extended lessons learned and evaluations integrated ... · in virtual factory, the cluster...
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Project ID 604674 FITMAN – Future Internet Technologies for MANufacturing
30/09/2015 Integrated Deliverable D12.5 – D13.5 – D14.5
D12.5 – D13.5 – D14.5
Extended Lessons Learned and Evaluations
Integrated Deliverable
Document Owner: Thomas Fischer (DITF)
Contributors: Eva Coscia, Silvia Crippa, Jacopo Cassina (Holonix), Toni Ventura (Datapixel), Aitor
Romero (Datapixel), Karl Hribernik, Marco Franke (BIBA), Silke Balzert, Jan Sutter
(DFKI), Konrad Pfleiderer (DITF), Ioan Toma, Benjamin Hiltpolt (STI), Sonja.Pajkovska-
Goceva (COMPlus), Gash Bullar (TANET), June Sola (Innovalia), Mirla Ferreira
(CONSULGAL), Marek Eichler (VW), Javier Martinez (AIDIMA), Roberto Sanguini
(AW), Nenad Stojanovic (FZI)
Dissemination: CONFIDENTIAL
Contributing to: WP12-13-14 Extended lessons learned and evaluations (final)
Date: 14/11/2015
Revision: 1.1
Project ID 604674 FITMAN – Future Internet Technologies for MANufacturing
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VERSION HISTORY
Version Date notes and comments
0.1 24/09/2015 DOCUMENT STRUCTURE AND TABLE OF CONTENT,
FIRST INPUT 14.5
0.2 05/10/2015 FIRST CONTRIBUTIONS FOR 12.5
0.3 13/10/2015 UPDATED CONTRIBUTIONS FOR 12.5
0.4 20/10/2015 CONTRIBUTION 13.5
0.5 26/10/2015 INPUT KPI Part 1
0.6 05/11/2015 INPUT KPI Part 2
1.0 06/11/2015 FINALISATION
1.1 14/11/2015 FEEDBACK FROM PEER REVIEW INCLUDED
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Table of Contents
EXECUTIVE SUMMARY 5
ACRONYMS AND ABBREVIATIONS 6
LIST OF FIGURES 7
1 DELIVERABLE 12.5 8
1.1 WHIRLPOOL USE CASE 8 1.1.1 Introduction 8 1.1.2 Challenges for big data analytics for process improvement 9 1.1.3 Our approach for big data clustering 10 1.1.4 Use Case: Whirlpool washing machine testing 11 1.1.5 Results 14 1.1.6 Evaluation of the Experimentation 19 1.1.7 Visualization of Deviation Maps 21 1.1.8 Lessons Learnt 22 1.1.9 Conclusion 23
1.2 TRW USE CASE 23 1.2.1 Introduction 23 1.2.2 Integration architecture 24 1.2.3 Visualization examples 25 1.2.4 Lessons learnt 26
1.3 REFERENCES 26
2 DELIVERABLE 13.5 27
2.1 INTRODUCTION AND DOCUMENT SCOPE 27 2.2 EVALUATION METHODOLOGY 27 2.3 METHODOLOGY MAIN STEPS 27
2.3.1 Feedback collection channels 27 2.3.2 Interview structure 27 2.3.3 Interviews and reporting 28
2.4 IMPROVEMENTS AND EXTENSIONS RELATED TO END USER FEEDBACKS 28 2.4.1 CONSULGAL TRIAL 28 2.4.2 AIDIMA TRIAL 33 2.4.3 VW TRIAL 34 2.4.4 WHIRLPOOL TRIAL (Digital factory) 34 2.4.5 TRW TRIAL (Digital Factory) 35
2.5 INTERVIEWS WITH END USERS 36 2.5.1 Interviews in CONSULGAL 36 2.5.2 Interviews in AIDIMA 38 2.5.3 Interviews in VW 41 2.5.4 Interviews in WHIRLPOOL 43 2.5.5 Interviews in TRW 45
2.6 LESSONS LEARNT 46 2.6.1 Lessons Learnt in AIDIMA 46 2.6.2 Lessons Learnt in CONSULGAL 49 2.6.3 Lessons Learnt in VW 50 2.6.4 Lessons Learnt in TRW and WHIRLPOOL 51
2.7 CONCLUSIONS 53
3 DELIVERABLE D14.5 54
3.1 INTRODUCTION 54 3.2 GENERATION AND TRANSFORMATION OF VIRTUALIZED ASSETS (GETOVA) 54
3.2.1 Short overview of GeToVA SE 54 3.2.2 Experiments and Results 56 3.2.3 Lessons learned 61
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3.3 ADVANCED MANAGEMENT OF VIRTUALIZED ASSETS (MOVA) 61 3.3.1 Short overview of MoVA SE 61
3.4 EXPERIMENTS AND RESULTS 61 3.4.1 Data Modelling 61 3.4.2 Importing 62 3.4.3 Cluster Search 67 3.4.4 Integrating MoVA with SME Cluster 67 3.4.5 Lessons learned 70
3.5 INTERVIEW WITH TANET 71 3.6 CONCLUSIONS 72
4 UPDATE ON KPIS IN THE TRIALS 73
4.1 AGUSTAWESTLAND 73 4.2 AIDIMA 78 4.3 VOLKSWAGEN 80
4.3.1 MR Update Cost 81 4.3.2 MR Update Time 81 4.3.3 Average lead time to access experts knowledge 82 4.3.4 Evaluation Accuracy 82 4.3.5 Inquiry Respond Time 82 4.3.6 Inquiry Respond Cost 83
4.4 CONSULGAL 83 4.5 TRW 86
4.5.1 Trial Results and Progress 86 4.5.2 TRW KPIs Analysis 87 4.5.3 Consolidated Trial Experience 89
4.6 TANET 90 4.6.1 Overview 90 4.6.2 General Comments about KPI’s 91
4.7 COMPLUS 92 4.7.1 Network Transparency For More Efficient Supplier Search 92 4.7.2 Transparency And Consistency Of ITs And Documents 92
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Executive Summary
This joint deliverable D12.5 D13.5 and D14.5 documents the final results of the evaluation of
work in Tasks 12.3, 13.3 and 14.3. These Tasks focus on involving the FITMAN Trials in the
technical evaluation of the customized platforms and solutions that have been implemented in
WP4+WP12; WP5+WP13 and WP6+WP14. In this sense, this joint deliverable is the summa
of all the experiences gained in our 10 industrial Trials. Trials (end users and their IT
partners) have been asked to experiment with the trial integrated systems and to report
essentially about their correctness (the solution behaves as expected), their completeness (the
business processes are supported as expected and all the key functionalities have been
implemented) and their quality (regarding performance, security and user friendliness). The
result of this evaluation has been analysed to extract lessons learnt at both trail and single
component point of view and therefore derive possible improvements for them.
Interviews have been conducted with the IT specialists and end users, to capture their
evaluation of the provided solutions and to know from them in particular if critical
functionalities were missing and how difficult has been for them to start using the software.
Overall, all comments have been largely positive and encouraging, with the confirmation
from the end users that the provided solution fully meets the expectations and supports the
selected business processes. Moreover, trials foresee the possibility to continue using the
solutions after the closure of FITMAN. This will be reported in the exploitation report of
WP9.
An update of the KPI analysis of some of the trials, based on the results of the latest 6 Months
of experimentation concludes this deliverable.
As a consequence of the evaluations performed in this phase, in Smart Factory the
components for data clustering had to be extended in order to deal with huge amount of data
as in the WHIRLPOOL trial. The components for visualization which could help validating
the results needed to be adapted accordingly.
In Digital Factory, some performance requirements asked for optimisation of the SEs
involved. One example is a virtual meeting with a high number of concurrently active
widgets. Some of the SEs, such as the 3DWV, might require some time to complete the
computation/transformation tasks and thus are slower in responding. Therefore, some
extensions and adaptations have been made to solve these issues.
In Virtual Factory, the cluster modelling for the automatic suggestion of suitable clusters was
a real challenge. The ontology modelling and search algorithms needed to be extended
accordingly. In addition, the import and export of data proved to be a challenge which was
met by optimised interfaces and APIs.
The intense experimentation activity in all the trials throughout the full lifetime of FITMAN
highlights the enormous interest of the industrial partners in the project.
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Acronyms and Abbreviations
3D PCAP 3D Point Cloud Analysis Processing
BP Business Process
BS Business Scenario
C3DWV Collaborative 3D Web Viewer
DCC Digital Content Creation
CAD Computer Aided Design
DF Digital Factory
GE Generic Enabler
GUI Graphic User Interface
MR Machine Repository
SE Specific Enabler
SEMed Semantic Mediator
SF Smart Factory
TSC Trial Specific Component
VF Virtual Factory
VW Volkswagen
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List of Figures
Figure 1: Correlation between power and rotation speed ......................................................... 12 Figure 2: Power parameter of multiple tests ............................................................................. 12 Figure 3: Comparison of two signals – red signal is shifted even more ................................... 13 Figure 4: Generated medoids .................................................................................................... 14
Figure 5: First cluster ................................................................................................................ 15 Figure 6: Second cluster ........................................................................................................... 15 Figure 7: First anomaly ............................................................................................................ 16 Figure 8: Second anomaly ........................................................................................................ 16 Figure 9: Third anomaly ........................................................................................................... 17
Figure 10: First cluster (large dataset) ...................................................................................... 17
Figure 11: Second cluster (large dataset) ................................................................................. 18
Figure 12: Third cluster (large dataset) .................................................................................... 18 Figure 13: Fourth cluster (large dataset)................................................................................... 19 Figure 14: Bigger dataset makes validation/visualization of analysis more complex .............. 21 Figure 15: Web App for the Visualization of Deviation Maps ................................................ 22
Figure 16: Class diagram for extension of TRW use case ....................................................... 24 Figure 17: Final architecture .................................................................................................... 25 Figure 18: Examples of two employee positions during work ................................................. 26
Figure 19: the original unique Dam Zone view ........................................................................ 30 Figure 20 The view Dam zones shows the list of the concrete compositions used in a slected
zone of the dam......................................................................................................................... 31 Figure 21: The new "Concrete Operations" view ..................................................................... 32 Figure 22: The NPAB details ................................................................................................... 33
Figure 23. Whirlpool part used for the trial. ............................................................................. 34
Figure 24: Diagram of the structure of the numerical dataset .................................................. 35 Figure 25. TRW spindle used in the trial.................................................................................. 35 Figure 26. TRW spindle used in the trial.................................................................................. 47
Figure 27: GeToVA Architecture ............................................................................................. 55 Figure 28: GeToVA integrated with MoVA in TANET trial ................................................... 56
Figure 29: TANET cluster generated by GeToVA .................................................................. 57 Figure 30: Individual Profile in JSON extracted by GeToVA from LinkedIn ......................... 58 Figure 31: Individual Profile in JSON extracted by GeToVA from LinkedIn ......................... 59
Figure 32: GeToVA in COMPlus trial ..................................................................................... 60 Figure 33 MoVA: Startscreen and model ................................................................................. 62
Figure 34: MoVA: Add new supplier ....................................................................................... 64 Figure 34: MoVA: Imported suppliers ..................................................................................... 64
Figure 38: MoVA: Restful API Import suppliers ..................................................................... 67 Figure 35: MoVA: Data of MoVA in SMECluster .................................................................. 68 Figure 36: MoVA: Restful API Result of Level 0 search ........................................................ 69 Figure 37: MoVA: Restful API Result of Level 1 search ........................................................ 69 Figure 38: MoVA: Restful API Result of Level 2 search ........................................................ 69
Figure 39: MoVA: MoVA Cluster Search Result in SMECluster ........................................... 70
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1 Deliverable 12.5
This deliverable summarizes the results and the lessons learnt in WP12, focusing on the two
trials Whirlpool and TRW. Other trials such as Piacenza had already completed their
experiments.
1.1 Whirlpool Use Case
One of the most important challenges in manufacturing is the continuous process
improvement that requires new insights about the behavior/quality control of processes in
order to understand the optimization/improvement potential. This deliverable elaborates on
usage of big data-driven clustering for an efficient discovering of real-time anomalies in the
processes. Our approach extends traditional clustering algorithms (like k-Means) with
methods for better understanding the nature of clusters and provides a very efficient big data
realization. We argue that this approach paves the way for a new generation of quality
management tools based on big data analytics that will extend traditional statistical process
control and empower Lean Six Sigma through big data processing. The proposed approach
has been applied for improving process control in Whirlpool (washing machine tests, factory
in Italy) and we present the most important finding from the evaluation study. We note here
that the results of this deliverable will be published IEEE BigData 2015, Special Session -
From Data to Insight: Big Data and Analytics for Smart Manufacturing Systems [8].
1.1.1 Introduction
Due to dynamically changing business environment and especially permanently increasing
competition, one of the most important challenges in manufacturing nowadays is the
continuous process improvement (CPI) - defined as an ongoing activity aimed at
improving processes, products and services through sustainable changes over a period of time.
Most CPI strategies incorporate the Lean Six Sigma1 principle, which is a combination of
techniques and tools from both the Six Sigma Methodologies and the Lean Enterprise2. The
Six Sigma methodology is based on the concept that a "process variation” can be reduced
using statistical tools. The goal of Lean is to identify and eliminate non-essential and non-
value added steps in a business process in order to streamline production, improve quality and
gain customer loyalty.
Lean Six Sigma practitioners have been improving processes for years through statistical
analysis of process data in order to identify the critical parameters and the variables that have
the most impact on the performance of a value stream and to control their variations.
However, the main constraint is the complexity of the statistical calculations that should be
applied on the (large) datasets. A well-known example is the multivariate statistical analysis.
Standard quality control charting techniques (e.g., Shewhart charts, X-bar and R charts, etc.)
are applicable only to single variable and cannot be applied to modern production processes
with hundreds of important variables that need to be monitored. Indeed, the diversity of
process measurement technologies from conventional process sensors to images, videos, and
indirect measurement technologies has compounded the variety, volume, and complexity of
process data3. For example, it is typical in a modern FAB (semiconductor manufacturing) that
1 https://en.wikipedia.org/wiki/Lean_Six_Sigma
2 https://en.wikipedia.org/wiki/Lean_enterprise
3 http://wenku.baidu.com/view/59f0c1bd84254b35effd346f.html?re=view
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over 50,000 statistical process control charts are monitored to control the quality of over 300
manufacturing steps in the fabrication of the chip [1].
Moreover, generated data can be extremely big: e.g. in-process monitoring in additive
manufacturing (3D printing) produces 100MB – 1GB data, whereas in-process geometry
inspection generates 1- 10GB data volume per part4.
Although process operations are rich in data, without effective analytical tools and efficient
computing technology to derive information from data, it is often the case that data is
compressed and archived for record keeping and only retrieved for use in emergency analysis
after the fact rather than being used in a routine manner in the decision-making process.
In this deliverable we report the testing and experimentation of a novel big data approach for
continuous process improvement that exploits above mentioned advantages for enabling
better understanding of a (dynamic) nature of a process and boosting innovations.
We argue that by performing Big data analytics on the past process data we can model what is
(statistically analyzed) usual/normal for a selected period and check the variations from that
model in the real-time (as Six Sigma requires). Additionally, these data-driven models can
support the root-cause analysis that should provide insights about what can be eliminated as a
waste in the process (as Lean requires). However, due to the above mentioned variety and
volume of data, the analytics must be a) robust – dealing with differences efficiently and b)
scalable - realized in an extremely parallel way.
The proposed approach has been applied for improving process control in Whirlpool factory
in Italy based on washing machine tests. In this deliverable we present the most important
finding from the evaluation study.
This section is organized in the following way: we start with description of the challenges for
big data analytics and then we continue with presenting our approach for big data clustering.
We provide details about the case study and lessons learnt from the trial. Finally, we
summarize the results.
1.1.2 Challenges for big data analytics for process improvement
In the nutshell of improving a process is the understanding of the nature of the process – what
is its normal/usual behavior? However, the pace of change is continuously increasing and
introduces new computational challenges for continuous process improvement. There are two
main issues that challenge traditional Lean Six Sigma approach for continuous improvement:
the number of parameters that can be measured in a process and corresponding size of
data to be analysed is exploding (note that it is strongly influenced by the supply-chain
networks, which expands the space of interest dramatically) and
process variations cannot be checked against predefined (expert) rules – the dynamics
of the process context requires the dynamicity in rules to be applied.
Therefore the detection of variations is not anymore the question of optimizing formulas from
statistics, but rather the challenge for defining what is “normal/usual” in the dynamically
changing business environment. This is where Big Data comes to the game:
by being inherently data-driven, big data processing of manufacturing data is able to
generate valid models of process behaviour.
by being very scalable, big data processing is able to work in high-dimensional spaces
of interests with low latency.
4 Sigma Labs, In-process Quality Assurance, Industrial 3D Printing Conference
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by using unsupervised learning, big data processing can continuously improve/adapt
the performances of the underlying task.
In this deliverable we present how variations in a manufacturing process can be detected
using unsupervised data analytics, namely big data clustering.
We define the following three requirements that should be satisfied in a big data approach for
detecting anomalies
R1: Precision: how to define what is similar (metrics) – what is usual?
R2: Interpretation: how to understand why something is not similar – why it is
unusual?
R3: Scalability: how to ensure that by using as much as possible data, the results of
processing will be calculated as fast as possible?
1.1.3 Our approach for big data clustering
Clustering algorithms tend to identify groups of similar objects and produce partitions for a
given dataset. A number of clustering algorithms exist, from partitioning algorithms such as
K-means5, over hierarchical algorithms that form a tree of clusters (dendogram) by
performing clustering on different levels, to density based algorithms such as DBSCAN [2]
that group objects based on the neighborhood of each object. All of these algorithms have
their own purpose, advantages and weaknesses, so a great caution is needed while choosing
the appropriate clustering method.
K-medoids6 is a partitioning algorithm, similar to K-means, that uses medoids to represent
clusters. Unlike the K-means algorithm where centroids are used to represent clusters, in case
of K-medoids, medoid is one of the objects from the dataset that is the best representative of
the cluster. PAM (Partitioning Around Medoids) is the most common realization of this
algorithm. The basic steps of K-medoids algorithm are initialization, assignment of objects to
closest medoid and new medoid selection for each of the clusters. Medoid selection is the
most expensive procedure of the algorithm. FAMES is a medoid selection algorithm that tries
to overcome this problem.
K-medoid algorithms try to find optimal medoids in the dataset, while finding a single
medoids requires O(n2) distance calculations. This makes this algorithm practically unusable
for bigger datasets. FAMES (FAst MEdoid Selection) [3] represents an improvement of K-
medoids algorithm by offering a fast selection of good representatives.
Our solution represents a combination of scalable K-means|| [4] (K-means parallel)
initialization and K-medoids like algorithm that relies on FAMES for medoid selection. K-
means|| represents an improvement of K-means++ [5] algorithm. The major downside of the
K-means++ is its inherent sequential nature, which makes it difficult to use in case of big data
because it requires k passes over the data to find good initial set of centers. On the other hand,
K-means|| is able to find good initial medoids in n iterations, where n is usually much smaller
than k. Another good property of this initialization approach is that it can be easily distributed
therefore achieving greater speed than K-means++ and can be used in the case of much bigger
datasets.
