Complex Supply Chain Research Articles

The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.

Configuration of last-mile distribution networks for an encroaching manufacturer

AbstractThe surge of e-commerce has revolutionized distribution channels, escalating from simple single-channel frameworks to complex multi-channel and omni-channel networks. In particular developments in information technology and rising customer expectations have popularized the transition from multi- to omni-channel distribution, where the classic brick-and-mortar stores can also be part of the omni-channel distribution strategy. This evolution poses intricate challenges for manufacturers, especially in the integration and optimization of these channels. Thus, there is a strong need for an in-depth analysis of how manufacturers navigate the transition across diverse distribution channels to meet the varying needs of different customer segments. To this end, we investigate single-, multi-, and omni-channel distribution strategies for the case of a manufacturer selling both standard and customized products to different customer segments with varying preferences. A central contribution of this research is the creation of an integrated optimization model that resolves a location-routing problem, designing a complex and realistic supply chain configuration suitable for an omni-channel distribution system. This model strategically serves to fragmented customer demands through multiple shopping and delivery options. The outcomes of our study indicate that an omni-channel distribution system is a viable approach, capable of serving more customer segments while simultaneously minimizing logistics costs. In addition, we offer a detailed analysis of the cost implications of in-store pickup versus home-delivery options, providing a comprehensive evaluation of their respective impacts on total logistics costs and customer responsiveness.

Use of Risk Management to Support Business Sustainability in the Automotive Industry

Today’s companies operate in a dynamic, constantly evolving, and highly competitive environment. The globalization of markets has significantly changed the economy, where companies operate within increasingly complex supply chains. The ever-increasing expectations of customers and company stakeholders, as well as the need to incorporate a comprehensive approach to the life cycle of manufactured products in corporate strategies, expose companies to a whole range of risks. The research was based on the need of organizations operating in the (automotive) industry to manage the dynamics of the business environment. This was accomplished using an appropriate model that, through its universality, would help to ensure the effective risk management in a holistic approach, protecting their performance and meeting the needs of the relevant stakeholders. The main idea of the research was to create and implement a dynamic model of risk management in the environment of a production organization based on the use of available methods such as SWOT, PESTLE, brainstorming, affinity diagrams, risk matrix, SIPOC diagram, risk, and the results of questionnaire surveys. The research was conducted in two directions: (1) designing the structure of the dynamic risk management model for the strategic management process; and (2) verifying the effectiveness of the proposed model in specific cases and evaluating the technical and economic benefits. To support the dynamics of the model, three basic management tools have been proposed: process review, internal audit, and management review, enriched with features that support the concept of risk-based thinking.

AN ANALYTICAL STUDY OF SELECTED TEXTILE COMPANIES

The paper is based on Analytical study of selected Textile Companies. Ratio analysis tool is used for this purpose. The study uses earnings ratios, profitability ratios and liquidity ratios for analyzing the financial performance of the Textile companies. The study period is from 2017-2018 to 2021-2022. The researcher also makes use of statistical tools such as mean, graphs and standard deviation to study the growth and deviation in stability of various ratios. Drawing upon a combination of quantitative data analysis and qualitative research methods, this study examines key aspects such as market dynamics, technological advancements, supply chain complexities, and sustainability initiatives within the chosen textile sectors. By analyzing industry-specific metrics and case studies, this research offers insights into the factors shaping the competitive landscape and driving innovation. Furthermore, this paper delves into the challenges confronting the textile industry, including labor issues, environmental concerns, and geopolitical uncertainties, and evaluates strategies adopted by industry stakeholders to address these challenges. Through a systematic examination of current practices and future prospects, this study aims to provide a comprehensive understanding of the contemporary textile industry landscape. Ultimately, this research contributes to the body of knowledge surrounding the textile industry by synthesizing empirical evidence and theoretical frameworks, thereby informing policymakers, industry practitioners, and academics about the opportunities and challenges inherent in this dynamic sector.

The role of European chemical manufacturing companies in promoting effective communication of conditions of safe use by workers.

