This paper is a case study conducted to present an approach to the process of designing

new products using virtual prototyping. During the first stage of research a digital geometric

model of the vehicle was created. Secondly it underwent a series of tests utilising the

multibody system method in order to determine the forces and displacements in selected

construction nodes of the vehicle during its movement on an uneven surface. In consequence

the most dangerous case of loads was identified. The obtained results were used to conduct

detailed strength testing of the bicycle frame and changes its geometry. For the purposes

of this case study two FEA software environments (Inventor and SolidWorks) were used. It

has been confirmed that using method allows to implement the process of creating a new

product more effectively as well as to assess the influence of the conditions of its usage more

efficiently. It was stated that using of different software environments increases the complexity

of the technical process of production preparation but at the same time increases the

certainty of prototype testing. The presented example of simulation calculations made for

the bicycle can be considered as a useful method for calculating other prototypes with high

complexity of construction due to its systematized character of chosen conditions and testing

procedure. It allows to verify the correctness of construction, functionality and perform

many analyses, which can contribute to the elimination of possible errors as early as at the

construction stage.

The application of artificial intelligence (AI) in modeling of various machining processes has

been the topic of immense interest among the researchers since several years. In this direction,

the principle of fuzzy logic, a paradigm of AI technique, is effectively being utilized

to predict various performance measures (responses) and control the parametric settings of

those machining processes. This paper presents the application of fuzzy logic to model two

non-traditional machining (NTM) processes, i.e. electrical discharge machining (EDM) and

electrochemical machining (ECM) processes, while identifying the relationships present between

the process parameters and the measured responses. Moreover, the interaction plots

which are developed based on the past experimental observations depict the effects of changing

values of different process parameters on the measured responses. The predicted response

values derived from the developed models are observed to be in close agreement with those

as investigated during the past experimental runs. The interaction plots also play significant

roles in identifying the optimal parametric combinations so as to achieve the desired

responses for the considered NTM processes.

The objective of this research is to investigate the perception of owner – managers and

their employees regarding entrepreneurial leadership. To develop the research, two questions

are raised related to the similarities or differences of the perceptions of both groups

with what is established in the literature and between the self – evaluation of the owner –

managers and their employees on whether the former perform as an entrepreneurial leader.

As a research method, both groups are asked to perform, first individual evaluations and

then to match certain behaviours and the levels at which they should appear at certain levels

of entrepreneurial leadership capacity. The data gathered during the investigation were

processed using the Categorical Principal Components Analysis and revealed the similarities

and differences between the perceptions of the owner-managers and their employees on

entrepreneurial leadership. In spite of not finding significant differences between what is established

in the literature and among the perceptions of the groups under study, interesting

nuances stand out that, if not identified and understood, could have a negative effect on

the performance of SMEs. The results of the research demonstrated the importance of the

approach of behaviour and perception in the study of entrepreneurial leadership.

This paper explores selected heuristics methods, namely CDS, Palmer’s slope index, Gupta’s

algorithm, and concurrent heuristic algorithm for minimizing the makespan in permutation

flow shop scheduling problem. Its main scope is to explore how different instances sizes

impact on performance variability. The computational experiment includes 12 of available

benchmark data sets of 10 problems proposed by Taillard. The results are computed and

presented in the form of relative percentage deviation, while outputs of the NEH algorithm

were used as reference solutions for comparison purposes. Finally, pertinent findings are

commented.

The objective of the milk-run design problem considered in this paper is to minimize transportation

and inventory costs by manipulating fleet size and the capacity of vehicles and

storage areas. Just as in the case of an inventory routing problem, the goal is to find a periodic

distribution policy with a plan on whom to serve, and how much to deliver by what

fleet of tugger trains travelling regularly on which routes. This problem boils down to determining

the trade-off between fleet size and storage capacity, i.e. the size of replenishment

batches that can minimize fleet size and storage capacity. A solution obtained in the declarative

model of the milk-run system under discussion allows to determine the routes for each

tugger train and the associated delivery times. In this context, the main contribution of

the present study is the identification of the relationship between takt time and the size

of replenishment batches, which allows to determine the delivery time windows for milkrun

delivery and, ultimately, the positioning of trade-off points. The results show that this

relationship is non-linear.

Redundancy based methods are proactive scheduling methods for solving the Project

Scheduling Problem (PSP) with non-deterministic activities duration. The fundamental

strategy of these methods is to estimate the activities duration by adding extra time to the

original duration. The extra time allows to consider the risks that may affect the activities

durations and to reduce the number of adjustments to the baseline generated for the project.

In this article, four methods based on redundancies were proposed and compared from two

robustness indicators. These indicators were calculated after running a simulation process.

