Search results

The purpose of this paper is to examine high-tech small-to-medium-sized enterprises (SMEs) supply chain partnerships. Partnerships are considered at the level of business function rather than the entire organisation. Second, the drivers of SMEs to engage in partnerships are assessed to see whether functions engage in partnerships for different reasons. Third, performance per function is assessed to see the differential effect of partnerships on the function’s performance.

In this study, the relationship between the drivers of SMEs to engage in partnerships, four types of partnerships (marketing and sales, research and development (R&D), purchasing and logistics, and production) and four types of functional performances of firms (marketing and sales, R&D, purchasing and logistics, and production) are examined. The data have been collected from 279 SMEs. The proposed hypotheses are tested using structural equation modelling.

The results indicate that there are considerable differences between business functions in terms of the degree of involvement in partnerships and the effect of partnerships on the performance of these functions. This paper contributes to research by explaining the contradictory results of partnerships on SMEs performance.

This study helps firms understand which type of partnership should be established based on the firm’s drivers to engage in supply chain partnership; and which partnership has a significant effect on which type of business performance of the firm.

The originality of this study is to investigate the relationship between different drivers to engage in supply chain partnership and different types of partnerships and different functional performance of firm in a single model.

Earlier studies have generally shown a positive relationship between entrepreneurial orientation (EO) and the overall performance of the firm. The purpose of this paper is to understand in more detail how EO influences firm performance. It adds to the literature by distinguishing performances of different functions in a firm and by exploring how the dimensions of EO influence these functional performances and, in turn, overall firm performance.

This study examined the relationship between three dimensions of EO (innovativeness, proactiveness, risk-taking), three types of functional performances of firms (R&D performance, production performance, marketing and sales performance) and the overall performance of firms. The data are collected from 279 high-tech small-to-medium-sized enterprises (SMEs) using a postal survey. The proposed hypotheses are tested using structural equation modeling (SEM).

The results indicate that the dimensions of (EO) are related in different ways to the performance of functions in a firm. A positive relationship is observed between innovativeness and R&D performance and between proactiveness and marketing and sales performance. A negative relationship exists between risk-taking and production performance. The results also show a sequential positive relationship from R&D via production and marketing and sales to overall performance of firms. Therefore, it is concluded that the R&D, production and marketing and sales functions reinforce each other in a logic order and are complementary in their effect on overall firm performance.

The results imply that the three functions, R&D, production and marketing and sales, in a firm play different roles, both in the firm’s EO and in their contribution to overall performance. Managers can use the findings to monitor and influence the performance of different functions in a firm to increase overall firm performance.

The first contribution of this study is that it unravels (i) which dimensions of EO have an effect on the performance of separate functions in a firm, indicating that functions contribute in different ways to entrepreneurial orientation of the firm. A second contribution is assessing how the performance of these functions influence the firm’s overall performance. This paper fills a gap in the literature by exploring internal firm variables mediating the relationship between EO and overall firm performance and contributes to the discussion on the contradictory results regarding the relationship between risk-taking and firm performance.

Port performance and port choice have been treated as separate streams of research. This hampers the efforts of ports to anticipate on and respond to possible future changes in port choice by shippers, freight forwarders and carriers. The purpose of this paper is to develop and demonstrate a port performance measurement methodology, extended from the perspective of port choice, which includes hinterland performance and a weighting of attributes from a port choice perspective.

A review of literature is used to extend the scope of port performance indicators. Multi-criteria decision analysis is used to operationalize the context of port choice, presenting a weighted approach using the Best-Worst Method (BWM). An empirical model is built based on an extensive port stakeholder survey.

Transport costs and times along the transport chain are the dominant factors for port competitiveness. Satisfaction, reputation and flexibility criteria are the other important decision criteria. The results also show how the availability of different modal alternatives impact on the position of a port. A ranking of routes for hinterland regions is done.

The paper focuses on two extensions of port performance measurement. So far, not all factors that determine port choice have been included in port performance studies. Here, first, factors related to hinterland services are included. Second, a weighting of port performance measures is proposed. The importance of factors is assessed using BWM. The approach is demonstrated empirically for a case of the European contestable hinterland regions, which so far have lacked quantitative analysis.

Urbanization and globalization in India have led to the depletion of resources and degradation of the environment to meet the demands. Because of these issues, researchers and practitioners have begun to study various strategies to reduce the level consumption of resources to utilize it for present and future needs. In pursuit of finding solutions to the problems, sustainable building construction is found as the best key to avoid depletion of resources. Sustainable material selection is found as a vital strategy in construction. The paper aims to discuss this issue.

A three-phase methodology is proposed for framing the assessment model for construction industries to select materials for construction. In the first phase, a total of 23 sub-criteria of triple bottom line (TBL) and four brick materials as alternatives were identified. The second phase finds the weights and ranks of criteria and sub-criteria using the best worst methodology (BWM) the third phase involves ranking of materials concerning sub-criteria weights determined in phase II using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The objective of study is fixed to identify the criteria list for the selection of material in construction industries from the literature review especially for Indian construction industries; to rank the criteria for selection of materials with the help of the BWM approach; and to prioritize the identified materials in the view of sustainability with the help of Fuzzy TOPSIS in construction industries perspective. This study analyzed and choosing right sustainable materials by the three pillars of sustainability which are the environment, economic and social, also called TBL, for Indian construction companies by framing a sustainable material assessment model.

