Abstract
Purpose
The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.
Design/methodology/approach
A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.
Findings
This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.
Research limitations/implications
This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.
Practical implications
Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.
Social implications
As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.
Originality/value
This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.
Keywords
Citation
Zhong, R., Xu, X. and Wang, L. (2017), "Food supply chain management: systems, implementations, and future research", Industrial Management & Data Systems, Vol. 117 No. 9, pp. 2085-2114. https://doi.org/10.1108/IMDS-09-2016-0391
Publisher
:Emerald Publishing Limited
Copyright © 2017, Ray Zhong, Xun Xu and Lihui Wang
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Food industry plays an important role in providing basics and necessities for supporting various human activities and behaviors (Cooper and Ellram, 1993). Once harvested or produced, the food should be stored, delivered, and retailed so that they could reach to the final customers by due date. It was reported that about one-third of the produced food has been abandoned or wasted yearly (approximately 1.3 billion tons) (Manning et al., 2006). Two-third of the wasted food (about 1 billion tons) is occurred in supply chain like harvesting, shipping and storage (Fritz and Schiefer, 2008). Take fruit and vegetables for example, such perishable food was wasted by 492 million tons worldwide in 2011 due to the inefficient and ineffective food supply chain management (FSCM) (Gustavsson et al., 2011). Therefore, FSCM is significant to save our food.
FSCM has been coined to depict the activities or operations from production, distribution, and consumption so as to keep the safety and quality of various food under efficient and effective modes (Marsden et al., 2000; Blandon et al., 2009). The differences of FSCM from other supply chains such as furniture logistics and supply chain management are the importance reflected by factors like food quality, safety, and freshness within limited time, which make the underlying supply chain more complex and difficult to manage (La Scalia et al., 2016). The complexities are significant in the case of perishable products where their traversal time through FSCM and the use warehouses or buffers against demand and transportation variability are severely limited. Additionally, as the coordination from worldwide scale, the complexities have been compounded, thus, the focus from a single echelon such as food production was shifted to the efficiency and effectiveness of holistic supply chain. That means the resources like trucks, warehouse facilities, transportation routes, and workers within the food supply chain will be used efficiently so as to ensure the food quality and safety through effective efforts such as optimization decisions (Wu, Liao, Tseng and Chiu, 2016).
As the development of cutting-edge technologies, FSCM has been widely recognized both by practitioners and academia. Information technology (IT) has brought dramatic improvements to FSCM in terms of automatic food processing like cleansing and packing as well as freshness storage (King and Phumpiu, 1996; Caswell et al., 1998; Wang et al., 2015). However, the discipline of FSCM is still incapable of addressing many practical real-life challenges satisfactorily. The reasons for the inadequacy are attributed to low operational levels from farmers (Folkerts and Koehorst, 1997), information obstacle among different stakeholders (Caswell et al., 1998), and inefficient decision-making systems/models (Ahumada and Villalobos, 2009). Strategic decision-makers require comprehensive models to increase total profitability while data input into those models are usually ignored in most of traditional myopic models. In order to address current challenges, it is necessary to investigate better approaches to accommodate emerging global situations after taking a critical look at the current FSCM practices and conditions.
This paper selects total 192 articles from 1993 to 2017 by searching the key word “FSCM” in Google Scholar (until November 2016). Special concentration is placed upon the data-driven IT systems which are used for facilitating the FSCM with particular aims of re-designing and re-rationalizing current supply chain to a globally integrated fashion for food industry. Among these articles, there are seven reports from website, 25 papers are case studies, and the others are typical research papers related to FSCM. Most of these reviewed papers are from leading journals such as International Journal of Production Economics (19), European Journal of Operational Research (4), Journal of Cleaner Production (10), Food Control (13), Supply Chain Management: An International Journal (7), Journal of Operations Management (3), British Food Journal (4), etc. Figure 1 presents the selected papers in a yearly view. As demonstrated, there is only a few studies about data-driven IT systems in FSCM in early 1990s. Then, the related papers are fluctuated slightly from 2000 to 2014. Currently, as showing from the prediction curve, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, the quantity can be expected to increase in the future.
This paper categorizes related topics in a hierarchical organization. Figure 2 presents the scope of the review that each focus is dissected to organize this paper. Section 2 talks about the supply chain management for food industry that covers three themes such as frameworks, models, and worldwide movement. Section 3 presents two major IT systems – traceability systems and decision-making systems for FSCM. Section 4 demonstrates FSCM implementations in terms of reported cases and data-driven applications. Section 5 summarizes the current challenges and future perspectives in four aspects: supply chain network structure, data collection, decision-making models, and implementations. Section 6 concludes this paper through identifying some insights and lessons from this investigation.
2. Supply chain management for food industry
2.1 Frameworks
A framework for FSCM is a basis for manufacturing, processing, and transforming raw materials and semi-finished products coming from major activities such as forestry, agriculture, zootechnics, finishing, and so on (Dubey et al., 2017). In order to identify the relationships among different items, interpretive structural modeling (ISM) was used to establish a hierarchical framework (Faisal and Talib, 2016). This framework helps users to understand the interactions among logistics operators in a food supply chain. ISM-enabled framework was also used to support risk management in identifying and interpreting interdependences among food supply chain risks at different levels such as first-tier supplier, third-party logistics (3PL), etc. (Colin et al., 2011). It is observed that this framework was proven as a useful method to structure risks in FSCM through a step-by-step process on several manufacturing stages. Information plays an important role in making FSCM more efficient. In order to assess the information risks management, an ISM based framework was proposed by twining graph theory to quantify information risks and ISM to understand the interrelationships in FSCM (Nishat Faisal et al., 2007). As the global FSCM is emerging with international collaborations, ISM-enabled framework confines to explain causal relationships or transitive links among various involved parties. A total interpretive structural modeling was then introduced to analyze some enablers and barriers of FSCM (Shibin et al., 2016). In this paper, ten enablers and eight barriers are examined by separate frameworks to further understand the interactions within a dynamic era of globalization FSCM.
Value chains play a critical role in FSCM to benefit the producers and consumers. Stevenson and Pirog (2008) introduced a value chain framework for strategic alliances between food production, processing and distribution which seek to create more value in the supply chain. The proposed framework concerns about food supply chain economic performance that correspond to the organization, structure, and practices of a whole supply chain. Food traceability has been widely used in the last few decades with large number applications. However, frameworks for a general or common implementation are scarcely reported. To label whether a framework with respect to food traceability application, Karlsen et al. (2013) observed that with a common framework, traceability is prone to be similar and implementation processes are more goal-oriented and efficient. Thus, Regattieri et al. (2007) presented a general framework and used experimental evidence to analyze legal and regulatory aspects on food traceability. They designed an effective traceability system architecture to analyze assessment criteria from alphanumerical codes, bar codes, and radio frequency identification (RFID). By integrating alphanumerical codes and RFID technology, the framework has been applied for both cheese producers and consumers.
Currently, coordination in the food supply chain from production to consumption is significant to ensure the safety and quality of various food. Take agri-food supply chain for example, Hobbs and Young (2000) depicted a conceptual framework to achieve closer vertical collaboration in FSCM using of contracting approaches. This work has critical impacts on transaction cost economics by developing a closer vertical coordination. In an international food supply chain, Folkerts and Koehorst (1997) talked about a framework which integrates the chain reversal and chain management model to make vertical coordination. In their framework, an analytical service designed particularly for benchmarking food supply chain projects is used so that an interconnected system of high performance and effectiveness are achieved as an integrated supply chain. Facing a global FSCM, strategic decision-making is important since the profitability of an entire chain could be increased by the holistic efforts from an efficient framework. To this end, Georgiadis et al. (2005) presented a system dynamics modeling framework for the FSCM. In this framework, end-users are able to determine the optimal network configuration, inventory management policy, supply chain integration, as well as outsourcing and procurement strategies. Collaboration is becoming more of a necessity than an option despite some barriers which deteriorate coordination among enterprises in food industry all over the world. Doukidis et al. (2007) provided a framework to analyze supply chain collaboration in order to explore a conceptual landmark in agri-food industry for further empirical research. It is observed that, from this framework, supply chain collaboration is of critical importance and some constraints such as time and uncertainties arise due to the nature of agri-food industry.
2.2 Models
Globalization of food production, logistics and consumption have resulted in an interconnected system for FSCM whose models play crucial role in ensuring food products of high and consistent safety and quality (Choi et al., 2016). In this section, we present related work using various models for considering five major aspects like food quality, supply chain efficiency, food waste, food safety, and value chain analysis. An incomplete list of the leading authors covering these five aspects is shown in Table I. In order to better demonstrate the literature, key contributions for each paper are highlighted at the last column.
From Table I, it could be observed that food quality, supply chain efficiency and food safety are more concerned in these models. And multi-objectives are commonly considered, for example, food quality and safety are integrated in the decision models. However, food waste is specifically looked at without twining with other aspects. Recently, supply chain efficiency and value chain analysis are placed special emphasis since the global FSCM is becoming more and more significant.
2.3 Worldwide movement
Current movements on FSCM from major districts are presented in this section which covers Europe, North America, and Asia Pacific.
2.3.1 Europe
The food industry is the EU’s largest sector in terms of employed people and value added. From one report about the data and trends of EU food and drink industry 2014-2015, the employment is 4.2 million people with 1.8 percent of EU gross value added and the turnover is €1,244 billion (FoodDrinkEurope, 2015). The turnover is increased by 22.32 percent compared with that from the year 2011 (€1,017 billion). Despite the significant increase of turnover, European Commission recently pointed out that the EU food industry is facing a decrease in competitiveness caused by a lack of transparency in food supply chain (European Commission, 2016). In order to enhance global competitiveness, in November 2011, 11 EU organizations like AIM, FoodDrinkEurope, European Retail Round Table (ERRT), CEJA, EuroCommerce, Euro Coop, Copa Cogeca, etc. signed a Supply Chain Initiative document which is based on a set of principles of good practice. After two years, seven EU level associations agreed to implement the principles which have been converted into 23 languages.
Retailers play important roles in FSCM since they are selling thousands of different products each of which has its own supply chain with distinct features and complexities. ERRT, an organization including the CEO’s of Europe’s leading international retail companies conducted a framework of the EU High Level Forum for a better food supply chain that often involves large number of business partners. Under the framework, leading retailers are going to build up a well-functioning and competitive supply chain in maintaining good relationships with their suppliers so as to bring the best and most innovative foods and drinks to the customers (ERRT, 2013). Retailers in EU are also aware that it is their environmental responsibilities to delivery of foods via a more sustainable model by contacting with consumers and suppliers. Thus, in March 2009, in response to the European Commission’s Action Plan on Sustainable Consumption and Production, ERRT set up the Retailers Environmental Action Programme (REAP) which aims to reduce environmental footprint in the food supply chain. REAP not only facilitates the sustainability dialogue with food supply chain key stakeholders, but also stimulates retailers to adopt new FSCM models (European Commission, 2015).
Logistics is a bridge between food retailers and manufacturers. It was reported that, in 2012, there were 24 million people employed in the food supply chain and 21 percent of the employment comes from logistics-related companies (European Commission, 2016). European Logistics Association (ELA) is a federation with over 30 organizations from Central and Western Europe. Recently, in order to achieve green logistics, ELA developed a sustainable supply chain scheme for FSCM (ELA, 2012). From economic, environmental, and social perspectives, this scheme focuses on realistic financial structure, sustainable FSCM, and successful cases implementation which are should be truly sustainable. Take European Logistics Hub, Limburg – a province in the south of the Netherlands for example, high developed logistics facilities and modern logistics infrastructures offer an advanced logistics with lowest supply chain costs and environment impacts (Hemert and Iske, 2015).
