Data for SG&A
We help finance, procurement and shared services organizations to leverage data and AI for planning and automation
In the current economic context, it has become paramount to have the ability to rapidly reforecast business activities, without draining the time and energy of finance and operations teams.
Using AI helps accelerating forecast cycles and automating such processes. Machine learning models can leverage various sources of data to identify key drivers of revenues and costs, therefore facilitating P&L planning.
We have built and implemented such models with several finance teams: they valued our knowledge of finance processes and our ability to quickly prototype a data-driven model to facilitate business planning.
- Revenue forecast
- Automated P&L reforecast
INDIRECT Spend modeling
Predicting spend with a good accuracy helps operational and procurement team to better buy, plan and negotiate.
We combine our knowledge of procurement processes and our data modeling capabilities to develop predictive spend models. Such models are able to quantify the impact of key drivers on spend. We typically use invoices, accounting data, supplier information, category, product, and price data to power-up these initiatives. We also collect external data like index, product reviews, demographic data, etc., to feed our AI model.
We help clients in developing and industrializing data-driven models for procurement:
- Spend forecast by category
We help our clients to simplify, objectivize and automated standard cost models. These projects are usually co-lead by operations and finance teams. We help them use large sets of data and advanced modeling techniques to build an efficient standard cost model. We typically leverage years of invoices, client orders, bill-of-materials, and product data.
Using automated data-driven approach for standard costs allows to develop simple yet accurate costing models.
- Data-driven standard costing
- Profit margin forecast
NEW PRODUCT COST MODELING
In many industries, it can be quite complex to predict the COGS of new products. From raw material price variations, to manufacturing productivity, they are many variables that will influence the actual cost of a product.
For new products, we have developed a methodology to identify key cost drivers from similar products launched in the past. We also use machine learning to model manufacturing ramp-up, based on complexity, material, production site, etc.
- New product cost modeling
Using data and AI allows to precisely model the impact of price fluctuations on sales. These machine learning models go beyond traditional elasticity models done by product category, providing a much more detailed and accurate picture of predicted demand variations.
Combining predictive models with optimization models also allows to find a good balance mark-down allocation among a seasonal range of products.
- Mark-down budget optimization
- Price-elasticity modeling