5 https://en.wikipedia.org/wiki/K-means_clustering
6 https://en.wikipedia.org/wiki/K-medoids
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1.1.4 Use Case: Whirlpool washing machine testing
The problem setup for washing machine tests for Whirlpool use case is as follows:
Washing machines functional tests are provided by Whirlpool;
Functional test is performed on every washing machine that was assembled and it is
used to examine if machine functions properly;
Various parameters are measured, such as power, speed and water inlet;
The size of the data is very large (too large to be processed on a single machine);
The goal is to detect anomalies – washing machines that behaved strangely during
functional test;
By detecting anomalies during functional tests quality of production can be increased;
Data is provided in a compressed form, so the first step is to decompress it;
Based on analysis a solution for detecting anomalies automatically should be
implemented.
The goal is to find a way to define normal parameter values and implement a solution that
will be able to identify unusual patterns in functional tests. Dataset provided by Whirlpool
contains three parameters:
Power;
Speed;
Total water inlet.
Initial analysis of the dataset was performed in order to find correlations between parameters,
as presented in Figure 1. The goal of our analysis is to describe normal behavior and discover
anomalies as behavior that does not conform to the defined model. To do that, we are using
clustering approach described in the previous section. We elaborate shortly on the arguments
to use that approach.
First of all, we have defined the process of detecting anomalies as follows:
Cluster the data using some clustering algorithm that will not only produce clusters,
but will also produce cluster representatives;
Cluster representatives and cluster variances form the model;
Each new measurement is compared to the existing model and the dissimilarity from
the model determines its status as anomalous or normal.
Most of clustering algorithms that respect these conditions belong to the group of partitioning
algorithms. Before we describe the concrete algorithm that we use from that group, it is
necessary to take another look at the dataset, this time considering multiple tests at the same
time.
Power parameter values of multiple tests are presented on Figure 2. We can notice that even
though all the graphics present the value of the same parameter, they may have very different
shapes. This has a huge impact on our analysis. There is a need for robust measure that is able
to cluster time series based on the shape of the series.
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Figure 1: Correlation between power and rotation speed
Figure 2: Power parameter of multiple tests
Another question occurs due to the latest condition, clustering time series based on shape.
How should the cluster representative look like for a cluster that contains series of different
shape? Some of the algorithms that produce cluster representatives give some kind of a mean
or an average of all the elements in the cluster as a representative. But that would not be an
acceptable solution in our case for the following reason – different tests may be performed
under different conditions. By conditions we mostly mean different timings. In case of one
test, centrifuge can be started part of a second earlier than in the other, or can have slightly
greater duration. That part of a second makes mean of series impossible to use as a
representative. Because of that, we turn to another group of clustering algorithms, algorithms
that select one of the objects from the cluster as a representative of the cluster. That object is
the one that is the most similar to all the other objects in its cluster, and it’s usually called a
medoid. This is the reason we selected k-Medoids as clustering algorithm.
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First of all, we consider initial medoids selection. This question has more relevance than it
may seem. Quality of final clustering heavily depends on initial medoid selection due to
phenomenon of local minimum. One option would be to select k objects randomly from the
dataset, but that can result in poor clustering. That is why we use a different kind of
initialization in our implementation. The idea is to select objects that are placed somewhere in
the core of existing clusters as initial medoids. In this way, only a small number of iteration is
needed to produce final clusters, and its role is just to refine initial clustering. This makes our
algorithm faster and more precise than in case of random initialization.
Second, we consider the question of objects similarity. To perform clustering it is necessary to
define a measure that will describe how similar objects are. Similarity measure is often
compared to distance measure, since the objects are observed in N-dimensional space.
Maximal similarity between two objects can have a value of 1, which means that distance
between them is minimal, that is, equal to 0. There are different kinds of distance measures,
such as Euclidean, Manhattan or cosine, but we will see that they are not very useful in our
case. The reason for that is that we are comparing time series that can be shifted in time, or
skewed, and distance-measures like Euclidean do not tolerate this. That is why we approached
another kind of distance measures that considers shapes of two signals. The measure is called
Dynamic Time Warping (DTW) [4] and it is able to find the optimal alignment between two
signals. To show this we will observe two very similar time series, presented on Figure 3. It
should be noticed that one signal is actually a modification of another created by shifting the
first signal by a certain time interval. The figure represents a comparison of Euclidean, cosine
and DTW distances of the two signals.
It can be noticed that Euclidean distance is very large, and so is the cosine distance. DTW, on
the other hand, gives distance equal to zero. This implies that DTW is immune to the
phenomenon of shift, no matter how big it is. This could be very useful in our case, since
different conditions are used while performing functional tests. We could interpret this shift in
the following way – counter clockwise rotation was started later in the case of second signal
than in the case of the first signal, so every next step in the test (for example, centrifuge being
started) also gets shifted. But this doesn’t mean that something is wrong with the other test.
The values are normal; they are just shifted in time.
Figure 3: Comparison of two signals – red signal is shifted even more
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1.1.5 Results
In this section we present the main conclusion from the performance test.
We have initially considered Power parameter and took a small sample to validate our
methods. We used a variant of K-medoids that we have mentioned and got five clusters, while
three clusters where clusters singletons. Cluster singleton is a cluster that contains only one
test. We consider these clusters anomalous, since the number of objects they contain is very
small, so they differ from the rest of the dataset. Medoids that were produced are presented on
Figure 4, while clusters are presented from Figure 5 to Figure 9.
By observing the shape of signals contained in the sample we can conclude that there really
are two groups of signals. At the same time we can notice that signals belonging to clusters
singletons have different shape than signals that exist in non-singleton clusters. We must
emphasize that our primary goal is not to find anomalies while clustering, since this can be a
long-term operation, but to generate a model that can be used in real time to detect anomalies.
Even so, we may detect suspicious tests in the dataset, like in our example, so the best
solution is to report them, and remove them from the dataset, for safety reasons.
The initial sample was good for validation of the approach and the implementation, but
afterwards all the tests provided by Whirlpool were analyzed. The dataset currently provided
contains about 15.000 functional tests, but a much larger amount of data is expected (amount
that demands a Hadoop cluster for processing – learning what represents normal behavior).
Figure 4: Generated medoids
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Figure 5: First cluster
Figure 6: Second cluster
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Figure 7: First anomaly
Figure 8: Second anomaly
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Figure 9: Third anomaly
Clustering results for the whole dataset are now presented from Figure 10 to Figure 13.
Again, there are clusters of normal behaviors and there is a cluster of potential anomalies
(cluster presented on Figure 11).
Based on results we conclude that our algorithm represents a good solution for the problem of
detecting anomalies in functional tests provided by Whirlpool. We even got a confirmation
that we were able to detect tests that represent a problem that really exists in one of Whirlpool
facilities (Figure 10 represents an example of such tests), which they are aware of. We
developed a solution that is able to compare test series based on their shape, and to cluster
tests based on it. We also used that similarity and clusters being produced to determine which
tests look unusual comparing to normal examples found in the dataset.
Figure 10: First cluster (large dataset)
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Figure 11: Second cluster (large dataset)
Figure 12: Third cluster (large dataset)
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Figure 13: Fourth cluster (large dataset)
1.1.6 Evaluation of the Experimentation
In this section we summarize the lessons learnt from the trial.
1. What were the problems in using Engineering infrastructure for big data
experiment?
- Access to machines in Engineering infrastructure
o There was only one machine with public (static) IP address
o We were able to connect to that machine only from machines in our office
using SSH (there was a whitelist for server access)
o There were some problems with the specific port given by Engineering for
access
o Number of machines that could connect to machine with public access was
limited
o There were a lot of ssh connections “hanging”, even if have closed the
connection (which led to messages such as “ssh_exchange_identification:
Connection closed by remote host” and “ssh_exchange_identification: read:
Connection reset by peer”, which made it unable to connect to machines)
- Missing support for the components in Hadoop ecosystem
o There was lack of support (knowledge) for setting up (using) Oozie on
Engineering machines
o A lot of difficulties occurred due to insufficient permissions for starting-up
Hadoop jobs from Oozie coordinator
o Number of workflows limitation as a result of small cluster size
o Non-default YARN settings (port) led to more problems
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2. What was the main difference in the results (from the analytics point of view)
between the first and second experiment in Whirlpool
- More data that needed to be cleaned
o The dataset provided by Whirlpool was in a compressed form and after
decompressing it has been noticed that it contains some irregular records. For
example:
There were some records in which all the parameters had zero value all
the time
Values for a parameter are pipe-separated “|”, which is fine, but records
could be found that contain long sequences of such pipes without any
actual values in between. For example sequences such as “||||||” exist
Exceptions were thrown during decompression, such as
“java.io.IOException: incorrect data check”
o This requested data cleaning before the actual analysis
o In the first phase analysis was performed on a small sample
o In the second phase analysis was performed on the whole dataset
o The number of functional tests (records) significantly increased (the size of the
subset was 20, the size of the full dataset was about 15.000)
o With the dataset size increase the number of “dirty” records increased as well
o That means that cleaning the big dataset is a “big job” itself
- More difficult visualization/interpretation of results
o The greatest part of the analysis was based on clustering
o Clustering is an unsupervised machine learning method
o Clustering as an unsupervised method has its strengths (the dataset doesn’t
have to be labelled, for first)
o As an unsupervised method clustering has a lot of challenges (clustering
algorithm, distance measure, number of clusters, quality of clusters…)
o One of the biggest challenges is validation of the analysis
o With bigger dataset validation becomes even a greater challenge
o With the small subset it was easier to perform validation
o Visualization (which could help validating the results) is much more complex
in the case of the bigger dataset (following figure illustrates this)
o A lot of effort (and creativity) needs to be put so the orientation could be found
in the “big mess” that comes with large amount of high dimensional data
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Figure 14: Bigger dataset makes validation/visualization of analysis more complex
1.1.7 Visualization of Deviation Maps
Additionally to the results presented so far, a Web application for the visualization of
deviation maps for the Whirlpool use case was developed which can be deployed in any Web
server. The idea of a deviation map is to visualize the differences (i.e. deviations) of a
physical part from the CAD model which was defined for its design. For this purpose, usually
a point cloud is produced for the physical part. In the context of FITMAN this point cloud is
produced by a high accuracy laser scanner. The point cloud is then compared with the CAD
model which results in the deviation map. The scanning and computation of the deviation is
not part of the Web application. The Web app purely displays the deviation maps which were
already computed. The easiest way to access the Web application is to go to
http://xml3d.org/xml3d/scenes/magnifi/.
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Figure 15: Web App for the Visualization of Deviation Maps
Figure 15 displays a screen shot from this Web app for displaying deviation maps. The
deviation maps are in the first place provided as binary data which is, however, easy to
interpret. It consists of a list of triangles all of which have a deviation value assigned. For the
visualization of the deviation map in a browser on the basis of XML3D, it was necessary to
convert the data into XML3D. For this the pure model information (i.e. the triangles) were
extracted. The deviation information is given as a separate vector where the position of the
deviation values in the vector need to correspond with position of the respective triangle in
the mesh of the model to which the deviation information belongs.
Additionally to the Web application a DyVisual Web client was developed for the FiVES
synchronization server. The advantage of using the DyVisual Web client is that the deviation
map can be investigated cooperatively, i.e. several users located at geographically distributed
site can view the model simultaneously while all changes of the view in which the deviation
map is visualized is synchronized among all clients which are connected to the same
DyVisual server. The Web client provides a restful service interface where rest services for
uploading a new deviation map and for adding or modification of the deviation information
for a given model.
The idea of the synchronization is that a group of experts which might be located at different
sites can cooperatively investigate a given deviation map. The assumption is that only one
expert is manipulating the synchronized view. We assume that the experts have an audio
connection for their discussion which they can use to agree on who is allowed to modify the
view.
1.1.8 Lessons Learnt
From the experiences with the FITMAN trials one can conclude that DyVisual is a powerful
tool for the visualization of dynamic 3D content in the context of the World Wide Web. The
main issue to solve for an applications is to acquire the data for the 3D models in the first
place. The data representation format of this data is of course an issue. DyVisual supports the
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most important standards for representing 3D models and with this a large number of
applications. The pure visualization of the model in a browser is straightforward. However,
when the model should be dynamically modified or even animated, deeper knowledge of
XML3D, FiVES and 3D data in general is necessary. For applications like the visualization of
deviation maps special shaders might be necessary which also requires special knowledge of
XML3D. However, the generic enablers which form the basis for DyVisual, i.e.
XML3D/Xflow and FiVES are well supported and documentation can be found at
http://catalogue.fiware.org/enablers/3d-ui-xml3d and https://github.com/fives-team/fives.
1.1.9 Conclusion
The clustering of big data for the real-time discovery of deviations can be achieved by an
extension of traditional clustering algorithms. Such new approaches will extend traditional
quality management tools and will thus empower current frameworks such as Lean Six
Sigma.
The proposed approach has been applied for improving process control for washing machine
tests in Whirlpool factory in Italy and the results are very promising. We have started a large-
scale case study for the presented washing machine functional tests that should prove the
feasibility of the approach for production environment.
1.2 TRW Use Case
1.2.1 Introduction
The task was to integrate the detection movement and the joint positions it is reporting with
our DyCEP, whose results will be visualized using DyVisual.
The first step was to create a NGSI10-to-RabbitMQ mapping web service to import the
movement events into DyCEP. DyCEP is designed as a reactive component, reacting on
events published in real time, so the preferred (in Storm almost mandatory) way of input is a
broker/queue. NGSI10 is a request/response protocol built on top of HTTP and it is not quite
suited for communication including subscribers. However, since NGSI10 is mandatory, we
created a bridge accepting NGSI10 XML-based messages and transforming them into an
internal format, published onto a local instance of RabbitMQ. The service is written in Scala
using PlayFramework and we called it the Collection Service.
The Storm topology within DyCEP is subscribed to the broker and executes its algorithms.
The output of the DyCEP generally should include the last angle and risk per distinct joint for
the visualization to have appropriate input. We needed to slightly change the operation of
DyCEP since it initially did more of a statistical overview of the worker movement in the past
five minutes and the risk distribution. We had to change the pattern to make a view of only
the last movements and exclude the statistics and to create appropriate adaptation logic for the
DyVisual input.
DyCEP also has to map the joint movement from a numerical sensor id notation to explicit
joint label suitable for DyVisual. This is done by specification provided by Innovalia.
The DyVisual has a REST endpoint for updating the avatar position, with a configuration
string specifying all the joints position in 3d space. The missing coordinates have default
values. Configuration string is shown in the example.
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A set of classes is created which are used for generating the configuration string.
Figure 16: Class diagram for extension of TRW use case
1.2.2 Integration architecture
The following figure shows the architecture after the integration. Notice the change with the
output format of the CEP and the destination of that output, as well as the path which is used
to visualize the position and risk level.
{
"avatarID":"0",
"aniName":"dummy.bvh",
"configString":"update,Spine,y,50,RightForeArm,y,100"
}
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Figure 17: Final architecture
Kinect (2) detects worker (1) joint angles. After some adaptation to the NGSI10 interfaces the
sensor ids (where a sensor id corresponds to a joint) and joint angles are reported to the
collection web service, explained earlier. The service adapts the messages to a pub/sub
interface (the broker) and the CEP (5) consumes those messages executing the algorithms.
When a result is ready, which means a set of coordinated joint positions and calculated risks,
a message for the DyVisual API is created. In the previous architecture the message was
different; it was an overview of the risks for each joint in the last 5 minutes, while the new
one is a message with the latest angle position paired with the risk. The API call is placed to
the DyVisual backend, which is hosted on some web server. It could be on the same web
server as the collection web service, like on the image, or might not be. The backend updates
the visualization, presumably viewed from a web browser on a computer. On the image, the
computer viewing the avatar and the computer which has the Kinect connected are the same,
however that doesn’t have to be the case. The avatar can be viewed from multiple computers
in general.
It should be noted that the avatar position API call and the markers (the colored orbs) are set
in multiple calls. The avatar can be set in one call, while each orb is set by a separate. Before
updating any of the markers, all of them must be deleted since they stack up on joints.
1.2.3 Visualization examples
On these two images shown in Figure 17 we can see examples of two employee positions
during work. The avatar itself represents the body position, while the orbs around the joints
show the risk level of that joint. Red being the highest, risk level 3, and the green is the
lowest, risk level 1. If there is no orb than the joint is visualized in its default position, and
there is no movement detected.
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Figure 18: Examples of two employee positions during work
1.2.4 Lessons learnt
In this section we summarize the lessons learnt from this trial.
1. Real-time processing of the signals from Kinect can be important for different
working situation
2. The signal from Kinect is rather complex and requires a substantial pre-processing in
order to get the data in the proper form
3. Big data opens new possibilities for process optimization, based on data collected in
all phases
4. Big data analytics enables powerful observing/sensing and reacting if needed
5. The rules for quality control are defined manually (and are not conceptually sound)
1.3 References
[1] Qin SJ, Cherry G, Good R, Wang J, Harrison CA. Semiconductor anufacturing process control and monitoring: a Fab-wide framework. J Process Control 2006;16: 179–91.
[2] http://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf
[3] Adriano Arantes Paterlini, Mario A. Nascimento, Caetano Traina Jr., Using Pivots to Speed-Up k-Medoids Clustering, JOURNAL OF INFORMATION AND DATA MANAGEMENT Vol 2, No 2 (2011)
[4] http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
[5] https://www.math.uwaterloo.ca/~cswamy/papers/kmeansfnl.pdf
[6] http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf
[7] Black, A. W.; P. Taylor: Automatically clustering similar units for unit selection in speech synthesis. In: Proc. Eurospeech ’97
[8] Stojanovic N., Dinic M., Stojanovic L., Big Data Process Analytics for Continuous Process Improvement in Manufacturing, to appear in IEEE BigData 2015, Special Session - From Data to Insight: Big Data and Analytics for Smart Manufacturing Systems, Oct 29 – Nov 01, 2015.
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2 Deliverable 13.5
2.1 Introduction and Document Scope
The main objective of task T13.3 is to analyse and evaluate the experimentation provided by
T13.2 and derive from that the bottlenecks and opportunities for further expansion of the trials
and for improvements of the single SEs and PCs
This document is the update of the D13.4 deliverable, reporting final improvements operated
on the results delivered in T13.2 and the outcomes of the interviews with the end users,
conducted after their experimentation of the final versions of the integrated prototypes.
2.2 Evaluation Methodology
The first evaluation round (reported in D13.3) collected feedback about single components.
This feedback was provided by mail and phone. The second round was done via dedicated
experiments with end users.
Since the end users conducted an experimentation of the overall integrated solution for their
trial, with possibly an incomplete visibility of the exact role played by each single component
in the solution, it has been decided to first collect feedback and comment on the overall
solution and then, whenever possible, to try to make the evaluation report more specific for
the single components. If that was not possible, either the mediation of the IT partner has been
asked, or the IT partners of WP13 analysed the provided feedback to identify bottlenecks,
gaps and suggested improvements that can be reported to the SEs and PCs.
2.3 Methodology main steps
2.3.1 Feedback collection channels
Some channels and tools have been used to collect feedback directly from end users:
Emails, web meetings, excel file, and, at the end of the evaluation phase, finally direct
interviews with the end users. This feedback is reported here.
In addition to that, other feedback data have been mediated by the IT partners from WP5, who
were in charge of the trial platform and acted as interface with the end users for the technical
and integration issues: IPK has been responsible for the integration of the WP13 prototype in
the VW trial and UNINOVA did the same for the CONSULGAL one.
