D7.1 Business Plan - databio.eu · D7.1 – Business Plan H2020 Contract No. 732064 Final – v2.1, 6/2/2018 Dissemination level: PU -Public Page 3 Most of the pilots provide solutions

This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee.

Project Acronym: DataBio

Grant Agreement number: 732064 (H2020-ICT-2016-1 – Innovation Action)

Project Full Title: Data-Driven Bioeconomy

Project Coordinator: INTRASOFT International

DELIVERABLE

D7.1 – Business Plan

Dissemination level PU -Public

Type of Document Report

Contractual date of delivery M12 – 31/12/2017

Deliverable Leader UStG

Status - version, date Final – v2.1, 6/2/2018

WP / Task responsible WP7

Keywords: Business planning, KPIs

D7.1 – Business Plan H2020 Contract No. 732064 Final – v2.1, 6/2/2018

Dissemination level: PU -Public Page 2

Executive Summary

This deliverable contains a first business analysis for DataBio pilots. The business analysis

involved the following: First, the type of potential value resulting from the use of Big Data

Technology (BDT) is identified. The potential added value can either be a new business model

or improved operational processes through data-driven decision making. In case the use of

BDT results in a new business model, then first the respective business model is visualised

and described in depth with a business model canvas. In both cases, major Key Performance

Indicators (KPIs) are identified with the aim to measure the impact of BDT use. Finally,

potential major cost and income categories are identified and compared.

The analysis revealed that both types of added value can be identified in the DataBio pilots,

i.e. both new business models based on BDT, but also overall improvement of operation

efficiency by advanced decision making based on BDT. Examples of data-based business

models in agriculture include the sensor and cloud-based solution for data collection and

analysis in agriculture from NP and GAIA, as well as the smart tractors from Zetor. In forestry,

there is the mobile solution for forestry, namely Wuudis Forest from MHGS. Finally, in

fisheries, there are potential spin-offs that could stem in the area of oceanic tuna fisheries.

The majority of pilots result in improved operational processes due to more precise data-

driven decision making. Thus, the most important KPIs considered in the analysis are:

productivity and profitability. Increased profitability and productivity is achieved by reducing

production costs due to better decision-making based on BDT. In agriculture it is possible to

reduce visits to the field, to reduce used resources, such as for example fertilizes or irrigation

water, to choose better seeds and to determine the optimal time for harvesting in order to

get the highest yield possible. In forestry, due to more precise data, it is also possible to

reduce visits to the forest and to better plan forest management activities. In particular,

sophisticated BDT based forestry health monitoring and prediction can prevent immense

forest damage due to spread of various kind of pests. Fisheries is the most regulated industry

in particular from the yield point of view. Thus, BDT is applied explicitly for reducing costs per

catch. Data-driven decision making based on more precise information has the potential to

result in shorter and at the same time more productive routes with less consumption of

energy and fuel for fisheries vessels.

To measure the expected operational improvements from BDT use, major KPIs were

identified for each pilot and BDT providers. Based on the individual pilot KPIs, a first

aggregation of potential added value on industry and project level is possible.

The development of technical BDT solutions requires substantial resources that cannot be

afforded by smaller companies. As a consequence, in many of the pilots, leading institutions

assembling or developing BDT are state institutions or research organizations. These are

organizations that do not necessarily exploit technology in form of market-ready products. As

a result, there is a need for entrepreneurship in the area of BDT in all three sectors to foster

the adoption of BDT in agriculture, forestry and fisheries.



Most of the pilots provide solutions that have potential to improve operational processes of

end users. However, it also shows that these results can be amplified, if solutions address and

provide added value for all actors of the value chain at the same time.

Overall the analysis confirmed the expected potential of BDT, but it also reveals aspects that

might hinder its adoption: high investment costs and need for entrepreneurship and

companies that will bring the technology to the market in a secure and affordable way for

end users; critical mass of technology coverage and adoption by end users, and support for

the whole value chain in all three industries.



Deliverable Leader: Katarina Stanoevska-Slabeva, UStG

Contributors:

Katarina Stanoevska-Slabeva, UStG

Vera Lenz-Kesekamp, UStG

Viktor Suter, UStG

Jaroslav Smejkal, Zetor

Seppo Huurinainen, MHG Systems Oy Ltd

Claudio Lamacchia, CIAOT

Per Gunnar Auran, SINTEF

Nikolaos Marianos, Neuropublic

Reviewers:

Göran Granholm, VTT

Margus Freudenthal, CYBER

Efi Argyropoulou, GAIA

Irene Matzakou, INTRASOFT

Approved by: Athanasios Poulakidas, INTRASOFT

Document History

Version Date Contributor(s) Description

0.1 30.10.17 K. Stanoevska-

Slabeva (UstG) Document structure, first version

0.2 31.10.17 J. Smejkal (ZETOR) Contribution to Chapter on Agriculture

0.3 13.11.17 J. Smejkal (ZETOR) Contribution to Chapter on Agriculture


Slabeva (UStG) Integration of literature into deliverable

0.4 16.11.17 Vera Lenz-

Kesekamp (UStG)

Inclusion description business models and

KPIs


Slabeva (UStG) Definitive version of introduction, project KPIs

0.6 30.11.17

K. Stanoevska-

Slabeva (UStG), V.

Suter (UStG)

Integration of literature on forestry and

forestry business models

0.7 07.12.17

K. Stanoevska-

Slabeva (UStG), V.

Suter (UStG)

Integration of literature on fisheries and

fisheries business models

0.8 14.12.17

S. Huurinainen

(MHGS), K.

Stanoevska-Slabeva

(UStG)

Case Study Business modelling for forestry

and MHGS

0.9 21.12.17

Nikolaos Marianos

(NP), K. Stanoevska-

Slabeva (UStG)

Integration of input to agriculture and case

study agriculture



1.0 22.12.17

P.G. Auran (SINTEF),

K. Stanoevska-

Slabeva (UStG)

Integration input to fisheries

1.1 29.12.17

K. Stanoevska-

Slabeva (UStG), V.

Suter (UStG)

Contribution to all sections


Slabeva (UstG)

Writing section on business analysis of pilots

in agriculture


Slabeva (UStG)

Writing first version of conclusions to all

sections and some consolidation of text


Slabeva (UStG),

Writing Executive Summary and extension of

conclusion


Slabeva (UStG), Final corrections and sending out for review

1.6 26.01.18

Estrada Villegas,

Jesus Maria

(TRAGSA), K.

Stanoevska-Slabeva

(UStG),

Review by TRAGSA and finalization of business

models and analysis for TRAGSA forestry pilots


Slabeva (UStG), Correction according to review from Cyber


Slabeva (UStG), Correction according to review from GAIA

1.9 31.01.18

K. Stanoevska-

Slabeva (UStG), V.

Suter (UStG)

Correction of all section according to review

comments and final formatting


Slabev (UstG)

Inclusion of review comments and final

version for submission

2.1 06.02.18

I. Matzakou, A.

Poulakidas

(INTRASOFT)

Final QA.



Table of Contents

EXECUTIVE SUMMARY ..................................................................................................................................... 2

TABLE OF CONTENTS ........................................................................................................................................ 6

TABLE OF FIGURES ........................................................................................................................................... 7

LIST OF TABLES ................................................................................................................................................ 7

DEFINITIONS, ACRONYMS AND ABBREVIATIONS ............................................................................................. 8

INTRODUCTION .................................................................................................................................... 10

1.1 PROJECT SUMMARY ..................................................................................................................................... 10 1.2 DOCUMENT SCOPE ...................................................................................................................................... 13 1.3 DOCUMENT STRUCTURE ............................................................................................................................... 13

INTRODUCTION TO BUSINESS MODELLING, BUSINESS PLANNING AND KPIS ........................................ 14

2.1 IDENTIFICATION OF ADDED VALUE BY USING BDT ............................................................................................. 14 2.2 BUSINESS MODELLING AND BUSINESS PLANNING .............................................................................................. 16 2.3 DEFINITION OF KPIS AS QUANTIFICATION OF BUSINESS GOALS ............................................................................. 19

2.3.1 Overview of KPIs and their relationships ...................................................................................... 19 2.3.2 KPI Breakdown and Aggregation .................................................................................................. 20

2.4 INITIAL IDENTIFICATION OF COSTS AND BENEFITS FOR PILOTS AND PARTNERS .......................................................... 22

BUSINESS MODELS AND KPIS IN AGRICULTURE PILOTS......................................................................... 24

3.1 DEFINITION AND CLASSIFICATION OF KPIS IN AGRICULTURE TRIALS ....................................................................... 24 3.2 EXAMPLES OF BUSINESS PLANS AND BUSINESS MODELS IN AGRICULTURE .............................................................. 25

3.2.1 Business Opportunities of Neuropublic / GAIA Epicheirein in the Pilots: T1.2.1-A1.1, T1.3.1-B1.2,

T1.4.1-C1.1 and P1.4.2-C2.2 ........................................................................................................................ 25 3.2.2 Business Analysis of the Pilot T1.3.2-B2.1 “Machinery Management” ......................................... 29 3.2.3 Summary of business analysis of agricultural pilots ..................................................................... 32

BUSINESS PLANS AND KPIS IN FORESTRY TRIALS .................................................................................. 33

4.1 DEFINITION AND CLASSIFICATION OF KPIS IN FORESTRY TRIALS ............................................................................ 33 4.2 EXAMPLES OF BUSINESS MODELS, KPIS AND BUSINESS PLANS IN FORESTRY ........................................................... 36

4.2.1 The Business Model of MHG Systems related to Pilot T2.2.1 and Pilot T2.2.2.............................. 36 4.2.2 The case of MHG Systems with Pilot T2.3.2- Forest Damage Remote Sensing ............................. 40 4.2.3 Business Modelling and Analysis of Pilot T2.3.2 and T2.3.3 ......................................................... 44 4.2.4 Business Analysis of the T2.4.1 ..................................................................................................... 47 4.2.5 Business Analysis of T2.4.2 ........................................................................................................... 48 4.2.6 Summary of Business Modelling in Forestry ................................................................................. 49

BUSINESS PLAN AND KPIS IN FISHERY TRIALS ....................................................................................... 50

5.1 CLASSIFICATION OF KPIS IN FISHERY TRIALS ...................................................................................................... 50 5.2 EXAMPLES OF BUSINESS PLANS IN FISHERY ....................................................................................................... 52

5.2.1 Business Analysis of the Fisheries Pilot T3.2.1-A1 ......................................................................... 52 5.2.2 Business Analysis of T3.3.1-B1 ...................................................................................................... 54 5.2.3 Business Analysis of pilot T3.2.2-A2, T3.3.2--B2, T3.4.2--C2 ......................................................... 56 5.2.4 Business Analysis of T3.4.2-C2 ...................................................................................................... 59 5.2.5 Summarizing business model for fisheries pilots T3.2.1-A2, T3.3.2-B2 and T3.4.2-C2 .................. 62 5.2.6 Summary of Business Analysis of Fisheries Pilots.......................................................................... 63



SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ........................................................................ 64

REFERENCES ......................................................................................................................................... 66

Table of Figures FIGURE 1: CLASSIFICATION OF BIG DATA VALUE CREATION (SOURCE [REF-12]) ..................................................................... 15 FIGURE 2: THE BUSINESS MODELLING APPROACH ............................................................................................................. 16 FIGURE 3: THE BUSINESS MODEL CANVAS ....................................................................................................................... 17 FIGURE 4: OVERVIEW OF DATABIO KPIS AND THEIR RELATIONSHIPS .................................................................................... 19 FIGURE 5: BREAKDOWN OF THE KPI GROSS PROFIT ......................................................................................................... 21 FIGURE 6: BUSINESS MODEL OF NP FOR ALL FOUR PILOTS .................................................................................................. 26 FIGURE 7: BUSINESS MODEL CANVAS FOR T1.3.2-B2.1 .................................................................................................... 30 FIGURE 8: THE BUSINESS MODEL OF MHGS IN PILOT T2.2.1 AND T2.2.2 ............................................................................ 37 FIGURE 9: THE BUSINESS MODEL OF MHGS IN PILOT T2.3.2 ............................................................................................ 41 FIGURE 10: POTENTIAL BUSINESS MODEL FOR T2.3.2 AND T2.3.3 FROM PERSPECTIVE OF TRAGSA ........................................... 45 FIGURE 11: BUSINESS MODEL CANVAS OF A POTENTIAL SPIN-OFF FOR THE FISHERIES PILOT T3.2.1-A1 ...................................... 53 FIGURE 12: BUSINESS MODEL OF A POTENTIAL SPIN-OFF FOR T3.3.1-B1 ............................................................................. 55 FIGURE 13: FISHERY PILOT PILOT T3.3.2-A2 BUSINESS PROCESS VIEW (ARCHIMATE 3.0) ....................................................... 57 FIGURE 14: FISHERY PILOT PILOT T3.4.2-C2 BUSINESS PROCESS VIEW (ARCHIMATE 3.0) ....................................................... 60 FIGURE 15: POTENTIAL BUSINESS MODEL FROM PERSPECTIVE OF SINTEF ............................................................................. 63

List of Tables TABLE 1: THE DATABIO CONSORTIUM PARTNERS ............................................................................................................. 10 TABLE 2: EXAMPLE OVERVIEW OF COST AND BENEFITS FOR TRIALS AND DATABIO PARTNERS .................................................... 23 TABLE 3 AGRICULTURE PILOTS ...................................................................................................................................... 24 TABLE 4: OVERVIEW OF KPIS FOR PILOT T1.2.1-A1.1 ..................................................................................................... 27 TABLE 5: SUMMARY OF COST AND INCOME CATEGORIES FOR NP FOR FOUR PILOTS ................................................................. 29 TABLE 6: SUMMARY OF COST AND INCOME CATEGORIES FOR T1.3.2-B2.1 FROM PERSPECTIVE OF ZETOR .................................. 31 TABLE 7 DATABIO FORESTRY PILOTS .............................................................................................................................. 35 TABLE 8: SUMMARY OF COST AND INCOME CATEGORIES FOR PILOT T2.2.1 FROM PERSPECTIVE OF MHGS ................................. 40 TABLE 9: SUMMARY OF COST AND INCOME CATEGORIES FOR PILOT T2.3.1 FROM PERSPECTIVE OF MHGS ................................. 44 TABLE 10: SUMMARY OF COST AND INCOME CATEGORIES FOR PILOT T2.3.2 AND T2.3.3 FROM PERSPECTIVE OF TRAGSA ............. 47 TABLE 11 DATABIO FISHERY PILOTS ............................................................................................................................... 52 TABLE 12: SUMMARY OF COST AND INCOME CATEGORIES FOR T3.2.1-A1 ........................................................................... 54 TABLE 13: SUMMARY OF COST AND INCOME CATEGORIES FOR T3.3.1-B1 ............................................................................ 56 TABLE 14: EXAMPLES OF SPECIFIC KPIS RELEVANT FOR FISHERY TRIALS AND FOR THE OPERATION AND OPTIMIZATION OF FISHERIES FOR

T3.3.2-A2 ..................................................................................................................................................... 58 TABLE 15: SUMMARY OF COST AND INCOME CATEGORIES FOR PILOT T3.2.2-A2 FROM PERSPECTIVE OF FISHING VESSELS .............. 59 TABLE 16: OVERVIEW OF KPIS FOR T3.4.2-C2 ............................................................................................................... 61 TABLE 17: SUMMARY OF COST AND INCOME CATEGORIES FOR PILOT T3.4.2-C2 FROM PERSPECTIVE OF FISHING VESSELS .............. 61



Definitions, Acronyms and Abbreviations

Acronym/

Abbreviation Title

BDT Big Data Technology

CAP Common Agricultural Policy

CFP Common Fisheries Policy

DaaS Data as a Service

EO Earth Observation

IAS Invasive Alien Species

ICT Information and Communication Technology

IT Information Technology

IUU fishing Illegal, Unreported and Unregulated fishing

KPI Key Performance Indicator

MSC Marine Stewardship Council

NIR Near Infrared Data

PPP Public-Private Partnership

RPAS Remotely Piloted Aircraft System

SME Small and medium size enterprise

TCO Total Cost of Ownership

UAV Unmanned Aerial Vehicles

UI User Interface

W.D. Without Date

Term Definition

Business Plan A written document describing the nature of the business, the sales and

marketing strategy, and the financial background, and containing a

projected profit and loss statement1.

