Finance Decision Intelligence - how to bring it to life?
An example from the Consumer Retail industry
Here is the story of a large retail clothing company (K&M)
In late winter 202X K&M wanted to identify an optimal pricing for the upcoming summer season
Aim:
Optimise the revenue and subsequently the gross margin of the summer collection
Actions:
Predict the sales of clothing for the summer collection
Optimise the promotional price that enables the highest gross margin across the summer collection
Key variable:
Weather (long-term forecast from external provider)
Approach using Finance decision support technologies
a) Predicting and Scenario Modelling:
We predicted revenue and gross margin, modelling six different weather scenarios, using three types of data:
External data: weather forecast
Internal data: financial
Internal data: non-financial
b) Machine Learning & Mathematical Optimisation:
Step 1: we considered constraints such as inventory levels, production capacity, staff availability, and we calculated optimal revenue and gross margin for the scenarios modelled in a)
Step 2: we used Machine Learning as K&M progressed through the spring months to learn how the external weather data and internal financial and non-financial data have influenced the optimal revenue and gross margin
Step 3: we again considered constraints, this time to calculate the optimal promotion price for clothing for the rest of the season, ensuring the optimal revenue and gross margin will be reached
c) Prescriptive Analytics:
We made use of prescriptive technology to guide K&M towards the best course of action under the scenarios calculated in a) and b). Therefore, Finance helped the organisation plan resources with more precision, and with higher confidence in reaching its aim: “Optimising the revenue and subsequently the gross margin”
Deep dive into the key technologies for Finance decision support
The approach taken by the retailer in the example above is detailed on the next slide, with a visualisation of the three key technologies used:
Predicting and Scenario Modelling,
Machine Learning & Mathematical Optimisation
Prescriptive Analytics
Finance Decision Intelligence – key ingredients for success