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The Indian marble and stone industry has got the potential to contribute well to the development of the emerging economy. However, unlike the other Indian industries, stone and marble industries are highly underrated sectors, which may become a critical factor for development. This paper analyses the sustainability factors in supply chain management practices.

A literature review is used to identify the barriers and drivers in sustainable supply chain management practices. Interpretive structural modeling has been used to obtain a hierarchy of barriers and drivers along with driving power and dependence power analysis. Further, MICMAC analysis is used for segregating the barriers and drivers in terms of their impact on sustainability.

The findings of the work of this research are that the attention of society, government, and commercial banks should be more toward the unorganized condition of stone and marble sector. There should be an increase in the commitment of stakeholders to reduce pollution and install safety, by enforcing more relevant laws and regulations and creating the importance of environmental awareness.

The main contribution of this research is to identify the barriers and drivers of sustainable supply chain management in a stone and marble industry. The paper proposes a sound mathematical model to prioritize the critical factors for responsible production and consumption of resources from sustainability perspectives of stone industry.

The ever-widening competitive global markets demand food business to demonstrate safe food supplies across the world. The causes of food borne illness are complex to determine and require a careful evaluation of all stages of food supply chain and food safety practices. The purpose of this paper is to systematically investigate the factors responsible for the assessment of food supply chain and evaluation of food safety system in India.

The study utilizes a combination of qualitative and quantitative approach by exploring expert’s opinion systematically using a semi-structured interview followed by careful grouping of responses using the grounded theory approach to build the research theme. The prioritization of the critical factors is carried out using Pareto analysis. The methodological review was carried out to identify factors and categorize them based on their impact on hierarchical logical relationship using total interpretive structural modeling approach to determine the enablers.

This paper attempts to deliver an inimitable list of seven vital factors for the effective design of evaluation system for food safety practices. The study provides a careful insight on the issue pertaining to designing of assessment system including competence building for assessor and availability of well-defined technical protocol. The recommendation for developing a robust food safety inspection system by implementing stricter regulation, enhancing competence and design initiatives is provided.

The study provides a unique list of factors for the assessment of food safety practices and develops the relationship. Food safety assessment is an integral part of food safety study which is systematically explored and conceptualized in this paper. The study is carried out using the opinion of Indian experts.

India is in the process to achieve an important place in $2,000bn global food trade. In order to achieve this goal, there is a need to develop a food safety system which is well written down in line with international practices that are highly coordinated based on self-compliance to assure consumer protection. Accordingly, many organizations undergo assessment of their food safety system to verify compliance internally as well as externally. The purpose of this paper is to provide insight on the critical factors and benefits by evaluating the food safety assessment practices.

A questionnaire-based survey is conducted among 96 Indian food business operators and regulators involved in assessment practices to obtain critical factors for the assessment of food safety practices. The questionnaire captures indicators for motivations or challenges and benefits of food safety assessment to identify critical factors using exploratory factor analysis. Model for the food safety assessment practices was developed based on multiple regression analysis by determining the impact of factors on the benefits of food safety assessment.

This paper identifies four factors responsible for assessing food safety practices, namely, business-centric approach, legislative needs, technical practices and organization resentment as a combination of reasons and challenges along with two benefits risk: protection and organization reinforcement. The regression analysis indicates that the organization reinforcement gets positively impacted by business and technical practices and negatively by organization resentment. Risk protection has a significant relationship with legislative needs.

This is the first attempt to systematically explore the factors around the assessment of food safety practices in India. This study provides inputs for the practical application of managers and regulators.

This paper compares impact of Industry 4.0 / emerging information and communication Technologies (ICTs), for example, Internet of things (IOT), machine learning, artificial intelligence (AI), robotics and cloud computing, on 22 organisational performance indicators under nine combinations of Lean Six Sigma (LSS) and quality management systems (QMS).

Survey of 105 Indian organisations was done about their experience of using QMS, Lean Six Sigma and emerging ICTs. Respondents included both manufacturing and service enterprises of different scales and sectors. The responses collected were compared, and statistically significant difference among them was evaluated using chi-square test.

The study confirmed statistically significant difference among 20 organisational performance indicators under different combinations of QMS, LSS and ICTs. These indicators include quality performance, delivery performance, sales turnover, inventory level and so forth. However, for two indicators, namely, absenteeism and throughput, significant difference in responses was not established.

All possible combinations of QMS, LSS, only LSS tools and ICTs were not studied because of either theoretical impossibility (e.g. using LSS without LSS tools) or practically rare situations (e.g. organisations using ICTs and LSS without QMS). Furthermore, the impact from different sequences of implementing QMS, LSS and ICTs can be studied.

Using this study, practitioners can identify which LSS, Quality System and ICT combination results in best performance and quick success. On theoretical front, the study confirms impact of LSS and QMS on organisational performance.

This study evaluates organisational performance under several possible combinations of QMS, LSS, and emerging ICTs, which was so far unexplored.

The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.

IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).

The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.

The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.

The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

The purpose of this paper is to find the driving factors for effective and efficient management of international manufacturing network (IMN) which has become increasingly important due to the intensive competition existing in the manufacturing industry. This paper presents a magnified view of different constructs of IMN and identifies the qualitative factors which are broadly classified under different constructs like political, economic, social, technological, legal and environmental.

Principal component analysis is applied to club identified factors into political, economic, social, technological and legal categories. PESTLE–SWOT approach is used to shortlist the identified factors using the elimination algorithm. Using analytical hierarchy process, weightages and rank of the identified factors are evaluated. Interpretive structural modeling is applied to understand inter-relationship among factors, and to analyze the driving power and dependence of the factors.

The results obtained from the above approaches are compared to identify most significant factors and the list of IMN enablers is presented using Venn Diagram. Government stability, Economic freedom, economic stability, environmental sustainability and raw material availability are coming out to be the driving factors across all techniques. Finally, the paper provides weights of the driving indicators common across all multi-criteria decision-making techniques considered.

The proposed work provides significant information about the qualitative factors needed to be considered while designing and developing IMN. As identified by literature review and through expert opinions, not all 29 factors responsible for IMN development are crucial. Many factors are either interdependent or driven by others. The paper provides a useful analysis about the driving factors that need to be taken into account.

The study presents a comprehensive analysis of the IMN enablers. Furthermore, it provides managerial and theoretical implications to deal with the complexities encountered while establishing IMN.

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues.

This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters.

The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10.

The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.

In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.

The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.

The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.