Brazilian Journal of Operations & Production Management

A Structural Equation Model For Adopting Additive Manufacturing in the Footwear Firms Supply Chains

2024-11-11T20:08:56-03:00

Goal: The objective of this research is to present a theoretical framework and explore how additive manufacturing (AM) techniques affect supply chain complexity (SCC) in the footwear sector.

Design/Methodology/Approach: This study developed theoretical framework that includes AM best practices and SCC through extensive literature review. Using 1-5 likert scale surveys, data were gathered from 205 professionals working in 29 Ethiopian footwear industries in the period October 20 to December 23, 2023. The collected questionnaires were tested for reliability and validity, measurement and structural model fit test were checked using confirmatory factor analysis. Structural Equation Modeling using AMOS v23 was used to evaluate the proposed correlations.

Results: The confirmatory factor analysis test result revealeld that measurement and structural equation model fit test fulfill the model fit test requirements, i.e. χ²/df < 5, CFI, GFI and TLI > 0.9, RMR and RMSEA < 0.08. The findigs of the study confirmed that additive manufacturing best practices (time, inventory, operation, and resource, energy and waste related factors) have positive effects on static and dynamic supply chain complexity.

Practical implications: This study helps the firm to focus on adoptation of AM for improving supply chain complexity. Furthermore, this study extended earlier research in the domains of SCM by building a theoretical framework that connects AM best practices with supply chain complexity factors.

Originality/value: This work bridges the scientific knowledge gap by combining supply chain complexity and AM best practices. Among others, it can contribute to the existing literature by illustrating the benefits of adopting AM technology particularly in footwear sector.

A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries

2024-11-22T08:47:23-03:00

Goal: The paper aims at the customization of supplier schedules upon the priority of key articles. It aims at the prediction of an appointment to a supplier based on the key aspects. The objective of this study is to investigate whether the machine learning algorithms can be used to predict the delivery dates of the products based on the trained data.

Design/Methodology/Approach: The prediction method uses a machine learning approach. Prediction algorithms namely, Logistic Regression (LR), K-Nearest Neighbour (KNN), and Random Forest (RF) are used for forecasting. The appointment is assigned to a supplier based on the delivery date of a previous supplier order. If a supplier requests a prior date, then the average sales and opening stock of the products is verified and the prior date of n-3 will be assigned (‘n’ represents the assigned days of delivery).

Results: Clustering is used to visualize the group of products based on the days of delivery and quantity ordered. This helps in creating the floor space for the on-time delivery of fast-moving products and reducing the manual process for the reordering team. The present work can be used for the procurement of key articles for the fulfillment of customer demands. The combination of K-Means with prediction and classification is giving lesser expected delivery date using RF compared to LR and KNN method.

Limitations: The limitation of the current work is that it is applicable for small scale industries in the developing countries. Also deployment of the application in agricultural sectors allow for greater transparency and accountability in the supply chain.

Practical Implications: Further, the work will also provide recommendations for retail companies looking to implement machine learning algorithms in their supply chain management.

Originality/ Value: The case study developed a model for Retail industries to manage the supplier delivery appointments.

Supply chain performance in the age of Industry 4.0

2024-12-20T12:04:04-03:00

Goal: This study investigates the impact of Industry 4.0 adoption on supply chain performance in Bangladesh’s manufacturing sector, emphasizing the mediating roles of digital supply chain integration, supply chain innovation, and supply chain visibility.

Design/methodology/approach: A quantitative research design was employed, utilizing a structured questionnaire distributed to 570 manufacturing professionals in Bangladesh, with 350 valid responses collected. Structural equation modeling (SEM) using SmartPLS was applied to analyze the data and test the hypothesized relationships.

Findings: The findings reveal that Industry 4.0 adoption significantly enhances digital supply chain integration (DSCI), supply chain innovation (SCI), and supply chain visibility (SCV), which collectively improve overall supply chain performance. Specifically, Industry 4.0 adoption strengthens DSCI by enabling real-time communication, reducing process fragmentation, and supporting more efficient decision-making. It also fosters SCI by driving innovative practices, adaptability, and continuous improvement within the supply chain. Furthermore, Industry 4.0 adoption improves SCV, enhancing traceability, transparency, and risk management. These mediating factors demonstrate the critical role of Industry 4.0 technologies in achieving superior supply chain outcomes.

