About Us

ILS has a founding team who have worked together for about five years in developing and implementing new logistics optimization systems. They will prepare for the launch of the software and will involve, among other things, developing mathematical models and solution algorithms, designing the web-based software, and building a brand that is marketable. Their backgrounds span logistics and supply chain management, mathematical optimization, and computer development:

Mohsen S. Sajadieh, Chief Executive Officer

Mohsen is assistant professor in Industrial Engineering & Management Systems Department, Amirkabir University of Technology, Iran. He earned his PhD in industrial engineering from Sharif University of Technology, Iran in 2009. His research interests center on logistics and supply chain management. Mohsen is an expert, working for 14 years as consultant and project manager of 15 logistics and supply chain projects for well-known companies in Iran.

Academic Background:

Team Members:

Team Member Role Experience
Mohammad Shahryari Chief Technical Officer Over 14 years' experience as a software developer and 5 years' experience as a Technical Manager LinkedIn
Mahdi Maleki Tehrani Chief Marketing Officer Over 12 years experience as a sale and marketing manager of product and service businesses LinkedIn
Farhad Hassanzadeh Logistics Advisor Senior Director of Business Intelligence & Analytics at XPO Logistics - Supply Chain, North Carolina, USA LinkedIn
Amin Dehghanian Mathematical Optimization Advisor Postdoctoral Research Fellow at Georgia Institute of Technology, Milton Stewart School of Industrial and Systems Engineering, Georgia, USA LinkedIn
Mehdi Asghari Marketing Advisor Director, Strategy and Business Transformation at The Hackett Group LinkedIn

Vision

Our vision is to be,1 fully-integrated total distribution planning optimization system in North America.

Goal

Our goal is supply and distribution operations excellence.

Mission Statement

ILS mission is to replace the transportation planning teams of people in logistics organizations by an intelligent machine in order to reduce total costs, improve logistics decision timeliness, improve customer experience, and boost clients’ competitiveness.