-
This project has received funding from the European Union’s
Horizon 2020 research and innovation programme under the Grant
Agreement No 690588.
Towards a Shared European Logistics Intelligent Information
Space
D7.20 Living Labs operation learning conclusions
and other SELIS Value propositions (version 1)
Ref. Ares(2018)4477188 - 31/08/2018
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D7.20 Living Labs Operation Learning Conclusions and other SELIS
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Document Summary Information
Grant Agreement No 690558 Acronym SELIS
Full Title Towards a Shared European Logistics Intelligent
Information Space
Start Date 01/09/ 2016 Duration 36 months
Project URL www.selisproject.eu
Deliverable D7.20
Work Package 7
Contractual due date 31.08.2018 Actual submission date
31.08.2018
Nature Report Dissemination Level Public
Lead Beneficiary Inlecom Systems
Responsible Author Makis Kouloumbis
Contributions from DHL, ELU, IBM, ICCS, EUR, ZLC, AK,
Revision history (including peer reviewing & quality
control)
Version Issue Date Stage Changes Contributor(s) Comments
0.1 10-Oct-16 Initial Skeleton Makis Kouloumbis
0.2 03-Mar-16 Assessment template improvement Makis
Kouloumbis
0.3 16-Mar-17 Enhanced template, added Best Practices
Makis Kouloumbis
0.6 08-Mar-18 Added LL8 input Makis Kouloumbis
0.7 11-Mar-18 Enhanced template streamlining sections and table
sections
Makis Kouloumbis
0.8 18-Mar-18 Update Template based on T7.10 participants
input
Steve Rinsler (ELUPEG), N.H. Gebreyesus (RSM)
0.9 25-Mar-18 Adding LL5 AK ICCS Content Makis Kouloumbis
1.0 30-Apr-18 Added LL3 SUMY input Pierre Geron, SUMY
1.1 4-May-18 Added LL5 ICCS input Nikos Provatas
1.2 18-May-18 Added LL4 input Oliver Klein
1.3 21-May-18 Added LL3 SARMET input Toai Truong
1.4 29-May-18 Added LL8 ELGEKA input Britta Balden, Stathis
Revvas
1.5 31-May-18 Added LL3 ZANARDO input Roberta Desidera
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1.6 1-Jun-18 Added LL7 CONEX input Kaye Cheri
1.7 7-Jun-18 Added LL8 SONAE input Tiago Oliveira
1.8 14-Jun-18 First Pass Consolidation and Conclusions Makis
Kouloumbis
1.9 18-Jun-18 Consolidated Value Propositions & additional
LL input
Makis Kouloumbis
2.0 20-Jun-18 Initial Conclusions Consolidation Makis
Kouloumbis
2.1 28-Jun-18 ELUPEG Refinements Steve Rinsler
2.2 16-July-18 Final Consolidation Makis Kouloumbis
2.3 15-Aug-18 Applied proposed Review recommendations
Makis Kouloumbis
Executive Summary Purpose of this deliverable is to consolidate
all SELIS Living Labs learning conclusions and value propositions.
Living Labs mid-term evaluation taken on month 23 of the project,
generated a thorough report with assessment of each LL’s results
and performance, based on structured “LL Evaluation &
Assessment Template” introduced to the LL Owners at the end of the
first year of the project. The collection of the required input to
generate this report, has also involved an iterative improvement
and optimization process, purposed not only to gather evidence
(operational measurements) of the improvements, but also to
identify both refinement paths, as well as lessons learned that
will enhance the positive environmental impact.
Disclaimer
The content of the publication herein is the sole responsibility
of the publishers and it does not necessarily represent the views
expressed by the European Commission or its services.
While the information contained in the documents is believed to
be accurate, the authors(s) or any other participant in the SELIS
consortium make no warranty of any kind with regard to this
material including, but not limited to the implied warranties of
merchantability and fitness for a particular purpose.
Neither the SELIS Consortium nor any of its members, their
officers, employees or agents shall be responsible or liable in
negligence or otherwise howsoever in respect of any inaccuracy or
omission herein.
Without derogating from the generality of the foregoing neither
the SELIS Consortium nor any of its members, their officers,
employees or agents shall be liable for any direct or indirect or
consequential loss or damage caused by or arising from any
information advice or inaccuracy or omission herein.
Copyright message
© SELIS Consortium, 2016-2019. This deliverable contains
original unpublished work except where clearly indicated otherwise.
Acknowledgement of previously published material and of the work of
others has been
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D7.20 Living Labs Operation Learning Conclusions and other SELIS
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made through appropriate citation, quotation or both.
Reproduction is authorized provided the source is acknowledged.
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Table of Contents
1 Introduction
...................................................................................................................................................
9 1.1 Addressing the SELIS Description of Action
...........................................................................................
9 1.2 Deliverable Implementation Plan
..........................................................................................................
9 1.3 Document Structure
............................................................................................................................
10
2 Evaluation and Assessment Template
..........................................................................................................
11 3 Performance Assessment & Lessons Learned
..............................................................................................
13
3.1 Living Lab 1 – DHL
...............................................................................................................................
13 3.2 Living Lab 2 – Port of Rotterdam
.........................................................................................................
23 3.3 Living Lab 3 – Urban Logistics – SUMY
................................................................................................
29 3.4 Living Lab 3 – Urban Logistics – SARMED
............................................................................................
33 3.5 Living Lab 3 – Urban Logistics – ZANARDO
..........................................................................................
37 3.6 Living Lab 4 – ISL
.................................................................................................................................
40 3.7 Living Lab 5 – Adria Kombi
..................................................................................................................
45 3.8 Living Lab 7 – CONEX
...........................................................................................................................
48 3.9 Living Lab 8 – ELGEKA
..........................................................................................................................
54 3.10 Living Lab 8 – SONAE
...........................................................................................................................
60
4 SELIS Value propositions
..............................................................................................................................
64 4.1 Structures and Behaviours
..................................................................................................................
66 4.2 Key Process Findings
...........................................................................................................................
66 4.3 Conclusions and Economic Benefits
....................................................................................................
67 4.4 Next Steps
...........................................................................................................................................
69
Annex I: Living Lab Use Cases & ELGSs Mapping
..................................................................................................
70
List of Tables
Table 1: Deliverable’s adherence to SELIS objectives and Work
Plan
.....................................................................
9
Table 2 LLx - Objectives & Operational Measurements
........................................................................................
11
Table 3 – LLx – Learning Outcomes & Conclusions
...............................................................................................
11
Table 4 - LLx - Proposed Refinements
..................................................................................................................
12
Table 5 – LL1 – Conclusions & Economic Benefit Analysis
....................................................................................
12
Table 6 – LL1 - Objectives & Operational Measurements
..................................................................................
13
Table 8 – LL1 - Proposed Refinements
................................................................................................................
21
Table 9 – LL1 – Conclusions & Economic Benefit Analysis
.................................................................................
21
Table 9 – LL2 - Objectives & Operational Measurements
....................................................................................
24
Table 10 – LL2 – Learning Outcomes &
Conclusions.............................................................................................
25
Table 11 – LL2 - Proposed Refinements
...............................................................................................................
26
Table 12 – LL2 – Conclusions & Economic Benefit Analysis
..................................................................................
28
Table 14 – LL3 SUMY – Objectives & Operational Measurements
.......................................................................
29
Table 15 – LL3 SUMY – Learning Outcomes & Conclusions
..................................................................................
30
Table 16 – LL3 SUMY – Proposed Refinements
....................................................................................................
31
Table 17 – LL3 SUMY – Conclusions & Economic Benefit
Analysis
.......................................................................
31
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Table 18 – LL3 - Objectives & Operational Measurements
.................................................................................
33
Table 19 – LL3 SARMED – Learning Outcomes & Conclusions
...........................................................................
35
Table 20 – LL3 SARMED – Conclusions & Economic Benefit
Analysis
...............................................................
36
Table 21 – LL3 - Objectives & Operational Measurements
.................................................................................
37
Table 22 – LL3– Learning Outcomes & Conclusions
...........................................................................................
38
Table 24 – LL3 – Conclusions & Economic Benefit Analysis
...............................................................................
