D25.2 Requirements for analyzing service ecosystems … 8 – SpagoBI features table 53 Table 9 –...

Project ID 284860 MSEE – Manufacturing SErvices Ecosystem

Date: 16/10/2012 Deliverable D25.2

D25.2 – “Requirements for analyzing service

ecosystems by business analysis tools”

Document Owner: Boyé HARDIS, Loichate TECNALIA,

Contributors: Gredat HARDIS, Storelli ENG, Maggio ENG, Martínez TECNALIA

Dissemination: Public

Contributing to: WP2.5

Date: 16/10/2012

Revision: 2


Date: 16/10/2012 Deliverable D25.2 – M12 issue

MSEE Consortium Dissemination: Public 2/62

VERSION HISTORY

VERSION DATE NOTES AND COMMENTS

0.1 02/07/2012 INITIALISATION OF DOCUMENT

0.9 27/08/2012 INTERNAL DIFFUSION OF HARDIS CONTRIBUTION

1.0 21/08/2012 INTEGRATION OF COMMENTS FROM TECNALIA

1.1 22/08/2012 UPDATE BY HARDIS + TECNALIA CONTRIBUTION

1.3 28/08/2012 INTEGRATION OF ENG CONTRIBUTINO (SPAGO BI)

1.4 06/09/2012 INTEGRATION OF HARDIS REFINMENTS (EXEC SUMMARY,

CONCLUSION & BI TOOLS EVALUATION)

INTEGRATION OF POLIMI CONTRIBUTION

1.5 12/09/2012 INTEGRATION OF PENTAHO AND SENTISTRENGHT TOOLS

EVALUATION (TECNALIA) AND SPAGO (ENG).

CONCLUSIONS SECTION REFINEMENT FROM TECNALIA.

1.6 12/09/2012 INTEGRATION OF RAPIDMINER, MICROSOFT BI, SAS BI AND

JASPERSOFT TOOLS EVALUATION (HARDIS)

1.7 13/09/2012 FORMAT CORRECTION, COMMENTS ANSWER/DELECTION, FIRST

GENERAL REVIEW (TECNALIA)

1.8 17/09/2012 INTEGRATE COMMENTS COMING FROM THE FINAL REVIEW DONE

BY EVERY PARTNER

2 16/10/2012 ADRESS COMMENTS REGARDING INTERNAL PEER-REVIEW

DELIVERABLE PEER REVIEW SUMMARY

ID Comments Addressed ()

Answered (A)

1

Title of deliverable it is not clear. In the Executive

Summary its is in somehow clarified, but it should be

better explained in the introduction in order to make

clear the contents of the document meets the title and

aims foreseen.

() This is the official deliverable

title, mentioned in the DOW. See

refinement in the introduction

chapter.

2

The Executive Summary should include a summary of

the major results and conclusions of the deliverable,

in order to be possible just reading the Executive

Summary to have a complete understanding of the

main outcomes reported in this deliverable, including

(). A paragraph is added to the

executive summary.




the results and main remarks.

3

The Introduction section needs to be more elaborated.

It is very short, and its message as it is does not justify

an independent section. Motivation for the

deliverable, its integration in SP2 work, problem

addressed should be described in detail. Then, the

methodology that supports the development of the

work that results in the contents of the deliverable,

should be presented, also including and justifying the

2 incremental approaches.

(). See refinement in the

introduction chapter.

4

In the Introduction, it is not properly justified why the

focus in on the performance, and not in other

characteristics. This should be well justified, in the

context of the objectives of the tasks where this

deliverable has been developed.

(). See refinement in the

introduction chapter.

5

Section 2.1.1 Conceptual model of an ecosystem:

the criteria for the identification and selection of the

dimensions that should be taken into account to assess

BI tools are not justified. This section intends to

present a conceptual model of an ecosystem, and the

model is not presented clearly in there.

(A). This section, moreover point 2 in

general, does not intend to present a

conceptual model, just to point the

basis of what a MSE (manufacturing

service ecosystem) should be; this

starting point info is needed to better

understand the posterior BI functions

selection and BI tools analysis, and

for that, a brief summary of info

coming from T25.1 task has been

done.

6

Section 2.1.2 has similar issues. “Accordingly to task

T25.1, we can sort these processes sets in the

following categories”. What is the rational and

methods that task 25.1 put in place to reach such

conclusion ? Clear and detailed justification should be

provided.

(). Makes no sense deeply

explaining here the work carried out

in T25.1, so a reference to T25.1 has

been included just in case somebody

wants t obtain deeper knowledge on

the subject.

7

Section 2.2.1. Similar problem, e.g., “Typically, we

can characterize these three levels of decision at the

level of a single enterprise, as follows”. In what basis

this conclusion is achieved ? There are not any

references supporting the statement, that leads to a

conclusion that will steer the next activities for SP2.

This needs to be justified with the methods used, and

the background in terms of concepts based in a

consolidated state of the art.

(). Included the references

supporting the three levels of

decision.

8

This is a generic remark for all the sections.

Again in section 2.2.3 “Therefore we can assume that

the performance of ecosystems should be evaluated

(). This conclusion does not obey

to any formal reference justification

or theory, it’s a simple out of the box




on the basis of how effectively the strategic objectives

of the ecosystem are met.”

Under which basis (e.g., criteria ? scientific reasoning

?) this is assumed ?

There are many “assumptions” without sufficiently

elaborated justifications.

conclusion that relies and relates

performance monitoring of ecosystem

with the achievement of strategic

long-term objectives. Fixed the text to

better explain the link to reach this

conclusion.

9

The conclusion are well elaborated, but they don’t

reflect in the same extend what was described in the

previous sections. The section need to be extended

and complemented, to support the conclusions

presented.

(A). This document and therefore

conclusions are related to a first step

analysis of T25.2, which has another

deliverable for M18, Thus, although

provided conclusions may seem

general and obvious externally, the

process to reach them has been very

elaborated, and are considered

suitable for this first stage, Of course,

these conclusions will be extended

and complemented in the second

stage of the task, where concrete

requirements for BI tools, and by the

way, for MSEs, will be pointed out.

10

The bibliography is incomplete. Along the text, many

statements should be supported by appropriate

references, including those from the BI tools

analysed, and also the EC projects indicated.

(). Added missing references and

bibliography. Moved to OpenXML

format to avoid problems with

reference support.




TABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................................................................................................... 9

1 INTRODUCTION ..................................................................................................................................... 10

2 CONCEPTS FOR SERVICE ECOSYSTEMS PERFORMANCE MANAGEMENT ........................ 11

2.1 SERVICE ECOSYSTEM CONCEPTS AND CHARACTERISTICS ............................................................................. 11 2.1.1 Conceptual model of an ecosystem ................................................................................................ 11 2.1.2 Governance Processes ................................................................................................................... 14

2.2 PERFORMANCE OF SERVICE ECOSYSTEMS ................................................................................................... 15 2.2.1 Conceptual decision levels ............................................................................................................ 15 2.2.2 Applicability to MSEE organizational structures .......................................................................... 17 2.2.3 Performance in manufacturing service ecosystems ....................................................................... 18 2.2.4 Characteristics for the implementation of business intelligence solutions .................................... 33

3 REVIEW OF BUSINESS ANALYSIS TOOLS ...................................................................................... 35

3.1 BUSINESS INTELLIGENCE SOLUTIONS ......................................................................................................... 35 3.1.1 Functional architecture ................................................................................................................. 35 3.1.2 BI solutions categories .................................................................................................................. 37

3.2 BI PRODUCTS ............................................................................................................................................ 37 3.2.1 Spago BI suite ................................................................................................................................ 37

3.2.1.1 Reporting ................................................................................................................................................... 37 3.2.1.2 Dashboard.................................................................................................................................................. 38 3.2.1.3 Analysis ..................................................................................................................................................... 38 3.2.1.4 Data Mining ............................................................................................................................................... 39 3.2.1.5 Driven Data Selection ................................................................................................................................ 39

3.2.2 Pentaho .......................................................................................................................................... 39 3.2.2.1 Reporting ................................................................................................................................................... 39 3.2.2.2 Dashboards ................................................................................................................................................ 40 3.2.2.3 Analysis ..................................................................................................................................................... 41 3.2.2.4 Data Mining ............................................................................................................................................... 41 3.2.2.5 Data Integration ......................................................................................................................................... 41

3.2.3 RapidMiner .................................................................................................................................... 41 3.2.3.1 Rapid Miner – Data analysis client ............................................................................................................ 42 3.2.3.2 Rapid Analytics ......................................................................................................................................... 43 3.2.3.3 Rapid net ................................................................................................................................................... 43 3.2.3.4 Rapid Sentilyzer ........................................................................................................................................ 43 3.2.3.5 Rapid Miner – Extensions ......................................................................................................................... 43

3.2.4 Microsoft BI tools .......................................................................................................................... 43 3.2.4.1 Reporting ................................................................................................................................................... 44 3.2.4.2 Dashboards ................................................................................................................................................ 44 3.2.4.3 Analysis ..................................................................................................................................................... 44 3.2.4.4 Data Warehousing ..................................................................................................................................... 45

3.2.5 SAS Enterprise BI Server ............................................................................................................... 45 3.2.5.1 Reporting ................................................................................................................................................... 45 3.2.5.2 Portal and dashboards ................................................................................................................................ 45 3.2.5.3 Query and analysis .................................................................................................................................... 46

3.2.6 JasperSoft ...................................................................................................................................... 46 3.2.6.1 Reporting ................................................................................................................................................... 46 3.2.6.2 Dashboards ................................................................................................................................................ 46 3.2.6.3 Analysis ..................................................................................................................................................... 46 3.2.6.4 Data Integration ......................................................................................................................................... 46

3.2.7 SentiStrength .................................................................................................................................. 46

4 EVALUATION OF BI TOOLS ................................................................................................................ 48




4.1 EVALUATION OF BI FUNCTIONS ................................................................................................................. 48 4.1.1 BI functions recommended for MSE metric types .......................................................................... 48 4.1.2 BI functions recommended for SE characteristics ......................................................................... 49

4.2 BI PRODUCTS COMPARISON MATRIX .......................................................................................................... 51

5 CONCLUSIONS ........................................................................................................................................ 60

