Trends in product recalls within the agri-food industry:Empirical evidence from the USA, UK and the Republic of Ireland

The increasing frequency of product recalls within the agri-food industry has led many to question food safety. Research studies also often focus on biological hazards without considering how past, present and emerging risks change over time. We undertake a systematic review of the different biological, operational and chemical hazards within the agri-food industry using a dataset of 2070 registered food recalls in the USA, UK and Republic of Ireland between 2004 and 2010. We show product recalls have become more frequent over time and operational hazards, rather than biological and chemical hazards, are the most frequent recall type within the agri-food industry. © 2012 Elsevier Ltd. All rights reserved.

A framework for designing robust food supply chains

After years of emphasis on leanness and responsiveness businesses are now experiencing their vulnerability to supply chain disturbances. Although more literature is appearing on this subject, there is a need for an integrated framework to support the analysis and design of robust food supply chains. In this chapter we present such a framework. We define the concept of robustness and classify supply chain disturbances, sources of food supply chain vulnerability, and adequate redesign principles and strategies to achieve robust supply chain performances. To test and illustrate its applicability, the research framework is applied to a meat supply chain.

Using vulnerability performance indicators to attain food supply chain robustness

High effectiveness and leanness of modern supply chains (SCs) increase their vulnerability, i.e. susceptibility to disturbances reflected in non-robust SC performances. Both the SC management literature and SC professionals indicate the need for the development of SC vulnerability assessment tools. In this article, a new method for vulnerability assessment, the VULA method, is presented. The VULA method helps to identify how much a company would underperform on a specific Key Performance Indicator in the case of a disturbance, how often this would happen and how long it would last. It ultimately informs the decision about whether process redesign is appropriate and what kind of redesign strategies should be used in order to increase the SC's robustness. The applicability of the VULA method is demonstrated in the context of a meat SC using discrete-event simulation to conduct the performance analysis.