39
Table 25 – LL4 - Objectives & Operational Measurements
................................................................................
40
Table 26 – LL4– Learning Outcomes & Conclusions
..........................................................................................
42
Table 27 – LL4 - Proposed Refinements
.............................................................................................................
43
Table 28 – LL4 – Conclusions & Economic Benefit Analysis
...............................................................................
44
Table 29 – LL5 - Objectives & Operational Measurements
................................................................................
45
Table 30 – LL5– Learning Outcomes &
Conclusions...........................................................................................
46
Table 31 – LL5 - Proposed Refinements
..............................................................................................................
47
Table 32 – LL5 – Conclusions & Economic Benefit Analysis
...............................................................................
47
Table 33 – LL7 - Objectives & Operational Measurements
................................................................................
48
Table 34 – LL7– Learning Outcomes & Conclusions
...........................................................................................
51
Table 35 – LL7 - Proposed
Refinements..............................................................................................................
52
Table 36 – LL7 – Conclusions & Economic Benefit Analysis
...............................................................................
53
Table 36 – LL8 – SC Visibility Objective & Operational
Measurements
.............................................................
54
Table 37 – LL8 – SC Finance Objective & Operational
Measurements
..............................................................
55
Table 38 – LL8– Learning Outcomes & Conclusions
..........................................................................................
57
Table 39 – LL8 – Conclusions & Economic Benefit Analysis
...............................................................................
58
Table 41 – LL8 - Objectives & Operational Measurements
................................................................................
60
Table 42 – LL8– Learning Outcomes & Conclusions
.............................................................................................
61
Table 43 – LL8 - Proposed Refinements
...............................................................................................................
62
Table 44 – LL8 – Conclusions & Economic Benefit Analysis
..............................................................................
62
Table 44 – LL UCs & EGLSs Mapping
.....................................................................................................................
70
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Glossary of terms and abbreviations used
Abbreviation / Term Description
3PL Third party logistics provider
A2D Answer to the depositor
AIS Automatic Identification System
ATA Actual time arrival
B2B Business to business
BPMN Business Process Model and Notation
CAPA Corrective and preventive actions
CPFR Collaborative planning, forecasting and replenishment
DSO Days Sales Outstanding, time until an Invoice is paid by the
customer
DSS Decision Support Systems
ECB European Central Bank
EDI Electronic Data Interchange
EDI Electronic Data Interchange
EGLS European Green Logistics Strategy
ERP Enterprise Resource Planning
ETA Expected time of arrival
EU European Union
DoA Description of Action
FMCG Fast Moving Consumer Goods
FTE Full Time Equivalent (an employee working full time)
GDP Gross domestic product
GPS Geo positional system
ICT Information and communication technologies
KG Knowledge Graph
KPI key performance indicators
LL Living Lab
LSP Logistics service provider
PoD Prove of Delivery
PoR Port of Rotterdam
https://en.wikipedia.org/wiki/Automatic_identification_system
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P/S (Pub/Sub) Publish/Subscribe Communication Infrastructure
R&D+I Research, development and innovation
RAD Rapid Application Development
SC Supply Chain
SCF Supply Chain Financing
SCN SELIS community node
SCV Supply Chain Visibility
SFA Sales Force Automation
SELIS Towards a Shared European Logistics Intelligent
Information Space
SKU Stock-keeping unit
SME Small and Medium Enterprises
TBC To be confirmed
TMS Transport Management System
UC Use case
UML Unified Modelling Language
VAT Value added tax
WC Working Capital
WMS Warehouse Management Systems
WP Work package
https://es.wikipedia.org/wiki/Stock-keeping_unit
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1 Introduction
As audience of this document, we consider all SELIS internal
stakeholders, who can exploit the findings of the Living Labs,
either as Users, or as Technology providers, as well as external
organizations, interested to evaluate and implement SELIS best
practices and solutions for their own business cases.
This is a mid-term evaluation and assessment, of the work and
outputs generated by each and every Living Lab, and it extensively
addresses specific KPI measurements and predictions, valuable
learnings for the logistics community, required performance and
integration improvements where applicable and an early financial
impact analysis. The document summarizes the respective conclusions
drawn on a Use Case, Living Lab and respective Logistics
Communities level.
1.1 Addressing the SELIS Description of Action
The following table maps Grant Agreement’s Deliverable (D7.20)
and Task (ST7.10.1, STF10.1.2) requirements, with the actual
chapters of this document.
Table 1: Deliverable’s adherence to SELIS objectives and Work
Plan
SELIS GA requirements
Section(s) of present deliverable addressing
SELIS GA Description
D7.20 (v1) Sections 3,4
Living Labs operation learning conclusions and other SELIS Value
propositions (version 1)
Mid-term evaluation of the Living Labs
ST7.10.1 Section 2 LL Evaluation & Assessment Template
ST7.10.1 Section 3
Mid-term evaluation and assessment report of all Living Labs,
including operational measurements, learning outcomes and
conclusions, proposed refinements in deployment and integration and
a preliminary Economic Benefits analysis
ST7.10.2 Section 3,4 Living Labs operation learning conclusions
and SELIS Value Propositions (Section 3: per LL, Section 4:
consolidated and per Logistics Community)
1.2 Deliverable Implementation Plan
Based on the skeleton formalized by SELIS Description of Action
the following timeline has been planned and implemented (to this
date):
1. Define and validate LL Evaluation & Assessment Template
a. Early LL Evaluation & Assessment Template (m6) b. Second LL
Evaluation & Assessment Template (validated & broadcasted)
(m14) c. Final LL Evaluation & Assessment Template (m20)
(utilizing input from T7.10 partners)
2. Initiate formal collection of Living Labs’ input (m21) 3.
Refine Living Labs’ input (m22-23) 4. Mid-term Evaluation: Living
Labs Operational Learnings and Value Propositions (m24)
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5. Follow-up mid-term assessment refinement action plan
(m25-m32) 6. Initiate formal collection of Year 3 Living Lab inputs
(m33) 7. Final evaluation and assessment for all Living Labs,
including detailed economic benefits analysis. Final
Reporting (m36)
As per DoA, beyond the above formal implementation milestones,
the documentation of Living Labs operation learning conclusions and
Value propositions as well as the close monitoring of corrective
actions, has been an iterative improvement and optimization
process, consolidating operational measurements and learning
outcomes, and continuously feeding SELIS solutions, consequently
leading into refinements both in deployment and integration.
1.3 Document Structure
Purpose of this document is to facilitate a mid-term evaluation
and assessment of all SELIS Living Labs and validate the business
value and economic benefits materialized through SELIS Community
Node and the solution built on it. For this reason, the document is
structured as follows:
Chapter 1: to introduce document goals, map document sections
with respective DoA components, outline implementation timeline and
explain document structure,
Chapter 2: to present the template used to collect Living Labs’
input, in a structure, disciplined and unified manner.
Chapter 3: contains the input collected from all Living Labs, as
per the provided template, paying particular attention to KPIs
evolution through the project lifetime as well as best practices
applicable to the respective Logistics Communities,
Chapter 4: summarizes Learning Outcomes, Value Propositions
segmented per industry along with expected economic benefits.
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2 Evaluation and Assessment Template
Living Labs’ Performance Assessment, Lessons Learned & value
propositions collection, involved an iterative and highly
interactive process, purposed not only to collate operational
measurements, learning outcomes and conclusions, but also drive
refinements both in deployment and integration.
Mid-way project implementation, a structure template was
formalized to facilitate a disciplined collection of input on
Operational Measurements, Best Practices and Refinements. The
following four tables were broadcasted and utilized by each Living
Lap to host the requested information. Each table cell on the right
provides a detailed description of the requested input.
Table 2 LLx - Objectives & Operational Measurements
Objective # / UC# 1 / UC1
Description Describe your organization’s objective either from
the business perspective, or the technical one (or both).
SELIS Applied Concepts / Innovations
Outline which SELIS Concepts / Innovations were applied, and how
they supported in the achievement of the envisioned objective
Measurement Method
State the KPIs (exact metrics) used and the methodologies
applied to measure the KPI associated with the success or otherwise
of the LL objectives.
