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The purpose of this paper is to present a conceptual supply chain (SC) hierarchical framework to measure the effectiveness and efficiency of the collaborative SC based on the Maslow’s hierarchy of needs and supply chain integration (SCI), supply chain visibility (SCV) and information sharing (IS).

Multimethods were applied. The first part of the research was undertaken using a deductive approach with rigorous and systematic analysis of the research material and the use of a survey instrument. The data collected through the survey are analysed and presented with descriptive statistics and cross-tabulations. The second part of the research is an extension of the analysis of the first part linking with the Maslow’s hierarchy of needs to form the basis for development of the SCI hierarchy.

The levels of SCI, SCV and IS are significantly different for organisation dimensions such as size, industry type and region of operations. The SCI hierarchy based on Maslow’s hierarchy of needs provides a number of indices to measure the effectiveness of SCI, IS, SCV in a collaborative SC.

The research focusses only on manufacturing, 3PL, fast-moving consumer goods and retail industries.

The research work provides some insights for practitioners on best practices for SC collaboration and measurement of collaborative effectiveness.

The primary value of this paper is the development of standardized levels of SCI, IS and SCV and the proposed measurement of the collaborative effectiveness of the SC. It could be a proposed approach for empirically evaluating the levels of SCI, IS and SCV subject to organisational dimensions.

This study aims to investigate how buyer-assisted lean intervention in garment supplier factories affects garment suppliers' productivity and production capability development.

Employing a qualitative research approach and a lean intervention design, a multiple case study method was adopted for this study. Quantitative data on productivity performance and qualitative data on production capability development were collected from a Danish buyer and their four corresponding garment suppliers. Collected data were analysed using standard lean measurement tools and qualitative data analysis techniques.

This study demonstrates that buyer-assisted lean intervention is a useful strategy for garment suppliers to enhance their productivity and production capability. However, suppliers need to select the right lean tools, ensure seriousness and commitment to lean initiatives, substantial involvement of top management and workers, arrange formal and informal training, provide performance-based financial/non-financial incentives and nurture a learning culture to facilitate suppliers' production capability development.

This study implemented few lean tools in a single sewing line in four supplier factories for a short intervention duration. Thus, there is a scope for future studies to investigate the impact of the lean intervention on a large scale.

The findings of this study might bring new insights to the management of buyer and supplier firms concerning how buyers could involve in suppliers' lean intervention initiatives and what suppliers need to ensure to develop production capability.

For the first time, this study engaged a buyer in suppliers' lean intervention initiatives to improve productivity and production capability in the garment industry of a developing country.

Investigate the potential of using “lean” in garment supplier factories to enhance productivity and compliance capability, so as to fulfill a buyer's expectation of lower price, shorter lead time, and higher occupational health and safety (OHS) standards.

By means of an intervention, lean tools integrated with OHS elements were introduced in four Bangladeshi key garment suppliers of a Danish buyer. By employing a qualitative approach, both quantitative and qualitative techniques were used to collect data on productivity, OHS, and buyer–supplier efforts.

This study demonstrates that lean tools, integrated with OHS elements, can be used to improve the productivity and compliance capabilities of supplier firms, which meet a buyers' changing demands for lower prices, shorter lead times, and higher compliance standards. This study suggests that the improvement of productivity and OHS in supplier firms through lean implementation could be a better choice for buyers than switching to new suppliers with uncertainties in productivity and delivery, as well as OHS compliance problems.

The findings of this study suggest that garment suppliers can benefit from implementing lean, thereby improving their capacity to meet buyers' expectations. Therefore, both suppliers and buyers have a mutual interest in the application of lean in suppliers' production facilities.

By considering both buyer and supplier perspectives, this research is a unique attempt to investigate the possibilities of lean implementation at the shop-floor level to meet the market challenges in the context of a developing country.

The authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.

SSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.

The case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.

The study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.

This study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.

The purpose of this study is to review and conceptualize a holistic framework for lean manufacturing (LM) implementation, enabling the manufacturing industries to administer the implementation process successfully toward organizational development and customer satisfaction.

An extensive literature survey on the input and output manifests of lean production is carried out. An attempt is made to build an exhaustive list of all the technical and human inputs necessary for LM implementation, coupled with a similar exhaustive list of the benefits accrued from its successful implementation. The determinants and outcomes of successful LM implementation are further mapped onto 11 distinct input latent constructs and 3 distinct output latent constructs, respectively.

A conceptual structural model for administering LM implementation is framed based on the manifests considered in the literature.

The model conceptualized in this study may be treated as a guide for successful LM implementation in a manufacturing enterprise.

This research is expected to provide further insights for lean implementation and will be very helpful to integrate the lean principles, tools and benefits into a unified coherent complete manufacturing system.

To the best of the authors’ knowledge, this work is one of the very first research studies to have a comprehensive all-inclusive study of the input and output variables for LM implementation. It intends to bridge the gap between theory and practice and has come up with a comprehensive framework which will aid the lean practitioners toward successful LM implementation, as well as, assist them in assessing the effect of implementation.

The purpose of this paper is to add to the quality management body of knowledge by solidifying the connection between operational and strategic aspects of lean transformation. Previous research has examined these issues in isolation, demonstrating mixed results in financial and operational efficiencies. The authors show that when operational and strategic changes are jointly considered the likelihood of success for lean transformation increases.

