Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.
We seek to guide operations management (OM) research on the implementation of supply chain traceability systems by identifying business requirements and the factors critical to successful implementation. We first motivate the need for implementing traceability systems in two very different industries—cobalt mining and pharmaceuticals—and present business requirements and critical success factors for implementation. Next, we describe how we carried out thematic analysis of practitioner and scholarly articles on implementing blockchain for supply chain traceability. Finally, we present our results pertaining to the needs of different stakeholders such as suppliers, consumers, and regulators. The business requirements for traceability systems are curbing illegal practices; improving sustainability performance; increasing operational efficiency; enhancing supply‐chain coordination; and sensing market trends. Critical success factors for implementation are companies’ capabilities; collaboration; technology maturity; supply chain practices; leadership; and governance of the traceability efforts. These findings provide a nascent measurement model for empirical work and a foundation for descriptive and normative research on blockchain applications for supply chain traceability.
Daniel Nascimento, Viviam Alencastro, O. Quelhas
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Purpose The purpose of this paper is to explore how rising technologies from Industry 4.0 can be integrated with circular economy (CE) practices to establish a business model that reuses and recycles wasted material such as scrap metal or e-waste. Design/methodology/approach The qualitative research method was deployed in three stages. Stage 1 was a literature review of concepts, successful factors and barriers related to the transition towards a CE along with sustainable supply chain management, smart production systems and additive manufacturing (AM). Stage 2 comprised a conceptual framework to integrate and evaluate the synergistic potential among these concepts. Finally, stage 3 validated the proposed model by collecting rich qualitative data based on semi-structured interviews with managers, researchers and professors of operations management to gather insightful and relevant information. Findings The outcome of the study is the recommendation of a circular model to reuse scrap electronic devices, integrating web technologies, reverse logistics and AM to support CE practices. Results suggest a positive influence from improving business sustainability by reinserting waste into the supply chain to manufacture products on demand. Research limitations/implications The impact of reusing wasted materials to manufacture new products is relevant to minimising resource consumption and negative environmental impacts. Furthermore, it avoids hazardous materials ending up in landfills or in the oceans, seriously threatening life in ecosystems. In addition, reuse of wasted material enables the development of local business networks that generate jobs and improve economic performance. Practical implications First, the impact of reusing materials to manufacture new products minimises resource consumption and negative environmental impacts. The circular model also encourages keeping hazardous materials that seriously threaten life in ecosystems out of landfills and oceans. For this study, it was found that most urban waste is plastic and cast iron, leaving room for improvement in increasing recycling of scrap metal and similar materials. Second, the circular business model promotes a culture of reusing and recycling and motivates the development of collection and processing techniques for urban waste through the use of three-dimensional (3D) printing technologies and Industry 4.0. In this way, the involved stakeholders are focused on the technical parts of recycling and can be better dedicated to research, development and innovation because many of the processes will be automated. Social implications The purpose of this study was to explore how Industry 4.0 technologies are integrated with CE practices. This allows for the proposal of a circular business model for recycling waste and delivering new products, significantly reducing resource consumption and optimising natural resources. In a first stage, the circular business model can be used to recycle electronic scrap, with the proposed integration of web technologies, reverse logistics and AM as a technological platform to support the model. These have several environmental, sociotechnical and economic implications for society. Originality/value The sociotechnical aspects are directly impacted by the circular smart production system (CSPS) management model, since it creates a new culture of reuse and recycling techniques for urban waste using 3D printing technologies, as well as Industry 4.0 concepts to increase production on demand and automate manufacturing processes. The tendency of the CSPS model is to contribute to deployment CE in the manufacture of new products or parts with AM approaches, generating a new path of supply and demand for society.
Customer service has evolved beyond in-person visits and phone calls to include live chat, AI chatbots and social media, among other contact options. Service providers typically refer to these contact modalities as "channels". Within each channel, customer service agents are tasked with managing and resolving a stream of inbound service requests. Each request involves milestones where the agent must decide whether to keep assisting the customer or to transfer them to a more skilled -- and often costlier -- provider. To understand how this request resolution process should be managed, we develop a model in which each channel is represented as a gatekeeper system and characterize the structure of the optimal request resolution policy. We then turn to the broader question of the firm's customer service design, which includes the strategic problem of which channels to deploy, the tactical questions of at what level to staff the live-agent channel and to what extent to train an AI chatbot, and the operational question of how to control the live-agent channel. Examining the interplay between strategic, tactical, and operational decisions through numerical methods, we show, among other insights, that service quality can be improved, rather than diminished, by chatbot implementation.
