Internet of things for perishable inventory management systems: an application and managerial insights for micro, small and medium enterprises.

Micro, Small, and Medium Enterprises (MSMEs) operating in the food retailing sector encounter two main concerns with respect to their perishable inventory management system, i.e., the product's shelf life and investment in warehouse monitoring systems. New technologies like the Internet of Things (IoT), automated inventory control platforms, and automatic storage and retrieval systems offer effective solutions to these issues. However, MSMEs are reluctant to adopt these technologies due to their prior perception of higher implementation costs and the expected benefits. The present study aims to optimize IoT implementation in MSMEs' inventory management systems and to provide tangible proof of its feasibility and usefulness. In so doing, we propose a mathematical model and analyze the impact of IoT through two case studies. The model provides a cost-benefit analysis of IoT investments that aim to increase products' shelf life. We adopted the fractional program method, solved by particle swarm optimization on MATLAB software. The findings demonstrate the positive correlation between adopting IoT and reduced inventory costs supporting IoT deployment for improved perishability performance in MSMEs. The study offers several insights and practical guidelines in considering IoT deployment in MSMEs.

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries.

There has been an increased interest among scholars to investigate supply chain resilience (SCRes) in manufacturing and service operations during emerging situations. Grounded in the SCRes theory, this study provides insights into the impact of the COVID-19 outbreak on the automobile and airline supply chain. Both the short and long-term response strategies adopted by the two supply chains are assessed, using a combination of qualitative and quantitative techniques in three distinct phases. In phase one, we use a sequential mixed-method for resilience evaluation, integrating Time-to-Recovery (TTR) and Financial Impact (FI) analysis. In phase two, we conduct an empirical survey involving 145 firms to evaluate the short-term SCRes response strategies. In the third phase, we conduct semi-structured interviews with supply chain executives both from the automobile and airline industries to understand the long-term SCRes response strategies. Our findings indicate that: (i) the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies. (ii) The airline industry on the other hand, perceived that the immediate need was to get ready for business continuity challenges posed by COVID-19, by defining their operations both at the airports and within the flights. (iii) Importantly, both the sectors perceived Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by COVID-19. (iv) Cooperation among supply chain stakeholders is perceived, as needed to overcome the challenges of the pandemic, and to accelerate the use of digital technologies.

Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda.

While the potential benefits of integrating digital technologies and supply chain management have been widely reported, less is known concerning the current state-of-the-art literature on big data-driven sustainable supply chains. Therefore, this study aims to systematise published studies which address the implications of big data for sustainable supply chain management. Through a systematic literature review, this work makes three significant contributions: (a) it provides an overview of extant literature on this topic in recent years; (b) it proposes seven gaps in the literature in order to foster future investigations on big data-driven sustainable supply chains; (c) it offers four lessons for business practitioners aiming to use big data for sustainable supply chain practices. These lessons suggest that: developing big data analytics capability has to become a business priority in order to effectively build competitive sustainable supply chains; big data has benefits for each of the dimensions of the triple-bottom-line in supply chains; the implementation of big data for sustainability in supply chains presents some challenges for firms; the development of complementary organizational capabilities is needed to overcome challenges and facilitate the benefits of big data technology for sustainable supply chain management.