1. The Role and Functioning of the OECD 2. Sustainable Management of Resources 2.1 Waste Management 2.2 Biodiversity 3. Protection of Health and Safety 3.1 Testing of Chemicals 3.2 Good Laboratory Practice 3.3 Mutual Acceptance of Data 3.4 High Production Volume Chemicals 4. Climate Change 4.1 Energy Efficiency 4.2 Environmentally Sustainable Transport 5. Biotechnology 5.1 Human Health 5.2 Agriculture and Food 5.3 Environmental and Industrial Applications 6. Conclusion Glossary Bibliography Biographical Sketch
This paper studies government subsidies for green technology adoption while considering the manufacturing industry’s response. Government subsidies offered directly to consumers impact the supplier’s production and pricing decisions. Our analysis expands the current understanding of the price-setting newsvendor model, incorporating the external influence from the government, who is now an additional player in the system. We quantify how demand uncertainty impacts the various players (government, industry, and consumers) when designing policies. We further show that, for convex demand functions, an increase in demand uncertainty leads to higher production quantities and lower prices, resulting in lower profits for the supplier. With this in mind, one could expect consumer surplus to increase with uncertainty. In fact, we show that this is not always the case and that the uncertainty impact on consumer surplus depends on the trade-off between lower prices and the possibility of underserving customers with high valuations. We also show that when policy makers such as governments ignore demand uncertainty when designing consumer subsidies, they can significantly miss the desired adoption target level. From a coordination perspective, we demonstrate that the decentralized decisions are also optimal for a central planner managing jointly the supplier and the government. As a result, subsidies provide a coordination mechanism. This paper was accepted by Yossi Aviv, operations management .
António Cardoso Monteiro, Sérgio Santos, Pedro Gonçalves
Simple Summary Precision agriculture has the potential to contribute to the broader objective of meeting the growing demand for food, ensuring the sustainability of primary production, based on a more accurate and resource-efficient approach to crop and livestock management. The aim of this paper consists of a brief review of the recent scientific and technological tools and sensors in precision agriculture and their application in crop and livestock farming. This literature review allowed us to realize that precision agriculture has been proven to be a highly researched and constantly evolving area due to the needs of farmers to use resources in a more optimized way. Abstract In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.
With the increasing severity of global water scarcity, a myriad of scientific activities is directed toward advancing brackish water desalination and wastewater remediation technologies. Flow-electrode capacitive deionization (FCDI), a newly developed electrochemically driven ion removal approach combining ion-exchange membranes and flowable particle electrodes, has been actively explored over the past seven years, driven by the possibility of energy-efficient, sustainable, and fully continuous production of high-quality fresh water, as well as flexible management of the particle electrodes and concentrate stream. Here, we provide a comprehensive overview of current advances of this interesting technology with particular attention given to FCDI principles, designs (including cell architecture and electrode and separator options), operational modes (including approaches to management of the flowable electrodes), characterizations and modeling, and environmental applications (including water desalination, resource recovery, and contaminant abatement). Furthermore, we introduce the definitions and performance metrics that should be used so that fair assessments and comparisons can be made between different systems and separation conditions. We then highlight the most pressing challenges (i.e., operation and capital cost, scale-up, and commercialization) in the full-scale application of this technology. We conclude this state-of-the-art review by considering the overall outlook of the technology and discussing areas requiring particular attention in the future.
Abstract Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing intelligent scheduling techniques. Production scheduling can have significant impact on energy saving in manufacturing system from the operation management point of view. Resource flexibility and complex constraints in flexible manufacturing system make production scheduling a complicated nonlinear programming problem. To this end, a multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints. Then, an enhanced genetic algorithm is developed to solve the problem. Finally, comprehensive experiments are carried out to evaluate the performance of the proposed model and algorithm. The experimental results revealed that the proposed model and algorithm can solve the problem effectively and efficiently. This may provide a basis for the decision makers to consider energy-efficient scheduling in flexible manufacturing system.
We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decision model is validated in a real-world application at a leading manufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditional methods. This paper was accepted by Charles Corbett, operations management.
