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بخشی از مقاله انگلیسیعنوان انگلیسی:Integrating harvesting decisions in the design of agro-food supply chains~~en~~

Abstract

The inefficiencies observed in current agro-food supply chains, and recent trends in agro-food industry such as consolidation, increase the need for further studies on supply chain management in this field. Models are required for the complex task of determining the optimal supply chain configuration in order to improve their economic and environmental performance, while taking into account the specific characteristics of agro-food supply chains. This paper provides a general description of the supply chain design problem in agro-food industrial chains, considering the role of seasonality and harvesting decisions, perishability, and processing. A general model formulation is presented, which accommodates for these characteristics and for forward and backward flows along the chain. The general model is applied to a case study of the sugar beet processing chain in the Netherlands. The pareto-efficient frontier between maximizing the total gross margin and minimizing the global warming potential in CO2-eq is explored. Uncertainties in demand and harvest yield are taken into account using a stochastic version of the model. Results show that a supply chain design model tailored to the specific characteristics of an agro-food supply chain with its uncertainties leads to identifying better performing supply chain configurations. In the case study, supply chain configurations can be found in which the performance on both the economic and the environmental objective is better than the modelled current performance. Additionally, we observe that supply chain configurations with decentralized processing or pre-processing are an interesting topic for future research in the context of agro-food industry.

Introduction

The current set-up of Food Supply Chains (FSCs) is an important cause for inefficiencies in food production (van der Goot et al., 2016). These inefficiencies should be reduced to guarantee food security for a growing world population, and improve the future responsible production and consumption of food products (in accordance with the United Nations sustainable development goals, UN General Assembly, 2015). The strategic redesign of FSCs can contribute to meet these challenges.

Inefficiencies in food production result in the creation of waste along the FSC, and a loss of the associated resources, capital, and labour invested. In Europe, the food industry represents almost 13% of the total manufacturing industry turnover (ECSIP Consortium, 2016), and the related FSCs contribute to 31% of the global warming potential (Perrot et al., 2016), while 31% of the food produced is wasted (Gustavsson et al., 2011, Timmermans et al., 2014). Addressing these issues improves the ability of FSCs to meet the demands of the population in a cost effective way while reducing their environmental impact, which is important both from an economic and an environmental perspective (Soysal et al., 2012, Tsolakis et al., 2014, Perrot et al., 2016).

The economic and environmental performance of FSCs is strongly related to their supply chain (SC) configuration, i.e. the number, type, and location of facilities and their interconnecting flows (Akkerman et al., 2010, de Keizer et al., 2017). Hence, reassessing the configuration of FSCs can improve their performance and address inefficiencies in the chain to improve its sustainability (Mota et al., 2018).

However, determining the optimal SC configuration for FSCs is a complex problem, due to the specific characteristics of food products and processes (e.g. de Keizer et al., 2015a, Soto-Silva et al., 2015). Seasonality in production and demand, the perishability of products, and product specific requirements for transportation and storage are a few examples that make the management of FSCs different from other SCs (van der Vorst and Beulens, 2002, Bourlakis and Weightman, 2004, Aramyan et al., 2007). Not every characteristic is however relevant for each FSC. Perishability and seasonality in production are for instance very important in FSCs dealing with fresh fruits, but less important for FSCs in which the products do not spoil that rapidly, as is the case for many pulses. Pulses are generally dried to prevent spoilage, although the weather conditions after harvesting could lead to post-harvest losses for these and other crops. Alternatively, processing plays an important role in for instance dairy SCs, but not in fresh fruit and vegetable SCs such as cabbages.

Due to the wide variety between FSCs, several sub-types are identified and studied in literature. Often, FSCs are categorized based on differences in shelf life (short and perishable, long and non-perishable), origin (animal, plant based), processing (fresh without product conversions, processed), or industry (agro-food, food manufacturing). Each of these subgroups has a specific set of FSC characteristics.

This article investigates decision support modelling for the strategic redesign of SC configurations for agro-food industry, where the main concern of the industry is the conversion of agro-materials into a set of semi-finished and finished products. Recent trends in this industry, such as consolidation, have increased the need for and applicability of models and tools (Ahumada and Villalobos, 2009, Tsolakis et al., 2014). Due to its dependency on agro-materials, seasonal production, harvesting, and processing are important characteristics of these Agro-Food SCs (AFSCs)(Jonkman et al., 2017). Additionally, agro-materials and their derived products are often perishable, and processing can both positively and neg atively influence this perishability. Moreover, the natural variability of agro-materials and weather conditions leads to uncertainties in harvest yields (supply), quality, demand and product prices. Due to these specific characteristics and their influence on the management of SCs, standard SC models have to be adapted to include these characteristics (Ahumada and Villalobos, 2009, Rajurkar and Jain, 2011).

These specific characteristics of AFSCs require taking into account harvesting decisions. The seasonal availability of a crop in combination with the perishable harvested agro-material puts restrictions on processing, and therefore affects the optimal SC configuration. Although the need for integrated support for the design and planning of AFSCs was identified (Ahumada and Villalobos, 2009, Tsolakis et al., 2014), the recent literature review of Kusumastuti et al. (2016) observes there is little work done in developing and applying a model that integrates harvesting and processing in this context.

This article contributes to the literature by presenting a Mixed Integer Linear Programming (MILP) model in which tactical decisions at the harvesting stage (area used for cultivation and time of harvesting) are integrated with strategic decisions on the AFSC design (number, location, and capacity of facilities, and the type of processing pathway to operate). It presents an overview of common characteristics of AFSCs and applies the integrated approach to a case study, while accounting for global warming impacts and the presence of uncertainty in harvest yield and demand.

Section 2 gives an overview of specific characteristics of AFSCs. Additionally, the related literature on the strategic and tactical design of AFSCs is presented. In Section 3, the decision problem is further detailed, and a general description of the model requirements and model formulation is developed. This general approach is applied to a case study building on the work of Kolfschoten et al. (2014) and Jonkman et al. (2017) on sugar beet processing in the Netherlands, as described in Section 4. The case study results are presented and discussed in Section 5, and further conclusions are drawn in Section 6.

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