A stream of recent research considers the practice of random price discounts (a.k.a randomized pricing) when selling to forward-looking customers and shows that such pricing strategies can mitigate strategic customer waiting and boost seller profit. In practice, random price discounts are often offered together with price guarantees, in which customers are refunded the price difference if the price is lowered within a given time window after purchase. This paper investigates the efficacy of price guarantees under randomized pricing. To that end, we consider a model in which a firm adopts Markovian pricing and interacts with customers over an infinite time horizon. The following results are obtained. First, while Markovian pricing allows firms to price discriminate customers based on their monitoring costs, price guarantees further allow firms to price discriminate customers based on their willingness to pay. Second, offering price guarantees under Markovian pricing can help retain customers effectively by inducing high-valuation customers to purchase early, regardless of their arrival time. Third, even with price guarantees, Markovian pricing can dominate static pricing only when high-valuation customers are more likely to have a high monitoring cost, which illustrates that customer composition plays a crucial role in the effectiveness of the firm’s pricing strategy. Fourth, the optimal duration of price guarantees is closely related to customers’ lifetime duration. Finally, perhaps surprisingly, offering price guarantees can decrease the aggregate customer surplus since the firm offers sale prices less often under price guarantees.
In this article, we explore the impact of implementing an algorithmic supervisor, a data analytics tool that offers real-time feedback to employees, on their overall performance. We collaborate with a leading service company that initiated the transition from human supervisors to an algorithmic supervisor for managing call agents in their outbound call center on September 18, 2019. We collect data for 220,085 calls over a span of 6 months—that is, 3 months before and 3 months after the introduction of the algorithmic supervisor. We find that the introduction of the algorithmic supervisor improved the agents’ service quality in terms of customer satisfaction; however, this came at the expense of quantity as fewer customers were served. In particular, the adoption of the algorithmic supervisor resulted in a 15.80% reduction in the number of served customers, accompanied by a 14.91% increase in the number of satisfied customers. Moreover, our study uncovers a gender-related dimension to the impact of the algorithmic supervisor. Before its adoption, male agents outperformed their female counterparts. However, after its adoption, female agents exhibited an 11.52% increase in the number of served customers, and a remarkable 14.00% increase in the number of satisfied customers as compared to their male counterparts. Our results suggest that the adoption of the algorithmic supervisor strikes a new economic balance between service quality and service quantity and generates a social impact in differently affecting service performance of female and male employees.
We assess how a retailer can manage inventories and labor under an extreme condition such as a temporary negative demand shock due to a pandemic or an economic turmoil. This analysis incorporates labor market frictions whereby firms incur deadweight costs associated with hiring and firing employees, as well as the option to furlough labor. We examine the impact of these frictions on a retail firm's optimal operating policies around inventory level, furlough, and layoffs. We find that labor market frictions condition the inventory and the level of employment in two ways. First, they lead to underinvestment in inventories, which limits recovery and employment in the postshock period. Second, high labor market frictions motivate the firm to conservatively downsize workforce during the negative demand period leading to higher employment, when compared to downsizing without friction. A significant contribution of our study lies in delineating the optimal furlough decisions and quantifying the impact of the furlough option on inventory and labor decisions. We demonstrate the conditions under which it is optimal for the retailer to either (i) fully downsize labor and leverage the furlough option in the labor market, or (ii) maintain excess labor while also opting to furlough a portion of the workforce. During an extreme event such as a temporary negative demand shock, our results highlight the need for a coordinated effort when implementing governmental subsidy policies on alleviating labor and inventory reductions by accounting for labor market frictions and furlough support.
Muhammad Haris Saeed, Muhammad Saeed, Atiqe Ur Rahman
et al.