Recent years have seen considerable debate about the practicability of a global quantity/price commitment to control carbon emissions and tackle environmental issues. In this paper, we study the impact of the cap-and-trade policy (quantity commitment) and the carbon tax policy (price commitment) on a firm’s technology investment and production decisions. The main feature captured in our model is that there exist correlated uncertainties between the sales market (demand uncertainty) and the permit trading market (permit price volatility) under the cap-and-trade policy. The correlation relationship stands on the following intuition. The demands for final products affect firms’ production output, which generates the needs of emission permits and influences the permit price. We show that under the cap-and-trade policy, with the uncertainty of the future emission price, the firm could flexibly adjust its production quantity to enhance its profit, resulting in low incentives to invest in clean technology. However, as the (positive) correlation between the sales market and the permit trading market increases, the production flexibility is constrained so that the firm has to increase its technology investment to hedge against the future risk of a high emission price. Making a comparison between the cap-and-trade and carbon tax policies, we find that when the correlation coefficient is moderate, the carbon tax policy generates a multiwin situation (i.e., more technology investment, higher expected profit and consumer surplus, and fewer carbon emissions). Case studies are provided to illustrate the implications and model variants are examined to check the robustness of the main results. Overall, our analysis sheds light on recent debate over carbon pricing and identifies the important role of correlated uncertainties in carbon policy design. This paper was accepted by Charles Corbett, operations management.
Bahareh Deljoo, Rohollah Ghasemi, Mohsen Moradi moghadam
et al.
The food industry has been transformed by the Fourth Industrial Revolution, particularly through the application of the Internet of Things (IoT). This technology enhances efficiency by connecting various components of the factory—both wired and wirelessly—and paves the way for smart factories aligned with sustainability goals. The aim of this research is to analyze the capability–attractiveness of IoT applications in the food industry based on sustainability indicators and the readiness of selected companies in the food industry of Tehran province to implement these technologies. First, a systematic literature review was conducted to identify relevant IoT applications in the food industry, along with sustainability-based attractiveness indicators and capability criteria. The case study is selected companies in the food industry of Tehran province and their subsidiaries, which are currently deploying IoT technologies across various areas. Using the Best-Worst Method (BWM), the weights of the indicators were determined. Then, decision matrices were developed separately for evaluating the applications based on attractiveness (sustainability) and capability indicators, and each application was scored accordingly. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was then used to obtain final rankings. Based on the capability–attractiveness matrix, the most promising IoT applications identified for implementation in the company include “real-time data collection,” “inventory management and shelf replenishment,” “energy consumption management,” and “smart fire detection systems.” The findings offer valuable insights for identifying and adopting IoT applications in the food industry, considering the capacities and infrastructure of companies.
Introduction
The food industry, a fundamental sector for human needs, faces increasing demand, customer expectations, and intense competition. To ensure food safety and profitability, companies are adopting advanced technologies like the Internet of Things. IoT, through networks of sensors and smart devices, enables intelligent interaction between equipment, machinery, and information systems, enhancing efficiency, streamlining processes, and supporting sustainable development. The Iranian food industry faces challenges such as high food waste, weak supply chain traceability, inefficient resource management, and ongoing quality concerns. IoT can effectively address these issues, yet many companies have not fully adopted it. This research provides a systematic approach to examine and prioritize IoT applications based on sustainability attractiveness and the capability of active companies in Tehran province. The main research question is: What is the implementation priority of IoT applications in active companies in Tehran province based on the attractiveness of each application and the companies' capability to acquire this technology?
Research Background
The Iranian food industry confronts significant challenges that threaten its sustainability and competitiveness, including extensive food waste during production, storage, and distribution, weak traceability in the supply chain, inefficient management of critical resources such as water and energy, and persistent product quality and safety risks. Lack of effective infrastructure for tracking and verifying food authenticity reduces consumer trust and enables food fraud. In this context, IoT technology emerges as a modern and efficient solution. By employing smart sensors to monitor storage and transportation, tracking systems in the supply chain, real-time monitoring of raw materials and products, and wearable devices to enhance worker safety, IoT can increase productivity, improve food safety, reduce waste, and strengthen consumer confidence. Targeted IoT adoption can address structural problems and enhance Iran’s national and international food industry standing. Despite this potential, many companies have not yet fully embraced IoT. This research seeks to provide a systematic approach to examining and prioritizing IoT applications while considering their sustainability benefits and internal company capabilities.
