Benchmarking circularity in supply chains: A systematic literature review.

The challenges posed by unsustainable practices in today's economy underscore the urgent need for a transition toward a circular economy (CE) and a holistic supply chain (SC) perspective. Benchmarking plays a pivotal role in managing circular SCs, offering a metric to gauge progress. However, the lack of consensus on the optimal benchmarking approach hampers effective implementation of circular business practices. To address this gap, we conducted a systematic review of the literature, identifying 29 pertinent publications. The analysis revealed 30 unique attributes and sub-attributes for benchmarking circularity, which were clustered into five main attributes. The main attributes are goals, subjects, key performance indicators (KPIs), data sources, and evaluation methods, while the sub-attributes are organised as features of the main attributes and depicted as a feature model. Drawing from selected publications, we illustrated each feature with examples. Our model offers a comprehensive benchmarking reference for circularity and will be a valuable tool for managers in the transition toward circularity. Supply chains seeking to benchmark their transition to circularity can apply the reference model to ensure that their benchmarking strategy is consistent with state-of-the-art knowledge. By providing a generic circularity benchmarking approach that is valid for diverse economic sectors, our findings contribute to theoretical efforts to address the lack of generic frameworks for CE.

A framework for modelling and designing transparency systems: A case of a Vietnamese pork supply chain.

The three major meat supply chains in emerging markets are traditional wet markets, integrated supply chains, and the more recent collaborative supply chains. Customers in these markets are increasingly demanding safe and high-quality meat, which requires more transparency in the supply chain. This paper presents a generic framework for modelling and designing transparency systems in meat supply chains, with special attention to the needs of emerging markets like Vietnam where all the three supply chain types co-exist. The framework consists of domain, product flow, business control, business process and transparency data models. The main novelty of the proposed framework is its complementarity to cross-industry reference architectures and generic traceability standards, and its stakeholder-centric approach. The framework is demonstrated in the three pork supply chain types that are also widely present in Vietnam and are representative of the pork supply chains of emerging markets in general. The applicability of the framework is described in detail in a case study of a collaborative supply chain of independent members, which is one of the three pork supply chain types. The case study is selected for detailed analysis because the members work closely together to provide safe and traceable pork meat to consumers.

Application of machine learning to improve dairy farm management: A systematic literature review.

In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions to predict various variables of interest, most of which were related to incipient diseases. The objective of this article is to identify, assess, and synthesize the papers that discuss the application of machine learning in the dairy farm management context. Using a systematic literature review (SLR) protocol, we retrieved 427 papers, of which 38 papers were determined as primary studies and thus were analysed in detail. More than half of the papers (55 %) addressed disease detection. The other two categories of problems addressed were milk production and milk quality. Seventy-one independent variables were identified and grouped into seven categories. The two prominent categories that were used in more than half of the papers were milking parameters and milk properties. The other categories of independent variables were milk content, pregnancy/calving information, cow characteristics, lactation, and farm characteristics. Twenty-three algorithms were identified, which we grouped into four categories. Decision tree-based algorithms are by far the most used followed by artificial neural network-based algorithms. Regression-based algorithms and other algorithms that do not belong to the previous categories were used in 13 papers. Twenty-three evaluation parameters were identified of which 7 were used 3 or more times. The three evaluation parameters that were used by more than half of the papers are sensitivity, specificity, RMSE. The challenges most encountered were feature selection and unbalanced data and together with problem size, overfitting/estimating, and parameter tuning account for three-quarters of the challenges identified. To the best of our knowledge, this is the first SLR study on the use of machine learning to improve dairy farm management, and to this end, this study will be valuable not only for researchers but also practitioners in dairy farms.

Identification of potential vulnerable points and paths of contamination in the Dutch broiler meat trade network.

The poultry meat supply chain is complex and therefore vulnerable to many potential contaminations that may occur. To ensure a safe product for the consumer, an efficient traceability system is required that enables a quick and efficient identification of the potential sources of contamination and proper implementation of mitigation actions. In this study, we explored the use of graph theory to construct a food supply chain network for the broiler meat supply chain in the Netherlands and tested it as a traceability system. To build the graph, we first identified the main actors in the supply chain such as broiler breeder farms, broiler farms, slaughterhouses, processors, and retailers. The capacity data of each supply chain actor, represented by its production or trade volumes, were gathered from various sources. The trade relationships between the supply chain actors were collected and the missing relationships were estimated using the gravity model. Once the network was modeled, we computed degree centrality and betweenness centrality to identify critical nodes in the network. In addition, we computed trade density to get insight into the complexity of sub-networks. We identified the critical nodes at each stage of the Dutch broiler meat supply chain and verified our results with a domain expert of the Dutch poultry industry and literature. The results showed that processors with own slaughtering facility were the most critical points in the broiler meat supply chain.