The market of consumer goods requires nowadays quick response to customer needs. As a consequence, this is transferred to the time restrictions that the semi-finished product manufacturer must meet. Therefore the cost of manufacturing cannot determine how production processes are designed, and the main evaluation function of manufacturing processes is the response time to customers’ orders. One of the ideas for implementing this idea is the QRM (Quick Response Manufacturing) production organization system. The purpose of the research undertaken by the authors was to develop an innovative solution in the field of production structure, allowing for the implementation of the QRM concept in a Contract Manufacturer, which realizes its tasks according to engineering-to-order (ETO) system in conditions defined as High Mix, Low Volume, High Complexity. The object of the research was to select appropriate methods for grouping products assuming that certain operations will be carried out in traditional but well-organized technological and/or linear cells. The research was carried out in one of the largest producers of sheet metal components in Europe. Pre-completed groupings for data obtained from the company had indicated that – among the classical methods – the best results had been given by the following methods: King’s Algorithm (otherwise called: Binary Ordering, Rank Order Clustering), k-means, and Kohonen’s neural networks. The results of the tests and preliminary simulations based on the data from the company proved that the implementation of the QRM concept does not have to be associated with the absolute formation of multi-purpose cells. It turned out that the effect of reducing the response time to customer needs can be obtained by using hybrid structures that combine solutions characteristic of cellular systems with traditional systems such as a technological, linear, or mixed structure. However, this requires the application of technological solutions with the highest level of organization.

Industry 4.0 promises to make manufacturing processes more efficient using modern technologies like cyber-physical systems, internet of things, cloud computing and big data analytics. Lean Management (LM) is one of the most widely applied business strategies in recent decades. Thus, implementing Industry 4.0 mostly means integrating technologies in companies that already operate according to LM. However, due to the novelty of the topic, research on how LM and Industry 4.0 can be integrated is still under development. This paper explores the synergic relationship between these two domains by identifying six examples of real cases that address LM-Industry 4.0 integration in the extant literature. The goal is to make explicit the best practices that are being implemented by six distinct industrial sectors

Industry 4.0 is expected to provide high quality and customized products at lower costs by increasing efficiency, and hence create a competitive advantage in the manufacturing industry. As the emergence of Industry 4.0 is deeply rooted in the past industrial revolutions, Advanced Manufacturing Technologies of Industry 3.0 are the precursors of the latest Industry 4.0 technologies. This study aims to contribute to the understanding of technological evolution of manufacturing industry based on the relationship between the usage levels of Advanced Manufacturing Technologies and Industry 4.0 technologies. To this end, a survey was conducted with Turkish manufacturers to assess and compare their manufacturing technology usage levels. The survey data collected from 424 companies was analyzed by machine learning approach. The results of the study reveal that the implementation level of each Industry 4.0 technology is positively associated with the implementation levels of a set of Advanced Manufacturing Technologies.

Studies linking the use of lean practices to company performance have been increasing as

markets are becoming more competitive and companies are eager for reducing waste and

therefore implementing the Lean Management (LM) philosophy to improve performance.

However, results from these studies have found various and different impacts and some light

is needed. Extant literature was reviewed and, to achieve the research objective, a metaanalysis

of correlations was carried out. The obtained results suggest a positive relationship

between some lean practices and performance measures. Furthermore, the presence of moderators

influencing the relationship between lean practices and performance outcomes is

highlighted in our results. To our best knowledge, this is the first research that proposes

a comparison of results from primary studies on Lean implementation, by analysing the

linear relationship between lean practices and enterprise performance. It fills this gap and

therefore represents an important contribution.

Recent rapid developments in information and network technology have profoundly influenced manufacturing research and its application. However, the product’s functionality and complexity of the manufacturing environments are intensifying, and organizations need to sustain the advantage of huge competitiveness in the markets. Hence, collaborative manufacturing, along with computer-based distributed management, is essential to enable effective decisions and to increase the market. A comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework and to shed light on the future research avenues. In this review paper, the use of technology and management by means of collaborative and cloud manufacturing process and big data in networked manufacturing system have been discussed. A systematic review of research papers is done to draw conclusion and moreover, future research opportunities for collaborative manufacturing system were highlighted and discussed so that manufacturing enterprises can take maximum benefit.