Methodology
This applied research adopts a quantitative, descriptive-survey, and cross-sectional approach, utilizing both library and field methods for data collection. The initial phase involved a systematic literature review to identify IoT applications relevant to the food industry, along with sustainability-based attractiveness indicators and capability criteria. Through this process, twelve key IoT applications were identified, such as real-time data collection, smart fire detection systems, and energy management. Additionally, nine sustainability indicators were defined across economic dimensions—including operational cost savings—social dimensions, such as customer satisfaction, and environmental dimensions, like waste reduction. Furthermore, eight capability indicators were established, covering areas such as platform development, security, and regulatory compliance.
The study targeted experts from the food industry in Tehran province, all with a minimum of five years of experience in IoT-related projects. A judgmental sampling method was employed, and data were collected from seven selected experts. To determine the weights of the attractiveness and capability indicators, the Best-Worst Method (BWM) was applied. The experts completed BWM questionnaires, and the final group weights were derived by calculating the arithmetic mean of their responses. Consistency ratios were also computed to verify the reliability of the comparisons.
Following this, separate decision matrices were constructed to evaluate the twelve IoT applications based on the weighted attractiveness and capability indicators. Each application was scored by the experts using a 10-point Likert scale. The TOPSIS method was subsequently employed to process these matrices, yielding final scores and rankings for the applications according to each dimension.
Finally, a Capability-Attractiveness Matrix (ACM) was developed. The TOPSIS scores for attractiveness, represented on the vertical axis, and capability, on the horizontal axis, were plotted for each application. The mean scores of all applications served as cutoff points, dividing the matrix into four distinct quadrants and thereby enabling strategic prioritization of the IoT applications.
Findings
The BWM analysis revealed the relative importance of the indicators. For attractiveness, the economic dimension was the most critical (0.725), followed by the social (0.175) and environmental (0.100) dimensions. Among all sub-indicators, "operational cost savings" (EC1) had the highest final weight (0.494), underscoring its paramount importance. For capability, "IoT platform development" (CAP3) was the most significant indicator (0.305), followed by "application development" (CAP4) and "security capability" (CAP5). All consistency ratios were within acceptable limits, confirming the reliability of the expert judgments.
The TOPSIS analysis provided separate rankings based on attractiveness and capability. Based on attractiveness (sustainability benefits), the top applications were "real-time data collection" (A1), "inventory management" (A10), and "energy consumption management" (A11). Based on capability (ease of implementation), the top applications were "smart fire detection" (A2), "real-time data collection" (A1), and "energy consumption management" (A11).
The integration of the TOPSIS results into the ACM yielded the final strategic prioritization. The applications were categorized into four quadrants: Quadrant 1 (High Attractiveness, High Capability) contained the most promising applications for immediate implementation: A1 (Real-Time Data Collection), A10 (Inventory Management), A11 (Energy Management), and A2 (Smart Fire Detection). These represent the first priority. Quadrant 2 (High Attractiveness, Low Capability) included applications A5 (Operational Cost Control), A4 (Process Automation), and A8 (Remote Facility Control). They are desirable but require capability-building efforts, marking them as a second priority. Quadrant 3 (Low Attractiveness, High Capability) contained applications A6 (Quality Monitoring) and A7 (Worker Health Monitoring). While companies have the capability, the perceived sustainability benefits are lower. These could be developed after Quadrant 1 applications. Quadrant 4 (Low Attractiveness, Low Capability) included applications A12 (Supplier Tracking), A3 (Worker Tracking), and A9 (Environmental Monitoring), indicating the lowest priority for implementation.
Discussion and conclusion
This study identified and prioritized IoT applications for the food industry in Tehran province using a structured Capability-Attractiveness framework. The findings indicate that the primary focus for companies should be on applications in Quadrant 1, which offer high sustainability benefits and align with existing organizational capabilities. The prominence of real-time data collection, inventory management, and energy management aligns with global trends emphasizing operational efficiency and resource optimization.
