Hasil untuk "Production management. Operations management"

Hąbek Patrycja

The Corporate Sustainability Reporting Directive (CSRD) introduces stringent and standardised environmental, social, and governance (ESG) reporting obligations across the European Union, presenting significant challenges for manufacturing companies due to their resource-intensive operations and complex value chains. This study investigates the digital transformation of sustainability reporting by evaluating how ESG software tools support CSRD compliance. A comparative analysis was conducted on eight widely used ESG software solutions. Thirteen assessment criteria were applied, covering functionality, regulatory compliance, integration capabilities, cost transparency, and scalability. The role of artificial intelligence and advanced analytics in enhancing ESG data quality, automating reporting processes, and generating actionable insights is also explored as a critical enabler of reporting efficiency and accuracy. The findings show that while no tool meets all needs universally, specific solutions offer comprehensive capabilities for large manufacturers, while others are well-suited for SMEs. The study concludes with targeted software selection recommendations and outlines future research directions.

Ciachorowski Damian, Lis Anna Maria, Miranda Elem

This paper investigates how existing literature has approached the concept of sustainability in the context of digital transformation.

Prabhakar Maheswari, Purushothaman Raja, Manoj Karkee et al.

Deep learning plays an important role in automating various operations in fruit crop production including irrigation, nutrition management, yield estimation and harvesting. Yield estimation is essential in fruit crop production as it helps farmers optimize cultivation, harvesting, logistics and marketing operations. Furthermore, fruit detection and localization is a very important step in the development of an automated fruit harvesting system. Hence, an intelligent system was proposed in this study for apple fruit detection and localization using modified DeepLabv3+, semantic segmentation based architecture. The finetuned customizations (such as modifying the activation function, optimization technique and loss function) were performed in the original architecture of DeepLabv3+ and its performance was analyzed. The modified model was trained with the training dataset of 2600 apple tree images. Images were split into 80 % of training and 20 % of validation. The modified architecture was also compared with the other variants of DeepLabv3+ architectures. After training, the model was tested with the unobserved test dataset of 101 images. The test results demonstrated the Mean Accuracy (MAcc) of 98.58 % and the Mean Intersection over Union (MIoU) of 96.66 % without compromising the inference time (i.e., 15 ms). The proposed model revealed the improved results than the original model which attained a MAcc of 92.12 % and MIoU of 88.94 % for the same dataset with the inference time of 40 ms. To ascertain further, the modified model was compared with other single stage detectors, including Fully Convolutional Network (FCN) and U-Net. FCN attained a MAccandMIoU of 77.5 % and 77.27 %, respectively whereas U-Net resulted a MAcc and MIoU of 83.95 % and 81.09 %, respectively. Results demonstrated that the modified DeepLabv3+ with ResNet18 is capable of detecting the apple fruits by mitigating the effects of class imbalance which is the major drawback in single stage detectors. Further, better detection and localization of apple fruits can lead to the precise picking by the robotic system.

Antonio Brogi, Stefano Forti, Carlos Guerrero et al.

Orchestrating next gen applications over hterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisation capabilities of bacteria colonies, we propose a declarative, fully decentralised application management solution, targeting pervasive opportunistic Cloud-IoT infrastructures. We present acustomisable declarative implementation of the approach and validate its scalability through simulation over motivating scenarios, also considering end-user's mobility and the possibility to enforce application-specific management policies for different classes of applications.

Travon Lucius, Christian Koch, Jacob Starling et al.

We present a reinforcement-learning (RL) framework for dynamic hedging of equity index option exposures under realistic transaction costs and position limits. We hedge a normalized option-implied equity exposure (one unit of underlying delta, offset via SPY) by trading the underlying index ETF, using the option surface and macro variables only as state information and not as a direct pricing engine. Building on the "deep hedging" paradigm of Buehler et al. (2019), we design a leak-free environment, a cost-aware reward function, and a lightweight stochastic actor-critic agent trained on daily end-of-day panel data constructed from SPX/SPY implied volatility term structure, skew, realized volatility, and macro rate context. On a fixed train/validation/test split, the learned policy improves risk-adjusted performance versus no-hedge, momentum, and volatility-targeting baselines (higher point-estimate Sharpe); only the GAE policy's test-sample Sharpe is statistically distinguishable from zero, although confidence intervals overlap with a long-SPY benchmark so we stop short of claiming formal dominance. Turnover remains controlled and the policy is robust to doubled transaction costs. The modular codebase, comprising a data pipeline, simulator, and training scripts, is engineered for extensibility to multi-asset overlays, alternative objectives (e.g., drawdown or CVaR), and intraday data. From a portfolio management perspective, the learned overlay is designed to sit on top of an existing SPX or SPY allocation, improving the portfolio's mean-variance trade-off with controlled turnover and drawdowns. We discuss practical implications for portfolio overlays and outline avenues for future work.

