Production companies face the challenge of choosing a suitable process optimization method

from a variety of methods, even though their effect on operational processes is uncertain.

This study shows, using a statistical hypothesis test, the impact of the methods Kanban

and Standard Worksheet on an autonomous team in comparison to a team that applies

these methods. For this purpose, 44 companies – of different size and operating in various

industries – across Germany completed a business game and generated data regarding the

KPIs adherence to delivery date, number of reworks and inventory costs. Based on these

data, the team’s performance could be ascertained and compared with each other.

Digitalization and sustainability are important topics for manufacturing industries as they

are affecting all parts of the production chain. Various initiatives and approaches are set

up to help companies adopt the principles of the fourth industrial revolution with respect

sustainability. Within these actions the use of modern maintenance approaches such as

Maintenance 4.0 is highlighted as one of the prevailing smart & sustainable manufacturing

topics. The goal of this paper is to describe the latest trends within the area of maintenance

management from the perspective of the challenges of the fourth industrial revolution and

the economic, environmental and social challenges of sustainable development. In this work,

intelligent and sustainable maintenance was considered in three perspectives. The first perspective

is the historical perspective, in relation to which evolution has been presented in the

approach to maintenance in accordance with the development of production engineering. The

next perspective is the development perspective, which presents historical perspectives on

maintenance data and data-driven maintenance technology. The third perspective, presents

maintenance in the context of the dimensions of sustainable development and potential opportunities

for including data-driven maintenance technology in the implementation of the

economic, environmental and social challenges of sustainable production.

Today, the changes in market requirements and the technological advancements are influencing

the product development process. Customers demand a product of high quality and fast

delivery at a low price, while simultaneously expecting that the product meet their individual

needs and requirements. For companies characterized by a highly customized production, it

is essential to reduce the trial-and-errors cycles to design new products and process. In such

situation most of the company’s knowledge relies on the lessons learnt by operators in years

of work experience, and their ability to reuse this knowledge to face new problems. In order

to develop unique product and complex processes in short time, it is mandatory to reuse

the acquired information in the most efficient way. Several commercial software applications

are already available for product lifecycle management (PLM) and manufacturing execution

system (MES). However, these two applications are scarcely integrated, thus preventing an

efficient and pervasive collection of data and the consequent creation of useful information.

The aim of this paper is to develop a framework able to structure and relate information

from design and execution of processes, especially the ones related to anomalies and critical

situations occurring at the shop floor, in order to reduce the time for finalizing a new product.

The framework has been developed by exploiting open source systems, such as ARAS

PLM and PostgreSQL. A case study has been developed for a car prototyping company to

illustrate the potentiality of the proposed solution.

The current industrial constraints on production systems, especially availability problems

are complicating maintenance managers’ mission and making longer and further performance

improvement process. Dealing with these problems in a wiser managerial vision respecting

sustainability dimensions would be more efficient to optimize all resources. In this paper, and

after addressing the lean/sustainability challenge in a the literature to define main research

orientations and critical points in manufacturing and then maintenance specific context, two

case studies have been conducted in two production systems in Morocco and Canada, within

the objective to set a clearer scene of the lean philosophy implementation in maintenance

and within the sustainability scope from an empirical perspective. To activate the social dimension

being often non-integrated in the lean/sustainability initiatives, the article authors

reveal an original research direction assigning maintenance logistics as the leading part of our

approach to cover all sustainability dimensions. Furthermore, its management is discussed

for the first time in a sustainable framework, where the authors propose a new model considering

the lean/sustainable perspective and inspired by the rich Human-Machine interaction

memory to solve daily maintenance problems exploiting the operators’ experience feedback.

A project scheduling problem investigates a set of activities that have to be scheduled

due to precedence priority and resource constraints in order to optimize project-related

objective functions. This paper focuses on the multi-mode project scheduling problem concerning

resource constraints (MRCPSP). Resource allocation and leveling, renewable and

non-renewable resources, and time-cost trade-off are some essential characteristics which are

considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid

algorithm is proposed based on non-dominated sorting ant colony optimization and genetic

algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to

improve the efficiency of the ant colony algorithm. The test problems are generated based on

the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed

algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result

verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II

algorithm.

