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Shelf Planner by Quick Assortments AB

Reference number
Coordinator QUICK ASSORTMENTS AB
Funding from Vinnova SEK 300 000
Project duration November 2019 - April 2020
Status Completed
Venture Innovative Startups
Call Innovative Startups step 1 autumn 2019

Important results from the project

Shelf Planner is a sophisticated forecasting and order management engine for online retailers, that improves sell through and profit, optimises inventory and reduces waste. Project goal was to develop a functional prototype of the cloud-based forecasting engine, develop machine learning algorithms and allow the engine to generate predictive pattern recognition order proposals based on ideal stock. The prototype has been successfully developed and initial testing started according to plan. First results and outputs are promising and form a solid base for future development.

Expected long term effects

After successful development of the prototype, testing started with partners. Initial output of the forecasting engine shows promising results and fine-tuning and optimisation of algorithms was initiated. Two pilot stores were set up and tracking of benefit generation for the customers has started. Next step is to reach out to other candidates and collect more data from various industries and create a sound business case to both support proof of concept and further development.

Approach and implementation

Pivotal in execution of this project has been an agile and collaborative development. The project has been executed in 6 phases: 1-2.) Planning and Detailed Analyses, 3.) Solution Design, 4.) Solution Development, 5.) Testing / Proof Of Concept, 6.) Hypercare and Stabilisation. Weekly stand-ups with core team and developers were installed to guarantee an agile development of the prototype. Bi-Weekly stand-ups with customers were installed to align on progress and functional requirements. On site alignments were cancelled due to travel bans, instead they were performed remotely

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 3 July 2020

Reference number 2019-04058