Research limitations/implications: The study's limitations include the use of convenience sampling and data from a single industry sector within a developing country, which may limit the generalizability of the findings. Future research could explore additional variables and contexts to further validate these results.

Practical implications: The study provides actionable insights for manufacturing firms in developing economies on leveraging Industry 4.0 technologies to enhance supply chain performance. It also offers guidance for policymakers in supporting digital transformation initiatives.

Social implications: By highlighting the benefits of digital supply chain integration, the study contributes to broader societal goals of economic development and industrial competitiveness in emerging markets.

Originality/value: This study is among the first to empirically examine the impact of Industry 4.0 adoption on supply chain performance in Bangladesh, providing valuable insights for both practitioners and researchers.

Enterprise risk management manufacturing industry quality determinants

2024-10-22T22:11:31-03:00

Goal: Large manufacturing companies face significant risks due to importing raw materials and exposure to fluctuating exchange rates. Enterprise Risk Management (ERM) is vital in this sector to manage its distinctive risks. This study finds ERM factors in Indonesia and Malaysia that affect company value and empirically evaluates risk management in their organizations.

Design / Methodology / Approach: The factors that influence the quality of a company's ERM examined in this study include auditor quality, ownership concentration, board monitoring, gender on the board of commissioners, gender on the board of directors, and human capital. This study's population is 2018-2020; 300 Indonesian and 252 Malaysian manufacturing companies contributed 552 research observations.

Results: This study shows that a qualified auditor, board monitoring, gender ratio on the board of commissioners, and human capital may uncover ERM implementation issues. On the other hand, higher share ownership does not affect ERM. Implications for practitioners and suggestions for future researchers are also described.

Limitations of investigation: The crucial role of Enterprise Risk Management (ERM) in mitigating these unique industry risks, this study underscores the limitations inherent in factors such as auditor quality, ownership concentration, board monitoring, and gender diversity within corporate leadership.

Practical implication: The necessity for manufacturing companies to prioritize specific aspects of Enterprise Risk Management (ERM) in their organizational strategies.

Originality / Value: This research illuminates the nuanced interplay of factors shaping Enterprise Risk Management (ERM) efficacy within the dynamic context of the manufacturing industry.

Challenges in data collection for enhancing productivity in Brazilian industrial processes

2024-12-23T14:28:12-03:00

Goal: To understand the underlying reasons for low productivity in medium-sized metalworking companies and to evaluate the effectiveness of the productivity improvement techniques that have been implemented.

Design / Methodology / Approach: The research adopted an exploratory design, employing both qualitative and quantitative methods. This included content analysis and semi-structured interviews with company managers. Twelve medium-sized enterprises in the metalworking sector were selected, and data were collected from thesis projects conducted between 2017 and 2019. The interviews were conducted via phone or video conferencing with managers in 2023.

Results: The findings indicate that productivity improvement techniques, including Lean Six Sigma, PDCA, and Value Stream Mapping, were partially implemented in some companies but were abandoned in others due to the pandemic and internal cultural barriers. Only a minority of companies succeeded in sustaining their productivity programs.

Limitations of the Investigation: The primary limitations include the reluctance of companies to provide detailed data, challenges in maintaining improvement programs due to the pandemic, and restricted access to internal information owing to industrial confidentiality.

Practical Implications: This study underscores the necessity for enhanced methodological strategies to foster company collaboration and robust data collection in productivity research, offering insights into the challenges faced by medium-sized companies in Brazil.

Originality / Value: This research provides a unique comparison of productivity challenges encountered by medium-sized Brazilian companies in the metalworking sector, particularly in the context of the COVID-19 pandemic, thus contributing to the discourse on industrial competitiveness and productivity enhancement in developing economies.