AI chatbots (AICs) have the potential to increase the sustainability of a manufacturing supply chain (SC) through sales engagement and customer engagement to accomplish various activities related to logistics and SC in real time. Industry 4.0 (I4.0) has opened up several opportunities with internet-based technologies, along with challenges for small and medium enterprises (SMEs). SMEs are beginning to adopt such technologies for their competitive advantages and the required sustainability in the manufacturing supply chain. AICs may help in accomplishing supply chain visibility (SCV) to enhance sustainable supply chain performance (SSCP). Innovation capability (IC) is also due to disruptive technologies being adopted by SMEs. The present research investigates the role of AICs in SCV and IC, which lead to SSCP, by employing structural equation modeling (SEM). An empirical study based on dynamic capability (DC) theory was carried out using 246 responses, and later Smart PLS-4.0 was used for SEM. The analysis revealed that AICs positively influence SCV and IC to support SSCP. SCV and IC also partially mediate the relationship between the adoption of AICs and SSCP.

Business enterprises such as small and medium-sized enterprises (SMEs) play a significant role in economic development but struggle for sustainability. A business enterprise such as a manufacturing unit tries many technological innovations and strategic initiatives to accomplish sustainability in the manufacturing system. Lean manufacturing implementation is one such initiative that helps SMEs manufacture value-added products with increased profitability and waste minimization. However, lean implementation in SMEs is challenging. Hence, it is essential to follow a systematic framework and control the critical success factors (CSFs) in attempting lean implementation. The purpose of this research is to find, evaluate, and rank the CSFs of lean implementation of SMEs so that they may be controlled to accomplish successful lean implementation. The CSFs of lean implementation found by an in-depth assessment of the literature are modeled using the interpretative structural modeling (ISM) approach. MICMAC analysis is also used in classifying and understanding the significance of each lean implementation CSF. ISM and MICMAC provide the relationship modeling to reveal the inter-relationships of each lean implementation CSF. Subsequently, the ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) has been applied to rank the CSFs of lean implementations. The results show that sustainability in a manufacturing system, financial capability, and employee involvement hold significant importance in lean implementations in manufacturing SMEs. Practicing managers may benefit from revisiting their lean implementation plans and respective aligned strategies. They will also be in a position to identify and focus on the scarce resources required for the subsequent lean implementations.

The main objective of this review is to study some important nanomaterials and their impact on the performance of geopolymer concrete. This paper is an investigation into trends and technology in the development of different nanomaterials to develop higher structural performance geopolymer concrete. The effect of the alkaline to binder and sodium silicate to sodium hydroxide ratio on the performances of geopolymer performances is studied. The relationship between setting time and slump is evaluated through the ternary plot, the variation in compressive strength values is evaluated using the kernel density plot, and the relationship between split tensile and flexural strength is investigated using the scattering interval plot. Regression analysis is carried out among water absorption and bulk-density result values obtained from previous literature. As the molarity and alkaline to binder (A/B) ratios increase, the strength development of geopolymer concrete increases up to a specific limit. The addition of a small quantity of nanomaterials, namely, nano silica, nano alumina, carbon nano tubes, and nano clay, led to the maximum strength development of geopolymer concrete. Incorporating these nanomaterials into the geopolymer significantly refines the structural stability, improving its durability. The various products in GP composites emerging from the incorporation of highly reactive SEM, XRD, and FTIR analysis of nanomaterials reveal that the presence of nanomaterials, which enhances the rate of polymerization, leads to better performance of the geopolymer.

Past research reveals that many lean implementation barriers hinder lean implementation in small and medium-sized enterprises (SMEs). Among many sectors, the manufacturing sector suffers more as it generates more waste while carrying out manufacturing processes. Many manufacturing units make unsuccessful attempts to implement lean principles in their manufacturing systems. Hence, such units must eliminate the prevailing lean barriers to accomplish successful lean implementation. Moreover, the contextual relationship of lean barriers must be studied to understand the effect of such barriers. This paper uses interpretive structural modeling (ISM) to explore lean barriers, their relationships, and their influence on other lean barriers. The present research also reveals the most significant classification of lean barriers into various categories of independent, dependent, autonomous, and linkage using the (MICMAC) Matrice d’Impacts Croisés Multiplication Appliquée á un Classement analysis. ISM and MICMAC together provide relationship modeling and reveal the interrelationship between each lean implementation barrier and its categories, respectively. The ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) is used to rank the barriers. The three significant lean implementation barriers revealed through the IRP include “lack of lean understanding”, “lack of strong quality policy”, and “risk of sustainable practice implementation”. The present research will help practicing managers of SMEs in the manufacturing sector to understand the mutual influence of lean barriers before introducing lean implementation. It is suggested that SMEs work on independent barriers so that dependent barriers can also be overcome with the least amount of resources and effort.

Highway construction projects are important for financial and social development in the United States. Such types of construction are usually accompanied by construction delay, causing liquidated damages ($LDs$) as a contractual provision are vital in construction agreements. Accurate quantification of $LDs$ is essential for contract parties to avoid legal disputes and unfair provisions due to the lack of appropriate documentation. This paper effort sought to develop an ensemble machine learning technique ($EMLT$) that combines algorithms of the Extreme Gradient Boosting ($XGBoost)$, Categorical Boosting ($CatBoost$), k-Nearest Neighbor ($kNN$), Light Gradient Boosting Machine ($LightGBM$), Artificial Neural Network ($ANN$), and Decision Tree ($DT$) for the prediction of $LDs$ in highway construction projects. Key attributes are identified and examined to predict the interrelated correlations among the influential features to develop accurate forecast models to assess the impact of each delay factor. Various machine-learning-based models were developed, where the different modeling outputs were analyzed and compared. Four performance matrices such as Root Mean Square Error ($RMSE$), Mean Absolute Error ($MAE$), Mean Absolute Percentage Error ($MAPE$), and Coefficient of Determination ($R^2$) were used to assess and evaluate the accuracy of the implemented machine learning ($ML$) algorithms. The prediction outputs implied that the developed EMLT model has shown better performance compared to other ML-based models, where it has the highest accuracy of 0.997, compared to the DT, kNN, CatBoost, XGBoost, LightGBM, and ANN with an accuracy of 0.989, 0.988, 0.986, 0.975, 0.873, and 0.689, respectively. Thus, the findings of this research designate that the EMLT model can be used as an effective administrative decision adding tool for forecasting the $LDs$. As a result, this paper emphasizes ML’s potential to aid in the advancement of computerization as a comprehensible subject of investigation within highway building projects.

The management and supply of blood are critical yet considered a challenging exercise within the healthcare industry. The inventory managers within the sector concerned with blood storage and transportation always focus on their consumers’ effective and timely responses. A lack of blood during emergencies significantly impacts patients’ lives needing a blood transfusion. On the other hand, blood transfusion to needy patients must be effective and timely, requiring adequate transportation and supply chain policies. A weak transportation system could lead to blood shortages, delivery inefficiencies, and even contamination during the process. There are few blood donors globally; therefore, any wastage due to a poor delivery solution is ineffective. The current review aims to offer a comparative study of blood delivery within crowded cities, specifically using cars and motorcycles as the leading delivery solutions. Based on the systematic literature review, the most effective and responsive blood delivery system in a congested city may be identified considering SC costs, time availability, and emergency severity. The present research provides a comparative analysis of the available blood delivery systems in a congested city. It also helps the stakeholders to take quick and responsive decisions quickly.