Manufacturing Supply Chain Research Articles

Large-scale Probabilistic Forecasting of Consumer Engagement of CPG Products using Heterogeneous Web Data

Consumer Packaged Goods (CPG) play a pivotal role in customer-centric industries. Understanding the distinctive features of such products and how customers engage with them is essential for CPG manufacturers to create customer-winning products. In this study, we explore an innovative data-centric approach to forecast customer engagement for CPG products by monitoring their digital evolution, particularly in the Snacks category in the USA. Traditional methods for consumer analysis such as surveys, focus groups are time consuming, costly and ineffective with limited scope. They also lack a forecasting component, making it difficult for CPG companies to make forward-looking decisions. However, with the emergence of big data, we can leverage user generated public data from social media, web search, e-commerce platforms to estimate consumer engagement and make long-term forecasts. To achieve this, we propose a systematic approach to accumulate and prepare large datasets from heterogeneous web sources for CPG products. We then use state-of-the-art deep learning based time series forecasting models to efficiently train and predict consumer engagement for the next 12 months, benchmarking their computational efficiency and forecasting performance. Our findings indicate that the DeepAR model outperforms all other models, with the lowest NRMSE (=0.378), RMSE (=14.848) MSE (=220.457), MASE (=0.871), and sMAPE (=0.306) values. Furthermore, we demonstrate methods for computing single forecasting points and prediction intervals using the forecasted sample distribution from the probabilistic models. The proposed approach will provide CPG businesses with valuable insights to make informed decisions about product development, marketing strategies, and supply chain management.

Blockchain and Smart Contracts Based Agricultural Supply Chain

Supply chain management (SCM), product traceability, and product quality certification are some of the initial prominent uses of blockchain technology. This is because blockchain technology is built on trust and cannot be changed. The popularity of blockchain technology is increasing as it is trustworthy and cannot be changed; hence making traceability easier for information such as where food came from, thus essential for managing the agricultural food supply chain (SC). On the contrary, suggested SCM systems premised on smart contract technology and blockchain tend to be specifically apply to production and production processes. Through qualitative research, this paper suggests framework for adoption of smart contracts based on Ethereum in the agri-food SC, with the aim to establish means to adopt blockchain for checking food quality and origins in the agri-food supply chain. Remix IDE is utilised to build and test the smart contract. The significance of the framework is realised through ensuring the traceability of product safety and quality.

IDENTIFICATION OF INDICATORS FOR THE IMPLEMENTATION OF A TRACEABILITY SYSTEM FOR SHARK AND RAY PRODUCTS AMONG INTERMEDIARIES IN PAHANG, MALAYSIA

Traceability system implementation has been suggested to improve data collection in shark and ray product supply chains, thereby supporting their sustainable utilisation and trade. By providing sufficient supply chain information, this system can help to prevent illegal, unreported and unregulated (IUU) fishing and the extinction of these important species. This study aimed to identify the indicators for implementing a traceability system for shark and ray products among intermediaries in Pahang. The study employed a descriptive analysis utilising a reliability test, Exploratory Factor Analysis, and frequency analysis. Individual interviews were conducted using semi-structured questionnaires. The study identified 21 indicators categorised from four dimensions: Shark and ray resources, regulatory compliance and collaboration effort, commitment and skills, and information technology infrastructure. The government is recommended to enforce mandatory registration of intermediaries, require species-specific reporting, invest in management and financial support, increase awareness and training, improve current information system infrastructure, and maintain good collaboration with all stakeholders. Future studies should include consumers and the community to raise awareness about shark and ray products and promote their sustainable utilisation and trade.