Business Case A business case captures the reasoning for initiating a project or task. It

is often presented in a well-structured written document, but may also

come in the form of a short verbal argument or presentation. The logic

of the business case is that, whenever resources such as money or effort

are consumed, they should be in support of a specific business need. An

example could be that a software upgrade might improve system

performance, but the "business case" is that better performance would

improve customer satisfaction, require less task processing time, or

1 Source: https://www.entrepreneur.com/encyclopedia/business-plan

https://en.wikipedia.org/wiki/Project

https://en.wikipedia.org/wiki/Presentation

https://en.wikipedia.org/wiki/Logic

https://en.wikipedia.org/wiki/Resources

https://en.wikipedia.org/wiki/Software_upgrade

https://en.wikipedia.org/w/index.php?title=System_performance&action=edit&redlink=1

https://en.wikipedia.org/w/index.php?title=System_performance&action=edit&redlink=1

https://en.wikipedia.org/wiki/Customer_satisfaction



reduce system maintenance costs. A compelling business case

adequately captures both the quantifiable and non-quantifiable

characteristics of a proposed project2.

2 Source: https://en.wikipedia.org/wiki/Business_case



Introduction 1.1 Project Summary The data intensive target sector selected for the

DataBio project is the Data-Driven Bioeconomy.

DataBio focuses on utilizing Big Data to

contribute to the production of the best possible

raw materials from agriculture, forestry and

fishery/aquaculture for the bioeconomy

industry, in order to output food, energy and

biomaterials, also taking into account various

responsibility and sustainability issues.

DataBio will deploy state-of-the-art big data technologies and existing partners’ infrastructure

and solutions, linked together through the DataBio Platform. These will aggregate Big Data

from the three identified sectors (agriculture, forestry and fishery), intelligently process them

and allow the three sectors to selectively utilize numerous platform components, according

to their requirements. The execution will be through continuous cooperation of end user and

technology provider companies, bioeconomy and technology research institutes, and

stakeholders from the big data value PPP programme.

DataBio is driven by the development, use and evaluation of a large number of pilots in the

three identified sectors, where also associated partners and additional stakeholders are

involved. The selected pilot concepts will be transformed to pilot implementations utilizing

co-innovative methods and tools. The pilots select and utilize the best suitable market ready

or almost market ready ICT, Big Data and Earth Observation methods, technologies, tools and

services to be integrated to the common DataBio Platform.

Based on the pilot results and the new DataBio Platform, new solutions and new business

opportunities are expected to emerge. DataBio will organize a series of trainings and

hackathons to support its take-up and to enable developers outside the consortium to design

and develop new tools, services and applications based on and for the DataBio Platform.

The DataBio consortium is listed in Table 1. For more information about the project see

www.databio.eu.

Table 1: The DataBio consortium partners

Number Name Short name Country

1 (CO) INTRASOFT INTERNATIONAL SA INTRASOFT Belgium

2 LESPROJEKT SLUZBY SRO LESPRO Czech Republic

3 ZAPADOCESKA UNIVERZITA V PLZNI UWB Czech Republic

http://www.databio.eu/



4

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER

ANGEWANDTEN FORSCHUNG E.V. Fraunhofer Germany

5 ATOS SPAIN SA ATOS Spain

6 STIFTELSEN SINTEF SINTEF ICT Norway

7 SPACEBEL SA SPACEBEL Belgium

8

VLAAMSE INSTELLING VOOR TECHNOLOGISCH

ONDERZOEK N.V. VITO Belgium

9

INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ

AKADEMII NAUK PSNC Poland

10 CIAOTECH Srl CiaoT Italy

11 EMPRESA DE TRANSFORMACION AGRARIA SA TRAGSA Spain

12 INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EV INFAI Germany

13 NEUROPUBLIC AE PLIROFORIKIS & EPIKOINONION NP Greece

14

Ústav pro hospodářskou úpravu lesů Brandýs nad

Labem UHUL FMI Czech Republic

15 INNOVATION ENGINEERING SRL InnoE Italy

16 Teknologian tutkimuskeskus VTT Oy VTT Finland

17 SINTEF FISKERI OG HAVBRUK AS

SINTEF

Fishery Norway

18 SUOMEN METSAKESKUS-FINLANDS SKOGSCENTRAL METSAK Finland

19 IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD IBM Israel

20 MHG SYSTEMS OY - MHGS MHGS Finland

21 NB ADVIES BV NB Advies Netherlands

22

CONSIGLIO PER LA RICERCA IN AGRICOLTURA E

L'ANALISI DELL'ECONOMIA AGRARIA CREA Italy

23 FUNDACION AZTI - AZTI FUNDAZIOA AZTI Spain

24 KINGS BAY AS KingsBay Norway

25 EROS AS Eros Norway

26 ERVIK & SAEVIK AS ESAS Norway

27 LIEGRUPPEN FISKERI AS LiegFi Norway

28 E-GEOS SPA e-geos Italy



29 DANMARKS TEKNISKE UNIVERSITET DTU Denmark

30 FEDERUNACOMA SRL UNIPERSONALE Federu Italy

31

CSEM CENTRE SUISSE D'ELECTRONIQUE ET DE

MICROTECHNIQUE SA - RECHERCHE ET

DEVELOPPEMENT CSEM Switzerland

32 UNIVERSITAET ST. GALLEN UStG Switzerland

33 NORGES SILDESALGSLAG SA Sildes Norway

34 EXUS SOFTWARE LTD EXUS

United

Kingdom

35 CYBERNETICA AS CYBER Estonia

36

GAIA EPICHEIREIN ANONYMI ETAIREIA PSIFIAKON

YPIRESION GAIA Greece

37 SOFTEAM Softeam France

38

FUNDACION CITOLIVA, CENTRO DE INNOVACION Y

TECNOLOGIA DEL OLIVAR Y DEL ACEITE CITOLIVA Spain

39 TERRASIGNA SRL TerraS Romania

40

ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS

ANAPTYXIS CERTH Greece

41

METEOROLOGICAL AND ENVIRONMENTAL EARTH

OBSERVATION SRL MEEO Italy

42 ECHEBASTAR FLEET SOCIEDAD LIMITADA ECHEBF Spain

43 NOVAMONT SPA Novam Italy

44 SENOP OY Senop Finland

45

UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO

UNIBERTSITATEA EHU/UPV Spain

46

OPEN GEOSPATIAL CONSORTIUM (EUROPE) LIMITED

LBG OGCE

United

Kingdom

47 ZETOR TRACTORS AS ZETOR Czech Republic

48

COOPERATIVA AGRICOLA CESENATE SOCIETA

COOPERATIVA AGRICOLA CAC Italy



1.2 Document Scope This document provides the results of the initial business planning of DataBio partners and

pilots. The document provides business models, aggregated KPIs (Key Performance

Indicators) as well as first break down of expected costs and income categories from the

perspective of technology providers and users. It also provides examples of industry specific

KPIs as well as first case studies of partners business planning. The business plans and cost

and benefit analysis is in a very first stage based on the initial definitions of trials and roles of

partners in them. Thus, the provided business plans are an attempt to provide first business

plans on an aggregated level and will be detailed in the next version of the deliverable.

1.3 Document Structure

This document is comprised of the following chapters:

Chapter 1 presents an introduction to the project and the document.

Chapter 2 explains the business modelling and business planning approach.

Chapter 3 provides the business analysis of pilots in agriculture.

Chapter 4 provides the business analysis of pilots in forestry.

Chapter 5 provides the business analysis of pilots in fisheries.

Chapter 6 provides a summary, conclusion and recommendation.

Chapter 7 lists the references.



Introduction to Business Modelling, Business

Planning and KPIs One major aim of implementing Big Data Technology (BDT) is the increase in efficiency and

productivity of existing production processes or the creation of new business opportunities.

All DataBio trials aim to illustrate this potential of BDT in the three target industries of the

DataBio project: agriculture, forestry and fisheries. To achieve this goal, extensive activities

of business planning are part of the project.

This deliverable contains first business plans of DataBio pilots and partners that are defined

based on the initial definition of trials in the following DataBio deliverables [REF-01], [REF-02]

and [REF-03]. Business planning at this stage of the project is on a general level and should

provide the basis for measuring the performance of trials, as well as detailed business

planning until the end of the project. It is based on: 1) an initial business model, 2) an initial

analysis of reported KPIs related to expected business improvements described in the three

mentioned deliverables, and 3) an initial overview of expected potential costs and benefits

(revenues). To define the first business models, KPIs as well as cost and benefits’ overview,

the following methodologies and approaches are applied:

• identification of the added value resulting from use of BDT;

• visualisation of the emerging new business models or enhancements of existing

business models of partners with the Osterwalder Business Model Canvas ([REF-04]);

or identification of a business case;

• definition of KPIs that quantify major goals for trials and partners;

• identification of major cost and income categories for partners.

The four approaches to business planning are described in more detail in the following

subchapters.

2.1 Identification of Added Value by Using BDT The use of BDT is connected with major technical challenges such as data collection, storage,

processing and interpretation. A bigger challenge than that is however, to use BDT in a way

that it results in creation of added or new business value for companies [REF-07], [REF-12],

[REF-13]. In prevailing literature potential added value of BDT is classified from different

perspectives. According to [REF-07] there are two major ways how companies can create

value by using BDT: 1) by developing new business opportunities (models) with new, big data

based offerings (for an overview of potential business models see [REF-17]; and 2) by

improving existing business processes through better internal decision making or through

data-driven decisions. The first type of value from BDT, i.e. business model-oriented value,

results in increased revenues by facilitating the targeting of new markets and customer

segments or by enabling increasing revenues with existing business models. It can also result

in improved competitiveness of a company. The second type of value created by BDT results



in reduced costs of existing business processes by improving internal business decision

making that results in higher productivity, cost efficiency or economies of scale within existing

business paradigms (see calculation examples in [REF-18]). For example, based on a study of

179 large publicly traded firms, the authors of [REF-19] have found out that companies that

adopt data-driven decision-making have output and productivity that is 5-6% higher than

what would be expected given their other investments and information technology usage.

A.T.Kearney [REF-12] identifies two major sources of big data value resulting from improved

internal decision making (see also Figure 1): creation of strategic value and improve of

operational efficiency.

Figure 1: Classification of big data value creation (source [REF-12])

The authors of [REF-13] propose an additional classification of BDT value in two categories:

value resulting from BDT use to the company and value resulting to the customer. Value

resulting from use of BDT on company level can impact and create value on industry and

society level as well. For example, a broad adoption of BDT in companies from the agricultural,

forestry and fishery industry might result in positive effects from society perspective with

reference to these three industries.

To summarise, the use of BDT can result in new data-based business models or in a decrease

of costs due to better and more precise decisions. New data-based business models provide

new markets and income sources for companies. Thus, the appropriate instrument to

calculate the business potential of the solution is a business plan. The decrease of operational

costs and improved operations results typically in higher productivity and profitability. For

this type of BDT value, a suitable business analysis instrument is the calculation of an internal

business case, where the potential gains result from savings in operational costs. Given the



different type of business value resulting from use of BDT, two approaches for business

analysis of trials will be applied (see Figure 2):

Figure 2: The business modelling approach

In the subsequent business analysis of pilots, the specific added value of BDT use will be

identified and, as far as possible, quantified.

2.2 Business Modelling and Business Planning One of the goals of the DataBio project is the illustration how the use of BDT can result in new

business opportunities for partners involved in DataBio pilots and overall in the three involved

industries: agriculture, forestry and fisheries. The new business opportunities are either

completely new business model or enhancements of existing business models of partners.

The business models are illustrated by using the Osterwalder Business Model Canvas [REF-04]

(see Figure 3):



Figure 3: The Business model canvas

The business models are described with the Business Model Canvas / by identifying and

denoting the following business model components [REF-04]:

Customer Segments: This business model component refers to the different target groups of

private or organizational customers a company aims to reach and serve. The customer

segments can be broken down into sub-segments if their needs require and justify a distinct

offer (e.g. they are reached through different channels, they require different types of

relationships, they have substantially different profitability, or they are willing to pay for

different aspects of the offer). In context of DataBio this component of the business model

refers to new customer segments that can be gained based on use of DBT or existing customer

segments that can be served with an improved value proposition due to use of BDT.

Value Propositions: The Value Propositions component represents the collection of products

and services that create value for a specific customer segments. The value propositions may

be quantitative (e.g. price, speed of service) or qualitative (e.g. design, customer experience).

