Popularity bias reflects the positive impact of popularity information on consumer choices. There are two common display formats for popularity information on online retail platforms: the total-based cumulative sales format, where the total sales of a product are displayed since its launch, and the period-based cumulative sales format, where only sales within a specific recent period are shown. Both types of popularity information are continuously updated. This paper focuses on the multiproduct dynamic pricing problem with popularity bias. We employ the widely used multinomial logit model to investigate the impact of popularity bias on consumer choices. In particular, we examine how popularity bias affects marginal revenue, pricing decisions, and market shares. Moreover, we highlight that ignoring popularity bias can lead to a suboptimal outcome. As the multiproduct dynamic pricing problem suffers from the curse of dimensionality, we propose a semi-myopic pricing policy, which is computationally tractable, and demonstrate its asymptotic optimality under both formats. Our numerical simulations further indicate that ignoring popularity bias can result in substantial revenue losses, while the semi-myopic pricing policy consistently outperforms other heuristics under both formats. Finally, empirical tests on real data provide a comprehensive procedure for identifying the most appropriate choice models, which offer practical insights for implementation.
Firms often encourage existing customers to recruit new customers. In this paper, we analytically study the customer referrals in a two‐period newsvendor model, where the demand generated by referrals increases in the previous sales. In this framework, we establish the structural results for the optimal inventory level, as well as evaluate the value of referral programs. We find that the customer referrals are more attractive for firms selling nonperishable products than for those selling perishable products. Overall, this study underscores the operational value of customer referrals, particularly for firms selling nonperishable products.
Blessing Iyanuoluwa Adediran, Akudo Francilia Onyegbula, Stephen Olufemi Oyeyipo
et al.
Food loss continues to be a major global challenge that impacts environmental sustainability, economic stability and food security. An inventive strategy for lowering food loss across the supply chain is AI-driven monitoring. The foundation of human civilization has always been agriculture, which supplies the vital resources needed for growth and nutrition. Higher quality crops with improved nutritional value, increased resilience to pests and diseases and improved adaptability to varying climatic conditions are in greater demand as the world's population continues to grow. Despite their effectiveness, traditional agricultural methods frequently fail to effectively meet these objectives; therefore, an innovative strategy for raising crop quality is the incorporation of artificial intelligence (AI) into agricultural operations. This paper examines the role of AI-driven monitoring in reducing food loss, focusing on its applications, benefits and implications for the food industry. AI driven technologies like machine learning, IoT-based smart sensors and computer vision can enhance efficiency in food production, storage, transportation and retail. By utilizing AI-driven solutions, stakeholders can optimize resource utilization, reduce waste, and contribute to sustainable food systems. AI-assisted processing can optimize various stages of crop production, from planting and growing to harvesting and postharvest management, thereby improving the overall quality of agricultural produce.
This article presents a system for enhancing adaptive management through the integration
of fuzzy logic decision-making system in backed by blockchain supply chain smart-contracts
of an enterprise.
The adaptive management system is based on two main components: risk assessment
and supply chain optimization. To assess risk, fuzzy logic models analyze input variables such
as supply chain risks, financial risks, and operational risks.
An adaptive resource management system is characterized by the ability to respond
to changes in the external environment and internal processes. This system should integrate
advanced technologies for effective resource management and ensure strategic stability.
The system utilizes blockchain’s immutable ledger and smart contracts to automate
key processes such as manufacturing processes, inventory management, and regulatory
compliance, thus addressing issues like communication gaps, delays, and counterfeit risks.
However, the inherent rigidity of blockchain systems in adapting to dynamic manufacturing
environments prompts the incorporation of fuzzy logic.
Fuzzy logic offers a solution to this limitation by enabling more nuanced decision-making
through the processing of uncertain or imprecise data. The article details the integration of
fuzzy logic with blockchain, wherein fuzzy inference systems (FIS) are employed to evaluate
and interpret operational data under variable conditions. This combination allows for adaptive
responses to supply chain disruptions, such as supplier delays or inventory shortages. The
fuzzy logic system applies rules to determine the optimal course of action, which is then
executed through blockchain-based smart contracts.