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
Abstract Agriculture holds a crucial position in maintaining livelihoods and securing food sources, particularly in nations such as Ethiopia, where a substantial portion of the population depends on agricultural pursuits. However, meeting the growing demand for food production amidst population growth presents considerable challenges. Recent advancements in technology, particularly in the areas of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) offer promising solutions to address these challenges. This paper explores the potential of integrating ML, DL, and IoT technologies in agriculture to revolutionize the sector. By harnessing data-driven insights, farmers can make informed decisions regarding crop management, soil health, and weather patterns, leading to optimized resource allocation and increased productivity. Moreover, IoT devices enable the real-time monitoring and control of agricultural operations, enhancing sustainability and productivity. Despite the opportunities presented by these technologies, there are also challenges to overcome, such as data quality, connectivity issues, and the need for farmer education. However, with concerted efforts and investment, Ethiopia and other agricultural regions can unlock the full potential of ML, DL, and IoT technologies to ensure food security, alleviate poverty, and drive economic development. This review paper offers perspectives on the present status, challenges, and future possibilities regarding the integration of ML, DL, and IoT in agriculture. It underscores the transformative potential of these technologies within the sector.
Nathaniel David Smith, Yuri Hovanski, Joe Tenny
et al.
Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine’s utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.
Abstract This paper examines the impact of gender diversity on financial reporting quality (accrual and real earnings management). We use a sample of 78 Egyptian listed companies over the period 2009–2021. The quality of financial reporting is measured using different models of earnings management (accrual and real earnings management). Accrual earnings management (AEM) is detected through four different models developed by modified Jones model, the Kasznik model, Kothari model, Raman and Shahrur model, while real earnings management (REM) is measured using six different model which are abnormal cash flows from operations (ABCFO), abnormal production costs (ABPROD), abnormal discretionary expenditures (ABDISEXP) and three aggregate proxies (RM1, RM2, RM3). Using the system generalized method of moments, companies with more gender diversity are more effective in reducing accrual earnings manipulation (AEM). The exception is the modified Jones model. Moreover, we find that gender diversity is positively and significantly correlated with financial reporting quality based on proxies of real earnings-based activity, except for RM2. The study found a non-significant and negative relationship between board diversity and RM2 as a proxy for REM. Overall, the empirical results based on accrual and real earnings management models (AEM and REM) support the notion that enterprises with more gender diversity on the board are more effective in controlling earnings manipulation practices. The predictions of corporate governance theories are confirmed. Policy makers should continue to promote and support gender diversity in leadership positions within organizations. This can be achieved through initiatives such as diversity quotas, mentoring programs, and leadership development opportunities for women.
Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.
This research study explores the relationship between customer focus and the performance of construction projects undertaken by small companies. Additionally, process management in these companies has been studied as a mediator in explaining the relationship between customer focus and project performance. A moderated mediation model has been proposed to investigate the role of strategic planning and its effects on project outcomes. Data was gathered from 326 staff members working at different levels of management in some of Pakistan’s emerging construction companies owned by young entrepreneurs. The study’s findings revealed a positive relationship between customer focus and project performance mediated by process management. Moderation analysis indicates a significant relationship between process management and project performance when moderated by strategic planning. Constraints to the study have been identified, and suggestions for future research have been offered.
Smart farming uses advanced tools and technologies such as intelligent agricultural machines, high-precision sensors, navigation systems, and sophisticated computer systems to increase the economic benefits of agriculture and reduce the associated human effort. With the increasing demands of individualized farming operations, the internet of things is a crucial technique for acquiring, monitoring, processing, and managing the agricultural resource data of precision agriculture and ecological monitoring domains. Here, an internet of things-based system scheme integrating the most recent technologies for designing a management platform for agricultural machines equipped with automatic navigation systems is proposed. Various agricultural machinery cyber-models and their corresponding sensor nodes were constructed in a pre-production phase. Three key enabling technologies—multi-optimization of agricultural machinery scheduling, development of physical architecture and software, and integration of the controller-area-network with a mobile network—were addressed to support the system scheme. A demonstrative prototype system was developed and a case study was used to validate the feasibility and effectiveness of the proposed approach.