Food production as source of FSCM is extremely important in Europe since about 9.12 million people were employed in agricultural industry including planting, harvesting, and so on. There are approximately 1,700 food manufacturers from 13 European countries. European Federation of Associations of Health Product Manufacturers (EHPM) aims to develop a sort of regulatory frameworks throughout the EU for health and natural food. Recently, EHPM is in support of producing the harmonization of health, safety, and qualified aspects for food supplements through an optimization of positive economic impacts on Food Supplements sector in the EU market (EHPM, 2013). Advanced technologies bring large benefit to food industry globally. A food Tech innovation Portal was launched by European Commission to apply innovative technology, such as biotechnology, nanotechnology and information and communications technology (ICT) to help food manufacturers to provide more health, safe, and natural foods (European Commission, 2014).
2.3.2 North America
North America is the second largest food industry in the world with a turnover of about €650 billion in 2013. Take USA for example, from an incomplete report in 2013, there were 40,229 grocery stores with $634.2 billion in revenues, 154,373 convenience stores with $165.6 billion annual sales, and 55,683 non-traditional food sellers with $450 billion turnover (Global Strategy, 2013). Consisting of multi-tiered food supply chains in North America, FSCM is both large and complex so that innovations are highlighted in food industry to meet the steady growing rate of 2.9 percent yearly.
Companies from North America are aggressively viewing new food market with large numbers of potential consumers. Thus, a far reaching and more sophisticated food supply chain is prone to risks caused by disrupted disasters, oil prices’ fluctuations, and political upheavals, which greatly influence food production and transportation (Lan et al., 2016). Using advanced technologies such as bio-tech and ICT, food production and harvesting are innovatively improved (Fraser et al., 2016). Genetically modified organisms for instance with higher productivity and stronger anti-viruses are used in plants, mammals, fish, etc. (Hemphill and Banerjee, 2015).
For innovative warehousing of food, robotics and automation have been adopted in North America in food and beverage supply chain. Given the improved efficiencies in terms of sorting, packing, and processing, funding sources, in recent years, have invested in warehouse automation significantly. In 2012, the US Government granted $50 million to research institutes and universities for robotics aligning with creation of the next generation of collaborative robots from the Obama administration’s National Robotics Initiative (Pransky, 2015). With the assistance from robots, warehouses for food and beverage are the most technologically advanced for facilitating FSCM.
Logistics and transportation are innovatively improved from improving the railroad, flight routes, marine and land roads. North America has the comprehensive and satisfactory logistics network. Currently, Genesee & Wyoming Inc. agreed to acquire Providence and Worcester Railroad Company (P&W) for approximately $126 million to meet customary closing conditions following the receipt of P&W shareholder approval in the fourth quarter of 2016 (BusinessWire, 2016). 3PL plays a major role in food supply chain. The top 3PL and cold storage providers in 2016 are AFN, Niles, Ill., Allen Lund Company, La Canada, Calif., and Americold, Atalanta, Ga. who are the top listed companies using latest technologies in transportation management systems, warehouse management systems (WMSs), and logistics scrutiny systems for a better food supply chain services.
2.3.3 Asia Pacific
China, as the third food and drink producer has a turnover of €767 billion in 2011 which is the largest food entity in this area (European Commission, 2016). As the biggest country in Asia pacific, China has around 400,000 food-related companies. Japan with €466 billion turnover between 2012 and 2013 employs 1.4 million workers. India, Australia, South Korea, and New Zealand, as major food producers in this area, their turnovers (2012-2013) are 95, 62, 32, and 27 billion Euro, respectively. It is no debate that this area is the most important food and beverage supplier from its enormous turnovers. However, FSCM in this area is mainly based on sacrificing manpower, for example China used 6.74 billion employees to achieve the total turnover, which is one-third more people than that in the EU.
With small margins attainable in most links of food supply chain in Asia Pacific, consolidation across various food categories and levels of the FSCM was necessary to reduce cost and maximize profits. To this end, a robust logistics and FSCM network program was initiated to enhanced focus on food availability and growing number of organized retail outlets for food supply chain development (Simatupang and Sridharan, 2002). Take India for example, the government proposed a multi-tiered network design plan which upgrade current city/urban and rural supply chain to hyper/mega centers, urban, semi-urban, and rural structure in 2025 by full use of automation, verticalization, and lean principles as well as 3PL innovations (Venkatesh et al., 2015). Thus, organizations in India are going to rethink their mega food center supply chain models so as to handle higher variety and faster transitions within food supply chain. Yeole and Curran (2016) used tomato post-harvest loses from Nashik district of India for example to demonstrate reduced intermediaries in the supply chain network will save the losses. Additionally, supply chain operations like improper packaging techniques and lack of cold storage facilitates are need to be improved for the network.
Chinese-made food products are prone to be low price, low quality, and low safety (Roth et al., 2008). The main reason is the weak management in food supply chain. Despite China has the largest number of food companies, most of them are small and medium-sized enterprises (SMEs) which are extremely difficulty for the government to manage. Currently, Chinese Government proposed a set of regulations for ensuring the food safety from various aspects such as GB (Guo Biao – a national standard). Moreover, after some significant food scandals, Chinese Government put more efforts on the supervision of the food manufacturing and distribution (Lam et al., 2013). Food logistics facilities are also concentrated on from both government and companies since China’s connections to global food markets have important effects on food supply. Unfortunately, weak implementations are needed to be improved although the government has depicted to strength regulation, establish scrutiny systems, reform laws, and increase investment on basic infrastructures in FSCM. It is still far to say Chinese foods are low price, high quality and high safety.
Japan and South Korea always follow the strict monitoring within the total FSCM because they believe that their foods represent their culture. Thus, a food-obsessed country like Japan or South Korea uses national natural cuisine uniquely to reflect the pure environment. Since global integration of food supply chain, companies from both countries adopted supply chain strategies to improve relationship between diversification and a firm’s competitive performance (Narasimhan and Kim, 2002). Food supply chain facilitates from both countries in production, warehouse, and distribution maybe the best in Asia Pacific. Take Japan for example, fishing industry plays an extremely crucial role in Japanese culture. Due to limited space for refrigerators and food storage spaces, its fish supply chain uses time-constraint multiple-layered supply chain network to guarantee freshness and quality (Watanabe et al., 2003). Recently, these countries moved into a smart FSCM using advanced technologies such as Internet of Things (IoT). Different types of sensors are used to facilitate various operations within entire food supply chain (Park et al., 2016).
Australia and New Zealand, as major food suppliers for the world, have mature FSCM in terms of consolidation of food industry partners and supply chain integrations. Australia proposed a green supply network where the consumers are able to seek to secure food (Smith et al., 2010). Recently, the Commonwealth Scientific and Industrial Research Organization launched a digital agriculture plan to help Australian farmers and food industry parties to improve productivity and sustainability. Smart solutions for modern farming and FSCM are placed on specific attention by developing information systems which are used for ingesting, processing, summarizing, and analyzing data from multiple sensor systems (Devin and Richards, 2016). New Zealand with its clean waters, fertile land, and excellent climate is a heaven for producing quality foods. This country is famous for its highly skilled workforce who is generating thousands of foods for the whole world with high standards in food quality and freshness (Campbell et al., 2006). Besides skilled workers, efficient and effective FSCM also makes the great success of food industry which is the largest manufacturing sector in New Zealand. The Ministry for Primary Industries is the primary food safety regulating authority in New Zealand, aiming to ensure food quality, safety, and reduce risks. Currently, New Zealand planned to take the leading role in global food security by adopting cutting-edge technologies such as Auto-ID which is a key technology of IoT for tracking and tracing animal products like cows and sheep (Ghosh, 2016). As a result, food products from this country could be monitored from sources to consumption phase, which makes real total lifecycle management for each food.
3. IT systems for FSCM
It is no debate that IT systems are essential for FSCM where so many things can go wrong such as trucks, food suppliers, data entry, etc. This section takes the traceability and decision-making systems for FSCM as examples to review the state-of-the-art situations that are useful for practitioners when they are implementing IT-based solutions.
3.1 Traceability systems
Traceability of a food refers to a data trail which follows the food physical trial through various statuses (Smith et al., 2005). As earlier as two decades ago, US food industry has developed, implemented, and maintained traceability systems to improve FSCM, differentiate foods with subtle quality attributes, and facilitate tracking for food safety (Golan et al., 2004a). Some systems deeply track food from retailer back to the sources like farm and some only focus on key points in a supply chain. Some traceability systems only collect data for tracking foods to the minute of production or logistics trajectory, while others track only cursory information like in a large geographical area (Dickinson and Bailey, 2002).
This section analyzes total 19 key papers published from 2003 to 2017. Table II presents a categorized analysis in terms of tracing objects, technology, district, and features.
From Table II, it could be observed that food traceability is paid much attention from EU where people do care more about the food safety and quality. Associated technologies are developing fast so that cutting-edge techniques are widely used for various food tracing and tracking. Take RFID for example, 73.68 percent of the reviewed papers adopt this Auto-ID technology for food traceability. Moreover, agri-foods are placed special attention to trace and track because as the most important perishable products, their freshness and quality are eyed by the consumers.
3.2 Decision-making systems
Besides the traceability systems in FSCM, other decision-makings such as integration/collaboration, planning/scheduling, fleet management, and WMS are also widely used in food industry. This section presents a review of total 26 papers which are related to the above topics. Table III reveals these papers from 2005 to 2007 with specific decisions, countries/area (identified by the corresponding author), used technologies, and features.
We selected two typical publications in each year for forming Table III from which several observations could be achieved. First, European countries are prone to be more use of systems to assist decision-makings in FSCM. Second, systems used in earlier stage are based on internet solutions. Currently, model-based systems using advanced technologies are widely reported in FSCM decision-makings. Third, focuses of decision-making shift from supply chain integration in earlier years to sustainable and specific problem solving cases in recent years.
4. Implementation of FSCM
4.1 Reported cases
Case studies from implementing various IT systems in FSCM are significant to get some lessons and insights, which are meaningful for industry practitioners and research academia. This section reports several cases using different systems for facilitating their operations or decision-makings in food supply chain from 2007 to 2017. They are categorized in the following Table IV which includes key information like company name, district, system, and improvement.
From the reported cases, it could be observed that, European countries have much more successful cases on using various IT support systems in FSCM. While, cases from Australia, China, etc. are scarcely presented. Another interesting finding is that before 2010, IT systems are used for optimization or supply chain coordination decision-makings. However, currently, companies are more concentrating on the sustainability and environmental performance in the food supply chain. For example, environmental influences like CO2 emissions and waste reduction are widely considered.
4.2 Data-driven implementations
Data, usually used for decision-makings, have been considered in FSCM for various purposes. Data-driven implementation in FSCM is categorized into two dimensions in this paper. First is the simulation-based modeling which focuses on adopting different data for FSCM optimization or decision-making. The other is data collection from practical implementations for supporting IT systems for various purposes such as traceability, risk assessment, and so on.