Key advancements include the development of a modified smart contract framework
that uses fuzzy logic to adjust supply chain parameters dynamically. For example, supplier
reliability is assessed using fuzzy membership functions, leading to adjustments in pricing
and supply quantities based on real-time evaluations. This approach enhances the flexibility
and responsiveness of manufacturing operations, ensuring that decisions are based on
comprehensive data analysis rather than static rules.
A fuzzy logic system processes ambiguous information using linguistic variables and
fuzzy sets that help interpret uncertainties in operational data. The key element of the system is
the fuzzy inference system, which performs basic steps such as fuzzification, rule evaluation,
aggregation, and defuzzification. This results in more refined decision outputs based on fuzzy
rules that can take into account different conditions such as supply quantity and supplier
reliability. Combining fuzzy logic with smart contracts facilitates dynamic adjustments in
supply management, such as pricing and modification of supply quantity based on supplier
reliability.
It is evaluated how residual networks and deep multi-level transformations
can be used in combination with a fuzzy logic system to improve performance. The
concept of global mean pooling and fully connected levels is applied to classification
tasks, and cross-entropy loss functions improve model accuracy. Additionally, the use
of membership functions such as trapezoidal and triangular sets allows for accurate modeling of factors such as delivery timeliness and product quality.The proposed
system provides a robust solution for managing production processes amidst fluctuating
conditions, combining the transparency and security of blockchain with the adaptive
capabilities of fuzzy logic. This integration aims to optimize production efficiency and
maintain operational continuity in the face of unpredictable challenges.
Marcelo dos Santos Póvoas, Jéssica Freire Moreira, Severino Virgínio Martins Neto
et al.
This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges.
Fallou Niakh, Alicia Bassière, Michel Denuit
et al.
The financial viability of renewable energy projects is challenged by the variability and unpredictability of production due to weather fluctuations. This paper proposes a novel risk management framework combining parametric insurance and peer-to-peer (P2P) risk sharing to address production uncertainty in solar electricity generation. We first design a weather-based parametric insurance scheme to protect against forecast errors, recalibrated at the site level to mitigate geographical basis risk. To handle residual mismatches between insurance payouts and actual losses, we introduce a complementary P2P mechanism that redistributes the remaining basis risk among participants. The method leverages physically based simulation models to reconstruct day-ahead forecasts and realized productions, integrating climate data and solar farm characteristics. A second-order theoretical approximation links heterogeneous local models to a shared weather index, making risk sharing operationally feasible. In an empirical application to 50 German solar farms, our approach reduces the volatility of production losses by 55\%, demonstrating its potential to stabilize revenues and strengthen the resilience of renewable investments.
In practice, new entrants often bypass intermediary retailers and engage in direct competition against incumbents. However, this might place entrants under the burden of integration costs. This paper models the strategic interactions between an incumbent and an entrant, incorporating the integration cost incurred by the entrant and the associated estimation bias exhibited by the incumbent. In the baseline setting with an unbiased incumbent, we find that the entrant’s higher integrative capability (i.e., lower integration cost) always hurts the incumbent, and can even hurt the entrant itself. We further investigate how the incumbent’s estimation bias affects firm performance, and find that increasing the bias can actually benefit the entrant and, intriguingly, the incumbent as well. In particular, a mutually beneficial situation can emerge where a higher level of bias potentially improves profits for both the incumbent and the entrant. Additionally, we extend our analysis to settings with random integration costs, where the bias can be linked to either the mean or the variance of these costs. Our findings offer practical implications for entrants in choosing their integrative capability strategy, and for incumbents in deciding whether to pursue bias reduction.
Xianjun Geng, Nicholas G Hall, Rakesh R Mallipeddi
et al.
While sports are enjoyed by several billion fans globally, the business of sports has evolved into a multi-billion dollar industry. Multi-disciplinary research on developing competitive strategies for drafting players and making on-field decisions is growing rapidly. However, there remains a notable gap in operations management research focusing on the business aspects of the sports industry. This paper addresses this gap in the literature to encourage future research in this domain. We highlight new opportunities for operations management researchers, emphasizing five research themes that are of importance to the sports industry: (1) Revenue management, (2) new technologies in sports business, (3) betting, (4) rule and competition changes, and (5) service operations. We identify important and relevant open-ended research questions and discuss related trade-offs to address the research questions. This paper provides a basis for future operations management research to address the unique challenges faced by sports organizations.