Factors Influencing on the Evaluation System of Cold Chain Logistics Services for Agricultural Products in Guangxi Province, The People’s Republic of China

The objectives of this study were: (1) To study the factors that affect the evaluation system of cold chain logistics services for agricultural products in Guangxi Province, the People’s Republic of China. (2) To study the relationships between influence factors and the evaluation system of cold chain logistics services for agricultural products in Guangxi Province, the People’s Republic of China. (3) To build a model of relationship between quality management and the evaluation system of cold chain logistics services and propose suggestion for improving the evaluation system of cold chain logistics services for agricultural products in Guangxi Province, the People’s Republic of China. The study employed a mixed research methodology. In the quantitative analysis section, a questionnaire survey was conducted using simple random sampling. In the qualitative part of the analysis, in-depth interviews were conducted with eight senior managers from agricultural production departments, logistics management companies, fresh agricultural product sales companies, hotels, and government departments. The data analysis utilized statistics including frequency, percentage, mean, standard deviation, and structural equation model. Major Findings: the factors influencing the evaluation system of cold chain logistics services for agricultural products in Guangxi Province were examined. The identified influencing factors included agricultural product cold chain logistics quality management, logistics environment, and transportation efficiency. It was found that agricultural product cold chain logistics quality management has a significant positive impact on the logistics environment, transportation efficiency, and agricultural product cold chain logistics service evaluation system. At the same time, the logistics environment and transportation efficiency have a significant positive impact on the agricultural product cold chain logistics service evaluation system. This study presents the issues with the development of the cold chain logistics service evaluation system for agricultural products in Guangxi, along with countermeasures and suggestions for improvement.(1)Full utilization of existing resources to promote industrialization and modernization of fresh agricultural product logistics; (2) Development of a systematic agricultural product cold chain logistics system to support the agricultural product industry; (3)Strengthening the construction of cold chain supply chains for urban and rural agricultural products; (4)Expediting the introduction of energy-efficient and environmentally sustainable long- and short-distance cold chain transport vehicles; (5)The application of information technology is To be encouraged with a view to enhancing the efficiency and transparency of cold chain logistics.

Innovative decision support tools for perishable food supply chain management

PurposeWe introduce decision support tools aimed at optimizing perishable food supply chain management, effectively balancing conflicting objectives such as the exporter’s product collection cost and the importer’s profit. This involves considering factors like perishability, selling price, discount rate, and order quantity to achieve optimal outcomes.Design/methodology/approachThis study considered a three-echelon supply chain comprising farmers, a single exporter, and a single importer providing a single, random-lifetime, perishable product under deterministic customer demand. The proposed mathematical model derived the optimal order quantity, selling price, and discount rate for the entire supply chain. This integrated optimization model treats both demand and supply sides as a multi-objective problem, employing a nonlinear program and a two-stage capacitated vehicle routing problem formulation. Numerical examples and a case study focusing on Thailand durian supply chain were conducted to illustrate the approach of the proposed model.FindingsTaking into account both the importer’s profit and the exporter’s product collection cost, the proposed integrated supply chain model and tools maximize profitability, minimizes waste, and meets demand by optimizing perishable product collection costs and proposing a discount system for selling prices.Research limitations/implicationsLimited to a single perishable product in a three-echelon international food supply chain. Future research can explore different products and supply chain contexts.Practical implicationsThe tools enhance decision-making for supply chain managers, improving efficiency, reducing costs, and enhancing customer satisfaction in the perishable food industry.Social implicationsThe proposed model aids in local workforce management by forecasting required manpower for upcoming seasons. By factoring in product quality and pricing, it ensures customers receive fresh products at fair prices. Furthermore, the near-zero waste concept enhances storage conditions at importers' facilities, contributing to improved environmental hygiene.Originality/valueThe integrated model and decision support tools offer a novel approach to address complexities and conflicting objectives in perishable food supply chains, providing practical insights for researchers and practitioners.