6 REFERENCES ........................................................................................................................................... 62




LIST OF FIGURES

Figure 1 – Ecosystem structural pattern ................................................................................... 11

Figure 2 – Ecosystem processes ............................................................................................... 14

Figure 3 – Functional Architecture of BI solutions .................................................................. 35

Figure 4 – Spago BI Dashboard view ....................................................................................... 38

Figure 5 – Pentaho Reporting view .......................................................................................... 40

Figure 6 – Pentaho Dashboard view ......................................................................................... 40

Figure 7 - Rapid-I products map............................................................................................... 42

Figure 8 - Rapid Miner Client - Process design View .............................................................. 42

Figure 9 - Microsoft BI components ........................................................................................ 44

Figure 10 - SentiStrength architecture ...................................................................................... 47




LIST OF TABLES

Table 1 – Conceptual decision levels 16

Table 2 – Interactions of MSEE organizational structures 17

Table 3 - Ecosystem process decomposition: strategic / tactical / operational 32

Table 4 - Characteristics for the implementation of BI solutions 34

Table 5 – Main components of BI solutions 36

Table 6 – Role of BI functionalities regarding metrics typologies 49

Table 7 – Role of BI functionalities regarding structural characteristics of MSEs 50

Table 8 – SpagoBI features table 53

Table 9 – Pentaho features table 54

Table 10 – SentiStrength features table 55

Table 11 – RapidMiner features table 56

Table 12 – Microsoft BI features table 57

Table 13 – SAS BI enterprise server features table 58

Table 14 – JasperSoft features table 59




Executive Summary

This deliverable is part of the WP2.5 ‘Service based Innovation Ecosystems’ of the MSEE

project, which at a glance, aims at defining a reference framework for service based

innovation ecosystems, including all the main elements, concepts and ideas that need to be

considered for their development.

More specifically, this deliverable belonging to task T25.2 ‘Service ecosystems requirement

by business analysis tools’ focuses on the performance management aspects of manufacturing

service ecosystems, and to accomplish this purpose, the study concentrates on the definition

of requirements for business analysis tools when analysing service ecosystems, which also

brings in parallel requirements and characteristics for ideal service ecosystems. In order to

proceed with this analysis, the followed methodology starts with a review of ecosystem basic

concepts (chapter 2), and after that, taking into account that service ecosystems are created to

collaborate and tackle business opportunities, make business in plain text, the analysis

concentrates in performance monitoring, as a key factor when taking decisions, when doing

business. To accomplish this goal, a decisional model is proposed, including metrics and

potential indicators for services ecosystem, structured around three decision levels commonly

used in single enterprises but also with application in ecosystems: strategic, tactic and

operational decision levels. The methodology continues with a third chapter presenting a

review of existing Business Intelligence solutions, starting with an introduction of the main

functionalities provided by BI tools available in the market offer. Then, the review

concentrates in the presentation and analysis of five representative business intelligence

products, which deserve attention, considering either their reputation on the market or their

wide spread usage in single enterprise contexts. Finally, the fourth section evaluates the

generic functionalities of BI tools regarding the kind of metrics which are relevant for

ecosystem performance management and applies this evaluation, in form of a comparison

matrix, to the selected business intelligence tools, introduced in the previous section.

It is worth noticing that this study is divided in two iterative and incremental phases, and this first

deliverable of the task exposes the first-round high level analysis and some preliminary

conclusions. These results will be refined in the second iteration and its corresponding

deliverable, with a clear exposition of the desired requirements for business analysis tools when

tackling service ecosystems.

As a conclusion of the first iteration of this study, we will explain that existing Business

Intelligence approaches for single enterprises provide efficient means to address technical

challenges for the implementation of performance management tools. Although, the most

important players on the market (either open source or commercial) do not provide particular

support to implement of metrics related to ecosystem performance. Therefore, a significant

amount of work is still needed to integrate and configure such BI tools, to analyse performance of

MSEs.




1 Introduction

As stated in the title of the deliverable, the main goal of this study is to leverage the

identification of specific requirements, regarding the analysis of manufacturing service

ecosystem with the help of Business Intelligence Tools. To do so, the underlying task T25.2:

“Service ecosystems requirements for business analysis tools” is expected to provide two

major outcomes which are: 1) an analysis of the existing offer for BI tools and their

applicability for MSEs ; 2) the identification of specific requirements related to MSE

performance management approaches and tools.

The two iteration of this deliverable will follow the 2 main cycles of the MSEE project, and in

parallel, the concepts for Manufacturing Service Ecosystem and its governance aspects will

be developed in other deliverables of SP2 (in particular, D25.1).

As a first step of the task T25.2: “Service ecosystems requirements for business analysis

tools”, this document analyses and points out the strengths, weaknesses and suitability of

existing BI approaches and tools when analysing and monitoring performance in service

ecosystems. As a remark, it is worth pointing from the very beginning the key role the word

‘performance (monitoring)’ has on this document and task in general. The main reason is that

manufacturing services ecosystems are built and evolved to enterprise collaboration and to

tackle business opportunities in the market, to make business in other words; and this,

translated to the world of enterprise strategic decision making processes, means continuous

performance monitoring of many of the activities inside the ecosystem, at different levels, but

of course, supported by IT tools, especially by BI tools and approaches.

To provide such a result, the analysis will follow this process:

Identification of the peculiarities and guidelines for performance management in the

specific context of service ecosystems

Review of existing performance management approaches

Evaluation of existing business analysis tools

Based on this first stage analysis, the second deliverable of this task will concentrate in

elicitating concrete requirements and characteristics future ideal service ecosystems should

have, from a business analysis tools perspective, in an attempt also of defining and preparing

these tools and approaches to properly deal and analyze service ecosystems.




2 Concepts for service ecosystems performance

management

2.1 Service ecosystem concepts and characteristics

This point introduces and explains basic but important concepts regarding the analysis being

performed inside previous task T25.1 “Analysis of collaborative networks as playground for

service-based innovation”. This piece of information serves as a useful reminder to better

understand the particular characteristics service ecosystems have, and thus, to better digest

and orientate the study of BI Tools this task pursues.

2.1.1 Conceptual model of an ecosystem

A definition of an Innovation Ecosystem is given by the COIN project: “An innovation

ecosystem is a non-hierarchical form of collaboration, in the past mostly founded on a

territorial proximity like Smart Regions or Districts but nowadays extending globally

worldwide, where big OEMs, SMEs networks, ICT suppliers, universities and research

centers, local public authorities, individual consultants, customers and citizens work together

for promoting and developing new ideas, new products, new processes, new markets” (COIN

project).

This definition has been used as a basis and has been tailored in order to provide a definition

that fits better with the different features and concepts of MSEE project. Therefore, according

to MSEE “a Manufacturing Service Ecosystem (MSE) is a non-hierarchical form of

collaboration where various different organizations and individuals work together with

common or complementary objectives on new value added combinations of manufactured

products and product-related services. This includes the promotion, the development and the

provision of new ideas, new products, new processes or new markets. Inhabitants of such an

MSE could be big OEMs, SMEs and networked organizations from various branches, ICT

suppliers, universities and research centres, local public authorities, individual consultants,

customers and citizens.”

Hence, MSEE project is positioning Manufacturing Service Ecosystems in terms of

organisational structure, between a formally defined and governed network (such as a Virtual

Breeding Environment – VBE (ECOLEAD project)) and the “open universe” of all potential

partners for collaboration.

Figure 1 – Ecosystem structural pattern

“open universe” Virtual Breeding

EnvironmentEcosystem




Consequently, and based on the above mentioned definition of a Manufacturing Service

Ecosystem, several of its characteristics might be highlighted:

Formalisation: Partners recognise that they are part of an ecosystem. They must

agree on a common objective (to develop innovative solutions/services in a defined

domain). They sign an agreement to work together and to follow some rules. Due to

the fact that each partner brings knowledge and added value in the MSE, it is

normal to establish some rules to preserve and protect the shared knowledge.

Type of relationship between partners: The Ecosystem has a non-hierarchic

character. Usually it is self-organised, most decisions are decentralized: therefore it

is not “managed in the classical sense” by one of its members. Anyway the partners

must agree on the objectives and to adopt an appropriate structure to reach the

objectives. However, some partners can take a more active part and facilitate

coordination and decisions. Under these conditions the ecosystems which have

defined objectives can use KPI to check the achievement of such objectives.

Openness and Stability: The ecosystem is evolving; some organisations leave the

Ecosystem while others join it. Joining and leaving require semi-formal conditions

due to the fact that belonging to the MSE each partner gains knowledge and added

value. In principle MSE as such is not doing business, although it has a vision,

mission and objectives.

Moreover, the research carried out in Task 25.1 has identified several dimensions that help

characterizing Manufacturing Service Ecosystems and that shall be the basis for the definition

of MSE according to MSEE project. Therefore, in order to assess BI tools, these dimensions

should be taken into account as they will be the means to synthesize and formalize the main

components of a MSE. The dimension that shall be taken into account when assessing the BI

tools shall be the following:

Structural dimension: it is the structure or composition of the CN in terms of its

constituting elements (participants and their relationships) as well as the roles

performed of those elements and other characteristics of the network nodes, such as

location, time, etc. (Matos & Afsarmanesh, 2007).

Activities and processes (functional dimension): it addresses the “base operations”

available at the network and the execution of time-sequenced flows of operations

(processes and procedures) related to the “operational phase” of the CN’s life cycle.

Governance and behaviour: Principles, policies and governance rules that drive or

constrain the behaviour of the CN and its members, i.e. principles of collaboration and

rules of conduct, contracts, conflict resolution policies, etc. (Matos & Afsarmanesh,

2007).




Components: This dimension focuses on the individual tangible/intangible elements in

the CN (Matos & Afsarmanesh, 2007).

ICT: Aspects that describe the technical information infrastructure supporting the CN

and underlying IT paradigms.

Servitisation: Collaboration takes place encouraging idea generation and development

of a new service (MSEE project).

Manufacturing: Collaboration is related to manufactured products and product-related

services (MSEE project).

These dimensions will be made operational through the processes that are described in the

next chapter and that will allow the implementation of the activities of the ecosystem. The

definition of these dimensions and processes shall serve as guidelines and benchmarking

criteria when assessing the different BI tools available in the market.




