Support Supply Chain Research Articles

Strategical and tactical supply chain optimisation for smart production planning and control 4.0

Industry 4.0 (I4.0) technologies generate new opportunities for developing smart models, algorithms and tools to support supply chain (SC) production planning and control (PPC), or smart PPC (SPPC 4.0). Paired with opportunities, challenges arise in integrating sustainability and resilience in SC network design (SCND) according under I4.0. The main novelty of this paper is to propose two multi-objective models that integrate strategical and tactical PPC decisions into a sustainable-resilient SC under I4.0 towards SPPC 4.0. Sustainability is incorporated in terms of reducing costs and CO2 emissions, and a job creation factor. Resilience is incorporated in terms of contracting support suppliers. To solve the multi-objective models, the augmented epsilon constraint method (AUGMECON) was used. This algorithm minimises a target objective function while it treats the others as constraints. Our experiments indicate that AUGMECON is sensitive to the choice of epsilon values. Furthermore, a synthetic data generation tool called SR1-SR2_SynthDataGenerator was developed. This tool generates input datasets to validate and evaluate our models against benchmarks. A stochastic model (SR2) is also proposed that, with small datasets, takes 8.39 seconds to solve. The proposed models can be useful for industries that seek sustainable and resilient SCs to adapt to changing environments.

“ESG score” vs. “ESG rating”: a conceptual model for the sustainability assessment and self-assessment of European SMEs

This paper aims to contribute to the ongoing discourse regarding the distinctions and application of scoring and rating systems by presenting a conceptual model designed to assess and self-assess small and medium-sized enterprises (hereinafter also “SMEs”) on their sustainability transition. Indeed, SMEs operate in very different economic contexts and have simplified organizational and governance structures. These characteristics can be effectively captured through this conceptual model based on a customized questionnaire tailored to the specificities of SMEs. Following an analysis of existing literature and regulatory frameworks, a conceptual model is proposed that includes a questionnaire that, unlike commonly proposed industry questionnaires, is designed to generate an Environmental, Social and Governance (hereinafter also “ESG”) Score complemented by a forward-looking perspective. This model can be useful for SMEs, as it allows them to self-assess their strengths and weaknesses in the sustainability transition process, highlighting specific needs and suggesting actions to improve their sustainability transition. It also allows banks to make a more accurate assessment of the sustainability of their customers, facilitating the redefinition of green and social credit products according to the needs of SMEs. Additionally, it also supports supply chain leaders in ensuring a sustainable supply chain by facilitating the sustainability assessment of SMEs. This is so that everything complies with the new European Union (hereinafter also “EU”) regulations. Finally, the questionnaire is currently being tested at a local bank and has been proposed to SME suppliers in a specific supply chain. It will be possible to make changes to the questionnaire based on the feedback received during the administration phase.

The Role of Information Processing and Digital Supply Chain Technology in Supply Chain Resilience through Supply Chain Risk (Management in Manufacturing Companies in DKI Jakarta)

The 2019 COVID-19 pandemic highlighted the importance of supply chain resilience in the face of global disruptions. Supply chain disruptions caused by isolation measures during the pandemic triggered market turmoil, logistical uncertainty, and reduced production capacity, which sometimes led to shutdowns. In this context, research on supply chain resilience is crucial to prepare companies for future disruptions. This research aims to examine the impact of supply chain resilience on supply chain continuity in manufacturing companies in DKI Jakarta, focusing on the role of digital supply chains, information processing capabilities, and supply chain risk management. This research method uses a quantitative approach. Data were collected from 300 respondents in the basic and chemical industry, miscellaneous industries, and consumer goods sectors through purposive sampling techniques. The analysis was conducted using Structural Equation Modeling (SEM) to identify the relationship between variables. The results showed that supply chain resilience has a significant positive influence on supply chain continuity. In addition, digital supply chains, information processing capabilities, and effective supply chain risk management were shown to support supply chain resilience in the studied sectors. The implication of this research is that it is important for companies to invest in digital technology, develop better information processing capabilities, and implement effective risk management to strengthen their supply chain resilience and continuity in the face of future global uncertainty.

