Assessing Project Progress Planning using Control Diagrams and

Neural Network Prediction for Shipbuilding Projects in an

Ecuadorian Shipyard

Gerardo Mena Caceres

Project Management Office, MSc. Global Production Engineering & Management,

Global Production Engineering & Management, AV 8ANO, Guayaquil, Ecuador

Keywords: Project Management, Data Analysis, Data Prediction, Planning.

Abstract: The planning and scheduling of new shipbuilding projects, as in other engineering disciplines require a certain

degree of experience and knowledge in order to provide progress planning of feasible works to achieve the

goals of the project and the managerial expectation. As is mentioned, although having experience is necessary;

according to current technologies, the use of data analysis and the certainty that in the medium-term future

artificial intelligence will be used in decision-making, it is necessary that not only manufacturing be according

to the approaches of industry 4.0 but also, project management from its start-up phase to closure uses

mechanisms for continuous improvement in a more successful way. This case study focuses on the data

analysis of planned and executed projects to estimate acceptable percentages of periodic progress of projects

using parameters of reliability engineering and neural network model from ISPP IBM software, in such a way

that the planning can be in accordance with the shipyard behaviour.

1 INTRODUCTION

Projects of any kind comprise different stages of

development, which go through initiation and

planning, execution, monitoring and control and

closure, and also have as parameters or "natural"

constraints the scope, time, quality, and cost (Project

Management Institute, 2017). However, at the time of

preparing the project, it's planning and schedule, it is

based more on the experience of the project manager

rather than on data recorded by the companies, this

experience makes this activity inherent to whoever

owns it, causing Project Managers (PMs) to estimate

project base line empirically or simply to what a

planning program says without examining data based

on similar previous projects or business behavior

even when they already exist.

Scheduling and progress planning are essential in

order to understand the base line of the project and in

order to figure out the managers’ intentions, that in

the end combined with the balance of the project will

determine if it was successful or not.

https://orcid.org/0000-0002-9169-6765

This document focuses on showing the current

importance for project managers the data analysis for

planning and thus making estimations or predictions

based on an analysis of data behavior, for progressive

project planning in a way more accurate, using the

registered data in the case of a specific company

dedicated to the new shipbuilding projects in Ecuador.

2 PROBLEM STATEMENT

Projects are characterized because they have a time

limit, that is, they have a beginning and a specific

duration (Lledó, Rivarola, Mecaru, & Cucchi D,

2006) given this, it requires an effort that is definitely

not constant and varies according to the type of

project, its scope, and available resources, also

considering the natural constraints mentioned above.

The shipbuilding industry in Ecuador is not too much

developed in terms of technology, innovation and

manufacturing techniques, so, generally speaking

these kinds of business needs to be assessed

according to the actual situation seeing their own

Caceres, G.

Assessing Project Progress Planning using Control Diagrams and Neural Network Prediction for Shipbuilding Projects in an Ecuadorian Shipyard.

DOI: 10.5220/0009993100600068

In Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2020), pages 60-68

ISBN: 978-989-758-476-3

constraints, strengths and weaknesses in order to

establish the best solution for them.

To execute any kind of project, the initial outline

or route to follow is required, so that those involved

in its development have full knowledge of the

expected scheduled progress, this is obtained through

planning, which can be developed in some existing

software (Excel, Project, etc.), but how could we

determine if the planned and desired progress is in

accordance with the business reality? This question

leads us to ask whether the experience alone

guarantees adequate planning and scheduling of

activities, and whether it is ultimately feasible or not

that the goals set are met according to a plan.

3 GENERAL CONCEPTS

3.1 Variability

Every system has variability, it is independently of

what kind of system it represents and the variability

is determined by the standard deviation of the data in

relation to its arithmetic mean (average), which

allows establishing in addition to the variance, the

form, or dispersion of the existing data. Variability

should not be seen as a problem in systems, as it can

be good or bad depending on the group or situation

being analyzed (Hoop & Spearman, 2008).

