Garros Gong, Stanko Dimitrov, Michael R. Bartolacci
This work addresses the operational conflicts between visibility-driven mobilization and cost efficiency in disaster management scenarios involving wildfires. Using official wildfire reporting on the social media platform Twitter (now X), we develop a temporal gravity model to extract a signal of public attention for California wildfires (2007–2021) without the “noise” of spurious content. Interpreting this signal through the lens of behavioral disaster management operations, our analysis finds a "Visibility-Efficiency Paradox". This paradox shows that while social media visibility functions as a potent mobilization signal to the general public during wildfires and is associated with greater resource deployment, it simultaneously correlates with reduced cost efficiency under high resource use loads. We identify resource saturation as a boundary condition where heuristic signals appear to shift from valuable inputs to potential stressors. These findings challenge the assumption that high visibility of responders in a wildfire emergency is a direct proxy for operational urgency and effectiveness. We propose actionable strategies, including reverse audits and decoupling, in order to help counteract salience bias; thus, highlighting the potential for algorithmic governance to align public attention with sustainable resource management.
Real-time feedback applications are reshaping employee performance feedback in operations management. Their design and implementation significantly influence employee engagement, which is a key factor in the success of technology-driven business innovations. This study investigates an implementation strategy to optimize feedback app use by employing digital nudges to encourage regular engagement. Drawing on social norms theory and cognitive load theory, we examine how message framing and users’ cognitive states affect the quantity and content features of feedback, including ratings, review length, surprise, and recognition. We conducted two randomized experiments to evaluate the effectiveness of digital nudges. Experiment 1, a field study with over 250 users of the DevelapMe app in a financial firm, tested how message framing and timing influenced cognitive load. Experiment 2, an online randomized controlled trial, explored underlying mechanisms and validated cognitive load measures. The results show that while social norms-based nudges increase feedback volume, they are associated with shorter reviews and reduced recognition and surprise. Employees experiencing lower cognitive load provided more feedback but tended to give lower ratings. Importantly, the interaction between social norms and low cognitive load resulted in higher ratings and more detailed reviews. This suggests that reducing cognitive load can enhance the positive effects of social norms on feedback quality. This study underscores the moderating role of cognitive load in the effectiveness of digital nudges and offers insights into feedback design that promotes both participation and quality. Theoretically, it contributes to research on digital feedback systems by integrating social norms and cognitive load theories to explain employee feedback behavior. Practically, it provides guidance for managers in designing real-time feedback tools that strategically use digital nudges while minimizing cognitive load, fostering a culture of continuous feedback, strengthening engagement, and improving performance management systems.
Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving toward predictive approaches relying on historical data. In this paper, we create a nonparametric model using a spatiotemporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.
Benkhoff, Lana, Straub, Natalia, Thayaparan, Thanushan
et al.
With the increasing automation of production and logistics systems, the demand for autonomous mobile robots (AMRs) has risen significantly in recent years. They enable an efficient and flexible organization to optimize material flow. Besides greenfield projects, AMRs are particularly suited for brownfield applications due to their ability to integrate into existing plants and operating structures. These abilities come with particular planning and implementation challenges as they encompass both socio-technical and organizational aspects due to the interference with ongoing operations in established structures. Many companies struggle to find qualified personnel or to develop internal expertise. Against this background, a comprehensive approach to implementing AMRs in brownfield environments is required. This paper aims to provide a widespread overview of the scientific literature concerning holistic planning approaches for AMR deployment through a systematic literature review. These results provide an entry point for the development and consolidation of theoretical models, describing deficits regarding brownfield implementations as well as AMR specifications and identifying additional topics that should be included in the planning processes like change management, employee acceptance, and sustainability.
We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is to hire the best candidates while avoiding bias with regard to a certain protected attribute. Simply ignoring the protected attribute will not eliminate bias due to correlations in the data. We present a provably optimal fair hiring policy that depends on the protected attribute functionally, but not statistically. The policy does not set rigid quotas, and does not withhold information from decision-makers. Both synthetic and real data indicate that the policy can greatly improve equity for underrepresented and historically marginalized groups, often with negligible loss in objective value.
