This study aims to develop a model that quantifies the key issues influencing digital integration within the supply chain (SC) as a single numerical value, termed the integrity index. This index value helps organisations in identifying and addressing various SC issues related to digital integration. The model calculates the integrity index by analysing the interactions among different issues involved in digital integration. The graph theoretic approach (GTA) technique is applied to examine the integrity index value by analysing the interactions among different issues involved in digital integration. In GTA, the various issues influencing the digital integration are identified, and a model with the help of a digraph is prepared based on their interdependencies. From this digraph, an adjacency matrix is created, and the permanent function of this matrix is computed to derive the integrity index. The integrity index is evaluated by using GTA in the context of Indian small and medium enterprises (SMEs). By using the GTA framework, the integrity index is also calculated in the best- and the worst-case scenarios. Findings from the study reveal that financial issues are the most influencing ones impacting digital integration, followed by organisational and technological issues, providing key focus areas for improvement.

In spite of the potentially enormous benefits, small and medium Enterprises (SMEs) are falling behind in digital transformation. Emerging technologies provide a variety of applications that can help these companies to enhance their performance and get around the size-related obstacles that stand in the way of conducting business. SMEs play a major role in the economy as they generate jobs on a larger scale. In the era of industry 4.0, SMEs are also required to upgrade their supply chain with the integration of digital technologies, e.g., Cloud Computing, IoT, and Artificial Intelligence to meet global competition. During the pandemic COVID-19 suddenly a gap is created to keep business running smoothly due to supply chain disruption and the SMEs are most affected due to pandemic. A need for the integration of technology with the supply chain is created to make the supply chain agile and sustainable. This paper studies the challenges in order to adopt the digital transformation in Indian SMEs.

Conventional supply chain has been shown to be incapable of meeting the ever-increasing demands of customers as well as the requirements of innovation. Due to various uncertainty volatility, ambiguity, and intricacy, the sustainability of supply chain becomes a major topic for organisations. Now there is need to integrate the digital technologies like cloud computing, internet of things, artificial intelligence, big data analysis etc. which improve the performance of supply chain in efficient and responsiveness manner. Due to this digital integration, the system undergoes various changes at organisational, operational, performance and technological level. This study aims to identify nine major critical factors which are enablers to achieve the sustainability in digitally integrated supply chain. A TISM model is developed to address their interrelationship among them. The factors are classified as dependent and independent factors according to their driving and dependence power through the use of the MICMAC analysis. If is confirmed in MICMAC analysis that the factors Agile Organisational structure, Smart logistics Capabilities, Smart Manufacturing Process and financial planning enhance the sustainability of digitally enabled supply chain. This study provides a comprehensive list of enablers that are necessary to achieve sustainability of digitally integrated supply chains; nevertheless, the list is not exhaustive. This paper was written with the intention of contributing a pool of knowledge on achieving sustainability in digitally integrated supply chain. This study has the potential to make it possible for market specialists and executives to focus on critical elements that lead to tactical decisions and maximise value for companies. It establishes a baseline from which future studies can build.

With the rising competition in market today, manufacturers need to alter their processes to deliver faster and to ensure improved response to customer needs. When a computer-controlled system is incorporated in a manufacturing system to add some levels of flexibility, the obtained system is called as Flexible Manufacturing System (FMS). Huge potential is there in FMS and thus, complex problems are also associated with the system. Main production related problems are part selection type i.e., grouping of machines, productivity ratio, allocation of resources and loading issues. Solving these problems to get optimum results is to be done to get the better output, machine and manpower efficiency. The present study identifies the loading issues related to FMS and model their interrelationships using Interpretive Structural Modeling (ISM).

In the current scenario of competitive and highly volatile market conditions, industries relay more on flexibility than on standardization of production. Many researchers have advocated the use of Flexible Manufacturing Systems to meet the challenges of global competition. Despite the potential benefits offered by FMS, their adoption rates are relatively slow, especially in developing countries like India. The conventional manufacturing industries are still reluctant in adopting these newer techniques of manufacturing although today there is much support from government and need for adoption due to cut throat global competition. The main reason for this is the lack of enough literature showing its implementation and stepwise adoption process. There is an utmost need for a generalized procedure showing the implementation process which is suitable for any kind of traditional industry which wants to adopt FMS. In this paper, an attempt has been made to propose a generalized methodology named as 'LAPTOP' to evaluate the transition of a conventional manufacturing system into FMS. A case study has been presented to validate the proposed methodology.

