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.

Lean 4.0 (L4.0) plays a significant role in reducing waste and enhancing productivity for a sustainable manufacturing supply chain in Industry 4.0 (I4.0). L4.0, with its soft and hard practices, may be well integrated into I4.0 to enhance its readiness. Small and medium enterprises (SMEs) are attempting to prepare themselves for I4.0 readiness. Hence, the present research explores L4.0 in terms of its soft and hard practices to understand its holistic relationship with I4.0’s readiness for delivering a sustainable manufacturing supply chain. To reap the maximum benefits, several traditional lean thinking practices and lean management principles should be combined with internet-enabled I4.0 technologies. The result of the present empirical analysis revealed that the soft L4.0 practices of top management leadership (TML), customer focus (CF), and employee training and learning (ETL) influence the hard L4.0 practices of total productive maintenance (TPM), statistical process control (SPC), and advanced manufacturing technologies (AMT) to have a positive significant influence on operational readiness (OR) and technological readiness (TR).

Lean 4.0 (L4.0) is a transformed form of traditional lean to suit Industry 4.0’s (I4.0) requirements. The L4.0 has a great deal of potential to match the I4.0’s challenges in terms of speed, dynamics, and efficacy once it has been digitalized. The study aims to identify and model the L4.0 CSFs for successful lean implementations in SMEs to suit I4.0 needs. The thorough analysis of the literature led to the identification of the L4.0 CSFs. The expert panel from SMEs was involved in selecting the relevant 22 L4.0 CSFs that suited the needs of manufacturing SMEs. Based on the feedback of the expert panel, a questionnaire survey was carried out. Further, collected responses were analyzed using an exploratory factor analysis (EFA). The EFA results identified four major groups: ‘worker-enabled technologies’, ‘IT-enabled technologies’, ‘management’, and ‘L4.0’ related CSFs. Furthermore, the study uses partial least square structural equation modeling (PLS-SEM) to simulate the L4.0 CSFs and identify the influence of each CSF toward successful lean implementation. The PLS-SEM results confirm that the lean CSFs have positive effects on successful lean implementation. The present research contributes to enhancing the knowledge and L4.0 practices of manufacturing SMEs. The results show that successful implementation will promote ‘productivity improvements (PI)’, ‘waste reduction (WR)’, ‘competitive advantage (CA)’, and ‘sustainable manufacturing system (SMS)’.

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.

In the era of Industry 4.0 (I4.0), automation and data analysis have undergone significant advancements, greatly impacting production management and operations management. Technologies such as the Internet of Things (IoT), robotics, cloud computing (CC), and big data, have played a crucial role in shaping Logistics 4.0 (L4.0) and improving the efficiency of the manufacturing supply chain (SC), ultimately contributing to sustainability goals. The present research investigates the role of I4.0 technologies within the framework of the extended theory of planned behavior (ETPB). The research explores various variables including subjective norms, attitude, perceived behavior control, leading to word-of-mouth, and purchase intention. By modeling these variables, the study aims to understand the influence of I4.0 technologies on L4.0 to establish a sustainable manufacturing SC. A questionnaire was administered to gather input from small and medium-sized firms (SMEs) in the manufacturing industry. An empirical study along with partial least squares structural equation modeling (SEM), was conducted to analyze the data. The findings indicate that the use of I4.0 technology in L4.0 influences subjective norms, which subsequently influence attitudes and personal behavior control. This, in turn, leads to word-of-mouth and purchase intention. The results provide valuable insights for shippers and logistics service providers empowering them to enhance their performance and contribute to achieving sustainability objectives. Consequently, this study contributes to promoting sustainability in the manufacturing SC by stimulating the adoption of I4.0 technologies in L4.0.