According to [REF-05] a company's value proposition is what distinguishes the company from

its competitors. The value proposition provides value through various elements such as

newness, performance, customization, "getting the job done", design, brand/status, price,

cost reduction, risk reduction, accessibility, and convenience/usability. In context of DataBio

the consortium members either create new, innovative value propositions for existing or new

customers or enhance existing value propositions with the use of BDT.

Channels: The Channels component describes how a company communicates with and

reaches its customer segments to deliver its value proposition. Channels represent a

company’s interface with its customers, and can include communication, distribution, and

sales. In the context of DataBio the channels define the way of how big data services are

delivered to customers or consumed by pilot partners.

https://www.google.ch/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjxo5regIjYAhXEJZQKHSw6CikQjRwIBw&url=https://www.sswm.info/es/category/step-rrr-business-development/module-4-business-development-part-i-entrepreneurial-adapta-4&psig=AOvVaw1NQYpQhG2tf1-ZzdZG0Yd4&ust=1513289473851470



Customer Relationships: The Customer relationships component describe the types of

relationships a company establishes with specific customer segments, and can range from

personal relationships to entirely automated interactions. The customer relationships are a

key issue in determining the overall customer experience. In context of DataBio customer

relationships can be supported by or created around an innovative use of BDT.

Revenue Streams: The Revenue Stream component describes how a company will generate

cash from each Customer Segment. The revenue stream has to take into account how much

customers will be willing to pay for the value the company delivers. There are two basic types

of revenue stream: revenues from one-time customer payments, and recurring revenues

from on-going payments. In context of DataBio specific revenue streams are defined around

innovative big data solutions such as revenue streams for sensors and sensor data streams,

revenue streams based on new data collections and similar.

Key Resources: The Key Resources component describes the most important assets within a

company that make a business model work. According to [REF-04], these generally include

physical resources (e.g. buildings, vehicles, etc.), intellectual resources (e.g. brands,

partnerships, proprietary knowledge, etc.), human resources (e.g. employees), and financial

resources (e.g. cash, lines of credit, etc.). In context of DataBio key resources are new big data

assets and infrastructures that will be built in the pilots.

Key Activities: The Key Activities component describes the most important things a company

must do to make its business model work. These can be activities to create and offer value

propositions, reach markets, maintain Customer Relationships, and earn revenues. General

categories for key activities include production, problem solving, and platform/networking.

In context of DataBio key activities are those necessary to deal with big data assets. For all

involved pilots and technology providers these activities have to be developed.

Key Partnerships: The Key Partnerships component describes the network of suppliers and

partners that make a business model work. Partnerships are essential in most businesses to

optimise their business models, reduce risk, or acquire resources. Partnerships can generally

be categorised into: strategic alliances between non-competitors, strategic partnership

between competitors, joint ventures to develop new businesses, and buyer-supplier

relationships. In context of DataBio key partnerships arise in combining BDT from different

providers to create integrated solutions for trials. It is expected that new ecosystems of BDT

providers will emerge to create common value propositions for pilots.

Cost Structure: The Cost Structure component describes all costs incurred to operate a

business model, for example in creating and delivering value, maintaining customer

relationships, and generating revenue. Cost structures can be divided into fixed costs, variable

costs, economies of scale, and economies of scope.

The business model canvas and its nine components will be used for the description of the

additional value that is created by DataBio partners and trials by using BDT. The resulting

business models will be analysed and recommendation will be provided for their

improvement.



2.3 Definition of KPIs as Quantification of Business Goals

2.3.1 Overview of KPIs and their relationships

The business model canvas provides a qualitative description of a business model and its

components. While this is a useful overview of a business model at one glance, it doesn’t

provide a quantification of business goals that are pursued with a specific business model. To

provide also quantifiable goals for the business models, in the first year of the project, along

with the definition of the trials in technical terms, the business goals of the trials have been

defined and quantified in the form of business Key Performance Indicators (KPIs). The

identified KPIs are broken down to measurable basic KPIs that will be measured before and

after the trials have been conducted. The KPIs will also be the foundation for more

comprehensive business planning and developing of business models and plans in the next

deliverables. The deliverable at hand focuses on the main KPIs identified for DataBio.

In general, KPIs are defined as measurable values, i.e. as a set of quantifiable measures that

demonstrate how effectively a company or a business unit is achieving key business

objectives. These metrics are used to determine a company's progress in achieving its

strategic and operational goals, as well as to compare a company's finances and performance

against other businesses within its industry. KPIs can be defined at multiple levels of

organisation: high-level KPIs, that focus on the overall performance of a company and low

level KPIs, that focus on specific processes or organisational units. DataBio KPIs are developed

on two levels: top-down, on the project and technology ecosystem level, and bottom-up, on

trial, company and technology-provider level. Figure 4 illustrates the developed KPIs and their

relationships:

Figure 4: Overview of DataBio KPIs and their relationships

At a top-down level, two major sets of KPIs are developed: the DataBio project KPI and the

big data Ecosystem KPIs. The project KPIs measure and define the performance of the

investments in the DataBio project. They consider at the one hand the overall performance



of the project and on the other hand the potential improvements due to the use of big data

from industry perspective for the three industries: agriculture, forestry and fisheries.

The second category of top-down defined KPIs are those for Big Data Ecosystems KPIs. One

goal of the project is to integrate existing big data sources and related big data processing

software relevant for the trials and the three industries in focus and to illustrate the added

value of integrated use of existing single big data technologies. The new emerging big data

ecosystems comprising single big data products and data processing software will provide

new business opportunities for all involved technology providers. Thus, the Big Data

Ecosystem KPIs will reflect the performance of new big data ecosystems emerging in the

project and illustrate their potential for all involved parties and their potential customers.

The bottom-up KPIs are developed by each pilot and by each BDT provider. Thus, we have

two categories of bottom-up KPIs: KPIs reflecting the performance of trials and KPIs reflecting

the performance of technology providers. Pilot KPIs measure the achievement of the main

goal of each pilot resulting from the use of big data technology. Thus, pilot KPIs are granular

and focus on one business aspect of companies involved in trials. This could for example be

the outcome of one specific field considered in the pilot and the use of less input material (i.e.

fertilisers) in the specific field. Similar granularity is evident also with respect to trials in

forestry and fisheries.

Big Data Technology KPIs measure the new opportunities arising from the use of big data

technology in the trials for big data technology providers. There are two versions of these

KPIs: measurement of nominal increase of data usage and measuring of potential income

increase by offering existing big data technology in a new form in the three DataBio industries.

After the top-down and bottom-up KPIs have been defined, in a next step the relationships

among them will be defined through KPI breakdown and aggregation. This process is

explained in more detail in the next section.

2.3.2 KPI Breakdown and Aggregation

The identified top-down and bottom-up KPIs will be further analysed in the following way (see

also Figure 2):

• Breakdown of pilot KPIs to measurable basic KPIs: The KPls, defined by the trials can

be on different level of abstraction. They might concern for example a specific field in

agriculture or a specific ship in fisheries. Thus, the breakdown of these KPIs will result

in single disaggregated KPIs that can be measured for the specific trial.

• Breakdown of project KPIs: Project KPIs are defined with a high level of abstraction

and will be broken down in order to get connection to the bottom-up KPIs.

• Aggregation of bottom-up KPIs of big data technology providers.

Example of an indicator breakdown on the example of the KPI Gross Profit Margin in

agriculture (see also Figure 5):



Figure 5: Breakdown of the KPI Gross Profit

• Goal of KPI analysis: Increased profitability in agriculture.

• KPI applied: Gross Profit Margin. This KPI reveals the portion of money left over from

Total Revenues after accounting for the Cost of the Goods Sold.

• KPI breakdown: Starting with the overall formula (see also [REF-08]):

Gross Profit = Total Revenues (Sales) – Total Costs

𝐺𝑟𝑜𝑠𝑠 𝑃𝑟𝑜𝑓𝑖𝑡 𝑀𝑎𝑟𝑔𝑖𝑛 =𝐺𝑟𝑜𝑠𝑠𝑃𝑟𝑜𝑓𝑖𝑡

𝑇𝑜𝑡𝑎𝑙𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠

The KPI Total Revenues can further be broken down as follows:

Total Revenues = Quantity of goods sold * Price

Total Costs = Quantity of Goods * Cost per Unit

The KPI Cost per Unit can further be broken down to different cost categories

denoting costs for different resources used to produce the goods.

An improvement of the overall profitability KPIs can be reached when either:

• Costs of used resources to produce certain goods are decreased, i.e. the quantity of

used resources is decreased. In context of agriculture potential input resources that

can be decreased are: used fertilizers on a farm or field, quantity of used water for

irrigation, cultivar selections and similar. The decrease of used resources to produce

the output can also result in improved sustainable use of natural resources such as for

example less or no lixiviation of chemicals into the aquifers.



• The output of goods is increased with same or smaller input of resources. In the case

of agriculture, the increase of output refers to the harvest yield achieved per space

unit (i.e. m2 or per plant and tree).

• When a better sales price can be achieved on the market. A better sales price can be

achieved by entering new markets or by creating better sales strategy based on

improved market data availability and analysis.

Overall the decomposition of top KPIs enables the identification of basic measurable KPIs that

can be considered for measuring the impact and goal achievement of BDT application.

2.4 Initial Identification of Costs and Benefits for Pilots and Partners The third instrument applied to analyse the impact of BDT is an overall comparison of costs

and benefits for applying BDT. In comparison to the KPIs described in the previous chapter

that are applied to measure the achievement of specific business goals related to the usage

of BDT, the cost and benefit analysis provides an overall overview which investments, costs

and benefits occur for BDT application. Based on the pilot descriptions and also first business

ideas and KPIs, it will be possible to identify the major cost and income categories for trials,

technology and data providers as well as users. This process will be guided by the Gartner

total cost of ownership approach. Gartner defines with the term total cost of ownership

(TCO) a comprehensive assessment of information technology (IT) or other costs across

enterprise boundaries over time. For example, for IT, TCO includes hardware and software

acquisition, management and support, communications, end-user expenses and the

opportunity cost of downtime, training and other productivity losses3. In analogy to the

Gartner Total Cost and literature (see for example [REF-06]) the cost categories and TCO of

applying BDT will be identified for trials and partners and compared to potential benefits

resulting from application of BDT.

The following cost categories can be envisioned:

- Investment costs: From the perspective of pilot users, investment costs are the upfront

costs necessary to set up the technology (trial) before it can be used in everyday

routines of pilot users. For example, to use BDT beyond the DataBio project, some

pilot user might have to invest in an initial set of sensors, installation of sensors,

education of their employees, and similar. Several sources might contribute to the

investments costs: research projects as DataBio and other similar projects, venture

capital, private capital by founders and own investment of the company. Depending

on how the investment costs are financed, i.e. if they have to be returned, the whole

investments costs or part of it need to be transferred in depreciation costs.

- Running fixed and variable costs: These costs occur through the everyday use of the

big data technology

3 https://www.gartner.com/it-glossary/total-cost-of-ownership-tco



The goal is to identify these two types of costs and income categories and summarize them

in a simplified business plan (see Table 2).

Table 2: Example overview of cost and benefits for trials and DataBio Partners

Cost Structure of Pilot (Partner) xx

Fixed Costs

- Depreciation

- Machinery

- Big Data infrastructure (platform)

Variable Data

- Energy costs for running sensors

- Costs for replacements of sensors

- Costs for external data streams

- …

Income Structure of Pilot (Partner) xx

- Increased income from increased harvest

- Increased Income due to increased productivity

- …

Besides an overview of costs, the cost-benefit table also contains an overview of potential

added value resulting from application of BDT. The added value of BDT will be analysed in the

specific context of BDT use in trials. As described in section 2.2 potential increase in company

income might result in two ways: from new business models based on BDT and the possibility

to create new products and to address additional markets and customer segments or by

increasing the outcome of activities supported by BDT.



Business Models and KPIs in Agriculture Pilots 3.1 Definition and Classification of KPIs in Agriculture Trials The agricultural sector is of strategic importance for the European society and economy. It

contains a broad spectrum of industries that at present are facing a series of challenges that

affect their production, productivity and profitability. Examples of these challenges on the

one hand are crop pests and diseases with increasing resistance, drastic changes due to

effects of climate change, and decreasing availability of certain resources such as irrigation

water. On the other hand, the fast-growing world population increases the demand for food.

To cope with these challenges, new innovative approaches in agriculture are necessary.

DataBio pilots aim to provide a contribution in agriculture and focus on the following

innovative developments in agriculture:

• Precision agriculture in: a) olives, fruits and grapes; b) vegetable seed crops; c)

vegetables (potatoes) – (3 pilots)

• Management in greenhouse eco-system – (1 pilot)

• Cereal and biomass crops – (4 pilots)

• Smart Machinery Management – (1 pilot)

• Insurance in agriculture - (2 pilots)

• Common Agricultural Policy (CAP) support – (2 pilots).

These are listed in the table below:

Table 3 Agriculture pilots

Task Subtask (or Pilot ID) Pilot

T1.2 Precision Horticulture including vine and olives

T1.2.1 A1: Precision agriculture in olives, fruits, grapes and vegetables

A1.1: Precision agriculture in olives, fruits, grapes (@Greece)

A1.2: Precision agriculture in vegetable seed crops (@Italy)

A1.3: Precision agriculture in vegetables -2 (Potatoes, @Netherlands)

T1.2.2 A2: Big Data management in greenhouse eco-systems

A2.1: Big Data management in greenhouse eco-systems (@Italy)

T1.3 Arable Precision Farming

T1.3.1 B1: Cereals and biomass crops

B1.1: Cereals and biomass crops 1 (@Spain)

B1.2: Cereals and biomass crops 2 (@Greece)

B1.3: Cereals and biomass crops 3 (@Italy)

B1.4: Cereals and biomass crops 4 (@Czech Republic)

T1.3.2 B2: Machinery management

B2.1: Machinery management (@Czech Republic, Italy)

1.4 Subsidies and insurance

T1.4.1 C1: Insurance C1.1: Insurance (@Greece)

C1.2: Farm Weather Insurance Assessment (@Italy)

T1.4.2 C2: CAP support C2.1: CAP Support (@Italy, Romania)

C2.2: CAP Support (@Greece)



The objective of the pilots is to illustrate through different usage scenarios that in agriculture

BDT use has the potential to result in new business models and/or optimised operational

processes. The respective KPIs to measure the added value can be classified in the following

basic categories:

• KPIs reflecting the use of resources: Examples of this type of KPIs are: fertilizer

consumption, use of irrigation water, working hours spent on paperwork.