Initial (Baseline) KPI
Specify the exact metrics utilized to measure the success of the
particular objective and their respective values before the
application of SELIS
SELIS KPI Specific’s KPI value, following the application of
SELIS EGLSs and Technologies
Economic impact Analyse the estimated Economic impact (including
investment) and explain what assumptions are made as well as the
overall reasoning. If Simulation has been applied, briefly describe
the Simulation method, results and relationship to the real
world.
Qualitative Business Impact Evaluation
Outline feedback from key players (roles). Further elaborate on
the Qualitative business impact recognized by each of the
stakeholders and role (do consider elements such a “user
satisfaction”, “image”, “reputation”, “social responsibility”,
“end-customer perspective”, etc. Also, log specific “user”
comments/remarks)
Repeat completion of the table above for all the envisioned
objectives of your Living Lab. Do note, in the occasion that: (a)
an objective is applicable to more than one Use Cases, you don’t
have to repeat the specific table, (b) similar objectives can be
“grouped” in a single table, you are encouraged to do so.
Table 3 – LLx – Learning Outcomes & Conclusions
Best Practice
Description
Describe the approach followed to accomplish the envisioned
goals, along with the related indicators. Do clarify, why the
applied approach can be considered as a best practice.
Reference Objective(s)
Identify (the number of) the objective(s) (from the above table)
for which the Best Practice has been identified
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SELIS Solution Outline key SELIS components (strategies,
applications, infrastructure) that have contributed in achieving
the specific objective(s)
Proposed Enhancement
Outline the recommended approach in order to further enhance the
effect of the particular practice, as well as institutionalize
and/or disseminate it. Include (where applicable) Organization
issues.
Investment Outline any investment required to implement the
enhancement
Expected Impact Outline what is the expected impact(s)
The table above should be completed if you consider that a
particular approach/technique applied in the Living Lab, led to
outstanding performance, and can be considered a Best Practice. Do
repeat, the table above where more than one Best Practices have
been identified.
Table 4 - LLx - Proposed Refinements
Refinement Description
Outline which LL envisioned objective has not yet achieved the
envisioned goals referencing to the related indicators and why.
Improvement Outline the improvement framework, describing the
revised goals and improvement actions (e.g. in the areas of
deployment, or integration), and how is this framework expected to
lead to the desired results. If Risk are to be addressed, outline
the Risk Mitigation Strategies. Also outline which portion of the
SELIS offering can be improved or become more effective, to support
reaching the desired KPI values.
Expected Impact Outline what is expected impact (potentially
over the SELIS last year timeline), if the proposed Improvement is
materialized.
Action Plan Identify specific actions, with due dates and owners
(for the last year of the project).
The above table should be filled only if you consider that the
performance of the particular Objective was not satisfactory, and
corrective actions are required. Do repeat, the table above where
more than one Refinements have been identified.
Table 5 – LL1 – Conclusions & Economic Benefit Analysis
Conclusions Summarize in this table, key learning outcomes and
conclusions. Do link where applicable with a preliminary Economic
Benefit Analysis.
Conclusion 1
Conclusion 2
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3 Performance Assessment & Lessons Learned
Scope of this chapter is to detail for each LL Performance and
document the Lessons Learned.
3.1 Living Lab 1 – DHL
DHL Iberia provides warehouse and transport solutions along the
entire supply chain for customers from a wide variety of sectors
(pharma, automotive, fresh food, consumer goods, retail, Portugal
customers and small customers).
The main problem of DHL is the high complexity to consolidate
and integrate diverse historic data coming from different sectors
(through multiple transport systems), making difficult the mapping,
sharing and integration of the complete transport information.
SELIS Community Node data integration adapters and normalization
engine, have been employed to address this situation, bringing
together information on all routes, visualizing it and allowing for
further trucks’ capacity utilization.
As a front end, SELIS will also build up a customizable
dashboard visualizing a number of common Freight Forwarder KPIs
(e.g.: Km, costs, type of vehicle, type of routes, CO2 etc.).
Furthermore, as a significant portion of the transport services are
outsourced, limiting the “visibility of the spot market”, SELIS
will engage it’s publish/subscribe infrastructure to effortlessly
collect hauliers’ current state, allowing DHL or any other Freight
Forwarder to subscribe to the respective services so as to retrieve
the required information.
Difficulty finding synergies: currently, it is extremely time
consuming and effort-intensive to predict how a new situation
(either introduction of a new customer, or loss of customer)
reverberates through the entire company (costs, benefits). Even
though there are limited internal resources skilful enough to do
this per sector; those people are disconnected with the other
sectors, and this significantly limits their capability to detect
and evaluate possible synergies amongst them. Working “in silos”,
is an obstacle expected to be eliminated by SCN, where all required
information will be gathered and structured in one place, allowing
for an integrated, shared decision making process increasing the
cross-sector efficiency.
In the DHL LL we have addressed the following two Use Cases:
• Use Case 1: Data Integration, consolidation and CAPA
Dashboard: It will gather and normalize data from multiple sources
in a single place. SELIS will provide consolidation, data
restructuring and visualization capabilities to show routes
information (maps) and consolidated accurate KPIs.
• Use Case 2: Business Intelligent system. This Use Case will
develop and implement a front-end application and services to
facilitate the decision-making process through increased visibility
and usability of the information provided. SCN will support the
prediction of how a new situation can affect the overall company
cost structure.
Table 6 – LL1 - Objectives & Operational Measurements
Objective 1 / UC1 & UC2
1 / UC1 & UC2 - Information consolidation
Description Goal is to consolidate and integrate diverse
historical data coming from 9 different transport systems. This
diversity leads to multiple internal and external systems feeding
data into the information supply chain. Each source uses its own
terminology to describe the goods or services it provides and has
its own identifiers for each party involved.
Therefore, the primary objective is through a SELIS-supported
integration and normalization, to increase data quality and
completeness, so that each party of the supply chain will have
accurate data to perform its operations.
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SELIS Applied Concepts / Innovations
For UC1, SELIS Communication Infrastructure will facilitate the
integration with the various legacy systems. In addition, SELIS
Normalization engine hosted in the SCN will identify business
objects of the same meaning, and complete them with the missing
information, along with all measurement data. The system will
include a set of normalization rules which the operator will be
able to modify based on the organization needs as well as to setup
new rules. This set of rules will be the base for the normalization
procedure in order to execute and perform the appropriate results.
Further to that, after each normalization procedure completion, the
engine itself will provide the operator with a new set of rules
based on the normalized data. These rules can be applied by the
operator in order to be part of the application and be used for
future normalizations. In this way, the more information is entered
into the normalization engine, the better it will be better
"trained", and the normalization process will be mature over the
time, achieving better and more accurate results.
A data-refining process receives raw data from many diverse
sources. The tool converts the raw data to a common, standard
structure. It then uses correlation and big data techniques to
correct and enhance data quality.
The data-refining process will also accommodate constant change.
When new data values begin to appear, the operator will be able to
determine whether they're valid. In addition, when the suppliers
update or modify their systems, they may start sending different
data values. To ensure data stay clean despite such inevitable
changes, automated systems are required checking transactions as
they occur. Also manual processes are needed to resolve or
accommodate changes.
Technologies applied are:
Content based P/S: Facilitate secure data transactions between
all involved parties
Connectivity Interfaces: Adapters developed to facilitate
communication between DHL and SELIS systems
Analytics and Machine Learning: Machine learning techniques
applied on the data normalization engine
Measurement Method
KPI 1: % of DHL data integrated in SELIS
The measurement method in this case is the “% of DHL data
integrated in SELIS”. The calculation method in this case is
simple, as nowadays we don’t have any data integrated in SELIS.
Calculation method:
KPI 2: % of Normalization Success Ratio (total and by major
client?)
In addition, and in order to prove improvement in data quality
integrated, we will calculate “% of Normalization Success
Ratio”:
KPI Improvements
KPI 1: As explained above, currently we don’t have any data
integrated in SCN. So, the initial KPI (baseline) is 0%. The
objective is to reach a 75% following full integration with
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SELIS Community Node.