2.1.2 Governance Processes

In order to implement its activities, an ecosystem will rely on a set of management processes

supporting its primary activities, related to service innovation and the management of tangible

and intangible assets; and there will also be support processes, focused on different areas,

with the main goal of supporting the primary activities and settling a proper governance

model for the ecosystem. Based on this process related assertion, as a next step, task T25.1

proposes to sort the ecosystem processes sets in the following categories:

Figure 2 – Ecosystem processes

Support processes:

1. Administrative processes: encompass the necessary tasks to sustain the operation of

the ecosystem (e.g.: member management, legal processes, finances management…)

2. Strategy processes: cover the necessary processes for coordinating the overall strategy

of the ecosystem, in alignment with individual member’s objectives.

3. Dynamizing ecosystem processes: focus on the stimulation of the ecosystem, in

particular to encourage and ease the creation of VMEs between members.

4. Marketing & external relations: relates to the management of ecosystem’s relations

with its external environment by promoting its activities and increasing its

attractiveness.

5. Performance management processes: include the necessary activity for quality

assessment and improvement, in relation to the strategy of the ecosystem.

6. Support to and monitoring of Virtual Manufacturing enterprises: it encompasses the

support to the creation and metamorphosis of VMEs within the ecosystem and the

monitoring of their life cycle.

7. Information systems processes: this category covers the tasks related to the

maintenance of the IT infrastructure, sustaining the processes of the ecosystem.

Besides the above mentioned support processes, two are the primary activities of the

ecosystem. The innovation process focuses on the management of this activity, covering the

initial phases of business opportunity identification and idea generation, up to the definition of

the requirements needed to create a VME. Moreover, tangible and intangible assets

1. Administrative management processes

3. Dynamizing the ecosystem

Primary Activities

Support processes

5. Support to and monitoring of VME

6. Information Systems

2. Strategy processes

4. Marketing / external relations

Innovation processes

Tanglible/intangible management processes

1. Administrative management processes

3. Dynamizing the ecosystem

Primary Activities

Support processes

5. Support to and monitoring of VME

6. Information Systems

2. Strategy processes

4. Marketing / external relations

Innovation processes

Tanglible/intangible management processes




management process comprises from the collection and assessment of tangible and intangible

assets to it evaluation.

2.2 Performance of service ecosystems

2.2.1 Conceptual decision levels

The organizational structure of a manufacturing ecosystem is built upon the organizational

structures of its virtual manufacturing enterprises and of its individual enterprises. The GRAI

model (Doumeingts, Vallespir, & Chen, 1998) describes the control of any kind of

organization using a combination of System Theory and Control Theory particularly

Hierarchical Theory. The GRAI decisional Grid aims to define the “set of decisions” from the

strategic level to the tactical and operational levels. In the context of performance

management, these three levels can be used to identify the impact of decision making

activities and to classify the underlying processes.

Typically, we can characterize these three levels of decision at the level of a single enterprise,

as follows:

Strategic: At the strategic level, the global strategy of the enterprise is decided, it’s the

long-term thinking of the company, where executive managers/Advisory Board decide the

roadmap of the company and establish objectives

Tactical: The tactical level is usually composed by multiple divisions, each controlling a

set of functions; the decisions taken here are related to the corresponding functions and

must comply with the strategy defined at the upper level. This level consists in identifying

the means and resources and properly organizing and planning a way to achieve the

strategic objectives. It’s mapped with the medium–term thinking of the company.

Operational: At the operational level, the core activities are carried out; the decision

power is limited to optimizing the specific production activities and business processes in

accordance with the main strategy. Day by day activities and concrete tasks are included

here, usually measured with specific KPIs.

The following table details the mutual characteristics of each of the three levels, along four

aspects: activity time-frame, business focus, user typology, metrics typology.

Strategic Tactical Operational

Time-Frame Month to year Days to month Intra-day or day-to-

day

Business focus Check the evolution

of the business

activity to achieve

long-term

objectives

Track and analyze

departmental

activities.

Manage tactical

initiatives to

Manage and

monitor and

optimize daily

business operations




achieve strategic

objectives

Users Executives and

business analysts

Managers (middle

management) and

business analysts

Front line workers

and end-users

Metrics Historical metrics.

High level and

refined metrics

(KPI’s)

Market and sector

trends

Worldwide

economy

Historical metrics

(aggregated data)

Short-time metrics

(daily or weekly

data)

Table 1 – Conceptual decision levels

We can notice that the most important discriminant of the three conceptual levels is related to

their “time frame” characteristic, which can be used for the decomposition of the objectives

and sub objectives of an organization. Finally, these three levels offer a natural model for the

decomposition of coarse grain objectives into fine grain and more operational objectives. For

instance, starting from high level objective such as : “Decrease the average delivery time of

all products of business unit Automotive Supplies to a maximum of 24 hours within the next

year”, we can observe that its time frame is “next year” ; a trivial quantitative measure would

be “the average delivery time” and that it could be decomposed into three sub objectives at

the tactical level, at a shorter timeframe:

1. “Increase production efficiency at assembly line V8 engine in factory XXX from level

8 to level 9 within the six months”

2. “Increase shipping efficiency of products shipped out of factory alpha by any logistic

partner from level 8 to level 9 within the six months”

3. “Reduce the average mean time between failure of production lines to 500 hours

within the next three months”

An equivalent refinement can be applied at the tactical level, and result in the definition of

operational goals, aligned with the tactical and strategic orientation of the enterprise. For

instance, the third example goal at the tactical level could result in such operational

objectives:

1. “Insure the maintenance team is constantly operational with 3 technicians available

on site”

2. “Maintain the availability of three units of sensitive spare parts in the maintenance

stock”




3. “Proceed to preventive maintenance verifications each two weeks”

These three conceptual levels will be used as a reference model for the analysis of

performance management in the frame of service ecosystems. Performance management

techniques, including business intelligence approaches and tools, and their applicability to the

context of manufacturing service ecosystems will then be evaluated in relation to these

conceptual levels.

2.2.2 Applicability to MSEE organizational structures

The following table summarizes the critical interactions between organizational structures

defined in the context of MSEE – Namely: Ecosystems, VME and single enterprises – in

order to underline the decisional level which should require a specific attention in each

context.

Context

Single Enterprise VME Ecosystem

Su

bje

ct o

f st

udy

Single

Enterprise

Relations between

single enterprises

follow operational

goals for the SE.

The involvement of

a single enterprise in

a VME sustains

tactical goals of the

single enterprise.

The involvement of

a single enterprise in

an Ecosystem is

aligned to the

strategy of the single

enterprise.

VME

In certain cases of

sustainable success, a

VME could eventually

become autonomous

in the form of a single

enterprise – e.g.: to

generate a spinoff.

During their lifetime,

it could be expected

that VMEs could

interact with others.

This would mostly

address operational

activities or strategic

goals.

The strategic goals

of a VME might be

directly influenced

by the Ecosystem

Ecosystem

From a tactical point

of view, Ecosystems

will tend to attract

single enterprises as

new members.

The generation of

VMEs is part of the

strategy of the

ecosystem.

(The interaction of

multiple ecosystems

is not included in the

study, as it is not

considered as

critical for its

performance.)

Table 2 – Interactions of MSEE organizational structures

Accordingly to the inherent nature of manufacturing service ecosystems, we can assume that

the involvement of single enterprises as members of an ecosystem is mainly related to the

pursuance of a strategic goal for this particular member. As well, considering the temporary

nature of VMEs and their business orientation, we can assume that the involvement of a

single enterprise as a part of a VME is related to tactical level goals for the enterprise.




Finally, we assume that the ecosystem strategy is mostly dedicated to the management of

VMEs and that the corresponding tactical goal will consist in attracting new single enterprises

as members of the ecosystem but also providing soft management methods and mechanism to

foster innovation and promote interaction and business opportunities within the service

ecosystem. As a consequence, the orientation for the study of performance management of

service ecosystem will mainly focus on the strategic and tactical decisional levels, in the

context of manufacturing service ecosystems.

2.2.3 Performance in manufacturing service ecosystems

As described previously, the reason of being for an ecosystem mostly resides in its potential

to generate effective innovation, i.e.: new ideas and opportunities for business development of

its members, at the medium or long term under the form of VMEs. Therefore, crossing this

main objective with the previously referenced three enterprise conceptual decision levels, the

conclusion is that performance of ecosystems should be evaluated on the basis of how

effectively the strategic objectives of the ecosystem are met. Among these leading objectives,

we can list the following examples:

Generating effective innovation

Influence the environmental market and trends

Attracting new and influent members

Generating successful VMEs

Positively benefit to the own business of its members

In order to identify a frame for the evaluation of business intelligence tools applied to the

management of service ecosystem performance, we propose to analyse each family of support

processes and to decompose their related objectives, along the 3 decisional levels described in

section §2.2.1. This classification of goals should help to determine the type of information to

be processed for monitoring specific metrics and to assess performance of MSE.




Class of process Overall

impact on

performance

Goals Metrics

Strategic Tactical Operational

Ecosystem

Administrative

Management

Processes

Low Provide administrative

support for the

ecosystem and its

members

Membership

management

Manage entry and exit

of members

Strategic:

- Number of transactions/partner

Tactical:

- Number transaction/industry sector

- Number of recruited partners

- Number of partners by type of

organization

- Reaction to complaints

- Number of complaints/partner

- Administrative costs/partner, revenue

Operational:

- Number of entries and exits

- Number of entries and exits by type of

organisation

Manage entry

conditions in the

ecosystem

Operational:

- Association fees

- Partners initial assessment:

o Organizational (e.g. size and

competencies)

o Financial (P&L, Operational

cash flow, turnover related to

latest 3 years)

o ICT Technologies

(Interoperability, Platform,




Security standards)

o Technological facilities (raw

material, physical product,

machinery)

o R&D (investments in

Technology,

o Number of resources and

industry expertise (human

capabilities and knowledge,

Seniority)

o Quality and reliability

o On time delivery

o Health and Safety Issues

o Price

Legal procedures

verification

Manage collaboration

agreement

(establishment,

satisfaction, etc.)