• KPIs reflecting the increase of agriculture outcome: increase of harvested quantity per

field, revenues, market share.

• Efficiency, productivity and profitability KPIs calculated by comparing use of resources

and resulting outcome.

In total there are 13 agricultural DataBio pilots. In six of them the main BDT providers are

companies and in the remaining seven pilots this are independent or state-owned research

institutions. The pilots, where companies are the main BDT providers, build upon their

existing offerings or research and development activities that are extended and verified in the

pilots. Given this and the early stage of business development of the pilots presented in this

deliverable, the business analysis focused on a selection of agricultural trials that either have

a company as main BDT provider or are the only pilot that is considered as representative of

a certain BDT usage scenario.

3.2 Examples of Business Plans and Business Models in Agriculture

3.2.1 Business Opportunities of Neuropublic / GAIA Epicheirein in the Pilots: T1.2.1-

A1.1, T1.3.1-B1.2, T1.4.1-C1.1 and P1.4.2-C2.2

Neuropublic (NP) is an innovative information and communication technology (ICT) and SME

company, specialized in the development of integrated information systems and high-

demand cloud-based applications. One of its business areas is providing services in the

agricultural application area not only for the Greek government, but increasingly also directly

to farmers. NP developed the information system GAIA EPICHEIREIN, which on a subscription

basis provides a variety of services to all players of the agricultural value chain. In particular,

NP has designed the GAIA Tron agri-sensor for the remote monitoring of environmental and

soil data and has placed such sensors on a considerable part of the Greek agricultural space.

The goal is to cover the whole Greek arable area with sensors until the end of 2018. Already

placed sensors have been used over several years to collect data. Thus, NP and GAIA possess

a valuable collection of agricultural data. In the DataBio pilots the sensor data will be

integrated and enriched with EO data and other external data sources of relevance. The

DataBio pilots are a favourable opportunity to verify the added value of the data and to show

its applicability and added value in several scenarios. Based on the existing data and its

extension and enrichment NP is main technology provider in the following agricultural pilots:

• A1.1: Precision Agriculture in olives, fruits, grapes

• B1.2: Cereals and Biomass Crop_2



• C1.1: Insurance

• C2.2: CAP Support (Greece)

Even though the application areas of the four pilots differ considerably and pose different

requirements upon the use of the available sensor data as well as upon its integration with

EO and other external data, the big data sensor infrastructure of all four pilots is the same.

Furthermore, also the main technological partner is the same, so that the four pilots actually

provide examples of four substantially new cloud-based services. Given this, the business

analysis was done in an aggregated manner for all four pilots and from perspective of NP /

GAIA EPICHEIREIN. In the remaining text of this section containing the results of the business

analysis of the four pilots, aspects relevant specifically only for one pilot are provided in

different colours: A1.1 can be considered as basic pilot upon which all the other three pilots

build. Text related to A1.1 is provided in black letters. Specific aspects for B1.2 in red, for C1.1

in blue, and for C2.2 in green.

3.2.1.1 Business Model of NP for T1.2.1-A1.1, T1.3.1-B1.2, T1.4.1-C1.1 and T1.4.2-C2.2

The aggregated business model of NP for all four pilots is illustrated in Figure 6. The business

model canvas shows that with some of the DataBio pilots NP is developing new data-based

services for new customer segments as insurance companies. Furthermore, the overview in

the value proposition component of the business model shows the spectrum of added value

that can be provided for the different customer segments based on BDT: data-driven decision

making has high potential to decrease the use of resources and operational costs in

agriculture.

Figure 6: Business model of NP for all four pilots



3.2.1.2 Overview of KPIs for NP

As illustrated in the business model canvas in Figure 6, the combination of remote sensing

data from fields with EO and other external data has considerable potential to provide added

value for all involved players. Following, the major KPIs for quantification of the added value

from perspective of the different players are described.

Major KPIs from perspective of NP / GAIA:

• % of agricultural land in Greece covered with sensors: The overall success of the

business model depends on the critical mass of placed sensors, i.e. the agricultural

space covered by sensors.

• Number of customers pro customer segment acquired: Economies of scale can only be

achieved if the high investment costs for the sensor infrastructure are divided among

many customer

• Number of subscriptions sold (per subscription type): This KPI is similar as the previous

one, but in financial terms. The modular architecture of the data and analytics services

allows NP to offer different versions of subscriptions. In case there are different

subscription types than also the number per subscription type is of relevance.

• Number of replaced sensors per month: Decreased number of sensors that need to be

replaced per month. In a similar way also other cost categories might be defined

Major KPIs from perspective of farmers, farm cooperatives and agronomist on the example

of pilot A1.1 are provided in Table 4.

Table 4: Overview of KPIs for pilot T1.2.1-A1.1

KPI short name

KPI description Base value Target value (year 1)

Target value (year 2)

Total change (%)

Unit of value

A1.1_1 Reduction in the average cost of spraying per hectare for the three (3) crop types following the advisory services at a given period.

Chalkidiki (olive trees): 250, Stimagka (grapes): 990, Veria (peaches): 810



Chalkidiki (olive trees)≈ 15, Stimagka

(grapes)≈

3.5,

Veria

(peaches)≈ 5

euros/ha

A1.1_1.2 Reduction in the average number of unnecessary sprays per farm for the three (3) crop types following the advisory services at a given period.

Chalkidiki (olive trees): 5 Stimagka (grapes): 4 Veria (peaches): 4



Chalkidiki (olive trees)≈ 80, Stimagka

(grapes)≈ 75,

Veria

(peaches)≈

75

number of sprays



A1.1_3 Reduction in the average cost of irrigation per acre for the three (3) crop types following the advisory services at a given period.




Chalkidiki (olive trees)≈ 30, Stimagka (grapes)≈ 30, Veria (peaches)≈ 30

euros/ha

A1.1_4 Reduction in the amount of fresh water used per hectare following the advisory services at a given period


Chalkidiki (olive trees): 695, Stimagka (grapes): 1588, Veria (peaches):1448


Chalkidiki (olive trees)≈ 30, Stimagka (grapes)≈ 30, Veria (peaches)≈ 30

m3/ha

Similar KPIs but for arable crops instead of olives, grapes and peaches are applicable also for

B1.2. An additional KPI for both trials might also be the following:

• %Decrease in environmental footprint due to use of less fertilizers fresh water for

irrigation and similar.

The potential added value from the CAP supporting services in C2.2 can be qualified with the

following KPIs:

• % Decrease in false crop type declarations following the supporting services compared

to what would be expected based on historical data

• % Accuracy in crop type identification

From perspective of insurance companies, the following KPIs are of relevance:

• Accuracy in damage assessment

• Decrease in required time for conducting an assessment.

Overall, the pilots have the potential to provide added value for many players involved in the

agricultural value chain.

3.2.1.3 Overview of Cost and Income Categories for NP

Table 5 provides an overview of the major cost and income categories for NP and the four

pilots. Again, costs and income categories that are specific for a certain pilot, are given in

different colours.

After the platform is implemented the following operational costs might incur:

- Support for users

- Operational costs for infrastructure for storing and processing data

- Personal costs for operating and further developing the platform



- Payments for partner data services

- Adaptation of infrastructure to changes in law and other regulation

- Marketing costs for acquisition of forest owners, forest contractors and timber buyers

Operational costs for support of users and for infrastructure for storing and processing data

might grow in leaps as soon as a certain number of users is reached. Furthermore, the

platform should be capable of dealing with suddenly increasing demand in case of extensive

large-scale damage.

Table 5: Summary of cost and income categories for NP for four pilots

Cost Structure of NP for A1.1, B1.2, C1.1 and C2.2

Investments Costs:

- Investments in additional sensors

- Development of algorithms for the collection, storage, management, integration and processing of EO and other external data

- Development of analytics and visualization algorithms suitable for the different customer segments

- Development of algorithms for identification of damages

- Development of CAP services

Operational Costs:

- Support for users

- Maintenance of BDT infrastructure for storing and processing data

- Maintenance of sensors in the field


- Marketing costs for acquisition of customers from the different segments

- Updates on procedures for identification of crop damage

Income Structure of NP for A1.1, B1.2, C1.1 and C2.2

- Subscriptions for DaaS per customer segment

- Sold special analysis and reports (i.e. damage reports)

- Sold CAP services

3.2.2 Business Analysis of the Pilot T1.3.2-B2.1 “Machinery Management”

Pilot B2.1 is focused on collecting telemetry data from machinery and analysing them in

relation with other farm data. The main technology provider, the Czech tractor manufacturer

Zetor, is now developing and implementing a first telematics systems for its tractors. The

second technology provider FederUnacoma is supporting this development with expertise in

electronic communication standards and machine-to-machine communication (M2M).

Lesprojekt is responsible for integration of data from different sources and their analysis.

Major end users of the pilot are farmers and tractor owners.

The business analysis presented in this section is primarily from Zetor’s point of view. For

Zetor the enhancements of its tractors with telematics systems is providing the foundation

for an extension of the existing business model. Besides selling enhanced tractors and offering



remote maintenance, Zetor can also create new business based on field data collected with

the tractors. Zetor’s business model can be summarized as illustrated in Figure 7:

Figure 7: Business model canvas for T1.3.2-B2.1

In the pilot description, Zetor has correctly mentioned that it is not only necessary to enhance

the own machines with telematics, but also to be able to connect and exchange data with

machines from other producers. A farmer typically owns machines from different producers.

In this context, it is important to consider which kind of relationship with external machine

provider are relevant for Zetor. One option is that Zetor becomes with his machines one

contributor to an overall farm management system provided by another company. In this case

Zetor will not have an overview over all data and an inferior role in the overall farm

management system and business model. Another option would be to aim for taking over the

leading role in the farm management system, by providing the dashboard and integrating

other machines in the own system. This approach would allow Zetor to keep the customer

relationship and to be the main contact for the customer. However, this will require additional

investments and development efforts.

3.2.2.1 Overview of major KPIs for T1.3.2-B2.1

According to D1.1 major KPIs of this pilot are:

• Number of tractors and agricultural machinery using DataBio solution

• Number of enhanced tractors sold

• Number of existing customers that want to upgrade the tractors they own with

telematics



• Number of various tractor brand/models tested

• Sold data and analytics services

Further KPIs that might be suitable to be measured during the project also from the

perspective of end users and related to BDT are:

• Decrease of non-operation status of machines

• Increased customer care by tractor producer

• Lower fuel consumption, less time consuming for various farm activities

• Farmer operating time savings

• Possibility to have focus on more demanding and more precise types of work with

higher utilisation value

• Accurate dosing of seeds, fertilizers, pesticides and other resources

• Possibility to create yield maps and optimally increase fertility

• Possibility to create statistics on the field and enable efficient work organization

3.2.2.2 Initial Identification of Cost and Benefits for T1.3.2-B2.1 from perspective of Zetor

The major investments from the perspective of Zetor are: investments in telemetry suitable

to be built into their tractors, development of analytics approaches for data collected by

tractors, investments into interfaces for connection with external data providers. The major

cost and benefit categories for B2.1 are summarized in Table 6:

Table 6: Summary of cost and income categories for T1.3.2-B2.1 from perspective of Zetor

Cost Structure of pilot B2.1 from perspective of Zetor

Investments Costs:

- Investments in machine telemetry

- Investment in interfaces for collection of third-party data

- Investment in collection, integration, storage, management and analysis of third-party external data as EO data, field boundary data and similar

- Investment in new visualisation and dashboards

Operational Costs:

- Support for users

- Maintenance of BDT infrastructure for storing and processing data

- Maintenance of sensors machines


- Marketing costs for acquisition of customers from the different segments

Income Structure of pilot B2.1 from perspective of Zetor

- Sales of telematics enhancements for already sold machines

- Sales of new tractors enhanced with telematics

- Sales of data services and analytics



3.2.3 Summary of business analysis of agricultural pilots

The business analysis of the agricultural pilots was done based on the example of four pilots

that have NP / GAIA as main BDT provider and are representing the four thematic areas in

which pilots are created. An additional example that was considered because of the specific

characteristics is the example of B2.1 with Zetor as main technology provider. Based on this,

five representative examples the following findings were identified:

• BDT has the potential to provide foundation for new business models and for

improvement of operational processes. Examples of new business models that will be

tested with the pilots are: Neuropublic’s sensor-based collection of agricultural data

and development of data services based on them as well as the enhanced smart

tractors and other agricultural engines of Zetor and new data services based on them.

Besides resulting in new business models for technology provider, these solutions

have the potential to provide additional added value for other players in the

agricultural value chain such as farmers, farmer cooperatives, insurance companies

and governmental institutions.

• At this early stage of business modelling, it was not possible to quantify the business

models completely so that only major KPIs and major categories of costs and revenues

were identified.

• The highest value from BDT can be achieved when all players of the value chain can

identify advantages. This can be illustrated on the example of Zetor: smart agriculture

machines are only relevant for the market in case they can provide substantial added

value and savings for farmers using them.

• The examples show that there is high potential to increase operational efficiency of

farmers with BDT. However, all pilots also show that substantial investments in BDT

are necessary to achieve the envisioned savings. Furthermore, to implement the

solution personnel with specific qualification is required. These are preconditions that

cannot be met by most of the farmers, in particular small farmers. Thus, there is a

need for entrepreneurship and companies that are dedicated to bringing BDT for

agriculture to the market in a form suitable for farmers. This holds even more

considering the fact that from 13 pilots, in more than half of them the BDT is

developed by independent or state-owned research institutions. If the pilots prove

that BDT provides indeed substantial added value, the next challenge will be to

productise the technology in a way that it can be offered on the market in an

affordable way.

• The success of BDT in agriculture depends also on the critical mass of farmers that are

willing to adopt it and the critical mass of coverage of agricultural land. If broad

acceptance cannot be assured, it will be difficult to refinance the high investment

costs.



Business Plans and KPIs in Forestry Trials 4.1 Definition and Classification of KPIs in Forestry Trials According to various communications and papers provided by the European commission (see

for example [REF-09], [REF-10]), European forests account for 5% of the world’s forests ([REF-

14]. European forests and other wooded land cover 40% of the EU’s land area, with a great

diversity of character across regions [REF-09]. In recent decades the EU’s forest area was

increasing by 0.4% per year. Furthermore, 60-70% of the annual increment is being cut and

the growing stock of wood is rising [REF-09]. By 2020 it is however expected that harvest rates

will increase by around 30% compared to 2010 [REF-09]. Some 60% of forests are owned by

several millions of private owners, with numbers set to rise as restitution of forest ownership

in some Member states continues.

