Abstract Big data analytics is becoming very popular concept in academia as well as in industry. It has come up with new decision tools to design data-driven supply chains. The manufacturing industry is under huge pressure to integrate sustainable practices into their overall business for sustainbale operations management. The purpose of this study is to analyse the predictors of sustainable business performance through big data analytics in the context of developing countries. Data was collected from manufacturing firms those have adopted sustainable practices. A hybrid Structural Equation Modelling - Artificial Neural Network model is used to analyse 316 responses of Indian professional experts. Factor analysis results shows that management and leadership style, state and central-government policy, supplier integration, internal business process, and customer integration have a significant influence on big data analytics and sustainability practices. Furthermore, the results obtained from structural equation modelling were feed as input to the artificial neural network model. The study findings shows that management and leadership style, state and central-government policy as the two most important predictors of big data analytics and sustainability practices. The results provide unique insights into manufacturing firms to improve their sustainable business performance from an operations management viewpoint. The study provides theoretical and practical insights into big data implementation issues in accomplishing sustainability practices in business organisations of emerging economies.
This study examines the composition of attributes among members of the Production and Operations Management Society (POMS). We analyze POMS membership data from 2017 to 2023 and conference registration data from 2011 to 2024. Specifically, the study seeks to: (1) describe the current composition of POMS members and conference registrants in terms of gender, academic rank, and country of affiliation, and (2) provide recommendations for improving representation within professional societies.
We consider a production-inventory system in which production relies on a self-renewable natural resource. The resource regenerates at a rate that depends on its current stock as well as random environmental factors. In each period, the firm decides on the production quantity, facing stochastic demand and limited resource. This type of system is common in industries such as fishing and logging. We model the resource’s self-renewal using a generating function and formulate the production optimization problem as a dynamic program. We find that because of the renewable nature of the resource, the optimal stock level does not necessarily increase with the available resource quantity, and could become higher than the optimal stock level for a benchmark system where resource is unlimited. In a special case with deterministic demand and environment, we show that it could be optimal to halt production even with zero inventory left, in order to preserve the growth of resource. On the other hand, it could also be optimal to raise the inventory level to be higher than demand, when overpopulation becomes an issue. Moreover, a higher market price, which stems from adverse environmental conditions and signals lower future yields, can incentivize the firm to reduce production and conserve resources for future growth. In a deterministic environment where the inventory is fully perishable, we analytically characterize the long-run dynamics of the resource quantity. In particular, sustainable production is more achievable for low-margin products, and a myopic production firm does not necessarily deplete the resource. We conduct numerical experiments using real-world salmon population data to test the robustness of our findings in general systems with stochastic environment and non-perishable products. The results show that ignoring either the resource constraint or the self-renewal dynamics, could lead to high operational costs and undermine resource sustainability.
Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R<sup>2</sup> value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R<sup>2</sup> = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control.
Background: Shared leadership is regarded as a fundamental approach to complexity leadership theory in terms of adaptability and flexibility. It emerges from communication among team members in a complex environment and consists of three dimensions: task coordination, personal support, and information sharing. Purpose: This study investigates shared leadership and its dimensions which are task coordination, personal support, and information sharing using social network analysis. By incorporating social network theory, the social and relational aspects of shared leadership can be revealed and emphasized. Study design/methodology/approach: Social network analysis was used to test the hypotheses on the data collected from the employees of a tourism organization. Findings/conclusions: The findings indicate that the individuals in task coordination, personal support and information sharing networks have a medium or low percentage of degree centrality in the social networks of their units or departments. The social networks of task coordination, personal support and information sharing have a high percentage of degree density when all individuals are treated as a total network and individuals in different departments and units as separate networks. This situation is led by the more balanced distribution of the power among the actors, dense communication between the members and intense network relations in task coordination, personal support and information sharing networks. Limitations/future research: The present study focuses only on internal network relations. As a future body of work, the study could be expanded to include both external and internal network relations to provide a wider understanding of the shared leadership concept. As another future body of work, to reach more generalizable results, this study can be expanded with a meta-analysis that will be performed on the results obtained by applying the survey on other organizations and processing the data collected with social network analysis methods again.
Production management. Operations management, Personnel management. Employment management
Aim: The main objective of this study was to evaluate the impact of biological watershed management measures on soil loss using the RUSLE model, remote sensing, and geographic information systems.
Material & Method: The study area, Chikan and Morzian watershed, is one of the sub-basins of the Darudzen dam basin. In this study, using the RUSLE model and based on remote sensing data, soil loss was estimated over 29 years at 4 time points (1992, 2003, 2017, and 2021). In order to estimate the amount of soil erosion, the map of rain erosion factors (R), soil erodibility (K), topography (LS), vegetation cover (C), and protection factor (P) at the catchment level was first prepared using the relevant instructions in the RUSLE model. In order to study the effect of biological watershed management measures, assuming that the other factors of the model remain constant during the study period, the changes in factor C under the effect of biological measures were studied.
Finding: The results of the C-factor estimations showed that this factor was 0.83±0.29, 0.80±0.28, 0.76±0.28 and 0.76±0.28 in the years 1992, 2003, 2017, and 2021, respectively. The average amount of soil erosion in the Chikan and Morzian basins during 1992, 2003, 2017, and 2021 was calculated as 16.88±20.19, 16.32±19.52, 15.41±18.29, and 15.80±18.82, respectively.