When scheduling the distribution of ordered items from a warehouse to customers, the transportation planning is generally done first and serves as input for planning warehouse operations. Such a sequential approach can lead to substantial inefficiencies when the customer deliveries are restricted by time windows, and the warehouse has limited resources available (both order pickers and space in the staging area). This paper studies the trade‐offs between warehouse operations and transportation planning. The goal is to understand the impact of three specific managerial interventions: adopting an integrated planning approach, expanding the available staging space, and expanding the delivery time windows. To this end, we propose a mathematical model for a general vehicle routing problem that incorporates order batching, order picker scheduling, staging, and vehicle loading. We introduce a novel idea to express the picking time of an order batch as a function of the batch size and develop a metaheuristic to solve this integrated problem. Furthermore, we develop exact algorithms to provide optimal solutions for the individual warehouse and transportation problems in a sequential planning approach. Managerial insights are distilled from case studies in two warehouses, one for ambient products and the other for refrigerated products, of a leading grocery retailer in the Netherlands. Our results show that integrated planning outperforms the other managerial interventions and generates cost savings between 9% and 11%. Savings are generally realized by executing larger order batch sizes to be picked in the warehouses at the expense of additional routing cost (around 2–3%). The second intervention in the form of time window expansions of only 15 minutes for customer deliveries can lead to cost savings between 4% and 6%, which results from a reduction in both transportation and warehousing cost. Expanding the capacity of the staging area is only meaningful when the staging space is highly utilized and only results in cost savings for the warehouse operations.
While the existing literature has studied the impact of demand uncertainty extensively within various monopoly settings, there is little research on its impact among competitive retailers. In this paper, we study the effects of demand uncertainty reduction in a setting with two newsvendor retailers competing on product availability. We parameterize the uncertainty level via a mean‐preserving spread and investigate the behavior of both retailers' equilibrium stocks and their expected profits in response to demand uncertainty reduction. We find that as one retailer's demand uncertainty decreases, it might move its order quantity away from the demand mean, which contrasts with the “pull‐to‐center” effect in the monopoly setting. Moreover, we show that as a retailer's demand uncertainty decreases, its own equilibrium profit increases while that of its competitor decreases.
Many individuals have strong preferences regarding COVID‐19 vaccines and would like to choose the vaccine they get. This practice, known as “vaccine shopping,” presents unique challenges to timely vaccine rollout: On the one hand, people may not get vaccinated if they are unable to receive their preferred vaccine, and on the other hand, a policy maker is inclined to distribute vaccines in a brand agnostic fashion to avoid wastage. We study whether a policy maker should allow individuals to choose their vaccine, and the optimal mix of single‐ and two‐dose vaccines to procure. We develop a stylized queueing game‐theoretic model that captures the main trade‐offs in the interaction between individuals and a policy maker to examine the impact of vaccine choice on the number of vaccinations. Individuals obtain a reward from the vaccine administered at a server and decide whether to get a vaccine based on the wait time, their inclination toward vaccinating, and the level of choice provided. We find that restricting choice results in a greater number of vaccinations when vaccine supply is low by administering doses as and when they become available. Contrary to popular belief that restricting choice wastes fewer vaccines, we find that fewer vaccines are wasted when patients who are moderately hesitant about vaccinating are allowed to choose their vaccine. In this case the possibility of being assigned a nonpreferred vaccine leads patients to forego vaccination, and allowing a choice alleviates this concern. Using a mathematical model for COVID‐19 transmission, we find that providing choice results in fewer infections in the United States than limiting choice, and the number of infections is lowest when a lower efficacy, single‐dose vaccine forms 5%–8% of the total vaccine dose supply. Our findings provide guidance to policy makers, especially as they plan to vaccinate effectively using a diverse vaccine supply.