2.3.2 Interview structure
The overall structure of interview is the following:
Evaluation of the integrated solution
o Questions for the IT partner in charge of the Trial platform: here the questions
are focused on installation and deployment issues, overall performances etc..
o Questions for the end user: here questions are about the coverage of the
selected business processes, the coverage of requirements and level of
completeness of the provide set of functionalities
Evaluation of the documentation and provided support: here the questions are on the
completeness and easiness of use of the SEs documentation available on the catalogue
and on the received support.
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Evaluation of specific SEs and PCs: in this part, questions about the completeness of
the solution, the adopted approach, missing functionalities, and
replicability/adaptability to other processes were asked.
2.3.3 Interviews and reporting
The end users have been asked to dedicate a 1-hour slot to conduct the interviews through
web conference channels.
The Trials experimenting with the SE (ADIMA, VW, and CONSULGAL, TRW and
WHIRLPOOL) received the interview structure in advance and had the possibility to get
prepared for the interview that has been conducted by the IT partners (one IT partner has been
appointed as responsible for each interview) using a web meeting tool.
The results of the interview have been shared within the WP13 partners, so that each IT
provider analysed the answers to identify the lessons learnt not only ad trail level, but also at
SE/PC level.
2.4 Improvements and extensions related to end user feedbacks
This section reports the minor modifications and improvements on the SEs, PCs and overall
integration solutions, developed for the trials during the experimentation phase to meet the
feedbacks received from the end users during the initial validation activities. We highlight
here the latest feedback and do not include feedback already processed earlier in the project,
for example provided by Augusta Westland.
2.4.1 CONSULGAL TRIAL
2.4.1.1 SEMed improvements
The configuration of SEMed was customized and deployed for the iLike views provided
before CONSULGAL suggested some re-organization of the information visualized. During
the specification phase additional attributes for the concrete operations view, changes in the
attribute mappings and rearrangement of attributes for the concrete operations view were
necessary. In consequence, the configuration of SEMed has been adapted, deployed and a
new set of SPARQL queries has been provided to enable the new content of the information
flows. The information flows which are mentioned in D13.3 are still consistent.
2.4.1.2 ILike improvements
During the experimentation with the provided version of iLike, CONSULGAL provided
clarifications on the semantic of some data that have been transferred from the Trial platform
to the SEMed and from there visualized in the iLike interface; moreover CONSULGAL
suggested some re-organisation of the information visualized in the iLike interface, to better
support the selected business process.
Such modifications are summarized in the table below:
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Visualisation Provided information Improvements/modifications
“Last concrete operations” offering a synthetic view of
the concreting operations
executed in the last period,
including links to visualize
the information of the
extracted samples; the
requested modifications
where;
This view was not existing
before: CONSULGAL
required it to better support
the activities of the
inspectors, that need to
search the system to retrieve
complete description of
concreting operations
“NPAB view” information on the concrete
composition, position in the
dam and approval date
Some data have been re-
named to reflect their
meaning; consistency check
with other views
implemented
“Dam Zone” providing access to the
information of each zone of
the Dam
This view was already
available in the previous
version; some corrections on
the structure of the visualized
data have been implemented
The first working version, proposed to the trial, offered a simple list of the concrete
operations filtered by the dam zones (shows the list of the concrete compositions used in a
selected zone of the dam). The details of the concrete composition and the samples were
contained in modal panel. This kind of view was developed in order to offer the simplest
interaction to the user but, after a first evaluation with CONSULGAL, the following weak
points have been identified:
the filters are limited at the dam zone while others important parameter are ignored
the dam zones must be identified by 4 coordinates instead of 3
the concrete operation was not identified uniquely
the details of document NPAB was not available from the views
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Figure 19: the original unique Dam Zone view
In order to address the identified problems and improve the navigation, the system has been
modified splitting the view (see following Figure 20) in two different views: “Concrete
Operations” and “Dam Zones”. Basically the set of displayed information are almost the same
but they are organized in two different ways to meet the needs of the user in different time of
the dam building.
The Dam zones view
The view “Dam Zones” is useful in the BP1: “Identification of concrete class and concrete
composition process”. This view allows the user to focus on the concrete class element, and
also to analyse the results of the testing on samples collected from the concrete operation that
use the selected concrete class. This view is useful in the long time because it allows
understanding the behaviour of a concrete during the time, but also it provides a summary of
the state of the dam area.
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Figure 20 The view Dam zones shows the list of the concrete compositions used in a selected zone of the
dam
The Concrete Operations view
The BP4 “Slump tests results for each concreting operations” requested to create a view with
a collection of the details of each concreting operation. From a concrete operation element, it
is now possible to retrieve the details of NPAB (the formal document approving the
concreting operation) and of the concrete composition; moreover for each concreting
operation it is possible to access the list of the samples taken during the single concreting
activities.
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Figure 21: The new "Concrete Operations" view
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Figure 22: The NPAB details
These examples show how the SEs involved have been optimised throughout the trials the
meet the needs of CONSULGAL.
2.4.2 AIDIMA TRIAL
2.4.2.1 C3DWV improvements
End users required some support to correctly transform the 3D models and make them
visualized in the C3DWV.
2.4.2.2 Virtual Obeya improvements
An updated version of the VO has been provided to AIDIMA, including templates that can be
used to easily create Obeyas and set up collaboration sessions.
Moreover, from some interactions with AIDIMA; it emerged that having the possibility to
share in the VO documents is critical during the design collaboration meetings. Whereas the
creation of widgets to share Google Docs is supported by the VO through a set of pre-defined
templates, AIDIMA and its associates cannot use this kind of documents, but rather share
documents using OneDrive. Therefore, Holonix and AIDIMA worked together to find a
solution that could be easy for OneDrive documents too.
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2.4.3 VW TRIAL
SEMed and 3DWV have been updated slightly throughout the trial.
2.4.4 WHIRLPOOL TRIAL (Digital factory)
The Whirlpool trial in the Digital Factory is focused on the dimensional quality control for
plastic parts produced in a Whirlpool manufacturing environment. In this trial the objective is
to use the 3DScan SE and the 3D Point Cloud Analysis Processing PC as a whole system to
produce dimensional data that will be used as an input by the Dynamic CEP SE to analyse
statistically the information obtained. The samples analysed can be also visualised
dynamically in the DyVisual SE, as well as stored and visualised using the 3DScan SE
storage and visualisation components.
The scanned part and the CAD model should share the same reference system to be
compared, so the colour mapping with the deviations can be calculated. This requirement,
together with the need of sharing and exchanging data together with the Dynamic CEP SE has
forced to make adjustments in the alignment mechanism of the part and the CAD model. As
the analysed part (see Figure 23) has revolution symmetry it is important that all the parts
belonging to the working sample are oriented in the same sense.
Figure 23. Whirlpool part used for the trial.
To use that revolution axis to install the reference system it has been necessary to develop a
small software customisation to accomplish with this requirement and make a specific
alignment. This is mainly to the fact that the part used in the trial has special geometrical and
functional properties, as it is revolution and rotating part.
The adjustment performed, from a conceptual perspective, consists of a mesh that is
calculated in the whole surface of the Whirlpool helix and divided in different regions with
triangle forms (see following figure). So the numerical dataset created after scanning the helix
is composed by a list of triangles (expressed with numerical coordinates, x, y, z, of the three
points that compose the triangle). Each triangle also contains a deviation that informs about
the difference between the scanned part and the CAD model (expressed in millimetres). In
some cases the deviation is zero, this usually means that the system has not been able to
calculate the deviation of this particular triangle. All the parts of the sample contain exactly
the same triangles, so each part can be easily compared with another one.
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Figure 24: Diagram of the structure of the numerical dataset
2.4.5 TRW TRIAL (Digital Factory)
The TRW trial is clearly oriented to the dimensional quality control of components for the
automotive industry produced by TRW in one of its manufacturing plants. In this trial the
objective is to use the 3D Scan and the 3DPAP as a relevant tool to identify parts (Figure 25
shows the part used in the trial) that do not meet the dimensional specifications and to
advance towards the Zero Defect Factory paradigm, where dimensional defective parts are
identified through the means of a control system.
The system deployed in TRW (3DScan + 3DPAP) is very close to the standard applications of
the technology of Datapixel. In any case, some specific customisations were necessary to
adapt the technology to the trial. The technology was installed and configured in TRW’s IT
infrastructure. The system has been deployed in TRW manufacturing plant and this requires
always some specific customisations in terms of configuration to adapt it to the IT
environment and to the particular production line.
An important aspect that has been agreed between Datapixel and TRW is the selection of the
part used during the trial. Finally the part selected is an endless spindle.
Figure 25. TRW spindle used in the trial.
Deviation (mm) D
D’ D’’
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Another important issue that Datapixel and TRW have agreed is which type of measurements
should be done and which type of defects should be detected. The clear definition of these
aspects has led to a reduction of the necessary steps for the usage.
2.5 Interviews with end users
These interviews summarise the final feedback at the end of the trials and after the
improvements made throughout the trials.
2.5.1 Interviews in CONSULGAL
2.5.1.1 Interview execution
The interview with CONSULGAL has been conducted on GoToMeeting and attended by
Paulo Rodrigues (CONSULGAL) and Sudeep Ghimire (Uninova)
During the interview, Mr. Rodrigues answered the questions about the completeness and
correctness of overall integrated prototype and of iLike, whereas the questions about SEMed
could be answered by UNINOVA, as the partner in charge of connecting the Trial Platform
with this SE.
2.5.1.2 Interview Results
2.5.1.2.1 Evaluation of the overall integrated prototype
As an IT integrator, UNINOVA considered the solution has no defects. The classification was
chosen according to the correct handling of the output of the web services of the
CONSULGAL Trial Platform. The easiness of the of application was classified at Level 2,
according to the fact that application requires fair amount of work for integration, specifically
caused by the constraints over the data model at the source. The resulting efficiency was
classified by UNIVOVA as expected for such solution and is “…acceptable for the scenario
we were working with…”. The reliability was classified by UNINOVA as high. A weakness
is the effort for configuration of the information flow which was classified as medium.
UNINOVA mentioned that the configuration of the system is time consuming and required
clear understanding of the documentation; it was worth it due to the value created.
CONSULGAL confirmed that the structure and the format of the information visualized in
the iLike UI corresponds to what the inspectors needs to know while performing their
activities.
Thus the solution is judged correct and complete.
Whereas the Trial platform developed in WP5 by Uninova covers the business processes BP1,
BP2 and BP3 of the CONSULGAL use case, WP13 solution covers the BP4 (Slump tests
results for each concreting operation) , requiring to compile, connect and make available all
the information collected during the other BPs.
The improvement in terms of efficiency provided by the solution is highly valuable, as
presently the information on concrete operations a slump tests must be searched across
different systems, the search covers and files and paper documents: with the Trial platform
and the solution from WP13, inspectors and other stakeholders can immediately retrieve this
information, even in mobility.
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The impact in terms of time reduction has not been precisely quantified, but it is extremely
high.
2.5.1.2.2 Evaluation of the documentation
The documentation provided on the FI-WARE catalogue supporting the usage of the SEMed
has not been used by CONSULGAL, as end user.
2.5.1.2.3 Evaluation of the single components
SEMed
The integration and application of SEMed were driven through the integrators namely BIBA,
HOLONIX and UNINOVA.
UNINOVA defines the role of SEMed in the Trial’s Business Scenario as the mediator for
data between CONSULGAL’s Trial platform and iLike. So, the data being produced and
stored by the CONSULGAL’s Trial platform are provided to iLike via SEMed to leverage the
functionalities provided by iLike which was an added value for the business scenarios of
CONSULGAL. UNINOVA mentioned that the requirements according to the data integration
in the business processes within the scope of FITMAN for CONSULGAL were fulfilled. In
addition, the overall supported data integration approach is flexible enough for data
integration via web services. SEMed maintains loose coupling between data producers and
consumers thus providing a cleaner data integration approach. On the basis of these
experiences UNINOVA declared “…SEMed approach is quite independent of the domain of
business scenarios. So, we strongly believe that SEMed is applicable for other data integration
challenges beyond the scope of FITMAN…”. HOLONIX mentioned that the existing
flexibility for data integration is given, but the creation of queries for solution provider should
be simplified. The application of SEMed in other business processes could be possible by
UNINOVA within the next two years. UNINOVA also mentioned that the application of open
source in a production environment would be possible in the case that the support would be at
the same level that they got within the scope of FITMAN. In cases where open source is not
applicable for production environments, UNIVOVA could imagine to implement an own
solution. The idea would be to extend the SEMed solution with functionalities that can arise
from new business requirements. This is a path that UNINOVA believes they could follow.
UNINOVA mentioned finally that the integration of SEMed required some efforts to get a
stable solution, but it was worth the effort due to the value created by integrating SEMed in
the trial solution and that “…We hope to use SEMed in future projects to deal with data
integration problems…”.
iLike
The iLike interface has been evaluated very positively: all the information are visualised as
required and the requests of modifications provided after the preliminary evaluation phase
have been implemented.
A remarkable comment is that the solution is considered extremely intuitive and easy to be
adopted, with almost no need of specific trainer for the professionals who will use it.
As for the applicability to other sectors, Mr. Rodrigues said that it can definitely be used to
support the supervision of concreting operations for other type of projects (i.e. hotels or road
constructions) as the information required to verify the quality of the concrete are exactly the
same one.
Moreover, the usage of the solution by other stakeholders, such as contractors or responsible
of the testing operation, is straightforward, as they need to know exactly the same data that in
FITMAN are provided to the inspectors. As the main Trial Platform supports workspaces for
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different categories of stakeholders, from these workplaces the users could be redirect to iLike
to visualise the information that has been entered into the system and that are of interest for
all of them.
Some improvements are suggested for the feature, to make the solution more complete and
therefore interesting for the market:
From the main platform of WP5, users should have the possibility to directly access,
through a button or similar, the iLike interface for the visualisation of the information
entered in the system
The guide number should be used as the unique identifier for the concreting operations
Filters and also drop down box should be available in the zones
Finally the solution should be able to manage several projects and thus to visualise
information about more than one dam.
Finally, a strong recommendation is to always verify the correctness of data transfer between
the main trial platform and the iLike interface, as the visualised information are very critical
and end users should fully trust the system.
2.5.2 Interviews in AIDIMA
2.5.2.1 Interview execution
The interview with AIDIMA has been conducted on GoToMeeting and attended by Maria
Josè Nunez and Fernando Gigante.
During the interview, all the questions of the first three sections have been analysed and
answered. The written answers have been provided on the day after the web meeting.
2.5.2.2 Interview Results
2.5.2.2.1 Evaluation of the overall integrated prototype
AIDIMA provided an overall very good evaluation of the platform as it has been delivered at
the end of the project.
It provided comments and feedbacks both from the IT integrator (here reporting also the
comments from UPV) and end user point of view (here representing the associated SMEs
from the furniture sector).
As an IT integrator and end user, the solution is considered correct; no defects remained after
some interactions with the IT developers to remove problems encountered initially in
configuring correctly the VM containing the integrated prototypes. Some adaptations have
been performed to successfully apply it to the existing environment, merely to solve
configuration issues.
The overall efficiency and reliability of the solution is considered high, considering that the
evaluation has been conducted on a Virtual Machine with quite important requirements. In
general, performances highly depend on the number and nature of widgets that are
simultaneously embedded into an Obeya rooms. Some of the SEs, such as the 3DWV, might
require some time to complete the computation/transformation tasks and thus are slower in
responding.
Configuration required some effort and time in the first evaluation (for the installation of the
first VM) but has been consistently improved for the configuration of the final version, and
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now AIDIMA says that the documentation provided was useful and adequate to perform the
installation and configuration of the tools. .
The business processes to be supported in the AIDIMA trial were:
1. Understanding customer requirements: Analyse external and internal information
sources such as social information analysis, trends, sales information, exchange of
ideas, etc.
2. Project management and functional design brief: Creation of a design brief
involving design specifications, environmental aspects, CAD files, manufacturing
issues, quality levels, price, etc., and exchanging opinions about.
3. Iterative sketch development and technical design rollout. Preparation,
presentation and selection of sketches according to design brief. Once approved,
technical design is generated involving cost calculation, BOM, revision and
validation of technical design until its final version is achieved.
All these business processes are fulfilled by the solution and no critical functionality is
missing in the system.
However, AIDIMA suggested some possible improvements for the future (see below) and
recommended to improve the documentation and training material.
The solutions are easy to understand and to learn by AIDIMA technical people, as end users,
with the provided manuals.
For SMEs, the learning processes could be long and presently requires external support, thus
it is suggested to improve the available documentation for the final users: the information in
the FI-WARE catalogues are good for IT developers and system integrators, but the final end
users (especially designers) need videos and tutorial to learn how to work with the integrated
solution-by examples.
As a suggestion, in the case of VO, tooltips in some actions could be included to ease the use
of the tool (i.e.: the “edit Obeya” control box).
The C3DWV is a bit more complex for end users so it provides more controls. Furthermore,
SEMed requires specific training.
Concerning the efficiency of the solution, it is difficult to make a benchmarking with similar
solutions already in use, as SMEs and designers are nowadays collaborating by exchanging
fields via cloud-based solutions, while the collaborative meeting are usually organized in
skype.
Thus, the collaborative platform offered by WP13 is a completely new approach that
rationalize and improve dramatically the as is situation. Moreover, the end users perceive as
of very useful not only the synchronous collaboration support offered by the virtual Obeya,
but also the possibility to access ad different times to the information sources and tools
embedded in the Obeyas room: that is particularly useful when people in the design team are
distributed geographically and might have difficulties in meeting together at the same time,
due to different time zones.
A very positive feedback on the interest of SMEs for the VO and for the integrated solution
provided by WP13 has been collected by AIDIMA during a workshop organized on 29th
September with associated SMEs. Some of them expressed their interest in attending a
training event organized by AIDIMA, to better learn how to use the solution.
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2.5.2.2.2 Evaluation of the documentation
The documentation provided in the FI-WARE catalogue supporting the SEs (SEMed and
3DWV) is considered of high quality and complete enough for the IT developers and
integrators. As mentioned, for the final end users it is expected, to introduce them to the usage
of the overall integrated platform and to give them a flavour of the possible application of the
SEs.
It is suggested that the documentation provided should include some FAQs in order to give
some clues about how to solve the most common issues when installing, configuring or using
the SEs. This could be considered a dynamic registry which can be built according to the user
experience.
2.5.2.2.3 Evaluation of the single SEs and PCs
SEMed For AIDIMA, SEMed provides significant flexibility in terms of connection of data sources
and performing specific queries.
The graphical interface provided by SEMed for the configuration of the data source is user
friendly. However, mapping and onto-related files need to be created by hand.
No missing features have been found at the moment. Further tests are useful to identify
specific missing functions.
In principle only Materializa (one feature of SEMed) was found to be useful to be integrated
in the collaborative environment but other kind of data sources such as ERP-related could be
made accessible via any tool by using SEMed.
In terms of applicability of the SE, it is commented that SEMed could be used to integrate
semantics in the learning platform used by AIDIMA to offer training services to its associates.
iLike
Designers benefit from the information provided by iLike through the prototype widgets.
Thus iLike is used as a backend tool that stores information and feed the prototype widgets; it
also communicates with the 3DWV and with Materializa, through the SEMed. Thus the iLike
interface is considered appropriate to this end.
As a remark, AIDIMA reports that the data model presented by iLike is not as flexible as
AIDIMA would like. In principle there are only 3 categorisation levels but in furniture
product data categorisation is not enough, thus additional levels should be introduced.