In 2006, the revised chemicals management legislation mandatedthat manufacturers of hazardous chemical substances conduct risk assessments for the entire substance life cycle. Additionally, they mustcommunicate use-specific safe handling advice (exposure scenarios) to their customer, asannex to the Safety Data Sheet (SDS). Despite significant efforts to develop workable solutions for chemical mixtures, this goal has not yet been fully achieved. Therefore, a Cefic research project (LRI B23) was commissioned on how to ensure meaningful health risk communication for workers across supply chains. The research project determined that risk-based safe use advice generated by manufacturers, often does not reach the intended end-user and was seen as not tailored to specific user needs. Recipients of the advice are also not prepared to act based on information developed by suppliers. From an industry perspective, the complexity of supply chains and substance life cycles are considered major barriers for effective safe use communication. Exposure scenarios for substance use in industrial work environments are often perceived as adding little value compared to existing safe use arrangements required by other health, safety, and environmental legislation applicable to employers and duty-holders. To attain meaningful use-specific safe handling advice for workers, including those at non-industrial premises who may benefit most from such advice, knowledge transfer and close collaboration between manufacturers and formulators remain key elements, supported by enhanced regulatory appreciation.

Trust and control dynamics in buyer–supplier relationships: The case of organic honey certification in Cuba

AbstractThe high complexity of international supply chain (SC) relationships requires mechanisms to build trust among stakeholders. Therefore, understanding the underlying mechanisms of developing and maintaining high trust levels in exchange relationships is essential to managing associated SC risks. Third‐party certification (TPC) is a widely used, control‐based governance mechanism in organic food production that aims to help build trust. Drawing on the concepts of trust and control, this article presents an analytical framework for assessing TPC's effects on trust–control dynamics within food SCs. By using qualitative data on a buyer–supplier relationship between Cuba and Europe, this case illustrates the mutually reinforcing relationship between trust and control. We also demonstrate how trust is built across multiple levels. By considering contextual factors, the case illustrates controls' impact on building institutional trust while also pointing out TPC's limited efficacy in enhancing collaboration in SCs.

A novel adjusted real-time decision-making for dynamic distribution in the grocery supply chain

PurposeThe current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.Design/methodology/approachThe constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.FindingsThe proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.Originality/valueIntroducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

Automated Pricing and Replenishment Decisions for Vegetable Products Based on Data Analysis

This study delves into the critical factors for the successful operation of vegetable products supermarkets: efficient inventory management and accurate market demand analysis. Given the perishability of vegetables, their variety, and the complexity of supply chains, a linear regression model and the method of least squares have been developed to estimate model parameters. Additionally, the ARIMA model is utilized to forecast the unit prices of commodities. In a more comprehensive approach, considering various influencing factors, a combinatorial optimization algorithm (integrating linear regression and greedy algorithm) is employed to determine the optimal replenishment strategy. Based on this, the study forecasts the sales volume and unit prices for various categories over the next seven days, and accordingly, establishes the pricing strategy and predicted replenishment volume for each category. The research underscores the necessity for retailers to precisely analyze sales data and market trends, optimize supply chain management, and formulate effective pricing strategies to cope with seasonal variations and limited sales space. These measures are essential to satisfy consumer demand and enhance profitability.

Cost-competitive decentralized ammonia fertilizer production can increase food security

The current centralized configuration of the ammonia industry makes the production of nitrogen fertilizers susceptible to the volatility of fossil fuel prices and involves complex supply chains with long-distance transport costs. An alternative consists of on-site decentralized ammonia production using small modular technologies, such as electric Haber–Bosch or electrocatalytic reduction. Here we evaluate the cost-competitiveness of producing low-carbon ammonia at the farm scale, from a solar agrivoltaic system, or using electricity from the grid, within a novel global fertilizer industry. Projected costs for decentralized ammonia production are compared with historical market prices from centralized production. We find that the cost-competitiveness of decentralized production relies on transport costs and supply chain disruptions. Taking both factors into account, decentralized production could achieve cost-competitiveness for up to 96% of the global ammonia demand by 2030. These results show the potential of decentralized ammonia technologies in revolutionizing the fertilizer industry, particularly in regions facing food insecurity.

Capturing key interdependences among supply chain disruptions and mitigation strategies to enhance firm performance

PurposeThis paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.Design/methodology/approachThis study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).FindingsThe results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.Originality/valueOur results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