Auto‐delivery is a subscription model widely employed in supply chains, whereby a supplier delivers products to a buyer (or multiple buyers) according to the buyer's choice of a constant shipping quantity to be delivered at prescheduled dates. The buyer enjoys a discount for the auto‐delivery orders and other benefits, including free subscription and cancellation. Because these benefits seem to all accrue to the buyer at the supplier's expense, the rationale for the supplier's decision to offer auto‐delivery and its impact on the profitability of both parties is an intriguing concern. We first develop a model that consists of a supplier and a single buyer, whereby the supplier offers a discount for the auto‐delivery orders and the buyer chooses the auto‐delivery quantity with the flexibility of cancelling the subscription. We derive the two parties' operating characteristics of their inventory systems and examine their optimal decisions. Our analysis shows that buyers benefit from the auto‐delivery discount; the supplier benefits from the demand‐expansion effect and the inventory‐reduction effect, a potential discount on the cost of the auto‐delivery units; and the supply chain benefits from reducing the bullwhip effect. We also find that channel coordination requires the supplier to pass the inventory‐related savings to the buyer through the auto‐delivery discount, which depends on the ratio of the two parties' holding cost rates. Moreover, we examine a model extension whereby the supplier announces a discount that is available for multiple buyers, we show that the supplier's optimal auto‐delivery discount under exponential demand can be determined based on the aggregate‐level demand information from all buyers. Finally, we discuss another model extension whereby the lead time of the supplier's recurring orders for auto‐delivery is longer than that of the regular orders and present a full analysis of the case when the lead time differential is one time period.
It is conventional wisdom that a manufacturer's encroachment into retail space will likely hurt an existing retailer. In contrast to this conventional belief, current research indicates that a retailer may welcome a manufacturer's encroachment despite the new competition in the final market. The encroachment may help the manufacturer have some “skin in the game” at the retail level, which will cause the manufacturer to make a selfish cost‐reducing investment that spills over to the retailer as a lower wholesale price. Such a spillover effect enhances the retailer's profit as long as the encroachment does not result in extreme retail competition by a certain degree of product differentiation, and ultimately generates Pareto gains in the supply chain. The spillover effect is so robust that the retailer's benefit from the encroachment remains even after considering potential mitigating factors such as selling costs, a nonlinear form of cost reduction, decentralized encroachment, additional retail competition, price competition, and a negotiation between the manufacturer and the retailer.
Long‐term vision depends upon history. Without a knowledge of history, both scholars and scholarship are incomplete. In a companion paper (Singhal & Singhal, 2022), we reviewed the history of technology, knowledge, and manufacturing before the Industrial Revolution, which began around 1760. Here, we continue this history from 1760 on.
A challenge for “Big Data” in the chemical production industry is not only to evaluate file storage but also to use online information to improve process performance. It should be spectral, vibration, thermal, and other sensors are more and more widely available. In today's harsh industrial conditions, accurate and reliable reviews or product quality assessments are critical. To predict important attribute factors utilizing quantifiable signals, information soft sensors dependent on Projection to Latent Structure (PLS) techniques are frequently used. However, due to changes in equipment, raw material, sensors, or management, most operations are carried out under real and stable conditions. The structure of the flexible sensors must be maintained at regular intervals. Reconstruction of the method using more recent sensor primary data focus of current design maintenance techniques, such as mobile window updates and recursive updates within the enterprise. In situations where data were collected with extremes, downtimes, and other transients in the non-stationary phase, this strategy was not sufficiently resilient. An alternative model update strategy was reviewed as part of this study. To assess the effectiveness of the current soft sensor approach, they modified two Key Performance Indicators (KPIs). The residue-dependent forecast KPI identifies long-term forecast damping models using a filtered estimation error. The KPI dependent on T2 would be a forecast KPI that checks the system's speculations to the expected original data. This updated strategy is effective in improving predictive accuracy without completely reconstructing the PLS model using research papers using industrial operations information. Finally, the KPI attributes and model upgrade mechanism could be used together. The researchers demonstrated that this update technique significantly improved the accuracy of the PLS soft detector predictions through the emulation of live behavior using industrial data. The configuration technique also made it possible to quickly identify underlying issues in situations where the original sample was ideal as well as informed engineers that a new method needed to be built.
