Maturity model for evaluating disaster and humanitarian operations

PurposeThis paper proposes a maturity model (MM) for assessing disaster operations and identifying strategies for organisations to evolve their maturity stages.Design/methodology/approachThis study applies a systematic literature review to identify state-of-the-art work related to maturity models for disaster operations. In addition, the study develops a case study to validate the proposed maturity model in a generic scenario and two real-life scenarios.FindingsThe analysis of 158 papers in the literature resulted in identifying 8 maturity models for disaster operations. Based on their structure, the authors proposed a new model with five maturity stages suitable for any of the four phases of the disaster life cycle (i.e. mitigation, preparedness, response and recovery). In addition, the research identified and presents 24 strategies for improving disaster operations according to each maturity stage transition. Finally, the research presents a case study that evaluates the disaster response operations from a Civil Defense organisation considering a response scenario disaster in general, a flood scenario, and the COVID-19 pandemic scenario.Originality/valueThis study provides the following three main contributions useful for academics and practitioners in the disaster operations area: a new maturity model for assessing disaster operations, a strategy guide for improving disaster operations based on a maturity evolution and an empirical study exploring the approximation between academia and professionals involved in real-life disaster operations management.

Odisha Craft – crafting the right expansion strategy

Learning outcomes Familiarize with the retail operations of handicrafts, facility location problem, apply multi-criteria decision through the goal programming approach and solving the same with MS Excel. Case overview / synopsis The case portrays a dilemma in the context of retail operations of a small-scale handicraft company known as Odisha Craft. Located in Odisha, Susanta Mohanty, the owner, was finding it a challenge to decide on the most promising location for his new retail outlet in the neighbouring city of Kolkata. He had five choices for the locations. Odisha craft was established by his father-in-law in 2009 with an objective to preserve and promote the rich culture of the handicrafts designed by the local artisans and ensure sustainable rural livelihood. The company had been facing numerous challenges and the pandemic has given a very formidable blow to the monthly revenues. The case brings out the multi-faceted dilemma of deciding on the facility location in 2020, involving a set of conflicting criteria. The case unfolds a systematic solution approach resolving the dilemma using MS Excel. Complexity academic level Courses such as operations research, operations management, service operations and retail operations for MBA students and trainings for junior-middle level executives. Supplementary materials Teaching notes are available for educators only. Subject code CSS 09: Operations and Logistics

Tax-Induced Inequalities in the Sharing Economy

The growth of sharing economy marketplaces like Airbnb has generated discussions on their socioeconomic impact and lack of regulation. As a result, most major cities in the United States have started to collect an “occupancy tax” for Airbnb bookings. In this study, we investigate the heterogeneous treatment effects of the occupancy tax policy on Airbnb listings, using a combination of a generalized causal forest methodology and a difference-in-differences framework. While we find that the introduction of the tax significantly reduces both listing revenues and sales, more importantly, these effects are disproportionately more pronounced for residential hosts with single shared-space (nontarget) listings versus commercial hosts with multiple properties or entire-space (target) listings. We further show that this unintended consequence is caused by customers’ discriminatory tax aversion against nontarget listings. We then leverage these empirical results by prescribing how hosts should optimally set prices in response to the occupancy tax and identify the discriminatory tax rates that would equalize the tax’s effect across nontarget and target listings. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

A literature review of smart warehouse operations management

E-commerce, new retail, and other changes have highlighted the requirement of high efficiency and accuracy in the logistics service. As an important section in logistics and supply chain management, warehouses need to respond positively to the increasing requirement. The “smart warehouse” system, which is equipped with emerging warehousing technologies, is increasingly attracting the attention of industry and technology giants as an efficient solution for the future of warehouse development. This study provides a holistic view of operations management problems within the context of smart warehouses. We provide a framework to review smart warehouse operations management based on the characteristics of smart warehouses, including the perspectives of information interconnection, equipment automation, process integration, and environmental sustainability. A comprehensive review of relevant literature is then carried out based on the framework with four perspectives. This study could provide future research directions on smart warehouses for academia and industry practitioners.

Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.

Social Queues (Cues): Impact of Others’ Waiting in Line on One’s Service Time

The traditional queueing literature assumes that service time is largely independent of social influences. However, queues are social systems; and social considerations are therefore likely to impact customers’ service time decision to the extent they have control. Through a series of experiments, we show that when others are waiting in line, customers tend to accelerate their own service time, and in doing so, sacrifice their own consumption utility. This behavior is driven by concern for others. Notably, the effect is diminished when they themselves have waited, as it is perceived as fair to let others wait if one also had to wait. We further show that obscuring the visibility between customers in service and those waiting in line diminishes the negative effect of others queueing on one’s own service time. This paper was accepted by Jay Swaminathan, operations management.

The Important Role of Time Limits When Consumers Choose Their Time in Service

A main challenge that service providers face when managing service systems is how to generate value and regulate congestion at the same time. To this end, classical queueing models suggest managers charge per-use fees and invest in capacity to speed up the service. However, in discretionary services, in which consumers value time in service and choose how long to stay, per-use fees result in suboptimal performance and speeding up does not apply. We study a queueing model of a service provider and rational consumers who are heterogenous in their requirements for service duration. Consumers incur disutility from waiting and choose whether to join and how long to spend in service. We consider time limits as a novel mechanism that may help in controlling congestion. Time limits put a cap on the maximum time that customers can spend in service. We analyze their effectiveness when combined with two price schemes: per-use fees and price rates. Time limits are effective because they reduce time in service and impact waiting times and joining behavior. Revenue maximizing firms and social planners who maximize social welfare benefit from implementing time limits in addition to price rates. Social planners who seek to maximize consumer welfare, however, focus on regulating congestion and should, therefore, offer the service for free but implement time limits if congestion levels are high. The attractiveness of time limits goes further. We show that time limits are not only a useful lever that works well when combined with simple price mechanisms, but they are in fact optimal when congestion is high. Service providers can achieve the first-best outcome and extract all customer surplus by coupling a time limit with an optimal price mechanism. The attractiveness of time limits stems from their ability to reduce not only the average time spent in service, but also its variance. This is highly effective in settings in which customers’ service times impose externalities on others’ waiting times. Thus, we conclude that providers of discretionary services should set time limits when congestion is an issue. This paper was accepted by Vishal Gaur, operations management.

Electricity Markets in a Time of Change: A Call to Arms for Business Research

Europe’s clean energy transition is imperative to combat climate change and represents an economic opportunity to become independent of fossil fuels. As such, the energy transition has become one of the most important, but also one of the most challenging economic and societal projects today. Electricity systems of the past were characterized by price-inelastic demand and only a small number of large electricity generators. The transition towards intermittent renewable energy sources changes this very paradigm. Future electricity systems will consist of many thousands of electricity generators and consumers that actively participate in markets, offering flexibility to balance variable electricity supply in markets with a high spatial and temporal resolution. These structural changes have ample consequences for market operators, generators, industrial consumers as well as prosumers. While a large body of the literature is devoted to the energy transition in engineering and the natural sciences, it has received relatively little attention in the recent business research literature, even though many of the central challenges for a successful energy transition are at the core of business research. Therefore, we provide an up-to-date overview of key questions in electricity market design and discuss how changes in electricity markets lead to new research challenges in business research disciplines such as accounting, business & information systems engineering, finance, marketing, operations management, operations research, and risk management.