The influence of consumer behavior on sustainable marketing efforts

This concept paper explores the dynamic relationship between consumer behavior and sustainable marketing efforts. As consumers increasingly prioritize environmental and social responsibility, businesses are adapting their marketing strategies to meet these evolving demands. The paper provides insights into the influence of consumer attitudes, beliefs, and values on sustainable purchasing decisions, highlighting the growing market for environmentally friendly products and the importance of transparent communication about sustainability efforts. Through a review of existing literature, the paper identifies key trends, challenges, and opportunities for businesses seeking to align their marketing strategies with sustainability goals. It proposes a model for understanding the influence of consumer behavior on sustainable marketing efforts, emphasizing the role of marketing strategies in shaping consumer perceptions of sustainability and influencing purchasing behavior. While businesses face challenges such as the high cost of sustainable production and the complexity of supply chains, the paper also highlights opportunities to differentiate themselves in the market and build stronger relationships with environmentally conscious consumers. By understanding consumer preferences and values, businesses can develop marketing strategies that resonate with their target audience and drive positive change. In conclusion, consumer behavior has a significant impact on sustainable marketing efforts, and businesses must adapt their strategies to meet the demands of an increasingly socially and environmentally conscious consumer base. The paper provides a foundation for further research and offers practical insights for businesses looking to integrate sustainability into their marketing strategies and achieve long-term success in a rapidly changing market landscape.. Keywords: Consumer Behavior, Sustainability, Marketing.

Enabling organizations to strategically manage risks in circular supply chains

AbstractCircular supply chains (CSCs) prioritize resource efficiency by reusing, remanufacturing, and recycling materials, offering environmental benefits and competitive advantages. However, they are facing various risks and organizational challenges that hinder their efficiency. To ensure long‐term sustainability, this study identifies the key risks and introduces a prioritization model for risk management in CSC strategies proposing a framework for the risk evaluation based on the set of criteria and prioritization model using a combination of three multicriteria decision‐making (MCDM) methods. The model applies fuzzy Delphi‐based analytical hierarchy process (AHP) method to determine the criteria weights and fuzzy axial distance‐based aggregated measurement (ADAM) method for ranking the alternatives. The findings of this study allow for the identification of the most important risks associated with CSC enabling stakeholders to allocate resources strategically and focus efforts on crucial areas. Results highlight that the most important risks are supply chain complexity, resource availability, and quality and technological challenges. The most important contributions are the identification of the most relevant risks that threaten the resilience and sustainability of CSCs, the establishment of the framework for their evaluation, and the development of a novel hybrid MCDM model for their ranking. These contributions are also the main theoretical implications of the study. On a practical level, it enables organizations to strategically manage risks, standardize risk assessment, and improve their competitive advantage by enhancing resilience, reducing disruptions, and lowering operational costs.

Leveraging machine learning for vaccine distribution in resource-limited settings: A synthesis of approaches

Vaccine distribution in resource-limited settings remains a crucial global health challenge, exacerbated by factors such as inadequate infrastructure, limited resources, and complex supply chains. Leveraging machine learning (ML) holds promise for optimizing distribution efficiency and ensuring equitable access to life-saving vaccines. This paper synthesizes various ML approaches aimed at addressing vaccine distribution challenges in resource-constrained environments. The literature review examines existing research on ML applications in healthcare and vaccine distribution, highlighting key findings and methodologies. Methodologically, criteria were established for selecting relevant studies, with a focus on ML techniques and their effectiveness in resource-limited contexts. Key ML approaches identified include predictive analytics for demand forecasting, route optimization algorithms for efficient vaccine delivery, and decision support systems for prioritizing distribution efforts. Case studies illustrate successful ML implementations in real-world settings, showcasing improved vaccine coverage and reduced wastage. Despite promising results, challenges persist, including data scarcity, model generalization, and ethical considerations. Future research directions include enhancing data collection methods, refining ML algorithms for specific contexts, and integrating ML solutions into existing healthcare systems. In conclusion, this synthesis underscores the transformative potential of ML in revolutionizing vaccine distribution in resource-limited settings. By addressing logistical barriers and optimizing resource allocation, ML-driven approaches offer a pathway towards achieving universal immunization coverage and mitigating the impact of infectious diseases on vulnerable populations. Keywords: Machine Learning, Vaccine Distribution, Resource-Limited Settings, Synthesis of Approaches.

Unlocking the potential of blockchain technology in enhancing the fisheries supply chain: an exploration of critical adoption barriers in China

The application of blockchain technology holds significant potential for improving efficiency, resilience, and transparency within the Fisheries Supply Chain (FSC). This study addresses the critical barriers hindering the adoption of blockchain technology (BT) in the Chinese FSC, recognizing the unique challenges posed by its intricacies. Through a comprehensive literature review, fourteen Critical Barrier Factors (CBFs) were identified, and a grey Delphi method was employed to distill this set. Five pivotal CBFs emerged, including "Regulatory Compliance," "Cost of Implementation," and "Complex Supply Chain Network". A subsequent grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis revealed the causal relationships among these factors, categorizing them into effect and cause groups. "Regulatory Compliance," "Cost of Implementation," and "Complex Supply Chain Network" were identified as primary influencing factors demanding attention for effective BT integration in the FSC. The findings serve as a valuable resource for FSC stakeholders, assisting in prioritizing efforts to address these barriers. The discerned causal relationships provide guidance for managers in optimizing resource allocation. Ultimately, this research advocates for the adoption of blockchain technology in the fisheries supply chain to enhance overall performance and operational efficiency.