3.2 Standard Deviation

It is defined as the square root of the variance (Hines

& Montgomery, 2004), the standard deviation is the

measure of dispersion most used to determine the

variability of a system.

3.3 Project Planning and Scheduling

Planning and scheduling are different but related to

them. We can say that scheduling is the lower level of

the planning, it focuses on the action that people need

to do in specific time, and planning involves the tasks

that the project needs to occur and how to do.

3.4 Project Progress

Although projects, like any process, require keep in

mind the integrity of the components of the system,

the development of this work will take into

consideration the planning in terms of percentage that

include all the activities entailed to complete the

shipbuilding project.

In other words, it is related to earned value index

which is commonly used to assess planning and

progress for the projects.

3.5 Statistical Control

Statistical control of processes through the use of

troubleshooting tools, allows, among other things, to

establish whether a system is under control, the level

of variability of the system, which allows the

application of continuous improvement and the

correction of possible process “failures”.

4 METHODOLOGY AND

SOLUTION DEVELOPMENT

To analyze the feasibility of data-driven planning, it is

necessary to collect existing data which is based on

reports generated by each project manager and/or those

presented by the functional units of a company. From

them, the following work methodology will be applied:

• Sort the data according to each project executed

and planned.

• Calculate the planned and executed monthly

progress of each project, as a differential.

• Calculate the standard deviation, the arithmetic

mean, variance coefficient.

• Perform statistical control of the process by

applying control diagrams

• Analyze and make estimates of minimum times

and average percentages based on data from

control theory.

• Apply neural network prediction on the sorted

data

• Analysis of the results.

It is noticeable that shipyards, like other companies,

collect certain data according to their policy, however,

the technical staff should make recommendations

about the kinds of data that are needed.

4.1 Input Data

4.1.1 Accumulated Progress Data by Project

Before making any kind of calculation, it is required

to know if any technological change has been

implemented, if the process has changed or improved,

and look for the factors that may have altered the

current state of the company which can be translated

into variability. In the event that the system has not

been implemented or adjusted to new technologies

and methodologies, it is recommended to use as much

Assessing Project Progress Planning using Control Diagrams and Neural Network Prediction for Shipbuilding Projects in an Ecuadorian

Shipyard

data as possible, and in case new technologies have

been implemented, the evaluation should be based on

this new business reality.

In the present case, since the company has not

carried out any implementation, the data collected

comes from all the projects in which the shipyard's

own workforce has been required, which uses the

same construction, control, and similar process

methodologies.

The data shown in Table 1 is an extract and it

represents the percentages of the accumulated

progress planned (PP) and executed (EP) by the

different projects, in this particular case, the data

corresponds for the monthly progress (T). However,

the data collection will depend on the policy

implemented, if it exists.

For the study of the present case, ten executed

projects during the last 4 years have been considered,

it is worth mentioning that, although they are not all

projects executed within the shipyard, these projects

have a register.

Table 1: Example of cumulative progress data planned.

T PP2,1 PP2,2 PP2,3 PP2,4 PP2,5 PP2,6

0 0% 0% 0% 0% 0% 0%

1 23% 23% 23% 23% 23% 23%

2 48% 48% 48% 48% 48% 48%

3 59% 59% 59% 59% 59% 59%

4 69% 69% 69% 69% 69% 69%

5 85% 80% 80% 80% 80% 80%

6 100% 90% 84% 84% 84% 84%

100% 93% 88% 88% 88%

100% 94% 92% 92%

100% 96% 94%

100

97%

100%

Within the data observed for this case study, the

different projects have been differentiated by using

generic acronyms which have data on project

planning, execution, and re-planning that can show

the changes that the projects have had since their

initial planning (PPi, 1), and until the last adjustment

made (PPi, j ), this means that in many cases the

execution of the project (EPi) is equal to the latest

version of the planning readjustment. For instance,

the project number one would be P1, the first

planning for this project will be represent by PP1,1 at

the end if the project number one has four changes in

https://voices.berkeley.edu/business/deconstructing-

project-management-process

the planning the identification will be PP1,4. This

mechanism is used to identify all the projects. On the

other hand, the executed projects are identified just

for one number, for the same given example the

execution for the project number one will be EP1.