KPI 2: Again, at the moment there is no automated normalization
mechanism, therefore current KPI baseline is 0%. With SCN’s
Normalization mechanism it is expected that within 10-15months the
Normalization Success Ratio, will be over 95%.
Economic impact In Spain DHL SC owns almost 60 warehouses and
has more than 3.700 employees. Only in Spain and Portugal we have
on average following movements/flows per year:
More than 500.000 routes
More than 1.500.000 trips legs
More than 150.000.000 km travelled
More than 16.000.000 pallets transported
Considering the integration of information of all systems, DHL
will be able to track more than 500.000 routes per year. This first
step of the project will allow DHL to gather all the information in
a single tool. With higher data quality, global companies are
enabled to make educated decisions, and therefore of optimize the
performance their supply chain.
At this stage, it is very difficult to assess economic impact
that DHL could reach. We are using an outsize quantity of data and
people involved in this process. The impact will depend on the
level of implementation in the DHL network. Transport service in
DSC Iberia is managed differently by 6 areas:
1. Grupag: transportation for food distribution
2. Retail: transportation for retail customers including
temperature controlled
3. Auto & Industrial: transportation for automotive and
industrial customers
4. LSHC: transportation for personal care & Life Science
Health customers including
temperature controlled
5. MC Network: transportation last mile & full truck load
services for mainly
consumer customers
6. Portugal: transportation for customers located in
Portugal.
These different Business Units are working independent to each
other and do not share standard procedures. Therefore, in a first
step we should standardize procedures, and integrate them one by
one. In this sense is very hard to appraise what would be the
impact due the complexity and the multitude of variables to take
into account.
Α concrete Economic Impact assessment will required to carry out
a financial study, nevertheless, the overall benefit is expected to
be several 100.000 €/year.
Qualitative Business Impact Evaluation
Qualitative Business impact by Role:
DHL Supply Chain Iberia (DSC Iberia)
Integration and normalization of data would mean for DHL the
following benefits:
Prompt identification of source and root cause of incorrect
data
Eliminating bottlenecks from data flows and IT processing
Reduce the time wasted chasing data and fixing errors
Dynamically analyse millions of data points
Increase reliability, therefore user satisfaction
Increase company image and reputation with the adoption of new
technologies (DHL is on the forefront of technology in the sector).
Therefore, DHL will have
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more possibilities to keep existing customers and increase new
business opportunities.
Reduction of people and time currently executing this work,
therefore reduction of company structural costs.
Greater accuracy of data and wider dynamic analysis of possible
solutions DSC Iberia Customers:
Improvement in data quality will bring better reliability of the
transport: fewer errors in collection, deliveries, better
utilization of vehicles, improvements in the network, etc. As a
result, customer satisfaction will be increased.
As better quality will bring better reliability of the
transportation, the image and reputation of the customer will
consequently increase as well.
Hauliers:
Data integrated and normalized regarding spot market options
will be available for Hauliers. Compared with the current
situation, it will help hauliers to allocate transport to a given
load in a more reliable and fast way, saving time and money. User
satisfaction will again increase.
Objective 2 / UC1
2 / UC1 & UC2 – Increase Visibility
Description Both Use Cases will produce a visualization
(Dashboard) and Simulation Tool (Business Intelligence Tool) aiming
at increasing visibility.
The implemented web application follows the dashboard concept to
help and support organizations to increase visibility on the
overall transport service. The web application will provide a map
visualization functionality that will support organization’s daily
operations. Apart from the normalized data (as described above),
the dashboard will also provide the Route Visualization
functionality by utilizing the consolidated data. Therefore, all
relevant information about the daily operations of the organization
will be collected by several existing legacy systems, and after
they will be normalized will then be presented in a single
Dashboard.
The Business Intelligent Tool will provide Big Data Analytics to
DHL business operations to support the prediction of how a new
situation can affect the overall company cost structure. The tool
developed will establish a common picture of the present and a view
of the future, which will allow managers the solid basis on which
to make decisions. This has not been possible until now.
SELIS Applied Concepts / Innovations
SELIS output will increase Visibility in the Supply Chain
process in two dimensions:
A Dashboard visualizing accurate, on-route data, consolidated
from multiple sources, on a map.
Business Intelligence: Big Data Analytics will be applied to DHL
business operations to facilitate the prediction of how a new
situation would affect the overall company cost structure. Lack of
visibility on the whole route planning making difficult to take
decisions from the strategic perspective.
The Dashboard will be used by the DHL operator in order to
visualize the routes of DHL and follow the daily transport
operations. The web application will provide a map visualization
functionality that will follow organization’s daily operations,
increase the
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visibility on the overall transport service and support
organization’s strategic decisions. The Dashboard will also allow
for the dynamic creation of customized reports, statistics and
measurable, delivered within the dashboard user interface.
To implement this integration of data among the different
business units, customers, vehicles and TMS systems in a single
dashboard, SELIS will again engaged it’s content based P/S
service.
In the case of the Business Intelligent Tool, it will allow to
dynamically analyse thousands of data points and model hundreds of
potential routes scenarios.
Technologies SELIS applied are similar to the previous
objective.
Measurement Method
KPI 1: Single Trip vs. Roundtrips
We will use “Single Trip vs. Roundtrips” measurement method. The
reason behind is that currently there is no way to identify
roundtrips opportunities between different business units as we
don’t have a cross-sector visibility. With the implementation of
the dashboard and the Business Intelligent Tool, DHL will be able
to identify more opportunities to use existing resources in crossed
way.
Initial (Baseline) KPI
KPI 2: Man-Effort:
We will compare Man-effort reduction for having a holistic &
accurate view in a single place. We will compare situation before
and after the implementation of the solution.
KPIs Improvements
KPI 1: As we don’t have common information for the overall
Spanish transportation data, it is difficult for DHL to calculate
the number of roundtrips at a given moment. Once data will be
integrated, a more accurate calculation could be performed on a
controlled sample for a limited period, including DHL and a few
hauliers and then extrapolate the results.
KPI 2: Again, at the moment, there is no systematic manual
effort to consolidate data in order to create a holistic view. We
do anticipate though that to create and maintain a simplistic
holistic view manually will require, by minimum 1-2 FTEs, which
through SELIS contribution, will be no more than 0.5 FTE.
Economic impact From one side we will gain visibility in
operations through the Visibility Dashboard, and from the strategic
point of view through the Business Intelligent Tool. Therefore, it
will allow DHL to dynamically analyse thousands of data points,
routes, model hundreds of potential scenarios, saving X man-hours,
which is translated in Y euros per year.
Similar to the previous Objective, economic impact is very
difficult to assess due to the size of the company and people
involved. We believe that for a concrete Economic Impact
assessment, we would need to carry out a financial study.
Qualitative Business Impact Evaluation
Qualitative Business impact by Role:
DHL Supply Chain Iberia (DSC Iberia)
Delivering better customer service
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Reducing premium freight costs
Simplifying Distribution Networks
Increase round trip possibilities
Improve distribution network
Decrease execution time as tools will simplify some complex
tasks
Increase job-satisfaction as it will minimize tedious and manual
tasks. DSC Iberia Customers: We expect similar qualitative impact
that in objective 1 Hauliers: We expect similar qualitative impact
that in objective 1
Objective 3 / UC2
3 / UC1 & UC2 – Increase Synergies potential
Description At the moment, it is nearly impossible to predict
the operational as well cost impact of arising a new situation (new
customer, loss of customer). A significant expert manual effort is
required to estimate the respective costs and benefits or losses.
With each sector, currently disconnected, it is practically not
possible to detect and evaluate possible synergies between them.
SCN and the introduced integrated structured information, will
allow for an integrated decision making process increasing the
cross-sector benefits.
With all information of different sectors and historical data
consolidated, SELIS Big Data Analytics will utilize this
information to predict how a new situation will affect to the
entire network. It will facilitate prediction of the new costs as
well as CO2 emissions forecast for a given period of time (duration
of the client contract).