Electric apparatus and materials. Electric circuits. Electric networks
Aiming to ensure current market needs, manufacturing companies search for tools and methodologies that would help them deliver their products efficiently and cost-effectively and enable them to become a part of Industry 4.0. Digital twins are a technology created based on the idea of the Fourth Industrial Revolution. The solution helps recreate physical devices in virtual space based on gathered data. It supports performance tests, configuration changes, and predictive maintenance without engaging existing machines. The paper aims to gain knowledge about the awareness level of the digital twin technology among industry representatives and identify the most important problems that stand in the way of implementing the technology in enterprises. The research focused on market awareness of the described technology. It also examined how companies use employee suggestions to improve their organisations and the factors that influence process efficiency. The methods used for the research were a literature review and cross-sectional survey conducted with 50 employees of manufacturing and IT companies. The research showed the need to implement digital twins in enterprises. Half of the survey respondents replied that the technology would help improve the efficiency of the company’s processes. The main benefit of the conducted research is identified awareness of the technology among industry representatives. In the future, the research will be extended to include the analysis of specific cases affecting the implementation of digital twins in enterprises.
Supply chain is definable as “the activities covering the procurement of raw materials, storage, production and assembly, stock control, distribution, order management, and delivery of the product to the end user, as well as the information systems necessary for monitoring and controlling these activities” (Author, Year, p. ??). Information technologies occupy a very important place in supply chains Due to the very close relationship that exists between them. Among the information technologies used in supply chains, barcode technology has been preferred as it provides traceability and accuracy in all operations of the supply chain and has currently reached a very important position. Meanwhile, stocking and warehouse operations are also important in supply chains as they occur at all links of the supply chain and have become the primary area of improvement for companies that want to increase supply chain efficiency. Stocking and warehouse operations are currently carried out using barcode technology, but changing and developing conditions have affected customer needs, accordingly also affecting all supply chain links and ultimately stocking and storage operations. The increases in the quality and quantity of the products to be stored have made warehouse management difficult. However, the fact that this process is still being carried out with barcode technology is now slowing down the processes. For this reason, radio frequency identification (RFID) technology has come to the agenda of businesses as a fast technology free from human error. RFID technologies promise an environment of fast, human-independent operation wherever possible. As a result, businesses have become aware of the need for effective stocking and storage operations for an effective supply chain and have begun investigating how RFID technologies can be used in these operations. This article includes an example that discusses how RFID technologies can improve storage and warehouse operations.
Throughout its history, the industrial engineering/operations management (IE/OM) field has relied heavily on axiomatic models and empirical studies of individual systems. But, unlike other engineering and management disciplines, it lacks a clear foundation in a descriptive science. Exceptional results like the famous “bullwhip effect” paper by Lee et al. (1997) hint at the powerful potential for a descriptive science of operations. But the very fact that such works are exceptional suggests that they are held to a high bar in the publishing process. This may be symptomatic of the cultural norms that have prevented our field from producing a rigorous scientific foundation. In this study, we make a case for why developing a unified science of operations is essential for IE/OM education, practice and research. We provide examples and a tentative framework to illustrate what such a science might look like and use this framework to generate a testable hypothesis about a powerful relationship between variability buffers in and operations system. We conclude with suggestions of measures we can take collectively to promote development of the science of operations.