Likewise, it includes planned projects that are in

execution (PPAi, j) with the same re-planning

characteristics (if they exist), and the current

execution that they carry (EPAi). This variable

differentiation allows to establish initially:

• There is new planning of the projects since there

are deviations in the baseline.

• The baseline has not been kept constant for any of

the existing projects.

• None of the planning carried out by the project

managers and their teams has been reached or,

saying in other way, they have not been carried

out correctly.

• The production behavior of the shipyard has not

been included during the planning phase.

This can be evidenced if the different plans are

drawn up, where the initially planned progress curve

can be verified and how the actual execution of the

project was, which gives us the basis for raising the

need of this study.

It is worth mentioning that, as in any project and

the stages of execution that each maintains, the

theoretically progressive proposed progress obeys a

Gaussian way as shown in Figure 1, therefore, it would

be expected that the plans have a similar relationship

where the effort is gradual both at the beginning and at

the end so that the project does not have excessive or

constant costs throughout its life-cycle.

Figure 1: Overlap between Project Management’s

Processes

The data collection is carried out by each project

manager and wrote down in excel document provided

by the project office. Nevertheless, each project

manager estimates the progress according to their

perspective. Shipbuilding is structured by different

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

components or disciplines: welding, auxiliary

systems, navigation, electrical and communication

system, carpentry, propulsion and steering system.

The data shown in this document is the summary of

all those shipbuilding components and the weight or

significance of each of them are not standardized.

Assign or determine a weight of each component is a

pending task not only for the Ecuadorian shipyards

(Arena, Birkler, Schank, & Riposo, 2005), but is a

task that production department or strategic one

should conduct.

4.1.2 Project Monthly Progress

To calculate the planned and executed monthly

progress of the projects, it is carried out simply by

subtracting terms, that is, these data are the

differential of those established in Table 1.

∆PPn = ∆PPi,j = PPi,j+1 – PPi,j (1)

Table 2: Example of progress data planned and executed by

projects.

PP2,1 PP2,2 PP2,3 PP2,4 PP2,5 PP2,6

23,0%

23,0% 23,0%

23,0%

25,0%

25,0% 25,0%

25,0%

10,5%

10,5% 10,5%

10,5%

10,5% 10,5%

10,5%

16,0%

10,5%

10,5% 10,5%

10,5%

15,0%

10,5%

4,00% 4,00%

4,00%

10,0%

9,50% 4,00%

4,00%

7,00% 6,50%

4,00%

6,00%

4,00%

2,83%

4,50%

2,83%

2,84%

Remember that there must be N-1 elements as

data.

4.2 Statistical Control of the Process

Table 3: Example of dispersion measurement calculation

results.

Mean Var.

Std.

Dev.

Coef.

Var.

Std.Err.

PP1 0,1075 0,00202 0,0449 41,823 0,0159

EP1 0,1000 0,00130 0,0361 36,156 0,0114

PP2,1 0,1666 0,00377 0,0614 36,872 0,0251

PP2,2 0,1428 0,00444 0,0666 46,645 0,0252

PP2,3 0,1250 0,00557 0,0746 59,713 0,0264

PP2,4 0,1111 0,00604 0,0778 69,995 0,0259

PP2,5 0,1000 0,00632 0,0795 79,512 0,0251

The calculation of the dispersion´s measures

mentioned above, although they can be done

“manually”, for this work the same computer tool that

will be used to estimate the results is used with the

following data as a representation:

The presentation of the dispersion measurement of

the other variables are presented in the annex and the

control diagrams have the same structure as in the

Figure 2.

