Prevai8l is a state-of-the-art demand planning application that can be used to create accurate Weekly Sales Forecasts. Prevail8 was developed to provide an integrated forecasting solution that is more flexible, powerful, responsive, secure and measurable than prior Prevail versions. Detailed forecasts developed within Prevail8 are designed to roll-up to highly accurate operational forecasts (by Product x Warehouse x Day) which are utilized within a Replenishment Planning System. Areté plans to retire the legacy Prevail7 application by the end of 2011, and we strongly encourage all clients to upgrade to Prevail8 as soon as possible to realize the significant benefits outlined below.
Flexible Forecasting Models
More Dimensions of Data
Prevail8 features six dimensions of sales data in order to produce the best possible forecast models and results. The lowest level of data granularity within Prevail8 is the Detail Combo, which is a unique combination of a warehouse, product, channel, account, territory, and distributor. Though the use of all six data dimensions is not required, dimensionality will be adapted to fit specific material, market and delivery information for each client.
Sophisticated Methodologies for Bundling and Slicing Data
Bundling is an intelligent way to group Detail Combos into Model Combos across dataset dimensions using different rules to produce optimal forecast models. Bundling is now more flexible than in prior versions, allowing multiple levels at which to model forecasts that did not exist in legacy versions.
Powerful Forecasting Techniques
Enhanced Ability to Handle Exceptions
Prevail8 features eight configurable Exception event types (e.g. Special Events, Summer Holidays, Winter Holidays, Special Packaging, Hot Price Promotion, etc.) and supports simultaneous occurrences of exceptions during a given sales week. Exception model calculations can now utilize outlet participation percentage in the specific exception to better predict the effects of promotion. These and other Prevail8 features will help facilitate product market launches in a time-phased fashion.
Advanced Statistical Tools
Prevail8 provides a more sophisticated statistical method for detecting sales outlers which should then be excluded from the Model-level data used to create forecasts. The application performs numerical analysis to evaluate multicollinearity of data more efficiently.
Improved management of Seasonality
Seasonality patterns will now be identified within any data dimension rather than just warehouse-product. Prevail8 more readily recognizes non-seasonal dips and spikes in sales and will utilize alternative smoothing parameters for weekly seasonality computations. Users can choose to apply separate smoothing algorithms for weekly forecasting versus monthly planning for flexibility and accuracy.
Prevail7 is a demand planning application designed by Areté, Inc. to support a collaborative forecasting process with forecasting partners. Prevail7 was developed with an emphasis on streamlining collaborative demand planning processes.
- Ability to graphically display historical sales/forecasts and current and future forecasts, as well as budget figures
- Increased forecast accuracy through facilitated consensus
- Reduced inventory write-off
- Ability to compare multiple forecasts at one time on one screen
- Functionality that allows the creation of demand versions that can be queried for reporting purposes
Prevail7 includes automated methodologies to merge demand plans from partners to support collaborative planning at defined levels. As a part of this collaboration, Prevail enables the creation of reports for comparison of actual vs. forecast at each level and enables an environment where CSRs can work towards consensus with their partners in order to achieve a single demand plan.
Prevail6 is a demand planning tool designed and supported by Areté, Inc. to better predict product sales across numerous locations. Areté designed Prevail to combine two separate and equally important approaches to create a good demand plan:
- Analysis of historical sales and statistical prediction of future behavior
- Direct input and analysis of demand by planners
More Accurate Demand Planning
Prevail6 is based on Café™ (Configurable Auto-Weighting Forecasting Engine). Café is Areté's proprietary technology to generate extremely accurate forecasts. Café methodically looks for subtle relationships between different types of market information (pricing, promotions, exceptions, trends, seasonality) and constructs multi-leveled models based on those that consistently predict sales historically.
More Productive Demand Planners
In Prevail 6.0, the planner can decide whether a particular demand combination should be forecasted in Market Groups (i.e. further broken down into Key Accounts or Channels) or whether it is sufficiently stable to be forecasted at the Operational Level.
With Prevail 6.0, the planner can focus their attention on those combos that drive sales variability. The planner does not need to enter or manage market information for op-only combos.
Prevail 6.0 Vistas™ allow the planner to manipulate large collections of data with a single entry. The planner can increase or decrease the forecast of any number of combos by defining a Vista Set. These definitions can then be stored for later use if desired. Vistas can also be used to effortlessly enter market information such as pricing or exceptions across a set of combos.
Planners can Focus on Forecasting, not Data
Prevail 6.0 comes with a rich and robust set of data interfaces. This allows Prevail to be entirely in synch with Enterprise Master Data. Locations, products, sales histories, etc. are all accessed seamlessly through the interfaces, which means there is no need to manage two sets of master data. Users can spend much less time sorting through and managing data, allowing them to concentrate on improving forecasts.
Planners and Managers can Easily Analyze Results
Prevail 6.0 offers scores of metrics for the demand planning process. Reports can be instantly defined using the new multi-dimensional Meta-Query Engine. The user selects how they want to view the data based on the location, product, market, or time hierarchies. For instance, a product may belong to a brand-flavor, which in turn belongs to a category. A user could choose to print sales, forecasting accuracy, and mean error grouped by category, location, and period.