d7.20 living labs operation learning conclusions and other … · 2019. 6. 25. · 1.2 deliverable...

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

D7.20 Living Labs Operation Learning Conclusions and other SELIS Value Propositions

© SELIS, 2016 Page | 2

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



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



made through appropriate citation, quotation or both. Reproduction is authorized provided the source is acknowledged.



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



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 30 – LL5– Learning Outcomes & Conclusions........................................................................................... 46

Table 31 – LL5 - Proposed Refinements .............................................................................................................. 47



Table 34 – LL7– Learning Outcomes & Conclusions ........................................................................................... 51

Table 35 – LL7 - Proposed Refinements.............................................................................................................. 52


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 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



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



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



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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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 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 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.


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.




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


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


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.




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


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.


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.


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


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


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


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.


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


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


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


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



version 3 (Visibility App with Decision Support) M28 M33



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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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.


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.


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)


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.


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.


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


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.


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.


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 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.


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.


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

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