For simulation-based modeling, studies mainly focus on establishing various simulation models which adopt different types of data such as product quality, customer demand for different decision-makings and predictions. In order to meet increasing demand on food attributes such as integrity and diversity, Vorst et al. (2009) proposed a simulation model which is based on an integrated approach to foresee food quality and sustainability issues. This model enables effective and efficient decision support on food supply chain design. FSCM is becoming more complex and dynamic due to the food proliferation to meet diversifying and globalizing markets. To make a transparent food supply chain, Trienekens et al. (2012) simulated typical dynamics like demand, environmental impacts, and social aspects to enhance the information sharing and exchanging. It is found that food supply chain actors should provide differentiated information to meet the dynamic and diversified demands for transparency information. As a wide application of Auto-ID technology for tracking and tracing various items (Zhong, Dai, Qu, Hu and Huang, 2013; Zhong, Li, Pang, Pan, Qu and Huang, 2013; Qiu et al., 2014; Guo et al., 2015; Scherhaufl et al., 2015), traceability data plays an important role in supporting FSCM. Folinas et al. (2006) introduced a model which uses the traceability data for simulating the act guideline for all food entities in a supply chain. The assessment of information underlines that traceability data enabled by information flow is significant for various involved parties in food supply chain to ensure food safety. Wong et al. (2011) used a model to evaluate the postponement as an option to strengthen food supply chain performance in a soluble coffee manufacturer. The simulation model shows that cost savings including reduction of cycle stock are obtained by delaying the labeling and packaging processes. Bajželj et al. (2014) simulated the food demand to examine the impacts of food supply chain on climate mitigation. This paper proposes a transparent and data-driven model for showing that improved diets and reduced food waste are critical to deliver emissions reductions. Trkman et al. (2010) used a structural equation model based on data from 130 companies worldwide to examine the relationship between analytical capabilities in FSCM. It is observed that the information support is stronger than the effect of business process orientation in food supply chain. Data-driven model was also proposed by developing a measure of the captured business external and internal data for food productivity, and supply chain value (Brynjolfsson et al., 2011). This paper obtains 179 firms’ data from USA where 5-6 percent increase in their output and productivity by using IT solutions. Low and Vogel (2011) used a national representative data on local food market to evaluate the food supply chain where small and medium-sized farms dominate the market. This paper finds that direct-to-consumer sales of food are greatly affected by climate and topography which favor perishable food production. Akhtar et al. (2016) presented a model by using data collected from agri-food supply chains to examine adaptive leadership performance in FSCM. This paper thus depicts that how global food supply chain leaders can use data-driven approach to create financial and non-financial sustainability. Hasuike et al. (2014) demonstrated a model to simulate uncertain crop productions and consumers’ demands so as to optimize the food supply chain profit. This simulation model is based on stochastic programming that accommodates surplus foods among stores in a local area. Manning et al. (2016) used a quantitative benchmarking model to drive sustainability in food supply chain. Li and Wang (2015) based on networked sensor data worked out a dynamic supply chain model to improve food tracking. Recently, Big Data is emerging as a crucial IT for instructing decisions in food supply chain. In order to differentiate and identify final food products, Ahearn et al. (2016) simulated environmental sustainability and food safety to improve food supply chain by using the consumer demands big data. This paper features a sustainability metric in agricultural production.
For practical data-driven system, various data are captured and collected to decision-makings in FSCM. Papathanasiou and Kenward (2014) produced a top level environmental decision support system by using the data collected from European food supply chain. It is found that socio-economic aspects have more influences on effective environmental decision support than technical aspects. Martins et al. (2008) introduced a shelf-life dating complex systems using sensor data to monitor, diagnose and control food quality. As the increasing focus on healthy diet, food composition and dietary assessment systems are significant for nutrition professionals. Therefore, Pennington et al. (2007) developed a system using the appropriateness of data for the intended audience. Most food and nutrition professionals will be beneficial from educating themselves about the database system. Perrot et al. (2011) presented an analysis of the complex food systems which are using various data such as supply chain dynamics, knowledge, and real-time information to make different decisions in FSCM. Tatonetti et al. (2012) illustrated a data-driven prediction system which is used for drug effects and interactions that US Food and Drug Administration has put great effects on improving the detection and prediction. Ahn et al. (2011), given increasing availability of information from food preparation, studied a data-driven system for flavor network and food pairing principles. Jacxsens et al. (2010) using actual microbiological food safety performance data designed a food safety management system to systematically detect food quality. The diagnosis is achieved in quantitative to get insight in the food businesses in nine European companies. Karaman et al. (2012) presented a food safety system by full using of data from plants where white cheese, fermented milk products and butter are produced. A case study from a Turkish dairy industry is demonstrated the feasibility and practicality of the presented system. In order to assess the lifecycle for sewage sludge and food waste, a system based on anaerobic codigestion of the organic fraction of municipal solid waste and dewatered sewage sludge was introduced (Righi et al., 2013). Environmental performances of various scenarios in the NE Italy case studies are evaluated to show energy saving using the data-driven system. Jacxsens et al. (2011) introduced a sort of tools for the performance examination and improvement of food safety management system by the support of food business data. These tools are able to help various end-users to selection process, to improve food safety, and to enhance performance. Food safety management systems usually use traceability and status data to examine food quality and freshness. Tomašević et al. (2013) took the Serbian meat industry for example to report food safety management systems implementation from 77 producers. Laux and Hurburgh (2012) reported a quality management system using food traceability data like maintain records for the grain scrutiny. A traceability index is used to quantify a lot size of grain in an elevator in this paper. Herrero et al. (2010) introduced a revisiting mixed crop-livestock system using farms’ data to achieve a smart investment in sustainable food production. By carefully consider the inputs of fertilizer, water, and feed, waste and environmental impacts are minimized to support farmers to intensify production. Tzamalis et al. (2016) presented a food safety and quality management system used in 75 SME by using the production data from the fresh-cut producing sector. This paper provides a best practice score for the assessment to ensure food quality and safety.
5. Current challenges and future perspectives
This section summarizes current challenges and highlights future perspectives in supply chain network structure, data collection, decision-making models, and implementations.
5.1 Supply chain network structure
Food quality and safety heavily rely on an efficient and effective supply chain network structure. As the increasing globalization demands for more healthy and nutritious food, current structure is facing several challenges. First, the concentration of design and development of a food supply chain network structure is placed upon a sole distribution system or a WMS. Mixed-integer linear programming models are widely used to suggest proper locations and distribution network configurations (Manzini and Accorsi, 2013). An entire and global structure is necessary. Second, optimizations are always considered within a network structure. However, the common considerations are planning, scheduling, profit and cost. Environmental impacts and sustainable performance are omitted. As increasing consumptions of various resources, a sustainable supply chain network structure considering waste reduction and greenhouse gas emissions is needed. Third, with the development of advanced technologies such as IoT, traditional network structure is no longer suitable for facilitating the food supply chain operations because large number of digital devices, sensors, and robots are equipped along the supply chain. Thus, an innovative and open structure for FSCM is required.
Future structure for food supply chain network will be focused on the following directions so as to address current challenges and meet future requirements:
An integrated global architecture: the final goal of this architecture is to control global food chain in both optimal and interdependent levels to make involved stakeholders for a closed-loop management and scrutiny. For achieving this purpose, new conceptual frameworks, effective supporting tools, integrated models, and enabled technologies are needed further investigation (MacCarthy et al., 2016; Talaei et al., 2016).
Sustainable food supply chain: in the future, sustainable business in food industry can be harvested by reducing the environmental impacts, enhancing food waste recycling, and strengthening facilities sharing. New mechanisms and coordinated development along with other industries like manufacturing and economy are basic supports for achieving the sustainability (Green et al., 2012; Irani and Sharif, 2016; Lan and Zhong, 2016).
Physical internet (PI) for FSCM: PI is an open global logistics system by using encapsulation, interfaces, and protocols to convert physical objects into digital items to achieve operational interconnectivity (Montreuil, 2011). Using the PI principle, FSCM for food handling, movement, storage, and delivery could be transformed toward global logistics efficiency and sustainability.
5.2 Data collection
Data-enabled decision-making plays an important role in FSCM so that without an approachable data collection method, it is difficult to carry out data-based analytics. Despite wide adoption of data collection approaches used in food supply chain, several challenges still exist so that data-driven decision-makings are confined. In the first place, manual and paper-based operations are common in food supply chain, especially in agri-food logistics. Data from these approaches are usually prone to be inaccurate and incomplete. As a result, decisions based on such data are unreasonable (Zhong et al., 2016). Moreover, various data collection devices such as sensors, smart phones, and GPS have different data formats that are usually unstructured and heterogeneous. Integration and sharing of these data among the food supply chain are extremely difficult (Pang et al., 2015). Finally, current data collection system cannot deal with huge number of data capturing in a simultaneous fashion. Due to the limited central calculation capacity and signal transmission methods, data collisions and jams could be happened occasionally.
Future data collection for food supply chain will be focused on the following dimensions which are featured by smart/intelligent devices:
IoT-enabled smart data collector: this type of data collection method is based on IoT technologies like smart Auto-ID and smart sensors which are designed with multi-functional ability. They are able to collect data under different situations such as temperature-sensitive condition for perishable products or wines. Thus, they are designed in a wearable or flexible way to be easily deployed and operated (Wu, Yue, Jin and Yen, 2016). A certain learnable ability is built upon each collector which is central managed and controlled by a knowledge-enabled super computer that works as human brain to coordinate vast number of collectors.
Adaptive smart robot: these data collectors are specially designed by twining robotics and smart sensors so that they are able to fulfill some operations and capture data in parallel. They are useful in some extremely hazardous environment like super low temperature for ice cream or frozen seafood. Such adaptive smart robot is based on advanced technologies which make it to perform like a human (Zhong et al., 2016). It can sense environment and adaptively make decisions based on real-time data from the environmental variations.
5.3 Decision-making models
As more and more data aggregated in food supply chain, decision-making models require associated knowledge from such data for more precise and systematic resolutions. Traditional approaches packaged or embedded into decision-making models are not able to deal with Big Data challenges. First of all, decision-making models in FSCM need various data for different purposes such as optimization of planning and scheduling, reduction of waste, etc. However, computational time will be so long that immense data are input into these models. Second, data-driven decision models used for food supply chain optimization do not have evaluation criteria to validate their effectiveness since numerical studies are commonly used in literature (Meneghetti and Monti, 2015). Such approaches may not be suitable under Big Data era. Third, current models are focusing on a specific problem driven by a single company or a particular food supply chain. Multi-functional models are scarcely reported. By making full use of food industry Big Data, multi-objective and generic models could be achieved.
To this end, future decision-making models for FSCM will be implemented as follows:
Multi-functional models: these models are able to make full use of Big Data from food supply chain. Some advanced and intelligent models or algorithms like deep machine learning will be integrated into these models so that multi-objectives could be defined (Balaji and Arshinder, 2016). They are capable of selecting associated data for different objective functions through training, learning, and calculating.
Smart decision models: future decision models can work collaboratively in a smart way. With the intelligent learning capability based on Big Data, a number of models will be created to perform smart decisions on real-time basis (Zhong et al., 2015). Advanced hierarchical or parallel frameworks for these models are required, thus, smart models are able to invite other models for seamless co-operation.
5.4 Implementations
FSCM implementations from real-life industries are based on cutting-edge technologies which are used for addressing some issues faced by food supply chain. Reported cases from literature mainly concentrated on verifying some hypothesis and presenting the improvements after using an IT system (Canavari et al., 2010; Soto-Silva et al., 2016). Few studies highlighted the natural characteristics of food supply chain or generic issues summarized from a set of companies so that the essence of FSCM could be figured out. After that, suitable technologies can be picked up to work out the solutions for the company or involved parties in food supply chain. Regarding the complexity of food supply chain, some important issues involving waste, re-use of resources, facility sharing, greenhouse gas emissions, and holistic lifecycle management are still unaddressed (Genovese et al., 2017). Take food waste for example, about 40 percent of total food produced in the USA goes as waste yearly which is equivalent of $165 billion (Pandey et al., 2016). Such vast wasted food not only physically influences our environment by polluting the water, but also significantly increases the CO2 emission since large number of pollution will be generated when they are deteriorating. Thus, reduction of food waste requires the actions at different echelons within food supply chain like food production, delivery, storage, retailing, and recycling. Regarding different echelons, associated solutions such as food production management system, WMS, logistics management system, etc. should be highly integrated in terms of data sharing and seamless synchronization.
