Purpose: With this work, we intend to promote research on the application of Operations Management tools in order to assist with decision-making in health crisis situations. During the first six weeks of the COVID-19 crisis in Spain, we have contacted a large number of hospital and health department managers in the Valencian Community and other regions of Spain. The result is that very few, at least when contact was made and at the time of writing this article, had consulted staff members in the Operations Management area for advice on this situation, and they are quite reluctant to do so. This is in spite of the fact that some medical sources also consider this crisis to be one of resources, not merely a medical crisis. Our opinion is that Operations Management can make a useful and valuable contribution to anticipate and improve the management of scarce resources, even in times of crisis. If those responsible for public health or heads of hospitals do not see this usefulness, then there is a huge gap between research and practice in Operations Management and what is transmitted to the healthcare sector. Our aim is to help reduce this gap.Design/methodology/approach: In the first part of the article, we will justify that this crisis, besides being a medical crisis, is also a crisis in terms of resources. We will briefly review some of the Operations Management tools that we believe are appropriate for managing this crisis. We will focus on those that we will illustrate in the second part of the article as a practical example of the action research protocol (only the action planning phase), in which we are currently involved.Findings: Due to the peculiarities of the COVID-19 epidemic, it is reasonable to think that the evolution and duration of each outbreak, and the way it is dealt with, will be different in each hospital and/or each health department. Anticipating the number of patients who will require care and forecasting the consumption of resources over time is complex, due to the long and variable incubation times and the uncontrolled evolution of the treatments needed by each patient. Furthermore, since the evolution of new patient admissions is not homogeneous, the forecasting of requirements is difficult. Providing real-time support and detecting with a certain degree of anticipation the potential levels of demand for health care will enable hospitals to define what stages of the contingency plan to apply and how to (re)assign the available resources in the most efficient way possible.Research limitations/implications: Hardly any current data are available, not only publicly, but also through public health ministries and managers; those that are available are not very reliable and come from studies with non-representative samples. For this reason, all forecasts must be taken with a note of caution, and in particular, designing review mechanisms every time a new set of updated reliable data becomes available (something that occurs on a weekly basis).Originality/value: In this work, we will show an example of a proposal for the practical application of Operations Management tools. Our proposal may be useful to hospital managers and public administrators in the healthcare sector (regional ministries or their equivalents). Each health department, hospital or hospital group will generate the aggregate data that facilitate the calculation of needs as input parameters for the model (available and occupied beds, daily admissions, mean lengths of stay in each healing phase and the percent of patients who evolve from one stage to another in the progression of the disease/healing process) and will be able to analyze how the system responds and evaluate different strategies or mitigating actions. These tools have been shown to be useful and reliable in manufacturing and service contexts that show a strong seasonal demand (both occasional and recurring) requiring a great deal of resources. We believe these situations are analogous to those faced by hospitals and the healthcare system in an epidemic like COVID-19. With them, we could facilitate the aggregate planning of resources for hospital administrators in order to provide them operational support during the COVID-19 contingency plan. However, the array of useful tools for this crisis is not limited to those that we will illustrate. Our intent is to encourage the Operations Management academic community to demonstrate how they contribute to the service of society in this COVID-19 epidemic, providing knowledge that can be extrapolated to other similar situations in the future that we may be unfortunate enough to experience.

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool’s predictions and illustrate how it can support managers in their daily decisions concerning the system’s capacity and ensure patients the access the resources they require.

Objective: To provide a working guide which facilitates the accuracy, transparency, and usefulness for academics and practitioners of case report papers as an article type which are fit for publishing in impact journals. In this way, we aim to increase the use and dissemination of recommendations in aid of the writing and publication of case study and action research. Theoretical framework: A case report paper presents a story in narrative form and includes current individual or organisational concerns, situational diagnoses, interventions, outcomes including adverse events, and follow-up. The narrative should include a discussion of the rationale for any conclusions and lessons learned. Method: We analysed the prevalence of these type of articles in publications as indexed in Web of Science (WOS) and in Scopus; we explain how we customised the case report guidelines (CAse REport or CARE) to optimise them for the scientific field of Management focussing particularly on Operations Management and Human Resources Management. We shared for discussion and feedback the adapted proposal among a group of academics and professionals in the field of Management. Outcomes/Implications: Robust reporting guidelines help to improve research performance, reduce bias risk, provide information on the implementation of management practices, support the training of management professionals, and enable informed editorial decisions by journal editors and reviewers.

This paper presents an integrated framework for capacity and operations planning in Spanish sheltered employment centers (SECs). Employment provides socio-economic opportunities for people with disability. Well functioning SECs that provide opportunities for people with and without disability to work alongside each other are an important component of Spain’s current labor market. To be economically sustainable, SECs need to satisfy their clients expectations in terms of price, flexibility and performance, whilst taking into account and strengthening the diverse skills and abilities of their workforce like specific learning and forgetting processes. The SEC studied herein, a non-for-profit organization whose mission is to create employment for people with disabilities its workforce is deployed directly on clients’ premises. Efficient management across this multi-site environment, whilst supporting the diverse employment needs of its staff, is of paramount importance. This paper contributes an integrated framework to support SEC capacity and operations planning, which prioritizes the explicit training needs of workers with disability at all levels (strategic, tactical, and operational) as a lever for achieving the organization’s goals.

The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5%.

The present work aims to analyse and identify a triple bottom-line business model of food retailing, in order to show a sustainable, replicable and non-scalable model in the agri-food sector, and to facilitate the development of a feasible market for small size producers-sellers. The methodology used for the business model identification analyses and relates elements and strategies, providing a design graphic for the model, as well as evaluating each triple sustainability dimension. All this following a protocol derived from the methodology of case study research. The case study shows a sustainable, replicable and non-scalable business model that emerges from a strategy defined by a responsible consumption group from a university. Using digital technology for the distribution and sale of products, and sharing capacity with a WISE, a business model is achieved that shortens the food products commercialization channel between producers and consumers, improving economic, social and environmental sustainability. The application of this methodology provides a tool that facilitates the replicability of sustainable business models in the agri-food business context, and allows identifying the level of their sustainability. The incorporation of this business model can contribute to the development of a triple bottom-line food market, whose purpose is to improve the survival of small size producers-sellers given the increasing centralization and globalization of the agri-food economy distribution.