Introduction

Shelf Planner is an automated forecasting and order management solution that gives superpowers to retailers and merchants.
Through our advanced algorithms in combination with the power of machine learning, we’re able to provide real-time, intelligent forecasting and order proposals for both online and brick ’n mortar retailers.
How does Shelf Planner work?
Shelf Planner uses historical data, planned campaigns and events and consumer spending behavior to generate a probability forecast that is maximising profit and optimizing stock levels.

 

Why is Shelf Planner better than a human decision?
In most cases, store owners do not have all the information they need to make the right decision. Moreover, even when the data is present, it can be overwhelming and requires complex forecasting models.
Shelf Planner eliminates the need to sift through all the data, it generates a clear forecast and order proposal.
Using machine learning and historic sales, the predictions will get more and more accurate over time.
Shelf Planner does not take away the human factor but merely supports in the decision making.

 

Appropriate Stocking ‘Ideal Stock’ instead of Min or Max.
By using predicative forecasting and probability models, Shelf Planner is able to generate an ideal stock for each product, category and store every week.
Every time transactions are made, or when new products are introduced, Shelf Planner will suggest how much to stock.
Competitive Landscape
Where our product stand out from our competitors, is foremost our focus on SME’s.
Our solution design is simple, efficient and easy to adopt.
At the same time, we offer a ‘modular system’, which allows customers to add functionality based on their own requirements.
SME’s typically don’t have armies of planners or buyers and information needs to be provided in a simple and intuitive manner to allow for faster decision making. That’s where our tools come in.
Secondly, we differentiate ourselves by using a combination of classic forecasting and planning logic in combination with machine learning and AI.
Historically, forecasting and inventory management has been a combination of statistical forecasting algorithms, in combination with manual parameterisation. This requires a unique skillset and years of experience.
Although this classic approach has proven effective, in recent years the use of machine learning for predictive analyses and pattern recognition is making ground.
Market leaders like BlueYonder, SAP and Oracle are all investing significantly in the development of AI and machine learning but have little or no focus on SME’s.
Finally, we are agnostic of any ERP or e-com platform, meaning our software works on Shopify, WooCommerce, Magento or any of the other major platforms out there. Seemlessly integrated, just plug and play.