Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters

Abstract

International trade in food knows no borders, hence the need for prevention systems to avoid the consumption of products that are harmful to health. This paper proposes the use of multicriteria risk prevention tools that consider the socioeconomic and institutional conditions of food exporters. We propose the use of three decision-making methods—Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), Elimination et Choix Traduisant la Realité (ELECTRE), and Cross-Efficiency (CE)—to establish a ranking of countries that export cereals to the European Union, based on structural criteria related to the detection of potential associated risks (notifications, food quality, corruption, environmental sustainability in agriculture, and logistics). In addition, the analysis examines whether the wealth and institutional capacity of supplier countries influence their position in the ranking. The research was carried out biannually over the period from 2012–2016, allowing an assessment to be made of the possible stability of the markets. The results reveal that suppliers’ rankings based exclusively on aspects related to food risk differ from importers’ actual choices determined by micro/macroeconomic features (price, production volume, and economic growth). The rankings obtained by the three proposed methods are not the same, but present certain similarities, with the ability to discern countries according to their level of food risk. The proposed methodology can be applied to support sourcing strategies. In the future, food safety considerations could have increased influence in importing decisions, which would involve further difficulties for low-income countries.

Multiple Criteria Decision Making for the Achievement of the UN Sustainable Development Goals: A Systematic Literature Review and a Research Agenda

“…The contributions of MCDM methods for the achievement of SDGs belonging to this category are highlighted in 56 studies, as follows: SDG 2-"Zero Hunger and Sustainable Agriculture" [106][107][108][109][110][111][112][113][114][115][116], SDG 3-"Good Health and Well-being" [117][118][119][120][121][122][123], SDG 4-"Inclusive and Quality Education" [124][125][126][127][128], SDG 7-"Affordable and Clean Energy" , and SDG 11-"Sustainable Cities and Communities" [153][154][155][156][157][158][159][160][161]. 4.4.1.…”

“…4.4.1. SDG2: Zero Hunger and Sustainable Agriculture Table 11 contains a summary of the reviewed articles related to this category [106][107][108][109][110][111][112][113][114][115][116]. In three studies [108][109][110], their authors combined a utility-based method (AHP) with Geographical Information Systems (GIS) in different localities, namely Bijnor (India), Emirate Abu Dhabi (UAE), and Benue (Nigeria), to access cropland suitability for choosing the best area for a specific crop and the areas where effective management is required.…”

“…In summary, most of the studies reviewed in this category employed utility-based methods (AHP and ANP) [107][108][109][110][111][112][113][114]116], followed by compromise methods (TOPSIS, CO-PRAS, SAW) [105,111,113,115,116], and outranking methods (ELECTRE III, PROMETHEE, and ELECTRE) [106,114,115].…”

Proposal of a New Orange Selection Process Using Sensory Panels and AHP

“…Retailers should therefore consider all these factors when choosing fresh fruit suppliers. However, in the literature, there are different proposals in the food industry that include green criteria [ 18 , 20 ], price, food safety concerns [ 19 ] and logistics [ 36 ]. Although all of them take food quality into account [ 17 ], none of them include it from a sensory point of view.…”

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

“…MCDM methods are ideal tools for the processes of decision-making between different alternatives. Although multi-criteria techniques have been used in decision problems related to the agriculture and food industries, it has rarely been used as a risk prevention appliance in the agriculture sector [39,40].…”