Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Profit efficiency analysis under limited information with an application to German farm types

Lack of information about technology and prices often hampers the empirical assessment of the profit maximization hypothesis (viz. by measuring the degree of profit efficiency). The non-parametric Data Envelopment Analysis (DEA) methodology can deal with such incomplete information. We exploit the implicit but largely neglected profit interpretation of the DEA model that builds on assumptions of monotone and convex production possibility sets. We show how its embedded assessment of necessary conditions for profit maximization can be strengthened given partial information in the form of monetary sub-cost/-revenue data (that are often easier obtained than the pure quantity data). Finally, we argue that a ‘mix’ efficiency analysis is naturally complementary to such a profit efficiency analysis. An application to German farm types complements our methodological discussion. By using non-parametric statistical tests, we further demonstrate the potential of the non-parametric approach in deriving strong and robust statistical evidence while imposing minimal structure on the setting under study. In particular, we look for significant efficiency variation over regions.     

An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility

We used an agent‐based modeling (ABM) framework and developed a mathematical model to explain the complex dynamics of microbial persistence and spread within a food facility and to aid risk managers in identifying effective mitigation options. The model explicitly considered personal hygiene practices by food handlers as well as their activities and simulated a spatially explicit dynamic system representing complex interaction patterns among food handlers, facility environment, and foods. To demonstrate the utility of the model in a decision‐making context, we created a hypothetical case study and used it to compare different risk mitigation strategies for reducing contamination and spread of Listeria monocytogenes in a food facility. Model results indicated that areas with no direct contact with foods (e.g., loading dock and restroom) can serve as contamination niches and recontaminate areas that have direct contact with food products. Furthermore, food handlers’ behaviors, including, for example, hygiene and sanitation practices, can impact the persistence of microbial contamination in the facility environment and the spread of contamination to prepared foods. Using this case study, we also demonstrated benefits of an ABM framework for addressing food safety in a complex system in which emergent system‐level responses are predicted using a bottom‐up approach that observes individual agents (e.g., food handlers) and their behaviors. Our model can be applied to a wide variety of pathogens, food commodities, and activity patterns to evaluate efficacy of food‐safety management practices and quantify contamination reductions associated with proposed mitigation strategies in food facilities.     

Introduction to the Special Issue on Humanitarian Operations and Crisis Management

The Genesis of this Special Issue came from the Board of the POMS College on Humanitarian Operations and Crisis Management (HO&CM). It was seen as a necessary initiative to define the field and examine research opportunities. This Special Issue shows that humanitarian operations pose challenges for P/OM researchers and practitioners that differ markedly from those of conventional supply chains associated with profitable enterprises. On the basis of the eight articles in this Special Issue, we have described and demonstrated the unique characteristics of the POM/HO&CM interaction. We have also identified those attributes that tend to overlap with conventional aspects of POM. In addition to wanting to be cost effective, the issue of equity fairness is pervasive in humanitarian operations, and so is the need to always base considerations on “last‐mile logistics,” that is, getting aid to those in most need. Research is essential to determine how to train researchers to scout out and map the territory of the real problems. One of the most vexing problems is the lack of robust data in the humanitarian domain which is as richly varied as the types of disasters that can occur.     

The Role of Media Exposure on Coordination in the Humanitarian Setting

Despite high demand and resource limitations, humanitarian organizations (HOs) typically do not share resources and/or coordinate in the field. While coordination enhances operational performance and saves costs, the general perception is that it dilutes the media attention that individual organizations might receive, and negatively influences their future donation income. In this study, we empirically unveil the impact of media exposure and operational performance on the donations obtained by HOs. Then, based on the empirical results, we develop a stylized model to characterize the structure of preferred coordination policies with respect to an organization's funding source and main mandate. Our findings shed light on the incentives and dynamics that drive behaviors in humanitarian operations and provide insights for policy makers on designing and implementing mechanisms that encourage humanitarian coordination.     

Estimation of Deprivation Level Functions using a Numerical Rating Scale

Evaluating and quantifying human suffering in humanitarian operations offers an innovative and potentially powerful way to assess the performance of humanitarian logistics (HL) and help build optimization models. Previous studies have suggested deprivation cost as a metric and have estimated deprivation cost functions for water using willingness‐to‐pay. Our study proposes deprivation levels, defined as the degree of human suffering caused by lack of access to a good or service, and estimates deprivation level functions using a numerical rating scale. Analyzing data collected from respondents with and without disaster experience, we find that individuals in the latter category estimate deprivation differently from the beneficiaries of disaster relief. Our study demonstrates that deprivation levels can be expressed as logistic growth functions with a typical S‐shape, and that these can be integrated into HL optimization models to better account for human suffering.     

Ideal Values and Counter‐ideal Values as Two Distinct Forces: Exploring a Gap in Organizational Value Research

Motives and values at work have long been key topics of business and management studies. In a focused review of the literature on the nature of human values, this paper identifies a disconnect with the literature on human motivation, despite the otherwise inherent relatedness of the two fields. Specifically, extant theory and research have conceptualized values generally in terms of ideals, namely desired end‐states that individuals strive to approach. Although values, by this definition, express motivational concerns, theories of human motivation suggest that there are two forces to consider, i.e. approach and avoidance motivation. By applying this ‘two forces’ perspective to value research, this paper identifies a gap in the literature on values: namely, the idea that individuals are also influenced by counter‐ideal values, i.e. end‐states that they deliberately seek to avoid. The identification of this gap opens up new opportunities for value research in general and organizational value research in particular. To pave the way for future research, this paper critically discusses the few studies that have taken first steps in that direction and outlines research questions that may follow for issues such as employer branding and person–organization fit. This paper closes by providing suggestions on how to tackle the issue in organizational practice.     

Clickstream Data and Inventory Management: Model and Empirical Analysis

We consider firms that feature their products on the Internet but take orders offline. Click and order data are disjoint on such non‐transactional websites, and their matching is error‐prone. Yet, their time separation may allow the firm to react and improve its tactical planning. We introduce a dynamic decision support model that augments the classic inventory planning model with additional clickstream state variables. Using a novel data set of matched online clickstream and offline purchasing data, we identify statistically significant clickstream variables and empirically investigate the value of clickstream tracking on non‐transactional websites to improve inventory management. We show that the noisy clickstream data is statistically significant to predict the propensity, amount, and timing of offline orders. A counterfactual analysis shows that using the demand information extracted from the clickstream data can reduce the inventory holding and backordering cost by 3% to 5% in our data set.