Inpatient flow management plays a critical role in care delivery, patient outcomes, and hospital operational and financial costs. Modeling and performance analysis of inpatient flow present unique features and challenges that differ from operations in other service industries. In this study, we review recent modeling and analytical advances in the setting of inpatient flow management, with a particular focus on service time models motivated from the observations of inpatient discharges. We first compare two new service time models developed to capture the time‐of‐day inpatient flow dynamics, and reveal interesting connections between the two models. We then review analytical methods developed to analyze systems with the new service time models. Based on one method, which is amenable to a one‐dimensional exact analysis under certain conditions, we further introduce its approximations that have explicit analytical forms and enable efficient computations in large systems. In particular, we showcase how to leverage a powerful tool, Stein’s method framework, in the hospital setting for steady‐state approximations and characterizing error bounds. We conclude this study by a literature review on other important aspects in inpatient flow management and propose future research directions, from both the modeling and analytical perspectives.
The rapid growth of “big data” provides tremendous opportunities for making better decisions, where “better” can be defined using any combination of economic, environmental, or social metrics. This essay provides a few examples of how the use of big data can precipitate more sustainable decision‐making. However, as with any technology, the use of big data on a large scale will have some undesirable consequences. Some of these are foreseeable, while others are entirely unpredictable. This essay highlights some of the sustainability‐related challenges posed by the use of big data. It does not intend to suggest that the advent of big data is an undesirable development. However, it is not too early to start asking what the unwanted repercussions of the big data revolution might be.
This study aims to evaluate the cost of agents’ self‐routing in a service system with inbound and outbound customers. We assume that inbound customers arrive over time depending on the waiting time offered, while outbound customers can be contacted at all times. Furthermore, agents are in control of routing decisions and are aware of the state of the system. Accordingly, they decide whether to serve an inbound or outbound customer, or to idle. The system manager seeks to provide a suitable trade‐off between agents’ choice of serving inbound and outbound customers by incentivizing their actions through linear payouts. Hence, there arises a problem of determining the cost of agents’ self‐routing, which can be interpreted as a variant of the principal‐agent problem where the agents’ efforts are directed toward selecting their routing policy. Through a Markov decision process, we show that the agents’ optimal policy is a reservation threshold policy for inbound customers, and express the compensation parameters that minimize staffing cost. We conclude that motivating idling decisions through linear payouts incurs high costs. This justifies the current practice of using automated routing in call centers. Moreover, paying for idling cannot reduce staffing cost. However, discriminating between delayed and non‐delayed customers in the reward structure presents a high potential of reducing agents’ pay. Finally, in situations where agents do not know the status of their colleagues, our analysis argues in favor of not revealing the state of the system to them through delay announcements when the objective waiting time is low.
This research was conducted to determine the impact of Covid-19 on the Company’s stock returns and trading volume activity. The covid-19 pandemic event is important for research because it includes investor’s assessment of the information generated in the capital market. This study was conducted to test the following hypotheses: before and after the Covid-19 pandemic was declared a national non-natural disaster, (1) there was a significant average change in the average abnormal return; (2) it is a significant average change in average trading volume activity. This research was conducted using event research methods. The sample for this study comes from 45 companies in the JII index. The analysis tool used is a regression with the SPSS. The descriptive statistic can be confirmed by calculating the standard deviation value. The result shows that the standard deviation range is 0.0002 to 0.03, so the research tool could be described as data obtained is suitable to the measurement variable. The conclusions explain that the events before and after the declaration of the Covid-19 outbreak as a national non-natural disaster have positive and a significant impact on the average abnormal return rate of stock activity and changes in trading volume activity.
Production management. Operations management, Management. Industrial management
farhad azizpoor, yaghob haghi, maghsod bayat
et al.
