Large Manufacturing Enterprises Research Articles

Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting

Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting

Shanghai oil futures prices and firms' investment decisions: Evidence from Chinese manufacturing firms

International crude oil futures prices play a key role in market pricing, price discovery, and reshaping the market order. As the world's largest importer of crude oil, China urgently needs to establish contracts based on Asian supply and demand to mitigate currency risk for refiners and consumers. Against the backdrop of the launch of yuan-denominated crude oil futures by the Shanghai International Energy Exchange (INE) on March 26, 2018, this paper examines the impact of crude oil futures prices on the investment scale of Chinese manufacturing companies based on data from listed manufacturing companies in China from 2016Q1 to 2021Q3. The study finds that increases in international crude oil futures prices suppress the investment scale of Chinese manufacturing enterprises, while price volatility has a promoting effect. The listing of Shanghai crude oil futures has a negative shock effect on the impact of international crude oil futures prices on the investment scale of Chinese manufacturing enterprises. Additionally, increases in operational leverage amplify the negative impact of international crude oil futures prices on the investment scale of enterprises and weaken the positive impact of changes in international crude oil futures prices on the investment scale. Furthermore, among large manufacturing enterprises, volatility and increases in international crude oil futures prices suppress their investment scale; whereas in small and micro manufacturing enterprises, price increases and volatility significantly boost their investment scale. This paper provides empirical evidence and policy implications for fully leveraging Shanghai crude oil futures to optimize corporate investment decisions and support the sustainable development of enterprises.

Firm performance and markets: survival analysis of medium and large manufacturing enterprises in Indonesia

AbstractThis study identifies the effect of firm performance, especially efficiency, on firm survival. This study applies efficiency calculations using a translog model based on both time-invariant and time-varying production functions and the Ackerberg–Caves–Frazer (ACF) model to overcome the endogeneity problem in the estimation of the production function. The data used are firm-level data, which are medium and large manufacturing company censuses with an observation period from 1995 to 2015. This study used two estimation techniques: the Cox proportional hazard model and Poisson regression. I estimate the Cox regression with firm-level data, whereas the Poisson regression is estimated with aggregate data for 2-digit ISIC. Estimates at the aggregate 2-digit ISIC level are intended to not only see the effect of efficiency on companies that survive but also on companies that enter and exit. Firm-level evidence shows that a company’s efficiency reduces the hazard ratio or increases its survival time. Moreover, consistent with firm-level results, the aggregate-level estimation shows that efficiency increases the chances of survival and entry of companies into Indonesia and reduces the rate of company exit from the Indonesian market. This shows that a company's level of technical efficiency makes an important contribution to the survival of manufacturing companies in Indonesia.

Improving approaches to product quality management at large manufacturing enterprises.

Dynamic conditions of the market environment force companies to seek and apply more progressive methods of quality management. While small companies are more flexible and can quickly adapt to new challenges, large enterprises find it more difficult to change their approaches due to the large number of internal standards, approval parties, and the degree of influence of quality standards on production operations. A reasonable combination of QMS approaches according to the ISO:9001:2015 standard and Lean principles improves the situation and helps improve production quality. However, in the conditions of modern competition, when production deadlines have a direct correlation with the financial wellbeing of the company, there is a need to introduce more modern, adaptive management technologies, for example, to make a transition to project management models even in such conservative areas as shipbuilding and construction. In this context, managing departments responsible for quality requires the manager not only to know the basic principles of forming and using project teams, but also to have the skills to apply these methods. The focus is gradually shifting towards “soft skills”, since the project management methodology, at its core, implies the correct distribution of roles between participants, identifying their strengths, which in turn is the result of a reasonable and professional approach to searching and hiring employees. Mental qualities and way of thinking prevail over applied knowledge and are more valuable for the leader and the company. This article analyzes the problems and challenges facing managers and the reasons for the decline in the overall quality level of large companies. In addition, the article provides practical examples indicating tools for improving the quality management system and describes the economic effect of introducing these tools.