Jesus Silva-Rodriguez, Xingpeng Li

The alternating direction method of multipliers (ADMM) is a powerful algorithm for solving decentralized optimization problems including networked microgrid energy management (NetMEM). However, its performance is highly sensitive to the selection of its penalty parameter \r{ho}, which can lead to slow convergence, suboptimal solutions, or even algorithm divergence. This paper evaluates and compares three district ADMM formulations to solve the NetMEM problem, which explore different methods to determine appropriate stopping points, aiming to yield high-quality solutions. Furthermore, an adaptive penalty heuristic is also incorporated into each method to analyze its potential impact on ADMM performance. Different case studies on networks of varying sizes demonstrate that an objective-based ADMM approach, denominated as OB-ADMM, is significantly more robust to the choice of \r{ho}, consistently yielding solutions closer to the centralized optimal benchmark by preventing premature algorithm stopping.

Etna Vianita

The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This  study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The  models evaluated  by  average  forecasting  error  rate  (AFER)  and  compared  with  existing methods.  AFER  value  1.24%  for  proposed method.

Mazur Magdalena, Korenko Maroš, Žitňák Miroslav et al.

The article deals with the use of the 5S methodology in an organization. It focuses on the implementation of 5S in an organization involved in the production and processing of metal components for the automotive industry. 5S is a Japanese methodology that was first applied in the automotive industry to improve productivity. The article is dedicated to the basic principles of lean manufacturing.The practical part analyses the state of the workplaces before the introduction of 5S and the actual implementation of this methodology. The implementation of 5S brings a number of benefits to the organization, including:

Michiel Van Kenhove, Maximilian Seidler, Friedrich Vandenberghe et al.

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications.

Ali Chouman, Dimitrios Michael Manias, Abdallah Shami

Experimentation in practical, end-to-end (E2E) next-generation networks deployments is becoming increasingly prevalent and significant in the realm of modern networking and wireless communications research. The prevalence of fifth-generation technology (5G) testbeds and the emergence of developing networks systems, for the purposes of research and testing, focus on the capabilities and features of analytics, intelligence, and automated management using novel testbed designs and architectures, ranging from simple simulations and setups to complex networking systems; however, with the ever-demanding application requirements for modern and future networks, 5G-and-beyond (denoted as 5G+) testbed experimentation can be useful in assessing the creation of large-scale network infrastructures that are capable of supporting E2E virtualized mobile network services. To this end, this paper presents a functional, modular E2E 5G+ system, complete with the integration of a Radio Access Network (RAN) and handling the connection of User Equipment (UE) in real-world scenarios. As well, this paper assesses and evaluates the effectiveness of emulating full network functionalities and capabilities, including a complete description of user-plane data, from UE registrations to communications sequences, and leads to the presentation of a future outlook in powering new experimentation for 6G and next-generation networks.

Saumya Kothari, Harsh Shah, Utkarsh Prajapati et al.

Mid-cap companies, generally valued between \$2 billion and \$10 billion, provide investors with a well-rounded opportunity between the fluctuation of small-cap stocks and the stability of large-cap stocks. This research builds upon the long-short equity approach (e.g., Michaud, 2018; Dimitriu, Alexander, 2002) customized for mid-cap equities, providing steady risk-adjusted returns yielding a significant Sharpe ratio of 2.132 in test data. Using data from 2013 to 2023, obtained from WRDS and following point-in-time (PIT) compliance, the approach guarantees clarity and reproducibility. Elements of essential financial indicators, such as profitability, valuation, and liquidity, were designed to improve portfolio optimization. Testing historical data across various markets conditions illustrates the stability and resilience of the tactic. This study highlights mid-cap stocks as an attractive investment route, overlooked by most analysts, which combine transparency with superior performance in managing portfolios.

Endy Tuhumury, Martinus Parnawa Putranta, Mpholle Clement Paepae

The quest to provide excellent services causes the aviation industry face challenges that can impair the well-being or “happiness” of its employees. Therefore, managing employee well-being is crucial for the industry to help its employees feel positive while serving the companies. This research aimed to examine the roles of followership styles, job satisfaction and perceived supervisor support in promoting psychological well-being in the Indonesian air transport operator contexts. Specifically, the research examined the impact of job satisfaction on psychological well-being and assessed whether job satisfaction itself was influenced by employees’ followership styles and their perceived supervisor support. A test was also performed to examine the moderating role of perceived supervisor support in the relationship between followership styles and job satisfaction. On-line questionnaires were distributed to potential respondents using a combination of convenience and purposive sampling.  A number of 109 non-managerial employees from several Indonesian commercial and non-commercial air transport operators involved.  Structural Equation Modeling was adopted to test the proposed hypotheses. The findings showed the majority of respondents enacted “exemplary followership” styles. This style positively related to job satisfaction. However, perceived supervisor support was not found to moderate the relationship. The managerial implication of the findings is outlined.