RELATIONSHIP BETWEEN LOGISTICS SECTOR AND ECONOMIC GROWTH IN ASEAN COUNTRIES

In the modern economics, any manufacturing companies have increasingly relied on professional logistics firms, leading to rapid growth in the logistics sector as a service industry and its significant contribution to economic development. With increasing globalization and competition, countries have focused on enhancing their logistics systems to ensure stable economic growth and strengthen global trade competitiveness. In the ASEAN region, logistics plays a critical role, facilitating cross-border trade and regional connectivity. This study aims to investigate the relationship between the logistics sector, specifically air freight transportation, and economic growth in ASEAN countries, given the sector's central role in supporting supply chain management and trade activities. The primary objective of this study is to determine whether there is a causal relationship between air freight transportation (AFT), a key logistics activity, and gross domestic product (GDP) growth in six ASEAN countries. The study seeks to provide insights into how investment in logistics infrastructure, particularly in air freight, may influence economic growth in the region. The study uses panel data for six ASEAN countries from 1996 to 2021. Data on GDP growth and air freight transportation were sourced from the World Bank’s World Development Indicators database. A series of statistical tests, including the Cross-Sectional Augmented Dickey Fuller (CADF) test, Pesaran’s slope homogeneity test, and the Bootstrap Granger causality test, were performed to examine cross-sectional dependence and the causal relationship between GDP growth and air freight transportation. Descriptive statistics revealed significant disparities in air freight transportation capacity among the countries studied, with Indonesia and Thailand showing substantial air freight volumes compared to the Philippines. However, the results of the panel causality tests, including the Dumitrescu and Hurlin panel causality test, indicate that no causal relationship exists between air freight transportation and GDP growth in the six ASEAN countries.

Accelerate demand forecasting by hybridizing CatBoost with the dingo optimization algorithm to support supply chain conceptual framework precisely

Accelerate demand forecasting by hybridizing CatBoost with the dingo optimization algorithm to support supply chain conceptual framework precisely

(Invited) Industrialization of PEM Electrolyzer Stack Platforms: Driving Lab-Scale Innovation to Multi-MW Implementation

The Hydrogen production plays important role as part of energy transition strategy in Germany as well as in visions and subsequent policy of European Green Deal activities. Schaeffler [1] is driving further technological development with strong financial backing of European (CHP) and German funding (H2Giga). PEM water electrolysis is regarded as powerful technology, bringing advantages of limited installation footprint, broad range of operating conditions, especially in terms of dynamic load range and featuring high efficiency between 75-90% (based on HHV) [2].Minimizing the Levelized Cost of Hydrogen demands reduction in CAPEX, OPEX, maintenance whilst extending lifetime to secure full Return of Investment [3]. In addition, production of green hydrogen needs to anticipate the intermittent and distributed nature of renewable energy sources whilst functioning in a decentralized energy distribution network. Enabling maximum uptime sets high demands on the stack performance requirements and its material specifications. Here we show examples of how component development and design iterations make a huge impact on the commercial viability of electrolysers in industrial applications.Efficiency is key when converting renewable electricity and water into green hydrogen and other chemicals, even though PEM electrolyser stack technology is deemed capable of increasing current densities up to 10 A/cm2. Therefore, State-of-Art commercial stacks are rated at current densities between ~1-3 A/cm2, but this anticipated trend to increase power density underlines the necessity to reduce the total electrical resistance and avoid Ohm’s losses.Critical to success is selecting materials that can sustain functionality, stability and compatibility within an operational window being pushed to higher current density to increase stack output, whilst reducing PGM content and overall cost. Progress has been achieved by combining formed metal components with durable, conductive coatings, and establishing optimum compression.Most promising results published in open literature are based on small cells on laboratory scale (5-25 cm2 active area). So-called “screeners” are ideally suited for studying phenomena under well-controlled conditions with a plethora of diagnostic tools. In contrast, large-area stacks operated in the field don’t always operate under ideal operating conditions and can show variability in performance characteristics that escalate into unexpected degradation issues. Of course, stack behavior is dependent on its local environment within the BOP system, relative to its peers. We show how continuous remote monitoring during operation helps identify non-uniform stack operation and flag off-spec conditions, which could form the basis of Accelerated Stress Tests for testing in the laboratory.In this work we estimate how much stack technology can be improved further by forming BPP metal structures and optimizing the component design using CFD. Especially when scaling stack hardware to larger capacity, there is much emphasis on enhanced thermal management to avoid hot-spots and mitigate non-uniform degradation mechanisms and premature stack failure. Simulation results will show how flow designs could be adapted maintain uniform conditions when scaling up the active area or adding more cells to the stack.Robust Stacks are demanded for market ramp-up, demonstrating predictable performance over a long lifetime and insensitivity to a wide range of possible operating conditions. However, but to the best of our knowledge, no component- nor stack- supplier is willing to provide extensive guarantees. Fortunately, stacks quality can be improved significantly by strong automotive and manufacturing industries stepping into the PEM electrolyzer arena and introducing their automated assembly, mature manufacturing and controlled processes to guarantee reproducibility, tight tolerances, superior quality and support supply chain development. Before ramping up global production capacity massively, all players would benefit from standardizing general geometric specifications of stacks so we can leverage a stronger collective learning curve with increasing numbers.

Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method

The "Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting

Demand forecasting has become the most crucial part for supporting supply chain decisions. However, accurate forecasting in time series demand forecasting, particularly within supply chain operations, is challenging because of short-term data features, such as limited volume, nonlinear datasets, and near history disturbances. As one of the most promising deep learning models, long short-term memory shows superior performance in extracting implicit patterns from datasets of various areas. Thus, a novel forecasting framework, attLSTM is constructed combining enhanced bidirectional LSTM (LSTM) and self-attention mechanism. The forecasting performance of attLSTM is verified by testing six randomly selected datasets and eight additional datasets with different volumes from a given database. The proposed attLSTM is compared with seasonal autoregressive integrated moving average, support vector machine, random forest, and LSTM through two commonly applied evaluation metrics and a specially designed newsvendor cost model. Extended experiments are conducted on four benchmark datasets from other fields. These analyses demonstrate that attLSTM shows comparable performance in supporting the supply chain demand forecasting and operational decisions. The proposed framework has robust generalization capability in univariate time series demand forecasting.

Human resource strategies for resilient supply chains in logistics and transportation: A critical review

This study systematically reviews and analyzes the literature on Human Resource Management (HRM) strategies for enhancing supply chain resilience in the logistics and transportation sector, identifying the pivotal role HRM plays in fortifying supply chains against disruptions. Utilizing a systematic literature review and content analysis, this research examines articles from peer-reviewed journals, focusing on publications from 2004 onwards to capture contemporary HRM practices and their impact on supply chain resilience. The methodology involves a detailed search strategy across multiple databases, employing specific inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed. Key findings reveal that workforce agility, technological integration in HRM practices, leadership development, and the cultivation of a resilient organizational culture are essential components of effective HRM strategies in this context. These elements collectively contribute to the resilience of supply chains, enabling organizations to maintain operational continuity during disruptions. Based on these insights, the study proposes strategic recommendations for industry leaders and policymakers, emphasizing the importance of investing in employee development, adopting technology-enhanced HRM practices, and fostering ethical and sustainable HR practices to support supply chain resilience. Concluding, the study highlights the need for further research, particularly empirical studies that examine the direct impact of HR strategies on supply chain performance. This research underscores the critical role of strategic HRM in enhancing supply chain resilience, offering a roadmap for future efforts in the logistics and transportation sector.