From the control diagrams, it can be established

whether the process was under control or not, and

because the projects presented have had a certain

degree of re-planning, it will be possible to show the

changes that the standard deviation, mean and the

coefficient of variability has one with respect to

another.

Figure 2: Control Diagram - variable EP3.

As can be seen in figure 2 for the case of the

variable EP3 that represents the execution of project

# 3, it can be said that the process was under control,

however, as part of the project management tasks, it

should be examined why three points were considered

"out of control", the fact that the points are outside the

limits of the process, although they give the alert that

something happened, will not always mean that

something was "wrong", and as was mentioned it is a

task of management of projects that must be included

in the lessons learned with their respective analyzes

that will allow continuous improvement of the

process.

As can be seen in Table 3, different coefficients of

variability are given for each of the parameters and it

can be seen that the variable EP5 has the highest value,

even when the variability of initial planning is less,

which translates into that not necessarily the entire

construction processes ended with a low variability due

to the lengthening of the execution period, but the

Assessing Project Progress Planning using Control Diagrams and Neural Network Prediction for Shipbuilding Projects in an Ecuadorian

Shipyard

average percentage of execution was less than

planning, this, in turn, varies the control limits.

4.3 Analysis of Variables and

Estimation of Percentages of

Progress

Among the analysis, the first point is the presentation

of the frequencies, which will allow establishing the

progress values within a "normal" working range of

the company, for example, in the shipyard, it can be

seen that the majority of the time used an effort of up

to 9.6% more frequently, so the values greater than

10% per month are not realistic.

Figure 3: Actual direct frequency diagram.

The first action to be taken is to define those

variables that will be included for the calculation of

the predictions, for a better understanding by way of

example, the following idea can be proposed:

• If the project is intended to be executed in a

planned time T, the projects to be chosen must

have at least T + 1 amount of data.

• The data of projects that are in execution, they can

be considered for the estimation as well as their

planning as long as the previous condition is met.

• The data pertaining to the actual execution

completed for each project will be selected, that

is, EPi.

Choosing the data is a vitally important task since

the regressions to calculate the number of X

approximations require the same amount of data as

the base, that is, it uses data one by one.

On the other hand, there is information regarding

project management concepts, such as the use of EV

which is closely linked to the costs and resources

used, although this is the standard methodology for

project evaluation, it is worth mentioning that the

percentages of Execution of shipbuilding projects

presented here are carried out with the joint

evaluation of the project management component and

the execution component (manufacturing).

The proposed methodology will be applied to two

similar shipbuilding projects that have different

schedules, different project managers, and different

execution times, showing the results of the proposed

data analysis numerically and graphically.

4.3.1 Estimated Monthly Percentages of

Progress

If we apply the neural network method or any other

existing tool, such as the tree regression method or

any other regression model, it will show us different

results. It is clear that the examination of these results

is crucial to know if they are adequate or not.

When neural networks are applied, the output data

is based on "weight" values before continuing to its

next phase of internal analysis, so the larger the data

for the dependent variables, the approximation and

mean error will decrease, however, as mentioned,

they act on dependent and independent variables or

factors, in this particular case and since the

percentages recorded are total values and there is no

data on the partial values that compose it, it will be

assumed that the dependent variables are the

schedules of the current projects to evaluate, and the

dependents will be the data recorded as actual or

executed progress of the projects.

The neural network based on Multi-layer

perceptron (MLP) was used to develop this case,

having a maximum number of hidden layer 50 and

“batch” training. Batch training is used in case where

the dataset is small, which is this case.

Applying neural networks in both projects and

superimposed on the total progress curves we will

have the following results:

Figure 4: Accumulated Progress Curve PPA7.2.

The orange line represents the MLP result, and the

blue one the baseline planned by the project manager.

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

Figure 5: Accumulated Progress Curve PPA8.1.