Gila E Fruchter, Ashutosh Prasad, Thomas Reutterer
In a globalized economy, companies face a range of challenges and opportunities related to relocating production activities to a new country. Relocation can yield significant cost savings and other benefits, but there are also risks including potential damage to the brand image. Thus, firms need to carefully evaluate when to relocate and when to stop production in a particular location. We formulate an optimal control model and derive analytical as well as numerical results to provide insights into the optimal relocation timing and production stoppage decisions. We show that factors like higher relocation costs, higher production costs in the relocation country but high brand image in the country of origin, can postpone production relocation. Competitive effects alter relocation timing, particularly when the firm faces direct competition and asymmetric negative cross-image spillover effects with the rival brand in the home or relocation country. The paper discusses illustrative examples and derives implications for the timing of relocation and the duration of production in the relocation country.
Additive manufacturing, or 3D printing, has become very common in professional applications in many industries. The 3D printing technology is especially suitable for making prototypes, demonstrators and small-batch production. The stiffness and strength of 3D prints depend on many factors, including among others infills, which are specific to this technology, as well as the orientation of the object during 3D printing. Where the stiffness or strength of an element is crucial, the only way is to empirically assess its properties. The advantage of 3D printing, i.e. incomplete infill of the interior of an object with the use of different types of infills (patterns) and different amounts of material, means that its mechanical properties differ from those of a solid element. The application of numerical tests, i.e. the finite element method (FEM), requires the creation of a 3D model while taking this infill into account. The modelling of elements for performing numerical strength calculations is time-consuming and labour-intensive. The article presents a proprietary original analytical method for generating various types of infills with varying infill density. The method was developed for typical infills (Grid, Triangular, Honeycomb). It was next implemented in the CAD environment using the iLogic tool of Autodesk Inventor. As a result, a tool for creating 3D models of objects consistent with those obtained from 3D printing was obtained. The method and tool were verified. Next, the influence of selected parameters of the 3D print on its mechanical properties was presented on three real objects. The results of numerical analyses revealed measurable benefits of such tests. The research conclusions also constitute recommendations for selecting the type and infill density of an object and its orientation in the printer with regard to the strength and stiffness obtained.
Background: Socio-demographic changes increase the need for long-term elderly care. Consequently, providing formal institutional service in elderly care homes is an interesting opportunity for entrepreneurs. However, the entry strategy decision is influenced by numerous external variables. Purpose: The main goal is to answer what determines market concentration as one of the most important market entry determinants. Study design/methodology/approach: A linear regression model has been formed and tested on the Croatian elderly care home market, observed on a county level, using data for 2021. Further, a cluster analysis, as a decision-support tool, has been made to assess market characteristics that are more likely to attract new entrants to the elderly care home market. Findings/conclusions: Results indicate that demand for long-term care services plays a significant role, and the market with more elderly will attract more competitors. When the level of GDP per capita and the unemployment rate are observed together, markets with stronger economies tend to attract entrepreneurs. In other words, it is more likely that someone will open an elderly care home in a densely populated county with individuals that can afford formal institutional long-term care for themselves or family members. Limitations/future research: The shortcomings are mainly related to the lack of data on prices and quality measures. Further, information on the number of beds in each elderly care home would enable an alternative calculation of the Herfindahl-Hirschman index, while data on service prices and structure of employees as a proxy for quality (medical and non-medical staff) would enable a more reliable comparative analysis of obtained results. Future studies on this subject include variables related to the portion of unemployed females in the market since female family members more often provide informal care, and at the same time, they are more likely to be employed in formal long-term care institutions.
Production management. Operations management, Personnel management. Employment management
Belinda Astari, Irzal Effendi, Tatag Budiardi
et al.