Emerging cutting-edge techniques may contribute to system integration in the near future. First, Cloud technology has been used to integrate segregated sector using minimum resources. It allows involved stakeholders to access various services via software as a service, platform as a service, and infrastructure as a service (Singh et al., 2015). Through Cloud-enabled solution, the information sharing and collaborative working principle could be achieved by using basic computing and internet equipment. Second, IoT technologies like Auto-ID and smart sensors have been widely implemented in manufacturing and aerospace industry (Zhong, Li, Pang, Pan, Qu and Huang, 2013; Whitmore et al., 2015). IoT-based solutions for FSCM are able to provide an entire product lifecycle management via real-time data capturing, logistics visibility, and quality traceability. Additionally, within an IoT-based environment, every objects with sensing, networking and calculating ability can detect and interact with each other to facilitate logistics operations and decision-making in a fashion that is ubiquitous, real-time, and intelligent. Third, Big Data Analytics for FSCM has received increasing attention since it is able to deal with immense data generated from food supply chain. Big Data Analytics can help food companies to make graphical decisions with more accurate data input by excavating hidden and invaluable information or knowledge which could be used for their daily operations. With such information, ultimate sustainable food supply chain could be realized by optimal decisions.
In the future implementation, giant companies play important roles in leading the food supply chain toward a green and sustainable direction. To this end, collaborations with green relationships could lead to a win-win situation that large companies will get the economic benefits, and in turn the food supply chain members like SMEs could also be benefited. That green relationship is based on the joint value creation by using new business models in terms of internal and external green integration which will be enabled by advanced technologies (Chiou et al., 2011; Gunasekaran et al., 2015). So these companies may take initial actions to be equipped by advanced IT systems, while up-stream and down-stream parties within food supply chain can follow up for a green future.
Finally, the implementations need the involvement of government bodies which are going to work out strategic plans for guiding and supporting various enterprises toward a better future. Thus, Big Data Analytics is extremely important for these bodies to figure out up-to-data statistics report, current status of a food supply chain, and industrial feedbacks. Further to identify the strategies, they can use advanced prediction models or data-driven decision-making systems for assisting deeper analysis. As a result, each individual end-user could be beneficial from future implementation.
6. Conclusions
As the increasing awareness of food quality, safety, and freshness, FSCM is facing ever pressure to meet these requirements. How to upgrade and transform current FSCM to suit the ever increasing demands in the future? This paper presents a state-of-the-art review in FSCM from systems, implementations, and worldwide movements. Current challenges and future perspectives from supply chain network structure, data collection, decision-making models, and implementations are highlighted.
Based on the reviewed papers, some ideas and observations are significant for academia and industrial practitioners:
advanced technologies like Big Data Analytics, Cloud Computing, and IoT will be employed to transforming and upgrading FSCM to a smart future;
data-driven decision-makings for FSCM would be adopted for achieving more sustainable and adaptive food supply chain; and
FSCM implementations will be facilitated by the cutting-edge technologies-enabled solutions with more user friendliness and customization.
Figures
List of models for FSCM
| Model | Food quality | Supply chain efficiency | Food waste | Food safety | Value chain analysis | Contributions |
|---|---|---|---|---|---|---|
| Caswell et al. (1998) | X | X | Proposes a Metasystems-enabled model Enhances product quality Considers the transaction costs and system efficiencies |
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| Vorst (2000) | X | Introduces a KPIs-based model Assessment of the key impact factors in FSCM |
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| Reiner and Trcka (2004) | X | Introduces an improved product-specific supply chain design model Enhances the performance |
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| Gorris (2005) | X | Introduces a food safety objective model Concerns operational food safety management at different food chains |
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| Beulens et al. (2005) | X | X | X | Introduces a network-based supply chain model Improves the products quality, safety and food chain transparency |
||
| Taylor (2005) | X | X | Applies lean value chain improvement Proposes a value stream analysis (VSA) model Uses a multi-echelon structures |
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| Kim et al. (2006) | X | Presents a modified three-stage methane fermentation model Reduces the food waste |
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| Manning et al. (2006) | X | X | Introduces an organizational business model Analyzes the efficiency in the integrated FSCM |
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| Aramyan et al. (2007) | X | Illustrates a performance measurement Uses a balanced scorecard model Proposes applicable performance appraisal indicators |
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| Trienekens and Zuurbier (2008) | X | X | Concern marginal costs and standards Revalue the cost/effectiveness of the food production |
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| Oliva et al. (2008) | X | Introduces a system dynamic model Ensures the food quality |
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| Akkerman et al. (2010) | X | X | X | Reviews the literature on related models in strategic network design, tactical network planning, and operational transportation planning | ||
| Ruiz-Garcia et al. (2010) | X | X | Establishes an architectural model Keep the quality and reduce waste |
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| Parfitt et al. (2010) | X | Uses a data model Examines losses at immediate post-harvest stages |
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| Marucheck et al. (2011) | X | X | Presents the operation management theory models and methodologies Examines food safety and values in FSCM |
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| Garnett (2011) | X | Introduces a model to estimate food-related greenhouse gas emission Improves the total value of food supply chains |
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| Wu and Pagell (2011) | X | Proposes a theory-building model Balances short-term profitability and long-term environmental sustainability |
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| Gustavsson et al. (2011) | X | Introduces a hierarchical model Analyzes the food waste within a whole food supply chain |
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| Rong et al. (2011) | X | Proposes a decision-making model Uses a mixed-integer linear programming model |
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| Zarei et al. (2011) | X | X | Uses quality function deployment model Improves the efficiency and increases the food value chain |
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| Zhang and Li (2012) | X | X | Analyzes an agri-food supply chain management Optimizes internal costs and productivities |
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| Kummu et al. (2012) | X | Presents a data analytic model Conducts food waste Examines food wastes’ influences on freshwater, cropland, and fertilizer usage |
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| Zanoni and Zavanella (2012) | X | Proposes a joint effects model Considers different objective functions |
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| Yu and Nagurney (2013) | X | Proposes a network-based FSCM model Increases the value chain |
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| Aung and Chang (2014) | X | X | Presents an information-based traceability model Considers safety and quality in the food supply chain |
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| Meneghetti and Monti (2015) | X | X | Introduces an optimization model Considers specific characteristics Increases the whole cold chain value |
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| Chadderton et al. (2017) | X | X | Introduces a decision support model Considers site-specific capabilities and supply chain efficiency |
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| Eriksson et al. (2016) | X | Proposes an environmental and economic model Enhances the biogas production from food waste |
Traceability systems for FSCM
| Systems | Tracing objects | Technology | District | Features |
|---|---|---|---|---|
| Kelepouris et al. (2007) | Batched foods | RFID | UK | This paper outlines both an information data model and a system architecture that make traceability feasible in a food supply chain |
| Angeles (2005) | Biscuits, cakes, prepared foods | RFID | UK | Implementation guidelines for managers are summarized to conduct real-time visibility into supply chains |
| Opara (2003) | Agri-food | EID GIS |
Oman | This paper introduces technological challenges in implementing traceable agricultural supply chain management |
| Hu et al. (2013) | Vegetable | UML RFID |
China | A systematic methodology for implementing vegetable supply chain traceability is presented |
| Dabbene and Gay (2011) | Batched food | Barcode RFID ICT |
EU | Novel criteria and methods for measuring and optimizing a traceability system are introduced |
| Bosona and Gebresenbet (2011) | Agri-food | Barcode EID Tag EDI GIS |
EU | This paper points out that the full understanding of food supply chain is important to conduct food traceability |
| Hsu et al. (2008) | Fish | RFID CRM |
Taiwan | A RFID-enabled traceability system for live fish supply chain is presented |
| Bertolini et al. (2006) | Durum wheat pasta | FMECA | EU | An industrial engineering tool “Failure Mode Effect and Criticality Analysis” (FMECA) is used for critical points tracing |
| Patterson et al. (2003) | Perishable food | RFID Barcode Geo-Coded |
USA | This paper proposes a model to examine the key factors which are greatly influence the supply chain technology adoption |
| Regattieri et al. (2007) | Italian cheese | RFID Alphanumerical Codes, Barcode |
EU | This paper provides a general framework for the identification of key mainstays in a traceability system |
| Golan et al. (2004b) | Agri-food | Electronic coding system | USA | This paper examines the US food traceability systems in agriculture supply chain management |
| Gianni et al. (2016) | Agri-food | BI IMS |
EU | This paper proposes a business intelligence (BI) wise solution using integrated management systems (IMS) approach |
| Pizzuti et al. (2014) | Agri-food | RBV Communication |
UK | This paper introduces a framework using resource based view (RBV) to examine strategic impacts of food traceability system technologies |
| Kondo (2010) | Fruits and vegetable | Machine vision Near infrared inspection |
Japan | This paper presents an automation technology-based system for fruit and vegetable traceability |
| Choe et al. (2009) | Agri-food | Barcode RFID IT |
South Korea | This paper presents an uncertainty mitigation approach in the context of the food traceability system |
| Thakur et al. (2011) | Fish | EPCIS UML |
Norway | EPCIS framework and UML statecharts are used for modeling traceability information in FSCM |
| Badia-Melis et al. (2015) | Wheat flour | RFID Cloud Computing |
EU | This paper introduces latest technological advancements in food traceability systems |
| Muñoz-Colmenero et al. (2017) | Candies | PGM PCR-CS |
EU | This paper uses Ion Torrent Personal Genome Machine (PGM) in analyzing candies supply chain |
| Dabbene et al. (2016) | Agri-food | RFID Barcode Big Data |
EU | This paper introduces a latest technologies for food traceability systems |
Decision-making systems for FSCM
| Systems | Decisions | Technologies | Area | Features |
|---|---|---|---|---|
| Vorst et al. (2005) | Logistics network integration | ICT | The Netherlands | Innovative developments of physical means, human skills and competences are integrated with ICT for enhancing logistics network integration |
| Henson and Reardon (2005) | Supply chain coordination | Internet-based IT | Canada | For ensuring private food safety and quality, an internet-based system is designed to achieve supply chain coordination |
| Maloni and Brown (2006) | Supply chain co-operation | Internet-based framework | USA | An internet-based framework using corporate social responsibility is used in the food supply chain for various co-operation |
| Taylor and Fearne (2006) | Planning scheduling |
Data-based framework | UK | A data-enabled framework is built to improving demand management within a number of food supply chain |
| Attaran (2007) | Transportation Fleet |
RFID Internet |
USA | An RFID-enabled system is used to improve the food retailer supply chain |
| Baker et al. (2007) | Fleet Scheduling |
Optimization Heuristic |