On the other hand, linear programming was applied as the solution technique to generate

the baselines of 480 projects analyzed. Finally, the results obtained allowed to identify the

most adequate method to solve the PSP with probabilistic activity duration and generate

robust baselines.

The objectives of this study were to develop a framework of the collaboration network, operational

performance, and reverse logistics determinants on the performance outcomes of the

auto parts industry, and to study the direct, indirect, and overall effects of the factors that

influence the performance outcomes of the auto parts industry. This quantitative research

utilized a questionnaire as the tool for data collection, which was completed by the managers

in the auto parts industry from 320 companies. According to the analysis with the Structural

Equation Modeling (SEM), it was found that the collaboration networks, operational

performance, and reverse logistics positively affect the performance outcomes; whereas, the

collaboration networks mainly affect the development of organizations by causing performance

outcomes to continue growing unceasingly, including the enhancement of sustainable

competitive capacity and the operational results of the auto parts industry.

With the increasing demand of customisation and high-quality products, it is necessary for

the industries to digitize the processes. Introduction of computers and Internet of things

(IoT) devices, the processes are getting evolved and real time monitoring is got easier.

With better monitoring of the processes, accurate results are being produced and accurate

losses are being identified which in turn helps increasing the productivity. This introduction

of computers and interaction as machines and computers is the latest industrial revolution

known as Industry 4.0, where the organisation has the total control over the entire value chain

of the life cycle of products. But it still remains a mere idea but an achievable one where IoT,

big data, smart manufacturing and cloud-based manufacturing plays an important role. The

difference between 3rd industrial revolution and 4th industrial revolution is that, Industry

4.0 also integrates human in the manufacturing process. The paper discusses about the

different ways to implement the concept and the tools to be used to do the same.

Rescheduling is a frequently used reactive strategy in order to limit the effects of disruptions

on throughput times in multi-stage production processes. However, organizational deficits

often cause delays in the information on disruptions, so rescheduling cannot limit disruption

effects on throughput times optimally. Our approach strives for an investigation of

possible performance improvements in multi-stage production processes enabled by realtime

rescheduling in the event of disruptions. We developed a methodology whereby we

could measure these possible performance improvements. For this purpose, we created and

implemented a simulation model of a multi-stage production process. We defined system

parameters and varied factors according to our experiment design, such as information delay,

lot sizes and disruption durations. The simulation results were plotted and evaluated

using DoE methodology. Dependent on the factor settings, we were able to prove large improvements

by real-time rescheduling regarding the absorption of disruption effects in our

experiments.

The article presents tools, methods and systems used in mechanical engineering that in

combination with information technologies create the grounds of Industry 4.0. The authors

emphasize that mechanical engineering has always been the foundation of industrial activity,

while information technology, the essential part of Industry 4.0, is its main source of innovation.

The article discusses issues concerning product design, machining tools, machine tools

and measurement systems.

Industrial engineers gather knowledge during their bachelor studies through lectures and

practical classes. The goal of practical class might be an extension of knowledge and/or a

consolidation and application of already gathered knowledge. It is observed that there exists

a gap between theory learnt during lectures and practical classes. If practical classes require

holistic approach and solving complex tasks (problems), students strive with understanding

relations and connections between parts of knowledge. The aim of this article is to show an

example of a simple practical assignment that can serve as a bridge between lectures and

practical classes through discussion of interactions and relations between parts of theoretical

knowledge. It is an example of in-class simulating of a line and cellular layout considering

discussion of elements impacting and impacted by the type of layout (e.g. learning curve,

changeovers, etc.). In-class verification of the presented approach confirmed its usability for

teaching industrial engineers and bridging the gap between theory delivered through lectures

and more advanced practical classes.

The focus of this paper is to propose a method for prioritizing knowledge and technology

factor in companies’ business strategy. The data has been gathered and analyzed from

Malaysian-owned company of medium size type industry, employing around 250 employees

and listed in the Malaysian Bourse Stock of Exchange, since 2000. Sense and respond model

is used to determine competitive priorities of the firms. Then knowledge and technology

part of sense and respond questionnaire is used to calculate the variability coefficient i.e. the

uncertainty caused by technology and knowledge factor. The results show that the company

is not leading in term of technology (spear head technology share is around 33%). Therefore,

the enhancement of technology and knowledge to SCA values is not significantly seen in

this study. The usage of the core technologies is around 41% and it might seem relatively

enough. In terms of basic technology, while its share is the lowest (around 25%), it has the

highest source of uncertainties among technology types. In this case, the proposed model

helped to have a clear and precise improvement plan towards prioritizing technology and

knowledge focus.