Demand Forecasting

In the wake of the recent economic uncertainty and market volatility, teams need efficient ways to generate and disseminate accurate, real-time forecasts that reflect rapidly changing circumstances.

Making the crystal ball clearer

A proven track record of accurate forecasts creates trust about the numbers it generates and the trends they may reveal. Yet companies’ access to ever-larger data sets continues to complicate the forecasting process as much as it enlightens it, leading to even more variety in how forecasts are built.

Operational inputs are important leading indicators of performance; often, line leaders know how the company is faring months before the financial reports appear. And by creating ‘adaptive predictive systems’ in a production environment, SparkBeyond Discovery powers solutions from out-of-stock optimization to automated replenishment, and from procurement to logistics asset allocation.

Mindset is also important to successfully drive impact via demand forecasting – instead of building a better model, explore deeper reverse-engineering patterns of supply and demand for strategy and operations. The Discovery platform’s continuous root-cause analysis discovers new drivers as incoming data evolves and is updated.

Clients have found value in SparkBeyond as a platform for demand forecasting to continuously discover ever-changing underlying root causes for demand not evident in internal data alone.

Consider this example: a global electronics giant wanted to optimize supply chain logistics in order to minimize sellouts (no product on the shelf). The goal was to forecast sellouts and discover sales drivers for 8 key regions, 4 product models, across 52 weeks. 

The Discovery platform connected the dots between masked data of historical sales, geo-locations, promotions, product details and holidays to understand demand and sellouts. Over 6 weeks, SparkBeyond supplemented existing models, discovered and stack-ranked 60 million features, all the while connecting real-time SparkBeyond API service for running predictions.

These findings led to increased demand forecasting accuracy by 15%, translating into $80M in effective cost savings.

Many business-unit heads still base their forecasting models on hypotheses rather than evidence. As a counterweight to gut feelings and biases, forecasts using SparkBeyond Discovery are not only precise and accurate, they provide greater insight into the key drivers of demand.

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