Blogs > Unboxing the Black Box: Why an Explainable Forecast is Key to Building Trust and Driving Better Adoption
Unboxing the Black Box: Why an Explainable Forecast is Key to Building Trust and Driving Better Adoption
Thu Jun 06 2024
Author
Alok Joshi
Demand forecasting is the backbone of retail and manufacturing. It informs businesses about future demand, enabling them to keep up with supply and identify opportunities for further growth. It is an essential component to guide the planning of the entire value chain, from production and inventory management, pricing and assortment planning, to operations optimisation, and customer engagement.
However, embedding forecasts into daily business processes presents a well-known change management challenge, especially with the rise of sophisticated machine learning models and the associated black box problem. This is where jahanForecast Explainer comes into play, offering a solution that not only enhances forecasting accuracy but also builds trust and drives adoption among users.
The Importance of Explainable Forecasts
Forecasting is as much an art as it is a science. While advanced algorithms can predict future demand with remarkable precision, the complexity of these models often leaves users in the dark about how decisions are made. Even the most accurate predictions can be met with scepticism and resistance. Without understanding the “why,” users are more likely to reject and adjust forecasts, which may undermine the true benefits of better availability or waste reduction from using an accurate forecast.
Explainable forecasts bridge this gap by providing clear insights into the drivers behind the predictions. When retailers and manufacturers understand the factors influencing demand forecasts—such as promotions, seasonal trends, weather conditions, and market dynamics—they are more likely to trust the forecasts and integrate them into their strategic planning processes. This transparency not only fosters trust but also empowers decision-makers to take proactive actions with a better understanding of the quantifiable effects of each demand driver.
Introducing jahanForecast Explainer
We’ve recently launched the game-changing jahanForecast, a holistic machine learning demand forecast engine offering superior accuracy and performance. Since its launch, it has helped our clients achieve outstanding business outcomes with robustness and speed to value.
At jahan.ai, we also recognise that transparency and trust in demand forecasting are as important as accuracy. That’s why we developed jahanForecast Explainer. It’s a powerful UI module built on top of our highly accurate and holistic jahanForecast, designed to demystify the forecasting process by providing detailed explanations of the factors driving demand predictions.
How it drives trust and adoption
Here are four learnings to share from our experience, and where jahanForecast Explainer helps to land the forecast:
Gain better understanding of your demand drivers
Imagine you are a demand planner preparing for a promotion on an upcoming scorching hot Halloween weekend (promo + weather + event + weekend shape). You review the forecast and human intuition kicks in, questioning the prediction, and you’re tempted to adjust it. While user adjustments are important, our data shows they often underperform compared to automatic forecasts, especially in complicated scenarios.
This is where jahanForecast Explainer comes into play. It quantifies the effects of all demand drivers contributing to the forecast, helping you to review unbiasedly. Over time, with a better understanding of the effects of demand drivers, users can make more accurate and proactive plans, which can help drive further demand growth. The views are highly configurable to suit the needs of various stakeholders, ensuring that the right levels of information are shown to the right audience.
Deep dive into the forecast performance
Being clear about where the forecast excels and where it doesn’t is vital. Overstating its capabilities erodes trust, while acknowledging its limitations allows for informed decision-making and helps build confidence in the tool’s strengths. Users are more likely to accept known shortcomings rather than unexplained errors, which make it hard to take the right corrective action.
jahanForecast Explainer can help by tracking accuracy through a comprehensive list of metrics like RMSE, MAPE, and RMAD at all different granularities (e.g., products, locations, and time), making it clear where action may be required. The fully customizable UI allows users to keep track of the most relevant metrics and insights, tailored to their specific needs.
Stay ahead with custom forecast alerts
Custom forecast alerts enable businesses to proactively manage demand by flagging significant anomalies in their forecasts. By providing real-time notifications on unexpected changes in forecasted demand—such as significant deviations from historical trends or sudden spikes and drops—these alerts help businesses quickly adjust their strategies and operations. This proactive approach ensures that businesses can mitigate risks and capitalise on opportunities as soon as they arise.
jahanForecast Explainer offers a fully customisable alert function that allows users to define what constitutes an anomaly based on their unique business needs. Whether it’s an unexpected surge in demand for a particular product at a specific location or a notable decrease in forecast accuracy, users can set specific parameters to trigger alerts. This flexibility ensures that users are always informed about critical forecast deviations, enabling them to take timely review and actions (such as manually a trigger contingency plan).
Change Management Matters
Introducing a machine learning demand forecast requires thoughtful change management. Train your team to understand the technology, develop effective communication strategies to highlight its strategic benefits, and address concerns with a clear process and dedicated contact point.
jahanForecast Explainer is an excellent communication tool that helps users understand how the forecast works, how it can be leveraged to make their tasks easier, and how their feedback has been incorporated. By providing detailed explanations and fostering transparency, jahanForecast Explainer builds trust and ensures smoother transition and adoption.
Remember: A sophisticated demand forecast is a powerful tool, but it's only as valuable as its application. By leveraging jahanForecast Explainer and investing in user understanding, you unlock the true potential of your forecast.
Have you implemented explainability for your demand forecast? Share your experiences and insights in the comments below or contact us (info@jahan.ai) for a demo!
At jahan.ai, we build end-to-end AI twins for retail and supply chain businesses. We take the concept of a digital twin, a virtual replica of business processes, further with advanced AI, which not only mirrors these processes but also automatically optimises them. In this way, we support businesses in driving efficiency, going beyond their potential.