In terms of applicability to other processes, AIDIMA can be successfully applicable to other
domains in addition to the furniture one. Considering the scope of the scenario proposed,
iLike can be also useful to model data about production processes although this is not
considered for the proposed scenario
Virtual Obeya (VO)
The collaborative meetings approach provided by VO fulfils the requirement of having a
collaborative space supporting iterative sketch development and technical design rollout,
preparation, presentation and selection of sketches according to design brief, cost calculation
of approved design, BOM, revision and validation of technical design until its final version is
achieved. This approach is applicable to any other challenge: its success will depend on the
implementation/selection of appropriate widgets embedded in the Obeyas. AIDIMA suggests
that it can be used to support training/learning processes related to manufacturing and other
disciplines.
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The overall evaluation of the VO is high and AIDIMA can imagine running collaborative
meeting using the VO tool.
Some future improvements (beyond the expectations for the project) are suggested
The sharing of documents is directly implemented in VO for Google Docs; something similar
should be provided also for OneDrive, as presently it requires a tricky method to be achieved.
The visibility of widgets in the Obeya could be associated to roles, so that only selected
categories of users can see and use some widgets in the Obeyas.
Tracking of completed sessions could be included (i.e.: session log, modified widgets, logged
users, session length). The system could send notifications to all the users involved in the
session to inform about changes.
C3DWV
AIDIMA reports that the remote visualization and collaborative approach provided by
C3DWV supports the selected business processes and requirements: indeed the 3D
visualization increases the efficiency of the arranged meetings, mainly those in which
designers are involved as 3D models can be directly evaluated during the remote meeting
sessions.
2.5.3 Interviews in VW
2.5.3.1 Interview execution
The interview with VW has been conducted on GoToMeeting and attended by Marek Eichler
as end user, Frank-Walter Jäkel und Jan Torka as integrators and Marco Franke as
interviewer.
The questionnaire was sent to the end users and integrators before the interview appointment.
The filled out questionnaire was provided before the interview by IPK/VW.
During the interview, an update of the answers were discusses and minor changes created.
2.5.3.2 Interview Results
2.5.3.2.1 Evaluation of the overall integrated prototype
VW and IPK provided a good evaluation of the platform as it has been delivered during the
project.
The interview provided comments and feedbacks both from the IT integrator (IPK) and end
user point of view (VW).
As an IT integrator and end user, the solution is considered as relatively minor defects. The
classification was chosen according to the missing full automation of the workflow and the
varying performance in the solution. The ease of application was classified by IPK between
the range: “Applicable with significant amount of work” and “Applicable with some
adaptions”. IPK mentioned that the integration of all components occurred in work, which
required the support of the SE/PC owners. Faster response times and a more detailed
documentation could be possible approaches, which would improve the ease for the next time.
The overall efficiency was considered as expected for such a solution. In contrary, the
reliability varies significantly according to its performance. The usage of the whole solution
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in a collaborative environment requires a higher performance. The latency of specific
components varies too much, which are described in detail in the SE specific sections.
The effort for configuration and integration of all components were classified as high. IPK
mentioned that they haven’t used the documentation of the FITMAN catalogue. The
configuration of all components the configuration requires the involvement of the solution
providers. Finally, the instantiation and integration was possible and the solution’s efficiency
was good as expected.
The business processes are fully supported. VW explained that the requirements according to
the web based availability of the Machinery Repository (MR) with a GUI to all relevant
persons and the aggregating and abstraction of data from different sources are fulfilled in a
good way. From the perspective of VW, the usability of the solutions differs between the
contained components. The solution provides functionalities to create, submit and evaluate
inquiries and to manage the MR content. Most of these functionalities are easy to understand
and to learn. Only the data aggregation workflow with manual XML export from the PLM
system is a bit more complex. In this case, VW mentioned also the missing fully automated
data extraction process. BIBA mentioned that this is possible but would include a multistage
extraction process and more extended capabilities in SEMed. The performance of C3DWV
was mentioned as fact for the hard usage of WP13 solution. VW mentioned that the overall
solution improved the business processes according to its grouping of all important services
and data. Furthermore manual tasks were (semi) automated, which reduce the effort. The
efficiency of business processes could be more improved through adding the fully automated
tasks for the data integration, the intelligent support for the evaluation functionality in VO and
the full-JT support to import 3D models without the necessity of manual conversion tasks.
The provided documentation was not sufficient to solve all integration issues of VW and IPK.
Instead, VW and IPK take advantage of the support by BIBA, DFKI, and Holonix. In so
doing, a couple of telephone conferences and physical meetings were hold. The direct contact
to the developers of the solution provider was necessary to solve integration issues, such as
for example the transferring process of OPC to IPK cloud of C3DWV and a non-terminating
SEMed instance were issues. All these issues could be solved by DFKI and BIBA.
2.5.3.2.2 Evaluation of C3DWV
IPK and VW define the role of the C3DWV as visualization of the machinery to provide the
engineer a better impression of the machinery, which corresponds to the planned role of
C3DWV. The satisfied requirement was to show 3D models from the PLM system based on
JT-files. This is currently only possible via manual mapping of the JT-file to XML3D. Apart
of the functional requirements, VW mentioned that the performance when used in IPK cloud
is not acceptable. The overall visualization approach for the business processes increases the
overall impression of the machinery, but does not affect the performance or efficiency
directly. Furthermore, VW mentioned that especially the collaborative part of the C3DWV
could be used for meetings and feedback in a global frame (e.g. planning discussions with
China) and therefore could be used in other business processes. To gain a further aid in the
business processes VW mentioned that a full JT-support is required. The usage of C3DWV in
future project is possible: “…Generally yes, due to the big number of 3d data during the
product development process. But JT support must be provided…”.
2.5.3.2.3 Evaluation of SEMed
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IPK and VW define the role of the SEMed as the data extraction from “PLM XML export
file” into MR database, which corresponds to the planed role of the SE. The satisfied
requirement was to extract and generalize data from PLM into MR planning database. In
particular, the extraction of a single station’s information from PLM to MR is available.
Extraction methods, which required arithmetic capabilities, were not provided by SEMed but
by TSC (LogoLayout Extractor). This missing feature was mentioned by VW to enable a fully
automated data extraction process. The overall supported data integration approach slightly
improved the flexibility for connecting specific parts of data sources. IPK/VW mentioned also
that SEMed is also applicable for another integration challenge in their domain. The
evaluation of SEMed for another domain within the next two years depends on the future
evolution of the SEMed. One example would be the parameterisation by an ontology derived
from the end user needs. The creation of the ontology and mapping during the configuration
should be simplified and more end user driven. SEMed is an open source tool and VW
mentioned the application of open source in the production environment is general possible if
the security, trust and the maintenance aspects are ensured. In other cases, IPK/VW said the
normal way would be to create an own data integration solution on basis of the results of
SEMed.
2.5.3.2.4 Evaluation of VO
IPK and VW define the role of VO for the support of a professional user interface for the
business processes, which corresponds to the planed role of the PC. The satisfied
requirements were the support of PHP (Hypertext Preprocessor), providing of different user
roles and the display different widgets. The synchronization between widgets was a
requirement, which was not satisfied, but this missing functionality was known by IPK/VW
since the first introduction. VW mentioned that collaboration approach provided by the VO is
applicable for other remote cooperation challenges in the domain of DF. The combination of
VO and C3DWV is applicable for another business process of company/business
ecosystems/associates. To support business processes better, extended security support, such
as smartcards, would be necessary. IPK/VW mentioned that another evaluation within the
next year is foreseen but in general, they could imagine running collaborative meetings using
the VO.
2.5.4 Interviews in WHIRLPOOL
2.5.4.1 Interview execution
The interview has been answered by Pierluigi Petrali, Operations Excellence Manager,
belonging to the Manufacturing R&D division of Whirlpool during the last week of
September 2015 and by Mauro Isaja as ENG (IT provider) representative.
2.5.4.2 Interview Results
2.5.4.2.1 Evaluation of the overall integrated prototype
Whirlpool has provided, in general, a good evaluation of the integrated platform delivered
during the project, considering that the solution fulfils all the trial requirements. The objective
of the platform solution is to provide a 3D measure of a microwave fan.
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ENG, as the IT provider, considers that there are no major defects in the platform and that the
correctness degree is highly satisfactory. From an applicability point of view, ENG considers
that the platform could be applicable to the user environments with a little amount of work
and some extra actions. By the way, the solution has proved efficient, being capable to
provide the appropriate performance with very reasonable resources consumption. Regarding
the openness, the platform shows a degree of interoperability maturity that can be defined as
Baseline Unified Approach (International Standard exist) in the case of 3DPCAP private
component and a level of Open Unified Approach (No international Standard exists) in the
case of the 3DScan SE, which means that there is a strong possibility to interact with other
systems. In terms of openness the solution, always according to Innovalia, shows a no
barriers, allowing developers to view and study the requirements and implement them as they
wish, in the case of the 3DPCAP private component. A high degree is considered of this
parameter, in the case of the 3DScan SE, allowing to consult with the use cases about their
needs and contribute to the source repository, designing documents and bug reports.
Whirlpool, considers that the platform solution is reliable enough to be used and keep a
specified level of service when used in the factory environment and settings. According to
sustainability, both, Whirlpool and ENG, consider that the software solution is easy to
maintain and modify.
The Business Processes are fully supported. Whirlpool evaluates that the four defined
requirements
access to point clouds stored
upload and retrieve the point clouds to the storage unit,
visualization of the point cloud stored
visualization of the 3D results through a colour mapping representation)
are covered by the solution proposed. Whirlpool in this sense considers that in case of the
visualisation of the point cloud stored and the 3D visualisation by means of a colour mapping,
the solution could cover a slight variation of the business processes. For the other two
business processes (access to the point cloud stored and upload and retrieve of the point
clouds stored) the solution could cover medium variations.
2.5.4.2.2 Evaluation of the documentation
Whirlpool took advantage of DATAPIXEL’s configuration and did therefore not use the
provided administration documentation. In any case, some meetings were held, by telephone
and physically, to adapt the solution to the type of samples Whirlpool would use during the
trial. All these issues were solved together by Whirlpool and Datapixel, but implemented and
configured in the platform by Datapixel.
On the other hand Whirlpool used and found useful the user documentation.
Evaluation of 3DScan and 3DPCAPWhirlpool considers that 3DScan could be useful to solve
other industrial problems raised in its manufacturing environment. In this sense, Whirlpool is
fully open to use open source solutions in its production environment and could perfectly
adopt 3DScan or similar solutions in future projects. However Whirlpool suggests that
3DScan should include openness to other hardware solutions to be part of its IT infrastructure.
Whirlpool states that they could perfectly conceive a dimensional quality control system
based on 3DScan.
Whirlpool has used 3DPCAP PC always as an integrated subsystem of the platform solution.
So the evaluation of 3DScan can be assumed for 3DPCAP.
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2.5.5 Interviews in TRW
2.5.5.1 Interview execution
The interview has been answered by Ignacio Arcona, IT Director of TRW production plant in
Pamplona and by June Sola from Innovalia (IT provider). The interview was answered during
the last week of September 2015.
2.5.5.2 Interview Results
2.5.5.2.1 Evaluation of the overall integrated prototype
TRW has provided a positive evaluation of the integrated platform delivered during the
project, considering that the solution fulfils all the defined trial requirements. The role of the
solution in the trial is to scan an endless spindle manufactured by TRW, provide a 3D point
cloud and analyse it in order to produce 3D results, including measurements and deviations.
Innovalia, as the IT provider, considers that the solution has a high degree of correctness; no
defects are detected in its specification and implementation. From an application perspective,
Innovalia states that the solution is applicable to its production and IT environment with a bit
of work in configuration. In terms of efficiency, the solution shows a high performance in
relation to the amount of resources used. By the way, Innovalia considers that the solution
provided has an Open Unified Approach (No International Standard exists) related to
interoperability maturity, namely the capability of the software to interact with other systems
in the case of the 3DScan SE. On the other hand, the 3DPCAP private component presents in
this aspect a Baseline Unified Approach (International Standard Exists). However, Innovalia
evaluates a high degree of openness with respect to the 3DScan SE and a low degree in the
3DPCAP private component, defining this level as the possibility of developers to view and
study the requirements and implement them as they wish.
In relation to reliability, TRW has considered for the solution a high degree of the software to
maintain a specified level of performance when used in the factory environment and settings.
Finally, both, TRW and Innovalia, consider that the software solution has a high degree of
sustainability, e.g. that the software solution is easy to maintain and modify.
The business processes are fully supported. TRW evaluates the defined business processes are
fulfilled. BP5: The manufactured parts are correctly digitalised through 3D scanning
technologies, is mainly done through 3DPCAP. Once this is performed the point clouds are
conveniently stored by the 3DScan SE and can be uploaded and retrieved from the storage
unit. 3DScan also satisfies the need to visualise the point clouds stored and to visualise the 3D
results by means of a colour mapping. 3DScan finally has been proved to be useful to
evaluate if a part contains dimensional defects or not. This is mainly due to the fact the
3DPCAP is able to measure the surfaces obtained and to perform the dimensional analysis
defined.
2.5.5.2.2 Evaluation of the documentation
TRW has used the documentation provided, both the administration documentation and the
user documentation. According to the administration documentation TRW considers that the
documents provided clearly indicate the necessary steps to install and configure the SE and to
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use it, in the case of the user documents. In this sense, TRW states to be satisfied with both,
the administration and the user documentation.
2.5.5.2.3 Evaluation of 3DScan and 3DPCAP
TRW considers that both, 3DScan and 3DPCAP, could be used to solve other industrial
problems, as the use of 3D Point Clouds is not only devoted to the automotive sector.
Therefore these technologies integrated or by separate, could be used in any industrial sector
in which parts are manufactured and need a high level of dimensional quality. In this sense,
these technologies could be used by TRW in future projects in the coming years. Regarding
open source solutions, TRW states that the automotive industry is a very traditional sector
where confidentiality of the data managed is an essential issue. In any case, if open source
solutions offer a huge stress on security issues, there is no reason why this technology cannot
be adopted in TRW’s production environment. However TRW suggests that 3DScan should
include access control and other security functionalities so it can be part of its IT
infrastructure. Concerning dimensional quality controls, TRW, as an automotive supplier, is
deeply concerned on dimensional quality so any system that can help to improve it will be
welcomed.
2.6 Lessons Learnt
Finally, the present report concludes with lessons learnt from the validation activities
conducted so far. Whereas the initial lessons learnt included in D13.3 resulted mostly from
the validation of the T13.2 results conducted within the WP13, by the IT partners, these
lessons learnt results from the interactions and the feedback collected directly from the Trail
owners, after their experimentations.
2.6.1 Lessons Learnt in AIDIMA
From the interactions with AIDIMA carried on during the evaluation period, several elements
have been extracted to create an analysis of strengths, opportunities but also bottlenecks and
further improvements for both the integrated solution developed in T13.2, as well as for the
SEs an PCs provided by the WP13 partners.
2.6.1.1 Strengths and Opportunities
From the end user perspective, the solution has been judged as mature and complete w.r.t. the
expected coverage of the business processes and the provided functionality: presently, it
offers a completely new way of working for the team of designers and furniture SMEs in a
cooperative way and no major functionality is missing. The SMEs that have been presented
the solution demonstrated interest and AIDIMA is evaluating how to continue the
experimentation with some of them, after the end of the project.
The final version is easy to be integrated in the trial environment and does not require specific
configuration effort.
As a demonstration of the interest proved by AIDIMA in experimenting with the solution,
below are reported some pictures of the collaborative environments created by AIDIMA in
the Virtual Obeya, as examples to be demonstrated to the associated SMEs:
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Figure 26. TRW spindle used in the trial.
2.6.1.1.1 SEs/PCs specific benefits
Virtual Obeya The Virtual Obeya offers a new approach for the collaboration and it has been successfully
valuated by AIDIMA; it considers of great value the possibility of accessing the contents of
the Obeya both on synchronous and asynchronous modalities, thus overcoming the limitations
of web conference tools, currently used by companies to host remote collaboration sessions,
where it is possible to share documents or tool interfaces, but just once a time, with just one
partner having control of it and with no possibility of accessing the tools and the results of the
collaborative session.
AIDIMA expressed the interest to continue the experimentation of the tool, with the objective
of making it available for the associates, to conduct virtual meetings with them and possibly
explore the possibility to use it support the training activities.
iLike The widgets of iLike provide a simplified interface to interact with the underlying platform,
and the integration with the 3D viewer allows to easily visualizing within the VO the
prototype data that have been uploaded on iLike.
C3DWV
The C3DWV, integrated within the VO, offers a valuable service to designers that
collaboratively work on a project and through this SE can visualize the 3D models they are
ideating, making them visible to all the participants to the meeting. Moreover, in the provide
solution, the 3D models are directly linked to the technical data of the prototype, thanks to the
integration with iLike.
SEMed The SEMed has been positively evaluated by AIDIMA from the point of view of IT provider
and integrator, since it provides the integration of Materializa data within the widgets for the
prototype creation. Whereas further analysis and experimentation would be necessary to grasp
all the potentialities of the tool, the availability of a web interface for the configuration proved
to be helpful for the IT integrators
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2.6.1.2 Bottlenecks and improvements
AIDIMA has identified some potentialities to further improve the solution and thus increase
the adoptability by the final end users.
Some of these improvements are valid for the overall solution, some other ones ae SEs/PCs
specific.
Overall solution
There are essentially two main barriers that could prevent the full adoption of the solution.
The first one is the easiness to learn how to work with the platform as a whole and how to use
the single elements. The preparation of documentation and training material, in the form of
videos, online help and tutorials is strongly recommended.
Also, the simplification of the installation procedures and the formalisation of the suggestion
provided to AIDIMA by email, phone calls and written instructions during the evaluation
would be highly beneficial.
Furthermore, the responsiveness of the collaboration environment significantly reduced when
more than 5-6 widgets are embedded in the same Obeya. However, it is recognised that this is
an extreme situation, and in any case other tools currently used for collaborative meeting are
offering more severe limitation, as the web meeting tools usually allows sharing one screen
and cannot offer all participants the possibility to interact with the visualized document or
tool.
SEMed
Even if ADIMA has not experimented with the configuration of SEMed directly, as the
configuration for Materializa has been provided by BIBA, it is recommended to make it as
intuitive as possible and to reduce the steps to be performed manually.
Virtual Obeya
After having expressed its satisfaction for the tool, AIDIMA suggested some directions for
further improvement of the tool, to improve its usability and flexibility. Below are the initial
suggestions:
Simplify the mechanisms for sharing documentation, extending the mechanism of
document template creation already available for Google Docs
Introduce a mechanism for role-based control of the visibility of widgets in the
Obeyas
Clarify and, if necessary, improve, the level of security of the applications: companies
would like to know how secure is to share information within the Obeya; it is
recommended to guarantee the highest possible level of security, prevented
unauthorized access to these information.
A mechanism for recording the activities performed by participants during a virtual
meeting, in particular as for the modifications operated in the shared information and
documents, would be highly beneficial.
C3DWV
AIDIMA recommends improving the uploading mechanism of files, removing the problems
with COLLADA models that sometimes appear and, possibly, to reduce the computational
time that makes the application a bit slow (at least when embedded in the VO). Moreover,
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since 3D models cannot be changed directly in the viewer, an annotation service, able to
collect the comments and request of modifications of the user would be highly beneficial.
2.6.2 Lessons Learnt in CONSULGAL
During the evaluation period CONSULGAL conducted several very exhaustive testing
sessions, that complemented the testing performed by the IT partners: this has been very
important as the end user was able to detect some bugs and inconsistencies of data that only
someone knowing the semantic of the data could detect and that guided the completing of the
integration between the Trial platform and the WP13 solution.
