iconet ll2 - corridor centric pi network

ICONETLL2 - Corridor centric PI network

Sergio Barbarino, P&GCedric Galichon, P&GKostas Zavitsas, VLTN

Alessandro Vaglini, NGSDavid Cipres, ITANNOVA

Steve Rinsler, Bisham Consulting, ELUPEG

This project has received funding from the European Union’s Horizon 2020 research and innovationprogramme under the Grant Agreement No. 769119

Project Intro

01

ICONET Factsheet

Project start: 01/09/2018

Duration: 30 months

16 partners

EU Horizon 2020 funding through GA no: 769119

Coordinator: Inlecom

Website: www.iconetproject.eu

Consortium

ICONET Vision

Explore and create innovative PI network services that optimise cargo flows against

throughput, cost and environmental performance, based on Governance policies and SLAs, constantly and fully

aware of network operations and status

New business and governance models and

enablers for the PI operations, addressing

the barriers for collaboration and maturity

issues

Generic PI Case Study and Simulation models for

PI network design, addressing decision support with respect to the number and placement of PI nodes

PI Hyperconnectivity Open Reference Architecture

and Platform for enabling the required connectivity

at the digital level

ICONET Objectives

• A cloud-based PI framework and services

• Digital and physical interconnectivity through open and public APIs

PI Living Labs

PI Hub

• Hub types capabilities and the possible topologies

• PI containers travel according to synchromodality principles

PI Corridor

• Transformation (modelling) of TEN-T corridors into IoT-enabled PI corridors

e-Commerce as a Service

• PI impact on e-commerce fulfilment models

• Redesigning last-mile distribution centres to fulfil PI hub roles

• Investigating the role of other forms of mobile or multirole last-mile hubs fall within this scope.

Warehousing as a Service

• Investigates the role of the warehouse as a key PI node

• A dynamic buffer for flow between other PI hubs, to increase throughput of hubs, reduce congestion, etc

AgendaPresentation Sections

Project Introduction and ObjectivesStephen Rinsler, ELUPEG

01

Business Case Problem and Case for ReviewSergio Barbarino and Cedric Galichon P&G

02

Generic ICONET Solutions for PI Environment and Simulation Models David Cipres, ITAINNOVA

03Generic ICONET Solutions for this Business CaseKostas Zavistas VLTN and Alssandro Vaglini NGS

04

Summary of SuccessSergio Barbarino05

Q&A06

Proctor and Gamble LL Business Case

Kick Off MeetingAthens February 2018

Living Lab 2 – PHYSICAL INTERNETCorridor Lab

Procter & Gamble, Inlecom, VLTN, ITAINNOVA, CLMS

USE CASE DESCRIPTION & OBJECTIVES


ICONET WORKSHOP

15th January 2021

ICONET AB – Living Labs & Solutions

Use Cases – Business EnvironmentLiving Lab 2- PI Corridor


UC Business Environment – Corridor 01 - Mechelen - West Thurrock

MECHELEN ZEEBRUGGE PURFLEET WEST THURROCK

TRUCK TRUCKFERRY

Living Lab 2- PI Corridor


UC Business Environment – Corridor 02 - Mechelen - Agnadello

MECHELEN ZEEBRUGGE SEGRATE AGNADELLO

TRUCK TRUCKRAIL



Use CasesLiving Lab 2- PI Corridor

Intermodal Tracking

• End-to-end visibility through the entire corridor between Mechelen and West Thurrock

• Interfacing with PGBS back-end system

• Physical installation of Smart Tracker in the Corridor and activation of the tracking service

Smart-Contracts Monitoring

• IoT Sensor’s monitoring vibrations on a container carrying fragile goods

• Tracking Service transmitting vibration over a pre-defined threshold

• Shipment redirection to a warehouse due to SLA violation

Dynamic Routing

• Road Corridor monitoring detects long waiting times due to a road accident

• Smart Container carrying sensitive goods, transmits rapid temperature decline

• Rerouting to train is executed

Container Prioritization

• Containers with fast moving SKUs at risk of violating an SLA (being either late, or about to become late) to be handled with priority


Business GoalsLiving Lab 2- PI Corridor

• Enhanced Intermodal Transport Visibility utilizing IoT installed in Containers

• Qualification of a new Trade lane

• Utilized visibility data to create value in the Supply Chain

• Delivery reliability (on-time, ETA,...)