Exploring the mechanism of digital technology affordance on manufacturing enterprises’ digital competitive advantage

PurposeDigital technology is crucial to improving a firm’s core competitiveness. However, the existing research on the relationship therein shows heterogeneity. Using digital technology can enhance competitive advantage, which is crucial for enterprises and scholars. Thus, based on the digital technology affordance theory, this study explores the relationship between digital technology affordance and digital competitive advantage.Design/methodology/approachSurvey data were collected from 509 large and medium-sized manufacturing enterprises in China, and multiple regression and structural equation modelling were used to test the hypotheses. Specifically, we discuss the mediating role of digital business capability and the moderating role of organisational legitimacy.FindingsEditability, association and visibility positively affect digital competitive advantage, and their coordination is strong. Further, they can help enterprises gain a competitive advantage through the mediating role of digital business capability (digital strategy, digital integration and regulation). However, the influence effect and action path differ per in different dimensions. Organisational legitimacy positively moderates the mediating effect of digital integration and regulation, and there is a moderated mediating effect. However, the moderating effect on the mediating effect of digital strategy is not significant.Originality/valueExisting studies neglect the relationship between the coordination of digital technology functions and digital competitive advantage. This study provides a new theoretical explanation for an in-depth understanding of these issues. These findings promote the development of innovation theory and provide valuable insights for guiding the application of digital technology in enterprises.

Digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms in the Industry 4.0-based Slovak labor market

Research background: On the basis of an analysis of the current situation and expectations in the field of implementation of the elements of the Industry 4.0 concept, the purpose of this paper is to identify the effects on the labor market in large manufacturing enterprises in the Slovak Republic. Purpose of the article: The presented work has a theoretical-empirical nature and consists of a theoretical section and a practical section, which includes statistical indicator analysis and quantitative research. In the theoretical section, the paper discusses the issue of Industry 4.0 in general, with a focus on its impact on the labor market, thus laying the groundwork for future research on the subject. Methods: The output of this work is an analysis of selected indicators of the manufacturing industry sector in the Slovak Republic, based on the most recent employment data analysis in the first stage and quantitative research survey in the second stage, with the respondents being manufacturing industry companies operating in the Slovak Republic, and whose primary objective is to determine the current status of the implementation of the elements and technologies of Industry 4.0 in production companies in the Slovak Republic, as well as the factors influencing this situation, such as digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms. Findings & value added: The research findings indicate that the degree of digitization adopted by businesses in the Slovak Republic is comparatively less robust and more sluggish to adapt. This is primarily attributable to the underdeveloped educational system, population reluctance, self-actualization, and inadequate state support. Recommendations for the Slovak market aim to increase the digital proficiency of businesses and of the general populace through various means, such as reforming legislation, enhancing state support for entrepreneurs, and modifying the education system, constituting the added value of the work.

Collaborative Planning in a Two-Stage Supply Chain: Transparent Coordination, Distributed Response, Integrated Optimization

In the competitive global market, large manufacturing enterprises are striving to achieve a competitive edge through constructing a transparent collaborative supply chain. One of the important and effective ways is to cooperate with distributed response from suppliers in the operation of the supply chain, such as production planning. This article proposes a new collaborative planning mode in a two-stage supply chain considering the supplier part production and product assembly simultaneously. In this context, a mathematical model is formulated and the integrated optimization method is proposed. To reduce the complexity of the problem, the part production decisions of suppliers are first addressed by some lemmas. Then, the transformation process of the mathematical model is described as the decision variables gradually decrease. On this basis, we further propose a LIMA-GVNDS (less is more approach-general variable neighborhood decomposition search) algorithm to solve the integrated supply chain planning problem for the assembly manufacturing enterprise. The objective is to minimize the total supply chain costs. A new neighborhood structure is designed according to the problem characteristics and a series of variable neighborhood descent (VND) variants are proposed. Some computational experiments are carried out, and the results show that the proposed LIMA-GVNDS algorithm has obtained an average 3.69%, 13.07%, 2.47%, and 2.99% improvement of objective value against the other four meta-heuristic algorithms. The model transformation and the integrated optimization method proposed in this article can provide a reference for large manufacturing enterprises to formulate collaborative planning in a transparent supply chain network.