IntroductionWatershed management plans are the starting point for conserving natural resources and sustaining them to better exploit and prevent the loss of national resources, and by quantitatively evaluating them, a clear view of the effectiveness of such measures in watersheds can be obtained.The important point in this regard is the effectiveness and profitability of watershed management projects that require review and evaluation of watershed management operations performed so that based on the results, while determining the efficiency of watershed management operations in the above basin, decide on the implementation of similar projects in other basins Also provide watersheds. More than a decade has passed since the new era of watershed management in the country, which has been accompanied by a fundamental change in its structure and importance.The result of this development is the improvement of the qualitative and quantitative level of programs in different levels and departments of management, executive, study and coordination with other relevant departments in the fields of nature. The view of planners and agents of the watershed management sector of the implementation of a series of limited executive projects, with the aim of controlling sediment upstream of large dams, has now spread throughout the vast country of Iran with goals such as soil and water protection, drought control, flood control., Improving the conditions of green land cover, paying attention to agricultural infrastructure resources, improving the economic conditions of watersheds, etc. have evolved. Due to the social and economic conditions of rural communities, low efficiency of production factors in different parts of the rural economy, inability to use existing facilities and their improper use, are among the most obvious economic variables that reduce efficiency at the level of natural resources.The purpose of this study is to determine and identify the effects of watershed management projects on the socio-economic and environmental system of the villages located in the Kurdish watershed in Jahrom city.MethodologyThe present quantitative research has been conducted with the aim of determining and identifying the effects of the implementation of the watershed management plan on the socio-economic system of villages with watersheds in a survey using a questionnaire.The statistical population of the study includes the number of watershed operators, among the rural households living in the study basins (390 households) in 5 villages located in the Kurdish section, which has been selected as the statistical population.The statistical sample size is estimated as 190 samples according to Krejcie and Morgan table. In this study, 35 items were examined from the perspective of the rural community. After filling in the questionnaires, statistical information was extracted and analyzed using SPSS software and questionnaire coding. In statistical analysis, two methods of descriptive and analytical statistics have been used. In the method of descriptive statistics, frequencies and percentage of frequencies are presented and in the method of inferential statistics, factor analysis test is used.DiscussionRegarding the evaluation of the effects of the implementation of the watershed management plan, according to the results of reviewing resources and field studies, a list of effective indicators on the implementation of this plan related to rural areas was extracted. In this study, the research variables are reduced to a smaller number of variables called factors. This method of factor analysis is called principal component analysis and its purpose is to solve the problem of internal dependence of a set of variables and summarize them in several components or factors. In this factor analysis, 7 factors have a specific value greater than 1. It should be noted that all the above factors explain 51.74% of the total variance of the variables and the remaining percentage of the remaining variance is related to other factors that could not be predicted. Meanwhile, out of 35 variables, 26 variables were involved in explaining the factors and the remaining 9 variables were removed from the analysis due to the fact that their factor load was less than 0.5. According to the factor analysis, the 26 variables under study can be named as seven factors. In the present study, due to the high share of each of the variables loaded in each of the seven factors rotated, the factors that have the ability to explain the variances have been named, and indicates that the factor analysis and variables are satisfactory. These factors are: local participation, development of agricultural production resources, reducing the impact of natural hazards, increasing agricultural production, employment development, reducing rural-urban migration and increasing the price of agricultural land, among which the participation of local communities with 17.74% It has been the most influential compared to other factors in the implementation of this plan.ConclusionThe results of this study are derived using factor analysis of various factors, based on which these factors are placed in 7 groups with variables. In general, variables, communication with experts and promoters, the share of people's representatives in basin management, public participation, increasing the level of gardens, reducing flood damage to rural roads, reducing erosion and sediment, solving problems related to water shortage in the region and land prices Horticulture and agriculture have been the most important variables affecting the implementation of this plan.Examining similar examples of the effects of watershed management projects on rural development over the past few years shows different results, but the prevailing situation indicates changes in socio-economic and environmental dimensions in rural areas. In other words, studies such as(Ghanbari and Qudussi, 2008 - Mosaei et al, 2010 and Rezaei et al., 2012, etc.)show a trend of effective changes in all dimensions and in some studies to the social dimension (participation). has it. Of course, it should not be overlooked that these changes are not the same in all cases and have fluctuated relative to each other. Including a series of variables(reduction of natural hazards, development of production resources and increasing the price of agricultural land) that have been added in this study and as can be seen in the table of research findings, these variables have been effective in implementing the watershed management plan.