ICONET LL3: e-Commerce Centric Networks - ICONET project
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ICONET
LL3: e-Commerce Centric Networks
Joao Queiroga, SONAE
Kostas Zavitsas, VLTN
David Cipres, ITANNOVA
Steve Rinsler, Bisham Consulting, ELUPEG
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Grant Agreement No. 769119
Agenda
Presentation Sections
Project Introduction and Generic ICONET Solutions
01 Objectives 04 for PI Environment and
Stephen Rinsler, ELUPEG Simulation Models
David Cipres, ITAINNOVA
Business Case Problem and Case Summary of Success
02 for Review 05 Joao Queiroga, SONAE
Joao Queiroga, SONAE
ICONET Solutions for this
03 Business Case 06 Q&A
Kostas Zavitsas VLTN
01
Project Intro
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
Generic PI Case Study
governance models and PI Hyperconnectivity Open
and Simulation models for
enablers for the PI Reference Architecture
PI network design,
operations, addressing and Platform for enabling
addressing decision support
the barriers for the required connectivity
with respect to the number
collaboration and maturity at the digital level
and placement of PI nodes
issues
ICONET Objectives
• A cloud-based PI
framework and
services
• Digital and
physical
interconnectivity
through open
and public APIs
PI Living Labs
e-Commerce as a Warehousing as a
PI Hub PI Corridor
Service Service
• Hub types • Transformation • PI impact on e- • Investigates the
capabilities and (modelling) of TEN- commerce role of the
the possible T corridors into fulfilment models warehouse as a key
topologies IoT-enabled PI • Redesigning last- PI node
• PI containers travel corridors mile distribution • A dynamic buffer
according to centres to fulfil PI for flow between
synchromodality hub roles other PI hubs, to
principles • Investigating the increase
role of other forms throughput of
of mobile or hubs, reduce
multirole last-mile congestion, etc
hubs fall within
this scope.
Steve Rinsler @Bisham Consulting
ICONET João Queiroga @SONAE
LL3 - ECommerce Centric PI Network Kostas Zavitsas @VLTN
David Ciprés @ITAINNOVA
Use Case Business Scenario
SONAE MC is the leading food retailer in Portugal
ParaPharmacy
Food Retail Pet Goods
Home appliancesCurrent Fulfilment models
eCommerce current business Model
Centralized operations
• Order preparation is
centralized in large stores
• Small and Medium Stores
can only do C&C – they
cannot do home delivery nor
transport the order to a 3rd
party location
• Food and Non-Food
deliveries are made
separately
Note: Darkstore: For Lisbon and Porto area, the company has developed a dark-store from which fulfils the orders. The darkstore acts essentially as a supermarket (exclusive to eCommerce)
dedicated to pickers. Its assortment its composed by the 80% of the items generally present at every order. On this model, the remaining 20% (“long-tail”) is fulfilled at the nearest hypermarket
with the same process (support store).Use Case Business Scenario
Main challenges
Through its large scale, yet centralized setup, SONAE still faces challenges with respect to:
Orders’ fulfilment (stockouts)
High costs of picking and delivering,
Local urban transport, and their side-effects (e.g. CO2 emissions, congestion)
Lead times
COVID-19 (new)Development of new fulfilment models
Ability to suggest the most efficient way to fulfill an order
Cost Stock-Out Lead time
DEVELOPMENT OF BUSINESS SCENARIOS AND PI RESEARCH
1. Decentralized Order Preparation and Delivery
2. Multiple Company Scenario including other businesses (e.g. Wells, KASA, ZU)
3. Integrated and standardized store stock information (Networking)
4. Back end service for road network discovery (Networking)Simulation Findings
Practical impact of the learnings
STREAM 1 STREAM 2
IN OUR CURRENT SCENARIO TODAY NEW ECOMMERCE OPERATIONS
I. ORDERS ARE ASSOCIATED TO A SPECIFIC (PREPARATION STORES)
PREPARATION STORE BASED ON LOCATION. ARE OPEN IN AN INDIVIDUAL BASIS
II. 100% OF THE ORDER IS DONE IN THAT WITHOUT A INTEGRATED VIEW OF THE
SAME STORE (HIGH STOCKOUTS) NETWORK
SIMULATION OF THE IMPACT FOR PI OPTIMIZATION FOR IDENTIFICATION OF
FRAMEWORK APPLICATION IN TO E- ADDITIONAL E-COMMERCE PREPARATION
COMMERCE OPERATION STORES
Very Otimistic preliminary findings Preliminary ranking of storesLiving Lab 3 – Core PI Services
Kostas Zavitsas
VLTN
ICONET
Kick Off Meeting
Athens February 2018
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Grant Agreement No. 769119PI Services