The business case under investigation will use the information
available in SELIS to help the experts to identify how one of the
following scenarios: new business from a new customer, new business
from a current customer or loss of a customer, would affect to the
company as a whole, as this currently performed by an expert with a
huge manual effort working focused in a single sector
information.
The main goal of integrating different sets of data in the same
Dashboard is to increase the visibility but as well synergies in
the whole company. This will allow to different company’s sectors
and external stakeholders to cooperate and collaborate to find new
competitive advantages not possible on another way.
The improvements achieved will allow making a better use of
resources (external or internal), what will be reflected in CO2
emissions reductions
SELIS Applied Concepts / Innovations
Organizations are now embracing technologies to increase
operational efficiency, optimize internal business processes,
improve decision-making and gain a competitive advantage over
business rivals. In the case of SELIS, this is accomplished by
using following technology innovations aiming at increase
synergies:
- SELIS Communication Infrastructure for Data Normalization
(supported by Machine Learning techniques) and Consolidation.
- SCN’s Route-optimized matching consolidated demand to
consolidate available capacity
- Big Data Analytics (currently under investigation)
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Due to DHL size, an automation of some operational information
is a must as collecting and analysing data regarding business
performance consume a significant amount of time and workforce. A
Business Intelligence Tool will help to reduce time and resources
necessary to improve key performance indicators.
System will rapidly analyse and treat information presenting
synergies identified along the transport network and providing DHL
with comprehensive information regarding operational
requirements.
Measurement Method
KPI 1: CO2 Reduction
Synergies identified though the tools developed in SELIS will
help to produce savings in terms optimization of network,
utilization of the vehicles, etc. This will be translated by a
reduction of CO2 emissions.
Calculation Method: Tn.km based on GHG protocol
1. A tonne.kilometre (tkm) is the weight of freight carried by
the transport mode used multiplied by the distance travelled
2. We applied GHG Protocol coefficients to calculate CO2
emission Factor (EF)
3. Therefore, CO2 Emissions:
KPI 2: Capacity Utilization:
Capacity Utilization across all Business Units
Q: Quantity
D: Distance
i: pick up location
j: pick up and/or delivery location
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∑i∑j Qij Dij Xij: Summation of Quantity per Distance of all
trade lanes (trade lane ij)
∑i∑jDij Xij: Summation of distance of total trade lanes (trade
lane ij)
MC: Max Capacity
Initial (Baseline) KPI
KPI 1:
0,9202 tn-CO2 /km (September 2016)
KPI 2:
As we don’t have consolidated information for the overall
Spanish transportation data, we cannot state a % of the overall
capacity utilization of DHL fleet
SELIS KPI KPI 1:
Over 5% C02 reduction
KPI 2:
Approximately 5% Capacity Utilization increase
Economic impact
We will be able to optimize transport tacking advantage of
synergies identified:
Look for synergies between different business units inside DHL
(set up more roundtrips, instead two single trips, therefore less
km, less consumption, less C02 emissions, etc.) aiming to achieve
cross-sector transports.
Merging transports: Regarding transports with same origin and
destination, we will be able to combine different transport using
just one vehicle.
Consolidate different shipments in one vehicle to set up fix
milk run routes
We will be able to identify empty trucks, therefore prompt
identification of vehicles available (Save capacity and
resources)
Gain of time and work force (Optimize processes and
structures)
Gain of new customers
Increase in revenues
Lowering inventory levels
Generate saving opportunities by identifying inefficiencies of
the network distribution
Improve empty return operations
Model potential truck-route scenarios In this case, it is very
difficult to assess the amount of potential saving that DHL could
reach, but we could speak about several hundred thousand euros per
year. If we are cautious/prudent in our estimations, we can
consider that at least we will be able to save 125.000 euros per
year.
Qualitative Business Impact Evaluation
Qualitative Business impact by Role:
DHL Supply Chain Iberia (DSC Iberia)
Delivering better customer service
Reducing network cost
Reduce CO2 emissions, therefore increase social
responsibility
Increase identification of inefficiencies
Increase adaptability and flexibility
Enhanced decision making process
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Improve distribution network
Increase time saving and reduce manual efforts
Increase a better understanding of the end-customer
perspective
Increase job-satisfaction as it will minimize tedious and manual
tasks.
Reduce CO2 & emissions DSC Iberia Customers: We expect
similar qualitative impact as in objective 1 Hauliers: We expect
similar qualitative impact as in objective 1
Table 7 – LL1 - Proposed Refinements
Refinement Description
Objective 1, measure by KPI2 (Normalization Success Ratio), is
still under development, tested up to now, with a limited sample of
data. Therefore, the machine learning techniques have not yet
materialized, neither stable results nor high success ratio. It is
anticipated though that at the next phase, with significant amount
of data fed, the Normalization Engine, will be sufficiently
“trained” to recognize and correct wrong/incomplete entries.
Improvement One of the major benefits of normalized data is the
forced integrity of the data as data normalization process tends to
enhance the overall cleanliness and structure of the data. Data
Normalization engine could improve the accuracy of order deliveries
and the searching and sorting of data to enhance monitoring and
management since DHL has to handle incoming data from various
heterogeneous systems.
The next steps regarding the data normalization engine, are to
establish a feedback loop where all the normalized results will be
validated in the UI and any corrections will be fed to the engine
to improve the normalization process. In such a way, the engine
will learn from these corrections and be more accurate in the
forthcoming normalizations.
The training of the engine will be evaluated through a KPI,
measuring the accuracy of the results based on the training of the
data normalization engine.
Expected Impact Assuming a Normalization success ratio over 97%,
it expected impact the manual effort to correct the remaining 3%
would be less than the 1/10 of the effort currently invested to
correct all “unrecognizable” entries.
Action Plan The action plan, to bring the full value of the
Normalization engine in the next 6 months, include massive feed of
additional data for the first 3 months and close monitoring and
fine-tuning of the normalization algorithm as well as the machine
learning technique.
Table 8 – LL1 – Conclusions & Economic Benefit Analysis
Conclusion 1 Reduction of Time and effort (economics and
workforce)
As a result, we can expect an outstanding reduction of time in
data treatment, bringing in addition access to high quality data.
The improvement of data quality will enhance operations on a daily
basis and reduce errors. It will allow DHL to work in a faster
and
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reliable way, avoiding waste of time and inefficiencies due to
data errors. Further, the system’s capacity to self-learn also
minimizes future errors.
Manual efforts will be reduced significantly making easier and
less tedious some tasks in the supply chain process. The automation
will permit a reduction not only of time, but a reduction in cost
structure as fewer people would be needed to accomplish some of the
activities currently carried out.
Conclusion 2 DHL will gain a competitive advantage over it’s
competitors, as SELIS offering will enhance supply chain awareness
and processes, materialize operational efficiencies and therefore
improve strategic decisions making. The system will support the
establishment of significant synergies aiming at increasing
business performance at operational and strategic level. It is also
expected will reduce unnecessary resources and increase multiple
key performance indicators.
The application services that combine business intelligence and
software development will generate valuable features for use in
predictive analytics, making DHL stronger over business rivals.
In addition, process constitutes collaboration with system
integrators to determine client requirements and strengthen it
relationship. The Business Intelligent system provides enterprises
with comprehensive information regarding customer’s needs and will
help to make better investment decisions.
Not only customer will benefit from better, innovative solutions
and costs, but users will increase their satisfaction as they will
profit from a powerful tool making daily tasks easier and
faster.
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3.2 Living Lab 2 – Port of Rotterdam
Living Lab 2 is established around the logistics communities of
Shippers/Retailers, Logistics Service Providers (LSPs), in-land
transport operators and container terminal operators (both deep sea
and in-land). It utilizes a unique adaptation of WP2 pan-European
Green Logistics Strategies (EGLS 1 and 3) in conjunction with
dynamically enriched data sets and tools, with the goal to create
visibility on reliability of in-land transport and improve
reliability based on analytics, with consequently positive
environmental impact due to an expected modal shift and a better
utilization of barge and rail connections. The action is related to
‘measure and visualize reliability of the chain’. It must be
pointed out that it is primarily about bringing together the right
information to get an insight into (past) reliability and to act
upon that. A next goal is, using a predictive model on expected
reliability, including real-time data and analytics to support
decision making in the booking phase to opt for environmental
friendly transport options.
