CHAINMENT
CHAINMENT is a neologism
re-defining Supply Chain Management.
Chainment.com is a web platform that allows to manage, analyze, optimize and control in real time companies and factories in the industry 4.0 way.
The platform features algorithms and IoT tools that can support supply chain management geo-referenced big data analysis, real time planning optimization, driven by predictive AI and machine learning: for people, resources, processes, space, time and money.
Let’s start from Chainment
The market looks too much fragmented or too pricey for SME. Other software is very complicated and require a long-term investment concerning training and customization period.
Nowadays every company needs different pieces of software to track, improve and try to optimize the supply chain. A huge amount of information to be shared through several different systems, which is hard, sometimes even impossible since management software is not optimized by any kind of algorithms and different systems of different companies aren’t connected.
We need to “take a look” at what’s happening inside our company and outside our supply chain in real-time, in a social network style.
Our long-term goal is to improve expertise transfer between different generations of workers.
you need to be on CHAINMENT
with all your customers and suppliers!
Our platform aims to integrate different kind of planning software on a side and our new generation algorithms on the other side. We will also collect every other kind of data from all the suppliers and customers and then we will aggregate them in a real-time sharing model system.
We are providing a unique service/platform, managing optimization for every kind of supply chain. We will create value optimizing all the keys activity, tracked and matched in real time by our algorithms.
Our platform easily integrates all the main processes to create the best business intelligence ecosystem accessible through an easy-to-use interface.
We’re working on several hooks and API to collect data from many sources or to export them to several formats. We will also fully document all the API so new developers may extend their platform on ours.
Team
Filippo Sottovia
Transports & Logistics + R&D Lab on Computer Science & Applyed Maths for Industry4.0.
Italian Delegate, Inspiror, Motivator, Animator, Facilitator @ YES, MAMEYE, G20YEA from 2007.
My motto is “share, sync, act!”
Massimiliano Losego
in opensource solutions and their customization (CRM, ERP, E-commerce), project manager in projects
with agile development. Speaker at TedX, Digital Meet and co-founder of two associations involved
in “start-up” study.
Nicola Gastaldon
applied fields of mathematics, majoring in Operations Research and Numerical Analysis; he has
designed and developed optimization algorithms for Vehicle Routing; as PhD student he is now
further improving his proficiency.
Beatrice Liberi
preference for algorithms and operations research. For her dissertation she took part in a project
about scheduling algorithms analysis and implementation. She works as a web and android developer.
Achievements
Timeline
Green Logistics Expo 2018
Chainment.com – artificial intelligence applied to logistics and supply chain 4.0 – will be presented at Green Logistic Expo 2018, Padova
CNA Day – Enterpreneurs awards 2017 to Filippo Sottovia
Presentation of the algorithm to ODS conference in Sorrento (Naples)
Subject: Optimization and Decision Science
AIRO (Operations Research Italian Association) members are attending the conference
CHAINMENT at G20 YEA Berlin
Presentation of “CHAINMENT” project to Young Entrepreneurs delegates from all over the world.
Started Computational Mathematics PhD
Presentation of the algorithm at VeRoLog conference (Nantes)
Subject: Vehicle routing and logistic optimization
Trans-Cel R&D Project for PhD* approved by Padova University
Trans-Cel Transports & Logistics R&D Project for PhD in High apprenticeship PhD in Computational Mathematics approved by Padova University in Computer Science for Supply Chain of Industry 4.0
Scientific Publication: A heuristic for multi-attribute vehicle routing problems in express freight transportation
Authors: Luigi De Giovanni, Nicola Gastaldon, Ivano Lauriola, and Filippo Sottovia
Abstract: We consider a multi-attribute vehicle routing problem arising in a freight transportation company owning a fleet of heterogeneous trucks with different capacities, loading facilities and operational costs.
The company receives short- and medium-haul transportation orders consisting of pick-up and delivery with soft or hard time windows falling in the same day or in two consecutive days. Vehicle routes are planned on a daily basis taking into account constraints and preferences on capacities, maximum duration, number of consecutive driving hours and compulsory drivers rest periods, route termination points, order aggregation.
The objective is to maximize the difference between the revenue from satisfied orders and the operational costs. We propose a two-levels local search heuristic: at the first level, a variable neighborhood stochastic tabu search determines the order-to-vehicle assignment, the second level deals with intra-route optimization.
The algorithm provides the core of a decision support tool used at the planning and operational stages, and computational results validated on the field attest for an estimated 9% profit improvement with respect to the current policy based on human expertise.
Scientific Publication: A heuristic for multi-attribute vehicle routing problems in express freight transportation
Authors: Luigi De Giovanni, Nicola Gastaldon, Ivano Lauriola, and Filippo Sottovia
Abstract: We consider a multi-attribute vehicle routing problem arising in a freight transportation company owning a fleet of heterogeneous trucks with different capacities, loading facilities and operational costs.
The company receives short- and medium-haul transportation orders consisting of pick-up and delivery with soft or hard time windows falling in the same day or in two consecutive days. Vehicle routes are planned on a daily basis taking into account constraints and preferences on capacities, maximum duration, number of consecutive driving hours and compulsory drivers rest periods, route termination points, order aggregation.
The objective is to maximize the difference between the revenue from satisfied orders and the operational costs. We propose a two-levels local search heuristic: at the first level, a variable neighborhood stochastic tabu search determines the order-to-vehicle assignment, the second level deals with intra-route optimization.
The algorithm provides the core of a decision support tool used at the planning and operational stages, and computational results validated on the field attest for an estimated 9% profit improvement with respect to the current policy based on human expertise.
MIMPRENDO 2015 Top 10
CHAINMENT project reach top 10 – out of 80 participants – in MIMPRENDO 2015.
MIMPRENDO is an Italian National contest of graduating students and graduates who set up a team with the goal of realizing an entrepreneurial project supported by an entrepreneur.