Australia | A fleet optimization system is proposed to satisfy the constraints in FSCM |
| Bottani and Rizzi (2008) | Logistics Warehouse |
RFID EPC |
Italy | This paper introduces an RFID and EPC system for fast-moving consumer goods (FMCG) supply chain management |
| Tsoulfas and Pappis (2008) | Supply chain performance | Modeling | Greece | A model based decision system is proposed to analyze the environmental performance indicators in FSCM |
| Vorst et al. (2009) | Logistics integration | Simulation | The Netherlands | A new simulation environment is introduced to support integrated food supply chain to deal with uncertainties |
| Peidro et al. (2009) | Planning | Fuzzy model | Spain | A fuzzy model is introduced for food supply chain planning by considering supply demand and process uncertainties |
| Kuo and Chen (2010) | Warehouse distribution | Internet Mobile APP RFID |
Taiwan | A logistics service based on the advancement of multi-temperature joint distribution system (MTJD) is proposed for food cold chain |
| Hsiao et al. (2010) | Supply chain outsourcing | Hierarchical framework | Taiwan | A system for supply chain outsourcing decision-making is introduced for food manufacturers |
| Ali and Kumar (2011) | Supply chain collaboration | ICT | India | ICT is used in enhancing the decision-making across the agricultural supply chain |
| Knemeyer and Naylor (2011) | Logistics integration | Behavioral model | USA | A behavioral system is used to make logistics and supply chain decisions to achieve integrated FSCM |
| Wang and Li (2012) | Warehouse Logistics |
Data-based modeling Tracing |
UK | An information system is used for perishable food supply chains by using data captured from trace technologies |
| Trienekens et al. (2012) | Transparency Logistics |
Integrated information | The Netherlands | An integrated information system using intensified data exchange is used in complex dynamic FSCM |
| Kannan et al. (2013) | Supplier selection | Fuzzy technique | Denmark | An integrated approach with fuzzy multi attribute utility theory and multi-objective modeling is proposed for decision-making in FSCM |
| Zheng and Ling (2013) | Planning | Fuzzy Optimization |
China | A multi-objective fuzzy optimization system is proposed for transportation planning in FSCM |
| Validi et al. (2014) | Distribution | Multi-objective GA |
Ireland | A distribution system is proposed using optimization demand for two-layer FSCM |
| Agustina et al. (2014) | Vehicle scheduling Logistics |
Modeling Optimization |
Singapore | A distribution system is presented for food supply chains to make vehicle scheduling and routing decisions |
| Sitek and Wikarek (2015) | Logistics sustainability | Hybrid Modeling Optimization |
Poland | A system uses hybrid framework and optimization approaches for sustainable FSCM |
| Kilger et al. (2015) | Collaborative planning | Multi-objective Modeling Internet |
Germany | A planning system is introduced for the food supply chain to achieve collaborative processes |
| Govindan and Sivakumar (2016) | Supplier selection Coordination |
Fuzzy Multi-objective Modeling |
Denmark | A fuzzy technique based system is used for supplier selection in FSCM to achieve coordinated operations |
| Wang et al. (2016) | Sustainability Logistics |
Trial-based Modeling |
China | A system using decision-making trial and evaluation laboratory approach is used for FSCM |
| Pizzuti et al. (2017) | Logistics Integration | Ontology | Italy | An ontology-based system is used for supporting meet logistics management |
| Gunasekaran et al. (2017) | Sustainability Resilience |
Big Data Framework |
UK | A Big Data Analytics system is used for examine resilience in FSCM |
Reported cases using IT systems in FSCM
| Company | District | System | Improvement | Case |
|---|---|---|---|---|
| Tronto Valley | Italy | ARIS | Reduction of 3 types of costs Enhanced traceability |
Bevilacqua et al. (2009) |
| A medium size company | Turkey | Risk management system | Order fulfilled on-time increases to 90.6% Risk mitigation increases 9.9% |
Tuncel and Alpan (2010) |
| Parmigiana Reggiano | Italy | Traceability system | Improved traceability Enhanced customer satisfaction |
Regattieri et al. (2007) |
| A tomato Firm | The Netherlands | Performance measurement system | Improved efficiency and flexibility Improved food quality Quicker responsiveness |
Aramyan et al. (2007) |
| A food manufacturer | Japan | Customer co-operation system | Improved customer co-operation Enhanced internal environment management |
Zhu et al. (2010) |
| Pizza restaurants | USA | TQM Lean/JIT |
Improved information sharing Better quality Increased logistics efficiency |
Pagell and Wu (2009) |
| Convenience stores | Taiwan | RFID-based food traceability system | Improved operations Strengthened tracking Better operational efficiency |
Hong et al. (2011) |
| FoodRet | UK | Distribution management system | Improved corporation network Enhanced efficiency Reduced fuel consumption |
Walker et al. (2008) |
| A leading retailer of food | USA | Risk management system | Improved risk management ability Consolidated coordination |
Oke and Gopalakrishnan (2009) |
| Chicken and potato supply chains | UK | Sustainability assessment system | Improved supply chain efficiency Improved sustainability |
Yakovleva (2007) |
| A fresh producer | Belgium | Food safety management system | Improved food quality Better risk management ability |
Jacxsens et al. (2010) |
| SustainPack integrated project | Spain | Lifecycle management system | Reduced WIP Enhanced packaging Improved efficiency |
Dobon et al. (2011) |
| Sanlu Group | China | Quality control system | Improved safety inspection More efficient control mechanisms |
Chen et al. (2014) |
| A single company | Italy | LCA system | Higher specific production Improved ecoprofile of the crops |
Cellura et al. (2012) |
| Agri-food supply chain | Australia | H&S food decision-making system | More healthy diet More environmental sustainability |
Friel et al. (2014) |
| The Emilia-Romagna FSC | Italy | Distribution management system | Sustainable food chain Environmental food packaging |
Accorsi et al. (2014) |
| 6 Firms | Italy | FSCM system | Energy saving Avoided disposal cost Improved productivity |
Sgarbossa and Russo (2017) |
| A beef logistics company | Netherland | Logistics network system | Reduced transportation emissions Sustainable logistics |
Soysal et al. (2014) |
| A chestnuts company | Italy | Value chain management system | Improved sustainability Reduced CO2 emission Increased value chain |
Savino et al. (2015) |
| A mushroom manufacturer | The Netherlands | Supply chain management system | Increase total profitability by 11% Improved environmental performance |
Banasik et al. (2017) |
References
Accorsi, R., Cascini, A., Cholette, S., Manzini, R. and Mora, C. (2014), “Economic and environmental assessment of reusable plastic containers: a food catering supply chain case study”, International Journal of Production Economics, Vol. 152, pp. 88-101.
Agustina, D., Lee, C. and Piplani, R. (2014), “Vehicle scheduling and routing at a cross docking center for food supply chains”, International Journal of Production Economics, Vol. 152, pp. 29-41.
Ahearn, M.C., Armbruster, W. and Young, R. (2016), “Big data’s potential to improve food supply chain environmental sustainability and food safety”, International Food and Agribusiness Management Review, Vol. 19 No. A, pp. 155-172.
Ahn, Y.Y., Ahnert, S.E., Bagrow, J.P. and Barabási, A.L. (2011), “Flavor network and the principles of food pairing”, Scientific Reports, Vol. 1, pp. 1-7.
Ahumada, O. and Villalobos, J.R. (2009), “Application of planning models in the agri-food supply chain: a review”, European Journal of Operational Research, Vol. 196 No. 1, pp. 1-20.
Akhtar, P., Tse, Y.K., Khan, Z. and Rao-Nicholson, R. (2016), “Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets”, International Journal of Production Economics, Vol. 181, Part B, pp. 392-401.
Akkerman, R., Farahani, P. and Grunow, M. (2010), “Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges”, OR Spectrum, Vol. 32 No. 4, pp. 863-904.
Ali, J. and Kumar, S. (2011), “Information and communication technologies (ICTs) and farmers’ decision-making across the agricultural supply chain”, International Journal of Information Management, Vol. 31 No. 2, pp. 149-159.
Angeles, R. (2005), “RFID technologies: supply-chain applications and implementation issues”, Information Systems Management, Vol. 22 No. 1, pp. 51-65.
Aramyan, L.H., Oude Lansink, A.G., Vorst, J.G.V.D. and Van Kooten, O. (2007), “Performance measurement in agri-food supply chains: a case study”, Supply Chain Management: An International Journal, Vol. 12 No. 4, pp. 304-315.
Attaran, M. (2007), “RFID: an enabler of supply chain operations”, Supply Chain Management: An International Journal, Vol. 12 No. 4, pp. 249-257.
Aung, M.M. and Chang, Y.S. (2014), “Traceability in a food supply chain: safety and quality perspectives”, Food Control, Vol. 39, pp. 172-184.
Badia-Melis, R., Mishra, P. and Ruiz-García, L. (2015), “Food traceability: new trends and recent advances: a review”, Food Control, Vol. 57, pp. 393-401.
Bajželj, B., Richards, K.S., Allwood, J.M., Smith, P., Dennis, J.S., Curmi, E. and Gilligan, C.A. (2014), “Importance of food-demand management for climate mitigation”, Nature Climate Change, Vol. 4 No. 10, pp. 924-929.
Baker, S., Bender, A., Abbass, H. and Sarker, R. (2007), “A scenario-based evolutionary scheduling approach for assessing future supply chain fleet capabilities” in Dahal, K.P., Tan, K.C. and Cowling, P.I. (Eds), Evolutionary Scheduling, Springer, Berlin, Heidelberg and New York, NY, pp. 485-511.
Balaji, M. and Arshinder, K. (2016), “Modeling the causes of food wastage in Indian perishable food supply chain”, Resources, Conservation and Recycling, Vol. 114, pp. 153-167.
Banasik, A., Kanellopoulos, A., Claassen, G., Bloemhof-Ruwaard, J.M. and Vorst, J.G.V.D. (2017), “Closing loops in agricultural supply chains using multi-objective optimization: a case study of an industrial mushroom supply chain”, International Journal of Production Economics, Vol. 183 No. B, pp. 409-420.
Bertolini, M., Bevilacqua, M. and Massini, R. (2006), “FMECA approach to product traceability in the food industry”, Food Control, Vol. 17 No. 2, pp. 137-145.
Beulens, A.J., Broens, D.F., Folstar, P. and Hofstede, G.J. (2005), “Food safety and transparency in food chains and networks relationships and challenges”, Food Control, Vol. 16 No. 6, pp. 481-486.
Bevilacqua, M., Ciarapica, F. and Giacchetta, G. (2009), “Business process reengineering of a supply chain and a traceability system: a case study”, Journal of Food Engineering, Vol. 93 No. 1, pp. 13-22.
Blandon, J., Henson, S. and Cranfield, J. (2009), “Small-scale farmer participation in new agri-food supply chains: case of the supermarket supply chain for fruit and vegetables in Honduras”, Journal of International Development, Vol. 21 No. 7, pp. 971-984.
Bosona, T.G. and Gebresenbet, G. (2011), “Cluster building and logistics network integration of local food supply chain”, Biosystems Engineering, Vol. 108 No. 4, pp. 293-302.
Bottani, E. and Rizzi, A. (2008), “Economical assessment of the impact of RFID technology and EPC system on the fast-moving consumer goods supply chain”, International Journal of Production Economics, Vol. 112 No. 2, pp. 548-569.
Brynjolfsson, E., Hitt, L.M. and Kim, H.H. (2011), “Strength in numbers: how does data-driven decision making affect firm performance?”, SSRN eLibrary, pp. 1-33, doi: 10.2139/ssrn.1819486.
BusinessWire (2016), “Genessee and Wyoming to acquire providence and Worcester railroad for $126 million”, available at: www.businesswire.com/news/home/20160815005302/en/Genesee-Wyoming-Enters-Agreement-Acquire-Providence-Worcester (accessed October 5, 2016).
Campbell, H., Lawrence, G. and Smith, K. (2006), “Audit cultures and the antipodes: the implications of EurepGAP for New Zealand and Australian agri-food industries”, Research in Rural Sociology and Development, Vol. 12, pp. 69-93.
Canavari, M., Centonze, R., Hingley, M. and Spadoni, R. (2010), “Traceability as part of competitive strategy in the fruit supply chain”, British Food Journal, Vol. 112 No. 2, pp. 171-186.
Caswell, J.A., Bredahl, M.E. and Hooker, N.H. (1998), “How quality management metasystems are affecting the food industry”, Review of Agricultural Economics, Vol. 20 No. 2, pp. 547-557.
Cellura, M., Longo, S. and Mistretta, M. (2012), “Life cycle assessment (LCA) of protected crops: an Italian case study”, Journal of Cleaner Production, Vol. 28, pp. 56-62.
Chadderton, C., Foran, C.M., Rodriguez, G., Gilbert, D., Cosper, S.D. and Linkov, I. (2017), “Decision support for selection of food waste technologies at military installations”, Journal of Cleaner Production, Vol. 141, pp. 267-277.
Chen, C., Zhang, J. and Delaurentis, T. (2014), “Quality control in food supply chain management: an analytical model and case study of the adulterated milk incident in China”, International Journal of Production Economics, Vol. 152, pp. 188-199.
Chiou, T.Y., Chan, H.K., Lettice, F. and Chung, S.H. (2011), “The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan”, Transportation Research Part E: Logistics and Transportation Review, Vol. 47 No. 6, pp. 822-836.
Choe, Y.C., Park, J., Chung, M. and Moon, J. (2009), “Effect of the food traceability system for building trust: price premium and buying behavior”, Information Systems Frontiers, Vol. 11 No. 2, pp. 167-179.