The results of this study facilitate to frame an assessment model for evaluating and selecting sustainable building materials.

In a globalized environment, small and medium enterprises (SMEs) are facing formidable challenges. Not only do they have to keep up their profitability, but there is also a pressure from various stakeholders to add to their environmental and quality performance .The solution obviously lies in continuously adopting and improving upon lean-green practices in their operations. This work aims at identifying, classifying and building up a duly tested robust ranked-order model of such “enablers”, related to lean-green practices, that puts them (the enablers) in an order of being the most significant to being the least significant further to be accorded the same or similar weight in strategy formulation and implementation stage by Indian SMEs for enhancing their overall organizational performance.

The study identifies 20 enablers (12 lean and 08 green manufacturing enablers) through extensive literature review and experts' opinion survey and classifies them into three main categories. The ranking and significance of each of the main and subcategory enablers is evaluated according to its weight which is determined by the best-worst method (BWM) approach, one of the novel multi-criteria decision-making (MCDM) methods. Further, the results have been drawn after running accuracy check of the rankings (based upon optimal weights) and testing the robustness of the ranked-order model through sensitivity analysis.

The results of this study reveal that out of the three main category enablers, “operational performance enablers (E1)” and “quality performance enablers (E3)” are the most and the least significant enablers, while in the group of 20 subcategory enablers, “Kaizen (E17)” and “environment emission control (E28)” are the most and the least significant subcategory enablers, respectively.

The prioritization model or ranked-order model of the lean-green manufacturing enablers proposed through this study may serve as a standard model to managers to help them decide and allocate their efforts and resources accordingly in managing their operations. This will also help them adopt high-ranking lean-green manufacturing enablers in their firms and benchmark and standardize their existing practices accordingly, leading to greater competitive advantage.

The study identifies various green-lean manufacturing enablers in SMEs, classifies them into three main categories and ranks them using BWM approach. The findings of this study should be extremely relevant to managers, manufacturing engineers and practitioners in Indian SMEs from the perspective of developing deeper appreciation of these enablers as per their relative ranked importance to further formulating an effective and efficient strategy for their implementation resulting in optimal results.

Because of the significant differences in the features and requirements of specific products and the capabilities of various additive manufacturing (AM) solutions, selecting the most appropriate AM technology can be challenging. This study aims to propose a method to solve the complex process selection in 3D printing applications, especially by creating a new multicriteria decision-making tool that takes the direct certainty of each comparison to reflect the decision-maker’s desire effectively.

The methodology proposed includes five steps: defining the AM technology selection decision criteria and constraints, extracting available AM parameters from the database, evaluating the selected AM technology parameters based on the proposed decision-making methodology, improving the accuracy of the decision by adopting newly proposed weighting scheme and selecting optimal AM technologies by integrating information gathered from the whole decision-making process.

To demonstrate the feasibility and reliability of the proposed methodology, this case study describes a detailed industrial application in rapid investment casting that applies the weightings to a tailored AM technologies and materials database to determine the most suitable AM process. The results showed that the proposed methodology could solve complicated AM process selection problems at both the design and manufacturing stages.

This research proposes a unique multicriteria decision-making solution, which employs an exclusive weightings calculation algorithm that converts the decision-maker's subjective priority of the involved criteria into comparable values. The proposed framework can reduce decision-maker's comparison duty and potentially reduce errors in the pairwise comparisons used in other decision-making methodologies.

The primary aim of this research is to conduct a comprehensive investigation into the essential elements of Industry 4.0 implementation within Indian Micro, Small and Medium Enterprises (MSMEs). Acknowledging the MSME sector as a crucial contributor to the Indian economy and industrial development, the study delves into the assessment of MSMEs based on Industry 4.0 components. Additionally, it explores the profound impact of these components on various performance factors, including organizational performance, sustainability performance and human-related aspects. The paper further ranks these identified components based on their significance within the MSME sector.

Employing a combination of methodological approaches, the research utilizes the Best and Worst Method (BWM), Data Envelopment Analysis (DEA) and calculates the Maturity Index for Industry 4.0 components. The BWM, a recognized multi-criteria decision-making technique, is initially applied to determine the weights and rankings of the identified components. Furthermore, the study evaluates 30 MSMEs, spanning manufacturing and service sectors, through the DEA approach. Industry 4.0 components are treated as inputs, and performance factors serve as outputs. Data for the analysis are collected through questionnaires distributed to the selected MSMEs. Lastly, the Maturity Index for MSMEs is also calculated.

From the result of the BWM method “assistive manufacturing” was found to be a highly weighted key component of Industry 4.0. From the DEA analysis out of 30 MSMEs 13 SMEs were highlighted as being efficient, whereas 17 MSMEs were judged to be inefficient. Furthermore, from the maturity index calculation, overall Maturity Index was determined to be 3.33 which shows that Industry 4.0 is in its initial stage of implementation, but it has gained pace in its implementation.