High business competition demands business players to improve quality. The Six Sigma

with DMAIC phases is a strategy that has proven effective in improving product and service quality. This study aims to find the consistency of DMAIC phases implementation and

analyze the objective value in Six Sigma research. By using a number of trusted article

sources during 2005 until 2019, this research finds that 72% research in manufacturing industry consistently implemented DMAIC roadmap especially in case study research type

for problem-solving, while service industry pointed out the fewer number (60%). The causes

of variations and defective products in the manufacturing industry are largely caused by

a 4M 1E factor, while in service industry are caused by human behavior, and it’s system

poorness. Both manufacturing & service industry emphasized standardization & monitoring to control the process which aimed at enhancing process capability and organization

performance to increase customer satisfaction.

Lean has established itself as the primordial approach to obtain operational excellence. Its simple and intuitive techniques focus on reducing lead time through continuous improvement, involving all levels of employees in the organization. However, the rate of successful implementations has remained low. This paper contributes to the understanding of continuous improvement in a Lean context, by analyzing a database of almost 10.000 improvement actions, from 85 companies, covering the time frame 2010–2018. It discusses categories of actions, their impact and cost, as well as key characteristics of the companies. It proposes an objective criterion to identify “success” and “failure” in Lean implementation and tries to link these to operational results. It is probably the first time an analysis of this magnitude on the subject has been performed.

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.

One of the strategic decisions of any organization is decision making about manufacturing

strategy. Manufacturing strategy is a perspective distinguishing a company from other

present companies in that industry and creates a kind of stability in decisions and gives a special

direction to organizational activities. SIR (SUPERIORITY& INFERIORITY Ranking)

method and their applications have attracted much attention from academics and practitioners.

FSIR proves to be a very useful method for multiple criteria decision making in fuzzy

environments, which has found substantial applications in recent years. This paper proposes

a FSIR approach based methodology for TOPSIS, which using MILTENBURG Strategy

Worksheet in order to analyzing of the status of strategy of the Gas Company. Then formulates

the priorities of a fuzzy pair-wise comparison matrix as a linear programming and

derives crisp priorities from fuzzy pair-wise comparison matrices

Manufacturing levers (Alternatives) are examined and analyzed as the main elements of

manufacturing strategy. Also, manufacturing outputs (Criteria are identified that are competitive

priorities of production of any organization. Next, using a hybrid approach of FSIR

and TOPSIS, alternatives (manufacturing levers) are ranked. So dealing with the selected

manufacturing levers and promoting them, an organization makes customers satisfied with

the least cost and time.

Today’s manufacturing environment is highly uncertain, and it is continuously changing. It

is characterized by shorter life cycles of products and technologies, shorter delivery times, an

increased level of customization at the price of a standard product, increased product variety,

quality as well as demand variability and intense global competition. Academicians, as well as

practitioners, agree that uncertainty will continue to grow in the twenty-first century. To deal

with the uncertainties in demand variation and production capacity a manufacturing system

is required which can be easily reconfigured when there is a need at low cost. A reconfigurable

manufacturing system is such a type of system.

In the present work, the concept of the reconfigurable manufacturing system has been discussed

and reviewed. It has been compared with dedicated systems and flexible manufacturing

systems. Part family formation and barriers of reconfiguration also have been discussed.

This work is an attempt to contribute to the conceptual systematization of the reconfigurable

manufacturing system and reconfigurability by synthesizing the vast literature available after

a systematic review.

This paper presents the results of a metrological analysis of the additively manufactured (AM) copies of a complex geometrical object, namely the fossil skull of Madygenerpeton pustulatum. This fossil represents the unique remains of an extinct “reptiliomorph amphibian” of high importance for palaeontological science. For this research, the surface was scanned and twelve different copies were 3D-printed using various devices, materials, and AM techniques. The same digitized model was used as a reference to compare with the surfaces obtained by Mitutoyo Coordinate Measuring Machine (CMM) CRYSTA-Apex S 9166 for each copy. The fidelity of the copies was assessed through statistical analysis of the distances between compared surfaces. The methodology provided a good background for the choice of the most accurate copies and the elimination of the less accurate ones. The proposed approach can be applied to any object of complex geometry when reproduction accuracy is to be assessed.

Computational intelligence (CI) can adopt/optimize important principles in the workflow of 3D printing. This article aims to examine to what extent the current possibilities for using CI in the development of 3D printing and reverse engineering are being used, and where there are still reserves in this area. Methodology: A literature review is followed by own research on CI-based solutions. Results: Two ANNs solving the most common problems are presented. Conclusions: CI can effectively support 3D printing and reverse engineering especially during the transition to Industry 4.0. Wider implementation of CI solutions can accelerate and integrate the development of innovative technologies based on 3D scanning, 3D printing, and reverse engineering. Analyzing data, gathering experience, and transforming it into knowledge can be done faster and more efficiently, but requires a conscious application and proper targeting.

