By bridging the gap between technical and business expertise was possible to adapt the new forecasting tool.
With access to vast data and advanced tools, the challenge in forecasting lies in setup, adaptation, and deployment. Successfully utilizing forecasting tools demands understanding, customization, and overcoming skepticism to achieve meaningful results.
To improve results and build confidence, the approach focused on three key structures: enhancing system interpretability through actionable frameworks, improving accuracy by tailoring features to business needs, and using simulations to monitor and compare performance over time.
The system became more responsive to business needs, with over 10 percentage points improvement in accuracy and a more stable forecast compared to the initial design.
Presently, every company has access to huge sets of information and high-capacity processing power. Simultaneously, tools are widely spread and capable of serving multiple purposes in complex environments. Specifically for forecasting, there are multiple software programs prepared with several algorithms, pre-processing methods, and advanced UI – nowadays, the challenge of having a forecasting module relies more on reliability, adaptability, and deployment.
As a result, the difficulty falls on setup and adaptation to business requirements. More important than connecting the plug is knowing how to exploit and adapt all the modules provided. Faced with skeptical teams and poor results, successfully deploying a well-established forecasting tool has been a tough challenge.
To improve the results and foster the team’s confidence, the approach followed 3 key structures:
System interpretability – Explore and translate each module and key forecasting algorithm into an application-based framework focused on actionable use cases that help the user to understand and review each outcome.
Accuracy improvement– Leverage and adjust all features to be tailored by business needs (disable, adapt, and develop new modules). Subsequently, adjust the parameters for each set of products.
Simulation & Monitorization – Use advanced simulation to compare business KPIs between the old and the new process and carefully supervise forecasting accuracy over time.
To foster confidence and expertise within teams, using advanced simulation modules and explaining the concepts of each forecasting module were critical levers to success.
Consequently, an ambitious rollout plan was efficiently conducted with full support and comfort for each operational team.
By bridging the gap between technical and business expertise was possible to adapt the new forecasting tool. This allows the system to be more responsive to business specificities and adjusted to the features of each product category.
Besides gains on processes and interpretability, the results showed more than 10p.p. improvement on accuracy and a more stable forecast compared with the initial design.
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