Choi, T.M., Chiu, C.H. and Chan, H.K. (2016), “Risk management of logistics systems”, Transportation Research Part E: Logistics and Transportation Review, Vol. 90, pp. 1-6.
Colin, J., Estampe, D., Pfohl, H.C., Gallus, P. and Thomas, D. (2011), “Interpretive structural modeling of supply chain risks”, International Journal of Physical Distribution & Logistics Management, Vol. 41 No. 9, pp. 839-859.
Cooper, M.C. and Ellram, L.M. (1993), “Characteristics of supply chain management and the implications for purchasing and logistics strategy”, The International Journal of Logistics Management, Vol. 4 No. 2, pp. 13-24.
Dabbene, F. and Gay, P. (2011), “Food traceability systems: performance evaluation and optimization”, Computers and Electronics in Agriculture, Vol. 75 No. 1, pp. 139-146.
Dabbene, F., Gay, P. and Tortia, C. (2016), “Safety and traceability” in Iakovou, E., Bochtis, D., Vlachos, D. and Aidonis, D. (Eds), Supply Chain Management for Sustainable Food Networks, Wiley, Chichester, pp. 159-182.
Devin, B. and Richards, C. (2016), “Food waste, power, and corporate social responsibility in the Australian food supply chain”, Journal of Business Ethics, April, pp. 1-12, doi: 10.1007/s10551-016-3181-z.
Dickinson, D.L. and Bailey, D. (2002), “Meat traceability: are US consumers willing to pay for it?”, Journal of Agricultural and Resource Economics, Vol. 27 No. 2, pp. 348-364.
Dobon, A., Cordero, P., Kreft, F., Østergaard, S.R., Robertsson, M., Smolander, M. and Hortal, M. (2011), “The sustainability of communicative packaging concepts in the food supply chain. A case study: part 1. Life cycle assessment”, The International Journal of Life Cycle Assessment, Vol. 16 No. 2, pp. 168-177.
Doukidis, G.I., Matopoulos, A., Vlachopoulou, M., Manthou, V. and Manos, B. (2007), “A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry”, Supply Chain Management: An International Journal, Vol. 12 No. 3, pp. 177-186.
Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Shibin, K. and Wamba, S.F. (2017), “Sustainable supply chain management: framework and further research directions”, Journal of Cleaner Production, Vol. 142 No. 2, pp. 1119-1130.
EHPM (2013), “Position paper on botanicals”, available at: www.ehpm.org/images/olddocs/PP001OBOT080513botanicalsLongVersion.pdf (accessed July 5, 2016).
ELA (2012), “Sustainable supply chain management”, available at: www.elalog.eu/content/eurolog-2012 (accessed July 5, 2016).
Eriksson, O., Bisaillon, M., Haraldsson, M. and Sundberg, J. (2016), “Enhancement of biogas production from food waste and sewage sludge – environmental and economic life cycle performance”, Journal of Environmental Management, Vol. 175, pp. 33-39.
ERRT (2013), “Good practices in the food supply chain”, available at: www.errt.org/issues/good-practices-food-supply-chain (accessed June 8, 2016).
European Commission (2014), “High-tech: a key ingredient for the future of Europe’s food industry”, Horizon 2020, available at: https://ec.europa.eu/programmes/horizon2020/en/news/high-tech-key-ingredient-future-europe%E2%80%99s-food-industry (accessed June 18, 2016).
European Commission (2015), “Retail forum for sustainability”, Environment, available at: http://ec.europa.eu/environment/industry/retail/index_en.htm (accessed June 18, 2016).
European Commission (2016), “Food and drink industry”, Growth Internal Market, Industry, Entrepreneurship and SMEs, available at: https://ec.europa.eu/growth/sectors/food_en (accessed June 18, 2016).
Faisal, M.N. and Talib, F. (2016), “Implementing traceability in Indian food-supply chains: an interpretive structural modeling approach”, Journal of Foodservice Business Research, Vol. 19 No. 2, pp. 171-196.
Folinas, D., Manikas, I. and Manos, B. (2006), “Traceability data management for food chains”, British Food Journal, Vol. 108 No. 8, pp. 622-633.
Folkerts, H. and Koehorst, H. (1997), “Challenges in international food supply chains: vertical co-ordination in the European agribusiness and food industries”, Supply Chain Management: An International Journal, Vol. 2 No. 1, pp. 11-14.
FoodDrinkEurope (2015), “Data and trends European food and drink industry 2014-2015”, Data and Trends of the European Food and Drink Industry, Brussels, pp. 1-26.
Fraser, E., Legwegoh, A., Krishna, K., CoDyre, M., Dias, G., Hazen, S., Johnson, R., Martin, R., Ohberg, L. and Sethuratnam, S. (2016), “Biotechnology or organic? Extensive or intensive? Global or local? A critical review of potential pathways to resolve the global food crisis”, Trends in Food Science & Technology, Vol. 48, pp. 78-87.
Friel, S., Barosh, L.J. and Lawrence, M. (2014), “Towards healthy and sustainable food consumption: an Australian case study”, Public Health Nutrition, Vol. 17 No. 5, pp. 1156-1166.
Fritz, M. and Schiefer, G. (2008), “Food chain management for sustainable food system development: a European research agenda”, Agribusiness, Vol. 24 No. 4, pp. 440-452.
Garnett, T. (2011), “Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)?”, Food Policy, Vol. 36, pp. S23-S32.
Genovese, A., Acquaye, A.A., Figueroa, A. and Koh, S.L. (2017), “Sustainable supply chain management and the transition towards a circular economy: evidence and some applications”, Omega, Vol. 66 No. B, pp. 344-357.
Georgiadis, P., Vlachos, D. and Iakovou, E. (2005), “A system dynamics modeling framework for the strategic supply chain management of food chains”, Journal of Food Engineering, Vol. 70 No. 3, pp. 351-364.
Ghosh, D. (2016), “Food safety regulations in Australia and New Zealand food standards”, Journal of the Science of Food and Agriculture, Vol. 96 No. 9, pp. 3274-3275.
Gianni, M., Gotzamani, K. and Linden, I. (2016), “How a BI-wise responsible integrated management system may support food traceability”, International Journal of Decision Support System Technology, Vol. 8 No. 2, pp. 1-17.
Global Strategy (2013), “United States of America food and beverage market study”, Global Strategy, Inc. International Business Development, Oviedo, FL, pp. 1-55.
Golan, E., Krissoff, B. and Kuchler, F. (2004a), “Food traceability”, Amber Waves, Vol. 2 No. 2, pp. 14-21.
Golan, E., Krissoff, B., Kuchler, F., Calvin, L., Nelson, K. and Price, G. (2004b), “Traceability in the US food supply: economic theory and industry studies”, Agricultural Economic Report, Vol. 830 No. 3, pp. 183-185.
Gorris, L.G. (2005), “Food safety objective: an integral part of food chain management”, Food Control, Vol. 16 No. 9, pp. 801-809.
Govindan, K. and Sivakumar, R. (2016), “Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches”, Annals of Operations Research, Vol. 238 Nos 1-2, pp. 243-276.
Green, K.W. Jr, Zelbst, P.J., Meacham, J. and Bhadauria, V.S. (2012), “Green supply chain management practices: impact on performance”, Supply Chain Management: An International Journal, Vol. 17 No. 3, pp. 290-305.
Gunasekaran, A., Subramanian, N. and Rahman, S. (2015), “Green supply chain collaboration and incentives: current trends and future directions”, Transportation Research Part E: Logistics and Transportation Review, Vol. 74, pp. 1-10.
Gunasekaran, A., Papadopoulos, T., Fosso-Wamba, S., Dubey, R., Childe, S. and Altay, N. (2017), “The role of big data in explaining disaster resilience in supply chains for sustainability”, Journal of Cleaner Production, Vol. 142 No. 2, pp. 1108-1118.
Guo, Z.X., Ngai, E.W.T., Yang, C. and Liang, X.D. (2015), “An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment”, International Journal of Production Economics, Vol. 159, pp. 16-28.
Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R. and Meybeck, A. (2011), “Global food losses and food waste”, Food and Agriculture Organization of the United Nations, Rome.
Hasuike, T., Kashima, T. and Matsumoto, S. (2014), “Data-driven food supply chain optimization under uncertain crop productions and consumers’ demands”, Innovation and Supply Chain Management, Vol. 8 No. 4, pp. 150-156.
Hemert, P.V. and Iske, P.L. (2015), “Framing knowledge-based urban development and absorptive capacity of urban regions: a case-study of Limburg, the Netherlands”, International Journal of Knowledge-Based Development, Vol. 6 No. 4, pp. 314-349.
Hemphill, T.A. and Banerjee, S. (2015), “Genetically modified organisms and the US retail food labeling controversy: consumer perceptions, regulation, and public policy”, Business and Society Review, Vol. 120 No. 3, pp. 435-464.
Henson, S. and Reardon, T. (2005), “Private agri-food standards: implications for food policy and the agri-food system”, Food Policy, Vol. 30 No. 3, pp. 241-253.
Herrero, M., Thornton, P.K., Notenbaert, A.M., Wood, S., Msangi, S., Freeman, H., Bossio, D., Dixon, J., Peters, M. and Steeg, J.V.D. (2010), “Smart investments in sustainable food production: revisiting mixed crop-livestock systems”, Science, Vol. 327 No. 5967, pp. 822-825.
Hobbs, J.E. and Young, L.M. (2000), “Closer vertical co-ordination in agri-food supply chains: a conceptual framework and some preliminary evidence”, Supply Chain Management: An International Journal, Vol. 5 No. 3, pp. 131-143.
Hong, I.H., Dang, J.F., Tsai, Y.H., Liu, C.S., Lee, W.T., Wang, M.L. and Chen, P.C. (2011), “An RFID application in the food supply chain: a case study of convenience stores in Taiwan”, Journal of Food Engineering, Vol. 106 No. 2, pp. 119-126.
Hsiao, H., Vorst, J.V.D., Kemp, R. and Omta, S. (2010), “Developing a decision-making framework for levels of logistics outsourcing in food supply chain networks”, International Journal of Physical Distribution & Logistics Management, Vol. 40 No. 5, pp. 395-414.
Hsu, Y.C., Chen, A.P. and Wang, C.H. (2008), “A RFID-enabled traceability system for the supply chain of live fish”, Proceeding of IEEE International Conference on Automation and Logistics, IEEE, Qingdao, September1-3, pp. 81-86.
Hu, J., Zhang, X., Moga, L.M. and Neculita, M. (2013), “Modeling and implementation of the vegetable supply chain traceability system”, Food Control, Vol. 30 No. 1, pp. 341-353.
Irani, Z. and Sharif, A.M. (2016), “Sustainable food security futures: perspectives on food waste and information across the food supply chain”, Journal of Enterprise Information Management, Vol. 29 No. 2, pp. 171-178.
Jacxsens, L., Luning, P., Marcelis, W., Van Boekel, T., Rovira, J., Oses, S., Kousta, M., Drosinos, E., Jasson, V. and Uyttendaele, M. (2011), “Tools for the performance assessment and improvement of food safety management systems”, Trends in Food Science & Technology, Vol. 22, pp. S80-S89.
Jacxsens, L., Luning, P., Vorst, J.V.D., Devlieghere, F., Leemans, R. and Uyttendaele, M. (2010), “Simulation modelling and risk assessment as tools to identify the impact of climate change on microbiological food safety – the case study of fresh produce supply chain”, Food Research International, Vol. 43 No. 7, pp. 1925-1935.
Jacxsens, L., Uyttendaele, M., Devlieghere, F., Rovira, J., Gomez, S.O. and Luning, P. (2010), “Food safety performance indicators to benchmark food safety output of food safety management systems”, International Journal of Food Microbiology, Vol. 141, pp. S180-S187.
Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A. and Diabat, A. (2013), “Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain”, Journal of Cleaner Production, Vol. 47, pp. 355-367.
Karaman, A.D., Cobanoglu, F., Tunalioglu, R. and Ova, G. (2012), “Barriers and benefits of the implementation of food safety management systems among the Turkish dairy industry: a case study”, Food Control, Vol. 25 No. 2, pp. 732-739.
Karlsen, K.M., Dreyer, B., Olsen, P. and Elvevoll, E.O. (2013), “Literature review: does a common theoretical framework to implement food traceability exist?”, Food Control, Vol. 32 No. 2, pp. 409-417.
Kelepouris, T., Pramatari, K. and Doukidis, G. (2007), “RFID-enabled traceability in the food supply chain”, Industrial Management & Data Systems, Vol. 107 No. 2, pp. 183-200.
Kilger, C., Reuter, B. and Stadtler, H. (2015), “Collaborative planning”, in Hartmut, S., Christoph, K. and Herbert, M. (Eds), Supply Chain Management and Advanced Planning, Springer, Berlin and Heidelberg, pp. 257-277.
Kim, J.K., Oh, B.R., Chun, Y.N. and Kim, S.W. (2006), “Effects of temperature and hydraulic retention time on anaerobic digestion of food waste”, Journal of Bioscience and Bioengineering, Vol. 102 No. 4, pp. 328-332.
King, R.P. and Phumpiu, P.F. (1996), “Reengineering the food supply chain: the ECR initiative in the grocery industry”, American Journal of Agricultural Economics, Vol. 78 No. 5, pp. 1181-1186.
Knemeyer, A.M. and Naylor, R.W. (2011), “Using behavioral experiments to expand our horizons and deepen our understanding of logistics and supply chain decision making”, Journal of Business Logistics, Vol. 32 No. 4, pp. 296-302.
Kondo, N. (2010), “Automation on fruit and vegetable grading system and food traceability”, Trends in Food Science & Technology, Vol. 21 No. 3, pp. 145-152.
Kummu, M., De Moel, H., Porkka, M., Siebert, S., Varis, O. and Ward, P. (2012), “Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use”, Science of the Total Environment, Vol. 438, pp. 477-489.
Kuo, J.C. and Chen, M.C. (2010), “Developing an advanced multi-temperature joint distribution system for the food cold chain”, Food Control, Vol. 21 No. 4, pp. 559-566.
La Scalia, G., Settanni, L., Micale, R. and Enea, M. (2016), “Predictive shelf life model based on RF technology for improving the management of food supply chain: a case study”, International Journal of RF Technologies, Vol. 7 No. 1, pp. 31-42.
Lam, H.M., Remais, J., Fung, M.C., Xu, L. and Sun, S.S.M. (2013), “Food supply and food safety issues in China”, The Lancet, Vol. 381 No. 9882, pp. 2044-2053.
Lan, S.L. and Zhong, R.Y. (2016), “Coordinated development between metropolitan economy and logistics for sustainability”, Resources, Conservation and Recycling, doi: 10.1016/j.resconrec.2016.08.017.
Lan, S.L., Zhang, H., Zhong, R.Y. and Huang, G.Q. (2016), “A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process”, Industrial Management & Data Systems, Vol. 116 No. 5, pp. 1024-1042.
Laux, C.M. and Hurburgh, C.R. Jr (2012), “Using quality management systems for food traceability”, Journal of Industrial Technology, Vol. 26 No. 3, pp. 1-10.
Li, D. and Wang, X.J. (2015), “Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain”, International Journal of Production Research, Vol. 55 No. 17, pp. 5127-5141.
Low, S.A. and Vogel, S.J. (2011), “Direct and intermediated marketing of local foods in the United States”, USDA-ERS Economic Research Report, Vol. 128, pp. 1-38.
MacCarthy, B.L., Blome, C., Olhager, J., Srai, J.S. and Zhao, X. (2016), “Supply chain evolution – theory, concepts and science”, International Journal of Operations and Production Management, Vol. 36 No. 12, pp. 1696-1718.
Maloni, M.J. and Brown, M.E. (2006), “Corporate social responsibility in the supply chain: an application in the food industry”, Journal of Business Ethics, Vol. 68 No. 1, pp. 35-52.
Manning, L., Baines, R. and Chadd, S. (2006), “Quality assurance models in the food supply chain”, British Food Journal, Vol. 108 No. 2, pp. 91-104.
Manning, L., Soon, J.M., Griffith, C. and Griffith, C. (2016), “Development of sustainability indicator scoring (SIS) for the food supply chain”, British Food Journal, Vol. 118 No. 9, pp. 2097-2125.
Manzini, R. and Accorsi, R. (2013), “The new conceptual framework for food supply chain assessment”, Journal of Food Engineering, Vol. 115 No. 2, pp. 251-263.
Marsden, T., Banks, J. and Bristow, G. (2000), “Food supply chain approaches: exploring their role in rural development”, Sociologia Ruralis, Vol. 40 No. 4, pp. 424-438.
Martins, R.C., Lopes, V.V., Vicente, A. and Teixeira, J. (2008), “Computational shelf-life dating: complex systems approaches to food quality and safety”, Food and Bioprocess Technology, Vol. 1 No. 3, pp. 207-222.
Marucheck, A., Greis, N., Mena, C. and Cai, L. (2011), “Product safety and security in the global supply chain: issues, challenges and research opportunities”, Journal of Operations Management, Vol. 29 No. 7, pp. 707-720.
Meneghetti, A. and Monti, L. (2015), “Greening the food supply chain: an optimisation model for sustainable design of refrigerated automated warehouses”, International Journal of Production Research, Vol. 53 No. 21, pp. 6567-6587.
Montreuil, B. (2011), “Toward a physical internet: meeting the global logistics sustainability grand challenge”, Logistics Research, Vol. 3 Nos 2-3, pp. 71-87.
Muñoz-Colmenero, M., Martínez, J.L., Roca, A. and Garcia-Vazquez, E. (2017), “NGS tools for traceability in candies as high processed food products: Ion Torrent PGM versus conventional PCR-cloning”, Food Chemistry, Vol. 214, pp. 631-636.
Narasimhan, R. and Kim, S.W. (2002), “Effect of supply chain integration on the relationship between diversification and performance: evidence from Japanese and Korean firms”, Journal of Operations Management, Vol. 20 No. 3, pp. 303-323.
Nishat Faisal, M., Banwet, D.K. and Shankar, R. (2007), “Information risks management in supply chains: an assessment and mitigation framework”, Journal of Enterprise Information Management, Vol. 20 No. 6, pp. 677-699.
Oke, A. and Gopalakrishnan, M. (2009), “Managing disruptions in supply chains: a case study of a retail supply chain”, International Journal of Production Economics, Vol. 118 No. 1, pp. 168-174.
Oliva, F., Revetria, R., Mastorakis, N., Mladenov, V., Bojkovic, Z., Simian, D., Kartalopoulos, S., Varonides, A., Udriste, C. and Kindler, E. (2008), “A system dynamic model to support cold chain management in food supply chain”, Proceedings of WSEAS International Conference on Mathematics and Computers in Science and Engineering, Heraklion, July 22-24, pp. 361-365.
Opara, L.U. (2003), “Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects”, Journal of Food Agriculture and Environment, Vol. 1, pp. 101-106.
Pagell, M. and Wu, Z. (2009), “Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars”, Journal of Supply Chain Management, Vol. 45 No. 2, pp. 37-56.
Pandey, P., Lejeune, M., Biswas, S., Morash, D., Weimer, B. and Young, G. (2016), “A new method for converting foodwaste into pathogen free soil amendment for enhancing agricultural sustainability”, Journal of Cleaner Production, Vol. 112, pp. 205-213.
Pang, L.Y., Zhong, R.Y., Fang, J. and Huang, G.Q. (2015), “Data-source interoperability service for heterogeneous information integration in ubiquitous enterprises”, Advanced Engineering Informatics, Vol. 29 No. 3, pp. 549-561.
Papathanasiou, J. and Kenward, R. (2014), “Design of a data-driven environmental decision support system and testing of stakeholder data-collection”, Environmental Modelling & Software, Vol. 55, pp. 92-106.
Parfitt, J., Barthel, M. and Macnaughton, S. (2010), “Food waste within food supply chains: quantification and potential for change to 2050”, Philosophical Transactions of the Royal Society of London B: Biological Sciences, Vol. 365 No. 1554, pp. 3065-3081.
Park, D.H., Kashyap, P. and Visvanathan, C. (2016), “Comparative assessment of green supply chain management (GSCM) in drinking water service industry in Lao PDR, Thailand, and South Korea”, Desalination and Water Treatment, Vol. 57 No. 59, pp. 28684-28697.
Patterson, K.A., Grimm, C.M. and Corsi, T.M. (2003), “Adopting new technologies for supply chain management”, Transportation Research Part E: Logistics and Transportation Review, Vol. 39 No. 2, pp. 95-121.
Peidro, D., Mula, J., Poler, R. and Verdegay, J.L. (2009), “Fuzzy optimization for supply chain planning under supply, demand and process uncertainties”, Fuzzy Sets and Systems, Vol. 160 No. 18, pp. 2640-2657.
Pennington, J.A., Stumbo, P.J., Murphy, S.P., McNutt, S.W., Eldridge, A.L., McCabe-Sellers, B.J. and Chenard, C.A. (2007), “Food composition data: the foundation of dietetic practice and research”, Journal of the American Dietetic Association, Vol. 107 No. 12, pp. 2105-2113.
Perrot, N., Trelea, I.C., Baudrit, C., Trystram, G. and Bourgine, P. (2011), “Modelling and analysis of complex food systems: state of the art and new trends”, Trends in Food Science & Technology, Vol. 22 No. 6, pp. 304-314.
Pizzuti, T., Mirabelli, G., Grasso, G. and Paldino, G. (2017), “MESCO (MEat supply chain ontology): an ontology for supporting traceability in the meat supply chain”, Food Control, Vol. 72, pp. 123-133.
Pizzuti, T., Mirabelli, G., Sanz-Bobi, M.A. and Goméz-Gonzaléz, F. (2014), “Food track and trace ontology for helping the food traceability control”, Journal of Food Engineering, Vol. 120, pp. 17-30.
Pransky, J. (2015), “The Pransky interview: Dr Robert Ambrose, chief, software, robotics and simulation division at NASA”, Industrial Robot: An International Journal, Vol. 42 No. 4, pp. 285-289.
Qiu, X., Luo, H., Xu, G.Y., Zhong, R.Y. and Huang, G.Q. (2014), “Physical assets and service sharing for IoT-enabled supply hub in industrial park (SHIP)”, International Journal of Production Economics, Vol. 159, pp. 4-15.
Regattieri, A., Gamberi, M. and Manzini, R. (2007), “Traceability of food products: general framework and experimental evidence”, Journal of Food Engineering, Vol. 81 No. 2, pp. 347-356.
Reiner, G. and Trcka, M. (2004), “Customized supply chain design: problems and alternatives for a production company in the food industry: a simulation based analysis”, International Journal of Production Economics, Vol. 89 No. 2, pp. 217-229.
Righi, S., Oliviero, L., Pedrini, M., Buscaroli, A. and Della Casa, C. (2013), “Life cycle assessment of management systems for sewage sludge and food waste: centralized and decentralized approaches”, Journal of Cleaner Production, Vol. 44, pp. 8-17.
Rong, A., Akkerman, R. and Grunow, M. (2011), “An optimization approach for managing fresh food quality throughout the supply chain”, International Journal of Production Economics, Vol. 131 No. 1, pp. 421-429.
Roth, A.V., Tsay, A.A., Pullman, M.E. and Gray, J.V. (2008), “Unraveling the food supply chain: strategic insights from China and the 2007 recalls”, Journal of Supply Chain Management, Vol. 44 No. 1, pp. 22-39.