• Quality (temperature, bumps,...)

• Leadtime (Value Stream Mapping,...)

• Based on the enhance end-to-end Visibility exploit the PI Concept

• Alternative Routing (Modal Shift)

• Synchromodality (Dynamic Rerouting)

• Smart Contracts (Driving automated reactions)

• Arrival Slots assignments (P&G PSC Poland)


KPIsLiving Lab 2- PI Corridor

• % increase of cross transportation mode routes currently tracked (visible end-to-end)

• % increase ETA accuracy

• Average decrease of reaction time to SLA violations or triggered by Network disruptions

• Anticipated Modal Shift (% of intermodal containers re-routed to train)

• % decrease of CO2 emissions on the Corridor due to modal shift

• % reduction of transportation cost on the Corridor (due to modal shift / synchromodality /

better resource planning)

• % increase of intermodal containers being prioritized

• % decrease in failing to deliver on time (“first-come first-serve” vs prioritization)

• Impact on transportation cost through prioritizing intermodal containers & alternative

routing




RESULTS & LEARNINGS


ICONET WORKSHOP

15th January 2021

9

Living Lab 2- PI CorridorEnd-to-End Visibility Value add

• Value Stream Mapping: Enable better understanding where waiting time is caused due to the synchronization of transport modes

• Eliminate waiting time: allowing better service levels and consequently resulting in modal shift

• Dynamic synchromodal routing: this is the front-end-innovation which results from the increased visibility (Symphony)

• Increased visibility on intermodal lanes is a breakthrough on its own

• At this moment shippers are very reluctant to make the shift to intermodal due to perceived low service levels

• Visibility on intermodal lanes and the actions related to improve this visibility will be very convincing as such

10

Living Lab 2- PI CorridorTesting Results & Involved PI Services

# KPI PI Service & Business Value Add

1 % increase of cross transportation mode routes currently tracked

Tracking Service (Shipping Layer): ad-hoc secure standardized IoT Architecture implemented through new tracker & gateway hardware, seamlessly increasing end-to-end visibility

2 Average decrease of reaction time to SLA violations or triggered by Network disruptions

Web Logistics Service hosting SLA creation and monitoring, consuming Shipping & Network Services events and triggering re-routing (service)

3 Anticipated Modal Shift (% of intermodal containers re-routed to train)

Routing Service identifying modal shift opportunities, utilizing a complete up-to-date Network status

4 % decrease of CO2 emissions on the Corridor due to modal shift

Modal shift to train and PI Node resources timely engaged driven by the Networking & Routing Services and supported by real-time situational awareness (Tracking Service and a uniform up-to-date Network status)

5 % reduction of transportation cost on the Corridor (due to modal shift / synchromodality / better resource planning)

6 % increase of intermodal containers being prioritized

Node & Route optimization (as part of the Routing & Encapsulation Services) prioritizing SLA-critical PI containers, considering Network Status and current container position7 % decrease in failing to deliver on time

Key Lessons Learned

• Intermodal Visibility is expected to improve understanding how lack of synchronization of transport modes affects waiting times

• Eliminating waiting time, will drive improved service levels and promote modal shift• Challenges:

• Currently shippers are very reluctant to make the shift to intermodal due to perceived low service levels

• SC Actors willingness to share information, is limited if there is no clear and direct benefit.

• Ability to materialize real life benefits offered by PI, could convince current SC actors to come on board. Application of simulation models do provide strong indication

• There is difficulty to engage container owners to allow installation of IoT devices on their containers






Q&A TIME


ICONET WORKSHOP

15th January 2021


Living Lab 2 – PHYSICAL INTERNETCorridor Lab Team

Marc Verelst, Cedric Galichon, Sergio Barbarino Procter & GambleMakis Kouloumbis, Inlecom

Kostas Zavitsas, VLTNClaudio Salvadori, NGS

David Cipres, ITAINNOVAKatia Sarsempagieva, CLMS


[email protected]

Sergio Barbarino

PGBS


[email protected]

Makis Kouloumbis

Inlecom Group

Contact Details

PI core services

PI Services

ICONET – Meeting ID

Integrated Representation of PI Transport Process

• Alignment with PI work (& PI layers functionality)• Definition of case specific challenges