Mechanical Turk (MTurk), an online labor market run by Amazon, provides a web platform for conducting behavioral experiments; the site offers immediate and inexpensive access to a large subject pool. In this study, we review recent research about using MTurk for behavioral experiments and test the validity of using MTurk for experiments in behavioral operations management. We recruited subjects from MTurk to replicate the inventory management experiment from Bolton and Katok ( 2008 ), as well as the procurement auction experiment from Engelbrecht‐Wiggans and Katok ( 2008 ), and the supply chain contracting experiment from Loch and Wu ( 2008 ). We successfully replicate individual biases in the inventory management and procurement auction experiments, but learning in the individual tasks occurs more slowly on MTurk compared to the original studies. Further, we find that social preference manipulations in the supply chain experiment are ineffective in changing the behavior of MTurk subjects, in contrast to the original study. We conducted an additional replication study of the supply chain contracting experiment using student subjects in a standard laboratory. Results from this laboratory replication also fail to replicate the original laboratory study, indicating that the effect of social preferences on supply chain contracting may not be robust to alternative subject pools. We conclude that factors potentially influencing the differences observed on MTurk are less related to the online environment, but more related to the diversity and characteristics of subject pool on MTurk. Overall, MTurk appears to be an important and relevant tool for researchers in behavioral operations, but we caution researchers about slower learning of the MTurk subjects and the use of social preference manipulations on MTurk.
Vania Beatrice, Werner R. Murhadi, Arif Herlambang
The purpose of this study was to examine the influence of demographic factors such as gender, age, education, occupation, income, and investment experience on investor behavior bias such as overconfidence bias, disposition effects, herding bias, and mental accounting. This type of research was causal research with a quantitative approach, and the analytical method used was the analysis of SEM (structural equation modeling). This research was conducted by distributing questionnaires to investors listed on the Indonesia Stock Exchange with a minimum age of 17 years. The results showed that overconfidence bias was influenced by investment experience while disposition effect was influenced by age, income level, and investment experience. Herding bias was influenced by age and occupation while mental accounting was influenced by income level.
Production management. Operations management, Management. Industrial management
This paper reviews the state of the art in Productions and Operations Management (POM) academic research regarding outsourcing in supply chain contexts. We first acknowledge the “Theory of the Firm” (ToF), the venerable and vast body of thought regarding where the firm draws the boundary between what it performs in‐house and what it outsources. Despite the clear linkage between outsourcing and POM, the ToF literature is most closely associated with the fields of strategy and economics. This disconnect might in part be due to a difference in theoretical lenses and terminology, which we address for the POM audience by providing a ToF tutorial. Our review of publications by the POM community from 2000 to 2016 includes a framework that organizes the in‐scope papers and a structured summary of each work. We partition the research into empirical/conceptual and analytical sub‐literatures, each of which gets its own critical assessment and discussion of open opportunities. Along the way, we articulate the features of the POM lens that distinctively position POM researchers to contribute further to the ToF, a convergence which we hope to encourage through this study. A deeper conversation among strategy, economics, and POM would enrichen the rigor and the relevance of each field.
In this paper, we study customers’ learning behaviors in service operations systems, when the customers hold incorrect beliefs about the population distribution (“projection effects”). We propose a basic model wherein the customers are heterogeneous in both delay sensitivity and awareness of service quality, which explains “rational hesitations”: The uninformed and patient customers obfuscate the quality signaled by the queue length for the uninformed and impatient customers, who wait and join when the queue becomes longer. Ironically, with such bounded rationality, the customers who are more averse to waiting will react more sensitively to the observed long queue, which leads to an overestimation of the service quality and waiting in the long queue. Such bounded rationality also impedes effective learning by inducing decision errors, which, among other consequences, reduces the social welfare due to a blind “buying frenzy” even if the service quality is low.
Kostetska Kateryna, Khumarova Nina, Umanska Yuliia
et al.