The Path from Green Innovation to Supply Chain Resilience: Do Structural and Dynamic Supply Chain Complexity Matter?

At the heart of supply chain innovation lies the challenge of complexity, a pivotal force shaping the pathways to resilience and sustainable success in today’s business environment. Drawing from the resource-based view, dynamic capabilities, and contingency theories, this study examines the impact of green innovation strategies on supply chain resilience through the mediation role of green logistics management practices and the moderation effects of dynamic and structural supply chain complexity. Leveraging a quantitative approach, this study surveyed 404 managers from manufacturing firms in Turkey using a combination of physical and electronic questionnaires. Our analysis robustly supports the interconnected roles of green innovation strategy and logistics management practices in bolstering supply chain resilience. A green innovation strategy significantly enhances green logistics management practices and supply chain resilience. Further, green logistics practices contribute positively to supply chain resilience, acting as a crucial mediator in translating green innovation strategies into heightened supply chain resilience. Additionally, the effectiveness of green innovation strategies in improving green logistics management practices is amplified in less structurally complex supply chains. In contrast, the impact of green logistics practices on supply chain resilience becomes more pronounced in environments with lower dynamic complexity, highlighting the nuanced influence of supply chain complexity on sustainability efforts. The study’s findings contribute a novel perspective to the sustainability discourse, emphasizing complexity’s nuanced role as a determinant of supply chain resilience.

Sustainable and ethical supply chain management: The role of HR in current practices and future directions

This study systematically reviews the integration of Human Resources (HR), sustainability, and ethics within the framework of supply chain management, illuminating the pivotal role of HR practices in enhancing the sustainability and ethical integrity of supply chains. Employing a systematic literature review coupled with content analysis, this research scrutinizes scholarly articles and case studies across various databases, focusing on the period from 2000 to 2024. The methodology hinges on precise inclusion and exclusion criteria to distill relevant literature, which is then subjected to rigorous content analysis to identify prevailing themes and insights. Key findings reveal that strategic HR practices, including talent management, training and development, and leadership cultivation, are central to embedding sustainability and ethical considerations into supply chain operations. These practices not only foster a culture of sustainability and ethics but also drive organizational change towards more sustainable supply chain models. The review identifies a dual challenge for sustainable supply chain management: the complexity of global supply chains and the increasing demands for transparency and ethical conduct, presenting both significant challenges and opportunities for innovation. Finally, the study underscores the critical role of HR in sustainable and ethical supply chain management, highlighting the necessity of integrating sustainability into HR practices. Strategic recommendations for organizations and HR professionals are provided to navigate the evolving landscape of supply chain sustainability. The study calls for further research into the dynamic interplay between HR practices and supply chain sustainability, particularly in the context of emerging technologies and global supply chain complexities.

Risk management and HR practices in supply chains: Preparing for the future

This study explores the strategic integration of risk management and Human Resources (HR) practices within the context of supply chain management, aiming to identify how such integration enhances organizational resilience and sustainability. Employing a systematic literature review and content analysis, the study examines scholarly articles from key databases, focusing on the period from January 2000 to the present. The methodology involves a comprehensive search strategy, stringent inclusion and exclusion criteria, and a detailed selection process to distill insights from relevant literature. Key findings reveal that strategic HR practices, such as talent management, leadership development, and fostering a risk-aware culture, significantly contribute to mitigating supply chain risks and bolstering resilience. Furthermore, the study highlights the pivotal role of technological advancements, including artificial intelligence, blockchain, and the Internet of Things, in supporting the dynamic integration of risk management and HR practices. This integration not only addresses operational efficiencies but also aligns with strategic objectives towards sustainability and adaptability in the face of global disruptions. The study concludes that the strategic importance of integrating risk management with HR practices cannot be overstated, emphasizing its necessity for building agile, resilient, and sustainable supply chains. As organizations navigate the complexities of modern supply chains, this integrated approach offers a pathway to not only survive but thrive amid uncertainties, underscoring a blueprint for sustainable competitive advantage in evolving global markets. This review contributes to the existing body of knowledge by providing a comprehensive overview of current practices and future directions for integrating risk management and HR practices in supply chain management.