From these testing reports and from the above reported interview, BIBA and Holonix
extracted very useful information to create an analysis of strengths, opportunities but also
bottlenecks and further improvements for both the integrated solution developed in T13.2, as
well as for the SEs an PCs provided by the WP13 partners.
2.6.2.1 Strengths and Opportunities
Overall solution
From the end user perspective, the solution has been judged as, literally, “nearly perfect” as it
fully covers the business process BP4 and offers access to all the necessary information, in the
way suggested by CONSULGAL.
SEs/PCs specific benefits
SEMed
The role of SEMed as data integration solution achieved good results and its applicability for
other business processes were assigned by UNINOVA in its role as integrator. Furthermore,
SEMed’s integration capabilities were recognized as “…quite independent of the domain of
business scenarios,,,”. Both assignments increase the chance of application of SEMed in near
future projects. ..
iLike
Its usage is highly intuitive and no specific training is required to start using the solution. This
is due to the fact that the information is visualized in the way inspectors need them.
Moreover, the iLike interface is of interest for other stakeholders in addition to the inspectors
(contractors, testing labs) that could access exactly the same information, with no need of
implementing filtering mechanisms to hide/display data depending on the user role.
Similarly, the same solution, with almost no major modification, could be used to support the
concreting control process for other projects.
2.6.2.2 Bottlenecks and improvements
No specific bottlenecks are reported by the end user, and also the performances in terms of
responsiveness and reliability of the solution are positive.
CONSULGAL has identified some potentialities for further improvements that could improve
the adoptability by the final end users.
Such improvements are related to the overall solution, with no distinction between SEMed
and iLike components.
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Improvements of the overall solution
Basically, it is envisaged to use the same solution for accessing information related to
different projects and constructions that are supervised by CONSULGAL or similar
organisations, as inspectors normally control more than one concreting process and on
different projects.
A seamless integration between the Trial platform interface and the iLike one is missing and
might cause confusion or trouble to the end user to access two different systems: this can be
easily solved by adding an icon or a tab in the trial platform interface to redirect the user to
the visualization of data in iLike.
In addition, searches should be as intuitive and quick as possible and here having dropdown
boxes and auto completion of words could be useful.
From the point of view of BIBA and Holonix, an important lessons learnt is the importance of
acquiring a deep understanding of the meaning of the technical data managed by
organisations such as CONSULGAL, so to be able to test the correctness and coherence of the
data acquired from external systems and visualized in iLike before making the system
available to the end users.
In addition to that, the involvement of the end users in all the phase of the development for
early validation is extremely important to be sure that the way data are visualized or searched
into the system is fully responding to the end user requirements and the interface is highly
usable and intuitive for the final consumers of the data.
2.6.3 Lessons Learnt in VW
Information has been extracted to create an analysis of strengths, opportunities but also
bottlenecks and further improvements for both the integrated solution developed in T13.2, as
well as for the SEs an PCs provided by the WP13 partners
2.6.3.1 Strengths and Opportunities
Overall solution From the end user perspective, the fulfilled requirement has been judged as “…fulfilled in a
good way…” In particular the provided functionality “…web based availability of the MR
with a GUI to all relevant persons and the aggregating and abstraction of data from different
sources…” covers the addressed business processes. The impact of the solution was
summarized as “… The solution is more efficient due its grouping of all important services
and data. Furthermore manual tasks were (semi) automated….” In addition the overall
solution, each of the single solutions was suggested to be applicable to other business
processes/ domains in the DF.
SEs/PCs specific benefits
C3DWV The C3DWV offers a valuable service to visualization of the machinery to provide the
engineer a better impression of this machinery. Through the web-based access, the
visualization increases the overall impression of the machinery. In particular, the
collaborative part of the C3DWV could be used for meetings and feedback in a global frame.
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SEMed
The SEMed offers a valuable service to data extraction and abstraction from PLM into MR
planning database. SEMed was suggested by IPK/VW to be suitable for data integration
challenges in other business processes and domains.
Virtual Obeya
The VO offers a professional user interface for a web based collaborative environment which
offers in combination with C3DWV a valuable service. IPK/VW mentioned that the
combination is applicable for other business processes and business ecosystems in the domain
of DF.
2.6.3.2 Bottlenecks and improvements
Overall solution
IPK/VW mentioned that the performance is varying between the SEs/PCs in the solution and
results as a common outcome. It was estimated as too low. The performance of the
visualization is an important bottleneck, which should be prioritized and solved. Apart of the
performance some functionality should be extended to increase the semi-automated processes
to a fully automated which in particular address the 3D models and the PLM data extraction
processes.
C3DWV The non-functional bottleneck of this SE is the performance within the proposed cloud
infrastructure. A detailed investigation of reasons and corresponding adaption would improve
the applicability in daily usage significantly. Apart of the performance, the semi-automated
transformation of a JT model into a XML3D model to a fully automated transformation would
also increase the applicability for the spontaneous usage of C3DWV in daily usage.
SEMed The functional bottleneck of this SE is to provide additional functionality to enable not only a
semi-automated but also fully automated data integration. In particular, an arithmetic function
is required to count amounts of information in data source and to be capable of offer an auto
increment function for keys in the information forwarding processes. This extension would
enable the fully automated data integration approach so far. Apart of the functional
capabilities of SEMed the creation process of the configuration must be simplified and driven
towards the perspective of the end user. In particular, this focuses on the creation and
maintenance of the information models that are the basis for SEMed.
Virtual Obeya
The functional bottleneck of this PC is to provide the capability to synchronize the data
between the widgets.
2.6.4 Lessons Learnt in TRW and WHIRLPOOL
The feedback from TRW and WHIRLPOOL was submitted in a slightly different structure
which was maintained in order to remain authentic.
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Good results:
The trials performed within FITMAN have shown advantages and positive results with
respect to the technology implemented. The following paragraphs point out the main positive
aspects of the technology in both, the TRW and the Whirlpool Trials.
Easiness to configure the solution:
Both trials have shown that the solution does not present great difficulty to be configured. It’s
true that a first configuration could require some specific know-how, but this can be easily
learned by the IT personnel of the final user and be deployed for future configurations or
maintenance. It is believed that this is a great advantage in comparison to other existing
solutions that may require much more know-how to configure the solution.
Expected acceptance:
The solution presented has been accepted by the end users of both trials, mainly due to the
added value that the information obtained can offer, combined with the fact that it is easy to
use and to adopt. From this point of view, the solution has been accepted in terms of usability
and adoptability, as it does not present a significant learning curve for the quality experts of
the final user. As well, it does not present a great challenge for the IT department in terms of
infrastructure and deployment. These two issues (added value and easiness to adopt and use)
facilitate its acceptance in the end user organisations.
Added value:
The results obtained by the deployed solution have an important added value for the final
user, as they deliver dimensional information about the production explaining if the analysed
components accomplish with the defined specifications. This can be one of the key processes
to advance to a Zero Defects Factory paradigm, where the whole production can be controlled
and defective parts identified easily in the production plant, as the TRW trial shows. The
Whirlpool trial, on the other hand, shows that the production can be controlled, from a
dimensional point of view, attending to trends that could help to anticipate and predict future
deviations.
Open issues and suggestions for further improvements:
The implementation of the technology in the two trials has left some open issues or unsolved
problems. These issues can be used to do some suggestions for further improvements that
should finally enhance the solution for next implementations in industry applications.
Integration:
In the Whirlpool trial the integration of 3DScan and 3DPAP with the statistical analysis
system (Dynamic CEP) shows the importance of standardization of data formats. This
standardization includes 3D Point Clouds and CAD models. In both cases it would be equally
essential to define and use standardized models in the market. Currently there are no
standardized models available in the market and this has been clearly a weak point that in the
near future that could be easily improved by defining common standards. In the Whirlpool
trial, to integrate both systems, the developers have finally agreed to use ASCII format for the
3D Point Clouds and stl for the CAD models. The colour mapping has been shared using a
specific de facto standard used for this kind of deviation maps.
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Traceability of the samples:
Both trials, especially the Whirlpool one, have shown that it would be very interesting to
connect the solution with a reliable traceability system. This could easily improve the global
quality system, as it would allow to have more control over the whole production and to
deploy hypothetical action plans, once a quality problem has been detected.
2.7 Conclusions
In T13.3, end users have been asked to experiment and evaluate the solutions that WP13
partners developed in T13.2 to support the business processes of the trials and implement the
requirements defined in T13.1.
These evaluations have been conducted by providing the solutions to the end users for testing,
either as VMs to be installed in the Trail environment (as for AIDIMA and VW), or as web-
accessible solutions, integrated with the main platform (as in the case of CONSULGAL).
In TRW the solutions has been implemented in the trial environment and in the case of
Whirlpool the analysed parts have been analysed in a platform were data transfer between its
components has been performed virtually.
Useful feedback has been collected through different channels: during the evaluation, end
users and IT partners asked for clarifications, additional support and reported bugs by email,
phone and web calls; this feedback has been very useful to identify bugs and minor
improvements implemented by the end of the project.
After the end of the evaluation, end users have been interviewed by the WP13 partners; the
structure of the interview covered both an overall evaluation of the integrated prototype and
more SE and PC specific questions that end users could (partially) answer or have been
answered by the IT partners responsible for the integration with the main trial platform.
The evaluation outcomes are, overall, highly positive and in all the trials the end users
confirmed the correctness and completeness of the solutions, which covered all the selected
business processes. The end users also suggested improvements for further developments of
the solutions and to better meet the expectations of the market and improve the adoptability of
the SEs and PCs.
During the evaluation and after an analysis of the feedback, useful lessons learnt have been
elaborated and will guide the future improvements of the SEs/PCs and potentially will support
the usage of the SEs in the FI-PPP.
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3 Deliverable D14.5
3.1 Introduction
The objective of this document is to report on the lessons learnt and evaluation results of the
FITMAN Specific Enablers (SE) generated in the context of FITMAN Task T14.3
specifically focusing extended virtual platform.
The APR trial did focus on other components than MoVA and GeToVa and the
experimentation did already end a bit earlier, therefore this trial is not reported here.
Being an accompaniment document to the technical prototype there is one dedicated chapter
for each of the SE implemented; each of which includes:
- general information
- experiments done in the context of FITMAN Trials and their results
- lessons learnt
The conclusions provide an overview about how the SEs are implemented
3.2 Generation and Transformation of Virtualized Assets (GeToVa)
3.2.1 Short overview of GeToVA SE
Extracting knowledge from multiple data sources, representing it in a meaningful, structured way,
as well as clustering, visualization and transformation into various formats in order to support
interoperability is one large requirement manufacturing enterprises usually have. The data sources
can vary from webpages, e-mails, text documents, spreadsheets to news articles, collaborative
posts, and patents. The FITMAN Specific Enabler for Generation and Transformation of
Virtualized Assets is aiming at providing a state-of-the-art Information Extraction-driven
semantic tool for (semi-)automatic Virtualized intangible Assets in order to heavily reduce
manual data entry for the population of the FITMAN-CAM Specific Enabler. The GeToVA
Specific Enabler provides the following core functionalities:
1. Extraction of Virtualized Assets information from real-world semi-structured
enterprise and network resource;
2. Generation of semantic representation of Virtualized intangible Assets according to
ontological models;
3. Clustering of Virtualized intangible Assets enabling better search of such assets
4. Multi-format ontology transformation between various formats, mapping and
exchanging Future Internet (FI) data e.g. USDL
The GeToVA Specific Enabler is provided as a set of RESTFul services being implemented on
top of the FITMAN baseline VF Platform. The GeToVA services APIs have been designed as
fully compatible with FITMAN Platform components, namely the Data.SemanticsSupport for the
GeToVA multi-formation ontology transformation and the Apps.Repository for registration of the
assets generated by GeToVA. During the last months of the project, we have refined and extended
the GeToVA architecture taking into account the shortcomings discovered during the
experimentations in the trials. The high-level, final architectural of FITMAN-GeToVA is
depicted in the following figure:
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Figure 27: GeToVA Architecture
FITMAN-GeToVA Specific Enabler includes seven components which are briefly recapped
below (a detailed description is available in D14.1, D14.2 and D14.3).
GeToVA includes several components:
1. Extraction - responsible for extracting information from real-world semi-structured
enterprise and network resource. It relies on the GATE system and includes as well
support for tagging and annotations. The Tagging and Annotations allow the user to define
annotations that can reused to automatically spot properties within semi-structured data.
Extraction is done either automatically (e.g. from LinkedIn profiles) or semi-automatically
using GATE Support.
2. Transformation - responsible for transforming the Base RDF formatted generated by the
FormatHandler into various formats, according to various ontologies. In includes
subcomponents such as the Europass Format Handler which is used to manage
structured data i.e. XML according to Europass Format and the Converted which is able
to transform between semantic formats (JSON-LD, XML, RDF) using SPARQL
Constructs. An Ontology Manager is used to create RDF data that is valid to the used
ontologies within our platform.
3. Clustering - which provides clustering of Virtualized intangible Assets
4. Search - provide Full Text search among our data
5. Database and Search Engine – for storing the raw and processed information
6. RESTful API - exposed in a unified RESTful API all the functionalities / components
mentioned above.
7. Dashboard – build on top of the RESTful API providing an intuitive user interface to
consume GeToVA functionality.
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3.2.2 Experiments and Results
GeToVA has been deployed and used in the context of two FITMAN Virtual Factory Trials,
namely TANET and ComPlus as follows.
3.2.2.1 TANET
In the context of the TANET trial GeToVA is used to import suppliers and tenders from
unstructured and semi-structured sources (e.g. The Welsh Automotive Forum and
Sell2Wales). The integration and usage of GeToVA SE in the TANET trial is illustrated in the
following figure.
Figure 28: GeToVA integrated with MoVA in TANET trial
Being integrated in the TANET trial, GeToVA provides the following functionalities. Given a
set of suppliers and tenders GeToVA is able to semi-automatically extract information about
these companies from unstructured and semi-structure data sources such as raw documents
and web sites. The information extraction is performed using the knowledge Extractor
GeToVA component and then represented internally in GeToVA as RDF using the Ontology
Manager component. Information is also transformed in other formats using the Converter
component. The information in RDF is than imported into our sister SE, MoVA which offers
additional functionalities for the SME Cluster in TANET (see section on MoVA for more
details).
GeToVA was also used to cluster, i.e. create groups of suppliers with similar profiles. A
cluster created by the GeToVA Clustering component using TANET trial data is shown in the
following figure.
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Figure 29: TANET cluster generated by GeToVA
For the TANET trial we generated 143 tender opportunities from the Sell2Wales-website. An
example of a generated virtual asset is listed below.
_:g2157029680 <http://fitman.sti2.at/company/hasLegalName> "CastAlum\n" .
_:g2157029680 <http://fitman.sti2.at/company/hasDescription> "Diecast and
machined alumnium components, Design for manufacture\n" .
_:g2157029680 <http://fitman.sti2.at/company/hasWebsite>
"www.Castalum.com\n" .
_:g2157029680 <http://fitman.sti2.at/company/hasLegalAddress> "Buttington
Cross Enterprise Park\nWelshpool\n, Powys, SY21 8SL\n" .
_:g2157029680 <http://fitman.sti2.at/company/hasHQAddress> "Powys, SY21
8SL\n" .
Benjamins-MacBook-Air:ditf benjaminhiltpolt$ cat 'Magor Designs
.rdf'
_:g2157293240 <http://fitman.sti2.at/company/hasLegalName> "Magor
Designs\n" .
_:g2157293240 <http://fitman.sti2.at/company/hasDescription> "Design
Engineering, Precision Engineering\n" .
_:g2157293240 <http://fitman.sti2.at/company/hasWebsite>
"www.magordesigns.co.uk\n" .
_:g2157293240 <http://fitman.sti2.at/company/hasLegalAddress> "Neath Vale
Business Park\nResolven\n, Neath, SA11 4SR\n" .
_:g2157293240 <http://fitman.sti2.at/company/hasHQAddress> "Neath, SA11
4SR\n" .
In addition we have generated 180 assets out of companies description in unstructured format.
An example is given below.
_:g2181915040 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://fitman.sti2.at/company/Company> .
_:g2181254880 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://fitman.sti2.at/company/Company> .
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_:g2181254880 <http://fitman.sti2.at/company/name> "Rumm Ltd" .
_:g2181254880 <http://fitman.sti2.at/company/hasLocality> " Mid Glamorgan"
.
_:g2181254880 <http://fitman.sti2.at/company/country> "Wales" .
_:g2181254880 <http://fitman.sti2.at/company/postalCode> " CF82 7EH" .
_:g2181254880 <http://fitman.sti2.at/company/hasWebsite> "
http://www.rumm.co.uk " .
_:g2181254880 <http://fitman.sti2.at/company/hasMail> "[email protected]" .
_:g2181254880 <http://fitman.sti2.at/company/locatedInRegion> " Ystrad
Mynach" .
_:g2181254880 <http://fitman.sti2.at/company/hasStreetAddress> "Tredomen
Gateway Centre Tredomen Business Park" .
_:g2181254880 <http://fitman.sti2.at/company/category> "Engineering and
Technical Development Services" .
_:g2181254880 <http://fitman.sti2.at/company/category> "Other Support
Organisations" .
_:g2181254880 <http://fitman.sti2.at/company/category> "Support for
management, productivity, accreditation and IT" .
Since the previous release the Extractor component was further developed to support the
needs of the TANET Trial. The extraction from LinkedIn individuals and companies profiles
is available. It extracts information automatically and makes it available as JSON. For
example extracting the https://at.linkedin.com/in/ioantoma LinkedIn profile will result in the
generation of the following JSON data:
Figure 30: Individual Profile in JSON extracted by GeToVA from LinkedIn
For the TANET trial we further increased our functionality by providing fully automated
LinkedIn profile page extraction. This is done by scraping LinkedIn profiles and store the
information as JSON. The JSON is available on our platform and accessible via our REST-
API. To access this functionality a simple Web frontend is available at
http://fitman.sti2.at/tanet_linkedins. The frontend allows the user to provide URLs to public
profiles, which are then scraped. It further allows to show, edit and remove scraped profiles
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The profiles for the TANET trials are available as REST endpoint at
http://fitman.sti2.at/tanet_linkedins. To extract the profiles we used a scraper based on the
opensource software available at https://github.com/yatish27/linkedin-scraper which is only
able to crawl public LinkedIn profiles. The REST-API allows the following methods:
• Base URL: http://fitman.sti2.at/tanet_linkedin
• GET /tanet_linkedins/:id to retrieve a specific LinkedIn profile (or all if no id is
provided)
• PATCH /tanet_linkedins/:id to update a LinkedIn profile
• PUT /tanet_linkedins/:id to manually add a LinkedIn profile
• DELETE /tanet_linkedins/:id to delete a LinkedIn profile
Further the endpoint: http://fitman.sti2.at/scrape_linkedin triggers the scraping of a provided
URL. One needs to do a GET on http://fitman.sti2.at/scrape_linkedin using as parameter the
LinkedIn URL of the form https://at.linkedin.com/LINKEDIN_PROFILE. Figure 31 shows
the extraction of a LinkedIn profile from the GeToVA dashboard.
Figure 31: Individual Profile in JSON extracted by GeToVA from LinkedIn
3.2.2.2 COMPlus
In the context of the COMPlus trial GeToVA is used for enrichment of the knowledge base
use by the network manager. By having a richer knowledge base, the network transparency is
improved and becomes easier for her/him to be aware of all possible choices of business
partners and chose the most appropriate ones for their business network. The integration and
usage of GeToVA SE in the COMPlus trial is illustrated in the following figure.