On the other hand, although the accumulated

progress curves are important, the monthly progress

effort curves must be analyzed, this will give

information to validate or not the presented approach,

to easily observe the result, the aforementioned

control charts are shown where the curves overlap

and the confidence intervals can be seen.

Confidence intervals are determined by

calculating the mean and standard deviation. Figure 6

and 7 show the confidence interval, the mean, and the

respective curves for both cases.

Figure 6: Monthly Progress Curve PPA7.2.

Figure 7: Monthly Progress Curve PPA8.1.

Maintaining the reference of the proposed colors,

in both figures, it can be seen how the estimated

values that apply the neural network present a low

effort at the beginning and later reach their peak to

decrease, and in the same way, in the first project

despite Being within the control limits, the effort is

constant for 80% of the time and, according to the

forecast, it cannot be completed on time.

On the other hand, in the second graph, we see that

the trends are similar, and the project can even be

completed before the planned time and be within the

confidence limits.

4 CONCLUSIONS

While some data exists in the shipyard, they are not

used by project managers and the project office for

analysis, it can be verified because the projects show

multiple rescheduling.

Statistical control supports the decision-making

because it can show an “apparent effort” and if

production personnel is applying the needed effort to

achieve the objective.

The next stage is to examine the shipbuilding

activities and processes such as: welding, piping,

electrical, equipment, carpentry and painting process,

which make up the total percentages used in this

document in order to determine the critical process

and the significances for the projects.

Since the progress percentages are directly related

to the work effort; translated into the use of human

resources, this form of planning will allow a better

distribution of the shipyard's resources.

Neural network prediction can be applied in order

to support the initial baseline estimation.

Using control parameters and reliability

engineering concepts is useful in determining the best

lead percentage distribution options.

Analyzing business behavior patterns are

necessary to improve decision-making in the case of

the shipyard in order to carry out projects with short

completion times (6-8 months) of complexities

similar to those already executed, the shipyard's

production department should increase its capacity

and capability, for this, it is necessary to analyze the

productivity parameters of the shipyard and its KPIs.

If the shipyard applies data analysis, problems like

delays or incorrect planning could be corrected.

From personal experience and knowledge, only

one shipyard in Ecuador uses a project management

methodology and records data, so this document can

help shipyards or dry docks in Ecuador to realize the

importance of data collection and analysis.

Assessing Project Progress Planning using Control Diagrams and Neural Network Prediction for Shipbuilding Projects in an Ecuadorian

Shipyard

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Evans, J. (1959). Basic Design Concept. Naval Engineers

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Hines, W. W., & Montgomery, D. C. (2004). Probability

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Hoop, W. J., & Spearman, M. L. (2008). Factory Physics.

Long Grove, Illinois: Waveland Press, Inc.

Kim, B. C. (December de 2007). Forecasting Project

Progress and Early Warnig of Project Overruns with

Probabilistic Methods. PhD dissertation. Texas: A&M

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King, S. C. (2016). Unerstanding the Industrial Design. En

Chapter 1 (págs. 1-35). O'REILLY.

Lledó, P., Rivarola, G., Mecaru, R., & Cucchi D, E. J.

(2006). En Administración LEAN de proyectos

Eficiencia en la gestión de múltiples proyectos (págs. 1-

7; 10; 15-31; 148-149). México: Pearson Pentice Hall.

Project Management Institute. (2017). A Guide to the

Project Management Body of Knowledge (PMBOK®

Guide). Newtown Square, Pennsylvania: PMI, Inc.

Shrouf et al. (2014). Smart Factories in Industry 4.0: A

review of the Concept and of Energy Management

Approached in Production Based on the Internet of

Things Paradigm.

Sukholov, Y. A. (2018). Industry 4.0: Industrial Revolution

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peculiarities of Industry 4.0 as a Sphere of Industry

(págs. 3-11). Moscow: Springer International

Publishing AG.

