Develop Deep Data Insights
Develop Deep Data Insights
Develop Deep Data Insights
Demand Planning (DP)
Most Demand Planning tools rely on a "tournament" based approach to forecasting, which often overlooks critical drivers like promotions, pricing, and marketing plans. This limitation leads to compromised accuracy and heavy reliance on manual overrides.
With Cauself, you can:
• Develop forecasts that incorporate various data inputs, from seasonality and trend to pricing, promotions, and marketing drivers.
• Create forecasts that adapt to variable periods such as holidays and peak selling periods.
• Incorporate market intelligence and buying behavior into your forecasts.
• Establish a direct link between sales and promotional plans and the resulting supply chain planning forecast.
• Utilize both top-down and bottom-up forecasting approaches.
• Harness all available data to generate powerful insights and enhance accuracy.
• Benefit from Cauself's flexible design, enabling ex-factory forecasting through detailed causal modeling.
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Successful Demand Planning comes down to 4 key areas:
Data Management
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History and driver data cleansing - improving data quality and allowing daily, weekly or monthly data sets and forecasts
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Optimized and flexible forecast modeling design - allowing simple trend-seasonal models through to more advanced models that recognise price, events, marketing plans, market factors and more.
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Capture information into a structure reporting environment for ad-hoc or targeted analysis
Flexibility
• Establish different forecasting approaches that are optimised for that channel or account
• Establish ex-factory forecasts for supply chain planning
• Recognise promotional plans in the final forecast used for procurement, and test different sales plan scenarios to stress test supply plans
• Integration into the TPM, Demand Planning and IBP processes
• Considering company and retailer financial metrics will reviewing forecasts
Insights
• Detailed driver analysis of your products - allowing you to develop demand insights like the price-volume relationship or other drivers
• Creation of advanced models recognising baseline and increment components.
• Allow analysis and forecasting of new products or ranging changes
• Considering company and retailer financial metrics will reviewing forecasts
• Allow top down and bottom up forecasting
Cross Functional
• Powerful friendly modelling designed for ease of understanding and maintenance
• Quick to develop and update models
• Streamlined data maintenance
• Single solution adaptable to meet the different modelling and forecasting needs
• Intuitive insight centric demand analysis
Cauself's unique design facilitates class leading implementation timelines, allowing you to migrate from your spreadsheet based approach in no time at all.
Get in touch if you want to learn more.
