Management and Production Engineering Review

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.