Management evaluation is essential to optimize finances by increasing egg production, survival rates, larval rearing, and nursery growth. Research aims to analyze production and financial performance in sustainable practices for producing hybrid grouper seeds such as cantang and cantik. The research was carried out for one year, from September 2022 to September 2023. Survey method with the selection of research locations carried out purposively utilized both primary and secondary data types. Primary data was collected through surveys using questionnaires, interviews, and direct observation of activities. Direct observations were conducted at egg production, hatchery, and nursery activities. Research shows that in one year the egg production is 97,300,000 cantang eggs and 17,800,000 cantik eggs. The final length mean harvested in the hatchery was 3.5 ± 0.28 cm with a survival rate of 8.3 ± 4.9 % for cantang hybrid grouper and 3.2 ± 0.25 cm with a survival rate of 10.1 ± 5.0 % for cantik hybrid grouper. The total length of the nursery harvested was 10.9 ± 0.30 cm with a survival rate of 71.5 ± 8.7 % for cantang hybrid grouper, and cantik hybrid grouper measured 10.1 ± 0.30 cm with a survival rate of 81.0±6.0 %. The highest net profit comes from nurseries, followed by larval rearing and egg production. Investments in egg production, larval rearing, and nursery businesses are financially advantageous, as both the R/C and B/C ratios > 1, demonstrating their economic viability. The increasing demand for hatcheries and seeds highlights the importance of successful egg production in fulfilling the growing needs of grouper hatcheries. Nursery operations play a crucial role in enhancing the success of grouper grow-out by improving survival rates and reducing rearing times. They focus on nurturing seeds from their early stages until they reach a suitable size and strength for the grow-out phase.
In 2022, the production rate of pomegranate is estimated at approximately 4.8 million metric tons. Unfortunately, these fruits are susceptible to many different kinds of diseases caused by bacterial, viral, and fungal infections. Such diseases can have a major negative impact on fruit quality, production, and the profitability of pomegranate cultivation. Nowadays, several machine learning and deep learning methods are used to identify pomegranate fruit diseases automatically and effectively. In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. This can improve disease identification accuracy, enabling more efficient disease control, lower crop losses, and better resource management. The proposed work introduces an intelligent deep learning-based approach for accurately detecting pomegranate diseases, begins with Improved Guided Image Filtering (Improved GIF) and resizing to pre-process fruit images, followed by feature extraction (shape, color, texture) using GLCM and GLRLM to streamline classification. Extracted features are then fed into a novel Hybrid Optimal Attention Capsule Network (Hybrid OACapsNet), which classifies the images as normal or diseased, conditions such as bacterial blight, heart rot, and scab. Our analysis indicates that the proposed classifier has a classification accuracy of 99.19 %, precision of 98.45 %, recall of 98.41 %, F1-score of 98.43 %, and specificity of 99.45 % compared to other techniques. So this approach offers a framework, which is a feasible solution for automated detection of diseases in fruits, thereby benefiting farmers and supporting their farming operations.
This research aimed to use a sustainable approach based on the internalisation of external cost analysis of intermodal transportation of freight to assess the impacts of these activities on the environment. This research used two approaches to develop a model that illustrates the internalisation of the external cost of freight transport. The first approach was used to calculate the cost of emissions for each route considering the transportation and its’ cost in the country of destination. The second approach calculated the external cost considering only the distance travelled by the vehicle. The results showed that the companies operating in the selected scenarios would have to pay an additional cost for the transportation of goods. The scenarios had different pollutants emitted during the transportation, which means that the negative impact on human health and the environment is evident. The urgency to limit carbon dioxide and other greenhouse gases in the atmosphere has increased concerns for all activity sectors. Climate change has drawn the attention of governments, companies, and academics, promoting initiatives that mitigate the impact of their activities. The model for measuring emissions was used due to the need for a comprehensive cost analysis to further assess the impact on the environment. Regarding the internalisation of the external cost emissions, the findings showed that different scenarios had a different pollutant emitted during the transportation, which means that the negative impact for human health and the environment is evident. Findings also indicate that to minimise the impact during the transportation, considering the “user-pays principle”, these impacts should be discussed in more detail between stakeholders.
Software‐as‐a‐service (SaaS) applications have experienced a decade of explosive growth, eliminating barriers in reaching users and enabling real‐time interchanges and intelligence. Using business analytics, SaaS applications are increasingly embedded in the day‐to‐day activities of businesses and consumers with competition and innovative pricing. Due to the evolution in cloud business models, new issues are surfacing to challenge practitioners and scholars. A number of issues encountered in the practice have not been properly addressed or even recognized. In this paper, we attempt to fill this important gap. We propose a framework of recent business research on SaaS in light of wide adoption of the SaaS business model. This framework broadly classifies SaaS research into two basic themes. For each theme, we review past work that has been instrumental in setting the direction of this line of research and discuss how emerging research opportunities can be addressed. For each research opportunity, we also propose an initial model and the applicable methodology. Further, in order to aid researchers, we identify the data sources wherever applicable, and even present some of the initial results. We conclude by describing promising directions on a roadmap for future research and explain why an integrative perspective of operations, marketing, and information systems is critical to SaaS. In this paper, we bridge the gap between research and practice by identifying the relevant industry problems that would help researchers who are interested in working in this area both to get a starting point and to address important theoretical and practical challenges.