Integrated Representation of PI Transport Process
• Based on PI layers functionality
• Integrated representation of PI Services communications and modules
• Definition of case specific challenges
• Stockouts – Store preparing each order
• Consolidate orders – Efficient van delivery rounds
• Expanding PI - New preparation stores
Generic PI
operation
ICONETPI Services
PI features for eCommerce
PI features for eCommerce:
• Multiple vendors
• Multiple stores per vendor
• Unknown fulfilment origin
• Multiple products per store
• Varying picking capacity
dark store
per store in store
• Multiple fulfilment
locations per order
• No fixed routes and PI
Mover services
• Vehicle capacity
ICONETPI Services
Networking - Functionality
• Consolidated information platform for
network discovery
• Integrated PI data structure
• Collects and standardizes data on:
• Multiple transport mode options
• Multiple warehouse storage options
• Real-time data/ updates:
• 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
ICONETPI Services
Networking – UC1 – Fulfilment origin
Stock
• Products (SKU, quantity)
store
• Synthetic order (SKU, quantity) customer
ICONETPI Services
Networking– UC1 – Fulfilment origin
Maximise Profit
• Considers delivery cost
store
customer
• First goal to fulfill as much as possible (not all)
• Second goal to minimize distance
• Utilize optimally nearby store capacity
• Linear
• Always returns a solution
ICONETPI Services
Networking – UC1 – Fulfilment origin
baseline
optimized dynamic
postcode based
fulfilment
order fulfilment
ICONETPI Services
Routing – Path finding/ Delivery round design
• Optimal path identification
• Shortest
• Cheapest
• Fastest
• Least polluting
• Most reliable
• Shipping instructions
• Considers time-windows
• Fleet size
• Van capacity
• Solves VRP to handle capacitated van
fleet routing problem
• Use of efficient/ Scalable algorithms
ICONETPI Services
Routing/ Encapsulation – UC1 – Consolidation
• IoT - Track capacity
availability of PI Movers
• Encapsulation – Fit orders
into e-commerce boxes
• Networking – Identify
fulfilment stores
• Routing – Assigns cargo to PI
Mover
ICONETPI Services
Shipping – UC2 – Network expansion
7 stores 79 stores NOT
preparing preparing
orders orders
ICONETPI Services
Shipping – UC2 – Network expansion
baseline – dynamic fulfilment 3 new stores 5 new stores
ICONETPI Services
Innovation & 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
ICONETContact
Details
VLTN
Kostas Zavitsas
k.zavitsas@vltn.be
ICONET
ICONETWP2 – Simulation Models
Living Labs
LL3 - e-Commerce centric PI Network
Mixed Digital/Physical Simulation Models
for PI Networks
David Cipres, ITAINNOVA
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Grant Agreement No. 769119LL3 - e-Commerce centric PI Network
Simulation Scenario
● The objective of this simulation model is to measure and
evaluate the impact of the PI framework in the fulfillment
process and operational cost, in an urban distribution
environment.
● Explore the benefits of sharing logistics assets (transport,
warehouse) in the eCommerce.
● Evaluation of different operative alternatives: Preparation
(Centralized/In Store) & delivery (In Store/Pick-up Point/Home
Delivery)
● Integration with ICONET Services (Networking, Routing,
Shipping services..)
● Evaluation of impact in transport efficiency and stock-outs.
ICONET ..LL3 - e-Commerce centric PI Network
Simulation Scenario
Dynamic Simulation Models
● Representation of retail supply chain with
Physical Internet components.
Simulation model Components:
● Customers (shown in blue)
● Stores (shown in yellow)
● Central warehouse (shown in green)
Model Scope: 7 stores and 80 customers. (Porto area)
ICONET ..LL3 - e-Commerce centric PI Network
Simulation Scenario
Scenario definition:
● Scenario 1. Local fulfilment
○ Orders are delivered from the store to the
customers depending on their postal
code.
● Scenario 2. PI Network fulfilment
○ The assignment of shops is made
according to criteria of proximity and
stock. (PI Services)
● Although in the second scenario the routes are longer and lead time increases by
30%, the vans are 29% fuller.
● Transport costs hardly increase by 1%, it is compensated by a lower activation cost
(two less vans are needed).
● Finally, since 25% more orders are completed, the total cost per order is 24%
lower.
ICONET ..Contact
Details
ITAINNOVA
Dr. David Cipres
dcipres@itainnova.esYou can also read
