Talos PowerBI Showcase – Retail Solution

Talos applied its experience working with retail companies to create a powerful forecasting tool. It uses several different prediction models to provide retail planning professionals with useful insights.

The combination of powerful Machine Learning methodologies and high-quality Business Intelligence UX Design make this solution highly effective. It enables the business to look forward and predict future trends. It gives the ability to give more reliable sales forecasts to management. Additionally, it allows the reduction of risk in over or understocking, increasing efficiency in supply chain.

Talos developed a tool that takes historical data and allows it to be viewed by category and time. It exposes the trends and any seasonal components in an easy to digest visual solution. This allows planners to check their understanding of the seasonal sales patterns of any product category across various locations.

The solution then constructs multiple prediction models using various Machine Learning models, statistic and neural network algorithms. This provides planners with a range of probable outcomes. Deeper exploration of the data can then expose which is the correct model to use to maximise reliability of the forecast.

Being able to anticipate the potential purchasing actions of consumers, the company now produces a more accurate sales forecast.

Many companies rely on the expertise of its planning professionals to determine forecasts. These are typically calculated in Excel and based on the estimating abilities of individuals.

We realised the need to develop a reliable, easy to use forecasting approach as the Excel forecasts struggled to properly account for variations in product behaviours across categories and seasons. Knowing the impact the forecasts had on inventory and merchandising strategies, we decided to apply more advanced capability using Business Intelligence and Machine Learning tools.

The new forecasting tool developed by Talos delivers value by increasing sales forecast reliability, which results in: