Introduction to the special issue on the role of operational research in future epidemics/ pandemics.

In this special issue, 23 research papers are published focusing on COVID-19 and operational research solution techniques. First, we detail the process from advertising the call for papers to the point where the best papers are accepted. Then, we provide a summary of each paper focusing on applications, solution techniques and insights for practitioners and policy makers. To provide a holistic view for readers, we have clustered the papers into different groups: transmission, propagation and forecasting, non-pharmaceutical intervention, healthcare network configuration, healthcare resource allocation, hospital operations, vaccine and testing kits, and production and manufacturing. Then, we introduce other possible subjects that can be considered for future research.

Collaboration-competition dilemma in flattening the COVID-19 curve.

Testing for COVID-19 is a key intervention that supports tracking and isolation to prevent further infections. However, diagnostic tests are a scarce and finite resource, so abundance in one country can quickly lead to shortages in others, creating a competitive landscape. Countries experience peaks in infections at different times, meaning that the need for diagnostic tests also peaks at different moments. This phase lag implies opportunities for a more collaborative approach, although countries might also worry about the risks of future shortages if they help others by reallocating their excess inventory of diagnostic tests. This article features a simulation model that connects three subsystems: COVID-19 transmission, the diagnostic test supply chain, and public policy interventions aimed at flattening the infection curve. This integrated system approach clarifies that, for public policies, there is a time to be risk-averse and a time for risk-taking, reflecting the different phases of the pandemic (contagion vs. recovery) and the dominant dynamic behavior that occurs in these phases (reinforcing vs. balancing). In the contagion phase, policymakers cannot afford to reject extra diagnostic tests and should take what they can get, in line with a competitive mindset. In the recovery phase, policymakers can afford to give away excess inventory to other countries in need (one-sided collaboration). When a country switches between taking and giving, in a form of two-sided collaboration, it can flatten the curve, not only for itself but also for others.

On the use of evidence in humanitarian logistics research.

This paper presents the reflections of the authors on the differences between the language and the approach of practitioners and academics to humanitarian logistics problems. Based on a long-term project on fleet management in the humanitarian sector, involving both large international humanitarian organisations and academics, it discusses how differences in language and approach to such problems may create a lacuna that impedes trust. In addition, the paper provides insights into how academic research evidence adapted to practitioner language can be used to bridge the gap. When it is communicated appropriately, evidence strengthens trust between practitioners and academics, which is critical for long-term projects. Once practitioners understand the main trade-offs included in academic research, they can supply valuable feedback to motivate new academic research. Novel research problems promote innovation in the use of traditional academic methods, which should result in a win-win situation: relevant solutions for practice and advances in academic knowledge.

The experience trap.

When companies put seasoned managers in charge of important projects, they don't expect missed deadlines, budget overruns, and rampant defects. However, that's what researchers found when they tested hundreds of experienced project managers with computer games that simulated software development projects. The study, conducted by two professors from Insead and one from Naval Postgraduate School, strongly suggests that veterans in complex environments suffer a breakdown in the learning process. The research reveals three reasons for the breakdowns: Time lags between causes and effects make it difficult to see how they're connected; fallible estimates color the chain of decisions that determine a project's outcome; and a bias toward the initial goals prevents managers from setting revised, more appropriate, targets when project circumstances change. Sticking to an initial low budget goal after a project grew in scope, for instance, led subjects to ignore quality assurance, which led to soaring defect rates--and costs. Companies can take practical steps to fix the learning cycle. They can provide feedback that shows the relationships between important variables in the environment. Such feedback might reveal, say, the 20-day ramp-up that a new quality assurance team needs before becoming fully effective. Tools that apply formal models to calculate such things as the effect of turnover on team productivity also help. Setting goals for behavior, instead of targets for performance, is critical as well. Finally, firms can create project "flight simulators" that mimic actual learning environments but don't let complexity overwhelm trainees. Managers can continue learning only if they get decision support tailored to the challenges they face. Firms would do well to focus more on training people higher up in the organization and stop leaving them to fend for themselves.

Inventory-driven costs.

In the 199os, Hewlett-Packard's PC business was struggling to turn a dollar, despite the company's success in winning market share. By 1997, margins on its PCs were as thin as a silicon wafer, and some product lines hadn't turned a profit since 1993. The problem had everything to do with the PC industry's notoriously short product cycles and brutal product and component price deflation. A common rule of thumb was that the value of a fully assembled PC decreased 1% a week. In such an environment, inventory costs become critical. But not just the inventory costs companies traditionally track, HP found, after a thorough review of the problem. The standard "holding cost of inventory"--the capital and physical costs of inventory--accounted for only about 10% of HP's inventory costs. The greater risks, it turned out, resided in four other, essentially hidden costs, which stemmed from mismatches between demand and supply: Component devaluation costs for components still held in production; Price protection costs incurred when product prices drop on the goods distributors still have on their shelves; Product return costs that have to be absorbed when distributors return and receive refunds on overstock items, and; Obsolescence costs for products still unsold when new models are introduced. By developing metrics to track those costs in a consistent way throughout the PC division, HP has found it can manage its supply chains with much more sophistication. Gone are the days of across-the-board measures such as,"Everyone must cut inventories by 20% by the end of the year," which usually resulted in a flurry of cookie-cutter lean production and just-in-time initiatives. Now, each product group is free to choose the supply chain configuration that best suits its needs. Other companies can follow HP's example.