Jorge Luiz Dias Agia, Ernesto Michelangelo Giglio, Oduvaldo Vendrametto
O trabalho investiga como ocorre a moderação da governança colaborativa (G.C.) para a organização das redes (O.R.) do Programa Nacional de Alimentação Escolar nos municípios de Cubatão-SP e Itanhaém-SP. O PNAE é uma rede complexa, com tarefas de especialidades entre vários atores, com importância social e econômica para os municípios. Para investigar o tema foi realizada uma pesquisa qualitativa, descritiva, explicativa e comparativa. Coletaram-se dados de fontes secundárias e entrevistas com roteiro estruturado, construído a partir dos indicadores de G.C. e O.R. Os dados permitem afirmar o benefício do avanço no conhecimento sobre a moderação da G.C. na O.R., já que surgiram exemplos de ajustes de regras e normas na rede. O benefício metodológico é a oferta de uma matriz de indicadores que se mostrou operacional e confiável.
Production management. Operations management, Production capacity. Manufacturing capacity
This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (DMOps). We advocate for the use of IAA in predicting the labeling quality of individual annotators, leading to cost and time efficiency in data production. Additionally, our work highlights the potential of IAA in forecasting document difficulty, thereby boosting the data construction process's overall efficiency. This research underscores IAA's broader application potential in data-driven research optimization and holds significant implications for large-scale data projects prioritizing efficiency, cost reduction, and high-quality data.
Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling $37.5$ times faster than the optimization while outperforming state-of-the-art forecasting techniques.
The article considers the trends of the COVID-19 pandemic impact on the economic activity of certain sectors of the economy, the consequences of quarantine restrictions in the national and world economy. The introduction of quarantine in the economic system provoked a decrease in purchasing power and income, there was a halt in transport and financial operations and communications, there was a need to develop social and medical spheres. There was a reduction in industrial production, small business and trade. Sectors of transport, tourism, hotel and restaurant business underwent crisis changes. This required a redistribution of capital and increased reserves. As a result, it was possible to reduce the rate of decline in key macroeconomic indicators. Some sectors of the economy in global challenges to refocus on digital technologies and successfully apply them. This provoked the development of the markets of online education, gambling, and e-commerce. Such changes have allowed the preservation of international contracts and economic relations. In addition, the current economic crisis has the specifics of a cognitive economy. this involves building direct links between the producer or seller and the end consumer. In the future, the management of international contracts in the post-crisis period will be based on digital communications, availability of IT technologies and logistics of product distribution, socio-psychological and institutional influences.
<i>Background:</i> Several product lifecycle management systems (PLMs) have been implemented in the industrial sector for managing the data of the product from the design up to the disposal or recycling stage. However, these PLMs face certain challenges in managing the complex and decentralized product lifecycles. <i>Methods:</i> To this aim, this work investigates the currently implemented PLMs used in industries through the exploration of various software reviews and selection websites. Accordingly, these existing PLMs are quantitatively compared and analyzed. <i>Results:</i> The analysis shows that most of the existing PLMs do not contain all the required features; therefore, industries integrate different software to create a full-fledged PLM system. However, this practice results in reducing the overall system efficiency. In this context, this paper assesses and recommends a blockchain-based innovative solution that overcomes the challenges of existing PLMs, hence increasing the overall system efficiency. Furthermore, this work argues, in a logical way, that the recommended blockchain-based platform provides a secure and connected infrastructure for data handling, processing, and storage at different stages of the product lifecycle. <i>Conclusions:</i> This work can be considered among the first to compare the currently implemented PLMs with a novel blockchain-based method. Thus, the stakeholders can utilize the outputs of this research in their analysis and decision-making processes for implementing the blockchain in their organizations.
Transportation and communication, Management. Industrial management