Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review

European IoT Use in Homes: Opportunity or Threat to Households?

The residential sector is characterized by new digital challenges. The Internet of Things (IoT) is a key-driver of innovation and operations management. This study aims to measure and assess IoT devices at the level of individuals, which are households, in European countries. For this scope, through the multi-criteria decision analysis (MCDA), we analyse data from Eurostat providing a mix of indicators allowing information to be aggregated at the level of individual Europeans and disaggregated by age group. The results highlight that only four countries (Netherlands, Denmark, Sweden and Malta) are classified as a high cluster in the examined scenarios. The 16-24 age group is the most involved in the uses of IoT devices, but the previous three northern European countries also show very high values for the 35-44 age group. IoT devices serve as a springboard for achieving a powerful propulsion toward technological innovation in the new business models, identifying opportunities and being a way to make many routine tasks more agile. Training programs and awareness campaigns are policy suggestions for the development of IoT devices favouring a cultural change on their use. However, there is an emerging need for studies that monitor environmental health impacts to prevent possible threats.

Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19

The outbreak of the COVID-19 pandemic has significantly augmented the complexity of information, adding to the challenges that firms face in effectively processing and grasping accurate information. As a result, the production uncertainty of firms has been seriously intensified during the pandemic, disrupting the normal operation of firms and their supply chains. Digital technologies serve as salient tools that help firms to process and analyse information, consequently enhancing firm resilience in the face of supply chain disruptions. This study aims to examine how digital technologies affect firm resilience in the context of COVID-19 through the lens of information processing theory and a large-scale survey conducted among Chinese manufacturers. Specifically, our study evaluates the mediating effect of supply chain integration (internal integration, customer integration and supplier integration) and the moderating effect of information complexity. The results show that supply chain integration plays a mediating role in the effect of digital technologies on firm resilience, and the mediation effect is particularly significant for customer integration. Furthermore, digital technologies have a stronger impact on firm resilience when information complexity is high. The findings advance our understanding and recognition of the resilience implications of digital technologies and provide important managerial implications for improving firm resilience in the context of COVID-19.

Analysis of efficiency and performance of global retail supply chains using integrated fuzzy SWARA and fuzzy EATWOS methods

The current paper aims to fill the two severe and significant gaps in the literature related to global retail chains. First, it presents the criteria set identified by performing comprehensive fieldwork together with experts highly experienced and have extensive knowledge of the retailing industry and a detailed literature review. Secondly, it proposes a robust, applicable, and powerful novel integrated MCDM framework dealing with many complicated uncertainties. As one of the significant practical and managerial implications, the current paper highlights the significance of sustainable retailing operations to better global retail chains. After the proposed model was implemented, a comprehensive sensitivity analysis was performed to test the validation of the model and its obtained results. According to the validation test results, A12 Walmart&ASDA has remained the best option for all scenarios. It has been observed that there are slight changes that did not change the overall results in the ranking performance of some decision alternatives. As a result, the analysis results prove that the proposed integrated fuzzy approach can be applied to solve highly complex decision-making problems encountered in various fields and the retailing industry.

Modelling the strategies for improving maturity and resilience in medical oxygen supply chain through digital technologies

Purpose

Improving the medical oxygen supply chain (MOSC) is important to cope with the uneven demand and supply seen in the MOSC when India faced the second wave of COVID-19. This improvisation increases the supply chain (SC) maturity and consequently the efficiency and resiliency to tackle oxygen shortage across the country and to prevent another similar scenario from ever happening. The purpose of this study is to identify and prioritize the solutions to overcome the issues faced by the MOSC during the second wave of COVID-19 cases in India and in turn reduce the extent of casualties in the expected third wave.

Design/methodology/approach

This paper uses best worst method (BWM) and fuzzy technique for order performance by similarity to ideal solution to classify the sub-criteria for solutions to solve major SC issues. BWM is used to determine the weights of the sub-criteria and fuzzy technique for order performance by similarity to ideal solution for the final ranking of the solutions to be adopted.

Findings

The result of this study shows that the Internet of Things based tagging system is the best solution followed by horizontal and vertical integration of SC in making a resilient and digitized MOSC capable of handling general bottlenecks during a possible third wave.

Research limitations/implications

The research provides insights that can enable the personnel involved in MOSC. Proper understanding will help the practitioners involved in the SC to effectively tailor the operations and to allocate the resources available in an effective and dynamic manner by minimizing or eliminating the pre-existing bottlenecks within the SC.

Originality/value

The proposed framework provides an accurate ranking and decision-making tool for the implementation of the solutions for the maturity of the MOSC.

Analysis of the MORT method applicability for risk management in supply chains

Although the Management Oversight and Risk Tree (MORT) method is considered a respected analytical procedure to investigate causes and contributing factors of accidents and incidents in different types of businesses, its application in Supply Chain Risk Management was not found in the literature (SCRM). Given this gap, the general objective of this research is to analyze the applicability of the MORT Method in SCRM. Methodologically, the research was supported by an in-depth case study in a cylinder requalification company located in Rio de Janeiro (Brazil). The case study included document analysis, direct observation and structured interviews based on questions previously defined by the MORT Method. As the main theoretical contribution, the research applied, in an innovative way, the MORT Method in SCRM. As the main practical contribution, sequenced, detailed and exemplified steps were developed, so that the MORT Method can be reproduced by other professionals in supply chains. As a limitation, it is observed that the case study only covers the cylinder requalification process, focusing on endogenous risks and failing to address exogenous risks in the supply chain. As a main result, it was possible to identify all the factors that contributed to the damage to cylinder valves; damage that has the potential to have severe consequences for other companies in the supply chain.

Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review

The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps.

IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.