Major manufactures are moving towards a sustainability goal. This paper introduces the results of collaboration with the leading company in the packaging and advertising industry in Germany and Poland. The problem addresses the manufacturing planning problem in terms of minimizing the total cost of production. The challenge was to bring a new production planning method into cardboard manufacturing and paper processing which minimizes waste, improves the return of expenses, and automates daily processes heavily dependent on the production planners’ experience. The authors developed a module that minimizes the total cost, which reduces the overproduction and is used by the company’s manufacturing planning team. The proposed approach incorporates planning allowances rules to compromise the manufacturing requirements and production cost minimization.

In this study, we propose a cooling structure manufactured using a specialized three-dimensional (3D) printing design method. A cooling performance test system with complex geometry that used a thermoelectric module was manufactured using metal 3D printing. A test model was constructed by applying additive manufacturing simulation and computational fluid analysis techniques, and the correlation between each element and cooling efficiency was examined. In this study, the evaluation was conducted using a thermoelectric module base cooling efficiency measurement system. The contents were compared and analyzed by predicting the manufacturing possibility and cooling efficiency, through additive manufacturing simulation and computational fluid analysis techniques, respectively.

In the era of smart manufacturing and Industry 4.0, the rapid development of modelling in production processes results in the implementation of new techniques, such as additive manufacturing (AM) technologies. However, large invest-ments in the devices in the field of AM technologies require prior analysis to identify the possibilities of improving the production process flow. This paper proposes a new approach to determine and optimize the production process flow with improvements made by the AM technologies through the application of the Petri net theory. The existing produc-tion process is specified by a Petri net model and optimized by AM technology. The modified version of the system is verified and validated by the set of analytic methods safeguarding against the formal errors, deadlocks, or unreachable states. The proposed idea is illustrated by an example of a real-life production process.

In mid-1992, Japanese consultant Yamada Hitoshi was tasked with modifying the production systems of Japanese companies as the existing configurations at manufacturing plants no longer satisfied unstable demands. He made improvements to the overall production system by dividing the long assembly lines into several short ones called cells or seru. Although of the advantages, it is still unclear about how to manage this new production system, and what variables really promoted the desired benefits. We identify in total 39 articles from 2004– 2020 about the progress of the seru production system, and we observe some possibilities to improve the effectiveness of this type of the production system. The first is the possibility of manufacturing the product in flexible sequence, in which the operations are independent among them. We show through the developed example that the makespan may be different. We noted when converting the in-line production system to one pure seru, the makespan tend to increase. Nevertheless, when analyzing the effectiveness of serus working concomitantly considering splitting the same lot, makespan and the cost may be reduced. And finally, when converting to one of pure serus, the performance may be similar to that obtained when serus working concomitantly.

The modern companies, which are competing on product market, need to use innovative solutions, in order to become potential leaders. One of the modernization methods is rearrangement of organizational structure and redistribution of competence. The article describes the Advanced Manufacturing Engineering Department in production plant, which is an innovative initiative in worldwide organizational management. Some aspects including AME application in plant processes are highlighted. Some advanced techniques are presented. In the article summary, perspectives for the development of AME are included.

The paper presents the technology and organization of the artistic cast production. On the basis of the actual cast production system, the

manufacturing process was shown, in particular sand–piece moulding, which is a very important process and a time-consuming part of the

entire manufacture of the casts. The current state of the production process as well as the organization of the work and production

technology were analysed with the use of methods and techniques of production improvement, the Lean Manufacturing concept and

computer systems. The results of the analysis and studies were shown with use of schemes and graphs of the layout of the production

resources, a flow chart of the production process, value stream mapping, and a costs table for the production and modernization of the

moulding stage. The work has shown that there are possibilities to improve the artistic cast production system. This improvement leads to

increased productivity, lower production costs of artistic casts and increased competitiveness of the foundry.