Demand Planning: Riding Disruptive Wave of AI and Accelerated Computing

Traditional demand planning methods often struggle to keep pace with the complexity, volatility, and vast datasets inherent in modern supply chains. Artificial intelligence (AI) offers a transformative solution, revolutionizing demand planning with its ability to analyze vast amount of data, identify complex patterns, and generate highly accurate forecasts. This paper explores the latest advancements in AI for demand planning, encompassing machine learning, deep learning, and natural language processing (NLP). The focus is on how these techniques enhance demand sensing capabilities, incorporating real-time market signals, external data sources, and unstructured text information. Furthermore, the potential of AI to optimize inventory management, enable scenario planning, and increase supply chain resilience in response to unexpected disruptions are examined. The paper also addresses practical challenges in implementing AI-powered demand planning solutions, and outlines areas for future research. Most importantly, the paper provides the robust methologies to integrate the emerging AI developments in demand planning process.

Prevention and Mitigation of Disruptions in Medical Device Supply Chains: A Policy Perspective

New technologies and their connectivity to the internet render medical devices and their supply chains a target for worldwide criminal attacks. Disruptions to a medical device supply chain (MDSC), including cybersecurity attacks, have increased rapidly, with some sophisticated attacks compromising the availability and operation of life-critical devices. Understanding the impact of disruptions on MDSCs is difficult due to the attendant supply chain complexity. The lack of a systematic classification of disruptions to MDSCs limits the transferability of mitigation strategies. Thus, there is a need for evidence-based, actionable policy guidance for corporations and government agencies that address disruption risks to MDSCs. This paper first presents a disruption classification scheme based on historical and anticipated disruptions to MDSCs. Then, it introduces a model, dubbed the Focal-firm Supply-chain Integrated Model (FSIM), that integrates a focal-firm view of supply chains and attendant disruptions, illustrated through an MDSC example. Based on lessons learned from constructing a disruption classification and creating an FSIM map, we describe the following actionable insights: a) implementing procedures and technologies that enable early-detection of disruptions, b) accurate and timely information flows, c) continual monitoring of physical inventory levels and comparing them with digital data, d) enhanced and repeated testing at various supply chain testing sites, e) standardized metrics that measure the impact of disruptions, f) developing in advance a preparedness strategy and a planning process to allocate resources effectively, equitably, and transparently during disruptions, and g) the need for a common framework that bridges the medical device regulatory gaps across countries. These insights can guide MDSC stakeholders, including manufacturers, suppliers, service providers, healthcare providers, policymakers, and government agencies.

Social hotspots in the automotive industry’s aluminum value chains—combining primary and generic data

Abstract Purpose The automotive industry and its suppliers have been facing increasing challenges in the sustainable sourcing of raw materials on environmental and social levels. To identify social risks in the value chain of aluminum in the automotive sector, this study aimed to examine how a generic hotspot analysis based on S-LCA can be conducted and support the reporting according to new legislative requirements such as the German Act on Corporate Due Diligence Obligations in Supply Chains. Methods This study investigated the social risks in the supply chains of two selected aluminum components of an aluminum company using a generic social hotspot analysis based on the UNEP Guidelines for Social Life Cycle Assessment of Products and Organizations 2020. The study employed the generic PSILCA© database and different additional indicators for the assessment. The UNEP Guidelines 2020 used in combination with the associated methodological sheets were beneficial in structuring the procedure within the social hotspot analysis. They gave the inexperienced user a guide, which led to the results presented in this work. Results and discussion A high number of social hotspots in upstream processes outside Europe across the different investigated indicators for both supply chains assessed were detected. Most social risks were identified in Guinea, as well as in a European and an Asian Country in the stakeholder categories “Workers” and “Local community.” Especially, the alloy element production in the Asian Country and the bauxite mining in Guinea showed an above-average number of social hotspots. The implementation of the work was associated with an enormous effort in information collection about the supply chains to be investigated and depended on the direct suppliers’ willingness to contribute. The need for a safe and supporting environment for suppliers to enhance transparency was identified. Conclusions Combining generic and company data to conduct a social hotspot assessment (e.g., using PSILCA©) to meet legislative requirements proved to be a suitable approach for complex supply chains such as the automotive one. Despite this, limitations of the PSILCA© database and potential for improvement were acknowledged regarding industry-wide social assessments of supply chains. Additionally, the study serves as an application example of the UNEP Guidelines 2020 and the activity variables for science in practice. Further development and standardization of social indicators and activity variables for impact measurement were identified as a research priority.