Social Sustainability Risk Assessment of Fresh Produce Supply Chain in China under Computer Technology and Digital Transformation

This paper investigates the social sustainability risks in China’s fresh produce supply chain amid ongoing digital transformation. As computer technologies reshape supply chain operations, understanding and mitigating associated social risks become paramount. This research aims to provide a comprehensive assessment of these risks, examining factors such as data and cyber security, inequality, and ethical sourcing concerns. Ten social risk criteria are identified through literature review and expert opinion. In a Sichuan case study, by integrating hybrid methodology Fuzzy Failure Modes and Effects Analysis (FMEA) and Fuzzy VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), Data Privacy and Security Risks (SR1) stands out as the most critical risk criterion in the supply chain with the highest Q value, followed by Ethical Use of Technology (SR4) and Digital Dependency (SR5). This paper offers insights into how stakeholders can navigate the intersection of digitalization and social sustainability to foster more resilient and equitable fresh produce supply chains.

Tourism digital transformation and future supply chain competition: an integrated perspective on real options theory and digital competencies

PurposeThis study analyzes how digital technologies collaboration, and technological capabilities affect tourism products' advantage and supply chain resilience via virtual integration and customer service capabilities.Design/methodology/approachTo achieve the goals of this study, a digital transformation model was formulated based on the real option theory (ROT) and digital competencies perspective. Data were collected from travel agencies in Taiwan. This study uses the partial least square structural equation modeling (PLS-SEM) technique to analyze the research model, and 384 samples were collected from travel agencies for analysis.FindingsThe research results point out that digital technology collaboration and technical capabilities affect virtual integration and customer service capabilities; customer service capabilities should also be regarded as key influencing variables to improve tourism product advantages and supply chain flexibility.Originality/valueThis study shares a unique perspective on the digital transformation model, which includes antecedents, mediators and moderators, to construct the critical effects for analyzing the tourism products' advantage and supply chain resilience.

How to Complete Supply Chain Integration and Improve Supply Chain Performance Through Relationship Governance in the Digital Age

In the process of manufacturing supply chain development, because appropriate governance mechanisms are lacking, manufacturing supply chain stability, integration and other aspects face many challenges. Such as opportunism, “free riding” and data leakage affect supply chain development. Thus, many manufacturing firms are adopting supply chain relationship governance (SCRG) as a strategic to enhance performance. Using data collected from a survey of 295 manufacturing firms, this paper confirms the influence of SCRG on supply chain performance (SCP). The mediating effect of supply chain integration (SCI) and the moderating effect of digital capabilities (DCs) are explored. The results show that SCRG has a beneficial effect on SCP. SCI plays a partial mediating role in SCRG and SCP. DCs positively moderate the relationship between SCRG and SCI. This study is one of the first to explore the role of DCs in the relationship between supply chain partners and the impact on performance. It provides fresh perspectives and real-world evidence for determining the importance of SCRG strategies.

Black soldier fly larvae feed suitability for chicken and prospects for economic valuation and viability aspects

Abstract Utilization of black soldier fly larvae (BSFL) emerges as a promising avenue for organic waste recycling and production of protein rich biomass. The current study explored the suitability of BSFL protein feed for chicken and economic facets associated with BSFL rearing facilities, encompassing costs of goods sold (COGs), operating expenditure (OpExp), and capital costs (CapExp). Research revealed that incorporation of BSFL feed for chicken did not exert discernible influences on overall meat quality. However, the amino acid content variation of chicken meat particularly concerning polyunsaturated fatty acids (PUFA), are correlated with the fatty acid composition of the BSFL feed, showed substrate-specific characteristics. The recommended BSFL dosage ranges up to 13% (defatted larvae), 12% (partially defatted BSFL), 14% (dried larvae), and 48% (for whole larvae), which did not affect the chicken body weight and physiological traits, except for the fatty acids profile. However, complete substitution at 100% proved detrimental to body weight. The establishment of a BSFL rearing facility is deemed economically viable, considering large production scales and supply chain development in collaboration with waste material managers. Such collaboration is envisioned to curtail substantial portions of expenses, primarily attributed to transport and labor costs, which constitute 60-70% of the financial outlay in the developing countries context. Based on the operational, capital, and marketing expenditures, the cost for BSFL rearing setup in Asian countries (Pakistan) ranged from US $50,762 to US $100,000, while the estimated profit was US $10,762 to US $51,121. These figures may vary based on cost per product, net price and profit per product, number of products sold, and market share and growth traction facets.