The placement of environmental monitoring (A9) in the low-priority quadrant (4) contrasts with international research that emphasizes green technologies. This discrepancy may be attributed to weaker environmental regulations, lower technological infrastructure, or a primary focus on immediate economic gains within the Iranian context.
The prioritization based on the ACM provides a more comprehensive strategy than ranking by attractiveness or capability alone. It allows decision-makers to select applications that not only offer high value but also have a lower implementation risk, considering their specific resources and infrastructure.
In conclusion, this research enhances our understanding of IoT as an emerging and transformative technology in the food industry. It assesses various applications from economic, social, and environmental perspectives while evaluating implementation feasibility. The results can serve as a valuable guide for decision-makers and policymakers in the Iranian food industry, enabling a more strategic and effective adoption of IoT technologies. A limitation of this study is the lack of a detailed technical-economic feasibility analysis for each application. Future research should conduct in-depth studies on the selected applications to identify implementation challenges and provide practical solutions.
This paper studies the impact of product sharing on competing manufacturers under a platform’s different quality entry barrier strategies. We build a game-theoretic analytical model to examine the strategies of two manufacturers: A high-quality manufacturer producing a high-quality product and a low-quality manufacturer producing a low-quality product. We explore three markets: The N-S market where the sharing market does not exist, the L-S market, and the H-S market, where the platform sets low and high entry barriers, respectively. We study these three markets because they bracket the most common platform entry policies in practice—no sharing, low-barrier (open access), and high-barrier (quality-gated) participation. Our findings indicate that while product sharing makes it easier for the high-quality manufacturer to survive, it may not necessarily improve the survival likelihood of the low-quality manufacturer. Furthermore, we demonstrate that the existence of the sharing market may negatively impact both manufacturers. To the best of our knowledge, this study is the first to investigate how competing manufacturers, platforms, and consumers can cope with the sharing phenomenon. We show that, while the high-quality manufacturer prefers a low cost of quality in all three markets, the low-quality manufacturer, interestingly, prefers a high cost of quality in both product-sharing markets. Additionally, we illustrate that the platform should not always set a low entry barrier to allow more product types to join. Surprisingly, our results show that although product sharing always benefits social welfare, it could potentially harm consumers.
ABSTRACT Artificial intelligence and data analytics capabilities have enabled the introduction of automation, such as robotics and Automated Guided Vehicles (AGVs), across different sectors of the production spectrum which successively has profound implications for operational efficiency and productivity. However, the environmental sustainability implications of such innovations have not been yet extensively addressed in the extant literature. This study evaluates the use of AGVs in container terminals by investigating the environmental sustainability gains that arise from the adoption of artificial intelligence and automation for shoreside operations at freight ports. Through a comprehensive literature review, we reveal this research gap across the use of artificial intelligence and decision support systems, as well as optimisation models. A real-world container terminal is used, as a case study in a simulation environment, on Europe’s fastest-growing container port (Piraeus), to quantify the environmental benefits related to routing scenarios via different types of AGVs. Our study contributes to the cross-section of operations management and artificial intelligence literature by articulating design principles to inform effective digital technology interventions at non-automated port terminals, both at operational and management levels.
While we have seen some encouraging examples of firms that try to drive diversity, equity, and inclusion (DEI) practices into their supply chains, progress has been frustratingly slow. We argue that a key impediment today and a potential enabler tomorrow—and thus an important subject of operations and supply chain management (OSCM) research—is the array of public policies that sets the “rules of the game” in OCSM. We differentiate three types of public policy regimes—laissez-faire, regulatory, and transformative—and analyze how each differentially affects DEI practices in OSCM. Our analysis suggests that the laissez-faire type may not offer sufficient incentives to trigger a comprehensive change in DEI practices, while the regulatory and transformative types offer more incentives but, in many instances, do not work as expected. By analyzing the effects of distinct public policy activities for DEI-related practices—for example, through comparative empirical studies or modeling and simulation—OSCM scholars can make important contributions to a more comprehensive implementation of DEI practices.
