Wai Peng Wong’s research while affiliated with Monash University (Malaysia) and other places

In the contexts of commercial freight, shipment delivery, and smart factories, organizations adopt Industry 4.0 (IR4.0) for competitive transportation practices. Yet, the role of transportation as a key "transportation 4.0" sub-system has been overlooked by scholars, resulting in an incomplete transition towards IR5.0. To bridge this gap, we adopt the reductionist approach grounded from systems theory to systematically review literature. Our analysis highlights the integration of technologies in transportation, impacting ecosystems significantly. However, global progress on transportation 4.0 exhibits regional disparities. In response, we propose a transportation 4.0 framework to mitigate disparities and enhance competitiveness. Identifying research gaps, challenges, and prospects, we outline directions towards IR5.0. Our study clarifies the evolving landscape of transportation within the Industry 4.0 paradigm.

Purpose Technology is the lifeline for the logistics industry, and it has been immensely disrupted by the emerging blockchain technology. This paper has two main objectives. The first is to explore how the current blockchain technology can be implemented in the logistics industry with the aim of improving logistic services amongst the network of logistics service providers (LSPs). The second is to propose the development of a blockchain model for the small and medium logistics service supply chain. Design/methodology/approach A prototype blockchain-based logistics system has been created and tested in a case study with a real logistics company. The primary technologies for developing a blockchain model on the Hyperledger platform as well as how the system is designed based on the logistics service flow are explained. Findings The study has resulted in the successful implementation of the proposed prototype blockchain-based logistics system. In particular, the case company has managed to fully utilise the developed tracking and tracing system. Whilst utilising the prototype, the participants have been able to fulfil their responsibilities in an effective manner. The performance of LSPs has improved following the World Bank Logistics Performance Index (LPI) criteria. Originality/value This paper contributes to current research in the application of blockchain technologies in the domain of logistics and the supply chain to progress LSPs towards Logistics 4.0. The current frameworks for Logistics 4.0 and how blockchain as a disruptive technology revolutionises logistic services are reviewed. In addition, this paper highlights the benefits of blockchain technology that LSPs can leverage to further improve their performance based on the LPI criteria.

One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era.

In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

Background: Informatization has enabled global logistics and supply chains (LSC) to capitalize on data-driven analytics to improve logistics performance. At the country level, logistics performance is gauged through the logistics performance index (LPI), where globally 61.25% or 98 countries perform below the mean LPI score. Previous studies focused on logistics informatization in high and moderate LPI rank economies. The paper aims to conduct an exploratory case study in a low LPI performing country to assess the informatization practices of logistics entities and develop a logistics informatization continuum to unlock data analytics for other countries. Methods: The study implements qualitative methods to develop strategic recommendations to reduce global logistics imbalance. We employ a two-layer methodology consisting of thematic analysis and a novel strategic choice approach (SCA) to involve stakeholders for recommendations on obstruction. For thematic analysis, 16 semi-structured interviews were conducted from logistics companies, also onboard 10 trade associations and government representatives for the SCA analysis. Results: We observed many obstructions in informatization; low willingness on informatization, fear of information leakage by humans, low-reciprocity for collaboration, the myth of information and communication technologies (ICT) as an expensive tool, self-interest, and opportunistic behavior. Conclusion: Information-centric and integrated LSC enables data-driven technologies for real-time decision making, vigilance, and data analytics to distinguished the success of a country's logistics performance. Originality: This study explores the informatization conformity in the logistics sector to connect data analytics. We introduced a novel strategic choice approach in the technology domain for problem structuring. The paper further contributes by suggesting a logistics informatization continuum for low LPI countries to straighten digitalization in the logistics sector.

This study demonstrates how to profit from up-to-date dynamic economic big data, which contributes to selecting economic attributes that indicate logistics performance as reflected by the Logistics Performance Index (LPI). The analytical technique employs a high degree of productivity in machine learning (ML) for prediction or regression using adequate economic features. The goal of this research is to determine the ideal collection of economic attributes that best characterize a particular anticipated variable for predicting a country’s logistics performance. In addition, several potential ML regression algorithms may be used to optimize prediction accuracy. The feature selection of filter techniques of correlation and principal component analysis (PCA), as well as the embedded technique of LASSO and Elastic-net regression, is utilized. Then, based on the selected features, the ML regression approaches artificial neural network (ANN), multi-layer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and Ridge regression are used to train and validate the data set. The findings demonstrate that the PCA and Elastic-net feature sets give the closest to adequate performance based on the error measurement criteria. A feature union and intersection procedure of an acceptable feature set are used to make a more precise decision. Finally, the union of feature sets yields the best results. The findings suggest that ML algorithms are capable of assisting in the selection of a proper set of economic factors that indicate a country's logistics performance. Furthermore, the ANN was shown to be the best effective prediction model in this investigation.

The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.

Unexpected worldwide disruptions brought various challenges to supply chain management thus manipulating the research direction towards resilience. Since the supplier is one of the important supply chain elements, the challenges can be overcome through resilient supplier selection. Supplier selection is a multi-criteria decision-making problem where several criteria are involved. In this study, GRA-BWM-TOPSIS was proposed to evaluate resilient suppliers. Seven resilience criteria which were Quality, Lead Time, Cost, Flexibility, Visibility, Responsiveness and Financial Stability have been proposed and five experts were selected to provide judgments for the selection process. By using the proposed method, the criteria importance levels were obtained using GRA and the criteria weights were computed using BWM, together with a consistency test. TOPSIS was applied to evaluate the suppliers’ performances. Through a case study in a food manufacturing company, 10 suppliers were evaluated and ranked. A validation process was carried out and the managerial implications were provided to ensure the effectiveness of the proposed model. GRA-BWM-TOPSIS is suitable for resilient supplier selection when there are uncertainties and incomplete data.

Voluntary engagement (VE) creates a sense of coordination and harmonization to share knowledge. The eminence of knowledge sharing (KS) for supply chain (SC) innovation is undeniable to initiate development in products, services, and operations. However, KS process is undergoing challenges in sustaining KS engagement by SC partners. Hence, recent researchers call for the need to address this gap in the literature to assess VE barriers. This paper studies the causal relationship of VE barriers on two MNCs (i.e., Toyota and Suzuki) via the fuzzy DEMATEL approach. The case examination findings indicate culture's alignment as the prime cause of VE, and leadership commitment has stronger interdependence. The core problems which need elimination are fear of losing the job, prominence, and opportunistic behavior. The study concludes that companies need to instigate the natural attributes of employees’ VE by setting up earnest guidelines to practice free information and knowledge flow.