The automated model framework and consumption dashboard will accelerate the scale up of this
This initiative has enabled the client to gain better visibility into business processes and data landscape
The artefacts created during the 8-week sprint are modular and can be re-used in initiatives beyond this project
Discord Deep-dive’s a mode of root-causing that renders post-gaming insightful, with potential to reduce gaps in forecasts beyond what’s allowed by just math.
TheMathCompany partnered with the client to create an automated framework for demand forecasting that builds, evaluates and fine-tunes models based on an exhaustive list of model-parameters combinations.
The automated model framework created tries all these model-parameter combinations for each SKU and picks the combination that best captures the nature of that SKU. We included multiple models (6) in our solution and an exhaustive list of parameters (~5400) across all these models.
The Power BI dashboard (for consumption by account managers) is an interactive tool that is easy to interpret and consume and enables the user to visualize forecasts at multiple levels (As granular as SKU level and can be rolled up to a market category level). Our solution was designed in such a way that it could be deployed even in case of an expansion (From 250 SKUs in phase 1 to maybe 10000 SKUs in the future). Macroeconomic Indicators of the US Economy were encoded with automated API Feeds serving data to the model pipelines.”
The following forecasting techniques were applied, Univariate: Autoregressive Integrated Moving Average (ARIMA) Unobserved Components Model (UCM) Holt Winters Moving Average Multivariate: Extreme Gradient Boosting (XGBoost) Unobserved Components Model (UCM)