Supply chain management emerged as the ultimate management strategy to ensure the competitive advantages of companies in their markets. Suppliers are considered as inevitable sources of external risks in modern supply chains. In this respect, resonance is essential for the ability to adapt in resonance to disturbances and to restore in choosing suppliers. As suppliers of critical resources are vulnerable, choosing better suppliers to create resilience, and thereby reducing the risks in the supply chain as a whole. In recent years, emphasis has been placed on supply chain resilience and resilient suppliers, but few studies have been conducted on the evaluation and selection of resilient suppliers with multi-criteria decision making models. The main purposes of this study are a broad review of the literature on the resilient factor, factorization, efficiency of key factors in the reliance of suppliers and the ranking of resilient suppliers using the combined approach of SWARA and WASPAS. For this purpose, after a comprehensive review of Literature interview with the experts of petrochemical upstream industry, six key factors and overall resilience of suppliers were identified in eighteen factors. Then the weight of the dimensions was determined by using the SWARA method. The output of the method showed that supplier accountability and key performance factors were the most important factors in assessing the resilience of suppliers. Using the supporting method, five resilient suppliers were evaluated based on six dimensions and the final ranking of suppliers was determined. With this ranking, the industry will be a major step towards improving supply chain and increasing suppliers’ resilience to address disruptions and risks, improve supply and achieve competitive advantage and satisfy the consumers’ needs.

In this study, solar cells based on copper oxide and titanium dioxide were successfully manufactured using the reactive direct-current magnetron sputtering (DC-MS) technique with similar process parameters. TiO2/CuO, TiO2/Cu2O/CuO/Cu2O, and TiO2/Cu2O solar cells were manufactured via this process. Values of efficiencies, short-circuit current, short-circuit current density, open-circuit voltage, and maximum power of PV devices were investigated in the range of 0.02÷0.9%, 75÷350 µA, 75÷350 µA/cm2, 16÷550 mV, and 0.6÷27 µW, respectively. The authors compare solar cells reaching the best and the worst conversion efficiency results. Thus, only the two selected solar cells were fully characterized using I-V characteristics, scanning electron microscopy, X-ray diffraction, ellipsometry, Hall effect measurements, and quantum efficiency. The best conversion efficiency of a solar cell presented in this work is about three times higher in comparison with the authors’ previous PV devices.

This paper addresses the problem of part of speech (POS) tagging for the Tamil language, which is low resourced and agglutinative. POS tagging is the process of assigning syntactic categories for the words in a sentence. This is the preliminary step for many of the Natural Language Processing (NLP) tasks. For this work, various sequential deep learning models such as recurrent neural network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bi-directional Long Short-Term Memory (Bi-LSTM) were used at the word level. For evaluating the model, the performance metrics such as precision, recall, F1-score and accuracy were used. Further, a tag set of 32 tags and 225 000 tagged Tamil words was utilized for training. To find the appropriate hidden state, the hidden states were varied as 4, 16, 32 and 64, and the models were trained. The experiments indicated that the increase in hidden state improves the performance of the model. Among all the combinations, Bi-LSTM with 64 hidden states displayed the best accuracy (94%). For Tamil POS tagging, this is the initial attempt to be carried out using a deep learning model.

In this article conclusions from nearly 10 years of collaboration with Polish and German Engineer-to-Order (ETO) small and medium-sized enterprises (SMEs) from mechanical sector was presented. Research objective was to highlight common organizational problems they are dealing with, which prevent them from transition to Mass Customizers. As a result, a concept of 5 foundations for robust process design was proposed: procedures, product selection, machining philosophy, planning and storage, cross-functional teams. More practical solutions from this field have to be published to fill the research gap.

Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.

Occupational risk is closely related to work environment. For the same positions, but in different working conditions threats and level of risk can be different. For this also estimating the degree of damage hazard the largest possible should be adopted effects. However, when estimating probability occurrence of threats should include, among others: working conditions, events from the past, or possible employee behavior (in particular those that may be the cause of an accident at work). The source of the above information may be data from statistics or observations of work stations. The article presents the assessment of occupational risk at the position of the laser cutter operator, which was carried out using the Job Safety Analysis (JSA) method. According to this method, occupational risk is determined on the basis of two parameters, i.e.: consequences of C and probability of consequences P. In turn, the probability of consequences is the sum of three factors: frequency of hazard F, probability of event O and avoidability or damage limitation A.