Blockchain adoption and operational performance: A secondary data analysis on effects and contingencies

PurposeThis study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.Design/methodology/approachDrawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.FindingsCompared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.Originality/valueThis research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

Supporting Supply Chain Risk Management: An Innovative Approach Using Graph Theory and Forecasting Algorithms

Supporting Supply Chain Risk Management: An Innovative Approach Using Graph Theory and Forecasting Algorithms

Secured finance handling for supply chain integrated business intelligence using blockchain application scenarios

Business intelligence is becoming more essential for supply chain administrators to make good decisions. The globalization of supply chains makes their management and control more challenging. Blockchain is a distributed digital ledger technology that guarantees traceability, transparency, and security and promises to ease global supply chain management issues. This paper proposes the Blockchain-assisted Secure Data Management Framework (BSDMF) for financial data handling for supply chain integrated business intelligence models. Analyzing, collecting, and demonstrating data could be important to a business, its supply chain performance, and sustainability. The blockchain can interrupt supply chain processes for improved finance handling, distributed management, and process automation. The study’s experimental result will help organizations deploy blockchain applications with intelligent business strategies to support supply chain management effectively. The simulation outcome has been implemented, and the recommended method achieves a computation time of fewer than 2 hours, an efficiency ratio of 97.4%, an error ratio of 94.1%, data authentication of 92.1%, and a data management ratio of 98.7%.

Road Infrastructure, Supply Chain Costs Reduction, and Road Safety in Economic Geography Perspective

Purpose: While it is expected that road transport infrastructure may enhance supply chain cost reduction, the mechanisms and conditions under which road transport infrastructure may enhance supply chain cost reduction are less understood. Theoretical framework: Drawing insights from the economic geography perspective, and survey data from 359 senior managers of manufacturing firms in Ghana, we found support for the argument that road transport infrastructure is important in the quest to improve supply chain cost reduction. Methods: The hypothesized model was tested with survey data from beverage manufacturing firms in Ghana. The survey method is usually appropriate for strategic management studies. Results and Conclusion: Our findings also suggest that road safety does not just support supply chain cost reduction but serves as a transformative mechanism to reap superior supply chain cost reduction in a resource-constrained environment. Our findings make a contemporary contribution to the economic geography theory as well as important managerial guidance for managers in the supply chain management space in resource-constrained regions like SSA. Research implications: The outcome of this study offers an important extension to the frontiers of innovative literature in supply chain management. Originality/value: It also provides practical insight to practitioners in the manufacturing setting by emphasizing the need to pay crucial attention to road safety as well as invest in road infrastructure development in the quest to reduce supply chain costs.

The Application of Natural Elements in Industrial Materials

With the pursuit of a balance between industrial development and environmental protection, the importance of natural elements in the application of industrial materials is becoming increasingly prominent. The article mainly focuses on the definition and classification of natural elements in industrial materials, as well as their mechanism of action in improving material properties. The article analyzes the unique contributions of plant-based materials, mineral materials, and bio-based polymers in enhancing material properties. Meanwhile, the article also points out the cost, technological, and environmental regulatory challenges faced by natural elements in industrial applications, as well as innovative solutions to address these challenges. These solutions include technological innovation, policy support, and building sustainable supply chains. This comprehensive analysis not only reveals the key role of natural elements in industrial material innovation but also provides profound insights for future research and applications.

Supply Chain Complexity and Its Impact on Knowledge Transfer: Incorporating Sustainable Supply Chain Practices in Food Supply Chain Networks

Background: The dynamics of supply chain networks have changed due to increasing complexities. Global expansions and knowledge transfer in supply chain networks bring efficiency and effectiveness to companies. However, the probability of supply chain complexity has also been seen increasing. The barriers to sustainable supply chain networks need to be tackled in an effective manner as they impact business operations. Therefore, it is essential to eliminate and reduce the supply chain complexities, as it will facilitate the process of knowledge transfer and increase the implementation of sustainable practises in supply chain networks. In the previous research, four supply chain complexity drivers were identified. Previous research identified four supply chain complexity drivers by conducting a systematic review. This study investigates which of the four complexity drivers impacts knowledge transfer in the context of the food supply chain sector. Methods: In this research, knowledge transfer is therefore examined from the perspective of sustainable food supply chains. Thirty exploratory qualitative interviews were conducted in this study and analysed using Nvivo (v12) software. This study utilised thematic analysis techniques for the evaluation of the interviews to gather results. Results: The results illustrated six main factors classified under broad categories: integration of Knowledge Transfer, incorporation of technological advancements in supply chain networks, supply chain complexity solutions, supply chain complexity drivers, sustainable supply chain networks, and capability to reduce supply chain complexity. The findings of this study highlight that process complexity significantly influences the process of knowledge transfer in food supply chain networks. The research findings contribute to both academic and practical domains. This study contributes to the aggregation of supply chain complexity and its impact on Knowledge Transfer. Additionally, the findings support supply chain networks, which strive to achieve efficient Knowledge Transfer to attain sustainable value in business operations. Conclusion: This study has proven that robust knowledge transfer reduces supply chain complexity as it makes supply chain systems more resilient and well-coordinated in many potential ways.

The Interaction Between Supply Chain Relationships And Management Accounting: A Literature Review

This study explores whether and how supply chain relationships have developed ‘matters of concern’ among researchers regarding the relevance of management accounting practices. A literature review of accounting, operations, and management literature was conducted to identify the ‘matters of concern’ among researchers. ‘Matters of concern’, one of the notions of Actor-Network Theory, was employed as a theoretical framework in this study. The findings of this study highlight the inadequacy of the conventional wisdom of management accounting in managing and supporting supply chain relationships. It shows how researchers focus not only on the issues within management accounting per se but also on the complexities that prevail in managing supply chain relationships and the reciprocal effects between management accounting and such complexities. This study paves the way for researchers to empirically investigate the ‘matters of concern’ and find ways to drive ‘matters of facts’ in the context of management accounting and supply chains.

Improving Supply Chain Sustainability and Resilience via anyLogistix: Research Trends and Future Challenges

Supply chain management faces difficult challenges today, given the rapidly evolving global business landscape. The need for resilient supply chains capable of anticipating, adapting to, and recovering from disruptions has become paramount. At the same time, it has become increasingly important to help systems achieve sufficient levels of economic, environmental and social sustainability in order to remain competitive. Within this challenging scenario, any Logistix is emerging as a tool capable of supporting supply chain managers through the power of optimization and simulation. In this paper, an analysis of the existing scientific literature is conducted on the use of any Logistix to improve supply chain performance within case studies. The analysis of research trends shows that it is a valid tool for improving supply chain sustainability and resilience. Furthermore, crucial challenges are detected such as better exploiting the Digital Supply Chain Twin paradigm, improving the integration of any Logistix with other tools, pushing collaboration between supply chain actors, towards a more sustainable and resilient future.

Supporting Supply Chain Planning with APS Class Systems: A Case Study of SAP IBP – Advantages and Challenges

Supporting Supply Chain Planning with APS Class Systems: A Case Study of SAP IBP – Advantages and Challenges

Enabling Technologies to Support Supply Chain Logistics 5.0

Enabling Technologies to Support Supply Chain Logistics 5.0

The Importance of an Emotionally Intelligent Supply Chain Leader in Determining the Success of an Organization: The Case of DS Motors Unique

The study was conducted to investigate the importance of an emotionally intelligent organizational leader currently leading organization, involves in supply chain interactions and has supply chain networks. Interactions are an indispensable part of the process which supports supply chain management so it’s very critical to know the human mind-set that supports interaction. Research has revealed that humans bring their influenced sentiments to their workplace. These sentiments dominate the emotions of a person. We have very limited literature about feelings and emotions in supply chain management, even more limited is the literature on emotional intelligence of supply chain organizational leader. The purpose of this study is to understand emotional intelligence, what emotional intelligence traits should a leader or supply chain manager poses. Established on the comprehensive literature review and an interview from Director Operations of one of the leading manufacturing organization in Pakistan marks the essentiality of emotional intelligent supply chain leader in determining the success of an organization. It is commended that supply chain Leader sharpen the necessary emotional abilities and instincts in enhancing their interactions with their suppliers/buyers, and from the management view point.