Tactical:

- Number of applications/industry sector

Operational


- Average time to address the problem

- Number of established agreements

- Number of satisfied/unsatisfied

agreements

- Number of conflicts arisen, Number of

conflicts solved, mean revenue for

exploited result, timeframe in which the

result is exploited

- Legal costs/partner, revenue




Manage contracts (i.e.

with external suppliers)

Tactical:

- Number of contracts/industry sector

Operational:


- SLA

- Contract costs/partner, revenue

- Claiming costs, recalls, business

hold/partner

- Number of contracts,

- Number of suppliers

- Number contracts breached

- Number of long-term strategic

collaboration partnerships

- number of partnerships per country

Manage ‘damages’

caused to third-party

stakeholders outside the

ecosystem

Operational:


- Contract costs/partner, revenue

- Claiming costs, recalls, business

hold/partner

Ensure Intellectual

property protection

management

Support creation of

patents

Tactical:

- Number of approved patents

- Number of pending patents

- Number of refused patents

Operational:

- Patent/partner




- Patent costs/partner

- Mean number of partners involved in a

patent

Manage exploitation of

patents and incomes

generated

Operational:

- Value of incomes generated per period,

- Mean value of incomes per patent,

- Number of patents taking end,

- Number of patents used without

agreement

Finance procedures

management

Ensure accounting:

membership fees (if

they exist), subsidies,

etc.

Operational:

- Association fees

Evaluate financial

health of members /

potential members

Tactical:

- Financial backlog (short, medium, long

term)

Operational:

- P&L

- Operational cash flow

- Turnover

Strategy Processes High Lead the MSE and fulfil

the common objectives

Detected/Accomplished

business opportunities

Definition and

coordination of the

ecosystem strategy in

accordance with every

member’s objectives

Monitoring of KPIs:

incomes, expenses,

number of new VE,

generated spin-offs,

targeted sectors,

number and

classification of

tangible and intangible

Strategic:

- MSE Turnover

- Turnover/partner

- Turnover/industry sector

- Customer satisfaction (complaints

etc,..)

- SLAs




assets, reusability of

resources, etc.

Market surveillance

Tactical:

- Number of active VMEs

- Financial indicators concerning VMEs

(e.g. P&L, Operational cash flow,

turnover)

Operational:

- VME/sector

- Number of new markets analysed,

- Number of enterprises analysed

- Number of detected/accomplished


- Number of new strategic partnerships

- ICT Technologies (Interoperability,

Platform, Security standards)

- Technological facilities (raw material,

physical product, machinery)

- R&D (investments in Technology,

- Number of resources and industry

expertise (human capabilities and

knowledge, Seniority, Reusability of

resources)

- Quality and reliability

- On time delivery

- Health and Safety Issues

- Price

Dynamizing the High Stimulate ecosystem

members to exchange

Animate internal Ensure regular

communication with

Strategic:

- Number of knowledge exchange events




ecosystem knowledge and to foster

the creation of virtual

enterprises

communication partners through

different channels

(forum, conferences)

Tactical:

- Repository Size for Tangible and

Intangible assets management

- Number of VMEs (as a positive result

of knowledge sharing)

Operational:

- Trust, reputation liability models, both

internal (among Partners) and external

(e.g. eBay)

Provide internal

communication of

tangible and intangible

assets that are at MSE’s

disposal

Operational:

- Number of assets declared

- Number of assets used by

members/partners

- Typology of assets used by

members/partners

- Rates applied (in any) for the use of

each tangible/intangible asset

Promote networking

and trust

Organize face to face

meetings and

networking events

Support team building

Operational:

- Number of organized events

Inform about

knowledge and

activities available

Manage knowledge into

the ecosystem: inform

about who is who

Tactical:

- Number of shared documents

- Number of enquiries to the knowledge




inside the ecosystem (experts location),

support exchange of

best practices, etc.

management database

- Number of expert advice

Operational:

- Number of upgrading occurrences on

the knowledge management database

Inform about

stakeholder’s

activity/maturity inside

the MSE: silent, very

active in market

opportunities,

contributes with a lot of

knowledge

Operational:

- Number of services, patents, white

papers, percentage of responses to

demands for each stakeholder

- Number of assets put at MSE’s disposal

- Number of VME joining/partner

Promote existing VMEs

among the ecosystem

and its members

Ensure communication

of VME

activities/results

(newsletter, etc.)

Operational:

- Number of VME dissemination actions

- Number of newsletters

Marketing / external

relations

Medium Increase influence and

attractiveness of the

ecosystem

Disseminate

ecosystem activities

and results

Control of presence in

media

Strategic:

- Number of Periodical Issuing of MSE

KPIs and SLAs

Tactical:

- Number of communication events

(forum, meetings, advertisements)

Operational:




- Number of press releases, Number of

appearance in the media

- Number of published news

- Number of used communication

channel

- Number of information requests

Promote the

involvement of

independent individuals

(customers, consumers

…)

Strategic:

- Number of R&D projects in

cooperation with customers

Operational:

- Number of individuals/association

involved in R&D research

Improve reputation of

the ecosystem

Monitoring of MSE’s

reputation (press, social

networks…)

Tactical:

- Number of customer surveys

- Result (improvement or not) of

customer surveys

Recruit new members

Identification of

potential new members

for the MSE

Tactical:

- Number of potential new MSE

members identified

- Number of potential new members

proposed by MSE members

- Number of potential new MSE

members contacted

- Number of new MSE members

recruited




Support to and

monitoring of “Virtual

Manufacturing

Enterprises”

High Create, animate and

monitor successful

VMEs in the ecosystem

Support the creation

of VMEs

Analyse and select

potential partners and

assets for VME

according to its

“theoretical”

requirements

Propose partners and

assets for future VME

to the promoters

Support

metamorphosis of

VMEs

Identify, assess and

select potential new

partners and assets for

VME

Propose partners and

assets for VME’s new

requirements

Tactical:

- Number and typology of assets

requested by VMEs

- Number of requests for new

assets/partners of VMEs

- VME maturity level

Operational:

- Number and typology of partner/assets

available,

- Number and typology of partner/assets

assigned

Monitor VMEs life

cycle

Monitor VMEs

performance

Operational:

- Organizational (e.g. size and

competencies)

- Financial (P&L, Operational cash flow,

turnover)




- ICT Technologies (Interoperability,

Platform, Security standards)

- Technological facilities (raw material,

physical product, machinery)

- Number of resources and industry

expertise (human capabilities and

knowledge, Seniority, Reusability of

resources)

- Quality and reliability

- On time delivery

- Health and Safety Issues

- Price

Management of the

dissolution/inheritance

of the VME (legal or

administrative support

for VME dissolution

and inheritance issues)

Operational:

- Number of inherited

assets/outputs/contacts

- Number of created spin-offs

- Number of joint-ventures

- Mean share capital when creating a

spin-off

- Mean number of partners involved

- Phase out costs

Information systems

processes

Medium Support management of

information systems

inside the ecosystem

Assure correct

operation ,

maintenance and

upgrade of the

HW/SW solution

MSE relies on

Number of

modules/data

sources/apps. the tool is

connected to

IT Services MSE offers

internally/externally

Operational:

- SLA for ICT,

- uptime, MTTR, MTTF, MTBF,

- band width

- HW/SW interoperability and reliability




Provide

interoperability

between members IT

systems

Uniform data exchange

Shared data repositories

Operational:

- Number of transaction to the repository

- Number of failure logs

Intangible/Tangible

management process

High Put at MSE’s disposal

all the relevant

intangible/tangible

assets

Collection of

intangible/tangible

assets

Strategic:

- Number of tangible and intangible

assets available as a service

- Turnover/partner related to specific

tangible and intangible assets

Assessment of

intangible/tangible

assets

Operational:


assets used as a service/partner



Planning and

management of

intangible/tangible

assets

Operational:


assets used as a service/partner



Evaluation of

intangible/tangible

assets

Operational:

- SLA related to tangible and intangible

assets




- Customer satisfaction

- Claiming costs

Innovation processes High Take advantage of


to create innovative

services

Identify ideas to bring

new business ideas to

fruition

Identify business

opportunities

Strategic:

- Number of business opportunities

presented (per partner, per sector, per

country,..)

- Number of business opportunities

selected

Strategic:

- Number of new ideas developed in the

past years

- Number of patents copyrighted

- Number of R&D researches

Tactical:

- Number of new services actually

implemented/ number of projects

(ideas) generated

Operational:

- Value of new services

implemented/turnover

Generate ideas Strategic:

- Number of ideas generated per

business opportunity

Tactical:

- Number of “idea generation teams”




created

Operational:

- Number of participants per “idea

generation team”

- Number of experts outside MSE

participating in idea generation

Idea assessment Operational:

- List of assessment criteria, Rating of

assessed ideas

Idea selection Operational:

- Number of selected ideas

Define requirements

to create a VME

Define new VME

opportunity/ new

service

Tactical:

- Degree of innovativeness of the

service/VME

- Degree of deviation of the defined

opportunity from the performed

opportunity (objectives, markets,

clients)

Define requirements for

VME

Strategic:

- Degree of deviation of the defined

opportunity from the performed

opportunity (budget, resources,

capacities…)

Tactical:

- VMEs performance (incomes, new

clients, .)




Support innovative

culture within the

ecosystem

Motivation actions to

support innovative

culture in the MSE

Strategic:

- Number of incentives for MSE

members

Tactical:

- Number of creativity workshops

- Number of idea contests

Table 3 - Ecosystem process decomposition: strategic / tactical / operational




2.2.4 Characteristics for the implementation of business intelligence solutions

As manufacturing ecosystem do rely on a widespread and loosely coupled organization

model, it is important to identify particular characteristics which may introduce new

difficulties for the implementation of a BI solution, in comparison to the context of a single

enterprise. The following table points out specific characteristics which would have some

impact on the implementation of performance management processes and the evaluation of

supporting methods and IT tools.

Characteristics Functional Impact Technical Impact

Physical dissemination of

actors

Deal with physically

distributed data sources and

third-party enterprise

applications and systems.

Different languages and

currencies support.

Loose relationship between

members

Difficulty to define

governance processes and

corresponding roles and

responsibilities.

Difficulties to quantify data

and measurement indicators.

Performance management

cannot rely on SLAs between

members.

Need to provide different

access levels to the

information, depending on

the actor profile and its role

in the ecosystem.