The article considers international trends and directions of inclusive growth which is considered as an inclusive economic growth and is measured by heterogeneous growth indicators, as an index of inclusive development. Considering the above, was analysed the existing state of the country’s growth considering the environmental, economic, social and technological components as prerequisites for inclusive environmental management. Thus, the main focus of this article is on the formation of prerequisites for inclusive nature management in socio-economic and environmental practices and their subsequent methodological support. So segments of population prosperity means not just material consumption, but social vision formation and the institutional support creation for enables everyone to participate in the socio-economic achievements. The main gaps in the institutional support of the inclusive environmental management process are disclosed: in the social sphere: limited access to economically viable means that meet the real needs of the population in terms of health care, social assistance, basic education and awareness; in the ecological and economic sphere there is no effective and efficient management of providing the population with products that comply with the requirements of eco-certification and eco-labelling, which negatively affects the replenishment of the state budget and the promotion of the rational use of natural resources. Therefore, in order to create a favourable climate and institutional support of inclusive environmental management, in this article, will conduct a thorough analysis of the status of its components and assess the compliance of the existing conditions with current international requirements for inclusiveness. Inclusive growth requires environmental inclusion, which can be achieved through the introduction of new metrics and resource value indicators in regional development projects and programs. In doing so, measures should be developed and recommendations made to improve further planning and control.
Idea generation and selection are fundamental activities in innovation. Scholars in many disciplines have written about these activities, addressing diverse perspectives. In this study, we synthesize the research findings most applicable to the management of technology. First, we present findings on the process of idea generation: the importance of problem recognition and the many decisions made in organizing the effort. Second, we present findings about the process of idea selection, focusing on the different types of information that can be used in that decision. Third, we turn our attention to the organizational context in which both idea generation and selection occur: the corporate culture, use of incentives, organizational structure, and use of teams. Finally, we conclude, emphasizing that although idea generation and selection are as old as human decision making, changes in technology still affect these fundamental processes.
Nicolas Devaux, Thomas Crestey, Corentin Leroux
et al.
Aim: The aim of this short note is to provide first insights into the ability of Sentinel-2 images to monitor vine growth across a whole season. It focuses on verifying the practical temporal resolution that can be reached with Sentinel-2 images, the main stages of Mediterranean vineyard development as well as potential relevant agronomic information that can be seen on the temporal vegetation curves arising from Sentinel-2 images.
Methods and results: The study was carried out in 2017 in a production vineyard located in southern France, 2 km from the Mediterranean seashore. Sentinel-2 images acquired during the whole vine growing cycle were considered, i.e. between the 3rd of March 2017 and the 10th of October 2017. The images were used to compute the classical normalized difference vegetation index (NDVI). Time series of NDVI values were analyzed on four blocks chosen for exhibiting different features, e.g. age, missing plants, weeding practices. The practical time lag between two usable images was closer to 16 days than to the 10 theoretical days (with only one satellite available at the date of the experiment), i.e. near 60% of the theoretical one. Results show that it might be possible to identify i) the main steps of vine development (e.g. budburst, growth, trimming, growth stop and senescence), ii) weed management and inter-row management practices, and iii) possible reasons for significant inter-block differences in vegetative expression (e.g. young vines that have recently been planted, low-productive blocks affected by many missing vines).
Conclusions: Although this experiment was conducted at a time when Sentinel-2b was not fully operational, results showed that a sufficient number of usable images was available to monitor vine development. The availability of two Sentinel satellites (2a and 2b) in upcoming seasons should increase the number of usable images and the temporal resolution of the time series. This study also showed the limitations of the Sentinel-2 images’ resolution to provide within-block information in the case of small blocks or blocks with complex borders or both.
Significance and impact of the study: This technical note demonstrated the potential of Sentinel-2 images to characterize vineyard blocks’ vigor and to monitor winegrowers’ practices at a territorial (regional) scale. The impact of management operations such as weeding and trimming, along with their incidence on canopy size, were observed on the NDVI time series. Some relevant parameters (slope, maximum values) may be derived from the NDVI time series, providing new insights into the monitoring of vineyards at a large scale. These results provided areas for further investigation, especially regarding the development of new indicators to characterize block-climate relationships.
Ovidiu Csillik, John Cherbini, Robert Johnson
et al.
Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.