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Supply Chain

Demand Sensing

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‎05-09-2016 12:00 AM
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‎05-09-2016 12:00 AM
Anaplan for Demand Sensing helps organizations deliver higher levels of customer service by including current demand signals throughout the supply chain. By applying industry-standard statistics designed to sort through the masses of downstream data, such as point-of-sale information by product and customer combination, you can adjust short-term shipment forecasts and develop accurate replenishment and demand plans.

Data harmonization: View data submission status for each data source. Load new data files for POS sales, on-hand inventory, and actual shipments. View imported data in the same format in which it was submitted. Ensure accuracy by making adjustments to the POS and on-hand inventory metrics. Set the calendar week definition for each data import.

Demand planning with downstream data and other demand drivers (such as promotions). Plan short-term sales by SKU and by banner. See immediate impact of plans on various "what-if" scenarios. Compare demand plan against marketing forecast and resolve variances.

Trade and promotions management: Plan promotions, including spend and associated volume lift. View high-level spend across all product families and customers. Compare promotional plans to prior-year actuals.

Statistical forecasting: Leverage out-of-box statistical forecasting and modify these methods as needed. Test different demand forecast scenarios in response to change. Allow demand planners to change any aspect of the demand planning model to suit changing business needs.

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Information

Business Function
Supply ChainSupply Chain Planning
Industry
All Industries
App Last Updated
May 9th, 2016
Size
1.02 GB
Language
English
Connects to
Data Loading Modules Connect to Major Retailer Data
Terms & Conditions

App stats

Modules
39
Roles
3
Formulas
213
Reports
18
Complexity
Advanced

Why Anaplan for Demand Sensing?

  • Collaborate quickly and easily
  • Flexible statistical forecasting and modeling
  • Driver-based demand forecasting
  • Increase forecast accuracy and reduce bias
  • Daily or weekly downstream data integration
Collect inputs at any level and include quantity and price from web, mobile, and Excel® add-ins from Sales, Marketing, and other business functions, as well as customers. Define consensus-building rules based on input accuracy and time horizons.
Leverage out-of-box statistical forecasting and modify these methods as needed. Test different demand forecast scenarios in response to change. Allow demand planners to change any aspect of the demand planning model to suit changing business needs.
Model and integrate promotions directly into your plans. Collaborate with Marketing on promotions by product and by customer, and see the impact of these new promotions in real time.
Identify accuracy and value-add of manual overrides and automated forecast. Understand reasons for demand forecasting changes via driver-based modeling and cell change history. Leverage POS, CRM opportunities, market data, and open orders as leading indicators of demand with transaction drilldowns in your demand planning process. Form a closed-loop process by integrating plans from other business functions like Finance, Sales, and Marketing that are created in other systems or created in the same Anaplan environment. Push decisions back to detailed planning and execution systems. Collaborate with trade partners like suppliers, distributors, and customers—all in one place.
Moving to daily forecasting by leveraging downstream data such as point-of-sale information reduces latency and improves near-time shipping forecast and, subsequently, the replenishment plans. Using daily or weekly buckets allows you to recognize daily SKU location demand patterns, leading to better understanding of what your big customers are doing. Complex, rules-based statistical formulas allow you to understand how demand is being consumed by Ship-To Location.

 

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Collect inputs at any level and include quantity and price from web, mobile, and Excel® add-ins from Sales, Marketing, and other business functions, as well as customers. Define consensus-building rules based on input accuracy and time horizons.Leverage out-of-box statistical forecasting and modify these methods as needed. Test different demand forecast scenarios in response to change. Allow demand planners to change any aspect of the demand planning model to suit changing business needs.

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