• Contract monitoring• Multiple modes & transshipments• Network monitoring• Prioritization and levels of service

• Integrated representation of PI Services communications and modules

Generic PI operation

PI Services


Shipping - Functionality

• Initiates and Oversees freight transport process stages

• Checks upon arrival at hub for contract violations

• Checks upon arrival at hub for network status updates

• Check for real-time disruptions• Updates/ expedites routing

instructions

PI Services


Networking - Functionality

• Consolidated information platform for network discovery• Integrated PI data structure

• Collects and standardizes data on:• Multiple modes schedules• Enables deviations against network:

• Disruptions• Delays and queues• Uncertainty and unreliability

• Considers utilization and fill rate metrics (impacting emissions and cost)

• Packages information w.r.t. PI Order requests

PI ServicesNetworking – Network specification

PI Nodes• TENT-T hubs

• Warehouses• Transshipment

terminals• Customer facilities

PI Links • multiple modes

• distances• cost• travel times

• road traffic/ congestion

PI ServicesNetworking – Consolidation

• Tracks capacity availability of PI Movers

• Calculates the cost of consolidating more cargo into a shipment.

• Updates loading and running cost status

• Routing accounts for operating costs

• Performed in collaborationwith Shipping, Routing

PI ServicesRouting – Functionality

• Optimal path identification• Shortest• Fastest• Most reliable

• Shipping instructions• PI Mover service specific• Transhipments and Intermodal

stops• Limit mode options

• Use of efficient/ Scalable algorithms

PI ServicesInnovation & Impact

• Introduces a highly interconnected, and standardized system that includes multiple optimization and smart DSS processes that offers:• Interoperability & communication between stakeholders• Robust functionality/ stochasticity in transport process• Trackability of processes and functionality• Adaptability to various business cases• Efficiency in decision making

Service Impact

Shipping Robust and standardized shipment processing

Encapsulation Consolidated shipments; LL adaptability

Networking Integrated and standardized network discovery

Routing Efficient and flexible (goal) routing; LL adaptability


WP 3 / LL2 – IoT Solutions

A. Vaglini, New Generation Sensors S.r.l.

Workshop, 15th January 2021

AgendaPresentation Sections

SUMMARY Scope of this presentation is to show how NGS IoT Devices were implemented in LL2 and the results obtained

Who is NGS

Why IoT in LL2

LL2 Operative Scenario

01

02

03

LL2 / IoT results

Topic 7

04

05

06


07

Topic 6

NGS contribution to LL2

IoT systems and solutions

Multi-protocol gateway powered by battery or cable

Scalable solutions

Consulting and customization

- The IoT company

The Internet of Things enables the “virtualisation” of the physical objects, connecting these with the DI

Internet of Things as enabling technology of the Physical Internet

IoT “translates” the “Physical world” in the “Digital world” enabling digital twin

IoT enables the “virtualisation” of the physical objects, connecting them with the Digital Internet equivalent

IoT transforms containers/pallets/goods (physical objects) in Smart PI containers/pallets/goods (connected physical objects)

IoT gives the complete E2E visibility to the Supply Chain

Where? When? How?

Living Lab 2 - PI CorridorIoT and Digital Twins as an Enabler for the PI Concept

InternetCloud IoT

platformICONET PoC/PI

Information Flow

Operative scenario in LL2

Smart Container•Geolocation•Monitoring (T/H, gasses, motion, bump, …)•IoT network inside/outside•Smart seal / Predictive maintenance / Other Sensors

•Battery Powered (long battery life)•Internal memory•Global wireless communication capability

Standardised interoperation

• Every IoT Service Provider can contributewith data

• Every PI service/user can retrieve own data

StandardisedPI IoT services interface

Toward the supply chain complete visibilityShipping Layer: Tracking & Tracing Innovations & Value in PI


Example of Shipment report 1/2

Example of Shipment report 2/2

Results achieved

NGS IoT Devices were installed successfully on real containers

4 IoT devices were made available

Data from the IoT devices was received as expected

No packet loss

When the device had no mobile coverage data was stored in internal memory and sent later

When the server was out of service the devices kept data stored locally and sent it when the server was available

Battery life exceeded expectations (more than 3 months with transmissions every 10min/h24)

Processed data were used to generate metadata, reports and data was shared with stakeholders

Real time sharing of data not achieved due to NDA limitations (container’s owner not being a partner of ICONET)

Limited number of shipments monitored due to low rate of usage of the containers (3 smart containers were never put into use)

Maintenance of the IoT devices to be better developed

So far so good!!! Need of improvement

Contact Details

[email protected]

Alessandro VAGLINI

New Generation Sensors S.r.l.