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Figure 32: GeToVA in COMPlus trial
Being integrated in the COMPlus trial, GeToVA provides the following functionalities. Given
a set of company profiles GeToVA is able to semi-automatically extract information about
these companies from unstructured and semi-structure data sources such as raw documents
and web sites. The information extracted includes the company name, type, location, web site
address, industry branch, etc. Such information is extracted using the Knowledge Extractor
GeToVA component and then represented internally in GeToVA as RDF using the Ontology
Manager component. In this way we can generate structured, semantic representations of the
companies profiles which will be used by COMPlus to enrich the knowledge base In addition
the information is transformed in other formats using the Converter component. The
information in RDF is than imported in the COMPlus ontological based where it can be
queried and reasoned upon for the COMPlus improved network transparency case.
In total we have processed a total of 76 LED company profiles and generated virtual assets
from them using GeToVA functionality. An example of COMPlus LED company information
generated by GeToVA is provided below.
_:g2173020640 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://fitman.sti2.at/company/Company> .
_:g2173020640 <http://fitman.sti2.at/company/name> "spectral" .
_:g2173020640 <http://fitman.sti2.at/company/hasLocality> "Freiburg" .
_:g2173020640 <http://fitman.sti2.at/company/country> " Germany\n" .
_:g2173020640 <http://fitman.sti2.at/company/postalCode> "79111" .
_:g2173020640 <http://fitman.sti2.at/company/hasWebsite>
"http://www.spectral-online.de" .
_:g2173020640 <http://fitman.sti2.at/company/hasMail> "info@spectral-
online.de" .
_:g2173020640 <http://fitman.sti2.at/company/locatedInRegion> "
Germany\n" .
_:g2173020640 <http://fitman.sti2.at/company/hasStreetAddress> "
Bötzinger Straße 31\n" .
_:g2173020640 <http://fitman.sti2.at/company/produces> " Arbeits- und
Leseleuchten " .
_:g2173020640 <http://fitman.sti2.at/company/produces> " LED-Strahler " .
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_:g2173020640 <http://fitman.sti2.at/company/produces> " Lichtböden / -
decken " .
For the COMPlus trial we also made available the companies profiles as JSON. They are
available at http://fitman.sti2.at/complus.
3.2.3 Lessons learnt
During the second and last iteration of trial experiments we have faced a coupled of
challenges. We have imported data from different sources such as LinkedIn profiles, web
pages and text documents. Due to the high diversity of information sources from where we
want to extract knowledge, the Extraction component requires fine tuning and adaptation.
However once the right setup is done, extraction works with high accuracy. As experiences in
the TANET and COMPlus trials, GeToVA SE and its functionalities can be very easily used.
The RESTful API we provide covers the needs of the trials and MoVA, the other Virtual SE
developed in WP14. The dashboard also fully supports the users in consuming GeToVA
functionalities.
3.3 Advanced Management of Virtualized Assets (MoVA)
3.3.1 Short overview of MoVA SE
MoVA has been deployed and used in the context TANET FITMAN Virtual Factory Trial.
The architecture has already been described in previous reports. Thanks to the MoVA
flexibility it could be applied without any problem.
3.4 Experiments and Results
3.4.1 Data Modelling
In the context of the TANET trial MoVA is used to identify new clusters (groupings of
SMEs) responding to a new tender opportunity. The search needs to assure that the cluster fits
in terms of competence and tender requirements. Therefore the following structure was
modelled in MoVA.
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Figure 33 MoVA: Start screen and model
The model of the cluster and the lessons learned have already described in earlier reports
(D14.3). Throughout the trial all partners acknowledged the flexibility of MoVA.
3.4.2 Importing
Using the MoVA Backend and Plugin API the import was implemented. This step was crucial
for the acceptance and the success of the trial as the import saves a lot of time (one KPI).
The Import Routines are implemented using the MoVA Plugin API and MoVA Backend API.
The plugin are integrated in the repository by creating a folder for the plugin in the
repositories plugin directory. In the file register_application_components.php the following
code activates the import code.
$plugInPath = '../plugins/tanet/';
{ # Additional Functions
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$subPlugInPath = $plugInPath.'code/';
{ #
$r-
>register_JavaScriptFile($subPlugInPath.'importDomainsSuppliers.js');
$r->register_JavaScriptFile($subPlugInPath.'importTenders.js'); $r-
>register_JavaScriptFile($subPlugInPath.'importSuppliers.js');
}
}
The special import code for the three import functions is integrated in three JavaScript Files
which defines the menu structure for calling the import which is running on the server. Each
of the three import function has its own file with its own server side code.
function importSuppliers() {
[…]
}
function importTenders() {
[…]
}
function importDomainsAndSuppliers() {
[…]
}
For importing the suppliers the JSON content from GeToVa URL
http://fitman.sti2.at/tanet.json is read and parsed into an array. For each new content element
there is created a new supplier in MoVA. This can be done very easy by using the MoVA
Backend API. The values of the supplier can also be set using the MoVA API.
#Create new object
$O_Suppliers = $OT_Suppliers->addObject();
[…]
$O_Suppliers->setAttributeValue_noCheck( $OA_UUID_Suppliers_Supplier_name,
"value_text", $companyName, true );
if (property_exists( $importedAttributes, "c:hasDescription" )) {
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Description, "value_text", trim($importedAttributes-
>{"c:hasDescription"}), true );
}
$addressParts = array();
if (property_exists( $importedAttributes, "c:hasLegalAddress" )) {
#ignore hasHQAddress as the data is the same as
hasLegalAddress, but with less information
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Address, "value_text", trim($importedAttributes-
>{"c:hasLegalAddress"}), true );
$addressParts[] = $importedAttributes->{"c:hasLegalAddress"};
}
if (property_exists( $importedAttributes, "c:country" )) {
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$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Country, "value_text", trim($importedAttributes-
>{"c:country"}), true );
}
else if (property_exists( $importedAttributes, "c:locatedInRegion" ))
{
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Country, "value_text", trim($importedAttributes-
>{"c:locatedInRegion"}), true );
}
if (property_exists( $importedAttributes, "c:hasMail" )) {
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Email, "value_text", trim($importedAttributes-
>{"c:hasMail"}), true );
}
if (property_exists( $importedAttributes, "c:hasWebsite" )) {
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Website, "value_text", trim($importedAttributes-
>{"c:hasWebsite"}), true );
}
Figure 34: MoVA: Add new supplier
Figure 35: MoVA: Imported suppliers
For importing domain ontology the import function parses the Excel saved as CSV-File and
does the import. The plugin code used the MoVA Backend which is full object orientated.
Besides importing the domain ontology the suppliers related in the Excel are also imported, if
they are not yet in the system. For example they can also be imported by the
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importSuppliers() function. Also the relations between domain entry and suppliers are set,
even if the suppliers are already in the system. Furthermore the import routine sets some
default values and does some mapping of data.
function importDomainsAndSuppliers() {
[…]
# read import file
$inputArray = readCSVinArray(IMPORT_DOMAIN_SUPPLIERS_SOURCE);
$companyInformation = array_shift($inputArray);
$qualityInformation = array_shift($inputArray);
$costsInformation = array_shift($inputArray);
$timeInformation = array_shift($inputArray);
$suppliers = array();
# generate suppliers in mova
for ($i=10; $i<count($companyInformation); $i++) {
#prepare information
$companyName = $companyInformation[$i];
$companyQuality = $qualityInformation[$i];
$companyCosts = $costsInformation[$i];
$companyTime = $timeInformation[$i];
{ # default values
if (empty($companyQuality)) {
$companyQuality = 0.25;
}
if (empty($companyTime)) {
$companyTime = 0.25;
}
if (empty($companyCosts)) {
$companyCosts = 0.25;
}
}
{ # generate supplier if not existing
[…]
$searchAttributes = array();
$searchAttributes['searchType'] = "must";
$searchAttributes['search_text'] = $companyName;
$Os_Suppliers_AllreadyInSystem = $OT_Suppliers-
>retrieveBy_attributeValues( array($OA_UUID_Suppliers_Supplier_name =>
$searchAttributes) );
if (count($Os_Suppliers_AllreadyInSystem)) {
[…]
$O_Suppliers = array_shift (
$Os_Suppliers_AllreadyInSystem );
$O_Suppliers = $O_Suppliers['object'];
$countUpdatesSuppliers++;
} else {
#Create new object
$O_Suppliers = $OT_Suppliers->addObject();
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#Setting name
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Supplier_name, "value_text", $companyName, true );
$countInsertsSuppliers++;
}
}
{ # setting quality, cost, time information
$O_Suppliers->setAttributeValue_noCheck(
$OA_UUID_Suppliers_Time, "value_listKey", strval(floatval($companyTime)),
true );
[…]
}
{ # link supllier to facilitator
[…]
}
{ # update supplier
$O_Suppliers->update();
$suppliers[$i] = $O_Suppliers;
}
}
{ # generate domains in mova
foreach ($inputArray as $inputLine) {
set_time_limit(10);
{ # preparing domain entry
[…]
}
{ # generate domain if not existing
$searchAttributes['search_text'] = $domainName;
$O_Domain_Entity_AllreadyInSystem = $OT_Domain_Entity-
>retrieveBy_attributeValues( array($OA_UUID_Domain_Entity_Domain_Keyword =>
$searchAttributes) );
if (count($O_Domain_Entity_AllreadyInSystem)) {
$O_Domain_Entity = array_shift (
$O_Domain_Entity_AllreadyInSystem );
$O_Domain_Entity = $O_Domain_Entity['object'];
$countUpdatesDomains++;
} else {
#Create new object
$O_Domain_Entity = $OT_Domain_Entity->addObject();
#Setting name
$O_Domain_Entity->setAttributeValue_noCheck(
$OA_UUID_Domain_Entity_Domain_Keyword, "value_text", $domainName, true );
$countInsertsDomains++;
}
{ # setting additional values
$O_Domain_Entity->setAttributeValue_noCheck(
$OA_UUID_Domain_Entity_only_for_structuring, "value_listKey",
strval($domainOnlyForStructuring), true );
}
{ # link domain to domain - hierarchy
$currentDomain[$currentHierarchyLevel] =
$O_Domain_Entity;
if ($currentHierarchyLevel > 0) {
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{ #lookup top level domain
$O_HigherLevel_Domain =
$currentDomain[$currentHierarchyLevel-1];
}
{ # check if relation allready exists
[…]
}
{ #Linking Domain to subdomain - if not
allready linked
[…]
}
{ #Setting attributes on relation
[…]
}
}
}
{ # link domain to supplier
[…]
}
{ #update domain
$O_Domain_Entity->update();
}
}
}
}
{ # output
$output = array();
$output['updatesSuppliers'] = $countUpdatesSuppliers;
$output['insertsSuppliers'] = $countInsertsSuppliers;
$output['updatesDomains'] = $countUpdatesDomains;
$output['insertsDomains'] = $countInsertsDomains;
return $output;
}
}
Figure 36: MoVA: Restful API Import suppliers
3.4.3 Cluster Search
Using the MoVA Backend API and Plugin API the cluster search was implemented. The
Cluster Search is started navigating to Requirements. Several Requirements are already
imported. The article which should be produced is set under Domain Keyword. Again, this
was already described in D 14.3.
3.4.4 Integrating MoVA with SME Cluster
Using the MoVA Restful API SME Cluster has access to the data stored in MoVA. The
Restful API offers a lot of simple functions, which can be combined to complex function. For
example for requesting all suppliers there are needed many requests: Asking for get all object
types, then get all objects of the found object type with the name Suppliers. After receiving a
list of all objects from type Suppliers for each supplier it’s necessary to do a request to receive
the attributes of the object type.
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Figure 37: MoVA: Data of MoVA in SMECluster
Furthermore it’s possible to call the Restful API functions of the plugins.
The plugin code of the TANET plugin provides a cluster search. This cluster search can not
only be used by using MoVA directly but also by calling the Restful API function from
external system. This is done by the SMECluster Website to display the result of cluster
search directly in the website.
If calling the cluster search function there is returned a complex json array, which is parsed by
SME Cluster to do the output. Each level needs its own request:
{"suppliersWithBenchmark":{"domainName":"Foam
Manufacturing","subdomains":[{"consistsOf":{"calc":20,"value":
20,"unit":"%"},"domain":{"domainName":"Seating
Foam","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"u
nit":"%"},"domain":{"domainName":"Trim
Foam","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"u
nit":"%"},"domain":{"domainName":"Moulded
Foams","suppliers":[{"supplier":{"O_v_UUID":"7faa3052-1a57-
11e5-b0e9-02004e435049","name":"[…]
Ltd"},"benchmark":{"value":25,"unit":"%","parts":{"time":0.125
,"cost":0,"quality":0.125},"partsAsString":"Time: 0.25 \/
Cost: 0.25 \/ Quality:
0.25"}},{"supplier":{"O_v_UUID":"7d2d4ed9-1a57-11e5-b0e9-
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02004e435049","name":"[…]"},"benchmark":{"value":25,"unit":"%"
,"parts":{"time":0.125,"cost":0,"quality":0.125},"partsAsStrin
g":"Time: 0.25 \/ Cost: 0.25 \/ Quality:
0.25"}}]}},{"consistsOf":{"calc":20,"value":20,"unit":"%"},"do
main":{"domainName":"Open Cellular
Foams","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"
unit":"%"},"domain":{"domainName":"Polyurethane
Foams","subdomains":[]}}],"clusters":false}}
Figure 38: MoVA: Restful API Result of Level 0 search
{"suppliersWithBenchmark":{"domainName":"Foam
Manufacturing","subdomains":[{"consistsOf":{"calc":20,"value":
20,"unit":"%"},"domain":{"domainName":"Moulded
Foams","suppliers":[{"supplier":{"O_v_UUID":"7faa3052-1a57-
11e5-b0e9-02004e435049","name":"A2B Plastics
Ltd"},"benchmark":{"value":25,"unit":"%","parts":{"time":0.125
,"cost":0,"quality":0.125},"partsAsString":"Time: 0.25 \/
Cost: 0.25 \/ Quality:
0.25"}},{"supplier":{"O_v_UUID":"7s2d4ed9-1a57-11e5-b0e9-
02004e435049","name":"Applied Component Technologies
(ACT)"},"benchmark":{"value":25,"unit":"%","parts":{"time":0.1
25,"cost":0,"quality":0.125},"partsAsString":"Time: 0.25 \/
Cost: 0.25 \/ Quality:
0.25"}}]}},{"consistsOf":{"calc":20,"value":20,"unit":"%"},"do
main":{"domainName":"Open Cellular
Foams","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"
unit":"%"},"domain":{"domainName":"Polyurethane
Foams","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"
unit":"%"},"domain":{"domainName":"Seating
Foam","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"u
nit":"%"},"domain":{"domainName":"Trim
Foam","subdomains":[]}}],"clusters":false}}
Figure 39: MoVA: Restful API Result of Level 1 search
{"suppliersWithBenchmark":{"domainName":"Foam
Manufacturing","subdomains":[{"consistsOf":{"calc":20,"value":
20,"unit":"%"},"domain":{"domainName":"Moulded
Foams","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"
unit":"%"},"domain":{"domainName":"Open Cellular
Foams","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"
unit":"%"},"domain":{"domainName":"Polyurethane
Foams","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"
unit":"%"},"domain":{"domainName":"Seating
Foam","subdomains":[]}},{"consistsOf":{"calc":20,"value":20,"u
nit":"%"},"domain":{"domainName":"Trim
Foam","subdomains":[]}}],"clusters":false}}
Figure 40: MoVA: Restful API Result of Level 2 search
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Figure 41: MoVA: MoVA Cluster Search Result in SMECluster
3.4.5 Lessons learned
Before you can start the implementing the import routine you’ve to get familiar with the
MoVA Plugin and Backend API. If you’ve done this orientation you can implement the
import very easy.
The data provided by GeToVa for the suppliers is very good structured. So it was very easy to
specify the import. Also the data for the Assets and Domain Entities seems to be good. The
import could be done very easy. The GeToVa fields are mapped to the MoVA structure. By
inspecting the assets and domain entities there are was found a lot of unstructured data, which
GeToVa itself couldn’t filtered out, because the source data itself contains this wrong
unstructured data. Therefore the imported data has to be inspected and validated by hand.
The structure of the Excel file is very good, as it is easy to parse by the import routine. The
development of the plugin was very easy. The mapping to the suppliers, which are imported
from GeToVa wasn’t always possible, as the suppliers were listed with different (full and
shorted) name of the companies. Due to this, some companies were create two time, although
they are indeed the same.
As only a few requirements contain a link to a Domain Entity a cluster search can only started
for this small amount of requirements. The cluster search is working good and results suitable
data. As the Domain Entity model is still not fully built, the cluster search is only working for
level 0 and 1 at the moment. A facilitator has a lot of experiences which suppliers work well
together and which do not.
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The next steps were to clean up the Domain Entities from the wrong data and to build up the
full domain hierarchy. After that the cluster search can be evaluated again to test the results.
MoVA stores actual clusters responding to a requirement. The facilitator can evaluate their
overall performance [0%-100%] and their performance in terms of time, quality and cost.
This evaluation is considered when suggesting new clusters.
Using the MoVA Restful API the SME Cluster website has access to the data stored in
MoVA. The presentation is done very nice.
The MoVA SE proves to be very useful for the TANET trial as it combines hard facts
(domain keywords, sub-domains) with the human way of working (searching for either high
quality or quick delivery). MoVA is able to handle such fuzzy values and to integrate them
into the technically exact model.
MoVA requires apparently a little experience in modelling complex information systems. The
GUI fully supports the system and shows step by step what is happening. Hence, it supports
the user in gaining this experience.
3.5 Interview with TANET
The following interview was conducted towards the end of the trial phase with the general
manager of TANET. The other trial, COMPlus, stated its experience already earlier in the
project.
Question: Could you please briefly describe the challenge your Trial was facing before the
implementation of the Specific Enabler of the open call?
Answer: We wanted to enhance the services of our SMECluster services by giving the cluster
managers tools ad hand that would improve the quality of the services why reducing time.
Q: Describe your experience in implementing MoVA and GeToVa, please.
A: The feedback I received from the Technical implementers at Control 2K was that it was
relatively easy to implement both SE’s into the SMECluster Platform. The support provided
was excellent.
Q: How well did the Specific Enablers support the business logic of your trial?
A: They have enabled the SME Platform to be extended to connect to the Automotive Forum
data and pull out the relevant suppliers from the association and allow Tim Williams who is
the Chief Exec of the operation to create clusters of companies who are able to tender for
business.
Q: How easy or difficult was the technical integration into your system?
A: I believe it was easier than the previous GE’s and SE’s that we worked with. Please
consult our Engineers or look at other deliverables for more detailed information.
Q: What are the key functions and benefits of MoVA and GeToVa? Could you fully exploit
them in your trial?
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A: www.smecluster.com is live so you can see them operational in the platform. They are
helping the platform to provide much needed data in a format friendly to use. Especially the
clustering, the import and the advanced cluster search are beneficial. We are now also able to
integrate subjective evaluations and rankings of the cluster managers.
Q: Did the Specific Enablers open up your Trial by offering new opportunities and functions
you did not plan to exploit in the beginning?
A: In a way yes. We did need the added features but the initial GE’s and SE’s did not provide
the required functionality.