This study builds on an existing structural model developed to examine the influence of

leadership and organizational culture on innovation and satisfaction of engineers in Australian

public sectors (APS). The objective of this study is to increase the understanding of

innovation process with a focus on causal relationships among critical factors. To achieve this

objective, the study develops an assessment approach to help predict creativity and work

meaningfulness of engineers in the APS. Three quantitative analysis methods were sequentially

conducted in this study including correlation analysis, path analysis, and Bayesian

networks. A correlation analysis was conducted to pinpoint the strong association between

key factors studied. Subsequently, path analysis was employed to identify critical pathways

which were accordingly used as a structure to develop Bayesian networks. The findings of

the study revealed practical strategies for promoting (1) transformational leadership and (2)

innovative culture in public sector organizations since these two factors were found to be key

drivers for individual creativity and work meaningfulness of their engineers. This integrated

approach may be used as a decision support tool for managing the innovation process for

engineers in the public sectors.

Lean management has become a much-researched topic in operations management. Beyond

its technical aspects, nowadays the analysis of soft factors (corporate culture, organization,

management, human resource management, knowledge transfer practices) have come to the

fore. However, there are few sources available to the lean organization to find out what organizational

changes are taking place alongside the lean application, and what organizational

structures are being developed. In our study first we deal with the literature-based concepts

of lean organizational structure and with the international examples, and then through five

Hungarian corporate solutions and with help of the literature of organizational theories we

synthesize the lean organizational forms.

The main aim of this research is to compare the results of the study of demand’s plan and

standardized time based on three heuristic scheduling methods such as Campbell Dudek

Smith (CDS), Palmer, and Dannenbring. This paper minimizes the makespan under certain

and uncertain demand for domestic boxes at the leading glass company industry in Indonesia.

The investigation is run in a department called Preparation Box (later simply called PRP)

which experiences tardiness while meeting the requirement of domestic demand. The effect

of tardiness leads to unfulfilled domestic demand and hampers the production department

delivers goods to the customer on time. PRP needs to consider demand planning for the

next period under the certain and uncertain demand plot using the forecasting and Monte

Carlo simulation technique. This research also utilizes a work sampling method to calculate

the standardized time, which is calculated by considering the performance rating and

allowance factor. This paper contributes to showing a comparison between three heuristic

scheduling methods performances regarding a real-life problem. This paper concludes that

the Dannenbring method is suitable for large domestic boxes under certain demand while

Palmer and Dannenbring methods are suitable for large domestic boxes under uncertain

demand. The CDS method is suitable to prepare small domestic boxes for both certain and

uncertain demand.

In the article, the significance and essence of management of intelligent manufacturing in

the era of the fourth industrial revolution has been presented. The current revolution has

a large impact on the operation of the company. Through the changes resulting from the

application of modern technologies, production processes are also undergoing revolutions,

which results in changes in such indicators of business development. Management of intelligent

manufacturing is also a challenge for socially responsible activities; due to solutions of

Industry 4.0, enterprises directly and indirectly influence environmental protection, which

results in benefits for all mankind. In the article, the analysis and assessment of management

of intelligent manufacturing, using modern technologies during the production process,

has been carried out, with particular emphasis on the components of management such as:

monitoring, control, autonomy, optimization. Moreover, the impact of the above components

of management on changes in the following indicators (KPI – Key Performance Indictors)

has been evaluated, i.e. (1) quality, (2) rapidity of the production process implementation,

(3) performance and (4) productivity, (5) decrease in waste generated during the technological

process and (6) amount of consumed electricity. For the purposes of conducting the

research, a case study has been used, developed due to the information shared by the company

manufacturing machinery and equipment for the polymer processing industry, in which

intelligent solutions of Industry 4.0 are being applied. The presented article is a significant

contribution to the current development of knowledge in the field of implementing Industry

4.0 solutions for polymer processing. The article is a combination of theoretical and practical

knowledge in the field of management and practical industrial applications. It refers to the

most current research trends.

Scheduling of multiobjective problems has gained the interest of the researchers. Past many

decades, various classical techniques have been developed to address the multiobjective problems,

but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search

method and many more are being successfully used. Researchers have reported that hybrid

of these algorithms has increased the efficiency and effectiveness of the solution. Genetic

algorithms in conjunction with Pareto optimization are used to find the best solution for

bi-criteria objectives. Numbers of applications involve many objective functions, and application

of the Pareto front method may have a large number of potential solutions. Selecting

a feasible solution from such a large set is difficult to arrive the right solution for the decision

maker. In this paper Pareto front ranking method is proposed to select the best parents for

producing offspring’s necessary to generate the new populations sets in genetic algorithms.

The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling

process is solved using genetic algorithm based Pareto front ranking method. The algorithm

is coded in Matlab, and simulations were carried out for the crossover probability of 0.6,

0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent

for a crossover probability of 0.6.