An instance of the SELIS Community Nodes (SCNs) has been
constructed to serve the business needs of PoR and APMT (for use
case 1) and TEUBooker (for use case 2). UC1 focuses on connecting
systems and unlocking operational data to analyse and predict
reliability. The major concern here is integrating with legacy
systems to collect inland navigation data. For UC2, where the LL is
utilizing AIS as source to measure and predict reliability on
barges only, the key obstacle is privacy law.
Use case 1 (PoR and APMT)
Currently, there is no standard measure for in-land reliability.
The first ambition is to create the foundation and use the SELIS
network in developing the first steps in that direction. Some KPIs
have been developed for measuring the reliability of the different
events and modalities in the chain. To convince all involved supply
chain partners to share (historical) data, a lot of effort has been
focused on determining the various perceptions of reliability and
the value of improving performance. Clearly, cooperation in the
supply chain starts with trust and the willingness and ability to
share data. After establishing this (in multiple joint workshop
settings), a first step into the insight of performance is
developed. To unlock the data takes quite some time and effort from
the companies. This insight leads to increased demand of continuous
performance measurement. This however requires system connections
and data analytics. Reaching the SELIS ambition implies moving
towards more realistic information. The following dynamic data is
already an input:
AIS data on barges. Depending on who is using this input, there
are privacy issues.
Batches of weekly generated data from terminal operators
Gate-in and gate-out messages of terminals could be basis for
further information exchange with the
actors involved.
Container information from cargo owners
Initial instances of the SELIS in-land reliability measurement
tool and its dashboards have been used in three corridors:
1. Import retail chain with Hema as shipper and the use of barge
and truck for in-land transport
2. Import of automotive parts with an automotive company as
shipper the use of barge or rail and truck for in-land
transport
3. Export of food with LambWeston as shipper and the use of
truck and barge for in-land transport
The final objective is to develop a generic inland reliability
tool based upon a standard measurement of reliability that is
applicable at a European scale.
For the Rotterdam case the ambition is to potentially connect
the SELIS in-land reliability tool with the local port community
system PortBase. At the moment, PortBase is connected with most of
the actors in the port, but there is no insight and connected
information from the inland domain (apart from slot request).
The
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challenge is to unlock the information from the inland actors,
who are mainly working with legacy systems which are not setup to
share data easily.
The main target market for the SELIS inland reliability tool
would be organisations that provide port community systems and have
access to this inland data. Another target area could be to measure
performance on the main European corridors by connecting
infrastructure managers’ data as a start and establish insight on
modality performance. This is interesting for ports, freight
forwarders and logistics service providers. The business model for
such an analytic service still needs to be developed.
Use case 2 (TEU booker)
Use case 2 analyses reliability of barge transport using vessel
tracking (AIS) data. The focus taken is to analyse all inland
barges that sail between inland terminals in the Netherlands and
the port of Rotterdam. The goals are to discover pattern, trends
and sailing times, so that they can make general rules of thumb on
the reliability of specific inland corridors and specific (types
of) barges. Analysis on past data will be used to develop a
predictive model on expected reliability for barge transport. This
will support decision making of customers of TEUBooker, to use a
specific barge service for delivering a container with goods to
meet a required time-slot at an in-land location. The SELIS inland
barge visibility App will be connected with and eventually
integrated in the synchromodal control tower platform of TEUBooker,
to enable its users to make optimal choices between modalities. The
expectation is that this will contribute to modal shift goals and
CO2 emissions reductions.
ISL will develop a simulation environment for this living lab
that can be used to inform supply chain actors about the effects of
the inland reliability tool and to calculate expected effects on
several KPIs.
Table 9 – LL2 - Objectives & Operational Measurements
Objective # / UC# 1 / UC1+2
Description Creating visibility on reliability of in-land
transport and improving reliability based on analytics, with
consequently positive environmental impact due to an expected modal
shift and a better utilization of barge and rail connections. In
terms of concrete objectives this means:
1 Development of accepted way of measuring reliability 2
Development of predictive analytics model on reliability of inland
chains 3 Estimate the impact of improved reliability data on
decision making/modal
split
SELIS Applied Concepts / Innovations
Related to the objectives above:
1 Electronic visibility dashboard on reliability, based upon
integrating data from various sources
2 (big) data analytics and machine learning methods to derive
patterns on past data and develop a predictive model
3 Simulation modelling
Measurement
Method
1 Reliability is measured as 1) the level of variance on the
average lead time or 2) as a punctuality: 1) = Actual lead time –
Average lead time 2) = % ATA of trains < or > 30 min
2 Estimating changes in modal split by simulation modelling
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Initial KPI’s - Initially modal split: 53,3 % road, 10,5 % rail,
36,2 % barge - Punctuality of trains: 50% trains arrives < or
> 30 minutes from ETA
SELIS KPI - Achieved modal split through SELIS application: to
be determined - Achieved improvement of lead times: to be
determined - Reduction of CO2 because of modal split: to be
determined - Punctuality of Barges: to be determined - Punctuality
of trains: to be determined
Economic impact The economic impact is multiple:
- Cost reduction in inland transport due to more efficient
supply chains with a reduction of overall lead-times (hard figures
cannot be given)
- Improved capacity use of barges can enhance revenues of barge
operators (hard figures cannot be provided at this phase)
- Cost reduction with shippers due to higher reliability of the
transport chains (hard figures cannot be provided at this
phase)
- Reduction in supply chain buffer stocks
Qualitative Business Impact Evaluation
The work in the corridors has led to the following initial
business impact as brought up by the actors involved:
- Adoption of a standardized way to measure reliability as a
starting point for interaction and gaining trust between chain
partners
- Improvement of the stakeholders’ business by a more reliable
supply chain
Table 10 – LL2 – Learning Outcomes & Conclusions
Best Practice Description
Creation and adoption of a widely accepted standard for
measuring and displaying reliability of container hinterland chains
(as the first step for improving reliability of such chains)
Steps taken in the process:
initial research of potential meaningful and valuable ways of
measuring reliability: intensive interaction between researchers
and key partners from industry
developing simple prototype of dashboard with data from key
partners
discussing outcomes and improvement in the key SELIS LL2
team
presenting prototype to users and stakeholders in initially one
corridor, followed by two more
discussion on value, shortcomings, potential improvements and
impacts
making a strategy for widening the use in more corridors
Reference Objective(s)
This first best practice has led to:
acceptance of value of such reliability tool by users (objective
1)
start of improvement of operational practice in the chain
(collaborative planning and data-exchange (objective 3)
SELIS Solution In the first place, use case 1 has been developed
by means of a simple dashboard, based on past data, Integrated from
multiple sources. UC2 dashboard is still under development,
currently integrating the required level of detail and quality of
AIS data.
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Data integration will be based on SCN Pub/Sub
infrastructure.
Proposed Enhancement
Use case 1 delivers a tool for measurement of reliability and
intentionally predicting of reliability. To become a standard and
being used widely in the inland transport industry for better
decision making and better operational transport planning and
execution, the tool must be made generically applicable
(integration with user's and data providers' systems, analytical
model for measurement and prediction and data-specifications). At
the same time a lot of effort must be spent to get a wider
acceptance and usage beyond the Rotterdam environment.
Use case 2 delivers a tool for measurement of reliability and
intentionally predicting of reliability based on AIS data.
Investment Investment:
Man-effort required to ensure integration with internal systems
of users and data providers (including port community systems)
Dissemination effort required to get industry wide acceptance
for the use of the standard for measuring reliability for improved
decision making and inland transport operations.
Expected Impact Quantitative real time insight in reliability
shared by all chain actors leads to:
increased use of intermodal modes and enabling of a synchromodal
practice
more efficient chains: shortening of lead-times, higher
utilization rates
Lower GHG emissions
Table 11 – LL2 - Proposed Refinements
Refinement Description
1. Enhance alignment between technology providers and industry
partners (linked to Objective 1)
2. Implementation of Predictive Analytics (linked to Objective
2) 3. Implementation of the Simulation Model (linked with Objective
3)
Improvement Accelerate alignment, between technology partner and
industry partner on logistics business and case knowledge, and
software goals
Proceed with the implementation of predictive model and its
integration with SELIS Dashboard, planned for the second half of
2018 and first half of 2019.