Ruiz-Garcia, L., Steinberger, G. and Rothmund, M. (2010), “A model and prototype implementation for tracking and tracing agricultural batch products along the food chain”, Food Control, Vol. 21 No. 2, pp. 112-121.
Savino, M.M., Manzini, R. and Mazza, A. (2015), “Environmental and economic assessment of fresh fruit supply chain through value chain analysis: a case study in chestnuts industry”, Production Planning & Control, Vol. 26 No. 1, pp. 1-18.
Scherhaufl, M., Pichler, M. and Stelzer, A. (2015), “UHF RFID localization based on phase evaluation of passive tag arrays”, IEEE Transactions on Instrumentation and Measurement, Vol. 64 No. 4, pp. 913-922.
Sgarbossa, F. and Russo, I. (2017), “A proactive model in sustainable food supply chain: insight from a case study”, International Journal of Production Economics, Vol. 183 No. B, pp. 596-606.
Shibin, K., Gunasekaran, A., Papadopoulos, T., Dubey, R., Singh, M. and Wamba, S.F. (2016), “Enablers and barriers of flexible green supply chain management: a total interpretive structural modeling approach”, Global Journal of Flexible Systems Management, Vol. 17 No. 2, pp. 171-188.
Simatupang, T.M. and Sridharan, R. (2002), “The collaborative supply chain”, The International Journal of Logistics Management, Vol. 13 No. 1, pp. 15-30.
Singh, A., Mishra, N., Ali, S.I., Shukla, N. and Shankar, R. (2015), “Cloud computing technology: reducing carbon footprint in beef supply chain”, International Journal of Production Economics, Vol. 164, pp. 462-471.
Sitek, P. and Wikarek, J. (2015), “A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management”, International Journal of Production Research, Vol. 53 No. 21, pp. 6611-6628.
Smith, G., Tatum, J., Belk, K., Scanga, J., Grandin, T. and Sofos, J. (2005), “Traceability from a US perspective”, Meat Science, Vol. 71 No. 1, pp. 174-193.
Smith, K., Lawrence, G. and Richards, C. (2010), “Supermarkets’ governance of the agri-food supply chain: is the ‘corporate-environmental’ food regime evident in Australia?”, International Journal of Sociology of Agriculture and Food, Vol. 17 No. 2, pp. 140-161.
Soto-Silva, W.E., Nadal-Roig, E., González-Araya, M.C. and Pla-Aragones, L.M. (2016), “Operational research models applied to the fresh fruit supply chain”, European Journal of Operational Research, Vol. 251 No. 2, pp. 345-355.
Soysal, M., Bloemhof-Ruwaard, J. and Vorst, J.V.D. (2014), “Modelling food logistics networks with emission considerations: the case of an international beef supply chain”, International Journal of Production Economics, Vol. 152, pp. 57-70.
Stevenson, G. and Pirog, R. (2008), “Values-based supply chains: strategies for agrifood enterprises of the middle”, in Lyson, T.A., Stevenson, G.W. and Welsh, R. (Eds), Food and the Mid-level Farm: Renewing an Agriculture of the Middle, The MIT Press, Cambridge, MA and London, pp. 119-143.
Talaei, M., Moghaddam, B.F., Pishvaee, M.S., Bozorgi-Amiri, A. and Gholamnejad, S. (2016), “A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry”, Journal of Cleaner Production, Vol. 113, pp. 662-673.
Tatonetti, N.P., Patrick, P.Y., Daneshjou, R. and Altman, R.B. (2012), “Data-driven prediction of drug effects and interactions”, Science Translational Medicine, Vol. 4 No. 125, pp. 1-14.
Taylor, D.H. (2005), “Value chain analysis: an approach to supply chain improvement in agri-food chains”, International Journal of Physical Distribution & Logistics Management, Vol. 35 No. 10, pp. 744-761.
Taylor, D.H. and Fearne, A. (2006), “Towards a framework for improvement in the management of demand in agri-food supply chains”, Supply Chain Management: An International Journal, Vol. 11 No. 5, pp. 379-384.
Thakur, M., Sørensen, C.F., Bjørnson, F.O., Forås, E. and Hurburgh, C.R. (2011), “Managing food traceability information using EPCIS framework”, Journal of Food Engineering, Vol. 103 No. 4, pp. 417-433.
Tomašević, I., Šmigić, N., Đekić, I., Zarić, V., Tomić, N. and Rajković, A. (2013), “Serbian meat industry: a survey on food safety management systems implementation”, Food Control, Vol. 32 No. 1, pp. 25-30.
Trienekens, J. and Zuurbier, P. (2008), “Quality and safety standards in the food industry, developments and challenges”, International Journal of Production Economics, Vol. 113 No. 1, pp. 107-122.
Trienekens, J.H., Wognum, P.M., Beulens, A.J.M. and Vorst, V.D.J.G. (2012), “Transparency in complex dynamic food supply chains”, Advanced Engineering Informatics, Vol. 26 No. 1, pp. 55-65.
Trkman, P., McCormack, K., De Oliveira, M.P.V. and Ladeira, M.B. (2010), “The impact of business analytics on supply chain performance”, Decision Support Systems, Vol. 49 No. 3, pp. 318-327.
Tsoulfas, G.T. and Pappis, C.P. (2008), “A model for supply chains environmental performance analysis and decision making”, Journal of Cleaner Production, Vol. 16 No. 15, pp. 1647-1657.
Tuncel, G. and Alpan, G. (2010), “Risk assessment and management for supply chain networks: a case study”, Computers in Industry, Vol. 61 No. 3, pp. 250-259.
Tzamalis, P., Panagiotakos, D. and Drosinos, E. (2016), “A ‘best practice score’for the assessment of food quality and safety management systems in fresh-cut produce sector”, Food Control, Vol. 63, pp. 179-186.
Validi, S., Bhattacharya, A. and Byrne, P. (2014), “A case analysis of a sustainable food supply chain distribution system – a multi-objective approach”, International Journal of Production Economics, Vol. 152, pp. 71-87.
Venkatesh, V., Rathi, S. and Patwa, S. (2015), “Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using interpretive structural modeling”, Journal of Retailing and Consumer Services, Vol. 26, pp. 153-167.
Vorst, V.D.J. (2000), “Effective food supply chains; generating, modelling and evaluating supply chain scenarios”, PhD thesis, Wageningen University, Wageningen.
Vorst, V.D.J., Beulens, A.J.M. and Beek, P.V. (2005), “Innovations in logistics and ICT in food supply chain networks”, in Jongen, W.M.F. and Meulenberg, M.T.G. (Eds), Innovations in Agri-Food Systems, Wageningen Academic Publishers, Wageningen, pp. 245-292.
Vorst, V.D.J.G., Tromp, S.O. and Zee, D.J.V.D. (2009), “Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics”, International Journal of Production Research, Vol. 47 No. 23, pp. 6611-6631.
Walker, H., Sisto, L.D. and McBain, D. (2008), “Drivers and barriers to environmental supply chain management practices: lessons from the public and private sectors”, Journal of Purchasing and Supply Management, Vol. 14 No. 1, pp. 69-85.
Wang, X., Chan, H.K. and Li, D. (2015), “A case study of an integrated fuzzy methodology for green product development”, European Journal of Operational Research, Vol. 241 No. 1, pp. 212-223.
Wang, X.J. and Li, D. (2012), “A dynamic product quality evaluation based pricing model for perishable food supply chains”, Omega, Vol. 40 No. 6, pp. 906-917.
Wang, Z.G., Mathiyazhagan, K., Xu, L. and Diabat, A. (2016), “A decision making trial and evaluation laboratory approach to analyze the barriers to green supply chain management adoption in a food packaging company”, Journal of Cleaner Production, Vol. 117, pp. 19-28.
Watanabe, K., Schuster, E.W. and Center, M.A.I. (2003), The Impact of e-Commerce on the Japanese Raw Fish Supply Chain, Northwestern University, Chicago, IL, pp. 1-29.
Whitmore, A., Agarwal, A. and Xu, L.D. (2015), “The internet of things – a survey of topics and trends”, Information Systems Frontiers, Vol. 17 No. 2, pp. 261-274.
Wong, H., Potter, A. and Naim, M. (2011), “Evaluation of postponement in the soluble coffee supply chain: a case study”, International Journal of Production Economics, Vol. 131 No. 1, pp. 355-364.
Wu, K.J., Liao, C.J., Tseng, M. and Chiu, K.K.-S. (2016), “Multi-attribute approach to sustainable supply chain management under uncertainty”, Industrial Management & Data Systems, Vol. 116 No. 4, pp. 777-800.
Wu, L., Yue, X., Jin, A. and Yen, D.C. (2016), “Smart supply chain management: a review and implications for future research”, The International Journal of Logistics Management, Vol. 27 No. 2, pp. 395-417.
Wu, Z.H. and Pagell, M. (2011), “Balancing priorities: decision-making in sustainable supply chain management”, Journal of Operations Management, Vol. 29 No. 6, pp. 577-590.
Yakovleva, N. (2007), “Measuring the sustainability of the food supply chain: a case study of the UK”, Journal of Environmental Policy & Planning, Vol. 9 No. 1, pp. 75-100.
Yeole, S. and Curran, T.P. (2016), “Investigation of post-harvest losses in the tomato supply chain in the Nashik district of India”, Biosystems and Food Engineering Research Review, Vol. 21, pp. 108-111.
Yu, M. and Nagurney, A. (2013), “Competitive food supply chain networks with application to fresh produce”, European Journal of Operational Research, Vol. 224 No. 2, pp. 273-282.
Zanoni, S. and Zavanella, L. (2012), “Chilled or frozen? Decision strategies for sustainable food supply chains”, International Journal of Production Economics, Vol. 140 No. 2, pp. 731-736.
Zarei, M., Fakhrzad, M. and Paghaleh, M.J. (2011), “Food supply chain leanness using a developed QFD model”, Journal of Food Engineering, Vol. 102 No. 1, pp. 25-33.
Zhang, M. and Li, P. (2012), “RFID application strategy in agri-food supply chain based on safety and benefit analysis”, Physics Procedia, Vol. 25, pp. 636-642.
Zheng, Y.J. and Ling, H.F. (2013), “Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach”, Soft Computing, Vol. 17 No. 7, pp. 1301-1314.
Zhong, R.Y., Dai, Q.Y., Qu, T., Hu, G.J. and Huang, G.Q. (2013), “RFID-enabled real-time manufacturing execution system for mass-customization production”, Robotics and Computer-Integrated Manufacturing, Vol. 29 No. 2, pp. 283-292.
Zhong, R.Y., Newman, S.T., Huang, G.Q. and Lan, S.L. (2016), “Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives”, Computers & Industrial Engineering, Vol. 101, pp. 572-591.
Zhong, R.Y., Lan, S.L., Xu, C., Dai, Q.Y. and Huang, G.Q. (2016), “Visualization of RFID-enabled shopfloor logistics big data in cloud manufacturing”, The International Journal of Advanced Manufacturing Technology, Vol. 84 No. 1, pp. 5-16.
Zhong, R.Y., Huang, G.Q., Lan, S.L., Dai, Q.Y., Xu, C. and Zhang, T. (2015), “A big data approach for logistics trajectory discovery from RFID-enabled production data”, International Journal of Production Economics, Vol. 165, pp. 260-272.
Zhong, R.Y., Li, Z., Pang, A.L.Y., Pan, Y., Qu, T. and Huang, G.Q. (2013), “RFID-enabled real-time advanced planning and scheduling shell for production decision-making”, International Journal of Computer Integrated Manufacturing, Vol. 26 No. 7, pp. 649-662.
Zhu, Q.H., Geng, Y., Fujita, T. and Hashimoto, S. (2010), “Green supply chain management in leading manufacturers: case studies in Japanese large companies”, Management Research Review, Vol. 33 No. 4, pp. 380-392.