The manufacturing organisations adopt flexible manufacturing systems to meet the challenges imposed by today's volatile market standards. An FMS is designed to combine the efficiency of a mass production line and the flexibility of a job shop to produce a variety of products on a group of machines. Productivity is a key factor in a flexible manufacturing system performance. Despite the advantages offered, the implementation of FMS has not been very popular especially in developing countries as it is very difficult to quantify the factors favouring FMS implementation. For its successful implementation, technological considerations, cost justification as well as strategic benefits are to be weighted. Therefore an attempt has been made in the present work to identify and categorise various productivity factors influenced by the implementation of FMS in a firm, further these factors are quantitatively analysed to find their inhibiting strength using graph theoretic approach (GTA). GTA is a powerful approach which synthesises the inter-relationship among different variables or subsystems and provides a synthetic score for the entire system. So using this approach a numerical index is proposed in this work to evaluate and rank the various productivity factors so that the practising managers can have better focus.

Flexible manufacturing is a process which permits production systems to perform under high customised production needs. The goals of manufacturing systems like minimum inventories, improved quality, enhanced technical performance and reduction of cost can be achieved by implementing the FMS. Material handling system plays a very important role and consumes a significant amount of cost and time in flexible manufacturing system (FMS). Since a lot of factors affect the material handling system, so selection of best suitable material handling system which optimises all these factors is a big problem before any material manager. The purpose of this study is to present a multi-criteria decision making comparison model which would be very useful for choosing better alternative between automated and manual material handling system (MMHS). Analytical hierarchy process (AHP)-based comparison is performed upon these attributes to select the best alternative between manual material handling system and automated material handling system (AMHS).

In today’s versatile and dynamic industrial scenario more and more industries are adopting advanced manufacturing technologies and systems like flexible manufacturing system (FMS) which combines the efficiency of a mass production line and the flexibility of a job shop. Material handling equipment form an important component of FMS and using proper material handling equipment can enhance the production process, provide effective utilization of manpower, increase production and improve system flexibility. In this research the main material handling issues in a FMS are identified and further evaluated using ISM and TOPSIS approaches. The purpose of identification of these issues and their analysis is to allow researchers and practicing managers to pay proper attention to these issues which may help them in designing the material handling systems in their organisations in a better way.

Small and medium scale industries play an important role in Indian economy and their strength lays in the competitiveness of their products in the world market. Backed by a strong government support in India, they continue to improve their growth rate. Despite this high growth rate, huge potential amongst the Indian small and medium scale industries still remains untapped. So there is a need to upgrade these industries so that they bloom to their full potential. For achieving increased competitiveness, the manufacturing SMEs need to replace or supplement their previous methods of production with more flexible and dynamic production systems like, flexible manufacturing system (FMS) which is characterised by shorter product development and manufacturing lead-time, better quality and flexibility, team-based work organisations, low setup/changeover times, multifunctional workers. FMS as a manufacturing strategy can help a SME to improve its processes and align it to the requirements of its customers. So this paper discusses the issues of SMEs and presents the major attributes for the adoption of FMS in SMEs and further these attributes are modelled to give a structure amongst them by using two distinct, well established modelling techniques, ISM and TOPSIS.

The manufacturing organizations adopt Flexible Manufacturing Systems to meet the challenges imposed by today’s volatile market standards. An FMS is designed to combine the efficiency of a mass production line and the flexibility of a job shop to produce a variety of products on a group of machines. Flexibility is a key factor in a flexible manufacturing system performance. Despite the advantages offered, the implementation of FMS has not been very popular as it is very difficult to quantify the factors favouring FMS implementation. Therefore an attempt has been made in the present work to identify and categorize various flexibility factors influenced by the implementation of FMS in a firm further these factors are quantitatively analyzed to find their inhibiting strength using Graph Theory Approach (GTA).

The last few years have witnessed unparalleled changes in the world market and the manufacturing companies have to adjust to these unforeseen changes in the market quickly to survive. So more and more companies are now opting for low volume, high variety flexible manufacturing system (FMS) instead of the conventional mass production. Though FMS provides a lot of strategic and tactical benefits, yet all of these may not be possible with all installations. A manufacturing manager should know what are the specific benefits he is expecting from the FMS installation and what is the time span within which these benefits start coming in. In the present paper, an attempt has been made to accomplish the task of enlisting the factors affecting the productivity of FMS installation and further an attempt has been made to model these different factors using the interpretive structural modelling (ISM). This model is further strengthened using MICMAC analysis by defining the driving power and dependencies of these factors.