Electric Vehicle Supply Chain Risk Assessment Based on Combined Weights and an Improved Matter-Element Extension Model: The Chinese Case

In order to meet energy and environmental challenges, many countries will implement the replacement of fuel vehicles for the future clean energy transition; so, the number of electric vehicles (EVs) operating in cities will grow significantly. It is crucial to assess the risks of the electric vehicle supply chain (EVSC) and prevent them. Based on this, this paper proposes an EVSC risk research framework with combined weights and an improved matter-element extension model: (i) Firstly, the EVSC evaluation index system is constructed from the six stages of supply chain planning, sales, procurement, manufacturing, distribution, after-sales, and external risks. (ii) The subjective and objective weights are calculated by the decision laboratory method and entropy weight method, respectively, and then the minimum deviation method is used for a combined design to overcome the defects of a single method. (iii) An improved matter-element extension model (MEEM) is constructed by introducing asymmetric proximity degree and risk bias. (iv) The model is applied to a case study and its feasibility and superiority are verified through sensitivity analysis and comparative analysis. The final results show that the method and framework proposed in this paper are in line with EVSC risk assessment standards and superior to other models, which can help EVSC managers to identify potential risks, formulate appropriate risk prevention measures, promote the stable development of electric vehicles, and provide a reference for the development of energy and environment.

Optimizing Production Supply Chain With Markov Jump System for Logistics Collaboration

This study employs a novel Markov jump system model to address complexities and uncertainties in modern logistics management, particularly in supply chain logistics information networks. It introduces dynamic memory to tackle issues in traditional static networks, enabling modeling and control of this intricate system. By assessing decision node importance, a novel strategy optimization method is devised. Through information exchange and decision adjustments among cooperating nodes, the overall decision system performance is enhanced, resulting in a comprehensive logistics information coordination mechanism for production supply chains based on the Markov jump system. The research demonstrates that this approach considers node interactions and information exchange, using dynamic memory to improve system adaptability and robustness, ultimately enhancing overall decision performance and stability. This has practical value for decision support and system optimization in production supply chain logistics information networks, offering fresh insights into Markov jump system control.

Increasing willingness to pay in the food supply chain: a blockchain-oriented trust approach

Food products' quality information is advertised on labels but do customers trust them? This study investigates how the consumers' Willingness-To-Pay (WTP) for food products can be increased by deploying managerial effort and advanced technologies, such as the Blockchain Technology (BT). Our model explains how revealing verified information about product quality throughout the supply chain will generate optimal consumers' WTP and maximise profit. At each echelon of a multi-echelon supply chain, a buyer holds a Bayesian belief about the quality of the input to be procured. This belief is shaped by the accuracy and veracity of the information about this quality. Managerial effort is required both to enhance quality as well as ensure full and verified information. We show why this effort must be made across the chain and how opportunistic behaviour may be circumscribed. Using empirically grounded analytics and real prices of olive oil intermediate produce from various official bodies, we show how the application of BT may be financially justified. This research shows how trust and WTP can be further enhanced through the use of BT and additional smart technologies in a supply chain, which may be projected on other supply chains of organic and sustainable food products.

Routing and facility location optimization in a dairy products supply chain

Routing and facility location optimization is an important aspect of supply chain management. This is because the problem of determining which facilities within a supply chain echelon should cost-effectively supply products to facilities in the next echelon is usually encountered by supply chain managers and analysts. In this paper, a linear programming model for supply chain routing and facility location optimization has been proposed. The model can be solved using the PuLP optimization library implemented in Python programming language. The model has been applied to a small dairy products supply chain to determine the most cost-effective routes for products in the supply chain. The optimization results proffered the optimal contribution of each factory warehouse to each distribution warehouse in order to satisfy distribution warehouse demand while minimizing costs. The results of the study indicated that the model is efficient in solving the routing and facility location optimization problem usually encountered in supply chain management. Therefore, this study will aid supply chain analysts and managers in determining the most cost-effective route for distributing products between echelons or stages of supply chains under study.