Complex indicators

constantly updated

High turnover rate of

members and roles

Difficulty to maintain role

models and governance

processes.

Low group consistency, poor

team sentiment

By definition, user roles may

change over time, so the BI

tools have to easily accept

modifications; also rapid

substitution of members and

the management of

corresponding

reconfiguration should be

taken into account

Delay of integration of the

new members to the IT

solution for performance

monitoring.

Proper management (storage,




archive…) of the information

regarding individual

manufacturing enterprises,

for example, in case a

company exits de ecosystem.

Variety of members (e.g.

partners not in the same

business)

Difficulty to define

commonly accepted

objectives, decision

processes and related

indicators

Trouble in considering

different weights based on

each partner’s contribution

Deal with heterogeneous and

legacy operational

information systems and

subsequent interoperability

issues.

Divergence of strategic or

tactical objectives between

members

Difficulty to define a

commonly accepted

roadmap, indicators and their

mutual representation

Deal with various kind of

representation of indicators

(multiple dashboards,

reporting, …)

Provide data extraction to

single members for analysis.

Confidentiality, privacy

issues, data policies

Complex and frequently

updated data management

policies

BI Tools have to match these

policies and cover potential

privacy issues: different

access levels, data

encryption, secure

connections and storage

Legal issues (partnerships,

exploitation of results)

Agile agreements between

several partners…

Agile and easy to use online

interaction methods to reach

agreements

Finance Connection with payment

portals/bank IT systems

Relation with the real

world outside the

ecosystem

Uniform mechanisms to

interact with the outside

world

Build an ‘open ecosystem’

image

Easy methodology to adopt

new members

Define standardized

communication channels

Data must be attractive, and

easy to use (e.g.: dashboards,

web portal)

Define lightweight IT

processes to ease ecosystem

member administration

Table 4 - Characteristics for the implementation of BI solutions




3 Review of business analysis tools

3.1 Business Intelligence solutions

Although the concept of business intelligence exists since the early '70s1, the term itself was

coined by the Gartner Group (Gartner Group) in the mid 90s, when they started the

development of various definitions. Basically, BI has replaced terms such as decision support

and management information systems. Business Intelligence can be defined as the process of

turning data into information and then into knowledge. Knowledge is typically obtained about

customer needs, customer decision making processes, the competition, conditions in the

industry, and general economic, technological, and cultural trends. To be more representative,

we can define BI as a generic concept which brings together, business tools and computer

science, used to transform data into information, information in decisions and decisions in

actions.

The BI applications that support decisions facilitate many activities, including:

decision support systems,

reports and queries,

online analytical processing of data, multidimensional analysis (OLAP - On Line

analytics Processing),

statistical analysis,

sorting the data to identify patterns and relationships (data mining)

3.1.1 Functional architecture

The following diagram depicts a generic pattern gathering the components (operational

systems/data integration/data warehouse/tools (front-end) and functions (analysis)) of a

typical Business Intelligence solution, from a functional point of view.

Figure 3 – Functional Architecture of BI solutions

Dec

isio

n s

up

po

rt s

yst

em

Management

Operational systems

Data Integration (Extraction, Transformation, Loading)

Data Warehousing

Reports OLAP Data Mining …

Dat

a W

areh

ou

se

Act

ion

s

Fu

nct

ion

s




As shown in the previous figure, BI can be considered as an iterative process, starting from

the operational environment:

Data are extracted from this medium and stored in data warehouses (the data

warehouse is in the form of a central data container, separate from operational data);

Decision Support systems are used by the decider to extract data from the data

warehouse;

Holding this information, a decider can create action plans;

This change in the operational information leads to a new iteration of the cycle

business intelligence.

The following table briefly describes the three main stages of a typical BI architecture:

Component Description

Operational Systems Data are extracted from the operational systems of the ecosystem.

They can come from multiple and heterogeneous sources like

internal databases (OLTPS) or even office documents.

Data Warehouse Data Warehouse designers determine which data contains business

value for insertion. It stores raw data, aggregated data and meta-

data.

Before data enters the data warehouse, the ETL process (Extract,

Transform and Load) cleans the data and ensures its quality.

The Data Warehouse modeling design is optimized for data

analysis.

Tools/Functions BI provides a broad range of functions to satisfy the management

needs. Those tools and functions depends on the analysis which is

expected, it can be simple reports from rough data or complex

dashboards using aggregated data, complex queries or even

discovering hidden patterns with the help of data mining methods.

Common functions are:

- reporting

- online analytical processing

- data and text mining

- process mining

- complex event processing

- business performance management

- benchmarking

- predictive or prescriptive analytics

Table 5 – Main components of BI solutions




3.1.2 BI solutions categories

Many BI tools are available in several categories, including production reporting, business

activity monitoring, corporate performance management, data mining, and advanced analysis.

Considering the types of information required, an enterprise determines what BI functions is

needed and then chooses the BI tools it wants. Generally speaking, BI solutions can be

dispatched in three different categories:

Business process centric applications: few data sources from production system

(operational)

Intermediate Solutions: heterogeneous data sources, generally needs data integration

and provide deeper capabilities in BI analysis (operational/tactical)

Strategic BI platforms: end-to-end BI platform with a comprehensive approach to

various data sources, visualization and reporting. Broad range of BI functions

(operational/tactical/strategic)

These three categories include more or less BI functions depending on the information needed

to make decisions.

3.2 BI products

The main results obtained on topics such as OLAP, multidimensional modelling, design

methodologies, optimization and indexing techniques converged to define the modern

architectures of data warehousing (DW) systems, and were absorbed by vendors to form a

wide set of on-the-shelf software solutions. An essential feature of BI solutions is their ability

to simultaneously connect to multiple and consistent sources of data, which may be different

operating systems (accounting, ERP, CRM, SCM, MRP, etc..) market research results,

activity logs and access, or anything that may be relevant to the recipient organization, usually

structured information, but in formats ranging from text files (CSV) to structures stored in

mainframe machines.

3.2.1 Spago BI suite

SpagoBI (SpagoBI) is an integration platform for enterprise BI solutions, entirely developed

according to Free and Open Source Software philosophy (Free and open-source software).

The SpagoBI platform actually adopts open standards without any binding dependences

on products and tools and provides solutions to solve analytical issues by means of

different products. Entirely developed as an open source platform, it implements a distributed

and scalable architecture able to satisfy most of the BI requirements: KPIs, Reporting, OLAP,

Dashboards and charts, Data Mining, Free Inquiring (QbE), Dossier, Geo-referenced

analysis, Collaboration. Moreover, SpagoBI provides services for Data Management,

Profiling and Security, Administrator support (versioning, scheduling, approval iter,

import/export, etc).

3.2.1.1 Reporting

SpagoBI allows to create structured reports, using structured information views (ex. lists,

tables, crosstabs and graphs) and to export them using several formats (HTML, PDF, XLS,




XML, TXT, CSV, RTF). Thanks to its Worksheet engine, SpagoBI allows end-users to freely

create their own multi-sheet reports, by defining simple tables, cross-tables and different chart

types in their document layout. SpagoBI offers a specific engine allowing to produce tabular

reports, which are accessible according to the international law WCAG 2.0.

3.2.1.2 Dashboard

SpagoBI offers a graphical visualization engine, in SWF format, allowing the display of KPIs

(Key Performance Indicators) for real-time graphical performance views.

SpagoBI provide a specific graphics engine which allows to develop single ready-to-use

graphical widgets (such as histograms, pie graphs, bar graphs, area graphs, scatter diagrams,

line graphs, bubble graphs, dispersion graphs) and interactive ones (temporal sliders,

add/delete series), to be used separately, by choosing the properties to be included into the

reports for a richer data view. SpagoBI offers a specific engine for the realization of complex

cockpits which allow to aggregate several documents into a single view, connecting

them with one another, fostering their interactive and intuitive usage.

Figure 4 – Spago BI Dashboard view

3.2.1.3 Analysis

SpagoBI allows the multidimensional analysis through OLAP engines. After having set the

analysis axis according to hierarchies and observed measures, the users can monitor the

data on different detail levels and from different perspectives, through drill-down, drill-

across, slice-and-dice, drill-through processes. Also SpagoBI provides two geographical

engines allowing to set run-time connections between the geographical data and the business

data of the Data Warehouse: a GEO engine, which uses a static catalogue in order to display

data, allowing users to dynamically re-aggregate the information, according to geographical




hierarchies (ex. nation, country, city) and a GIS engine, which interacts with real spatial

systems, according to the WFS/WMS scheme.

3.2.1.4 Data Mining

SpagoBI allows advanced data analysis, thanks to Data Mining processes aiming to find

out hidden information patterns among a great amount of source data.

3.2.1.5 Driven Data Selection

SpagoBI offers a QbE engine, which is suitable for those cases in which the free inquiry of

data and the extraction of data are more important than their graphical structure and

structural layout. Users can define their own queries through an entirely graphical

modality. Moreover, they can execute queries, check the results, export them, save them for

future use and generate reporting templates.

3.2.2 Pentaho

Pentaho Business Analytics (Pentaho) is a commercial open source business analysis tool that

allows easy data access, analysis and visualization. It is a mature solution that provides

extensive data type including diverse data types and even big data. It is a complete solution,

featuring multi-platform support and very easy to deploy.

The unified suit is a set of different programs working tightly for data discovery, integration

and exploration, using techniques such as data mining. These solutions are written in Java,

and it is a very flexible solution used to cover a wide range of business needs.

3.2.2.1 Reporting

The reporting module is a solution based on the JFreeReport (JFreeReport) solution and

allows quickly report generation. Pentaho Reporting allows the distribution of the analysis

result in multiple formats. All the reports include the option to print or export to PDF, XLS,

HTML and plain text formats. Pentaho reports also allows scheduling and automatic execution

of reports with a certain periodicity.




Figure 5 – Pentaho Reporting view

3.2.2.2 Dashboards

All the components of the Pentaho Reporting and Pentaho Analysis modules can be part of a

Dashboard. In Pentaho Dashboard it is very easy to include any kind of images, tables and

speedometers (dashboard widgets) and used them in the JSP Portlets, where reports, images

and OLAP analysis can be displayed.