Ahmad Ebrahimi, laya olfat, Maghsood Amiri et al.

The current research has considered the design of the four-level supply chain of perishable goods, including manufacturing factories, distribution centers, wholesalers, and retailers, in conditions of uncertainty in important parameters. The aim is to make strategic and tactical decisions, including the location, number, and size of distribution centers and wholesalers, stock levels in stocking centers, determining the flow of goods between facilities at different supply chain levels, and choosing the type of means of transporting goods between facilities. This is achieved through a three-objective mathematical model. The goals include minimizing the expected total cost in the supply chain, achieving the shortest travel time of goods in the chain, and at the same time minimizing the amount of deviation from customer demand. The presented model tries to pay attention to environmental uncertainty and consider different operational scenarios, as well as the possible approach in important parameters. This takes into account the product life cycle, the different rate of spoilage of the goods in different storage facilities, the different capacity of the facilities in different scenarios, and considering different methods of product transportation with different rates of product spoilage. All of this aims to cover the lack of previous research in the field of perishable goods supply chain design. Considering the multi-objective nature of the model and the need to create flexibility in decision-making for decision-makers, this research uses Normal Boundary Intersection (NBI), which allows decision-makers to choose the most optimal solution according to the importance of different goals. GAMS 24 software and MILP solver were used to solve the mathematical model. Materials and Methods This study presents a multiobjective model for designing a four-echelon supply chain (SC) in the strategic and tactical levels for fixed lifetime perishable products. The targeted SC levels include production plants, distribution centers (DC), wholesalers, and retailers. The locations of the plants and retailers are predetermined, while the locations of DCs and wholesalers will be selected from potential locations. The elaborated model seeks to minimize the total cost and product transportation time in the SC and minimize expected demand deviation as well. The Normal Boundary Intersection (NBI) method is employed for solving the model, and GAMS software is used to determine the optimal values of decision variables. Results This study utilizes a case study of an Iranian broad dairy company that produces eleven product groups. Data for the study were collected from historical company records and expert interviews. According to the opinions of the experts, three different operational scenarios have been extracted, and the data related to each scenario, especially the customer demand, has been estimated according to historical data as well as the corrective opinions of the managers. The results of the proposed mathematical programming model showed that changes in demand did not have unexpected effects on the values of the objective function and did not change the general trend of the answer to the problem. On the other hand, changes in the percentage of perishability of the product had far less impact on the values of the objective functions as well as the membership function. The overall result is normal, and as a result, in general, these changes represent the stability of the model against the fluctuations of important parameters. A comparison of optimal results and reality reveals that the examined SC needs a redesign of its DCs and wholesalers' locations, and hybrid transportation methods should be used. Conclusion Supply chain design (SCD) of fixed lifetime perishable products at the strategic and tactical levels is indeed an important issue. By considering the research gap, this study developed a multi-objective and multi-level model for SCD of fixed lifetime perishable products, and new concepts such as varying perishability rates in storage and transportation facilities are considered. On the other hand, with regard to environmental uncertainty, important parameters such as demand and capacity of facilities are considered as probable parameters. Adding environmental and social factors as new objectives, hybrid transportation methods, and horizontal interactions in the same SC levels can be considered for model development. In order to solve the proposed model, NBI has been used, which has significant advantages compared to other solution methods. By turning the answer of the optimization model into a kind of decision-making problem, this technique gives flexibility to the decision-maker to choose the best solution for their supply chain design according to the weight of each goal. Also, the decision-maker can redesign and increase the adaptability of the supply chain by changing the important parameters of the problem over time.

Yohan Wismantoro, MG Westri Kekalih Susilowati, Amalia Nur Chasanah et al.

Environmental damage and sustainability become the center of attention along with climate change today. One of the interesting things is the change in behavior in the consumption of products categorized as "green", including in terms of choosing tourism destinations. Environmentally friendly tourism destinations or ecotourism is increasingly becoming an option for those who care about the environment. Ecotourism is also used by many countries, including travel agents, to increase their economic value or profit. This study aims to investigate how the role of Environmental Concern as antecendent of Ecoturism Visit Intention mediates the influence of Future Time perspective and Destination Image toward Ecotourism visit Intention.  This research involved 200 respondents who had visited ecotourism destinations at least twice, which was obtained using a convenience sample approach. The data were analyzed using Structural Equation Modelling (SEM) which were processed using the SmartPLS 4.0 statistical package. The result shows that Future Time perspective and Destination’s Image positively impact Ecotourism Intention.   Future Time perspective and Destination’s Image also influence Ecotourism Intention indirectly through Environmental Concern as the mediating variable. As an implication, the government and travel agents improve service readiness and perception of high-value propositions to develop a better destination image.

