Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, management of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simulation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management.
The main purpose of Management and Production Engineering Review is to publish the results of cutting-edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed.
We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials.
Letters to the Editor-in-Chief are highly encouraged.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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