Figure 6 – Pentaho Dashboard view




3.2.2.3 Analysis

Pentaho Analysis provides users with an advanced system to analyze information. Using

dynamic tables (pivot tables, crosstabs), generated by Mondrian and JPivot, the user can

navigate through data, with an advanced customization of data viewing, with visualization

filters, by adding or removing data aggregation. Data can be represented in SVG or Flash,

dashboard widgets, or they also can be integrated with data mining systems and web portals

(portlets). Finally, using Microsoft Excel Analysis Services, dynamic data can be analyzed in

Microsoft Excel (using the Mondrian OLAP server connection).

3.2.2.4 Data Mining

Pentaho features powerful data mining using the Weka software (Weka). Weka is a collection

of machine learning algorithms for data mining tasks such as data pre-processing,

classification, regression, clustering association rules, and visualization.

3.2.2.5 Data Integration

Data Integration is done using Kettle, a tool used to implement ETL processes using an

innovative meta-driven approach. It features an intuitive graphical editor (Spoon) where you

can define procedures stored in XML format to be interpreted by Kettle runtime using the the

graphical editor itself, a command line utility (Pan), a small server (Carte) and finally with a

database repository (Kitchen).

3.2.3 RapidMiner

RapidMiner (rapid-i) is an open source software for predictive analytics, data mining and text

mining that includes: Data Integration, Analytical ETL, Data Analysis, and Reporting

modules in one single suite.

It delivers a powerful but intuitive graphical user interface for the design of analysis

processes. Process, data and meta data are handled in repositories. RapidMiner supports meta

data transformation, on-the-fly error recognition and quick fixes. It is considered as one of the

most mature open-source system for Data Mining.

Rapid-I now delivers a complete software solution by including additional components.




Figure 7 - Rapid-I products map

Rapid-I provides a wide range of components and extensions from “Reporting and

Dashboards” to “Sentiments and opinions analysis”. With these extensions, RapidMiner

becomes a complete and flexible solution that delivers hundreds of data loading, data

transformation, data modelling, and data visualization methods.

3.2.3.1 Rapid Miner – Data analysis client

The data analysis client allows designing an analysis process as a sequence of calculation

steps. A typical data analysis process starts from an example data set, from which a model is

extracted under the form of a decision tree. This generated model is then applied to a data set

which is the object of the analysis and a target repository can be specified, to store the

calculation results. Once designed, the user has the possibility to execute the processes and to

browse the results in a dedicated perspective, to display the decision trees and to consult

performance metrics such as: accuracy and calculation time.

Figure 8 - Rapid Miner Client - Process design View




The tool provides helpful and accessible features to guide the user during the design of the

analysis process. Thanks to a WYSIWYG interface (WYSIWYG), it is possible to browse

through a component list and to build a consistent execution chain as the tool provides

notifications to the user, including warning and existing problems.

3.2.3.2 Rapid Analytics

This module provides a server component providing enterprise level integration features. Its

main functionalities include a process execution environment (from the models designed with

the Rapid Miner client), process scheduling, data repository, report server, user and

administration features, and a wide range of data source connectors.

3.2.3.3 Rapid net

Rapid net provides advanced features for visually exploring data. The tool is based on Rapid

Miner capabilities for data access and visualization, and gives the possibility to interactively

browse data analysis results.

3.2.3.4 Rapid Sentilyzer

Rapid Sentilizer is a proprietary module of the Rapid Miner suite which combines data

crawling and data mining techniques to provide sentiment and opinion analysis features. Such

a module finds its application for reputation analysis use cases, such as “how successful is my

marketing campaign ?”; “what are the expectations of my customers / users”.

Rapid Sentilyzer is built on top of RapidMiner data analysis engine and analytic server and

provides its functionalities through a portal solution, for multiple user access, delivering

dashboard display, statistics, data browsing, and the possibility to evaluate the source of

information the tool crawls. Rapid Sentilyzer has the capability of collecting by crawling

internet sources, such as news sites, forum and blog posts.

3.2.3.5 Rapid Miner – Extensions

With its wide range of extensions, Rapid Miner lets its users to benefit from its powerful

analysis features in combination with many environments, additional tools and data sources.

Such extensions increase the interoperability of the tool and decuples its potential. Among

them, we can mention a web extension, allowing Rapid Miner to access internet sources (RSS

feeds, web pages, web services) ; a community extension which allow to browse and share

analytic processes with the user community ; a parallel processing extension allowing to

optimize the calculation time, taking benefit of multicore architecture, thanks to new building

blocks for process design.

3.2.4 Microsoft BI tools

Microsoft BI tools (Microsoft Business Intelligence) constitute a complete BI solutions

including data storage, clean and consolidation, analyse and visualization. It regroups large

number of components to cover most of BI functions.




Figure 9 - Microsoft BI components

These tools empower users of all levels with new insights through familiar tools while

balancing the need for IT to monitor and manage user created content and deliver access to all

data types across structured and unstructured sources.

3.2.4.1 Reporting

Microsoft tools provides a wide range of reporting capabilities from highly interactive and

explorative self-service reporting for end users (Power View), to powerful operational report

authoring and rendering environments for IT professionals (SQL Server Reporting Services).

Furthermore, Microsoft products address complex reporting needs with familiar tools,

implanted in most enterprises.

3.2.4.2 Dashboards

Microsoft SharePoint Server provides a full set of rich dashboard and scorecard capabilities

like advanced filtering, guided navigation, interactive analytics and visualizations. To

enhance dashboards, Visio Services can display live data on diagrams created in Visio.

3.2.4.3 Analysis

SQL Server analysis services simplifies the process of building complex solutions with rich

modelling capabilities, it allows to build comprehensive, enterprise-scale analytic solutions.

It includes now a new capability called the BI Semantic Model: a single model enabling a full

spectrum of BI capabilities: reporting, analytics, scorecards, dashboards. SQL Server analysis

services include functions like Data Mining, complex events processes, Big data analysis,

predictive analysis and more.




3.2.4.4 Data Warehousing

SQL Server meets the most demanding data-warehousing requirements. Benefit from

enterprise-class scale and performance, and gain actionable business insights affordably with

integrated BI and ETL tools. Take advantage of hardware choice, data warehouse reference

architectures, and support for near real-time data. SQL server integrates data from any data

source with SQL Server Integration Services and maintains master data across the

organization with SQL Server Master Data Services. Data Quality Services is a tool to cleanse

organizational data and improve data quality by using organizational knowledge and third

party reference data to profile, cleanse, and match data.

3.2.5 SAS Enterprise BI Server

SAS Enterprise BI Server (SAS Enterprise BI Server) delivers extensive BI capabilities on

top of an open and integrated BI infrastructure, it does not provide data storage and

integration capabilities. Its main features include portal and dashboards, report viewing, report

building, advanced data exploration, Microsoft Office integration, guided analysis, metadata

management, guided SAS OLAP cube creation and application development. As a result,

users at all levels are able to quickly and easily obtain the information needed to make

decisions at the lowest overall cost to the organization.

SAS Enterprise BI Server provides extensive and robust visualization capabilities through

dynamic, interactive visualization environments, a comprehensive library of graphics for

presentations and customizable graphic generation. Business visualization delivers insights

and surfaces relationships that are not easily discovered in tabular formats. Business users can

interact with visual environments to explore ideas, investigate patterns and discover

previously hidden facts through visual queries. Providing business users with this level of

self-sufficiency reduces the overdependence on IT to service ad hoc requests.

3.2.5.1 Reporting

SAS Enterprise BI Server enables novice or casual business users to quickly create basic

queries and reports. Users can view reports in a self-service manner while IT maintains

control of the underlying data and security. Data is presented in terms business users

understand so nontechnical users can search and choose the information they need. As needs

evolve, more sophisticated layout and query capabilities are available. SAS empowers

business users with self-service access to query and reporting capabilities. SAS allows data

access, reporting and analytics directly from Microsoft Office via integrated menus and

toolbars. Those capabilities minimize training and support costs.

3.2.5.2 Portal and dashboards

SAS Enterprise BI Server includes a secure, role-based portal that provides personalized

interaction with information. Business users can access aggregated information via an easy-

to-use, Web-based dashboard utilizing Adobe Flash Player. Point-and-click dashboard

development enables users to create their own dashboards from multiple data sources.




3.2.5.3 Query and analysis

SAS provides self-service, visual query capabilities and wizards that enable information

producers to easily access and query their data. SAS can access virtually any data source with

the power and interoperability to query across multiple databases and platforms.

SAS allows exploring and analyzing OLAP cubes in an easy and user-friendly way.

3.2.6 JasperSoft

The Jaspersoft Business Intelligence Suite (Jaspersoft) is commercial open source software

that delivers integrated reporting, dashboarding, analysis, and data integration.

3.2.6.1 Reporting

Jaspersoft delivers a broad range of report types from relatively static and highly structure

reports to interactive and viewed online reports. Although those production reports are

generally created by IT professionals, Jaspersoft also provides ad hoc reports that are much

more dynamic, providing the business user with the data and a drag-and-drop web tool to

create their own reports.

3.2.6.2 Dashboards

The Web-based, drag-and-drop dashboard designer allows users to create ether single report

or dashboard-level parameters drive user interaction. Consolidated dashboard view combines

internal corporate information with external data sources.

3.2.6.3 Analysis

JasperSoft provides data analysis from any data source, including relational, Big Data, OLAP,

and custom sources. It delivers a single user interface (accessible through a standard browser

or iPad device) that supports ad hoc reporting and analysis. Data analysis engines allow in-

memory and OLAP analysis.

3.2.6.4 Data Integration

Jaspersoft data integration software extracts, transforms, and loads (ETL) data from different

sources into a data warehouse or data mart for reporting and analysis purposes. Jaspersoft

ETL builds integration jobs with an intuitive graphical interface that provides Business-

oriented models with a drag-and-drop process designer.

JasperSoft delivers a native connectivity to ERP and CRM applications (such as

Salesforce.com, SAP, and SugarCRM) and support for mainframe, transactional, and analytic

databases. We can also notice connectivity to Big Data environments (such as Hive for

Hadoop).