LL2 - Corridor-centric PI NetworkSimulation Scenarios

• The objective of this simulation model is to evaluate the benefits of using cloud PI Services for transport management in a long-distance corridor.

• Validation of the data model and service structure to exchange information between the services through the Cloud PoC integration

• Evaluation of the impact of some strategies like container prioritization or dynamic routing selection in the PI Network .

ICONET ..

LL2 - Corridor-centric PI NetworkUC2 Smart Contract Monitoring – Operational Level

Example of simulation and services integration for validation

• Simulation requests topology to Networking Service.• Networking Service returns nodes topology to the

Simulation.• Simulation request Routing service for the best path• Simulation executes the actions and runs the scenario.• At a certain point, the container sensors detect vibration

values above the limit (damage) and the Shipping Service notifies the Web-Logistics Service

• The Web-Logistics Service asks the Shipping Service for a new destination for the damaged container.

• Shipping service request to Routing Service a new route to the closest warehouse.

• Routing service sends the new route back to the Shipping service and visualizes it through the Simulation.

ICONET ..

http://drive.google.com/file/d/1XfmM174p9AQjXorJ2c0C6Nua_PTSC3yn/view

LL2 - Corridor-centric PI NetworkUC3 Dynamic Rerouting

ICONET ..

Example of dynamic rerouting due to congestion

• Simulation asks the Networking service for the hubs/nodes.• The Networking service returns the hubs/nodes to the

Simulation.• Simulation asks the Routing service for the best path

between the origin and destination of the order.• Simulation runs (executes transport movements)• At a certain point, the container sensors detect there is

traffic congestion in the Alps.• Web-Logistics asks the Shipping service for a new destination

for the container.• Shipping service asks the Routing service for a new route to

the final destination so that the cargo is transferred to a train to avoid congestion.

• Routing service returns the new route to the Simulation.• When the container reaches the destination by train the

Simulation ends.

http://drive.google.com/file/d/1yDDgL6v2GK7o6w77nTbL9dMd3gcwPX9S/view

LL2 - Corridor-centric PI NetworkUC4a Container Prioritization

• Objective : Demonstrate how prioritizing urgent containers significantly contributes into increasing the number of orders delivered on time.

• Simulation considers 80 orders from Belgium (Mechelen, Rumst) to P&G Italy (Agnadello, Gattatico) with 15% of those identified as of high priority (e.g. fast moving goods).

• At each intermediate node two prioritization options:○ (a) First-come-first-out (FIFO)○ (b) Prioritizing the most urgent

ICONET ..

UC4a Container Prioritization

Scenario Total Transport Cost

SLA Violation Cost(*)

Priority Handling Cost(*)

Lead Time (High Priority Orders)

% On-time delivered orders

S1 (FIFO) 98,923.0 € 200.0 € 0.0 € 37.42 hours 83.3%

S2 (Prioritization) 98,843.0 € 0.0 € 120.0 € 34.64 hours 100.0%

(*) possible applicable costs

LL2 - Corridor-centric PI NetworkUC4a Container Prioritization

• Objective : Demonstrate how prioritizing urgent containers significantly contributes into increasing the number of orders delivered on time.

ICONET ..

LL2 - Corridor-centric PI NetworkUC4b Route Optimization & Modal Shift – Macro Level

ICONET ..

UC4b

Scenario CO2 Emissions Lead Time Transport Cost Multimodal Share

S1 (Direct Transport) 76 t 31.1 hours 75,916.0 € 14.1%

S2 (Modal Shift) 51 t 31.2 hours 72,571.0 € 32.2%

• Objective is to demonstrate how transport costs and emissions decrease when increased network awareness and synchromodality drive increased modal shift.

• In the first scenario (S1) , all orders are transported directly from their origin to their destination without considering modal shift opportunities.

• In the second scenario (2), modal shift opportunities have been evaluated at each node.

Contact Details

[email protected]

Dr. David Cipres

ITAINNOVA

iconet ll2 - corridor centric pi network

Documents