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

APPENDIX

Table 4: Dispersion Measurement Calculation Results.

Proj. Mean Variance Std.Dev. Coef.Var. Std.Err.

PP1

0,107500 0,002021 0,044960 41,8235 0,015896

EP1

0,100000 0,001307 0,036157 36,1566 0,011434

PP2,1

0,166667 0,003777 0,061455 36,8728 0,025089

PP2,2

0,142857 0,004440 0,066637 46,6458 0,025186

PP2,3

0,125000 0,005571 0,074642 59,7136 0,026390

PP2,4

0,111111 0,006049 0,077773 69,9955 0,025924

PP2,5

0,100000 0,006322 0,079512 79,5124 0,025144

PP2,6

0,090909 0,006477 0,080482 88,5307 0,024266

EP2

0,090909 0,006477 0,080482 88,5307 0,024266

PP3,1

0,038708 0,001054 0,032470 83,8853 0,006494

PP3,2

0,032258 0,000824 0,028705 88,9842 0,005155

PP3,3

0,031216 0,000519 0,022789 73,0064 0,004029

PP3,4

0,027778 0,000625 0,024992 89,9705 0,004165

EP3

0,027778 0,000533 0,023077 83,0755 0,003846

PP4,1

0,027778 0,000172 0,013117 47,2223 0,002186

PP4,2

0,021277 0,000067 0,008174 38,4175 0,001192

PP4,3

0,021277 0,000049 0,006987 32,8372 0,001019

EP4

0,021277 0,000049 0,006987 32,8372 0,001019

PP5,1

0,100000 0,003831 0,061892 61,8921 0,019572

PP5,2

0,100000 0,005612 0,074911 74,9109 0,023689

PP5,3

0,083333 0,006026 0,077626 93,1514 0,022409

PP5,4

0,071429 0,006008 0,077510 108,5141 0,020715

PP5,5

0,066667 0,005919 0,076933 115,3993 0,019864

PP5,6

0,062500 0,005802 0,076170 121,8714 0,019042

EP5

0,062500 0,005802 0,076170 121,8714 0,019042

PPA6,1

0,055556 0,001608 0,040094 72,1691 0,009450

PPA6,2

0,047619 0,001476 0,038418 80,6782 0,008384

EPA6

0,030543 0,000554 0,023542 77,0780 0,006292

PPA7,1

0,083333 0,000110 0,010497 12,5969 0,003030

PPA7,2

0,083333 0,001679 0,040973 49,1676 0,011828

PPA7,3

0,071429 0,000798 0,028245 39,5435 0,007549

EPA7

0,090000 0,000720 0,026833 29,8142 0,010954

PPA8,1

0,062500 0,000873 0,029552 47,2835 0,007388

PPA8,2

0,062500 0,000900 0,030000 48,0000 0,007500

EPA8

0,050000 0,000440 0,020976 41,9524 0,008563

PPA9,1

0,030303 0,000387 0,019670 64,9107 0,003424

PPA10,1

0,062500 0,001033 0,032146 51,4328 0,008036

Assessing Project Progress Planning using Control Diagrams and Neural Network Prediction for Shipbuilding Projects in an Ecuadorian

Shipyard

Table 5: Cumulative Progress Data Planned and Executed by Projects (Example of 02 Projects).