Vulnerability of fresh agricultural products supply chain: Assessment, interrelationship analysis and control strategies

Most research in fresh agricultural products supply chain (FAPSC) vulnerability identification and evaluation is based on independent perspectives. Few studies have studied the interrelationships, transmission mechanisms, and control strategies from a network approach. Given this, this paper first identifies 7 vulnerability categories and 36 vulnerability factors based on empirical data collected from practitioners of FAPSC using grounded theory (GT). The interrelationships among the identified vulnerability factors are established immediately afterward based on complex network theory. Finally, key vulnerability factors and vulnerability impact transmission paths are identified based on the results by calculating the basic network characteristic indicators. The results show that market supply-demand imbalance and human resources issues are the key vulnerability factors. The five factors of market supply-demand imbalance, environmental pollution, socio-economic downturn, human resources issues, and arable land area as the intermediate transmission factors of the vulnerability network play a key role in vulnerability transmission. Based on this, strategies such as information sharing and measuring the environmental performance of fresh agricultural product suppliers are proposed. This study can help stakeholders control the vulnerabilities and ensure the stable operation of the FAPSC.

Sustainability performance of Dutch firms and the role of digitalization: The case of textile and apparel industry

The textiles and apparel industry is a major contributor to economic development while at the same time being one of the most polluting industries due to its lengthy supply chain and resource intensive production operations. To address these sustainability challenges, digitalization is seen as one of the potential solutions. Using the lens of sustainability and digitalization in Supply Chain Management (SCM), this paper analyses the sustainability and digitalization status of Dutch textile and apparel firms. We used a mixed methodology of quantitative text mining of 94 Dutch textile and apparel firms as well as qualitative thematic and coding analysis of experts’ views and opinions on sustainability and digitalization in the Dutch textiles and apparel industry. Quantitative analysis of website data shows that Dutch textile and apparel firms predominantly communicate the environmental, to a lesser extent social, and least of all economic sustainability factors. Keyword analysis also shows that the use of technological keyword indicators is less prominent, while certain technologies such as IoT, sensors and blockchain correlate mostly to environmental sustainability factors. Moreover, qualitative analysis reveals that to address sustainability via digitalization, it is important to link sustainability goals to Key Performance Indicators, which requires data for traceability. We recommend firms to: (1) re-evaluate their business models and assess the extent traceability can be incorporated in their sustainability strategy; (2) enhance stakeholder collaboration within and outside the supply chain to utilize traceability; and (3) proactively use traceability information to improve transparency and accountability to meet legal requirements and address greenwashing. This study contributes to literature by showing the importance of traceability for (a) linking sustainability and digitalization in SCM, b) achieving the ultimate goals of transparency and accountability, and c) predicting demand and supply to address overproduction and waste in the textiles and apparel sector.

COMPARATIVE ANALYSIS OF BUSINESS MODELS IN RETAIL: FAST CYCLE VS. LONG-TERM SUSTAINABILITY

This study conducts a comprehensive comparative analysis of fast-cycle and long-term sustainability business models in the retail industry. Through a mixed-methods approach, incorporating both quantitative and qualitative data, this research examines the key characteristics, underlying mechanisms, and impacts of these two contrasting models. The findings reveal stark differences in supply chain structures, design and production processes, marketing strategies, and consumer behavior. While fast-cycle models excel in speed, affordability, and trend responsiveness, they are associated with environmental degradation, ethical concerns, and a culture of disposability. Conversely, sustainable models prioritize ethical sourcing, environmental responsibility, and long-term value creation, but face challenges related to cost, scalability, and the need to balance timelessness with trend relevance. The implications for retailers, policymakers, and consumers are discussed, with a focus on the potential for hybrid models that combine elements of both approaches. This research contributes to the growing body of literature on sustainable business practices and provides valuable insights for retailers seeking to navigate the complex landscape of consumer preferences and sustainability demands.