3.2.7 SentiStrength

Sentistrength (SentiStrength) is able to estimate the strength of positive and negative

sentiments on informal short text, using methods to exploit de-facto grammars and spelling

styles (spelling correction algorithm, syntax analysis techniques). It is optimized to be applied

to social web information extracts written in English language. It also works with text from

different communications media in which text based communication seems to frequently

ignore the rules of grammar and spelling. The most remarkable case could be mobile phone

http://www.jaspersoft.com/reporting

http://www.jaspersoft.com/dashboards

http://www.jaspersoft.com/analysis

http://www.jaspersoft.com/data-integration




text language that includes abbreviations, emoticons and truncated sentences. SentiStrength

adapts to different contexts by using different dictionaries including language words, positive

and negative sentiments, etc, external to the application. The tool is free for academic

research and costs £1000 for a commercial license.

Figure 10 - SentiStrength architecture

Its core emotion detection algorithm was developed on an initial set of 2,600 MySpace

classifications used for the pilot testing. The algorithm is based on a sentiment word strength

list, composed of a classification of 298 positive terms and 465 negative terms, each one rated

with a sentiment strength value from 2 to 5. This dataset includes “standard” English words,

as well as common expressions on the social web. The initial ranking of this wordlist is then

updated by a training algorithm, tuning the emotional weight values. In addition to this core

algorithm, a set of rules and processes are applied to optimise the evaluation of sentiments

through a syntactical analysis (e.g.: punctuation detection, repeated letters or punctuation,

detection of moderating words, …).

The result of benchmarking experiments and comparison with other machine learning

algorithms along social networks excerpts showed encouraging results for positive sentiments

evaluation (Thelwall & et al.). It is to notice that SentiStrength offers no advanced mechanism

for data source integration “out of the box”, neither reporting capabilities nor dashboard

features. Thanks to its java version, it is possible to invoke its runtime through the command

line or its Java API.

Among the multiple success cases of SentiStrength we can find the first social media driven

light show, where public and attendees, with their real time tweets, were dictating what color

the London Eye has to be turned every evening, by using Olympic-related dictionary of 2750

terms (Barnet).




4 Evaluation of BI tools

4.1 Evaluation of BI functions

This section aims at assessing the importance of the main Business Intelligence components

on both functional aspects (i.e. the “essential” constraints which are inherent to the

governance process families of MSEs) and the technical aspects (i.e. the “accidental”

constraints which are inherent to the structure of MSEs). Both tables should constitute the

basis for an evaluation framework in form of a comparison matrix, which would serve as a

preliminary guide process between particular BI tools to be implemented in Manufacturing

Service Ecosystems.

4.1.1 BI functions recommended for MSE metric types

The purpose of the following table is to evaluate the role and the criticism of the main

Business Intelligence components in the production of metrics which are specific to the

governance of manufacturing services ecosystems. This list of components is based on the

main functionalities provided by the tools previously introduced. The second dimension of

this table is based on a classification of the metrics introduced in section §2.2.3. These metrics

are grouped together considering their typology in terms of consistence, structure and

aggregation principles.

Note that the following BI functions evaluation has not been done along strategic, tactical and

operational decision levels, normally related to which final decisions are taken; the current

approach is oriented to evaluate how BI Tools can support these decision making processes,

that’s why BI functions have been evaluated along each metric group and characterized by

their importance in the production of such metrics.

Metric types:

Deterministic (based on structured data representing facts from the past)

Reputation & opinion based (based on unstructured social data, representative of

either internal or external opinion and sentiments)

Prospective (metrics that gave insight into actions to take, not merely a reporting of

what had occurred)

Proactive (provides an early indication of the final quality or performance of an item

or service)

Type of metrics

Deterministic

(example: number

transaction/partner,

cost related metrics,

Reputation & opinion

based (example:

trust/reputation

liability models,

satisfaction studies...)

Prospective /

Proactive (example:

forecast

administrative costs)

BI Functions

Social analysis - Critical Optional

Text mining - Critical Useful




Data mining - Optional Critical

Querying facilities Useful Optional Optional

Reporting These components will be used independently from the nature of the

metrics to be presented but will fulfil needs at the strategic or tactical

level, to adapt the level of presentation of the indicators.

Dashboards

Data integration

(ETL)

This functionality deserves transversal / technical needs and provides

means to retrieve data from operational systems and clean it before

processing

Data warehousing This functionality deserves transversal / technical needs and provides

storage facilities for insulating BI data from the original data sources.

Table 6 – Role of BI functionalities regarding metrics typologies

4.1.2 BI functions recommended for SE characteristics

The role of this table is to identify a list of critical points that should require a specific

attention when evaluating BI software for its implementation in a MSE context. This list is

derived from the technical impacts that were identified in section §2.2.4; and describes which

Business Intelligence components should be impacted by these criteria.

Characteristics Evaluation Criteria

Physical dissemination of actors

Data source integration capabilities (ETL

component)

Distant access, web based access (mostly

Dashboard and Reporting components)

Access & security management Rights & authorization management (ETL,


Complex indicators

Aggregation capabilities, OLAP support (Data

Analysis component)

Data browsing and exploring capabilities

(Reporting & Dashboard components)

High turnover rate of members

Administration facilities for right management

(ETL, Dashboard and Reporting components)

Data archiving facilities (Data warehouse

component)

Multiple restitution levels

Data presentation levels and segmentation

capabilities (Dashboard and Reporting

components)

Security issues Data encryption, access security




Heterogeneity of actors & systems

Interoperability capabilities, types of connectors

(ETL component)

Accessibility, ergonomics, enterprise portal

integration, office suites integration (mostly


Table 7 – Role of BI functionalities regarding structural characteristics of MSEs




4.2 BI Products comparison matrix

This section provides, based on the functionalities and characteristics defined in the previous

two points, a crossing analysis for each of the selected BI Tools and approaches, answering to

each point in a “Yes/Partially/No” format, with the corresponding reasoning explanation.

Spago BI Suite

Social analysis

No. SpagoBI does not provide right now any specific

functionality regarding social analysis, but this option is

the wishlist for future versions of the suite.

Text mining

No. SpagoBI does not support text mining techniques, but

its open architecture allows the integration of external text

mining components.

Data mining

Yes. SpagoBI allows advanced data analysis, thanks to the

integration of the Weka engine, in order to find out hidden

information patterns among a great amount of data.

Data

integration

Yes. SpagoBI allows to load data into the data warehouse

and manage it. SpagoBI ETL engine integrates the open

source product TOS (Talend Open Studio).

Querying facilities

Yes. SpagoBI offers a QbE (Query by Example) engine,

which is suitable for those cases in which the free inquiry

of data and the extraction of data are more important than

their graphical structure and structural layout. Users can

build their own queries through an entirely graphical

modality. Moreover, they can execute queries, check the

results, export them, save them for future use and generate

reporting templates.

Reporting

Yes. SpagoBI allows to realize structured reports, using

structured information views (ex. lists, tables, crosstabs,

graphs) and to export them using several formats (HTML,

PDF, XLS, XML, TXT, CSV, RTF). SpagoBI uses four

reporting engines: JasperReport, BIRT, Accessible report,

BO. Moreover thanks to its Worksheet engine, SpagoBI

allows end-users to freely create their own multi-sheet

reports, by defining simple tables, cross-tables and

different chart types in their document layout.

Dashboards Yes. SpagoBI offers a specific engine allowing to produce

real-time monitoring consoles, to be used in Business,




applicative or BAM processes.

Data Warehousing No. SpagoBI can only be connected to external data

warehouse.

Data Analysis

Yes. SpagoBI allows the multidimensional analysis

through OLAP engines (Jpivot/Mondrian, JPalo/Mondrian,

JPXMLA), which are more flexible and user-friendly,

compared to structured reports. After having set the

analysis axis according to hierarchies and observed

measures, the users can monitor the data on different detail

levels and from different perspectives, through drill-down,

drill-across, slice-and-dice, drill-through processes.

Physical Dissemination

Yes. The suite is prepared to deal with distributed remote

datasources, allowed types are following: Oracle, MySQL,

SQL Server, Excel, CSV files……..

Access & Security

Management

Yes. SpagoBI robust behavioural model allows the user

access management as well as the role-based access

control, supporting the correct distribution of the

information to the end-users.

Complex indicators

Yes. SpagoBI offers all the necessary tools to create,

manage, view and browse KPI hierarchical models,

through different methods, calculation rules, thresholds and

alarm rules.

High Turnoverate of

members

Yes. Integration of new users of the BI environment is

handled through the role-based access control system,

which enables new members to be registered, profiled and

granted the appropriate access level to the different

functionalities. All SpagoBI functions check if the user can

or can't execute it, this is done from Spago Application

Framework. At the login, SpagoBI inserts all the

functionalities in User Profile and use it to check the

authorization. Each SpagoBI role has some functionlities,

the administrator can configure this association in SpagoBI

metadata DB.

Multiple restitution

levels

Yes. As mentioned in the “Data Analysis” row SpagoBI

allows the multidimensional analysis through OLAP

engines. The users can monitor the data on different detail

levels and from different perspectives, through drill-down,

drill-across, slice-and-dice, drill-through processes.




Security issues No. No specifc security problem is present to our

knowledge.

Heterogeneity of actors

& systems

Yes. Thanks to its open architecture SpagoBI can be

integrated with any external system.

Table 8 – SpagoBI features table

Pentaho BI Suite

Social analysis No.

Text mining Partially. Text mining is done using Weka data mining

library externally.

Data mining Partially. Data mining is done by using Weka data mining

library externally.

Data

integration

Yes. Data Integration is done thanks to the Ketle module

ETL capabilities. Includes Hadoop, NoSQL, OLTP and

analytic databases.

Querying facilities Yes. A Community Data Browser (CDB), uses a visual

OLAP browser called Saiku to create a query exists.

Reporting Yes. Pentaho Report Designer allows the creation of report

definitions in a graphical environment.

Dashboards Yes. A commercial plugin provided to enterprise edition

allows the user to create dashboards.

Data Warehousing No. Data warehousing is provided by external systems.

Data Analysis

Yes. Data Analisys is done by an open source OLAP

server codenamed Mondrian. There is a web based viewer

called PAZ too.

Physical Dissemination Yes. Data Access project allows the integration of data

available in remote data sources.

Access & Security

Management

Yes. Thanks to the security feature available since version

1.6 of the Pentaho BI Platform.