Management processes in an organization involve decision-making based on many criteria (MCDM), and in this process ranking of variables plays a vital role. This paper presents the analysis of key business issues of an Indian automotive organization using an efficient interpretive ranking (eIRP) approach. This paper integrates the Situation-Actor-Process (SAP) and Learning-Action-Performance (LAP) framework of the organization with eIRP. It evaluates the ranking of actions to be carried out in an organization with respect to performance parameters. The study highlights the area where the organization should focus on achieving desired business excellence. From the analysis, it is revealed that the top-ranked suggested action for the organization is the adoption of energy policy as a core business policy followed by technology management, maintenance management, and the use of information technology for cost management. This case study is one of the few that uses the SAP-LAP framework for ranking the actors and actions of the organization using the eIRP approach, to make MCDM an easy task.

Digital twin (DT) is a solution for presenting reality in a virtual world. DTs have been discussed in the literature only recently. The aim of this work is to review and analyse literature connected to DTs. Under a systematic literature review the authors searched databases for the information how DTs can support organization operations and how they can support sustainability of companies. A literature review was performed according to a developed research methodology, which covers research questions and keywords identification, selection criteria and results analysis. Databases, such as Web of Science, Scopus and Science Direct, were searched. The titles, abstracts and keywords were searched for works related to digital twins, sustainable development and manufacturing processes. Moreover, the search was focused on real-time monitoring, data, decision-making etc. The keywords used in the searching process are specified in the methodology. Afterwards, quantitate and qualitative analysis were performed taking into account number of publication, year of publications, type of publication, based on keywords and available information concerning the papers. Deeper analysis was performed on available full texts of the papers. The main goal of this paper was to assess how much the specified problem is discussed in literature in the context of production organizations and real-time and what kind of topics are present in publications to indicate future research needs.

The article is devoted to the development of a multicomponent adaptive strategy for managing Russian high-tech enterprises in modern crisis conditions. Adaptability and flexibility are considered as the most important indicators of the efficiency of structures, their ability to ensure sustainable operation and effective innovative development of high-tech enterprises. A significant place in the article is devoted to possible approaches and methods of adaptive management of the enterprise in crisis, with the help of which changes in the internal and external environment are monitored, which can be expected and random, make current operational decisions that contribute to the achievement of pre-set goals based on correction of certain tasks. The questions about the quality of adaptation, adaptive characteristics of the structure and their consistency with the level and quality of adaptation of other elements of the enterprise, conjugation of adaptability and flexibility and pace of changes are considered. A number of modern techniques related to the development of strategies for the development of high-tech enterprises in crisis conditions are analyzed. The necessity of conducting a comprehensive accounting of the crisis as the main factor associated with the uncertainty of the external environment at the stage of strategic analysis is substantiated. There is an option, in which it is possible to include the crisis and its main characteristics into the strategic three competence model of the enterprise, as an additional controlled parameter. There is a general specificity of the formation of the strategy of a high-tech enterprise in conditions of risk and uncertainty.

This paper is the first to optimize the friction stir welding (FSW) process considering the Clamp Pitch (mm) and Clamping Torque effect using the Various combinations of parameters were constructed using factorial design and responses, resulting in a comprehensive factorial analysis. Conspicuous changes in the tensile strength, yield strength, hardness, and power profile were observed for all amalgamations of parameters. Significant parameters of the FSW process have been considered in many optimization studies, however, the effect of the Clamp Pitch (mm) and Clamping Torque (Nm) has been never studied. Three levels of three parameters were used in the experiments: Clamp Pitch, tool rotational speed and Clamping Torque. The full factorial analysis was performed, was applied as an approach for selecting the values of the Significant factors of the parameters. For each result the three key parameters were important with p-values of less than 0.05, suggesting their significance in the phase of FSW. Mathematical models built with high R-sq. and least percentage error were adequate for the investigated responses. The findings were gained by important parameter values factors of 30 mm, 1800 rpm and 70 Nm for the take into consideration parameter range for the Clamp Pitch, rotational speed and Clamping Torque respectively.