Possibilities of automating the plant personnel recruitment

The presented research is devoted to determining the possibilities of introducing information technologies in the work of the human resources department of the enterprise for the personnel selection activities automation. The article substantiates the relevance of studying the problem of “personnel shortage” at Russian enterprises in general and in relation to the production enterprise of the Sverdlovsk region. The authors carried out a comparative analysis of the wages at the analyzed enterprise with the average level in the metallurgical industry and in the Sverdlovsk region. Based on the results of the empirical study, the recruitment process’ components are highlighted, the results of the analysis of the photograph of working hours of recruitment specialists of a large manufacturing enterprise in the Sverdlovsk region are presented. The reserves of increasing the productivity of labor time use of human resources specialists of the production enterprise through the automation of a number of processes have been revealed. Recommendations for the introduction of information technologies in improving the recruitment system of the production enterprise are developed. The research paper may be of interest to specialists of personnel management services of large production and industrial enterprises in terms of analyzing the possibilities of automating the business process of personnel recruitment.

How flexible leadership ability affects manufacturing enterprises' digital transformation willingness: The role of innovation commitment and environmental dynamics.

Existing studies have recognized the significance of leadership ability on enterprises' digital transformation. However, few of them pay attention to the mechanism of flexible leadership ability (FLA) on digital transformation willingness (DTW). This study aims to explore the influence mechanism of FLA on DTW based on the ability-behavior-purpose logical framework. Survey data is collected from 509 large and medium-sized manufacturing enterprises in China, and multiple regression and PROCESS Macro methods are used for hypothesis testing. This study mainly discusses the impact of FLA on DTW, as well as the mediating role of innovation commitment (IC) and the moderating role of environmental dynamics (ED). Results show that FLA promotes DTW directly (β = 0.574, p<0.001) and indirectly, that is, through IC (the main effect decreased from (β = 0.574, p<0.001) to (β = 0.40, p<0.001). Additionally, the moderating role of ED affects the direct path of FLA on DTW (β = 0.167, p<0.001) as well as the two indirect paths (β = 0.196, p<0.001; β = 0.104, p<0.01). The findings contribute to the advancement of flexible leadership theory, and provide practical advice for enterprises on how to implement digital transformation.

Product order-demand prediction model based on random forest

This study aims to develop a decision support model based on product order data analysis and demand forecasting. By analyzing the shipment data of a large manufacturing enterprise from September 2015 to December 2018, we establish an accurate prediction model for the demand in the next three months of a large manufacturing enterprise. Quarterly and monthly variables capture trends and seasonal variation by adjusting hyperparameters and cross-validation using a random forest algorithm. The results show that the mean absolute error (MAE) on the test set is 8.965, the root mean square error (RMSE) is 11.369, the relative mean absolute error (MAPE) is 8.256%, and the coefficient of determination (R²) is 0.826. These indicators confirm that the model can accurately predict the target variable, with little difference from the true value, and show good predictive power and fit. The monthly model has high accuracy and stability and can effectively support production and supply chain planning to meet future needs. This study confirms the potential of product order data analysis and demand prediction models to improve the efficiency and competitiveness of enterprises and provides a valuable reference for the research and practice in related fields.

Blade sequencing optimization of aero-engine based on deep reinforcement learning

The unreasonable sorting of single-stage rotor blades leads to the over-tolerance of rotor unbalance, which is the main cause of excessive engine vibration. Aiming at the problems of long search time, poor repeatability, weak adaptability and difficulty in obtaining global optimum by using heuristic algorithm for blade sorting, this paper presents a deep reinforcement learning method is proposed to solve the blade ordering problem. A pointer network model including an encoder and a decoder structure is established. For the case where the blade data cannot be obtained, the unbalance of the single-stage rotor is used as the reward function, and the pointer network model is trained by the Actor Critic reinforcement learning algorithm. The experimental results show that the trained enhanced pointer network model can directly perform end-to-end reasoning on the input sequence, avoiding the iterative solution process of traditional heuristic algorithms, and has high solution efficiency. Using the enhanced pointer network blade sorting optimization model in this paper to sort a set of blade sequences, the unbalanced value of the rotor after sorting is 14.78 g.mm, which is 84.8% better than the genetic algorithm, and the search speed is increased by 95.9%. The results show that the method can quickly and accurately give the arrangement order of the leaves, and the proposed model has generalization. It can provide a reliable measurement method for rotor assembly measurement of large engine manufacturing enterprises such as China Aero-Engine Company.

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization.

Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors such as reporting mistakes made by personnel or environmental changes, the actual data of the demand for spare parts are prone to abnormal variations. It is thus hard to capture the evolutional pattern of the demand for spare parts by traditional time series forecasting methods. The reliability of prediction results is also reduced. To address these concerns, this paper proposes a tensor optimization-based robust interval prediction method of intermittent time series for the aftersales demand for spare parts. First, using the advantages of tensor decomposition to effectively mine intrinsic information from raw data, a sequence-smoothing network based on tensor decomposition and a stacked autoencoder is proposed. Tucker decomposition is applied to the hidden features of the encoder, and the obtained core tensor is reconstructed through the decoder, thus allowing us to smooth outliers in the original demand sequence. An alternating optimization algorithm is further designed to find the optimal sequence feature representation and tensor decomposition factors for the extraction of the evolutionary trend of the intermittent series. Second, an adaptive interval prediction algorithm with a dynamic update mechanism is designed to obtain point prediction values and prediction intervals for the demand sequence, thereby improving the reliability of the forecast. The proposed method is validated using the actual aftersales data from a large engineering manufacturing enterprise in China. The experimental results demonstrate that, compared with typical time series prediction methods, the proposed method can effectively grab the evolutionary trend of various intermittent series and improve the accuracy of predictions made with small-sample intermittent series. Moreover, the proposed method provides a reliable elastic prediction interval when distortion occurs in the prediction results, offering a new solution for intelligent planning decisions related to spare parts in practical maintenance.

Technical potential of the SAP HANA platform

The article describes the technical capabilities of the SAP HANA platform and database, which is the basis for the deployment of the SAP S/4 HANA innovative ERP system and modules derived from it. The principles of operation, the main tools and capabilities of the platform are described. The requirements for software and hardware for the deployment and maintenance of this platform at large manufacturing enterprises are defined. Practical studies of the speed of work on this platform and an cverview of some tools have been carried out. The platform is developed using advanced technologies that provide high‑speed information processing and secure storage, meeting the requirements of modern business and users.

Why Does Developmental Feedback Foster Employee Job Performance? The Mediating Role of Job Crafting

As a kind of job characteristic, feedback often occurs in the organization in order to improve employee performance. Based on leadership identity theory, this study developed a conceptual model involving supervisor developmental feedback, job crafting and job performance. the authors then targeted a large manufacturing enterprise in Hunan, China, obtaining 337 valid data using an on-site questionnaire survey. The data was analyzed using a hierarchical linear regression method, showing that supervisor developmental feedback was positively related to job crafting and employee job performance, and this job crafting was positively associated with employee job performance and partially mediated the relationship between supervisor developmental feedback and employee job performance. Managerial implications and directions for further research are discussed.

Enhancing Cybersecurity in Large Manufacturing Enterprises: A Strategic Approach to Protecting Operational Technology Systems

In the transformative landscape of Industry 4.0, the robustness of cybersecurity measures for Operational Technology (OT) systems within expansive manufacturing enterprises emerges as a paramount concern.

Does green innovation mediate corporate social responsibility and environmental performance? Empirical evidence from emerging markets

PurposeThis paper examines the mediation role of green innovation in the relationship between corporate social responsibility and environmental performance of manufacturing firms in Ghana.Design/methodology/approachThe paper chose African emerging markets and surveyed managers from manufacturing firms. With 301 questionnaires qualified for this study’s final analyses, the authors adopt the multiple regression with mediation models to estimates the nexus among study variables.FindingsResults evidence that both corporate social responsibility and green innovation has a positive and significant impact on environmental performance. Interestingly, the authors find that corporate social responsibility significantly improves environmental performance through green innovation indicating that firms could essentially build their dynamic resource and innovation capabilities in sustainability leading to enhanced environmental performance.Research limitations/implicationsThis paper develops a dynamic resource-based view of firm environmental performance illustrating how firms use resources to build strategic capabilities for competitive advantage, which leads to improved environmental performance. The paper highlights the mediation role of green innovation on corporate social responsibility and environmental performance relationships.Practical implicationsThis study's results provide significant insights to owners and managers of manufacturing companies to integrate corporate social responsibility and green innovation to ensure environmental performance and sustainability. Furthermore, policy makers should encourage green innovation when design sustainable development systems in the manufacturing industry.Originality/valueThe paper provides a valuable model showing how green innovation mediates corporate social responsibility to improve environmental performance and build competitive advantages considering both small, medium, and large manufacturing enterprises in emerging countries.