The authors provide a literature review of 109 peer-reviewed papers on lean manufacturing and qualitative analysis of 23 Baldrige award winners (2000-2014) that implemented lean to assess the importance of strategic actions in achieving a sustainable competitive advantage through lean transformation.

The authors find that lean transformation yields mixed results unless strategic actions are taken by senior management. These strategic actions include but are not limited to knowledge management, human resources, and business growth and can result in performance heterogeneity by improving the output/input ratio of the firm. This performance can then manifest as either doing the same level of business with fewer resources (a profit play) or doing more business with the same resources (a growth play). As specific examples, the authors analyzed Baldrige award winners for evidence of lean strategic action to drive performance gains. The authors suggest further model validation through directed interview and/or survey research.

This paper clarifies the need for jointly implementing lean tools with strategic actions. The findings provide more deliberate strategic actions for organizations wishing to increase the likelihood of success of lean transformation and ultimately improve quality.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.

The purpose of this study is to develop a novel comprehensive three-dimensional computational model to predict the transient thermal behavior and residual stresses resulting from the layer-by-layer deposition in the direct metal laser sintering process.

In the proposed model, time integration is performed with an implicit scheme. The equations for heat transfer are discretized by a finite volume method with thermophysical properties of the metal powder and an updated convection coefficient at each time step. The model includes convective and radiative boundary conditions for the exposed surfaces of the part and constant temperatures for the bottom surface on the build plate. The laser source is modeled as a moving radiative heat flux along the scanning pattern, while the thermal gradients are used to calculate directional and von Mises residual thermal stresses by using a quasi-steady state assumption.

In this study, four different scanning patterns are analyzed, and the transient temperature and residual thermal stress fields are evaluated from these patterns. It is found that the highest stresses occur where the laser last leaves off on its scanning pattern for each layer.

The proposed model is designed to capture the layer-by-layer deposition for a three-dimensional geometry while considering the effect of the instantaneous melting of the powder, melt pool, dynamic calculation of thermophysical properties, ease of parametrization of various process parameters and the vectorization of the code for computational efficiency. This versatile model can be used for process parameter optimization of other laser powder bed fusion additive manufacturing techniques. Furthermore, the proposed approach can be used for analyzing different scanning patterns.

The purpose of this paper is to theorise and develop seven dimensions (strategic supplier partnership, level of information sharing, quality of information sharing, customer service management, internal lean practices, postponement and total quality management) into a supply chain management (SCM) practices (SCMPs) construct and studies its causal relationship with the conceptualised constructs of supply chain performance (SCP) and manufacturing firms’ performance (MFP). The study also explores the causal relationship between SCP and MFP.

Data were collected through a survey questionnaire responded by 249 Jordanian manufacturing firms. The relationships proposed in the developed theoretical framework were represented through three hypotheses: H1 – there is a significant relationship between SCMPs and SCP; H2 – there is a significant relationship between SCMPs and MFP; and H3 – there is a significant relationship between SCP and MFP. Linear regression, ANOVA and Pearson correlation were used to test the hypotheses. The results were further validated using structural equation modelling.

The results indicate that SCMPs have a positive effect on SCP (H1), which in turn also positively affect MFP (H3). Despite this intermediary positive effect of SCMP on MFP through SCP, the study also suggests that SCMPs have a direct and positive effect on MFP (H2).

This study provides hard evidence indicating that higher levels of SCMPs can lead to enhanced supply chain and firms’ performance. It also provides SC managers of manufacturing firms with a multi-dimensional operational measure of the construct of SCMPs for assessing the comprehensiveness of the SCMPs of their firms.

This study is among the very first SCM researches conducted on the Jordanian manufacturing sector, particularly, in relation to the practices that manufacturing firms in this country need to adopt to make their supply chains a solid competitive vehicle for their development. The results have broader implications for all manufacturing companies, particularly in developing economies where the growth of manufacturing and the development of integrated supply chains are key stages in economic development.

In the recent scenario, there has been an increasing trend toward lean practices and implementation in production systems for the improvement of an organization’s performance as its basic nature is to eliminate the wastes. The increasing interest of customers in customized products and the fulfillment of customers’ demand with good productivity and efficiency within time are the challenges for the manufacturing organization; that is why adopting lean manufacturing concept is very crucial in the current scenario.

In this paper, the authors considered three different methodologies for fulfilling the objective of our research. The analytical hierarchy process, best–worst method and fuzzy step-wise weight assessment ratio analysis are the three methods employed for weighting all the enablers and finding the priority among them and their final rankings.

Further, the best results among these methodologies could be used to analyze their interrelationships for successful lean supply chain management implementation in an organization. In this paper, 35 key enablers were identified after the rigorous analysis of literature review and the opinion of a group of experts consisting of academicians, practitioners and consultants. Thereafter, the brainstorming sessions were conducted to finalize 28 lean supply chain enablers (LSCEs).

For lean manufacturing practitioners, the result of this study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process.

This paper is the first of the research papers that considered deep literature review of identified LSCEs as the initial step, followed by finding the best priority weightage and developing the ranking of various lean enablers of supply chain with the help of various methodologies.