the project by determining the optimal treatment method and cleanup efficiency level that result in the lowest overall remediation costs. | Any | |

Lean Speed and Six Sigma Quality | ||

This financial-based model demonstrates how Lean Speed and Six Sigma Quality can be combined to uncover, measure, and reduce rework costs to address the "hidden factory" of defects that exists within an organization. While reducing "defects" (the target of Six Sigma) and reducing "lead time" (the target of Lean Principles) will independently offer some gains in cost savings, only by combining both techniques can you improve both speed and quality and achieve the lowest costs. Simulation is added to this model to incorporate and address the impact of variability on the Cost of Poor Quality and the Cost of Poor Process. | Any | |

Hotel / Project Design | ||

A downtown hotel is considering a major remodeling effort and needs to determine the best combination of rates and room sizes to maximize revenues. Currently the hotel has 450 rooms that can be segmented into three categories, each of which has a different price, daily occupancy rate, and price/demand elasticity. Given uncertainty around the elasticity, the owners want to set the optimal prices for each room type while maximizing their projected revenue. | Hospitality | |

Inventory | ||

The two basic inventory decisions that managers face are: (1) how much additional inventory to order or produce, and (2) 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. Given variable (uncertain) demand over a 52-week period, this model determines an optimal order quantity and reorder point that results in the lowest possible total annual costs. | Any industry with stock issues | |

Loan Application Process (Six Sigma) | ||

This example model is an overview of a loan application process. This transactional process, from the initial customer inquiry through to the loan disbursement, takes an average of 91 hours. Given a performance target of 96 hours of cycle time, the process would seem to be solid. However, the loan specialists have complained that quite often it can take over 130 hours and should be a project for process improvement. As a Six Sigma practitioner, you will apply simulation to better understand what is driving the variation around this business process and quantify the process capacity and Six Sigma quality level. | Bank | |

Location Distribution | ||

Deciding where to locate a new plant, warehouse, or other facility is an important and common management dilemma. On a purely logistical level (assuming minimal internal political considerations), the decision of where to locate a distribution facility must account for uncertainties in transportation costs from the location to the satellite stores, operating costs, and the required capital investment. This example model analyzes the costs associated with five potential distribution locations and enables you to select an optimal location with minimal investment and ongoing costs. | Logistic | |

Sales Forecasting | ||

This model shows the estimated gross profit resulting from newsstand sales of four of the company’s most popular magazines. The uncertainty in this forecasting model lies in the variation of demand (product sales). Because you have historic sales data for all of these magazines, you can use Crystal Ball's distribution fitting feature to create assumptions for each of the magazines. You can also use this model to experiment with the Batch Fit tool. | Any | |

Forecasting | ||

This example is based on a Bakery who is a rapidly growing bakery in Dubai, UAE. It opened in March of 2007, and it has kept careful records (in an Excel workbook) of the sales of the three main products: French bread, Italian bread, and pizza. With these records, it can better predict their sales, control the inventory, market products, and make strategic, long-term decisions. | Any | |

Multi Zone estimation | ||

When estimating reserves for wells or prospects with multiple producing zones, it is important to account for the dependencies that often occur not only between reservoir rock properties on a zone-by-zone basis, but also to quantify dependencies from one zone to another that may be the result of the geologic structural or stratigraphic framework associated with the pay zones. This model shows an approach that can be used to estimate multi-zone reserves accounting for uncertainty in each zone's reservoir parameters, and also incorporating in-zone and across-zone dependencies as a result of plausible observed or known reservoir and geologic information about the prospect. | O&G | |

Oil Field Development | ||

"Oil companies need to assess new fields or prospects where very little hard data exists. Based on seismic data, analysts can estimate the probability distribution of the reserve size. With little actual data available, your discovery team wants to quantify and optimize the Net Present Value (NPV) of this asset. In the process, you will optimize the number of wells to drill, the size of the processing facility, and the plateau rate of the field." | O&G | |

Production | ||

This model demonstrates how Crystal Ball can be used in the design phase of a project to determine the optimal specifications for production. In this case, the product is a piston assembly. The piston displacement needs to be within a certain range to meet customer requirements. The values that impact the piston displacement are defined as assumptions with the appropriate probability distributions. As a result, you can determine the likelihood of producing assemblies outside of the specification | Manufacturing | |

Return on assets | ||

An investor has $100,000 to invest in four assets. The source of uncertainty in this problem is the annual return of each asset. The more conservative assets have relatively stable annual returns, while the least conservative asset has higher volatility. The decision problem is to determine how much to invest in each asset to maximize the total expected annual return while maintaining the risk at an acceptable level and keeping within the minimum and maximum limits for each investment. | Investments | |

Return on assets 2 | ||

This model is a continuation of the Portfolio Allocation example model (please see that model for the basic Crystal Ball and OptQuest descriptions). This model examines two alternative methods for creating optimal portfolios: multiobjective optimization and Arbitrage Pricing Theory. A third method using Efficient Frontier Optimization is examined in a different model, Portfolio Revisted EF. | Investments | |

Return on assets 3 | ||

This model is a continuation of the Portfolio Allocation example model (please see that model for the basic Crystal Ball and OptQuest descriptions). In the example, you not only wish to determine an optimal portfolio allocation strategy, but you wish to view alternative optimal portfolios at different levels of risk. In this model, you will use OptQuest's Efficient Frontier option to achieve this goal. | Investments | |

Marketing and Forecasting | ||

This model examines a product marketing and forecasting analysis of an emerging media product, Interactive TV (ITV). Colorado Cable has created a discounted cash flow (DCF) analysis that examines the success of the product over a six-year period. Monte Carlo simulation and time-series forecasting are used to provide a greater understanding and quantification of the risks inherent in a spreadsheet-based business forecast. | any | |