Analyzing the Relationship between Digital Transformation Strategy and ESG Performance in Large Manufacturing Enterprises: The Mediating Role of Green Innovation

In the era of the digital economy, digital technology brings new opportunities for enterprises’ development. The degree of enterprises’ digital transformation determines their development level and potential. At present, China’s “double carbon” policy is having a profound impact on the industry. The relationship between digital transformation strategy and environmental, social, and governance (ESG) performance is analyzed based on the digitalization and sustainable development goals of enterprises, and on the basis of positioning enterprise digital transformation level through the strategic alignment model (SAM). Data are collected by questionnaire survey from 224 large manufacturing enterprises in China as a sample for empirical testing by the hierarchical regression method. The empirical results show the following. (1) Digital transformation strategy has a direct positive and significant impact on enterprise ESG performance. Specifically, it is reflected in the two dimensions of digital transformation strategy, namely business digitalization and platform digitalization, which have a significant direct positive impact on enterprise ESG performance. (2) Enterprise green innovation plays a significant intermediate role in promoting the relationship between digital transformation strategy and enterprise ESG performance. (3) The two dimensions of green innovation, namely green process innovation and green product innovation, both play a partial mediating role in promoting business digitalization and platform digitalization development level to enterprise ESG performance, respectively. It can be concluded that business digitalization and platform digitalization can improve enterprise ESG performance by promoting green process innovation and green product innovation. Therefore, enterprises should clarify the direction of development of their digital transformation strategy, emphasize green innovation, and continuously improve their ESG performance to create favorable conditions for achieving sustainable development.

Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.

Managers’ service time determined key motives and engagement

Introduction.The article is devoted to the problem of engagement and its relationship with the satisfaction of the key motives for the managers with different work experience in the company. The urgency of solving this problem is explained by the close relationship between the managers’ engagement and the company’s performance indicators, which, under the influence of structural transformation of socio-economic processes, stimulates the continuous search for optimal algorithms for managing it. Purpose. The keyobjective of the work is to present the results of a correlation analysis of the motivation sign and engagement values for the managers of a large industrialenterprise during the entire period of work in a company. Materials and Methods. The methodology of the research is based on modern theories of management and personnel management, the humanistic concept of people management. Empirically, the research exploits the results of a surveyamong 629 employees of the “managers” category (59.7% of the total numberof employees in this category) from a large manufacturing enterprise. Results. The paper justifies a close correlation between the satisfaction of the key motives at work and engagement of the managers from the analyzed enterprise.The authors show that there are significant differences in a number of key motives, their satisfaction, as well as in terms of indicators of involvement in the company’s activities, depending on the factor “service time at the enterprise”. Conclusion. The work can serve as a basis for further study of the possibilities of managing the involvement of managers, its relationship with the satisfaction of key work motives under the influence of various external and internal factors.

Family-Work Conflict and Successful Aging at Work of Employees in Manufacturing Enterprises in North China.

Successful aging at work is a new idea for enterprises to develop and utilize older employees under the background of population aging. However, there is a lack of research on the effect of family-work conflict on successful aging at work. This study explored how family-work conflict affective successful aging at work through the mediating roles of occupational future time perspective and the moderating role of flexible work arrangements perception. In study 1 (scenario-based experiment, N=107) recruited full-time employees working through the Credamo platform as experimental subjects, tested the causal relationship between family-work conflict and successful aging at work. In study 2 (questionnaire survey, N=349), questionnaires were distributed to large manufacturing enterprises in North China, and a two-wave time-lagged survey design was used to test the full model. The results show that family-work conflict has a negative impact on successful aging at work; occupational future time perspective plays a mediating role in the relationship between family-work conflict and successful aging at work; flexible work arrangements perception moderated the mediating path via occupational future time perspective, and the indirect effect of occupational future time perspective decreased when flexible work arrangements perception increased. This study enriches the research on the relationship between family-work conflict and successful aging at work in theory, and has important guiding significance for enterprises to build an inclusive and aging human resource management system in practice.