In recent years, red beetroot has received a growing interest due to its abundant source of bioactive compounds, particularly betalains. Red beetroot betalains have great potential as a functional food ingredient employed in the food and medical industry due to their diverse health-promoting effects. Betalains from red beetroot are natural pigments, which mainly include either yellow-orange betaxanthins or red-violet betacyanins. However, betalains are quite sensitive toward heat, pH, light, and oxygen, which leads to the poor stability during processing and storage. Therefore, it is necessary to comprehend the impacts of the processing approaches on betalains. In this review, the effective extraction and processing methods of betalains from red beetroot were emphatically reviewed. Furthermore, a variety of recently reported bioactivities of beetroot betalains were also summarized. The present work can provide a comprehensive review on both conventional and innovative extraction techniques, processing methods, and the stability of betalains.

In the face of global competition, competitive enterprises should pursue sustainable development, and strengthen their supply chain resilience to cope with risks at any time. In addition, big data analysis has been successfully applied in a variety of fields. However, the method has not been applied to improve supply chain resilience in order to reduce sustainable supply chain risks. An approach for enhancing the capabilities of big data analytics must be developed to enhance supply chain resilience, and mitigate sustainable supply chain risks. In this study, a decision framework that integrates two-stage House of Quality and multicriteria decision-making was constructed. By applying this framework, enterprise decision-makers can identify big data analytics that improve supply chain resilience, and resilience indicators that reduce sustainable supply chain risks. A case study of one of China’s largest relay manufacturers is presented to demonstrate the practicability of the framework. The results showed that the key sustainable supply chain risks are risks regarding the IT infrastructure and information system efficiency, customer supply disruptions, transport disruptions, natural disasters, and government instability. To reduce risk in sustainable supply chains, enterprises must improve the key resilience indicators ‘financial capability’, ‘flexibility’, ‘corporate culture’, ‘information sharing’, and ‘robustness’. Moreover, to increase supply chain resilience, the following most important big data analysis enablers should be considered: ‘capital investment’, ‘building big data sharing mechanism and visualisation’, and ‘strengthening big data infrastructures to support platforms and systems’. This decision framework helps companies prioritise big data analysis enablers to mitigate sustainable supply chain risks in manufacturing organisations by strengthening supply chain resilience. The identified priorities will benefit companies that are using big data strategies and pursuing supply chain resilience initiatives. In addition, the results of this study show the direction of creating a fruitful combination of big data technologies and supply chain resilience to effectively mitigate sustainable risks. Despite the limited enterprise resources, management decision-makers can determine where big data analysis enablers can be most cost-effectively improved to promote risk resilience of sustainable supply chains; this ensures the efficient implementation of effective big data strategies.

Given the increasing complexity of the global supply chain, it is an important issue to enhance the agilities of enterprises that manufacture new energy materials to reduce the ripple effects of supply chains. Quality function deployment (QFD) has been applied in many areas to solve multi-criteria decision making (MCDM) problems successfully. However, there is still lack of sufficient research on the use of MCDM to develop two house-of-quality systems in the supply chain of new energy materials manufacturing enterprises to determine ripple effect factors (REFs), supply chain agility indicators (SCAIs), and industry 4.0 enablers (I4Es). This study aimed to develop a valuable decision framework by integrating MCDM and QFD; using key I4Es to enhance the agility of supply chain and reduce or mitigate its ripple effects ultimately, this study provides an effective method for new energy materials manufacturers to develop supply chains that can rapidly respond to change and uncertainty. The case study considered China’s largest new energy materials manufacturing enterprise as the object and obtained important management insights, as well as practical significance, from implementing the proposed research framework. The study found the following to be the most urgent I4Es required to strengthen the agility of supply chain and reduce the key REFs: ensuring data privacy and security, guarding against legal risks, adopting digital transformation investment to improve economic efficiency, ramming IT infrastructure for big data management, and investing and using the new equipment of Industry 4.0. When these measures are improved, the agility of the supply chain can be improved, such as long-term cooperation with partners to strengthen trust relationships, supply chain information transparency and visualization to quickly respond to customer needs, and improving customer service levels and satisfaction. Finally, REFs, such as the bullwhip effect caused by inaccurate prediction, facility failure, and poor strain capacity caused by supply chain disruption, can be alleviated or eliminated. The proposed framework provides an effective strategy for formulating I4Es to strengthen supply chain agility (SCA) and mitigate ripple effects, as well as provides a reference for supply chain management of other manufacturing enterprises in the field of cleaner production.

With the development of economic globalization, the uncertainty of supply chains is also increasing, and alleviating the bullwhip effect has become an important issue. From previous discussions on alleviating the bullwhip effect, there was no research on alleviating it by enhancing supply chain agility through improving big data. Moreover, it has not been found that quality function deployment is used to analyze the interdependence between big data and supply chain agility, as well as between supply chain agility and the bullwhip effect. In particular, the interaction of bullwhip effect factors are not considered. In this study, the multicriteria decision-making integrated framework is proposed and the largest relay manufacturer in China is taken to identify key big data enablers to enhance supply chain agility and mitigate the bullwhip effect, thus providing an effective method for electronic equipment manufacturing enterprises to develop a supply chain that can quickly respond to changes and uncertainties. These big data enablers can enhance supply chain agility and reduce the bullwhip effect. This framework provides an effective method for electronic manufacturers to formulate supply chain agility indicators and big data enablers to mitigate the bullwhip effect and also provides a reference for other manufacturing enterprises in supply chain management.

No abstract