N Project N° 3 Project N° 4

T PP3,1 PP3,2 PP3,3 PP3,4 EP3 PP4,1 PP4,2 PP4,3 EP4

0,00% 0,00% 0,00% 0,00% 0,00% 0,00% 0,00% 0,00% 0,00%

0,20% 0,20% 0,20% 0,21% 0,21% 1,00% 1,00% 1,00% 1,00%

0,41% 0,41% 0,41% 0,42% 0,42% 2,00% 2,00% 2,00% 2,00%

0,63% 0,63% 0,63% 0,63% 0,63% 3,00% 3,00% 3,00% 3,00%

0,83% 0,83% 0,83% 0,84% 0,84% 4,00% 4,00% 4,00% 4,00%

1,04% 1,04% 1,04% 1,04% 1,04% 5,50% 5,00% 5,00% 5,00%

1,81% 1,81% 1,81% 1,33% 1,33% 7,00% 6,00% 6,00% 6,00%

2,36% 2,36% 2,36% 1,80% 1,80% 9,00% 7,00% 7,00% 7,00%

3,71% 3,71% 3,71% 2,27% 2,27% 11,00% 8,50% 8,50% 8,50%

5,10% 5,10% 5,10% 3,35% 3,35% 14,00% 10,00% 10,00% 10,00%

6,38% 6,38% 6,38% 4,26% 4,26% 17,00% 12,00% 12,00% 12,00%

7,55% 7,55% 7,55% 5,16% 5,16% 23,50% 14,00% 14,00% 14,00%

10,37% 10,37% 10,37% 8,76% 8,76% 27,00% 16,00% 16,00% 16,00%

13,26% 13,26% 13,16% 11,75% 11,75% 30,00% 18,00% 18,00% 18,00%

16,69% 16,73% 16,55% 15,52% 15,52% 34,00% 20,00% 20,00% 20,00%

21,86% 19,95% 19,73% 18,07% 18,07% 38,00% 23,00% 23,00% 23,00%

29,08% 23,20% 22,75% 21,04% 21,04% 42,00% 26,00% 26,00% 26,00%

36,48% 27,17% 26,49% 24,80% 24,80% 46,00% 29,00% 29,00% 29,00%

43,89% 31,59% 30,58% 28,77% 28,77% 51,00% 32,00% 32,00% 32,00%

50,65% 36,64% 34,84% 33,77% 33,77% 55,00% 35,00% 35,00% 35,00%

59,16% 44,50% 40,69% 39,77% 39,77% 59,00% 38,00% 38,00% 38,00%

67,16% 52,29% 48,19% 43,75% 43,75% 63,00% 41,00% 41,00% 41,00%

74,41% 59,87% 52,96% 45,74% 45,74% 67,00% 44,00% 44,00% 44,00%

82,32% 67,28% 58,51% 46,84% 46,84% 71,00% 47,00% 47,00% 47,00%

88,68% 75,54% 64,32% 48,39% 48,39% 74,00% 50,00% 50,00% 50,00%

96,77% 83,38% 71,19% 50,76% 50,76% 77,00% 52,00% 52,00% 52,00%

93,77% 90,30% 78,42% 51,00% 51,00% 80,00% 54,00% 54,00% 54,00%

98,64% 95,86% 85,52% 52,06% 52,91% 83,00% 56,00% 56,00% 56,00%

99,13% 98,41% 88,46% 57,24% 56,71% 86,00% 60,00% 60,00% 60,00%

99,61% 99,81% 91,65% 60,95% 60,60% 88,00% 62,00% 62,00% 62,00%

99,80% 99,99% 94,68% 68,39% 64,39% 90,00% 63,00% 63,00% 63,00%

100,00% 100,00% 97,72% 76,75% 69,32% 92,00% 65,00% 65,00% 65,00%

99,89% 86,26% 74,66% 94,00% 67,00% 67,00% 67,00%

10,00% 91,80% 77,63% 96,00% 69,00% 69,00% 69,00%

96,60% 82,50% 98,00% 71,00% 71,00% 71,00%

99,80% 92,50% 99,00% 73,00% 73,20% 73,20%

100,00% 100,00% 100,00% 75,00% 75,40% 75,40%

77,00% 77,60% 77,60%

79,00% 79,90% 79,90%

81,00% 82,10% 82,10%

83,00% 84,30% 84,30%

85,00% 86,60% 86,60%

87,00% 88,80% 88,80%

89,00% 91,00% 91,00%

91,00% 93,30% 93,30%

93,00% 95,50% 95,50%

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics