The manufacturing industry has been reshaping its operations using digital technologies for a smart production towards a more customized demand. Nevertheless, the flexibility to attend the production plan changes in real time is still challenging. Although the Internet of Services (IoS) has been addressed as a key element for Industry 4.0, there is still a lack of clarity about the IoS contribution for advanced manufacturing. Through a case study, the paper aims to validate the adherence of a theoretical model named Service-Oriented Manufacturing Architecture (SOMA) in two manufacturing companies that have been already engaged in Industry 4.0. As main results, it was concluded that IoS could suit in one case of Industry 4.0 flexible production process but not in a mass production one. Considering the scarcity of research that exemplifies the IoS contribution, the present paper brings an important assessment on a real manufacturing scenario.

The paper addresses a managerial problem related to ensuring cybersecurity of information and knowledge resources in production enterprises interested in the implementation of INDUSTRY 4.0 technologies. The material presented shows the results of experimental research of a qualitative nature, using two expert inventive methods: brain-netting and a fuzzy formula of inference. The experts' competences included the following three variants of the industrial application of the INDUSTRY 4.0 concept: (1) high production volumes achieved using a dedicated and fully robotic production line (2) the manufacture of short, personalized series of products through universal production cells, and (3) the manufacture of specialized unit products for individual customers. The Google Forms software was used to collect these expert opinions. The conclusions of the research carried out using the brain-netting method point to nine variants of the cybersecurity strategy of IT networks and knowledge base resources in manufacturing enterprises represented by the experts. The results of the research using the fuzzy formula of inference are numerically and situationally defined relations linking the above-mentioned nine strategies with five types of cyber-attacks. The summary record of these relations as the basis for managerial cybersecurity recommendations has a matrix form.

The automotive industry is characterized by a high degree of uncertainty. Companies are facing the challenge of producing different systems simultaneously. Additionally, the global quantity of electric vehicles is also expected to increase significantly. This results in the following capability to remain competitive: Effective and efficient adaptions of production systems to model variations and volume increases. While flexible production is identified as the most promising concept, defining the actual flexibility level of included production resources is essential for its proper realization. A literature review on existing flexibility assessment approaches revealed their emphasis on high-level enablers and limited practical applicability in the automotive industry. In contrast, focusing the assessment on single workstations supports the selection of appropriate production resources. Therefore, a simple and structured standard procedure for a production resource flexibility assessment was developed. This theoretical construct was subsequently complemented with practical insights through its application on two real-life case studies within one automotive engineering company. Summarizing and discussing the findings in combination with a conclusion completed this paper.

This paper proposes the application of the digital numerical control (DNC) technique to connect the smart meter to the inspection system and evaluate the total harmonic distortion (THD) value of power supply voltage in IEEE 519 standard by measuring the system. Experimental design by the Taguchi method is proposed to evaluate the compatibility factors to choose Urethane material as an alternative to SS400 material for roller fabrication at the machining center. Computer vision uses artificial intelligence (AI) technique to identify object iron color in distinguishing black for urethane material and white for SS400 material, color recognition results are evaluated by measuring system, system measurement is locked when the object of identification is white material SS400. Computer vision using AI technology is also used to recognize facial objects and control the layout of machining staff positions according to their respective skills. The results obtained after the study are that the surface scratches in the machining center are reduced from 100% to zero defects and the surface polishing process is eliminated, shortening production lead time, and reducing 2 employees. The total operating cost of the processing line decreased by 5785 USD per year. Minitab 18.0 software uses statistical model analysis, experimental design, and Taguchi technical analysis to evaluate the process and experimentally convert materials for roller production. MATLAB 2022a runs a computer vision model using artificial intelligence (AI) to recognize color objects to classify Urethane and SS400 materials and recognize the faces of people who control employee layout positions according to their respective skills.

The aim of the article is to present an exemplary system for recording and analyzing quality costs and to demonstrate that it is helpful in planning and assessing the effectiveness of continuous improvement processes at the operational and strategic level. Various approaches to defining quality costs are described, followed by indicators for assessing effectiveness and tools to collect data on the values of individual groups of quality costs and compare them with financial indicators. The practical part presents a case study on the quality cost accounting system in a medical company and the possibility of using quality cost accounting to plan and evaluate continuous improvement processes and make managerial decisions.

Simulations are becoming one of the most important techniques supporting production preparation, even in those industrial sectors with atypical technological processes, such as in metallurgy, where there is a multiphase material flow. This is due to the fact that in the conditions of a market economy, enterprises have to solve more and more complex problems in a shorter time. On the basis of the existing production process and the knowledge of the flow characteristics in a given process, a model is built, which, when subjected to simulation tests, provides experimental results in the scope of the defined problem. The use of computer techniques also creates new possibilities for the rational use of the reserves inherent in each technological process. Taking into account the existing demand and the state of modern technology, the computer model can be a source of information for further analysis and decision-making processes supporting company management. At work a model of the logistic system was made on the example of a hot-rolled steel strip mill, on which simulation experiments were carried out to improve the effectiveness and efficiency of the analysis production line. The presented article aims to disseminate the idea of ??Industry 4.0 in Polish companies from the manufacturing industry sector, taking into account simulation techniques.

Recently, the expand of industrial market has led to have long supply chain network. During the long shipment, the probability of having damaged products is likely to occur. The probability of having damaged products is different between stages and that could lead to higher percentage of damaged products when arrived at retailers. Many companies have rejected the entire shipment because the damaged product percentage was higher than that agreed on. Decision-makers have tried to reduce the percentage of damaged products that happened because the transit, loading unloading the shipment, and natural disasters. Companies started to implement recovery centers in the supply chain network in order to return their system steady statues. Recovery models have been developed in this paper to reduce the damaged percentage at minimum costs to do so. Results show that the possibility of implementing an inspection unit and a recovery centers in the system before sending the entire shipment to the retailer based on examining a sample size that has been selected randomly from the shipment and the minimum cost of committing type I and type II errors. Designing a methodology to minimize the total cost associated with the supply chain system when there is a possibility of damage occurring during shipping is the objective of this research.

So far, numerous studies have been published on the selection of appropriate maintenance tactics based on some factors affecting them such as time, cost, and risk. This paper aims to develop the TRIZ contradiction matrix by explaining the dimensions and components of each of the following Reactive maintenance tactics. The related findings of previous studies were analyzed by adopting “Rousseau and Sandoski” seven-step method to identify and extract the relationships between TRIZ principles and Reactive maintenance tactics. Thereafter, 5 Reactive maintenance tactics were replaced TRIZ’s 40 principles in the TRIZ contradiction matrix. Finally, the ANP method were used to extract and prioritize the appropriate Reactive maintenance tactics. The proposed matrix in this research was used in the desalination section of one of the oil companies to select on the appropriate Reactive maintenance tactics. The results of this research is useful for managers and maintenance specialists of units in making decisions to provide appropriate Reactive maintenance tactics for the desired equipment.

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.

The industry transformation to the digital model 4.0 will be a significant change from the perspective of the organisation and processes. In the context of the above, the research was undertaken, the principal aim of which constituted the attempt to answer the question concerning the technological advancement level of manufacturing companies operating in the agricultural machinery sector. It is about identifying what adaptation projects in the context of the fourth generation industry era should be undertaken by the Polish manufacturers operating in the agricultural machinery sector. The achievement of the main objective required formulation and implementation of partial objectives, which, according to the authors, include: C(1) – defining the Industry 4.0 axiom merit; C(2) – using the subject literature reconstruction and interpretation methods – nomination of areas, on the one hand essential from the perspective of the model 4.0, and on the other hand those that may demonstrate the maturity in the domain of the adopted desiderata; C(3) – compilation of the research model, in the form of an assessment sheet, being a resultant of literature studies and research conducted among deliberately selected domain experts; C(4) – based on the selected indicators, the technological advancement level recognition of the studied companies; specification of a technological gap (questioning among experts).