Q: Which additional features would you like to see developed for MoVA and GeToVa in the
near future?
A: Better integration with LinkedIn and tenders to be pulled from the Welsh Tendering portal
http://www.sell2wales.gov.uk/
3.6 Conclusions
In this deliverable we have reported on the final round of experiments, results and lessons
learned from using the two SEs developed as part T14.1 namely:
- advanced Management of Virtualized Assets (MoVA) aiming to support Virtual
Factories (VF) in intuitively generating, composing, and transforming virtual
representations of in-/tangible assets (VAaaS) within Manufacturing Ecosystems.
Design and implementation of an intuitive user-centric graphical interface for dynamic
discovery and flexible composition of Virtualized in-/tangible Assets (as a Service)
targeting at team building applications as well as advances in production networks;
- Generation and Transformation of Virtualized Assets (GeToVA) aiming to
support Virtual Factories (VF) in semi-automatic generation and clustering of
Virtualized intangible Assets (VAaaS) from real-world semi-structured enterprise and
network resources. GeToVa enables as well multi-format ontology transformation
between various representations of Virtualized in-/tangible Assets.
The Specific Enablers have been incorporated into the FITMAN Architecture and have been
used in the FITMAN pilots, especially in the Virtual Factories (WP6) that created the
specification for the open calls to which these SEs fulfil.
Both SEs contributed successfully and significantly to the success of the two trials where they
have been incorporated. Valuable insights to further streamline and optimise the SEs have
been gained.
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4 Update on KPIs in the Trials
In this section an update on the KPIs in the trials is provided.
4.1 AgustaWestland
AgustaWestland has performed the experimentation of SMART and DIGITAL trials. At the
end of this phase AW has been able to produce significant lesson learnt referring to the
collected results.
Trial results
Following is the list of gathered BIs, due to confidentiality purposes all Performance
Indicators were unified to percentage.
Trial - Scenario - BPI Progress_1 Progress_2 Progress_3
3-AGUSTAWESTLAND 70,4% 100,0% 100,0%
1- SUPPORT FOR MANAGEMENT OF DOCUMENTATION AND
REPORT CREATION 55,6% 100,0% 100,0%
ANDR_1-AVERAGE NUMBER DISCREPANCY REDUCTION 100,0% 100,0% 103,70%
RAT_1-REDUCTION OF AVERAGE TIME 11,1% 100,0% 100,0%
2- SUPPORT FOR MONITORING AND MANAGEMENT OF
TOOL TRACKING 100,0% 100,0% 100,0%
TDTM_2-TAILORED DATA FOR TRAINING MATERIALS 100,0% 100,0% 100,0%
Digital Trial
As a general comment the digital trails confirmed the initial expectations and the desiderata
target have been achieved.
With respect to the BI defined at the beginning of the project, a further BI was measured
relevant to a significant reduction of number of discrepancies between the analysed data from
different sources and complied by different persons, it’s linked especially to possible human
error of transcription inside the DB of Quality Production.
As regards the BPI “reduction of the average time”, the great improvement measured at
month 18 is due to various reasons:
at the beginning some GEs has been found not yet mature and not very stable.
consolidating of the application and fixing of the main issues that were present in the
first version tested at 12 months, in particular the connecting to the corporate
repositories;
tuning of the virtual machine where the application was running in order to manage
multiple services;
bugs fixing.
At month 27 further significant improvements were not measured even if some additional
functionaries were implements in the system such as possibility to filter the data sources or
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the display of image of the components (if available). These new features, although
appreciated by the users, have not brought substantial improvements on the average time to
collect data.
Additional improvements could be obtained by increasing the number of sources dates
interrogated by the system.
As regard the BPI “Average Number Discrepancy Reduction”, the expected result was
already achieved at month 12. The application has replaced the “past and copy” of data from
company archives to the Db Quality Production handmade by users. Furthermore we must
consider that this DPI is time independent research data that is measured instead by the
previous PBI.
The result was confirmed at 18 months, while a slight improvement was measured at 27
months when an automatic consistency check between data sources was implemented. This
new feature checks if there are mismatches between the data from different sources (e.g.: a
different part number for the same component provided by different vendors) and in this case
it selects the data contained in the source that has been defined as master (in example the
IETP is the master for the part number). The other new features (filters and image of
components) have not affected the BPI.
General speaking, times saving as well as reduction of discrepancies can contribute to
enhancing the efficiency of the activities performed by the Production Quality department that
is committed in the preparation of all documentation required and necessary for the delivery
of the helicopter to the customer, for example to have a real helicopter picture of “as built” vs.
“as design”. This is very important for the post-delivery activities (spare parts procurement,
updating of technical publications, training, etc.) that constitute a major portion of the AW
business.
An interesting indication has emerged during the trial in particular saving time during
documents searching, the possibility to use the system to support the instructors of AWTA
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(AW Training Academy) in preparation of the courses for pilots and maintenance technicians.
The availability of the information in a shorter time makes more efficient the activities of the
AW training organisation; the instructors, saving time in data collection, can focus on training
aspects and on preparation of training materials (training manual, presentation, multimedia,
etc.) used during type rating courses for pilots and technicians that are purchased by the
customer.
Purpose: The system searches the data linked to a specific helicopter through queries in 4
different sources and compiles the relative cell inside a Db of Quality Production
Business Performance Indicators:
PI1 Reduction of average time
PI2 Average number discrepancy reduction
Idl Trial Scen
ario PI_Description
PI_name AS IS (middle indicative value)
TO BE * (desiderata)
Ex-pect-ed Target
Comments PI_ Class
UM
3
AW
Digital Trial Case
Reduction of average time to make data in a digital format after/before the implementation during the period
PI1 Reduction of average time
50÷65 min
(for each person, monthly) (this is an indicative value, it’s a medium value per person)
5÷10 min
(for each person, monthly)
- 45÷55%
Target is a percentage of reduction of time to search, copy or write data from different sources inside the Database used by Quality Production for the Logbook data compilation
Lead Time in minutes (LT)
% reduction
3
AW
Digital Trial Case
Reduction of average data discrepancies after/before the implementation during the period (for each helicopter)
PI2 Average number discrepancy reduction
30÷40 (number
of discrepancy per each helicopter)
5÷10 (number of discrepancy per each helicopter)
- 25÷30%
Reduction of number of discrepancies between the analysed data from different sources and complied by different persons, it’s linked especially to possible human error of transcription inside the Database of Quality Production for the Logbook data compilation
Number
% reduction
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* Data regarding tool utilization are partially simulated as errors are not statistically frequent
enough in comparison to the pilot dynamics
Smart Trial
For the smart trial the BPI is qualitative in terms of availabily of documents reporting the
events which can occur to the tools (tool tracking) used during assembly of the helicopters at
the FAL or during maintenance activities of the helicopters at a service center.
Selected a period of time to be monitored, the following information are provided:
the last date in which the event happens;
the typology of event (for example not correct position of tool in the smart tools box at
the end of activity);
the tool connected / associated to the logged event;
the number of time that the event linked to the selected tool happens (this is linked to
the entire chronology, starting from the first use of the toolbox and referring tools).
These documents are used by stakeholder involved in FOD (Foreign Object Debris)
prevention to better define and tailor training courses to be provided to technicians as
prescribed by regulations for all workers employed in the aviation industry (for example
taking into consideration events that occur more frequently, tools most forgotten, etc.)
The aim is to keep high the awareness on safety that, in all its forms, is essential for the
avoidance of risk to helicopter’s users (passengers and crew).
The expected result was already achieved at month 12 and it was confirmed at month 18 and
month 27. Evaluation of BPI was made not only based on the availability of documents, but
also on their usefulness as confirmed by some senior instructor who appreciated the type of
information inside.
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In particular at month 27 additional features were implements in the system such as a more
rational organization of the collected information that have been grouped by source (smart
tool box) allowing to better understand the areas where the events occurred, or the possibility
to display the image of the tools (if available). The users really appreciated these new features
because provide them further information.
It is important to point out that additional benefits could be obtained using the results of the
trial as input of TELL ME (Technology Enhanced Learning Living Lab for Manufacturing
Environments) platform developed within the an integrated project in technology Enhanced
Learning for Manufacturing workplaces of the future. The aim of this project is to support the
Blue Collar Worker at the workplace providing specific training by using the latest
technologies and insights.
The TELL ME scenario developed for aeronautical sector refers to technicians that, at service
stations, carry out maintenance activity on AW helicopters. Also in this case FOD prevention
is one of the main topics and it was faced with an innovative approach based on Precision
Teaching methodology.
Purpose: The system produces period reports relative to Tools events recorded. The reports contain
data useful for the preparation of further and future tailored Training Material linked to Tools FOD
Prevention.
Business Performance Indicators: PI6_ more tailored data for training materials linked to results of
new tracking tools methodology
Id Tri
al Scenario
PI_Description
PI_name AS IS (middle indicative value)
TO BE * (desiderata)
Expected Target
Comments
PI_ Class
UM
3
AW
Smart Trial Case
Report of tools use tracking useful for the future preparation of further dedicate and tailored training material linked to Tools FOD prevention
PI6_ more tailored data for training materials linked to results of new tracking tools methodology
N/A
Not fully achieved
Qualitative value though a binary indicator: yes / no
After the implementation it will be possible to know if the system and the referring will be able to furnish data useful for the raw training material ( yes) or not (no)
Qualitative Indicator
Qualitative value
Document
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* Data regarding tool utilization are partially simulated as errors are not statistically frequent
enough in comparison to the pilot dynamics
Consolidated Trial Experience
The aeronautical manufacturing is a high-technology industry characterized by complex
processes that are also regulated by national and international bodies. This is meant to reach
and ensure the highest possible level of safety. The implementation of FITMAN solution took
into consideration these aspects in order to avoid any interference that could compromise the
safety of the flight and the airworthiness is why a testing environment has been set up as an
exact replica of the real environment.
In the same way AW guidelines and policies oblige that all data has to be treated with high
confidentiality and should not be accessible by public in any case. As a consequence a cloud
solution has been excluded preferring the use of the internal network.
Some problems were faced during implementation due do the software quality of FIWARE
GEs available in the catalogue that is still not mature and bug free.
The use of different operating systems (UNIX and MICROSOFT) has led to some problem of
interfacing.
The Future Internet technology such as that experienced in FITMAN trial 3 is in line with the
AgustaWestland strategy towards customers who are asking for more – more safety
assurance, more quality on delivery and more reliability and availability in service.
4.2 Aidima
Furniture Trends Forecasting for Product Development / UC1
Type Indicator
s
Descriptio
n
Unit Curren
t value
Future
expecte
d value
TO-
BE
Value
s
1, 2, 3
Impac
t level
Comments
Efficiency Search
time
process per
source
Reduction
of searching
time
(working
hours
saving) per
source when
browsing
electronic
sources,
identifying
weak
signals and
classifying
them.
Workin
g hours
8 hours
per
source
approx.
6 hours
per
source.
6
6
5.1
Resear
ch
Productivit
y
Sources Increase of
number of
electronic
sources
analyzed by
trends
experts due
Number 20 +40/year 25
25
60
Resear
ch
Software
allows to
analyze as
many sources
as analysts
input into the
system.
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to FITMAN
automated
solutions
Number of
sources can be
incremented
as needed
without limit
Productivit
y
Weak
signals
Increase of
number of
weak
signals
identified
due to
FITMAN
automated
solutions
Number 200
approx.
400/year 220
220
462
Resear
ch
Weak signals
are index
cards with any
score
Productivit
y
Index
cards
Increase of
number of
index cards
due to
FITMAN
automated
solutions.
Number 100
approx.
300/year 150
150
286
Resear
ch
Index cards
are weak
signals with a
score higher
than 3 stars
and that are
printed out.
The new
rating system
has a lot of
potential and
can increment
the number of
index cards
dramatically
Opinion Mining in Furniture Products / UC2
Type Indicators Descriptio
n
Unit Curre
nt
value
Future
expecte
d value
TO-
BE
Value
s
1, 2, 3
Impa
ct
level
Comment
s
Efficien
cy
Complaint
s
resolution
time
process.
Time
saving
when
addressing
customer
complaints
or negative
opinions.
Days >1 <1 1
1
0,2
Comp
any
Complain
resolution can
be reduced
dramatically
since the
answers can be
carried out via
Facebook or
Twitter. It all
depends on the
time dedicated
on a regular
working day Marketin
g
Opinion
retrieval
Number of
identified
Percenta
ge
0% -
10%
100% 30
30
Comp
any
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electronic
customer
opinions
about the
firm or its
products,
services and
brands
75
Social Identificatio
n of non-
reported
dissatisfacti
on
Increase of
cases of
non-
reported
customer
online
dissatisfacti
on related to
product
and/or
service. Not
directly
reported to
the
company
Percenta
ge
0% 100% of
online
comment
s. On
specified
sources
20
20
100
Custo
mers
Social Opinion
leaders
Identificatio
n of opinion
leaders
amongst
customers
(i.e.
bloggers,
etc.). Not
professional
.
Number 0 Up to 5 1
1
7
Custo
mers
4.3 Volkswagen
KPI Name AS-
IS
value
TOBE
1
TOBE
2
TOBE
3
Target
value
Progress
1
Progress
2
Progress
3
BP: Management of the Machinery Repository
MR
Update
Cost
100 85 75 60 50 30,00% 50,00% 80,00%
MR
Update
Time
100 80 70 50 46 37,04% 55,56% 92,59%
BP: Inquiry Service
Average
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lead time
to access
experts
knowledge
100 60 55 31 29 56,34% 63,38%
97,18%
Evaluation
Accuracy
100 90 85 65 50 20,00% 30,00% 70,00%
Inquiry
Respond
Time
100 95 90 83 80 25,00% 50,00% 85,00%
Inquiry
Respond
Cost
100 95 92 91 90 50,00% 80,00% 90,00%
4.3.1 MR Update Cost
Comment:
This KPI indicates the cost of updating or adding a machinery module inside the MR. This
task is done by the responsible engineer and the value is calculated by the effort in hours and
the hourly wage of the engineer.
Trend:
The numbers in the table above show a continuous reduction of the update cost over the
project period and the implementation/evolution of the trial system (final achievement 80%).
Reasons:
This reduction is achieved by using the FITMAN trial system. Due to its web-based services
the engineer can easily and fast accesses the MR to enter machinery data. One major
improvement is the semi-automated extraction of machinery data from the PLM system. This
function aggregates and abstracts the detailed and unsorted data from an XML file and stores
it into the MR. By this process the manual effort is reduced a lot.
4.3.2 MR Update Time
Comment:
This KPI indicates the time of updating or adding a machinery module inside the MR and to
make it public (accessible to all engineers). This task is done by the responsible engineer and
the value is the time in hours.
Trend:
The numbers in the table above show a continuous reduction of the update time over the
project period and the implementation/evolution of the trial system (final achievement
92,59%).
Reasons:
The engineer can easily and fast accesses the MR to enter machinery data. After the
successful updating/adding of machinery data, this data is instantly accessible by other
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engineers and no further manual distribution is needed. This results in a much shorter update
time.
4.3.3 Average lead time to access experts knowledge
Comment: This KPI indicates the time to get in contact with relevant experts in production planning and
to receive information about production topics.
Trend:
The numbers in the table above show a continuous reduction of the lead time to access experts
knowledge over the project period and the implementation/evolution of the trial system (final
achievement 97,18%).
Reasons:
Every user can get in direct contact with engineers who are responsible for different assembly
sections by using the FITMAN system web services. The user has only to choose the product
or assembly section and the inquiry will be forwarded to the responsible engineer. Due to this
no manual effort for searching the responsible person or department is needed and the process
is fastened.
4.3.4 Evaluation Accuracy
Comment:
This KPI indicates the accuracy of cost estimations during the production system planning
phase and is based on estimated and real cost. Unfortunately only a long term measurement
could provide reliable data for this KPI, which was not possible inside the frame of FITMAN
with respect to the implementation date of the system. To deal with this issue older car
projects were evaluated with the FITMAN trial system. The shown KPI values are based on
these results and by using a pessimistic approach. But even an achievement of 70% (equal to
an improved accuracy by 35%) is very good.
Trend:
The numbers in the table above show a continuous improvement of accuracy over the project
period and the implementation/evolution of the trial system (final achievement 70%).
Reasons:
This reduction is achieved by using the FITMAN trial system. The cost evaluation was
improved due to the aggregated and abstracted machinery data in the MR.
4.3.5 Inquiry Respond Time
Comment:
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This KPI indicates the time which is needed to receive, analyse and evaluate an inquiry and to
create a report.
Trend:
The numbers in the table above show a continuous reduction of time for the inquiry respond
over the project period and the implementation/evolution of the trial system (final
achievement 85%).
Reasons:
Thanks to the web services the engineer can easily and fast accesses the system to view and
analyse this inquiry. The evaluation of the inquiry is supported by the aggregated and
abstracted machinery data in the MR, which reduces the manual effort for data aggregation.
After finishing the evaluation an online report is created and sent back to the requester. This
report is instantly available on his/her computer or mobile device.
4.3.6 Inquiry Respond Cost
Comment:
This KPI indicates the costs which are spent for the evaluation of production related inquiries.
Its value is calculated by the effort in hours and the hourly wage of involved persons.
Trend:
The numbers in the table above show a continuous reduction of cost for the inquiry respond
over the project period and the implementation/evolution of the trial system (final
achievement 90%).
Reasons:
The engineer can easily and fast accesses the system to view and analyse an incoming inquiry.
The evaluation of the inquiry is supported by the aggregated and abstracted machinery data in
the MR, which reduces the manual effort for data aggregation and evaluation. The main
amount of the evaluation is still based on the engineer’s experience.
4.4 Consulgal
Performance indicators are measures that describe how well a program is achieving its
objectives. Following, we describe what the data show for each of the indicators measured
until M27:
PI1: Ratio: Average lead time to access the information relating to concrete
characteristics and concreting plan after/before the DV/AV implementation during the
concrete control process.
AS-IS: 4 hours. Target value: 98% of reduction.
This performance indicator is to provide information on the time saved by the elimination of
waiting time in the process.
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What the data show?
The values obtained represent a 99.96% of reduction in time. This value is very close
to the expected value for this PI (98% of reduction in time.)
PI2: Ratio: Average number of pages used in the test results recording, archival,
after/before the DV/AV implementation during one concrete operation.
AS-IS: 5 pages. Target value: reduce by 40%
This performance indicator provides information about the average number of pages used for
recording the test results during one concrete operation.
What the data show?
In the simulations made we did not print information but this will not be more than 2
pages per concreting operation. This represents 60% of reduction.
PI3: Average lead time needed to perform and record the test results after/before the
DV/AV implementation during one concrete operation.
AS-IS: 27.5 minutes. Target value: 30% of reduction.
This performance indicator is to provide information about the time saved due to automation
of the process.
The values obtained represented a 75.39% of reduction in time. This value definitely exceeds
the expected value for this this PI.
The application has removed the registration time in Excel, and additionally it reduced the
other times beyond our expectation, because it is not necessary anymore to register several
times the data that identify the concrete operation. This later aspect has not been considered in
our AS IS analysis and, for that reason, the value of this performance indicator significantly
exceeded our initial estimate.
PI4: Ratio: Average lead time needed to analyse the test results after/before the DV/AV
implementation during one concrete operation.
AS-IS: 39 days. Target value: 98% of reduction
This performance indicator is to provide information about the time we hope to save by the
elimination of waiting time in the process.
What the data show?
The time to analyse the test results is minutes. We consider the values 0 due to the
magnitude scale. This represents 100% of reduction.