Further develop the simulation model and apply it in the
Rotterdam case.
Enhance efforts for getting the standard for measuring
reliability including the analytical model and the dashboard
accepted.
Upgrade the reliability visibility and prediction prototype, to
be capable of Integration with the wider hinterland transport
industry.
For UC2 the following two actions should be executed to enhance
the overall impact:
Assess use of AIS data to develop a predictive model
Test SELIS Dashboard and integrate in TEUBooker Platform
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Expected Impact Measurable real time insight in reliability
shared by all chain actors leads to:
increased use of intermodal modes and enabling of a synchromodal
practice
more efficient chains: shortening of overall lead-times, higher
utilization rates of barges
Action Plan Action plan use case 1
activities Start finish
version 2 (Extended Reliability Analytics App) M22 M27
Development by CLMS M22 M26
Testing and demonstration / user involvement in 3 corridors M25
M27
version 3 (Live integration with PCS) M28 M33
Development by CLMS M28 M31
Testing and demonstration / user involvement in 3 corridors M30
M31
Reporting M32 M32
D7.7 (final version) M30 M34
Action plan use case 2
activities Start finish
version 1 (Barge Visibility App) M21 M24
Development by CLMS M21 M22
Testing and demonstration / user involvement TEUBooker customers
M24 M24
Reporting M21 M22
D7.6 (version 1) M24 M24
version 2 (Extended Barge Visibility App with Predictive
modelling) M25 M28
Development by CLMS M25 M26
Testing and demonstration / user involvement TEUBooker customers
M26 M27
version 3 (Visibility App with Decision Support) M28 M33
Development by CLMS M28 M31
Testing and demonstration / user involvement TEUBooker customers
M30 M31
Reporting M32 M32
D7.7 (final version) M34 M34
Simulation / KPI assessment
Provide datasets and definitions to ISL by PoR
Finalize simulation model by ISL
Test several scenarios and assess impact on KPIs
Reporting
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Table 12 – LL2 – Conclusions & Economic Benefit Analysis
Conclusion 1 Reliability is of major concern to all involved
parties, and it is important to promote awareness on the impact.
There is also need for measuring reliability in a standardized way
and extensively making use of dashboards
Conclusion 2 Insight based on historic data triggers interest
and willingness to improve. Increased reliability results in modal
shift (to barge).
Conclusion 3 Working on adaptation and specification of
potentially valuable electronic data-based tools requires a vast
effort into getting the actors aligned. This requires a careful
stepwise and foremost iterative and flexible process, starting with
very simple technology solutions and continuous interaction with
the industry to adapt and get it accepted. At the same time this
takes time as the industry has its own pace and interests. There is
a need to have continuous insight based on connected sources of
data (direct system integration). Due to legacy systems,
connections are sometimes hard to make.
Conclusion 4 To evaluate the actual improvements on modal shift,
either stated preference method types or simulation tools can be
applied.
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3.3 Living Lab 3 – Urban Logistics – SUMY
SUMY is a small LSP in the area of Brussels, with two main
scenarios: Delivery of fresh foods from Rungis platform in Paris to
ISPC in Brussels, focusing on optimization this transport with
enhanced information sharing, and delivery of fresh foods from ISPC
platform in Ghent to Brussels, focusing increasing their level of
collaboration with other local LSPs and shippers, with ultimate
goal to maximize load factors, improve quality of service and
consequently reduce the environmental footprint.
Through SELIS Urban Logistics Node all individual Brussels-based
stakeholders (e.g. shippers, service providers) would be enabled to
communicate their demand (i.e. freight transport orders) and offer
(i.e. available capacity) execute collaborative planning; collect
transport events and real-time traffic data in order to monitor the
transport progress and react to disturbances (e.g. rerouting,
notifications).
Table 13 – LL3 SUMY – Objectives & Operational
Measurements
Objective # / UC# 1 / UC1
Description Steadily increase the usage of available capacity of
the different vehicles out on delivery within the context of a
complex, short distance urban distribution environment. Improving
Urban LSP’s load factor will consequently lead in decreasing the
environmental impact (such as CO2 emissions), but it is also
expected to decrease operational costs and increase the overall
productivity of the collaborators.
SELIS Applied Concepts / Innovations
To achieve efficient collaboration, LL3 will actively engage
SELIS Content based Publish and Subscribe infrastructure that will
provide us a secure and continent communication channel for sharing
relevant information on a real time basis.
Measurement Method
For the KPIs calculation, the following data available in SCN
will be utilized: transported volumes, timestamps of all transport
events, costs per shipment/cargo, delivery points, distances,
frequency of service etc.
Each of the KPIs below will be calculated as follows:
Average Load factor: average for all segments of the total
volume of orders divided by the total available space of the truck
(Physical volumes ie. m3 or weight)
CO2 emission per m3 per order: we calculate the CO2 emission per
m3 of each segment by calculating the CO2 emission in function of
the distance and dividing it by the volume in the segment. For the
CO2 per m3 of an order, we find the sum of the segment used CO2 and
multiply it by the volume of the order.
Operational Cost per m3: we find the cost of an order or service
and divide it by the total volume of this order or service.
Initial (Baseline) KPI
Current SUMY Load Factor = 72%
Current CO2: no record of sufficiently detailed data to make the
equivalent KPI analysis required for comparison.
SELIS KPI SUMY Load Factor = 83% (expected KPI, assuming 7%
increase in transport order)
Assuming 15% increase in Load Factor, this will lead to 13%
reduction in CO2 emission and 13% reduction in Operational Costs
per m3.
Economic impact Our solution will have a direct impact on the
cost of the services given. This will allow
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for the Urban LSPs to increase their margin and profit of their
activities, by up to 15% (due to reduced operational costs)
Qualitative Business Impact Evaluation
With SELIS Urban Logistics Community Node effectively and
seamlessly facilitating the collaboration among the involved
stakeholders, it is expected that this will have positive and
direct impact on their visibility, which they currently struggle to
follow, and therefore increase both the user as well as the end
customer satisfaction. As a result, this will accelerate the
adoption of the solution to a wider audience and greater
extent.
SUMY CEO Evidence: “The SELIS platform does allow a much more
efficient collaboration with our partners”.
“Improved public image” – Urban LSPs considered more environment
friendly, therefore they are preferred transport service providers,
from environmentally sensitive shippers and customers. This is
expected to result in another 2-3% increase of transport demand, in
a period of 2-3 years after the full implementation of the
solution. Several of SUMY current clients made the choice of
working with them because they have this sustainable and ecological
image they want to integrate in their supply chain.
Table 14 – LL3 SUMY – Learning Outcomes & Conclusions
Best Practice
Description
EGLS2, applied in the LL, facilitated the Collaborative Planning
approach, encouraging multiple stakeholders to share their
transport demand and capacity, consequently allowing the urban LSPs
not only to optimize the situation per actor silo, but to break
these silos and find an optimal global solution.
The platform will use prioritized routing optimization as the
method to find the most efficient way to combine orders in a
service. With priorities we can express the business importance of
certain strategic client orders that should be serviced even if
this means excluding other demands.
The Cost allocator developed allows us to fairly allocate cost
between different consumers of the service, and by parametrization,
aligns the Business Model of the LSP to the calculation method.
This shows the collaborators an unbiased financial benefit for
participating in the platform.
Reference Objective(s)
Objective 1
SELIS Solution SELIS provides the Urban Logistics Community
Node, that can be fully integrated with external systems of the
respective community through APIs, allowing the users to share both
order data as well as logistics service data with one another.
User can configure the platform to customize the solution to the
user’s specificities. He can configure his vehicles, his volume
units, his delivery conditions etc.
We also provide a cost calculator where we calculate cost from
several parameters linked to the type of vehicle, vehicle use and
the distribution network.
SELIS provides a global optimizer which from a set of published
orders and logistic services it can calculate an optimal solution
considering the different routing options and priorities.