The socio-economic, environmental importance of forests is high as forests contribute to

[REF-09], [REF-10]:

• Rural development and provide around three million jobs [REF-09]

• Wood is considered an important source of raw material for emerging bio-based

industries [REF-09]

• Forest biomass is currently the most important source of renewable energy and

accounts for around half of the EU’s total renewable energy consumption [REF-09]

• Protection of settlements and infrastructure [REF-10]

• Protection of soil, freshwater supplies, conservation of biodiversity [REF-10]

• Regulation of local and regional weather [REF-10].

Against this background and given the importance of forests, a broad array of EU policies and

initiatives consider forests. Examples of goals of these policies and initiatives that are relevant

for DataBio are [REF-09], [REF-10]:

• To improve information about forests (forest inventories, forest classification, forest

condition monitoring, monitoring of forest damages as fire and similar)

• Increase efficiency and productivity in forest management

• Introduction of forest management plans on all levels

• Proper forest planning

• Sustainable mobilization and harvesting of forest

• Protection of forest from significant effects of storms and fire

• Protection of the health of forests

• Developing of a conceptual framework for valuing ecosystem services

The DataBio pilots will illustrate how BDT can support in achieving these goals. The pilots will

in particular contribute the following aspects [REF-02]:

• Improved and more precise information about forest

• Increased productivity of forest management activities

• Increased profitability of forests by reducing forest management costs



• Optimized use of tree resources

• Improved health protection of forests by providing forests health maps.

Given the above goals the main KPIs for forestry are: productivity and profitability.

Productivity: Productivity is defined in forestry differently by different authors depending on

the context in which it is considered: According to [REF-07] productivity in forestry from the

perspective of forest plantation is defined as “… the cubic meters of harvestable wood that

can be grown per year on forested site. ... Productivity not only covers harvestable wood, but

the quality of that harvested wood for various purposes. Even more broadly, it includes the

productivity of other goods and services an indigenous or plantation forest can, or could

provide.” According to the same authors some factors affecting plantation productivity in

forestry are [REF-07]:

- Access: forests are often remote or even inaccessible. So, getting to forests and

accessing forest for various management activities as plantation, thinning and

pruning, release and other management activities cause substantial costs. Access

furthermore requires energy to harvest forestry products.

- Site selection: soil depth and drainage, soil physical and chemical composition,

amount and pattern of yearly soil moisture availability, frequency and nature of

common and occasional winds, storms and fires, and the general climate of the area

are important components of site quality.

- Species choice: different tree species have different productivity potential.

Further factors that affect plantation productivity are: avoidance of both inbreeding and

dysgenic selection, spacing and age control, nursery practices, planting, post-planting care,

soil moisture and fertility, thinning and pruning, and biological diversity consideration. All

these factors that have influence on productivity in forestry can be divided in two major

groups: factors that are independent and cannot be affected by human activities and factors

that are part of forest management and can be affected by humans.

The definition of forest site productivity by the authors of [REF-11] accounts for the impact of

forest management and defines forest site productivity in the narrow sense as: “… the

production that can be realized at a certain site with a given genotype and a specified

management regime. This depends on both natural factors inherent to the site and on

management related factors. In managed forests, the inherent site potential is determined

largely by soil characteristics and climatic factors. Management can affect the production

potential through silvicultural options such as site preparation, choice of tree species,

provenance, spacing, thinning and regeneration method. Additionally, environmental

conditions in the surrounding forest (e.g. wind-sheltering effect of neighbouring stands) and

practical aspects of forest operations (e.g. damage to crop trees and soil compaction) may

also influence the production potential.”

Given the impact of natural factors that are always specific for a certain forest site, we can

conclude that forest productivity is related to a certain forest site and can be positively

influenced by increased efficiency of forest management activities. Both definitions reveal



that forest productivity is to a certain extent delimited by natural factors that are not

completely controllable. However, it also reveals that forest management activities such as

forest access, spacing and age control, site selection and similar activities have an impact on

forest productivity by influencing on the one side the potential yields from a given forest and

on the other side the costs of forest management. Efficient and precise forest management

activities such as spacing, planting, nursery practices and further similar activities can impact

forest growth and result in a higher output of wood. At the same time more precise and

efficient forest management activities can reduce overall input of resources used for forest

management.

Thus, the productivity of a forest site can be expressed in its simplest form as:

𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 =%𝐻𝑒𝑖𝑔ℎ𝑡𝐺𝑎𝑖𝑛 (𝑜𝑟 %𝑉𝑜𝑙𝑢𝑚𝑒𝐺𝑎𝑖𝑛)

𝑁𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝑉𝑖𝑠𝑖𝑡𝑠

The denominator of the formula “Number of Visits” can be replaced by other types of input

resources as number of seedlings and similar. If the goal is to analyse the impact of several

input resources together, then more complex formulas as proposed in [REF-11] need to be

considered.

The second important KPI for measuring the impact of BDT use in forestry is Profitability.

While the productivity measure expresses improvements in terms of nominal variables, the

profitability measure expresses improvements in monetary terms (see also [REF-15], [REF-

16]). As already explained in section 2.3.2, the profitability KPI compares forest sales and

incurred costs to produce the sold wood. While the productivity KPI is impacted by natural

factors, profitability is impacted in addition to that also by market prices. Profitability can be

calculated on different level of detail: gross profitability and gross margin, operating

profitability and operating margin as well as relative profitability.

In the following sections the above KPIs and the business modelling and analysing approach

described in Chapter 2 will be applied for the analysis of the DataBio forestry pilots. The

DataBio forestry pilots are listed in the table below:

Table 7 DataBio forestry pilots


T 2.2 Multisource and data crowdsourcing /e-services

T2.2.1 Easy data sharing and networking

T2.2.2 Monitoring and control tools for forest owners

T 2.3 Forest Health / Remote/Crowd sensing, Invasive species/damage

T2.3.1 Forest damage remote sensing

T2.3.2-FH Monitoring of forest health

T2.3.2-IAS Invasive alien species control and monitoring

T 2.4 Forest data management services (forecast/predict)

T2.4.1 Web-mapping service for the government decision making

T2.4.2 Shared multiuser forest data environment



4.2 Examples of Business Models, KPIs and Business Plans in Forestry In the forestry pilots T2.2.1, T2.2.2 and T2.3.1 the company MHG Systems Oy (MHGS) is the

main BDT provider. All three pilots are based on a major integration of basic forest data from

the Finnish Forest Centre Metsak (metsään.fi) and data from MHGS. However, while the two

pilots T2.2.1 and T2.2.2 are based on the crowdsourcing paradigm for collection of detailed

and up-to-date forest data, T2.3.1 is based on integration of earth observation data (EO) and

differs to a great extent compared to T2.2.1 and T2.2.2. T2.2.2 builds upon T2.2.1 and both

pilots have the same customers, partners and payment models. Given this, and to avoid

repetition in the analysis, the business analysis of T2.2.1 and T2.2.2 have been aggregated

into one business model.

4.2.1 The Business Model of MHG Systems related to Pilot T2.2.1 and Pilot T2.2.2

This chapter contains the business model analysis of the trials T2.2.1 – “Easy Data Sharing and

Networking” and T2.2.2 – “Monitoring and Control Tools for Forest Owners” based on their

description in [REF-02]. These two pilots are considered together from the perspective of the

main technology provider MHGS. Pilot T2.2.1 provides new data sharing services along the

forestry value chain that are relevant also for pilot T2.2.2.

4.2.1.1 The Business Model Canvas for Pilot T2.2.1 and Pilot T2.2.2

The core value proposition of MHGS is to collect and integrate forest data from different

sources and serve all stakeholders in the timber and biomass business value chain with easy-

to-use services based on seamless access to multiple sources of data as forest management

plan, meteorological, timber price and geospatial data. To achieve this in the DataBio project

the Wuudis platform of MHGS is connected to available forest data from Metsak (metsään.fi)

(Pilot T2.2.1). These data are enhanced and updated with data from additional data sources

such as for example data resulting from quality monitoring by forest owners and forest

contractors. Furthermore, an integration with existing forest monitoring functionality from

Spacebel is planned along with an integration with the DataBio platform operated by VTT and

the drone and hyperspectral camera monitoring by SENOP. Based on the integrated forest

data the focus of T2.2.1 is on enabling flexible data sharing features among forest owners,

forest contractors, timber buyers and forest authority experts. In T2.2.2 additional tools are

added for monitoring and controlling of forest by forest owners. The monitoring services of

T2.2.2 will be verified in Finland and the Czech Republic.

Based on business model relevant descriptions of pilot T2.2.1 and T2.2.2 in [REF-02], the

business models of the pilots T2.2.1 and T2.2.2 from perspective of MHG Systems (MHGS) as

main technology provider are presented in summarized form in Figure 8: Content written in

Figure 8 black is valid for the business models of both trials and content written in blue

denotes additional characteristics relevant for pilot T2.2.2. The same holds also for the

subsequent text of this section until the next Chapter 4.2.2. The data services in both pilots

are based on available forest data from matsään.fi that at present is updated with a frequency

of ten years. The new services developed by MHGS and available through The Wuudis mobile

app enable a more frequent update of data and an innovative way of quality control.



Figure 8: The business model of MHGS in pilot T2.2.1 and T2.2.2

Figure 8 reveals that the business relationship as well as payment model for forest contractors

and timber buyers is not verified yet.

Recommendation: With the new flexible data sharing services provided by Wuudis, forest

contractors and timber buyers will be able to search for potential customers (forest owners)

over the Wuudis platform. Furthermore, they will be able to send updates on the work or

logging in an efficient way. As the flexible data sharing provides value for forest contractors

and timber buyers, it might be possible for MHGS to charge transaction fees for each forest

management and logging transaction contracted and reported over the Wuudis platform.

The same observation holds for forest insurance companies and their consideration in the

business models as customers with specific needs. The access to the forest data might be

monetised through available SaaS options.

4.2.1.2 Analysis of the KPIs for pilot T2.2.1 and T2.2.2

With the enhanced Wuudis platform, MHGS is creating a new offering based on data. It

creates value for all involved stakeholders. Thus, relevant KPIs can be defined from the

perspective of all stakeholders.

For Wuudis Forest business model (MHGS) to be successful, it is necessary to acquire a critical

mass of forest owners as well as forest contractors and timber buyers. Without critical mass

of the different types of users, the usefulness of the collaboration features cannot be proven.

Thus, the most important KPIs from perspective of MHGS is the number of participating forest

owners, forest contractors and timber buyers. Another important KPI is the number of sold

data licensing. Thereby, the more users are participating the more attractive the Wuudis



platform and services becomes. The Wuudis platform was able to acquire about 5000 users

in 2017. For the next years the following end user numbers are planned: 10 000 users until

the end of 2018 and 50 000 users until end of 2019. From perspective of the pilot an important

KPI is also the number of users in the associated regions “Rangunkorven yhteismetsà” and

“Hippala”.

From the forest owners’ perspective, the use of the Wuudis platform provides the basis for

increased productivity and profitability by decreasing time and costs for forest management

activities. It also provides the opportunities to better implement requirements from forest

authorities and gain faster subsidies. Overall, forest owners can improve forest yield by

improved forest management activities. The major KPIs can be summarized as follows:

• Decrease of time for contracting with forest contractors and timber buyers

• Decrease of time for reporting

• Decrease of time to receive subsidies

• Increase of forest management activities and a as a consequence potential to increase

forest yield and forest profitability

• Decrease in damages in high biodiversity sites

• Decrease in costs for forest monitoring

From the perspective of forest contractors and timber buyers, the use of the platform would

allow for following improvements:

• Decrease of search time for customers

• Decrease of time for contracting


• Better customer service through real time operational reporting

• Decrease of costs for accessing the forest due to precise information

• Potential for getting more contracts within a given time

• Potential to increase number of contracts conducted during a certain period of time

• Support in maintaining up to date data.

From the perspective of forest insurance providers, the availability of more precise and timely

data about forest damages provides the opportunity for following improvements:

- Faster assessment of damage

- Precise quantification of economic losses for compensation payment

- Flexible insurance coverage based on speed of forest owner’s reaction upon damage.

Forest owners that react fast to deal with damages might get payment faster and

discounts on insurance. Forest owners that can prove to take care of their forest might

get discounts on insurance and similar insurance models might become possible.

From the perspective of forest authorities, the use of the platform would result in the

following improvements:

• Increased accuracy of data and more precise decisions regarding subsidies



• Decrease of costs for data update

• Increased speed of reaction upon damage

• Better advice in case of damage

• Faster reaction to damages and by that reduction of overall costs

• Seamless digital communication with forest owners without media breaks through the

Wuudis app

Overall, the data sharing platform would improve productivity and profitability for all involved

stakeholders by enabling a decrease of operational costs for forest management.

4.2.1.3 Cost Benefit Analysis for Pilot T2.2.1 and T2.2.2

Based on the planned activities a first cost benefit analysis for the Wuudis platform can be

calculated (see also the summary in Table 8). The investment costs can be summarized as

follows:

- Implementation of interface and API to metsaan.fi and to enable data exchange

- Enhancements of existing web services and mobile app

- Development of flexible collaboration features

- Development of Wuudis mobile app to enable work quality monitoring in a

standardized way. It is planned to collect the following information: forest estate,

geometry of compartments, type of the forest work, sample plot location, measured

data per sample plot, measurement averages per compartment, measurement date

and user information.

- Development of features for non-wood product monitoring needs

- Development of features for forest damage monitoring

- Development and integration of external functionality for identification of different

forest damages

Several sources might contribute to the investments costs: research projects as DataBio and

others, venture capital, private capital by founders and own investment of the company.

Depending on how the investment costs are financed, i.e. if they have to be returned, the

whole investments costs or part of it need to be transferred in depreciation costs.


- Support for users

- Operational costs for infrastructure for storing and processing data





Operational costs for support of users and for infrastructure for storing and processing data

might grow in leaps as soon as a certain number of users is reached. Furthermore, the

platform should be capable of dealing with suddenly increasing demand in case of extensive

large-scale damage.