PI5: Ratio: Time for data exchange between stakeholders after/before the DV/AV
implementation during the concrete control process.
AS-IS: 8 hours. Target value: 98% of reduction.
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This performance indicator is to provide information about the time expected to be saved by
improving the actual exchange information between stakeholders.
What the data show?
We made measurements in all the Business Scenarios. The values obtained represent a
99.97% of reduction in time. This value is very close to the expected value for this PI
(98% of reduction in time.)
PI6: Ratio: Average cost needed to perform and record the test result after/before the
DV/AV implementation during one concrete operation.
AS-IS: 2.04€. Target value: reduce by 30%.
This performance indicator is to provide information about the average cost of human
resources involved in the process.
The values obtained represented a 74.01% of reduction in time. This value definitely exceeds
the expected value for this this PI. The values obtained in this PI exceeded the value of the AS
IS, due to the values obtained in the PI3.
PI7: Ratio: Average cost needed to analyze the test result after/before the DV/AV
implementation during one concrete operation.
AS-IS: 1.41€. Target value: reduce by 65%
This performance indicator is to provide information about the average cost of human
resources involved in the process.
What the data show?
The values obtained represent a 67.37% of reduction in cost. This value exceeds the
expected value for this PI.
In general, we can say that the improvements that may be obtained from using the application,
in the concrete control process, have exceeded our expectations. Nevertheless, there is room
for significant improvements on what the user interfaces are concerned, the functionality of
some of the features, the versatility allowed and user-friendliness.
Table with the values of BPIs (ASIS, TOBE 1, 2, 3 , Target value)
7 CONSULGAL
1
IDENTIFICATION OF CONCRETE CHARACTERISTICS AND CONCRETING PLAN T
OB
E
1
TO
BE
2
TO
BE
3
Tar
get
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EXCH.TIME TIME FOR DATA EXCHANGE 28800 5,15 6,35 5,12 576
Values are in seconds. (8 hours = 28800 seconds) Target reduction in percentage is 98%
LT Char.&Plan
AVERAGE LT TO ACCESS INFORMATION 14400 7,5 5,39 4,73 288
Values are in seconds. (4 hours = 14400 seconds). Target reduction in percentage is 98%
2 SAMPLES COLLECTION AND TESTING
COST RES.
AVERAGE COST TO PERFORM AND RECORD RESULT 2,04 0,55 0,47 0,57 1,43
Values are in € Target reduction in percentage is 30%
EXCH.TIME TIME FOR DATA EXCHANGE 28800 8,2 5,1 4,89 576
Values are in seconds ( 8 hours = 28800 seconds) Target reduction in percentage is 98%
LT RES.
AVERAGE LT TO PERFORM AND RECORD RESULTS 1650 424 358 436 1155
Values are in seconds (27.5 minutes = 1650 seconds) Target reduction in percentage is 30%
NUM.PAG. AVERAGE NUMBER OF PAGES 5 2 2 2 3
Values are in number of pages. Target reduction in percentage is 40%
3 TEST RESULTS TREATMENT AND EVALUATION
COST AN.RES. AVERAGE COST TO ANALYZE RESULT 1,41 0,52 0,41 0,45 0,49
Values are in € Target reduction in percentage is 65%
EXCH.TIME TIME FOR DATA EXCHANGE 28800 8 10,5 8,9 576
Values are in seconds Target reduction in percentage is 98%
LT AN.RES. AVERAGE LT TO ANALYZE RESULTS 39 0 0 0 0,78
Values are in days Target reduction in percentage is 98%
4.5 TRW
TRW has performed the experimentation of SMART trial. At the end of this phase TRW has
been able to produce significant lesson learnt referring to the collected results.
4.5.1 Trial Results and Progress
The table in the next page summarises the expected target, the real values measured in the
TRW trial during the whole project and the progress of the measured KPIs..
TRW trial will use percentages of improvement and decrease of the business performance
indicator as measuring unit, avoiding the usage of absolute values. The main reason for this
choice is the misuse that external users can do with current data of TRW, getting them out of
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context and creating non-desirable image for a worldwide leader branch in the automotive
sector. Due to this unfortunate and possible situation, TRW will use percentages comparing
current and future values of each indicator.
Additionally, the most important target of TRW due to business performance indicator is to
not only assess the impact of the FITMAN system instantiation, but also report and
communicate this impact in the manufacturing and production activities thanks to FI
technologies deployment. In order to reach these objectives of assessment and
communication, percentage values of TRW indicators are as useful as absolute values, since
they are able to reflect the evolution of the business processes in the factory.
PI Name of the PI Expected Target TO
BE 1 TO
BE 2 TO
BE 3 Progress 1 Pro. 2 Pro. 3
TRW Trial 83,32% 123% 178,44%
BS1 - RISK MODELLING 78,3% 116,7% 184,7%
BS1PI 1
Number of standards and regulations (added) in the repository after/before the DV/AV implementation during a period
Increase of 5% Good
Increase of 7% Very good
Increase of 15% Excellent
4 6 10 80,0% 120,0% 200,0%
BS1PI 2/ BS2PI 1
Number of accidents and incidents in the factory after / before the DV/AV implementation during a period
Reduction of 10% Good
Reduction of 15% Very good
Reduction of 20% Excellent
9 13 17 90,0% 130,0% 170,0%
BS1PI 3
Number of risks that has been defined using the new system after / before the DV/AV implementation during a period
Increase of 30% Good
Increase of 45% Very good
Increase of 60% Excellent
25 40 50 83,3% 133,3% 166,7%
BS1PI 4
Number of preventive actions using the new systems after /before the DV/AV implementation during a period
Increase of 30% Good
Increase of 50% Very good
Increase of 70% Excellent
18 30 50 60,0% 100,0% 166,7%
BS1PI 5
Number of human errors in the design of prevention strategy planning after /before the DV/AV implementation during a period
Reduction of 10% Good
Reduction of 20% Very good
Reduction of 30% Excellent
- 10 22 100,0% 220,0%
BS2 - RISK DETECTION AND INFORMATION 88,3% 130,1% 172,2%
BS2PI 2
Number of deployed monitoring systems after / before the DV/AV implementation during a period
Increase of 55% Good
Increase of 75% Very good
Increase of 95% Excellent
50 70 95 90,9% 127,3% 172,7%
BS2PI 3
Number of risk detections, alarms and warnings set up after / before the DV/AV implementation during a period
Increase of 65% Good
Increase of 85% Very good
Increase of 100% Excellent
60 80 95 92,3% 123,1% 146,2%
BS2PI 4
Number of training sessions regarding safety after /before the DV/AV implementation during a period
Increase of 25% Good
Increase of 40% Very good
Increase of 50% Excellent
20 35 50 80,0% 140,0% 200,0%
4.5.2 TRW KPIs Analysis
The main reason for the high progress in the TRW trial (over 100%) is due to the inexistence
of customised and effective tools and systems for the optimisation of the preventive planning
design.
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Furthermore, these KPIs demonstrate that the use of the FITMAN system in the TRW
production line allow a successful prevention of the injuries and illnesses that later can
provoke important musculoskeletal disorders, enhancing the health and safety of the
workers.
BS1PI 1: Number of standards and regulations (added) in the repository after/before the
DV/AV implementation during a period
This performance indicator aims to measure the time invested and the reduction of
inefficiencies (time) in the broad application of current regulations and standards.
TRW is currently using REBA, NIOSH and OCRA standards, which are the most important
ones. With the new system, the time invested in the full application of these standards and the
range of information controlled (parameters controlled) has been optimised, not changing the
costs.
BS1PI 2/ BS2PI 1: Number of accidents and incidents in the factory after / before the
DV/AV implementation during a period
This is a key performance indicator, which ensures that the system is able to reduce the
number of injured workers and reduce the lost days in the production line.
The TRW trial has achieved a reduction of 17% in the accidents and incidents with the use of
the FITMAN system. As a result, the rates of injured workers with musculoskeletal disorders
have been significantly reduced, decreasing the number of lost days, with the important
savings that this supposes.
BS1PI 3: Number of risks that has been defined using the new system after / before the
DV/AV implementation during a period
The system allows setting up risks that can happen in the factory, specifying concrete
parameters and thresholds to detect them. The type of risks that can be found in the
production line are defined by important organisms, so these cannot be modified. But thanks
to the FITMAN system, several risk have been deeply configured and customised, which as a
result provides a better prevention and detection.
BS1PI 4: Number of preventive actions using the new systems after /before the DV/AV
implementation during a period
The system allows setting up preventive actions, linked to the risks detected. The increase of
50% of design of preventive actions is directly related to the previous KPI. However, more
important than the quantity is the quality of the preventive actions, and thanks to the FITMAN
system more accurate and customised actions are possible, getting support from the latest
technologies.
BS1PI 5: Number of human errors in the design of prevention strategy planning after
/before the DV/AV implementation during a period
This performance indicator is focused on checking that the human errors are reduced, which
is one of the main problem of current systems. Nowadays the prevention technicians are the
ones in charge of the risk detection and preventive action definition. Even if they have huge
experience, some errors might appear. In the TRW trial these errors have been decreased to
22%.
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BS2PI 2: Number of deployed monitoring systems after / before the DV/AV
implementation during a period
Nowadays there are hardly any IT systems supporting the prevention activities. In the TRW
trial of FITMAN new IT equipment and infrastructures have been deployed in the selected
production line, increasing in a 95% the usage of technology for this area.
BS2PI 3: Number of risk detections, alarms and warnings set up after / before the
DV/AV implementation during a period
This is a key performance indicator, since it determines the effectiveness of the systems in the
risk detection and preventive actions deployment. Due to the FITMAN system
implementation in the TRW factory, the number of risks detected and alerts send to the
workers has increased in a 95%, which has directly contribute in the enhancement of the
workers well-being. Additionally, new tools and mechanisms for performing the prevention
actions have been set up.
BS2PI 4: Number of training sessions regarding safety after /before the DV/AV
implementation during a period
The objective of this performance indicator is to probe the increase in the awareness of the
importance of H&S adoption in the TRW factory. Thanks to the information gathered and
analysed in the TRW trial, 50% increase in the training sessions has been performed.
Therefore, the workers have more knowledge on their postures and behaviours, which in the
end becomes into less injures and health problems.
4.5.3 Consolidated Trial Experience
To implement the FITMAN platform in the industry sector some points have to be
considered:
- Make sure all goals/objectives of the planned usage and all needed functionalities are
clarified.
- Check the needed functionalities with the provided platform components whether they
fit or not. Maybe additional components like SEs or TSCs are required.
- Make sure your infrastructure complies with the component’s requirements
(Hardware, Software, OS).
- Clarify existing guidelines and policies and check if they interfere with the FITMAN
platform components.
- Develop an implementation roadmap and experimentation plan. Maybe it is advisable
to test each component separately and to implement the components step-by-step.
- If problems or questions are occurring, contact the owner or developer of the
component. They can provide help or needed adaptions.
More concretely, and due to concrete aspects of the TRW trial, some other important factors
has to be taken into account. These differential aspects are the location of the trial in the shop
floor (in the production line) and the importance of workers safety and security in the trial.
The first important activity to be achieved is the calibration of the sensors deployed in the
shop floor. The point is that depending on different aspects such as the light, vibrations,
location, etc. the results provided by the sensors cannot be reliable. Therefore, some tests and
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calibrations have to be performed in the concrete location where the devices will be deployed
in the shop floor, to ensure their accuracy.
The second advice is related to the selection of the personnel involved in the trial. The
TRW trial monitors and processes information about the production line workers, adding
some difficulties to the technical implementation. Therefore, the selection of the personnel
participating in the trial has to be done very carefully, since those people will not only provide
their data for the benefit of the trial, but also will give some feedback and opinions about
functionalities, usability and other important aspects of the solution. The TRW solution will
be mainly used by the blue collar workers and prevention technicians, so they should be the
main source for developing an intuitive and easy-to-use solution based on the FI
technologies.
4.6 TANet
4.6.1 Overview
TANET
PI_Desc PI_Name AS-IS TO-BE1 TO-BE2 TO-BE3 Target Comments UM
IMPORT OF TENDER OPPORTUNITIES
FAC.NUM.
NUMBER OF ACTIVE
FACILITATORS 1 2 2 3 3
Current Value and Target are in number of active facilitators Added Welsh
Automotive Forum as facilitator into use case. Discussing inclusion with third
partner. M18 - still in talks with third partner, dependent on open call
integration ability to import member data. Third facilitator tentatively
onboard, based on GetOva import of suppliers.
number of active
facilitators Productivity (P)
SERV.PR.NUM.
NUMBER OF REGISTERED
SERVICES PROVIDERS 23 23 71 101 115
Current Value and Target are in number of registered service providers
SMECluster is not yet advertising for new service providers. This is a long-
term goal increase. M18 - improved by using GeToVA, available at:
http://fitman.sti2.at/companies - GeToVA pulled in third facilliators
suppliers.
number of registered
service providers Productivity (P)
TEND. TENDERS ACCRUED MONTHLY 3 3 12 18 20
Current Value and Target are in numbers of tenders No automated process
for acquiring tenders exists yet. M21 planned completion using open call
components. M18 - tender entry by facilitator using SMECluster platform.
Tend 3 - tenders entered by facillitator still, Getova SE was aimed more at
import of suppliers. number of tenders Productivity (P)
IMPROVEMENT OF FACILITATOR ROLE
CLUST. END-TO-END CLUSTERING 6 5 2 2 2
Current Value and Target are in hours Decrease in time due to use of CAM as
data store. SCAPP implementation expected to significantly reduce time by
providing negotiation tools. Open call components will also reduce time
through import of tender opportunities. M18 - SCAPP negotiation rooms has
hugely simplified the process of negotiation between facilitator and supplier
members -open call components were not used to automate tendering so no
changes to the end-to-end clustering time. hours Lead Time (LT)
TEND.AUT.
AUTOMATED TENDER INPUT
TIME 30 30 6 2 1
Current Value and Targets are in minutes Open call components will be used
to automate import of tenders - completion planned for M21. M18 - Majority
of data storage and representation supported by CAM, reducing input time;
input time will be further reduced using OCSEs - time was reduced further by
the addition of Mova which gives a better interface to the ontology thus
allows annotating in a better manner. Getova was originally to be used for
fully automating tendering however this was dropped as the focus became
further toward the import of suppliers. minutes Lead Time (LT)
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4.6.2 General Comments about KPI’s
4.6.2.1 Import of Tender Opportunities:
These all relate to improving the throughput of business opportunities by implementing the
GE’s, SE’s and TSE’s.
Number of Active Facilitators:
It was always envisaged that SMECluster as a tender platform would have been made
available to other Facilitator based organisations. The testing was conducted primarily with
the Welsh Automotive Forum who have always required such a platform and the additional 2
businesses interested were Aerospace Forum and Industry Wales.
Number of Registered Service Providers:
This was based on the number of providers that could offer services via the SME portal and
whilst expressions of interest increased through-out the trial, the figures reflect the likely
uptake in the local region.
Tenders Accrued Monthly:
This KPI is a calculated value based on connecting to sites such as www.sell2wales.co.uk and
scraping information about current tenders from the relevant areas. Also the importing of
suppliers was introduced to SMECluster via the newer GE’s such as MoVA and GeToVA.
4.6.2.2 IMPROVEMENT OF FACILITATOR ROLE:
This KPI was to measure the effectiveness of the new tools coming out of FITMAN to
improve the efficiency of the facilitators.
End to End Clustering:
This KPI was to measure the overall reduction of “processing” time for facilitators through
negotiating tools provided by FITMAN.
Automated Tender Input Time:
The numbers connections to pull opportunities from tender sites such as
http://www.tendersdirect.co.uk/ and www.sell2wales.co.uk increased the speed of processing
TANET 8
1 IMPORT OF TENDER OPPORTUNITIES AS
-IS
ToB
e1
ToB
e2
ToB
e3
Targ
et
Pro
gre
ss1
Pro
gre
ss2
Pro
gre
ss3
Comments
FAC.NUM. NUMBER OF ACTIVE FACILITATORS 1 2 2 3 3 50.00% 50.00% 100.00%
Current Value and Target are in number of active facilitators Added Welsh
Automotive Forum as facilitator into use case. Discussing inclusion with
third partner. M18 - still in talks with third partner, dependent on open
call integration ability.
SERV.PR.NUM.
NUMBER OF REGISTERED SERVICES
PROVIDERS 23 23 71 120 115 0.00% 52.17% 105.43%
Current Value and Target are in number of registered service providers
SMECluster is not yet advertising for new service providers. This is a long-
term goal increase. M18 - improved by using GeToVA, available at:
http://fitman.sti2.at/companies.
TEND. TENDERS ACCRUED MONTHLY 3 3 12 12 20 0.00% 52.94% 52.94%
Current Value and Target are in numbers of tenders No automated
process for acquiring tenders exists yet. M21 planned completion using
open call components. M18 - tender entry by facilitator using SMECluster
platform.
2 IMPROVEMENT OF FACILITATOR ROLE
CLUST. END-TO-END CLUSTERING 6 5 2 2 2 25.00% 100.00% 100.00%
Current Value and Target are in hours Decrease in time due to use of CAM
as data store. SCAPP implementation expected to significantly reduce time
by providing negotiation tools. Open call components will also reduce time
through import of tender opportunities.
TEND.AUT. AUTOMATED TENDER INPUT TIME 30 30 6 2,5 1 0.00% 82.76% 94.83%
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tenders compared to the original manual method. Further work is required to actually make
these opportunities to available to facilities.
4.7 COMPlus
4.7.1 Network Transparency For More Efficient Supplier Search
Type Indicators Description Unit Cur-rent
value
Future
expected
value
TO-BE
Values
1, 2, 3
Comments
Lead Time Time used
for
configuratio
n and data
entry
Reduction of
configuration
and searching
time due to the
configuration
of the supply
network.
% Reduc-
tion of
35%
Reduction
of 85%
5
20
35
This indicator
shows the level of
support to the
configuration
process of supply
network.
This KPI
improves during
the use of the
solution. As the
number of
included entities
into the
knowledge base
increase, the
maturity of the
system improves.
Produc-
tivity Level of
transparency
Improvement
of level of
transparency
of the supply
network
% Improve
ment of
50%
Improvem
ent of 80%
0
20
50
This indicator
shows the level of
achieved
transparency
within the supply
networks. With
the continuous use
of the solution,
the knowledge
base is being
enriched and
hence the level of
transparency if the
network
improves.
Lead Time Reduction of
the time
needed for
searching of
a supplier
Reduction of
time needed to
search for an
existing or a
new supplier
% Improve
ment of
35%
Reduction
of time to
search for
a supplier
of 80%
5
15
35
This indicator
shows the level of
decrease of time
needed to search
for a new or
existing supplier
within the
network
4.7.2 Transparency And Consistency Of ITs And Documents
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Type Indicators Description Unit Current
value
Future
expecte
d value
TO-BE
Values
1, 2, 3
Comments
Quality Reduction
of Mistake
and Errors
This
indicator
show the
ratio of the
reduction of
mistakes and
errors during
the
configuration
of the supply
network
% 35 80 5
15
35
The
improvement of
this ratio
improves with
the enrichment
of the supplier
knowledge base
and the maturity
of the solution.
Produc-
tivity
Standardise
d IT
Landscape
This
indicator
shows the
ration of the
standardised
IT
Landscape
% 30 55 15
25
30
This ratio
improves with
the number of
shared best
practices and
maturity of the
solution.