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.
Labor absenteeism is a factor that affects the good performance of organizations in any
part of the world, from the instability that is generated in the functioning of the system.
This is evident in the effects on quality, productivity, reaction time, among other aspects.
The direct causes by which it occurs are generally known and with greater reinforcement
the diseases are located, without distinguishing possible classifications. However, behind
these or other causes can be found other possible factors of incidence, such as age or sex.
This research seeks to explore, through the application of neural networks, the possible
relationship between different variables and their incidence in the levels of absenteeism. To
this end, a neural networks model is constructed from the use of a population of more than
12,000 employees, representative of various classification categories. The study allowed the
characterization of the influence of the different variables studied, supported in addition to
the performance of an ANOVA analysis that allowed to corroborate and clarify the results
of the neural network analysis.
Up to date, workload and worker performance in Small Medium-sized Enterprise (SMEs)
was assessed manually. KESAN (Kansei Engineering-based Sensor for Agroindustry) was
developed as a tool to assess worker workload and performance. The latest prototype of
KESAN was established. As the final step prior to the full-scale mass production, an industrial
design was required and must be designed based on the validation to user needs. This
research proposed an industrial design for mass production of KESAN using Kano model
and Quality Function Deployment (QFD). The user needs was extracted from attributive
analysis of Kano model. The matrix of House of Quality (HOQ) was utilized to connect
the user needs and technical requirement. The research result validated Thirteen (13) user
need attributes. The most important attribute was desktop application as an integrated
decision support system. Fourteen (14) technical requirement attributes were identified to
fulfil the user needs. Finally, a prototype was developed based on product final specification
and prioritized technical requirements.
The operation of thermal devices and installations, in particular heat exchangers, is associated
with the formation of various deposits of sediments, forming the boiler scale. The
amount of precipitate depends on the quality of the flowing liquids treatment, as well as
the intensity of the use of devices. There are both mechanical and chemical treatment methods
to remove these deposits. The chemical methods of boiler scale treatment include the
cleaning method consisting in dissolving boiler scale inside heat devices. Worked out descaling
concentrate contains phosphoric acid (V) and the components that inhibit corrosion,
anti-foam substances, as well as anti-microbial substances as formalin, ammonium chloride,
copper sulphate and zinc sulfate. Dissolution of the boiler scale results in the formation of
wastewater which can be totally utilized as raw materials in phosphoric fertilizer produc
Seasonality is a function of a time series in which the data experiences regular and predictable
changes that repeat each calendar year. Two-stage stochastic programming model
for real industrial systems at the case of a seasonal demand is presented. Sampling average
approximation (SAA) method was applied to solve a stochastic model which gave a productive
structure for distinguishing and statistically testing a different production plan. Lingo
tool is developed to obtain the optimal solution for the proposed model which is validated
by Math works Matlab. The actual data of the industrial system; from the General Manufacturing
Company, was applied to examine the proposed model. Seasonal future demand
is then estimated using the multiplicative seasonal method, the effect of seasonality was
presented and discussed. One might say that the proposed model is viewed as a moderately
accurate tool for industrial systems in case of seasonal demand. The current research may
be considered a significant tool in case of seasonal demand. To illustrate the applicability of
the proposed model a numerical example is solved using the proposed technique. ANOVA
analysis is applied using MINITAB 17 statistical software to validate the obtained results.
Low cost manufacturing of quality products remains an essential part of present economy
and technological advances made it possible. Advances and amalgamation of information
technology bring the production systems at newer level. Industry 4.0, factory for future,
smart factory, digital manufacturing, and industrial automation are the new buzz words of
industry stalwarts and academicians. These new technological revolutions bound to change
not only the complete manufacturing scenarios but many other sectors of the society. In this
paper an attempt has been made to capture the essence of Industry 4.0 by redefining it in
simple words, further its complex, disruptive nature and inevitability along with technologies
backing it has been discussed. Its enabling role in manufacturing philosophies like Lean
Manufacturing, and Flexible Manufacturing are also
Technological assurance and improvement of the economic efficiency of production are the
first-priority issues for the modern manufacturing engineering area. It is possible to achieve
a higher value of economic efficiency in multiproduct manufacturing by multicriteria optimization.
A set of optimality criteria based on technological and economic indicators was
defined with the aim of selecting the optimal manufacturing process. Competitive variants
and a system of optimization were developed and investigated. A comparative analysis of
the optimality criteria and their influence on the choice of optimal machining processes was
carried out. It was determine
The paper proposes three multi-criteria decision-making (MCDM) methods for the selection
of an industrial robot for a universal, flexible assembly station, taking into consideration the
technical and performance parameters of the robot. Fuzzy versions of AHP and TOPSIS
methods as well as SMART were chosen from the variety of MCDM methods as they represent
different attitudes to analysis. In order to minimise the impact of the method applied on
the final decision, a list of results of the analyses has been developed and a final classification
has been made based on decision makers’ preferences concerning selected parameters of the
robot.
This paper presents a new welding quality evaluation approach depending on the analysis
by the fuzzy logic and controlling the process capability of the friction stir welding of
pipes (FSWoP). This technique has been applied in an experimental work developed by
alternating the FSW of pipes process major parameters: rotation speed, pipe wall thickness
and travel speed. variable samples were friction stir welded of pipes using from 485 to 1800
rpm, 4–10 mm/min and 2–4 mm for the rotation speed, the travel speed, and the pipe wall
thickness respectively. DMAIC methodology (Defining, Measuring, Analyzing, Improving,
Control) has been used as an approach to analyze the FSW of pipes, it depends on the
attachment potency and technical commonplace demand of the FSW of pipes process.
The analysis controlled the Al 6061 friction stir welded joints’ tensile strength. To obtain
the best tensile strength, the study determined the optimum values for the parameters from
the corresponding range.
This study demonstrates application of Lean techniques to improve working process in
a sewing machine factory, focusing on the raw material picking process. The value stream
mapping and flow process chart techniques were utilized to identify the value added activities,
non-value activities and necessary but non-value added activities in the current
process. The ECRS (Eliminate, Combine, Rearrange and Simplify) in waste reduction was
subsequently applied to improve the working process by (i) adjusting the raw material picking
procedures and pre-packing raw material as per demand, (ii) adding symbols onto the
containers to reduce time spent in picking material based on visual control principle, and
(iii) developing and zoning storage area, identifying level location for each row and also
applying algorithms generated from a solver program and linear programming to appropriately
define the location of raw material storage. Improvement in the raw material picking
process was realized, cutting down six out of 11 procedures in material picking or by 55%,
reducing material picking time from 24 to 4 min or by 83%. The distance to handle material
in the warehouse can be shortened by 120 m per time or 2,400 m per day, equal to 86%
reduction. Lean techniques
The presented method is constructed for optimum scheduling in production lines with parallel
machines and without intermediate buffers. The production system simultaneously
performs operations on various types of products. Multi-option products were taken into
account – products of a given type may differ in terms of details. This allows providing for
individual requirements of the customers. The one-level approach to scheduling for multioption
products is presented. The integer programming is used in the method – optimum
solutions are determined: the shortest schedules for multi-option products. Due to the lack
of the intermediate buffers, two possibilities are taken into account: no-wait scheduling,
possibility of the machines being blocked by products awaiting further operations. These two
types of organizing the flow through the production line were compared using computational
experiments, the results of which are presented in the paper.
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