Additive manufacturing (AM) is a process that joins similar or dissimilar materials into application-oriented objects in a wide range of sizes and shapes. This article presents an overview of two additive manufacturing techniques; namely Laser metal deposition (LMD) and Wire arc additive manufacturing (WAAM). In LMD, metallic powders are contained in one or more chambers, which are then channelled through deposition nozzles. A laser heats the particles to produce metallic beads, which are deposited in layers with the aid of an in-built motion system. In WAAM, a high voltage electric arc functions as the heat source, which helps with ensuring deposition of materials, while materials in wire form are used for the feedstock. This article highlights some of the strengths and challenges that are offered by both processes. As part of the authors’ original research work, ­Ti-6Al-4V, Stainless steel 316L and Al-12Si were prepared using LMD, while the WAAM technique was used to prepare two Al alloys; Al-5356 and CuAl8Ni2. Microstructural analysis will focus on similarity and differences in grains that are formed in layers. This article will also offer an overall comparison on how these samples compare with other materials that have been prepared using LMD and WAAM.

The purpose of the present paper was to investigate the effect of shot peening on the condition of the surface layer and abrasion resistance of specimens made of Ti-6Al-4V titanium alloy produced by Direct Metal Laser Sintering (DMLS) process. The specimens have been produced by means of EOSINT M280 system dedicated for laser sintering of metal powders and their surfaces have been subjected to the shot peening process under three different working pressures (0.2, 0.3 and 0.4 MPa) and by means of three different media i.e. CrNi steel shot, crushed nut shells and ceramic balls. The specimens have been subjected to profilometric analysis, to SEM examinations, microhardness tests and to tribological tests on ball-on-disc stand in Ringer fluid environment. The general results of all tests indicate to favourable effect of shot peening process on the hardness and tribological performance of titanium alloy.

This article summarizes the arguments and counterarguments within the scientific discussion on identifying the enterprise’s state to evaluate its effectiveness and optimize the

target functions in solving enterprise development problems. The proposed scientific and

methodological approach to modeling the enterprise development management system under decentralization conditions and its practical implementation makes it possible to determine the dominant development parameters of manufacturing enterprises that influence

the United Territorial Community and to timely track the impulses and space of the United Territorial Community state, taking into account the PS state as parameters for its

development. The proposed analysis of the Production System state within the United

Territorial Community framework and evaluating its development dynamics shows the necessity of forming a system of generalized vector-scalar, situationally oriented indicators.

The main aim of the article is to develop a simulation model of flexible manufacturing

system with applying the ontology on flexibility. Designing manufacturing systems matching

both production and market requirements becomes more and more challenging due to the

variability of demand for a large number of products made in many variants and short

lead times. Manufacturing flexibility is widely recognised as a proven solution to achieve

and maintain both the strategical and operational goals of the companies exposed to global

competition. Generic simulation model of flexible manufacturing system was developed using

FlexSimr 3D software, then the example data were used to demonstrate the developed model

applicability. “The Ontology on Flexibility” was applied for evaluation of achieved flexibility

of manufacturing system.

The spread of digital technologies dramatically changes production processes. The fourth

industrial revolution opens up new opportunities for the introduction of technologies, having

a significant impact on the production cycle, starting with highly automated production lines

and ending with the large-scale implementation of technological solutions designed to improve

productivity, optimize costs, quality and reliability. Defining digital transformations,

primarily in the manufacturing industry, as a strategic imperative for the entire economy

based on opinions and intentions of entrepreneurs (short and medium-term), key aspects of

the digitalization process in Russian medium, high-tech and low-tech manufacturing industries

are revealed. A set of tendencies in the development of digital technologies by their main

types is presented, the level of industry participation in digital transformation is shown, as

well as many other important digital transformation processes in enterprises that are not

measured by quantitative statistics.

Due to fast-paced technical development, companies are forced to modernise and update

their equipment, as well as production planning methods. In the ordering process, the customer

is interested not only in product specifications, but also in the manufacturing lead

time by which the product will be completed. Therefore, companies strive towards setting

an appealing but attainable manufacturing lead date.

Manufacturing lead time depends on many different factors; therefore, it is difficult to predict.

Estimation of manufacturing lead time is usually based on previous experience. In the

following research, manufacturing lead time for tools for aluminium extrusion was estimated

with Artificial Intelligence, more precisely, with Neural Networks.

The research is based on the following input data; number of cavities, tool type, tool category,

order type, number of orders in the last 3 days and tool diameter; while the only output

data are the number of working days that are needed to manufacture the tool. An Artificial

Neural Network (feed-forward neural network) was noted as a sufficiently accurate method

and, therefore, appropriate for implementation in the company.