Supply Chain & Operations

Demand Planning

Development of a historical sales base, purged from outliers and events.

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Introduction

New Demand Planning approach

Baseline

Development of a historical sales base, purged from outliers and events

Consolidation of events data, in standardized and replicable format, so that they can be leveraged in the Generation phase

Generation

Definition of forecast horizon (short- vs long-term)

Introduction of automatic methods, reducing the manual effort and increasing robustness

Time series models combined with causal models (machine learning), grasping historical trends alongside event inputs, enabling more accurate sales forecasts

Validation

Implementation of a strategy to validate all generated forecasts, highlighting forecasts with low accuracy or deviating from the target

Analysis of similar products (brand, packaging, …) historical sales and forecast to enhance the validation process

Monitoring

Accuracy metrics definition, transversal to the whole organization

Monitoring dashboard with views customized to the needs of the stakeholders, allowing different types of analyses

Identification of KPI targets to assess improvement opportunities

Analytics Discovery

Boost the forecast
Generation

Time Series Models

Time Series Models

The forecasting model uses times series analysis that are able to capture trends and seasonalities (e.g. various exponential smoothing and autoregressive and moving average models).

Aggregation

Aggregation

Different levels of aggregation are automatically tested and evaluated in the history, namely both product and sales channel hierarchy.

Causal Models

Causal  Models

The model will further increase reliability by applying causal models (e.g., machine learning) to relate trends and seasonality to exogenous variables by including events info and other data.

Testimonials

Customer
Stories

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