The research contributes to practical implications by offering a more accurate assessment of the state of Industry 4.0 implementation within MSMEs. The introduced maturity index proves instrumental in pinpointing key components that have received inadequate attention. This information is crucial for MSME managers and policymakers, guiding them in allocating resources effectively, addressing areas requiring attention and facilitating progress in the implementation of Industry 4.0. The study serves as a valuable tool for MSMEs to enhance their overall operational efficiency.

The research’s originality lies in its application of a comprehensive approach, combining BWM, DEA and the introduction of a maturity index for Industry 4.0 components in the MSME context. By employing these methodologies, the study not only identifies influential components but also provides a nuanced understanding of their relative significance. The research contributes significantly to the broader understanding of Industry 4.0 adoption, particularly, in the vital MSME sector within the Indian context. The findings are valuable for researchers, practitioners and policymakers seeking insights into improving the efficiency and effectiveness of MSMEs in the era of Industry 4.0.

This study aims to analyze lean wastes for the poultry sector in Turkey and link lean tools to this study, focusing on identifying each lean waste that affects poultry production and proposing solutions for preventing these lean wastes in the sector. The proposed solutions aim to improve processes by suggesting different lean tools and their applications for the poultry sector.

The study consists of two different applications. First, the waste relationship matrix (WRM) was created to reveal the relationship between seven lean wastes and their importance order. Then, after determining lean tools for eliminating lean wastes, the optimum weight ranking and consistency ratio of the most suitable lean tools were calculated for these wastes and ranked with the best-worst method (BWM).

Results showed that overproduction is the most critical waste that impacts other wastes, followed by defect waste. Due to the nature of the sector, these wastes not only result in economic loss for the company but also in food waste and loss and issues related to animal welfare. Furthermore, the Kaizen approach and 5S implementation are the methods to eliminate these wastes. Detailed discussion on the link between lean tools and lean wastes is provided for the poultry sector.

This is the first study that theoretically and empirically identifies the potential lean waste affecting the poultry sector and provides lean tools for eliminating these wastes. Sector-specific explanations and discussions are presented in the study to show the applicability of lean approaches in the poultry sector to eliminate waste. In addition, this study is the first to integrate the WRM and BWM.

The purpose of this paper is to identify and prioritise supply chain finance (SCF) adoption enablers and develop a novel comprehensive framework to select supplier firms based on their SCF adoption capability.

The study deploys a three-phase method to identify and prioritise SCF adoption enablers, followed by developing a model to select suppliers according to their SCF adoption capability. An extensive literature review, followed by a Delphi approach-based expert interview, has been used to finalise the enablers. Using the Best Worst Method and the VIsekriterijumsko KOmpromisno Rangiranje technique, a supplier selection model has been developed in the context of a case company.

The financial health and technological advancement variables received the top priority, followed by collaborative efficiency, whereas the human resources and organisational variables received the slightest significance. A supplier selection framework has also been developed by using the adoption capability of these factors by the supplier partners. In this study’s model, Supplier 4 exhibited better SCF adoption capability and received the top priority.

Manufacturing supply chains in a developing country are the scope of the current study. Extensive future studies are required to derive a global consensus.

The proposed framework of this study can be used to select supplier firms based on their SCF adoption capability. Policymakers can emphasise the most critical enablers of SCF adoption to assist small supplier firms to be a part of the advanced global supply chains.

The current study established a novel comprehensive framework for supplier selection based on the Supply Chain Finance adoption capability of MSME supplier firms.

To attain a competitive edge, it is essential to realize the significant critical success factors (CSFs) that contribute to the adoption of Quality 4.0 (Q4.0) in manufacturing organizations. Therefore, the study aimed to analyze CSFs for Q4.0 implementation in manufacturing small and medium-sized enterprises (SMEs) using multi-criteria decision-making (MCDM) tool.

The present study begins with a systematic literature review of past studies about Q4.0 implementation in manufacturing, followed by the identification of CSFs. Further, a case study was conducted wherein 42 CSFs identified were grouped into five dimensions. Best–worst method is a MCDM tool applied as a solution methodology for the analysis of CSFs based on expert opinion and priority order of CSFs attained.

The priority order of CSFs is obtained. Based on the findings, significant CSFs are “Data prediction and Analytics,” “Organizational culture towards Quality 4.0” and “Machine to Machine communication.”

The shifting market dynamics incorporate Q4.0 inclusion for realizing zero defects and high traceability in automotive SMEs. The present study offers implications for industry managers and practitioners by delivering insights on how Q4.0 could be serving automotive systems and CSFs that industry authorities need to pay attention to effectively adopt Q4.0 in the current quality systems. The study will facilitate industry practitioners to meticulously examine CSFs for Q4.0 toward the improvement of SME performance.

The identification of CSFs for Q4.0 adoption in manufacturing SMEs, along with the prioritization of CFSs using the MCDM tool, is the original contribution by the authors.