@>> PSI Planner FC (Forecasting) for Windows

@>> PSI Planner FC (Forecasting) for Windows

Find recommendations and reviews PSI Planner FC (Forecasting) for Windows. For both price and quality. To help you make the best product.
PSI Planner FC integrates easily with all ERP and Accounting systems and is a collaborative tool which mixes "art and science" to allow you to come up with the most accurate forecasts possible. It fully supports a Sales and Operations Planning (S&OP) process to efficiently balance your organization's Supply and Demand. The system generates a statistical forecast for each item-location combination that has been set up by the user (PSI Planner FC uses regression analysis with a seasonality factor as well as other optional time series forecasting methods and our new "Best Method" option to generate this forecast). The user can then make adjustments to the system generated item-location (i.e. "SKU") forecasts and aggregate them nationally, regionally or by other criteria such as product, class or market zone. At this point, further adjustments can be made to the aggregates and be prorated back down to the item-location level to drive production and deployment decisions. A Forecast Variance Reporting System then "automates" the sometimes tedious but critical activity of measuring planned versus actual forecasts which can be used to improve future forecast accuracy. PSI Planner FC also gives users the option to calculate item-location safety stock quantities based upon desired customer service levels, and performs ABC analysis nationally or by location. For more information, go to "www.psiplanner.com"

Feature About PSI Planner FC (Forecasting) for Windows :

• ABC (Pareto) analysis nationally or by location.
• Ability to aggregate item-location forecasts nationally, make adjustments and prorate back to item-location level.
• Optimal safety stock requirements calculated for desired customer service levels.
• Customized Seasonality Index/Promotional Calendar.
• Item-location statistical forecasting using a variety of time series methods, linear regression and "best method" algorithms with optional seasonality and smoothing factors.

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