Educational equity serves as a cornerstone for a fair and flourishing society. However, existing research consistently highlights the presence of educational disparities between developed and developing regions. Remote learning, a transformative educational approach altering knowledge dissemination and access, takes center stage. Our study delves into the potential of remote learning to bridge this educational divide. Analyzing students’ performance in China’s National College Entrance Examination from 2018 to 2020, we exploit the pandemic-induced shift to remote learning as an external catalyst. Employing a difference-in-differences methodology, we gage remote learning’s impact on performance gaps across regions. Our findings illuminate a marked enhancement in learning outcomes for students in developing regions, surpassing their counterparts in developed ones. Furthermore, we unearth that the digital divide in access to technological resources constrains remote learning’s efficacy in reducing educational disparities. Our study underscores remote learning’s implications and underscores the pivotal role of digital technology capabilities.
The logistics network is considered the provider of logistics activities in supply chains. The fluctuating requirements of customers and the logistics network’s complex structure are only a few of the factors that cause challenges to its management. Industrial facilities are particularly vulnerable to challenges because material handling operations dominate in addition to manufacturing activities. Disruptions at industrial plants are disseminated through the logistics network, affecting all supply chain participants. As a result, reducing material handling time and costs to decrease material losses, pollution, and productivity is vital to their business. Due to their distinctive properties and significant share in finished goods, bulk materials are particularly vulnerable to issues during manufacturing. Accordingly, this study aims to rank and select technologies for handling bulk materials in an industrial plant where the production of construction materials is performed. This paper proposes four alternative solutions for the observed case study, and nine criteria were selected for the evaluation. A new hybrid multi-criteria decision-making model is proposed. The model combines Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), used to determine the weight of criteria, and the Axial-Distance-Based Aggregated Measurement (ADAM) method, used to rank alternative solutions. The model results indicate that the pneumatic conveyor is the best ranked alternative that significantly increases productivity, reduces losses, and improves working conditions. The key contributions of this study are its analysis of the efficiency of the technologies proposed for bulk material handling and the development and implementation of a model framework for the ranking of these technologies.
Garima Thakur, Saxena Nikita, Vinesh Balakrishnan Yezhuvath
et al.
The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber–physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.
Maximilian Burkhardt, Felix Jan Nitsch, Stefan Spinler
et al.
Humanitarian workers often operate under highly stressful conditions, view emotional scenes, and face high time pressures. This may bias managerial decision‐making in humanitarian logistics, but evidence is lacking. We model humanitarian logistics decisions in an adapted newsvendor setting. We experimentally expose participants to time pressure, noise, and emotional pictures. Using physiological and self‐reported data, we confirm that these manipulations have different effects on two components of the stress response (negative valence and arousal) and on decision‐making quality. Specifically, medium levels of arousal seem to boost decision‐making quality, independent of affective valence. However, high time pressure (characterized by high levels of arousal) leads to a collapse in decision‐making quality. Our results highlight that some level of stress may be beneficial for decision‐making owing to its action‐activating properties. However, excessive time pressure sharply overrides and outweighs these benefits. We discuss the generalizability of our results to other emergency situations (e.g., firefighters) and managerial contexts (e.g., crisis responses).
Environmental protection and production sustainability are the key actions required to the farming activities, especially to those with higher add value as wine production. Vineyard are one of the most demanding activities in terms of water consumption and environmental impacts, which can be mitigated only with a systematic approach based on smart agriculture to support and optimize vineyard management. This paper proposes a vineyard digital twin (VDT) based on a mathematical model able to predict the vegetative and productive growth of a vineyard (leaf area, shoot length, crop and yield mass), qualitative product parameters (sugar and acid) and the water footprint associated with production. The model implements a soil-atmosphere source-sink model, where water balance across vine is coupled with potential carbon demand functions to estimate water and temperature stresses and, through a mechanistic model for sugar accumulation and acid concentration, will evaluate the expected grape quality. The distinctive trait of this model is the integration and feedback among prediction of grapevine quality and vegetative growth, using a common boundary data set and integrating the agronomical operations on vineyard seasonal development. The VDT prototype will help producers to systematize, formalize, and accumulate knowledge to improve and optimize management processes to achieve sustainable production, increasing products healthy and reducing environmental footprint.