For the Wuudis platform potential new income categories are:

- Subscription by end users per month and year

- Platform licensing by business users

- Payed data services

These income streams result from new business transactions with end users and forest

authorities. Potential transaction relationships with forest contractors and timber buyers

have not been verified yet. One option is to collect transaction fees and other SaaS fees from

these stakeholders.

Table 8: Summary of cost and income categories for Pilot T2.2.1 from perspective of MHGS

Cost Structure of T2.2.1 and T2.2.2 from perspective of MHGS

Investments Costs:

- Implementation of interfaces and APIs to metsaan.fi and to enable data exchange

- Enhancements of existing web services and mobile app

- Development of flexible collaboration features

- Development of Wuudis mobile app to enable work quality monitoring in a standardized way (sample plots “kemera”).

- Development of features for non-wood product monitoring needs

- Development of features for forest damage monitoring

- Development and integration of external functionality for identification of different forest damages

Operational Costs:

- Support for users

- Infrastructure for storing and processing data



- Updates on procedures for identification of forest damage


Income Structure of T2.2.1 and T2.2.2 from perspective of MHGS

- Subscriptions from end users

- Sales of additional services to users (i.e. quality control modules)

- Sold platform licenses to business and authority users

- Sold data licenses to business users (Data as a Service)

- Sold special analysis and reports (i.e. damage report)

4.2.2 The case of MHG Systems with Pilot T2.3.2- Forest Damage Remote Sensing

This business model analysis is based on the description of Pilot T2.3.1 in [REF-02]and is

mainly conducted from the perspective of the main partner MHGS. The goal of Pilot T2.3.1 is

the development of a Forestry monitoring system for the Wuudis platform based on almost

real-time remote sensing (satellite, aerial, UAV) and field surveys. The pilot will develop a

comprehensive and near real-time quantitative assessment for forest cover, forest above-



ground carbon stock and carbon stock change over the project pilot areas (Rangunkorpi

cooperative forest and Hippala). This allows detecting and measuring damages, deforestation

and forest degradation, which is a major cause of loss of biomass and carbon stores.

Figure 9 summarizes the Pilot T2.3.1 business model from perspective of the main technology

provider MHGS.

Figure 9: The business model of MHGS in Pilot T2.3.2

Many characteristics of the business model are similar to the business model of T2.2.1 and

T2.2.2 as shown in Figure 8. The major difference is in the quantity of data and different

approaches for data analysis. One major difference from perspective of the business model is

also the fact that remote sensing is a service that can be ordered on demand for e certain

forest area, for example by forest owners or forest authorities. Thus, it can be priced per order

and size of the specific forest space.

Similar as in the business model of pilot T2.2.1, not all potential customer segments are

considered in form of potential revenue streams.

Recommendation: Development of specific offerings and monetisation strategies for forest

contractors, timber buyers and forest insurance providers.

The almost real-time forest monitoring solution developed in Pilot T2.3.1 promises to deliver

the critical and relevant mass of forest area (sites) covered by automatic collection of data

and monitoring faster than the crowdsourcing based solution of Pilot T2.2.1 and T2.2.2. Even

though users still contribute to achieving greater precision of the collected data, they are not

the main source of data. This makes the solution more reliable.



4.2.2.1 Analysis of the KPIs for pilot T2.3.1

This pilot creates value for all involved stakeholders and relevant KPIs can be defined from

the perspective of all stakeholders.

The MHGS business model in pilot T2.3.1 is able to provide added value for forest insurance

companies, forest contractors, timber buyers and forest authorities by providing more

accurate data. However, additional value is created, if due to the solution’s functionality end

users can be motivated to actively manage their forest. The business users and authorities

can use the data to create business models that also motivate forest owners. For example,

forest insurance companies might offer flexible insurance plans where forest owners who

actively manage their forest pay less and passive forest owners pay more. In a similar way,

forest authorities might stop paying subsidies to forest owners, who do not react on alerts

about forest damage and by late actions or no actions endanger also neighbouring forests

and forest owners. Given all these, the most important KPIs from perspective of MHGS are:

- Square meters of forest covered by the service

- Number of forest owners using the service and actively managing their forest with it

- Number of involved forest contractors

- Number of involved timber buyers

- Number of forest insurance providers

- Sold subscriptions and services on the platform

- Sold remote sensing operations

The Wuudis platform was able to acquire about 5000 users in 2017. For the next years the

following end user numbers are planned: 10 000 users until the end of 2018 and 50 000 users

until end of 2019. From perspective of the pilot an important KPI is also the number of users

in the associated regions “Rangunkorpi” and “Hippala”.

From the forest owners’ perspective, the use of the Wuudis platform provides the basis for

increased productivity and profitability by decreasing time and costs for forest management

activities. It also provides the opportunities to better implement requirements from forest

authorities, gain faster subsidies and to improve forest yield by improved forest management

activities. The major KPIs can be summarized as follows:

• Decrease of time for contracting with forest contractors and timber buyers


• Decrease of time to receive subsidies

• Increase of forest management activities and, a as a consequence, potential to

increase forest yield and forest profitability

• Decrease in costs for forest monitoring

From the perspective of forest contractors and timber buyers, the use of the platform would

allow for following improvements:

• Decrease of search time for customers

• Decrease of time for contracting




• Decrease of costs for accessing the forest due to precise information

• Decrease of time to money (from contracting to invoice preparation and payment)

• Potential for getting more contracts within a given time

• Potential to increase number of contracts conducted during a certain period of time

• Support in maintaining up to date data.

From the perspective of forest insurance providers, the availability of more precise and timely

data about forest damages provides the opportunity for following improvements:

- Faster and cheaper assessment of damage

- Precise quantification of economic losses for compensation payment

- Flexible insurance coverage based on speed of forest owner’s reaction upon damage.

Forest owners that react fast to deal with damages might get payment faster and

discounts on insurance. Forest owners that can prove to take care of their forest might

get discounts on insurance and similar insurance models might become possible.

From the perspective of forest authorities, the use of the platform would result in the

following improvements:

• Increased accuracy of data and more precise decisions regarding subsidies

• Decrease of costs for data update

• Increased speed of reaction upon damage

• Better advice in case of damage

• Faster reaction to damages and by that reduction of overall costs

Overall, the data sharing platform would improve productivity and profitability for all involved

stakeholders by enabling a decrease of operational costs for forest management. If a

sufficient precision of data is achieved, it will also decrease the need for travelling to and

visiting the forest for all players involved in the forest value chain.

4.2.2.2 Cost Benefit Analysis for Pilot T2.3.1

Based on the planned activities a first cost benefit analysis for the Wuudis platform can be

calculated (see also the summary in Table 9). The investment costs can be summarized as

follows:

- Implementation of interfaces and APIs to metsaan.fi to enable data exchange

- Development of interfaces to EO data

- Development and integration of external EO analysis features

- Development of UAV collection of data

- Integration, analysis and visualisation of data from different sources

- Development and integration of functionality for detection of forest damage

Depending on how the investment costs are financed, i.e. if they have to be returned the

whole investments costs or part of it need to be transferred in depreciation costs.




- Support for users


- UAV maintenance and replacement




Based on the services developed in T2.3.1, an additional income category are remote sensing

operations for a certain space of forest. These income streams result from new business

transactions with end users and forest authorities. Potential transaction relationships with

forest contractors and timber buyers have not been verified yet. One option is to collect

transaction fees from these stakeholders.

Table 9: Summary of cost and income categories for Pilot T2.3.1 from perspective of MHGS

Cost Structure of Pilot T2.3.1 from perspective of MHGS

Investments Costs:

- Implementation of interfaces and APIs to metsaan.fi to enable data exchange

- Development of interfaces to EO data

- Development and integration of external EO analysis features

- Development of UAV collection of data

- Integration, analysis and visualisation of data from different sources

- Development and integration of functionality for detection of forest damage

Operational Costs:

- Support for users





Income Structure of Pilot T2.3.1 from perspective of MHGS

- Subscriptions from end users

- Sales of additional services to users (i.e. quality control modules)

- Sold platform licenses to business and authority users

- Sold data licenses to business users (Data as a Service)

- Sold special analysis and reports (i.e. damage report)

4.2.3 Business Modelling and Analysis of Pilot T2.3.2 and T2.3.3

The two Pilots T2.3.2 and T2.3.3 are dedicated to remote forest monitoring and control of

forest health as well as to proactive calculation of forest health risk respectively. The main

technical Partner is Empresa de Transformación Agraria, S.A. (Tragsa). Tragsa is a state-owned

company that works for public authorities to the service of society. Thus, Tragsa does not

explicitly pursue a market-oriented business model. However, a cost benefit analysis should

provide an overview if used technology results in cost-effective procedures. Furthermore, it



might be possible to sell some of the services to business forest owners. Given this, despite

of lack of market goals a business model canvas was developed. A potential business model

for T2.3.2 and T2.3.3 from the perspective of Tragsa as main data integrator and BDT provider

is drafted in Figure 10.

Figure 10: Potential business model for T2.3.2 and T2.3.3 from perspective of Tragsa

In case the pilots are successful and significant improvements are achieved, a sustainable

business model will be necessary in order to keep it up to date and provide it as permanent

service. Sustainable financing can on the one hand be achieved by state financing and

additional contributions by business forest owners.

4.2.3.1 Analysis of the KPIs and Cost Benefits Analysis for T2.3.2 and T2.3.3

The goal of pilot T2.3.2 is to set up a methodology for monitoring of the health status of

forests on large areas of the Iberian Peninsula based on a combination of remote sensing

images (satellite, areal and UAV) and field data. The target solution should provide public

bodies with valuable information and tools that enable data-driven decision making. Public

authorities should be able to optimise forest health management.

The goal of pilot T2.3.3 is to develop a simple model for assessing invasion risk in Spain based

on a set of factors that strongly influence the geographic pattern and level of invasion.: 1)

Environmental similarity, calculated from bioclimatic variables; 2) Biodiversity similarity,

approached through biogeographic information; 3) Propagule pressure, estimated from data

on trade, tourism, immigration, population and terrestrial transport network; and 4)

Ecosystem disturbance, measured from land use and fire frequency. The pilot case is linked

to prevention, which is both an effective and efficient way of dealing with the problem of

biological invasions. In fact, prevention is the best way of control against forest pests as



affected forest is difficult to heal and economic and biomass losses are immense. Indeed, the

pilot will identify the areas in Spain at greatest risk of invasion and the most likely source

regions of Invasive Alien Species (IAS), thus providing crucial information for resource

prioritization and for a much better management of these species.

Based on [REF-02] the major KPIs for T2.3.2 and T2.3.3 relevant for business analysis from

perspective of Tragsa are:

• Surface processed (T2.3.2 and T2.3.3): Number of hectares of forest land monitored

using different EO data sources (satellite/aircraft/RPAS) for the two assessed pests

(Phytophtora & Gonipterus) for T2.3.2 and number of hectares of land monitored for

invasive pest prediction for T2.3.3 using the methodology developed within the pilot.

In T2.3.3 the assessment of hectares of land covered will be compared to the whole

of Iberian Peninsula, Canary Islands and Balearic Islands, (approximate surface:

596.270 km2). For both pilots achieving a critical mass on land and forest coverage is

important for the quality of achieved results and its cost effectiveness. The more land

can be covered with the same technology investment the more effective is the

technology.

• Invasion risk maps generation (T2.3.3): Risk assessment at a geographical level with

cells of 1 km per 1 km for the surface processed. The accurate assessment of the risk

has impact of number of field studies and is the bases for planning of preventive

measures and their efficiency.

• Reports of risk level (T2.3.3): at least NUTS2 level. This has the same impact as the

previous KPI.

• Generation of new products & services (T2.3.2) available for final users. How many

successful products and services can be developed?

• Estimation of potential users (T2.3.2): How many users can be attracted to use the

service

• Field visits saved: Can higher accuracy of data be achieved with less field visits? The

goal is to achieve 10% less field visits.

• Economic improvements of methodologies used (T2.3.2 and T2.3.3): an economic ratio

will be obtained to assess the performance of the new methodologies (€/ha) and

compare it with traditional methods.

From the perspective of end users of T2.3.2 and T2.3.3 can be summarized as follows:

• Field visits saved: If the developed algorithms provide precise results, less field visits

will be necessary to achieve the same accuracy of forest data and predictions. Thus,

an assessment of how much fieldwork (number of hours) can be saved by using an

adequate EO-based methodology will be performed.

• Increased productivity of forest management: If the new methodologies provide

results that are more precise and accurate, a better planning of corrective and

preventing measures can be achieved. This has direct impact of the decrease of costs



per hectares managed forest, i.e. less (€/ha). The goal is to achieve 10%

improvements.

• Hectare of forests or biomass saved: How much of the forest biomass can be saved

with more accurate data, by earlier prevention of larger damages and spread of

diseases and pests.

Overall the planned BDT in T2.3.2 and T2.3.3 has the potential to improve forest health and

decrease the costs for forest health management.

4.2.3.2 Cost Benefits Analysis for T2.3.2 and T2.3.3

Based on the planned activities, an identification of the major cost and income categories can

be identified from the perspective of Tragsa. The investments costs can be summarized as

follows (see also Table 10):

Table 10: Summary of cost and income categories for Pilot T2.3.2 and T2.3.3 from perspective of Tragsa

Cost Structure of T2.3.2 and T2.3.3 from perspective of Tragsa

Investments Costs:

- Implementation of imagery classification system

- Implementation of machine learning to produce a “Change Layer”

- Implementation of remote sensing in different form

- Implementation of visualisation

- Implementation of data collection

- Implementation of EO data - Implementation of algorithms to collect, integrate, store, analyse and visualise

integrated data

Operational Costs:

- Regular data collection



- Support for users


Income Structure of T2.3.2 and T2.3.3 from perspective of Tragsa

- Saving of forest due to better data and faster reaction

- Less operational costs due to less visits, fuel, less prevention activities

- Payment from state budget

- Payment from business users

4.2.4 Business Analysis of the T2.4.1

The goal of T2.4.1 is the development of a web-mapping service for governmental decision

making based on Sentinel-2 data. The leading partner and main user of the pilot is FMI. FMI

is a governmental agency established under the Ministry of Agriculture of the Czech Republic

for redistribution of subsidies for forest owners affected by decreased forest vigour. FMI

maintains a central database of forestry and hunting and provides a range of services based



on information about forests. Main users of the FMI services developed in the project are the

Ministry of Agriculture of Czech Republic, other Czech governmental institutions and forest

owners. The resulting technology will be transferred also to Wallonia, Belgium. Thus, this pilot

is clearly belonging to the pilots that are dedicated to improving operational excellence based

on data-driven decision making. No direct business outcome of the pilot is expected. The

business analysis will therefore focus on KPIs and an identification of major costs and saving

categories.