We consider singular control in inventory management under Knightian uncertainty, where decision makers have a smooth ambiguity preference over Gaussian-generated priors. We demonstrate that continuous-time smooth ambiguity is the infinitesimal limit of Kalman-Bucy filtering with recursive robust utility. Additionally, we prove that the cost function can be determined by solving forward-backward stochastic differential equations with quadratic growth. With a sufficient condition and utilising variational inequalities in a viscosity sense, we derive the value function and optimal control policy. By the change-of-coordinate technique, we transform the problem into two-dimensional singular control, offering insights into model learning and aligning with classical singular control free boundary problems. We numerically implement our theory using a Markov chain approximation, where inventory is modeled as cash management following an arithmetic Brownian motion. Our numerical results indicate that the continuation region can be divided into three key areas: (i) the target region; (ii) the region where it is optimal to learn and do nothing; and (iii) the region where control becomes predominant and learning should inactive. We demonstrate that ambiguity drives the decision maker to act earlier, leading to a smaller continuation region. This effect becomes more pronounced at the target region as the decision maker gains confidence from a longer learning period. However, these dynamics do not extend to the third region, where learning is excluded.
Our research investigates the relationship between eco-innovation and earnings management among 567 firms listed on the FTSE All-Share Index from 2014 to 2022. By examining how sustainability-driven innovation influences financial reporting practices, we explore the strategic motivations behind income smoothing in firms engaged in environmental initiatives. The findings reveal a positive association between eco-innovation and earnings management, suggesting that firms may leverage ecoinnovation not only for environmental signalling but also to project financial stability and meet stakeholder expectations. The analysis further uncovers that the propensity for earnings management is amplified in firms facing financial constraints, proxied by low Whited-Wu (WW) scores and weak sales performance, and in those characterised by high financial opacity. We employ a robust multi-method approach to address potential endogeneity and selection bias, including entropy balancing, propensity score matching (PSM), and the Heckman Test correction. Our research contributes to the literature by providing empirical evidence on the dual strategic role of ecoinnovation -balancing sustainability signalling with earnings management, under varying financial conditions. The findings offer actionable insights for regulators, investors, and policymakers navigating the intersection of corporate transparency, financial health, and environmental responsibility.
[Significance]With the rapid development of robotics technology and the persistently rise of labor costs, the application of robots in facility agriculture is becoming increasingly widespread. These robots can enhance operational efficiency, reduce labor costs, and minimize human errors. However, the complexity and diversity of facility environments, including varying crop layouts and lighting conditions, impose higher demands on robot navigation. Therefore, achieving stable, accurate, and rapid navigation for robots has become a key issue. Advanced sensor technologies and algorithms have been proposed to enhance robots' adaptability and decision-making capabilities in dynamic environments. This not only elevates the automation level of agricultural production but also contributes to more intelligent agricultural management.[Progress]This paper reviews the key technologies of automatic navigation for facility agricultural robots. It details beacon localization, inertial positioning, simultaneous localization and mapping (SLAM) techniques, and sensor fusion methods used in autonomous localization and mapping. Depending on the type of sensors employed, SLAM technology could be subdivided into vision-based, laser-based and fusion systems. Fusion localization is further categorized into data-level, feature-level, and decision-level based on the types and stages of the fused information. The application of SLAM technology and fusion localization in facility agriculture has been increasingly common. Global path planning plays a crucial role in enhancing the operational efficiency and safety of facility aricultural robots. This paper discusses global path planning, classifying it into point-to-point local path planning and global traversal path planning. Furthermore, based on the number of optimization objectives, it was divided into single-objective path planning and multi-objective path planning. In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios.[Conclusions and Prospects]Currently, the challenges for facility agricultural robot navigation include complex scenarios with significant occlusions, cost constraints, low operational efficiency and the lack of standardized platforms and public datasets. These issues not only affect the practical application effectiveness of robots but also constrain the further advancement of the industry. To address these challenges, future research can focus on developing multi-sensor fusion technologies, applying and optimizing advanced algorithms, investigating and implementing multi-robot collaborative operations and establishing standardized and shared data platforms.
Alejandra de la Rica Escudero, Eduardo C. Garrido-Merchan, Maria Coronado-Vaca
Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not supported by data in high volatility markets. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, we developed a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management, integrating the Proximal Policy Optimization (PPO) with the model agnostic explainable techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent suggestions. To the best of our knowledge, our proposed approach is the first explainable post hoc portfolio management financial policy of a DRL agent. We empirically illustrate our methodology by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time.
As the Internet of Things (IoT) industry advances, the imperative to secure IoT devices has become increasingly critical. Current practices in both industry and academia advocate for the enhancement of device security through key installation. However, it has been observed that, in practice, IoT vendors frequently assign shared keys to batches of devices. This practice can expose devices to risks, such as data theft by attackers or large-scale Distributed Denial of Service (DDoS) attacks. To address this issue, our intuition is to assign a unique key to each device. Unfortunately, this strategy proves to be highly complex within the IoT context, as existing keys are typically hardcoded into the firmware, necessitating the creation of bespoke firmware for each device. Furthermore, correct pairing of device keys with their respective devices is crucial. Errors in this pairing process would incur substantial human and temporal resources to rectify and require extensive communication between IoT vendors, device manufacturers, and cloud platforms, leading to significant communication overhead. To overcome these challenges, we propose the OTA-Key scheme. This approach fundamentally decouples device keys from the firmware features stored in flash memory, utilizing an intermediary server to allocate unique device keys in two distinct stages and update keys. We conducted a formal security verification of our scheme using ProVerif and assessed its performance through a series of evaluations. The results demonstrate that our scheme is secure and effectively manages the large-scale distribution and updating of unique device keys. Additionally, it achieves significantly lower update times and data transfer volumes compared to other schemes.