A new ViT-Based augmentation framework for wafer map defect classification to enhance the resilience of semiconductor supply chains

Wafer map defect classification plays a crucial role in sustaining the semiconductor supply chain during industrial disruptions by ensuring continuity, resilience, and efficiency while aligning with sustainability principles. Up to 30 percent of production costs is lost to chip testing and yield losses for semiconductor manufacturing supply chain. In the semiconductor practice, wafer map defects recognition plays a linchpin role in the front-end-of-line stage. Defect pattern recognition can directly pinpoint the assignable causes and provide the domain experts with actionable insights. However, various wafer defect types occur differently from each other, which makes the collected wafer map dataset to be highly imbalanced in defect classes. In the face of data imbalance, the classification model usually cannot provide satisfactory classification performance. In this aspect, this paper intends to investigate the wafer defect map classification problem by using Vision Transformer (ViT) as an alternative data augmentation approach. The primary purpose is to alleviate the class-imbalance issue and then the performance of a deep learning-based convolutional neural network for wafer defect classification can be effectively improved. The experimental results demonstrate that the proposed data augmentation method by using ViT proves to be a potential generative model for improving wafer map defect classification, particularly robust on the individual minority class. In a word, the proposed augmentation framework for wafer map defect classification facilitates a more targeted and efficient approach to quality control, resource utilization, and production optimization in maintaining a sustainable semiconductor supply chain, particularly in times of industrial disruption.

Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights.

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

Analysis of Market Entry Strategies for Foreign Multinational Corporations in India A Market Research Study

This research paper explores market entry strategies for foreign multinational corporations (MNCs) in India, aiming to provide actionable insights for navigating the complexities of this dynamic market landscape. Through a qualitative research design, semi-structured interviews were conducted with key industry experts and executives of foreign MNCs operating in India. The findings reveal that joint ventures are the preferred entry mode, with factors such as access to local knowledge, regulatory requirements, and cultural compatibility influencing entry mode selection. Performance evaluation metrics primarily focus on revenue growth, market share, and customer satisfaction, while adaptation strategies include product localization, marketing customization, and supply chain optimization. Challenges faced by foreign MNCs in market entry include regulatory compliance, cultural differences, and competition. Overall, this study contributes to filling the identified literature gap by providing a nuanced understanding of market entry strategies and their effectiveness in the Indian context. The implications of these findings extend to both academia and practice, offering opportunities for further research and guiding strategic decision-making for MNCs seeking to establish and expand their presence in India.

Comprehensive Threat Analysis in Additive Manufacturing Supply Chain: A Hybrid Qualitative and Quantitative Risk Assessment Framework

Additive Manufacturing (AM) offers revolutionary design capabilities but introduces cybersecurity vulnerabilities due to its digital integration. While existing research focuses on cyber threats, a significant gap exists in addressing physical and cyber risks. The current research focuses on cyber threats, but more attention needs to be paid to addressing physical and cyber risks. This paper proposes a new risk assessment framework that considers both physical and cyber threats quantitatively and qualitatively and helps safeguard the AM supply chain. The framework identifies, analyses, and prioritises potential threats based on their characteristics. It also considers the trustworthiness of data and the frequency of reported threats to estimate the likelihood of each threat. The risks are then prioritised based on a combined impact-likelihood score. The framework has been validated on the standard benchmark MITRE CVEs dataset and has proven effective in identifying critical vulnerabilities. This approach empowers manufacturers to proactively deal with security concerns and navigate the complexities of the rapidly evolving AM landscape.