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The cost allocator applied an analytical model which implement
several rules for allocating the cost of a collaboration between
different shippers served by an LSP. This cost allocator model has
been specifically designed to address the collaboration in urban
logistics networks. The analytical model allows the user to combine
different allocation rules (stand-alone, service point and
volume-based allocation model) by assigning a weight to each
rule.
Proposed Enhancement
In urban logistic, financial cost only covers the direct cost to
the LSPs. There are several negative externalities, more than just
CO2, that should be considered such as noise pollution or city
congestion, which have a cost the cities operated. The platform
should calculate this "external cost" to aim at reducing it in
parallel to the financial cost.
Based on the cost allocator, we should design a service for
unprofitable deliver points. This will provide a basis for decision
making for adjusting the transport price or outsourcing
unprofitable deliver points.
Expected Impact Reduce trucks empty runs in the urban
environment. The SELIS platform will reveal many new synergies in
the delivery map and help exploit them to make the services more
efficient and productive.
The cost model will increase the visibility on the different
costs of the orders and shippers. Ultimately it will increase the
profitability of the LSP.
Table 15 – LL3 SUMY – Proposed Refinements
Refinement Description
User Interface still needs to become more robust and user
friendly. Solution not yet verified with heavy SCN messages
“traffic” and multiple distribution points and service
providers.
Improvement Improve user experience to accommodate management of
massive data, allowing efficient filtering out of irrelevant
information.
Validate solution scalability to ensure that the optimization
algorithm can be efficient even when handling significantly more
complex problems.
Expected Impact With an easy to use and stable platform, the
user base would be more engaged and will clearly see the added
value the platform brings to their day to day.
Action Plan The platform could be analysed by UX specialist that
will describe the changes to be implemented. A filtering mechanism
is under development (currently in the design phase) for subscribed
data that is published.
In regards with ensuring scalability, a simulation solution will
be employed to test how the platform performs to numerous requests
that need to be optimized simultaneously.
Table 16 – LL3 SUMY – Conclusions & Economic Benefit
Analysis
Conclusion 1 Achieving a better load factor of logistic services
will have a wide range of beneficial results such as an important
economic improvement for LSPs and adds much needed visibility to
the customer.
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Conclusion 2 The impact the platform will have on the
environment will be highly positive as a much more efficient
logistic network means less wasted space on kilometres driven which
will also result in slump of trucks on the road.
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3.4 Living Lab 3 – Urban Logistics – SARMED
For Province of Greece, SARMED (LSP) is delivering the goods to
its customers through specialized Regional Agencies (RA). Each RA
has a Collection Centre (similar to a Distribution Centre) and
covers a specific region. A limited number of those RA’s are
currently sufficiently organized to automatically report back to
the LSP accurate information about the delivery’s status.
The Goal of this Living Lab is to seamlessly consolidate
information flows from multiple stakeholders (LSPs, RAs, Client,
End Customers) into the Urban Logistics SELIS Node, in order to
facilitate enhanced end-to-end visibility as well as dynamic
collaboration with the minimal cost and man-effort, for the
involved parties.
Table 17 – LL3 - Objectives & Operational Measurements
Objective 1 /UC1
1 / UC1 - Supply Chain Visibility
Description Transportation information of goods that are shipped
through Regional Agencies to the regions outside of Attica and
Thessaloniki lacks consistency and it is not timely delivered.
SELIS Applied Concepts / Innovations
In this Use Case, SELIS will be a Supply Chain Visibility
enabler, utilizing SELIS Connectivity Infrastructure and the
respective Pub/Sub mechanism, information fed from all Supply Chain
stakeholders will be seamlessly transformed and integrated, to
formalize the accurate real-time awareness of the current delivery
status. SCN Participants will be subscribed to the “content queues”
relevant to their interests, and at the same time “publish” their
data/events, transmitting the required information to the other
parties. Furthermore, the provided online Dashboard will facilitate
the effortless and reliable operation, providing real-time
end-to-end Supply Chain visibility of:
- (LSP) Client: the status of their customer orders - LSP: all
order delivery status - RA: all information relevant to their
operations
End Customer: status of their orders (picked, loaded, in
transit, in hub X, etc.)
Measurement Method
For this UC we considered the following KPIs, to be calculated
and extracted by SARMED’s legacy systems:
1. Reduction of operational costs - Operational workload for
exchanging information - Reduce workload to exchange information to
all parties - Measure time to exchange information
2. Delivery Information lead time improvement - Lead time to
share / provide information for the delivery - Reduce Lead time to
share / provide information for the delivery - Measure time to
provide information for the delivery
3. Reduce track time of deliveries - Time to track a delivery -
Reduce time to track information for a delivery - Measure time to
track a delivery
Initial (Baseline) KPI
Initial KPIs for UC1:
1. Time to exchange information = 2 mins / party 2. Time to
provide information for a delivery = 24 hours after the delivery 3.
Time to track a delivery = 1 hour
SELIS KPI Following SELIS full application, the expected KPIs
will be as follows:
1. Time to exchange information = 0 min / party
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2. Time to provide information for a delivery = less than 12
hours after the delivery 3. Time to track a delivery = 0 hour
Economic impact
Currently at SARMED, the Transport and the Customer service
departments (13 persons) have on daily basis to manually contact
the RAs and the customers to exchange information for the status of
deliveries. They have to contact approximately with 60 RAs and 100
customers, which means 60+100 = 160 X 2 = 320 mins daily. With a
full-scale application of SELIS this workload could be avoided.
Only for Sarmed the above saving is calculated to a 5% reduction
in operation (320/60/8 = 0,66 person. 0,66 /13 = 5%).
The other stakeholders will also spend less time and effort to
obtain information status for the deliveries.
Qualitative Business Impact Evaluation
Currently the client-assignor, the LSP-shipper and the End
customer-receiver do not have prompt information for shipment. With
SELIS Solution, all involved stakeholders will have full visibility
of the Supply Chain enabling them to significantly improve the
level of their customer service.
Objective 2/ UC2
1 / UC2 – RA Delivery Optimization – Maximize Load Factors
Description Currently, both RAs as well as LSPs have limited and
frequently last-minute knowledge of preferred delivery dates per
final point, and low to none capability to influence the delivery
dates in an efficient way. SCN will allow, not only to effortlessly
create a common view of the intended actions, but also automate the
transport-price vs delivery date negotiation among the Regional
Agent and the LSP, with the goal to optimize the cargo load-factor
on the preferred dates.
SELIS Applied Concepts / Innovations
In this UC the SELIS Urban Logistics Node, will facilitate
collaborative planning and value sharing among the LSPs and the
Regional Agents, allowing higher load factors for shipments to the
province, which due to the increased transport distances, has
significant impact to the CO2 emissions’ reduction. This is
achieved through full transparency of the operational facts and a
fast and effective SELIS facilitated “negotiation” among the LSPs
and the RAs, intended to fairly share the optimization
benefits.
Measurement Method
For this UC we applied the following KPIs, to be calculated and
extracted by SARMED’s legacy systems:
- Reduction of operational costs - Operational workload for
planning a route - Reduce workload to exchange information between
LSP and RAs - Measure time to exchange information
- Improve Load Factor - Load Factor - Improve Loading Factor
-Measure Loading utilization (in pallets(laden), in kilogram, in
volume)
- CO2 footprint reduction - Calculate CO2 for specific
routes
Initial (Baseline) 1. Time to exchange information = 2mins /
RA
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KPIs 2. Load Factor = 75% 3. CO2 for specific routes – CO2
calculation is a complicated procedure which has a lot
of parameters and require additional data. This is still in
progress.
SELIS KPIs 1. Time to exchange information = 0 min / RA 2. Load
Factor = 82,5% 3. CO2 for specific routes = 10% increase in Load
Factor will lead to 10% reduction in
CO2 emission
Economic impact
Daily a SARMED employee has to contact with approximately 30 RAs
to exchange information, therefore 30*2 = 60mins = 1 hour. 1h /8
working hours = 12,5% reduction in respective operational cost.
The RAs will also have spent less time and effort to obtain
information for the pla