4.2.4.1 KPI Analysis for T2.4.1

The expected improvements in operational costs can be measured with the following KPIs:

• Improved precision of forest data with less field surveys.

• Improved accuracy in subsidy distribution

• Shorter time to determine necessary subsidies

• Faster information to forest owners about the health status of their forest.

4.2.4.2 Cost and Benefit Analysis for T2.4.1

Major investment costs are development costs for the new services based on Sentinel-2 data.

To deliver the new service regularly, additional operational costs in terms of additional

personnel or infrastructure might arise.

Major quantifiable benefits of the new solutions are: less field visits by all involved state

agencies and forest owners, faster distribution of subsidies and faster reaction of forest

owners in forest management.

4.2.5 Business Analysis of T2.4.2

T2.4.2 is led by the Finnish Forest Centre (METSAK), which is a governmental institution

dedicated to collecting and managing forest data and providing information services for forest

owners and other end users as forest contractors. Major goal of METSAK is to motivate as

many forest owners as possible to actively manage their forest assets and to improve the

information flow of forest data among various players involved in forest management.

This pilot does not have dedicated new business goals but focuses on improvement of existing

operations. Thus, the major KPI is improvements in forest management, measured by

following KPIs:

• Increased coverage of forest space with detailed information and increase of forest

management activities available to all involved players as forest contractors and

operators

• Number of forest owners using METSAK services and actively managing forest assets

• Number of active forest operators using the data



4.2.6 Summary of Business Modelling in Forestry

The results of the business analysis of forestry pilots reveals the following major results:

Forest data is collected and maintained by different kind of governmental institutions. Thus,

a major user of BDT are governmental institutions. These institutions in most of the cases

develop forest data services themselves funded by government grants, or services are

developed by research institutions financed by government. In this context it is important to

convert successful pilots into sustainable solutions used by these authorities. One option that

will be further explored in the project is to offer the solutions to independent entrepreneurs

that can provide BDT and services in a sustainable way and under market conditions.

Besides governmental institutions, in forestry there a several other players involved in the

forestry value chain. The pilots show that major positive effects can be achieved when all

players of the value chain can profit from BDT solutions (see T2.1.2).

In most of the pilots, added value through use of BDT is expected due to improved operational

processes, i.e. due to better data-driven decisions. However, the example of MHGS and the

Wuudis platform shows that there are also opportunities for entrepreneurship and innovative

business models are possible based on forest data.



Business Plan and KPIs in Fishery Trials 5.1 Classification of KPIs in Fishery Trials The maritime economy is of essential importance for humans. It provides food, jobs,

transport, recreation and with new innovative technologies also pharmaceuticals, minerals

and energy [REF-22]. Fisheries are an important sector in the European maritime economy.

EU-28 is the fifth largest producer (catches and aquaculture) of fishery products in the world

and 3% to 5% of the EU’s gross domestic product comes from the maritime sector [[REF-22].

Almost 100’000 boats are in operation around Europe, either in fisheries or aquaculture [REF-

22]. In 2015, the EU-28 fishing fleet had a combined capacity of 1.6 million gross tonnes and

a total engine power of 6.4 million kilowatts (kW) [REF-20].

According to [REF-20], having peaked in 1995 at 7.6 million tonnes of live weight, the total

EU-28 fishery catch fell almost every year until 2007. “Thereafter, the weight of EU-28 catches

was relatively stable up until 2013, with a marked jump in 2014 (up 11.5 %). A smaller

reduction followed in 2015 (-5.0 %), with the total EU-28 catch amounting to 5.1 million

tonnes. This quantity was 7.0 % less than 10 years earlier and approximately one third lower

than in 1995” [REF-20].

Despite of their importance, fisheries as well as seas and oceans are endangered by climate

change, overfishing, illegal fishing and similar developments. Thus, the European Commission

is investing on the one hand over 7.5 million Euro through the European Maritime and

Fisheries Fund to boost innovation and create jobs in the blue economy. On the other hand,

to ensure a rigorously sustainable exploitation of the seas and oceans, the Commission has

issued among other policies also the Common Fisheries Policy (CFP). The CFP regulates total

allowable catches, fishing licenses, boat capacity management, minimum fish and mesh size,

design and use of gears, and closed areas and seasons [REF-23]. It was introduced in 1970 and

its last revision was 2015 [REF-23]. Overall, fisheries is the most regulated industry among the

three industries considered in DataBio.

Besides regulation, the European Commission also applies technology to monitor both

sustainable exploitation of the marine ecosystem and the application of the regulations in

practice (for an overview see [REF-26]). In addition, technology is used to facilitate also the

cooperation of all maritime players across sectors. In this context, BDT plays a central role, as

data is the basis for monitoring and sound decision making. The collection and sharing of

scientific data about fish stocks and the impact of fishing at the sea-basin level is an obligation

for all member states and a growing data base is available. Also, independent projects as

Google fishing watch4 [REF-24] or the WWF Smart Fishing initiative [REF-21] use satellite

tracking and data to monitor fishery activities worldwide. Besides using BDT on global scale

by regulation authorities and global initiatives, it is of increasing importance to involve in such

solutions all players of the fisheries value chain from the boats that catch the fish, over

intermediaries that fabricate the fish to the end consumer. To leverage the advantages of

4 http://globalfishingwatch.org/



BDT, data is increasingly used for better decision making by all players in the fisheries value

chain for example to identify better catch areas, to decrease costs of fishing, to better predict

the fish market and similar.

The DataBio project provides a contribution to the diffusion of BDT in fisheries by

concentrating on fishing vessels and developing and providing BDT solutions that can be used

by them. Given the fact that fisheries are a highly regulated area in particular in terms of

where, when and which fish in which quantity is allowed to be caught, fisher boats have less

opportunities to optimize their productivity and profitability by increasing the catch quantity.

The optimization efforts mainly concentrate on the resource and costs side of the profitability

and productivity equation (see also [REF-25]. The fisheries trials of DataBio concentrate on

the use of BDT to improve catch efficiency and profitability and to minimize operational costs

and risk of fisher boats. The specific focus and related KPIs of the fisheries trials can be

summarized as follows:

• Minimization of operational costs: KPIs in this category include reduction of time spent

on fish operations (e.g. steaming), improved vessel energy efficiency (propulsion

modes/engine configurations and electrical energy production) as well as reduced vessel

downtime and costs savings through condition-based maintenance.

• Sustainability and reduction of environmental impact and operational risks: Time and

energy savings by optimization of fishery operations, as well as preventive maintenance,

will help reduce CO2 and NOx emissions and risks of downtime and accidents. Better

usage of catch and fish observations from the fishing fleet in fish stock estimation will

reduce risk of overfishing, and data integration and transparency will help in reducing

Illegal, Unreported and Unregulated (IUU) fishing. KPI transparency for the end consumer

through certified sustainability fishery labelling of seafood like the blue MSC label (Marine

Stewardship Council), raise consumer awareness for sustainable fisheries and help drive

consumer preferences for protein food.

• Catch efficiency, productivity and profitability KPIs calculated by comparing the outcome

with the use of resources: Examples include income from fish catch sales versus time

(crew) and energy costs spent looking for, catching and delivering the fish, for example

quantified as energy consumption (kWh) and distance sailed (nm – nautical miles) per

kilogram fish. Profitability KPIs include marked aspects as price achieved in the market per

fish landing and in average per quota, as well as traditional cost versus income

considerations.

To summarize, the DataBio fishery trials create value of BDT by optimizing data driven

decision making and overall optimisation of the operation processes of fisheries ships. Given

this, the subsequent business analysis will focus mainly on the analysis of the business cases

based on the expected optimisation of the business processes of fishing vessels. For two

pilots, A1 and B1, there is an option for establishing a spin-off company, in case the trials are

successful. For these two trials and where appropriate also business models for the potential

spin-off are developed.

A list of the DataBio fishery pilots is provided in the table below:



Table 11 DataBio fishery pilots


T 3.2 Fishing vessels immediate operational choices

T3.2.1 A1: Oceanic tuna fisheries immediate operational choices

T3.2.2 A2: Small pelagic fisheries immediate operational choices

T 3.3 Fishing vessel trip and fisheries planning

T3.3.1 B1: Oceanic tuna fisheries planning

T3.3.2 B2: Small pelagic fisheries planning

T 3.4 Fisheries sustainability and value

T3.4.1 C1: Pelagic fish stock assessments

T3.4.2 C2: Small pelagic market predictions and traceability

5.2 Examples of Business Plans in Fishery

5.2.1 Business Analysis of the Fisheries Pilot T3.2.1-A1

5.2.1.1 Business Model of a Potential Spin-off from T3.2.1-A1

The goal of the fisheries pilot A1 is the development of data-based decision support system

to improve vessel energy efficiency and engine preventive maintenance. The main technology

partner of the pilot is the university EHU-UPV. The pilot end user is the Echebastar Fleet. The

basis for the pilot are on the one hand historical data of 117 measurements from the engine

and propulsion system every 10 seconds. On the other hand, external data as for example

data about sea currents, oceanographic and others. Both data sources, historical and current,

are combined and analysed for calculation of the route with the lowest energy consumption,

or shortest path for a certain catch. As data about the position of fishing vessels or their fishing

plans is business secret for security reasons, in this pilot there is strong requirement for highly

secure data transfer.

In case the pilot is successful, one potential option to further exploit the technology is the

creation of a spin-off company. Figure 11 provides a summary of a potential business model

in the business model canvas.



Figure 11: Business model canvas of a potential spin-off for the fisheries pilot T3.2.1-A1

Favourable condition for creating a company might be achieved under the following

conditions:

• The overall savings achievable through using the system are sufficient to cover the

costs of producing the decision-relevant data. This is relevant from the perspective of

the ship owning companies and from the perspective of the spin-off. In context of the

spin-off this means that there should be sufficient market potential for covering the

costs for producing the data.

• The BDT, in particular data transfer can reach the high security requirements suitable

for fisheries ships.

• The decision-relevant data is available also when the ship is in remote areas without

stable communication connection.

• The personnel of fisheries ships are capable to use the new decision data. With other

words, the data have to be presented at relevant point of time and in an easy

understandable visual form.

• There are no or only low costs necessary to make adjustments on the ship for the new

BDT solution.

• The BDT solution can be scaled to other ships and ship owning companies.

5.2.1.2 Analysis of the KPIs of T3.2.1-A1

The major KPIs from the perspective of the technology provider, i.e. the potential spin-off can

be summarized as follows:

• Number of ship customers acquired



• Number of analysis and reports sold

• Number of subscriptions

• Costs for data collection

From the perspective of the ships the main KPIs can be summarized as follows:

• Fuel consumption per sailed nautical mile

• Fuel consumed per catch unit of mass [kg fuel/fish ton or kg]

• Downtime hours due to main engine failure per year (or other time unit)

• Miles sailed per catch unit of mass [Nautical miles/fish ton (kg)]

5.2.1.3 Cost – Benefit Analysis of T3.2.1-A1

As already mentioned, from the perspective of the new company a sufficient profitability is

given, if the potential income is higher than the potential operation and investment costs.

Table 12 provides an overview of potential investment and operative costs and income

categories:

Table 12: Summary of cost and income categories for T3.2.1-A1

Cost Structure of A1

Investments Costs:

- Productisation of the BDT solution developed in the pilot

Operational Costs:


- Maintenance of infrastructure for storing and processing data

- Support for users


- Acquisition of new customers

Income Structure of A1

- Licensing of the data platform per ship

- Payment per specific analysis

5.2.2 Business Analysis of T3.3.1-B1

5.2.2.1 Business Model Canvas for T3.3.1-B1

The pilot B1 has similar objectives as A1. The goal is to improve profitability of ocean tuna

fisheries by using BDT for better decision-making. More precise information about where the

most promising catches are, provide the bases for optimisation of the vessels’ routes and

through reducing fuel costs through fish observation and fishing route observation. The main

technical partner is AZTI, while the main user is the Echebastar Fleet.

In case the expected added value of the BDT use can be validated with the pilots, it is expected

to create a spin-off that exploits the technology and offers the developed solution as a service

to other vessels owners. The potential business model of this company is depicted in the

business canvas in Figure 12.



Figure 12: Business model of a potential spin-off for T3.3.1-B1

5.2.2.2 Analysis of the KPIs of T3.3.1-B1

The major KPIs from the perspective of the technology provider, i.e. the potential spin-off can

be summarized as follows:

• Number of ship customers acquired

• Number of analysis and reports sold

• Number of subscriptions for DaaS

• Costs for data collection

From the perspective of the ships the main KPIs can be summarized as follows:

• Fuel consumption per sailed nautical mile

• Fuel consumed per catch unit of mass [kg fuel/fish ton or kg]

• Miles sailed per catch unit of mass [Nautical miles/fish ton (kg)]

5.2.2.3 Cost – Benefit Analysis of T3.3.1-B1

Furthermore, from the perspective of the new company the potential income has to be higher

than the potential operation and investment costs. Table 12 provides an overview of potential

investment and operative costs and income categories:



Table 13: Summary of cost and income categories for T3.3.1-B1

Cost Structure of B1

Investments Costs:

- Productisation of the BDT solution developed in the pilot

Operational Costs:


- Maintenance of infrastructure for storing and processing data

- Support for users


- Acquisition of new customers

Income Structure of A1

- Licensing of the data platform per ship

- Payment per specific analysis

5.2.3 Business Analysis of pilot T3.2.2-A2, T3.3.2--B2, T3.4.2--C2

The three fisheries pilots A2, B2 and C2 have all SINTEF as main technology provider and the

same end users. The end users are representatives of the Norwegian pelagic fishing fleet:

Ervik & Saevok, Eros, Kings Bay, and Liegruppen. The planned functionality of the pilots is

complementary and builds on each other. All three pilots are dedicated to optimization of

small pelagic fisheries:

• A2 with the title Small pelagic fisheries immediate operational choices combines on-

board measurements with available meteorological and oceanographic data, so that

the underlying connection between more parameters can be more accurately

modelled. The goal is a reduced fuel consumption for a certain amount of catches and

reduced maintenance costs and downtime of ships with precise event prediction prior

to fault tolerance.