Warehouse and inventory management is a recurring issue in many of the different supply chains in diverse industries, where the constant changes in the markets have a direct impact on the management of warehouses and inventories, either generating over-stocks or shortages. This paper presents a case study on warehouse and inventory management control. The company under study was having problems in this area, where over-stocks were generated frequently, leading to various incidents, such as having to store finished and packaged product in unsuitable places, with the associated risk of deterioration. To deal with this problem, control tools based on the KPI (Key Performance Indicator) concept were developed. To this end, the corresponding problem and the information management process within the Supply Chain department had to be analyzed. In this case, it was observed that the databases were not synchronized, therefore strategies were proposed to systematize the collection and updating of data. In addition, to summarize the information, we proceeded to the implementation of an interactive form that facilitates the visualization and interpretation of the evolution of the process, and to be able to apply an efficient control on it, and thus to propose corrective actions supported by evidence.

Unrelated Parallel Machines Scheduling Problem (U-PMSP) is a category of discrete optimization problems in which various manufacturing jobs are assigned to identical parallel machines at particular times. In this paper, a specific production scheduling task the U-PMSP with Machine and Job Dependent Setup Times, Availability Constraint, Time Windows and Maintenance Times is introduced. Machines with different capacity limits and maintenance times are available to perform the tasks. After that our problem, the U-PMSP with Machine and Job Dependent Setup Times, Availability Constraints, Time Windows and Maintenance Times is detailed. After that, the applied optimization algorithm and their operators are introduced. The proposed algorithm is the genetic algorithm (GA), and proposed operators are the order crossover, partially matched crossover, cycle crossover and the 2-opt as a mutation operator. Then we prove the efficiency of our algorithm with test results. We also prove the efficiency of the algorithm on our own data set and benchmark data set. The authors conclude that this GA is effective for solving high complexity parallel machine problems.

Small and medium-sized enterprises (SMEs) are facing barriers to grow due to the lack of structured procedures for upgrading and allocating the limited resources. To overcome these drawbacks and to improve business capabilities, a structured framework to conduct a comprehensive diagnostic and upgrading study is presented in this paper. The proposed framework involves four phases. First, the external and internal strategic factors, which can affect the enterprises’ performance are evaluated using strategic planning and assessment tools. Second, key upgrade performance indicators are developed and evaluated using multi-attribute rating techniques to guide, evaluate, and track progress of upgrading process. Third, a set of upgrade strategies are generated and evaluated using resource allocation model. Finally, a periodic re-evaluation plan is introduced to monitor the implementation progress. The developed framework for performance evaluation and upgrading is suitable to be used as a structured know-how procedure in manufacturing enterprises and can support entrepreneurs in their strategic decisions. To validate the proposed framework, a data set was collected from a local housecore company. As a result, one package of the efficient frontier strategies that represents the best use of resources was proposed for implementation.

Nowadays, the main challenge in maintenance is to establish a dynamic maintenance strategy to significantly track and improve the performance measures of multi-state systems in terms of production, quality, security and even the environment. This paper presents a quantitative approach based on Dynamic Bayesian Network (DBN) to model and evaluate the maintenance of multi-state system and their functional dependencies. According to transition relationships between the system states modeled by the Markov process, a DBN model is established. The objective is to evaluate the reliability and the availability of the system with taking into account the impact of maintenance strategies (perfect repair and imperfect repair). Using the proposed approach, the dynamic probabilities of system states can be determined and the subsystems contributing to system failure can also be identified. A practical application is demonstrated by a case study of a blower system. Through the result of the diagnostic inference, to improve the performances of the blower, the critical components C, F, W, and P should be given more attention. The results indicate also that the perfect repair strategy can improve significantly the performances of the blower, while the imperfect repair strategy cannot degrade the performances in comparison to the perfect repair strategy. These results show the effectiveness of this approach in the context of a predictive evaluation process and in providing the opportunity to evaluate the impact of the choices made on the future measurement of systems performances. Finally, through diagnostic analysis, intervention management and maintenance planning are managed efficiently and optimally.