Kalle Koivisto, Toni Taipalus

Knowledge is considered an essential resource for organizations. For organizations to benefit from their possessed knowledge, knowledge needs to be managed effectively. Despite knowledge sharing and management being viewed as important by practitioners, organizations fail to benefit from their knowledge, leading to issues in cooperation and the loss of valuable knowledge with departing employees. This study aims to identify hindering factors that prevent individuals from effectively sharing and managing knowledge and understand how to eliminate these factors. Empirical data were collected through semi-structured group interviews from 50 individuals working in an international large IT organization. This study confirms the existence of a gap between the perceived importance of knowledge management and how little this importance is reflected in practice. Several hindering factors were identified, grouped into personal social topics, organizational social topics, technical topics, environmental topics, and interrelated social and technical topics. The presented recommendations for mitigating these hindering factors are focused on improving employees' actions, such as offering training and guidelines to follow. The findings of this study have implications for organizations in knowledge-intensive fields, as they can use this knowledge to create knowledge sharing and management strategies to improve their overall performance.

Wen Chiat Lee, Boo Ho Voon

Service sector is an important sector in Sarawak and it contributes about 36 percent to Sarawak’s Gross Domestic Product (GDP) in 2020.  The sector also contributes 56 percent of total employment of the state.  However, there are some challenges in service sector. Among the key challenges are low productivity growth, shortage of skilled workforce and weak internet connectivity in rural area that restricted the development of service sector.  This paper presents the performance of service sector in Sarawak and the challenges of the service sector.  The proposed recommendations to face the challenges include, providing training to semi-skilled and low-skilled workers to improve the productivity, providing quality and cheap food to attract tourists, and developing internet infrastructure in rural Sarawak.

Ji Fang, Vincent CS Lee, Haiyan Wang

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service. An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

Naderi Reihaneh, Nikabadi Mohsen Shafiei, Alem-Tabriz Akbar et al.

Environmental threats of coal usage in the electricity production combined with the consumption of renewable and non-renewable resources had led to worldwide energy challenges. The cost of coal mining and economical and environmentally sustainable usage of mined coal could be optimized by efficient management of coal supply chain. This paper provides a mathematical model for improving coal supply chain sustainability including the cost of exergy destruction (entropy). In the proposed method, exergy analysis is used to formulate the model considering not only economic costs but also destructed exergy cost, while genetic algorithm is applied to efficiently solve the proposed model. In order to validate the proposed methodology, some numerical examples of coal supply chains are presented and discussed to show the usability of the proposed exergetic coal supply chain model and claim its benefits over the existing models. According to the results, the proposed method provides 17.6% saving in the consumed exergy by accepting 2.7% more economic costs. The presented model can be used to improve the sustainability of coal supply chain for either designing new projects or upgrading existing processes.

Rachel D. Scarlett, Mangala Subramaniam, Sara K. McMillan et al.

Stormwater has immense impacts on urban flooding and water quality, leaving the marginalized and the impoverished disproportionately impacted by and vulnerable to stormwater hazards. However, the environmental health concerns of socially and economically marginalized individuals are largely underestimated. Through regression analysis of data from three longitudinal surveys, this article examines if and how an individual's race, gender, and education level help predict one's concern about and willingness to participate in stormwater management. We found that people of color, women, and less-educated respondents had a greater willingness to participate in stormwater management than White, male, and more-educated respondents, and their concern about local stormwater hazards drove their willingness to participate. Our analysis suggests that physical exposure and high vulnerability to stormwater hazards may shape an individual's concern about and willingness to participate in stormwater management.

Kwangmin Jung, Donggyu Kim, Seunghyeon Yu

This paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out-of-target-portfolio information that may be missed when one considers the Value at Risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of dimensionality can be overcome in the use of financial big data and discuss where and when benefits occur from a large number of assets. In this regard, the proposed approach is the first to suggest the use of financial big data to improve the accuracy of risk analysis. We compare the proposed model with benchmark approaches and empirically show that the use of financial big data improves small portfolio risk analysis. Our findings are useful for portfolio managers and financial regulators, who may seek for an innovation to improve the accuracy of portfolio risk estimation.