• B2 with the title “Small Pelagic Fisheries Planning” aims to provide the crew and ship

owners with information which benefits fisheries planning. The main motivation for

this pilot is to improve catch revenue through improved fisheries planning, in

particular by improving catch efficiency through reducing the time spent looking for

fish and thereby reducing fuel consumption.

• C2 with the title “Small pelagic market predictions and traceability” aims to provide

information for predicting the development of various fish market segments, so that

the fisheries may be targeted against the most beneficial fishes. Another goal is to

provide consumers with detailed information about the products, so that he/she can

take into consideration quantitative aspects such as sustainability, environmental

impact, energy consumption per kg or fish and similar parameters while choosing the

fish.

In the following sub-sections, the business aspects of the three pilots will be described in

detail first. As all three pilots aim to improve operational processes of fisheries vessels, the

focus will be on business case definition and analysis. Then a business model will be



developed from the perspective of SINTEF as potential option for sustainable offering of the

developed services after the project.

5.2.3.1 Description of the Business Case and Overview of KPIs for T3.3.2--A2

The business process of fisheries is inevitably about balancing revenue from catch against

operational expenses incurred from crew, maintenance and fuel. Availability of fisheries

resources are scarce and the fisherman is limited by the quota. Therefore, the only control

the fisherman, or managing company, exerts on the immediate business of fisheries is to

increase operational efficiency during location and catching of the fish. The business view of

the small pelagic fishing operation is seen in Figure 13. The key business processes (inside of

main rectangle) are:

• Operational monitoring: Catch details are logged, while fuel consumption and

meteorological situation are monitored and analysed by the skipper.

• Analyse catch efficiency: The catch result is analysed, logged and auctioned for sale

and the catch efficiency calculated by the skipper.

• Manage fishing fleet operations: Optimization of fishing fleet operations by

predicting future efficiency through historic catch analyses and interactive analytics

visualization for decision support.

Figure 13: Fishery Pilot pilot T3.3.2-A2 business process view (ArchiMate 3.0)

With the help of BDT it is expected to improve activities in the process.



Table 14: Examples of specific KPIs relevant for fishery trials and for the operation and optimization of fisheries for T3.3.2-A2

Name Description Unit

SFO_NM Propulsion Engine Specific Fuel Oil volumetric consumption per sailed nautical mile in steaming condition. Steaming condition is defined as the condition when vessel is going from one point to other (manoeuvring condition not considered. Auxiliary engines not considered).

L FO/Nm

LFO_kgCatch Ship specific Fuel Oil volumetric consumption per kilogram of fish caught (total fuel oil consumption including auxiliary engines).

L FO / kg Catches

FO_consumption Total Fuel Oil Consumed by the vessel in a defined period of time analyzed.

L

SOGave Average ship velocity in steaming condition. Knot

kgCatches Total fish caught in a certain period of time analyzed. Kg

Sailed_NM Sailed nautical miles in steaming condition in a certain period of time analyzed.

Nm

Catch_Efficiency kgCatches / Sailed_NM Kg/Nm

LFO_day Fuel Oil consumed by the vessel per day of operation. L/day

Day_trip Average value of days spent per fishing trip (from departure to return to harbour).

day/trip

NM_trip Average value of sailed nautical miles in steaming condition per fishing trip (from departure to return to harbour).

Nm/trip

EL_rel_load The share of the electrical consumers’ power, which will together with main engine load indicates if there is extra capacity on the main engine to handle electric power production.

%

MC_rel_load Energy main consumers, share of consumption. This shows if a single power source dominates consumption

%

5.2.3.2 Cost-Benefit Analysis of Pilot T3.3.2-A2

Based on baseline data for the above KPIs and their comparison to necessary investments first

bottom-up necessary improvement by BDT use can be calculated. After the pilot is conducted

experimental data will show how high the actual improvements might be.



Table 15: Summary of cost and income categories for Pilot T3.2.2-A2 from perspective of fishing vessels

Cost Structure of Pilot A2 from perspective of fishing vessels

Investments Costs:

- Implementation of interface to exchange data and analytics result with BDT provider

- Improved infrastructure for handling data?

- Training foe employees

Operational Costs:

- Regular data collection and exchange

- Payment for data DaaS

Cost Savings of Pilot A2 from perspective of fishing vessel

- Fuel savings due to more precise data and adjustment of propulsion mode, loading and use of auxiliary and main engines

- Energy savings due to better operational decisions

- Lower degradation of catch quality due to improved vessel communication

- Savings due to reduced time on operation

- Increased revenue due to catch quota revenue

- Increased catch revenue from pelagic species

5.2.4 Business Analysis of T3.4.2-C2

5.2.4.1 Description of the Business Case and Overview of KPIs

Adapting the fisheries to the future market needs is one of the major challenges for the small pelagic fisheries. One tries to do the fishing when the fisheries are good, the prices are high and the quality is good. As the covariation of these aspects are not simple to predict, there is often room for improvements with regards to both income, quality and energy efficiency.

The goal of this pilot is to provide information for predicting the development of various

market segments, so that the most beneficial fisheries may be targeted. This pilot will also act

as a basis for providing the consumers with information about the products, so that he/she

can take into consideration quantitative aspects for sustainable fishery such as environmental

impact and energy consumption per kg fish caught. Figure 8 shows the business process view

for this pilot. The main differences compared to the previous pilot example is the business

processes related to on catch potential, market prediction and seafood traceability and

consumer awareness:

• Market price potential predictions: Economic catch potential is analysed both in the

near future, e.g. catch decision support for the vessel skipper for the ongoing fishery

operation (next few days/weeks), and aggregated for the longer term by the fishing

operations manager, e.g. quota fulfilment and fleet revenue optimization by

modelling the market dynamics for price development (next few

months/quarter/season). This management process is in essence similar to stock



portfolio optimization in the finance world, although we do not focus on the buying

and selling of quotas here but on the optimization within quota limits.

• Improve seafood traceability and awareness: The seafood consumer is an additional

stakeholder in this business model where increased transparency through labelling of

seafood sustainability is affecting the consumer's protein food preferences.

Figure 14: Fishery Pilot pilot T3.4.2-C2 business process view (ArchiMate 3.0)

Examples of KPIs focusing on market predictions and catch potential are summarized in Table

2.



Table 16: Overview of KPIs for T3.4.2-C2

Name Description Unit

Prediction_Accuracy Price prediction success rate on historical test sets and by comparing predictions vs. actual price development.

Classification error rate (%)

Expected_Catch_Revenue

Quantify what level current catch revenue is at using traditional methods without market prediction relative to quota value calculated as sum of maximal price/kg * catch volume.

% achieved vs. max incl. uncertainty

Hindsight_Potential Quantify revenue potential of alternative catches in historical data, e.g. what was the average accumulated value vs. max potential in the quotas when optimizing potential

% increase in potential catch vs.

Asset_Optimization Quantify optimized revenue potential by: - Making default/traditional catch plan (e.g. like

last year for example) - Alternative plan using market prediction tool

Calculate difference in percent increase.

% increase

User_Reach Number of users/stakeholder interested in the pelagic market information service, including portals visits, active use and growth rate. <Note: This takes time to establish>

Count % Growth % Use vs overall portal traffic

Table 2: Examples of KPIs relevant for fishery market predictions

5.2.4.2 Cost-Benefit Analysis of Pilot T3.4.2-C2

Based on baseline data for the above KPIs and their comparison to necessary investments first

bottom-up necessary improvement by BDT use can be calculated. After the pilot is conducted

experimental data will show how high the actual improvements might be.

Table 17: Summary of cost and income categories for Pilot T3.4.2-C2 from perspective of fishing vessels

Cost Structure of Pilot C2 from perspective of fishing vessels

Investments Costs:

- Implementation of interface to exchange data and analytics result with BDT provider

- Improved infrastructure for handling data?

- Training for employees

Operational Costs:

- Regular data collection and exchange

- Payment for data DaaS

- Maybe costs for communication of data



Cost Savings of Pilot C2 from perspective of fishing vessel

- Increased catch revenue by better price prediction and catch planning

- Increased revenue of catch by better choice of fish to catch

5.2.5 Summarizing business model for fisheries pilots T3.2.1-A2, T3.3.2-B2 and T3.4.2-

C2

As described in the subchapters the pilots A2, B2, and C2 these fisheries pilots have great

synergies and the BDT and data used and analysed builds upon each other. In all three pilots

the technological partner is SINTEF that cooperates with the same end users: representatives

of Norwegian pelagic fishing fleet. In case the pilots can provide evidence that the expected

added value of the use of BDT can be achieved, then a sustainable business model is necessary

in order to ensure that the advantages of the use of BDT can become part of the everyday

routines of the various players in the fisheries value chain.

As has been shown in the various pilot descriptions and their business analysis substantial

investments are necessary to create an appropriate and powerful BDT infrastructure where

data can be collected, stored, managed, analysed and visualised. Furthermore, personnel

with highly specialized knowledge and qualification is necessary in order to collect, manage

and process the data. These investments are probably too high for the prevailing shipping

companies. Furthermore, their core competence is not BDT and data analysis, but fishing.

Thus, even if added value can be proved for BDT use, fisheries might not be able to invest in

such solutions by themselves. Given all this, a potential business model has been sketched

from the perspective of SINTEF. SINTEF is an independent research organization that will

accumulate a lot of data and knowledge how to collect, manage, analyse, visualize and

transfer decision relevant data to fishing vessels. Given all this, a potential business model

canvas has been sketched in Figure 15 from perspective of SINTEF. In the business model

three different colours are used to denote the specific characteristics of the three pilots: black

for the A2, blue for B2 and red for C2.



Figure 15: Potential business model from perspective of SINTEF

5.2.6 Summary of Business Analysis of Fisheries Pilots

Fisheries is the most regulated sector of the three industries considered in DataBio. How

many fish can be caught by a ship, when and where in which area is strongly regulated? Thus,

the only control the fisherman, or the ship owning company has, is to control and optimize

the operational costs for a certain given catch.

The business analysis of the fisheries trials shows that BDT has potential to provide added

value for optimization of fisheries operational processes. However, the data collection

process is complex and requires cooperation from players in the fisheries value chain.

The technology providers in all fisheries pilots are a university and several state-financed or

independent research institutions. In case the pilots are successful, a sustainable way of

offering the services has to be found. One option are spin-offs or other forms of

entrepreneurships.

BDT has the potential to provide added value for improvement of operation processes by

providing data for more precise decision making.



Summary, conclusions and recommendations This deliverable contains the first business analysis and modelling of the DataBio project. At

this stage of the project, when pilots have not been executed yet, it is difficult to provide fully

quantified business models. Given this, the deliverable focuses on defining instruments for

business modelling that can be used throughout the project. It furthermore contains a first

overview of concepts for potential business models for BDT providers and end users. The

deliverable also identifies major KPIs and cost and benefits categories. However, these first

results of the business analysis are rather of qualitative and descriptive nature and on an

abstract level.

The business modelling of pilots in agriculture was based on a representative sample of five

pilots. In focus were pilots from all different areas of agricultural pilots and pilots that have a

company as leading technology provider. In forestry and fisheries almost all pilots were

considered.

In all three industries it was possible to identify both types of potential added value from BDT

use: new data-based business models and optimization of operational processes based on

more precise data-driven decision making. Examples of data-based business models in

agriculture are: the sensor and cloud-based solution for data collection and analysis in

agriculture from NP and GAIA and smart tractors from Zetor. In forestry this is the mobile

solution for forestry Wuudis Forest from MHGS. In fisheries this are potential spin-offs in the

area of oceanic tuna fisheries.

The majority of pilots focus on improvement of operational processes based on more precise

data-based decision making. In agriculture it is possible to reduce visits to the field, to reduce

used resources such as fertilizes, irrigation water, to choose better seeds and to determine

the optimal time for harvesting in order to get the highest yield possible. In forestry due to

more precise data it is also possible to reduce visits to the forest and to better plan forest

management activities. Fisheries is the most regulated industry in particular from the yield

point of view. Thus, BDT is applied explicitly for reducing costs per catch. Data-driven decision

making based on more precise information has the potential to result in shorter and at the

same time more productive routes with less consumption of energy and fuel.

The analysis of the pilots also shows that major improvements of processes on operational

level can be achieved, if all players of the value chain are considered. This can be illustrated

on the example of forestry pilots that are based on the Wuudis platform or on the example

of the sensor-based cloud platform of NP. Both platforms offer solutions for all major players

in the value chain and savings can be achieved in parallel at all players at the same time. This

results in high aggregated savings on industry level.

The fact that in the majority of pilots the major BDT providers are state-owned or

independent research institutions raises the question of the sustainability of the developed

solutions. If the pilots can prove the positive effects of BDT use, then there is need for

entrepreneurship to bring the solutions to the market in a way affordable for end users.



Apart from the first versions of business models, the KPIs for quantifying the added value of

BDT have also been defined. An attempt was made to identify the major KPIs for all involved

players in a pilot. It is clear that BDT use can only be successful if on the one side technology

providers can develop profitable business models and on the other side, end users are

motivated to buy such solutions in order to improve their productivity and profitability. As

investments in BDT are high, a critical mass of end users (customers) are necessary to enable

profitable business models of technology providers. This means that fast adoption of BDT is

necessary and technology providers have to also invest in activities to drive adoption of BDT

in agriculture, forestry and fisheries.

The analysis also revealed that due to the specific character and importance of the three

sectors considered in DataBio, governmental institutions are also big users of BDT. In all three

industries there are pilots that address the needs of governmental institutions. Governmental

institutions need more precise data in order to provide more precise regulations and subsidies

and to have an overview of available resources. Thus, governmental institutions might be also

the major investors in BDT in agriculture, forestry and fisheries. This raises the question for

new approaches of how to make data created by governments available for producers (i.e.

farmers, forest owners or fishers). New type of private-public-partnerships might be a

suitable solution in this context.

Overall, this first business analysis confirmed the potential of BDT use in agriculture, forestry

and fisheries, even though it was not possible to quantify the potential gains yet. However, it

also revealed some aspects that might hinder the fast adoption of BDT in the three industries.

These are: high investment costs and need for entrepreneurship and companies that will bring

the technology to the market in a secure and affordable way for end users; critical mass of

technology coverage and adoption by end users, and support for the whole value chain.



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D7.1 Business Plan - databio.eu · D7.1 – Business Plan H2020 Contract No. 732064 Final – v2.1, 6/2/2018 Dissemination level: PU -Public Page 3 Most of the pilots provide solutions