Product launch | ||

As businesses evaluate the development of new products, they must consider several different types of risks. This model is set up for a product that has three specific types of risks: technical feasibility, manufacturing capability, and marketability. | any | |

Product Mix Optimization | ||

This model is a classic optimization example where inputs are subjected to limits. Ray's Red Hots, Inc. manufactures five types of sausages, and there is variation in the number of pounds of the four main ingredients--veal, pork, beef, and casing--used per unit of product and in the profit generated per unit. When Ray has only limited amounts of each ingredient, his problem is to determine how many pounds of each product to produce in order to maximize gross profit, without running out of meat ingredients or casing during the manufacturing run. | any | |

Project cost Estimation | ||

This simple spreadsheet model estimates the cost of replacing an air filtration system at a major manufacturing plant. You have prepared a traditional contingency analysis, but are concerned that a bid of $82 million will significantly reduce your chances of winning the project. Your task is to find the lowest amount you company can bid, while remaining confident that there is only a 5% chance of exceeding your estimated costs and losing money on the project. | Projects | |

Project selection | ||

The R&D group of a major public utility company has identified eight possible projects. A net present value analysis has computed: (1) the expected revenue for each project if it is successful, (2) the estimated probability of success for each project, and (3) the initial investment required for each project. Using these figures, the finance manager has computed the expected return and the expected profit for each project as shown in the Model worksheet. Unfortunately, the available budget is only $2.0 million, and selecting all projects would require a total initial investment of $2.8 million. Thus, the problem is to determine which projects to select to maximize the total expected profit while staying within the budget limitation. Complicating this decision is the fact that both the expected revenue and success rates are highly uncertain. | investment | |

Project selection (Six Sigma) | ||

As a Six Sigma Champion, you have been presented with eight possible projects for the upcoming year. For each project, your Six Sigma experts have computed: (1) the expected change in revenue for each project, (2) the expected cost savings, or change in expenses, and (3) the initial investment required for each project. Using these figures, the finance manager has computed the gross profit and the Economic Value Added (EVA), or economic profit, for each project. Unfortunately, you have constraints on both the budget and labor, and many of the variables, including the Project Revenues, Cost Savings, Investments, and Staff Requirements, are highly uncertain. Thus, the problem is to determine, based on financial considerations, which Six Sigma projects to select to maximize the Total EVA while staying within the budget and labor limitations. | investment | |

Return on Investment | ||

This is a return on investment (ROI) model using a discounted cash flow (DCF) approach. The model calculates the net present value (NPV), internal rate of return (IRR), regular payback period, and discounted payback period of a project. In addition, terminal value ROI calculations using the Gordon Growth Model are also included. | Investment | |

Sales Projection | ||

This simple example uses simulation to forecast future sales for a three-year period. Two important features in this model are cell referencing for assumption parameters and the trend chart for analyzing the relationships of forecasts over time. | Sales | |

Design for Six Sigma (DFSS) | ||

This example model demonstrates the application of combining simulation with a designed experiment. The model uses the results from a designed experiment to simulate the variability in the response (part length) based on variability in each of three factors using a transfer function developed in Minitab. 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. | R&D | |

Gas Usage | ||

In this example, you work as a forecaster for the Residential Division of Toledo Gas Company. The Public Utilities Commission requires you to predict the gas usage for the coming year to make sure that the company can meet the demand. You have four data series, and usage is dependent on the three independent series. You will apply Predictor and multiple linear regression to create a usage forecast for the next year. | O&G | |

Tolerance Analysis | ||

An engineer at an automobile design center needs to specify components for piston and cylinder assemblies that work well together. To do this, he must perform an optimal stack tolerance analysis, where he calculates the dimensions of the components to be within certain tolerance limits, while still choosing the most cost-efficient methods. Given the variability in the statistical dimensions of seven separate parts, the engineer must choose optimal tolerance levels that meet the assembly gap design criteria. | Manufacturing | |

Risk Analysis | ||

This simple spreadsheet model predicts the cancer risk to the population from a toxic waste site. The pollutant at the waste site and the population close to the site are both sources of uncertainty, which complicates the calculation of a risk assessment value. Overestimating the population risk can mean a waste of resources on unnecessary remediation, while underestimating the risk can pose a very real danger to the local population. | CSR | |

Value Stream Analysis | ||

As businesses continually strive to evaluate and improve their critical processes, they increasingly employ techniques such as value stream mapping to guide their efforts and analysis. This order-to-cash example was developed to rapidly screen or "triage" a large number of process steps to focus on those most seriously in need of immediate improvement. This example combines concepts from Lean (value stream mapping and process efficiency), Six Sigma (quality), and Critical Path scheduling. The process model incorporates the stochastic measurement of cycle time (time), defect rate (quality), value-add time (process efficiency), and fixed/variable cost (cost). | Process Optimization | |

Workforce with Queuing | ||

Businesses that serve customers in queues typically need to balance their staff size with customer expectations. As more servers are available, each customer spends less time waiting in line. However, the cost to employ these additional servers must also be considered. Many businesses have the capacity to hire servers with varying levels of training, experience, and speed in service. The problem is to minimize costs while ensuring that customer requirements are met. This model illustrates how Crystal Ball and OptQuest can be used together to solve such a problem. In this example, we refer to the servers as tellers; however, they could also be checkers, waiters, or toll-booth attendants, among others. | Staff Utilization Optimization |

Inplenion is the sole Reseller of Crystal Ball in the MEA region, please contact us for more information at This email address is being protected from spambots. You need JavaScript enabled to view it.

Read More: Data Sheet

also look at Crystal Ball related to Six Sigma CB Six Sigma Data Sheet