Conclusion: The results indicated that the implementation of biological operations during this period decreased soil erosion. In conclusion, the implementation of biological operations is a useful and reliable measure to reduce soil erosion in degraded watersheds. However, other functions of this operation, such as fodder production, run-off reduction, recreational value, etc., can be added to its soil protection function.
Innovation: One of the most innovative and practical aspects of the research is using remote sensing techniques to study the amount of soil loss over time under the influence of biological watershed management measures.
The effective implementation of sustainable forest management depends largely on carrying out forest operations in a sustainable manner. Climate change, as well as the increasing demand for forest products, requires a re-thinking of forest operations in terms of sustainability. In this context, it is important to understand the major driving factors for the future development of forest operations that promote economic, environmental and social well-being. The main objective of this paper is to identify important issues concerning forest operations and to propose a new paradigm towards sustainability in a changing climate, work and environmental conditions. Previously developed concepts of forest operations are reviewed, and a newly developed concept - Sustainable Forest Operations (SFO), is presented. Five key performance areas to ensure the sustainability of forest operations include: (i) environment; (ii) ergonomics; (iii) economics; (iv) quality optimization of products and production; and (v) people and society. Practical field examples are presented to demonstrate how these five interconnected principles are relevant to achieving sustainability, namely profit and wood quality maximization, ecological benefits, climate change mitigation, carbon sequestration, and forest workers' health and safety. The new concept of SFO provides integrated perspectives and approaches to effectively address ongoing and foreseeable challenges the global forest communities face, while balancing forest operations performance across economic, environmental and social sustainability. In this new concept, we emphasize the role of wood as a renewable and environmentally friendly material, and forest workers' safety and utilization efficiency and waste management as additional key elements of sustainability.
Dinata Steven, Bawono Baju, Dharsono Wardana W.
et al.
Trolley rack is a material handling that significantly affects production productivity in Indonesian Tea factories. The design of this tool needs to consider the anthropometric approach. This study raises the problem of the dimensions of the trolley racks used in the industry without thinking about the anthropometric approach. Of the twenty-two trolley rack operators, 92.5% of workers experienced back pain when operating the equipment. This problem has an impact on musculoskeletal disorders (MSDs), which causes a decrease in effectiveness and workers. The REBA and Nordic Body Map Questionnaire methods are used to obtain optimal operator posture analysis related to the redesign of the trolley rack. Five anthropometric dimensions of a comfortable trolley rack were obtained, including shoulder height (SH), standing shoulder width (STW), leg length (FL), foot width (FW), and hand grip width (HGW). The final anthropometric dimensions of this tool are SH = 126 cm, STW = 33 cm, FL = 11 cm, FW = 13 cm, and HGW = 5 cm. Using new trolley racks in this study increased productivity by 80%.
ABSTRACT The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties.
As empresas são mais competitivas, se elas tiverem um controle eficiente dos seus suprimentos. A aplicação da pesquisa operacional, com a utilização de ferramentas computacionais, pode ser uma alternativa de auxílio para evitar problemas no processo ferroviário. Esta pesquisa teve como objetivo, utilizar da mensuração dos parâmetros oriundos da Teoria de Filas e da simulação com o software Arena, além de ter um melhor diagnostico da previsão dos suprimentos na restauração de truques em uma oficina da MRS Logística. Com o estudo de caso aqui descrito, foi possível ter uma reflexão sobre a importância da simulação na produtividade, pois verificou que a variação do tempo de produção da manutenção, pode causar problemas no planejamento orçamentário do processo de manutenção dos truques.
Production management. Operations management, Production capacity. Manufacturing capacity
Artificial intelligence (AI) models can help investors find portfolios in which the focus is to optimize the risk-return relationship. There are several algorithms and techniques in the literature that allow the application of tests to a set of historical data for the selection and validation of investment portfolios. Based on this, this research intends to examine the contribution of the main machine learning techniques used in portfolio management through a systematic literature review. By using the Methodi Ordinatio for selection and ranking of articles, we classified papers considering object of study, type of AI used, period of analysis, data frequency, balance and cardinality. In addition, we detail the main contributions and trends conceived until the year 2020. Therefore, our findings reveal gaps and suggest future works on the topic.
Production management. Operations management, Production capacity. Manufacturing capacity
Abstract The notion of Internet of Things (IoT), as well as related topics such as Cyber-Physical Systems, Industrie 4.0 and Smart Manufacturing are currently attracting a lot of attention within the process and manufacturing industries. Clearly, IoT offers many potential applications for automation, ranging from engineering installation of new plants to production management and more intelligent maintenance schemes including novel sensor technologies. The focus of this paper is, however, on the control and operations. Most likely IoT leads to new system architectures where open standards play a significant role. Through better connectivity, information will be much more easily available, which could result in that previously isolated functions will become more closely integrated. Here modeling at the right level of fidelity will be absolutely key. It can be expected that the importance of optimization will increase and this paper discusses some aspects related to the opportunities, challenges and changes triggered by IoT.