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).

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.

The fourth industrial revolution brought a paradigm shift in the present manufacturing system and its supply chain management (SCM). The evolution of Industry 4.0 (I4.0) brought several disruptive technologies like cloud computing (CC), blockchain, the Internet of Things (IoT), cyber-physical systems (CPS), etc. These disruptive technologies have changed the face of the modern manufacturing system and its manufacturing supply chain (SC). Several changes in manufacturing in terms of lead time, cost reduction, agility, flexibility, and response to market sensitivity are seen in almost all types of manufacturing. I4.0’s disruptive technologies influence lean SC, agile SC, leagile SC, and green SC. The current study examines how I4.0 technologies affect society on such supply chains (SCs), which leads to enhanced performance of the manufacturing SC. The effect of process innovation (PI) resulting from I4.0 innovations is also investigated. SEM-PLS-based modeling is constructed based on 195 responses received from manufacturing enterprises implementing various SC practices in managing their manufacturing SCs. The findings demonstrate a favorable correlation between I4.0 technology and the enhancement of various SCs. The result also revealed that there is a positive impact of I4.0 technologies on PI, which leads to manufacturing SC performance improvements.

Purpose: The organic reconfiguration in supply chain practices significantly varies with restaurant types, customers and the extent of the degree of serving organic food. The purpose of this study is to develop a quantitative application to measure the degree of serving Organic Food for Restaurant Supply Chains. Design/methodology/approach: The detailed semi-structured interviews, onsite visits and secondary customer reviews of 60 restaurants of different sizes, locations and cuisines from India are used to explore typologies of organic supply chains. In the second stage, an approach for evaluating the degree of organic food serving is developed on the basis of Supply Chain Operations Reference (SCOR) attributes, and differences between typologies are statistically tested using non-parametric Mann–Whitney U-test. Findings: The results show that the degree of serving organic food differs with the type of restaurants across SCOR attributes. Based on the variations in SC practices, the work presents two types of restaurant supply chains – Organic Food Enthusiast (Type-I) and b) Village Voyagers (Type-II). The difference between the degree of serving organic food for the “Make” and “Enable” attributes is statistically insignificant. Despite variations in the strategic positioning, customer type and geographical context, both the organic supply chains tend to preserve their core making process. Practical implications: The degree of serving organic food for each individual element of the SCOR model can be used by practitioners and managers to identify specific problems and opportunity areas where practical actions could be defined and taken to improve the organic food-based supply chain reconfiguration. Originality/value: This paper proposes the quantitative approach to evaluating organic food serving capabilities of restaurant supply chains. Further, it empirically demonstrates the typologies of organic food serving supply chains.