S. B. Goswami, Ramyajit Mondal, Sanjib Kumar Mandi
ABSTRACT Rice is the most residue-producing crop in Asia (826 million tons) contributing 84% of total production of the world. Traditionally, rice straw is removed from fields for use as cattle feed and other purposes in South Asia. On average, rice crop residues contain 0.7% N, 0.23% P and 1.75% K. Therefore, the amount of NPK contained in rice crop residues produced is about 22.13 × 106 and 26.26 × 106 t year−1 in Asia and the world, respectively. Recently, with the advent of mechanized harvesting, farmers have been burning in situ large quantities of crop residues left in the field which interfere with tillage and succeeding operations for the subsequent crop, causing loss of nutrients and soil organic matter (SOM). On-field residue retention benefits soil health, soil water conservation, soil productivity and environment but there are several challenges in residue incorporation – physical problem of soil incorporation, labor intensive, fallow period and N immobilization. There are several off-field options for managing rice crop residues – palatable livestock feed, economic roof thatch for rural poor, rural residue composting, edible mushroom cultivation, biogas production and packaging of non-consumable items for transport.
Protecting natural forests such as those identified as high conservation value (HCV) areas may facilitate crop production due to the benefit from ecosystem services provided by biodiversity spill-over from adjacent forests. To investigate the effect of protecting contiguous and isolated forests adjacent to oil palm plantations on crop health, we measured the distance between oil palm plots and the continuous forest and forest patch boundaries. We surveyed 715 oil palm sample plots comprising 613 plots in large-scale oil palm plantation and 102 plots in smallholdings that were at least 300 m apart and had a radius of 100 m. Satellite imagery and ancillary spatial data from 2016, 2018, 2019 and 2020 of Negeri Sembilan, Malaysia were used to determine elevation and vegetation indices (VIs). The VIs derived were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Moisture Index (NDMI). Both NDVI and EVI are used to measure the vegetation greenness. The NDMI is used to determine the water content of plants. The VIs are crucial for a variety of applications, including vegetation monitoring, drought research, and agricultural operations. We then used generalized linear models (GLMs) to examine the relationship between VIs and stand-and landscape-level variables. Each VI was used as a response variable, with elevation, distance from continuous forest or forest patches, and oil palm management system (i.e., smallholding and industrial plantation) as explanatory variables. Our results revealed that the chlorophyll sensitive NDVI decreased with increasing distance from continuous forest, but increased away from the forest patches. In contrast, the dense vegetation sensitive EVI increased away from continuous forest, but decreased when distance from forest patches increased. Proximity to continuous forests or forest patches had no effect on the NDMI. All the vegetation indices were lower in smallholdings than industrial plantations. None of the vegetation indices were significantly influenced by elevation. Given that these indices predict palm health and yield, this pattern could result in greater ecosystem services that benefit oil palm growers in oil palm closer to some forest types through the spillover effects of forest biodiversity from continuous forests and forest patches. This study suggests that conservation and industry stakeholders should work together to strengthen the conservation of biodiverse continuous forests and forest patches in HCV standard to develop more-sustainable oil palm agriculture, because of their potential role in supporting ecosystem services.
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Why do industrial agricultural operations continue to displace smaller family farms in spite of their continued pollution of the natural environment and degradation of rural communities? Large-scale, specialized agricultural operations, such as concentrated animal feeding operations (or CAFOs), persist because they have an economic advantage over smaller, diversified farming operations. They have higher ecological and social costs but lower economic costs. This economic advantage is commonly referred to as economies of scale.
In economic theory, there are two types of economies of scale. Internal economies of scale refer to differences in the costs of production associated with different sizes of production units. In animal agriculture, “scale” refers to the number of hogs, poultry, milk cows, or beef cattle in a single farming operation or production unit. In field crop and pasture-based animal production, scale refers to the acres of land in a single production unit. External economies of scale, on the other hand, refer to differences such as the costs of fertilizer or feed, or the cost of complying with government regulations, for different sizes of management units. Management units may include one or more production units under single management or control (Ross, 2022). A single farm or production unit may comprise multiple parcels of land, but a farm management unit may comprise multiple farms that are managed as a single economic entity or unit. . . .
The Cardinality-Constrained Portfolio Selection Problem (CCPSP) consists of allocating resources to a limited number of assets. In its classical form, it is represented as a multi-objective problem, which considers the expected return and the assumed risk in the portfolio. This problem is one of the most relevant subjects in finance and economics nowadays. In recent years, the consideration of cardinality constraints, which limit the number of assets in the portfolio, has received increased attention from researchers, mainly due to its importance in real-world decisions. In this context, this paper proposes a new hybrid heuristic approach, based on a Non-dominated Sorting Genetic Algorithm with Local Search structures, to solve PSP with cardinality constraints, aiming to overcome the challenge of achieving efficient solutions to the problem. The results demonstrated that the proposed algorithm achieved good quality results, outperforming other methods in the literature in several classic instances.
Production management. Operations management, Production capacity. Manufacturing capacity