AI powered supply and demand forecasting

Effective inventory planning for retail and supply chain

Stop relying on moving averages and gut-feel when you can utilise data and Artificial Intelligence to help business like yours make better decisions.

Improved forecasting accuracy enables your business to plan better, thereby reducing working capital and out-of-stock situations.

You can achieve your business goals by deploying our predictive modelling solution called PredictDemand

PredictDemand

UPGRADE YOUR BUSINESS

PredictDemand utilises machine learning and predictive modelling to forecast sales more accurately than any manual method. By analysing stock, sales, promotions and other historical stock movement data, your business will gain insight, thereby assisting you to make decisions that will enhance your demand forecasting process, including monetary value to your business.

How PredictDemand can benefit your business

Historical Data
Seasonality Sales
Promotional Sales
Future Dated Promotions
Patterns
Trends

Reduce working capital tied up in under performing items

Increase sales by reducing stock-outs

Reduce logistic costs by having the right item, at the right time , in the right location

Increase on-time deliveries

Maximise Return On Investment (ROI)

Improve sales budgets accuracy

Use Cases

Marketing & Sales

Retail & Supply Chain

Travel & Booking

E-Commerce

Business Analytics Lifecycle

A business analytics lifecycle is a continuous process of learning and improvements. Together with your team, we help to define key business goals and measurements then utilise advanced analytics to help you achieve them.
The data modelling process goes through multiple phases. Each phase refines the data to ensure data is clean and well structured. Given that data is the fuel that powers machine learning and predictive modelling, you could say that data quality is the most important ingredient in any advanced analytics project.

Data must be cleaned, merged and preprocessed. Relevant features will be extracted and/or created. Only then the process of training and evaluating models can begin.

Once the model is evaluated and deployed, applications and business processes are connected to begin extracting value. The value extraction process typically generates new sources of data, which is fed back into the model to improve the efficiency and accuracy on an ongoing basis.
Our development and integration teams will plug the models into your existing systems and business processes to minimise change management.