Complex indicators Yes. Pentaho Interactive Reporting (PIR) enables users to

create reports easily.




High Turnoverate of

members

Yes. The security module Pentaho integrates provides

flexibility and allows easy protection of data from external

members and being as transparent as possible for internal

members.


levels

Yes. Thanks to the reporting and dashboard capabilities

data can be segmented to show different presentation

levels.

Security issues Partially. There is access security support but not

encrypted data.


& systems

Partially. Pentaho does not include ad-hoc support for

connecting to external systems, but they can be created

thanks to a plugin system.

Table 9 – Pentaho features table

SentiStrength

Social analysis Partially. The tools was created with the aim of analyse

sentiments on text. It must be connected to social media.

Text mining No. It does not try to discover patterns.

Data mining No. The tool analyses only sentiment strength on text.

Data

integration

N/A.

Querying facilities N/A.

Reporting N/A.

Dashboards N/A.

Data Warehousing No. Data to feed the tool is external to itself.

Data Analysis No. The tool analyses only sentiment strength on text.

Physical Dissemination No. The tool only allows local execution.

Access & Security

Management N/A.

Complex indicators N/A.




High Turnoverate of

members N/A.


levels N/A.

Security issues N/A.


& systems N/A.

Table 10 – SentiStrength features table

RapidMiner (Rapid-I)

Social analysis Yes. Rapid Sentilyzer module provide sentiment and

opinion analysis features

Text mining Yes. RapidMiner is considered as one of the most mature

open-source system for Data/Text Mining

Data mining Yes. RapidMiner is considered as one of the most mature

open-source system for Data/Text Mining

Data

integration

Yes. RapidMiner provides connectors for all common

databases and formats and numerous transformations for

common ETL processes.

Querying facilities

Yes. Rapid Net module provides advanced features for

visually exploring data. The tool is based on Rapid Miner

capabilities for data access and visualization, and gives the

possibility to interactively browse data analysis results.

Reporting Yes. The RapidMiner Reporting extension supports various

output formats, including HTML and PDF.

Dashboards Yes. Allow to create dashboards (dynamic dashboards,

etc.)

Data Warehousing Yes. RapidMiner provides a preconfigured INGRES

database to build your data warehouse.

Data Analysis Yes. Provides numerous analysis functions that can be

completed with a wide range of extensions.

Physical Dissemination Yes. Data integration capabilities allow manipulating

remote and heterogeneous data sources.




Access & Security

Management

Yes. Provides shared repositories for collaborative access.

Security management is also available including Single-

Sign-On.

Complex indicators Yes. The broad range of analysis functions available allows

determining complex indicators.

High Turnoverate of

members

(To be studied)


levels



levels.

Security issues (To be studied)


& systems

(To be studied)

Table 11 – RapidMiner features table

Microsoft BI

Social analysis No

Text mining Yes. SQL Server Analysis services include Text Mining

functions.

Data mining Yes. SQL Server Analysis services include Data Mining

functions.

Data

integration

Yes. SQL Server Integration services provide data

integration and workflow applications.

Querying facilities Yes. Microsoft Office tools make querying easier.

Reporting

Yes. Microsoft covers the full range of reporting from

highly interactive and explorative self-service reporting for

end users (Power View). Users can create their own reports

through Office tools like Words, Excel, SharePoint, etc.

Dashboards Yes. Users can create their own dashboards through Office

tools like SharePoint.

Data Warehousing Yes. SQL Server is a database engine and provides

important data warehousing capabilities.

Data Analysis Yes. Provides a wide range of analysis functions including




Data Mining, complex events processes, Big data analysis,

predictive analysis and more.



Access & Security

Management

Yes. Microsoft products provide access and security

management: SSO, Active Directory and more.



High Turnover rate of

members

Partially. Microsoft Office tools allow operational users to

create and modify reports and dashboards. People can

quickly get autonomous with those tools.


levels



levels. Microsoft Office tools allow users to personalize

their reports and dashboards easily.

Security Issues (To be studied)


& systems

Partially. Microsoft products are well implanted in

existing companies but hardly compatible with other

systems.

Table 12 – Microsoft BI features table

SAS BI Enterprise Server

Social analysis Yes. SAS Sentiment Analysis automatically analyses

sentiment from the Web and electronic documents

Text mining Yes. SAS Analytics provides evaluated analysis functions

that include Text Mining.

Data mining Yes. SAS Analytics provides evaluated analysis functions

that include Data Mining.

Data

integration

Yes. SAS Data Management ensures data integration and

data quality

Querying facilities

Yes. SAS Business Intelligence offers an integrated, robust

and flexible presentation layer analytics capabilities. It

provides self-service, visual query capabilities and wizards

that enable information producers to easily access and

query their data. SAS also allows exploring and analyzing

OLAP cubes in an easy and user-friendly way

Reporting Yes. SAS provides web and desktop reporting.




Dashboards Yes. SAS provides portals and customizable dashboards


Data Analysis

Yes. SAS Analytics provides evaluated analysis functions.

SAS allows exploring and analyzing OLAP cubes in an

easy and user-friendly way.



Access & Security

Management

Yes. SAS provides multiple authentication methods (SSO,

domain authentication, token authentication, etc.) and

manages role and permissions of users.



High Turnoverate of

members

(To be studied)


levels



levels.



& systems

(To be studied)

Table 13 – SAS BI enterprise server features table

JasperSoft

Social analysis No

Text mining No

Data mining No

Data

integration

Yes. JasperSoft ETL builds integration jobs with an

intuitive graphical interface

Querying facilities

Yes. JasperSoft provides a unified analysis interface

accessible through a standard browser (or iPad device) that

supports ad hoc reporting and analysis

Reporting Yes. Provides highly interactive and formatted reports

containing interactive charts, images, sub-reports,




expressions, and more.

Dashboards Yes. Provides a web-based, drag-and-drop dashboard

designer that can be used by end-users.


Data Analysis

Yes. JasperSoft allows you to create a broad range of

report types to meet the needs of different users. It also

provides in-memory and OLAP powered data analysis

engines.



Access & Security

Management

Yes. JasperSoft provides Single-Sign-On authentication.

Common external authentication mechanisms include

LDAP, CAS, or SiteMinder.



High Turnoverate of

members

(To be studied)


levels



levels.



& systems

(To be studied)

Table 14 – JasperSoft features table




5 Conclusions

The analysis of Manufacturing Services Ecosystems for assessing and managing their

performance leads to tackle new challenges in comparison to the analysis of performance in

the context of single enterprises. The main differences identified along this study reside in:

the overall strategy of Manufacturing Service Ecosystems which mostly aims at

developing new innovative business opportunities, in form of services, between

partners

the organizational aspects of ecosystems, which relies on non-hierarchical

relationships and roles, and a distributed organisation of its members

the complexity of properly managing innovation and exploitation issues inside the

ecosystem

the sharing culture and reuse of individual and common assets, both tangible and

intangible, that promotes the ecosystem, and the ‘interpretation’ of this claim by each

stakeholder

All of the above characteristics strongly influence the analysis, towards a potential (re)use of

some functionalities of Business Intelligence tools currently available in the market, but also

the implementation of new functionalities and modules identified as real needs when

analyzing manufacturing service ecosystems. Through this first step of the analysis has been

demonstrated that existing BI tools and approaches partly offer technical means for the

extraction, analysis and presentation of the information which should be relevant for the

governance and performance measurement of service ecosystems. Indeed, thanks to widely

accepted technologies in today’s enterprises (ERPs, relational databases, OLTP, …), most of

the BI tools right now integrate with operational information systems of ecosystem members

and provide the capability to extract and aggregate operational data in order to produce

relevant metrics and reporting facilities; logically, these BI Tools need a kind of evolution

that should be reflected in the provision of functionality capable of performing these

operations in a more global and collaborative sense, not only targeting sole enterprises, but for

a set of companies playing together within a services ecosystem.

Besides, as Manufacturing Service Ecosystems rely on particular governance processes which

adapt to the non-hierarchical structure of ecosystems and their time evolving nature, the study

reveals that none of the existing BI tools and approaches on the market are ready to be used

by MSEs “out of the box”, providing relevant metrics with no further integration effort.

Consequently, although there is a good base right now, can be said that a significant amount

of work still remains necessary for the adaptation and the configuration of a BI software in

order to analyse performance of MSEs, as these tools currently mostly address technical

needs, which are independent of their utilization context.

In parallel, remarking again the key role of performance monitoring when doing business in a

collaborative way, this document points out a first set of metrics and indicators, which are




considered essential for the evaluation of ecosystem performance at the three decisional levels

(strategic, tactical and operational) of a single enterprise, but also of a manufacturing services

ecosystem. In order to support the related decision processes which will take into account and

somehow consume these indicators, BI tools have been identified as a very valuable option in

helping to extract the source data from the relevant systems (mostly operational information

systems); they currently provide great and proven functionality to accomplish this task of

acquiring data from heterogeneous data sources, as a first step that after some particular

processing, provides the indicators both an enterprise and an ecosystem need. From an

architectural point of view, the information sources exploited by BI tools should be clearly

identified across the ecosystem organization, be aligned and have uniform access

mechanisms, and of course, be maintained up to date in coordination with the ecosystem

processes, so that these data repositories and the data they contain, reflect the actual state of

the ecosystem. Taking this into account, it is important to consider that BI tools are a top level

component of the overall information system architecture of ecosystems. Therefore, a

successful implementation of BI tools will require that the source systems are correctly

orchestrated and aligned with operational and governance processes of MSEs.

As a conclusion, we can assess that the existing BI offer on the market provides solid

technical foundations to partially face the difficulties due to the specific characteristics of

service ecosystems and the heterogeneity of information systems of each member;

nevertheless, none of these tools directly provide functionalities for an immediate

implementation of performance metrics for MSEs, either at the operational, tactical or

strategic levels. This general conclusion opens future perspectives for establishing

prerequisites and requirements for the BI tools of the future, in order to guide the necessary

effort to accomplish potential implementation and configuration approaches for the

production of performance metrics needed by manufacturing service ecosystem, at the

strategic, tactical and operational decision levels.




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D25.2 Requirements for analyzing service ecosystems … 8 – SpagoBI features table 53 Table 9 –...

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