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The purpose of this paper is to propose an integrated gray-decision making trial and evaluation laboratory (Grey-DEMATEL) framework to evaluate the ICT adoption barriers in manufacturing small and medium enterprises (MSMEs) running in developing countries. The proposed model is also tested for Indian MSMEs.

The model consists of 16 potential ICT adoption barriers selected from existing literature and refined based on discussion with domain experts. While DEMATEL analyses causal relationship and prioritizes them, Grey approach tackles individual biases and data insufficiency.

The authors conclude that lack of awareness about benchmarking and lack of management vision are the most critical ICT adoption barriers in the case of Indian MSMEs.

Outcomes of the present research are based on the experts’ inputs which are subject to the biases related to their experience and exposure. In India practices adopted by SMEs have geographical and political influence that is also neglected.

This study provides a model consisting of 16 ICT adoption barriers for MSMEs in developing country and a framework to analyze causal relationships among the barriers with the flexibility of data input from their own domain experts. The framework is also capable of dealing with expert biases and data insufficiency.

This research proposes a text analytics–based framework that examines the utility of online customer reviews in evaluating automobile manufacturers and discovering their consumer-perceived weaknesses.

The proposed framework integrates aspect-level sentiment analysis with the house of quality (HoQ), TOPSIS, Pareto chart and fishbone diagram. While sentiment analysis mines and quantifies review-embedded consumer opinions on various automobile attributes, the integrated HoQ-TOPSIS analyzes the quantified opinions and evaluates the manufacturers. The Pareto charts assist in discovering consumer-perceived weaknesses of the underperforming manufacturers. Finally, the fishbone diagram visually represents the results in the form with which the manufacturing community is acquainted.

The proposed framework is tested on a review data set collected from CarWale, a well-known car portal in India. Selecting five manufacturers from the mid-size car segment, the authors identified the worst-performing one and discovered its weak attributes.

The proposed framework can help the manufacturers in evaluating competitor; identifying consumers' contemporary interests; discovering own and their competitors' weak attributes; assessing the suppliers and sending early warnings; detecting the hazardous defects. It can assist the component suppliers in devising process improvement strategies; improving their customer network; comparing them with competitors. It can support the customers in identifying the best available alternative.

The proposed framework is first of its kind to integrate the sentiment analysis with (1) HoQ-TOPSIS to assess the manufacturers; (2) Pareto chart to discover their weaknesses; (3) fishbone diagram to visually represent the results.

As an emerging technology, Radio Frequency IDentification (RFID) and blockchain have the potential to disrupt many areas of business and social structure. However, it is loaded with significant technical, social, legal, financial and ethical complications that bring difficulty in its widespread use within the public distribution system (PDS). This research aims to analyze the barriers to integrated RFID and blockchain adoption in developing countries' PDS. Furthermore, this study also aims to validate the proposed framework against the Indian PDS.

The proposed framework consists of 10 potential barriers to integrated RFID and blockchain adoption. To identify the barriers, this study referred to the extant literature followed by consultations with domain experts. This study prepared the DEMATEL-based questionnaires, collected the data from four domain experts and analyzed them using an integrated Grey-DEMATEL approach.

The obtained results provide a precise list of barriers and the correlations among them. From the results, it is concluded that the unavailability of a skilled workforce at affordable cost, lack of knowledge about privacy level and unclear return on investment and benefits are the most critical blockchain adoption barriers in the context of Indian PDS.

This research proposes a framework consisting of 10 integrated RFID and blockchain adoption barriers in relation to Indian PDS. It also proposes a method for analyzing causal interrelationships between the barriers while allowing for data input from domain experts. Consequently, the framework is capable of coping with experts' biases and data scarcity.

The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

The purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system.

The real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data.

The proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries.

The proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

To create a benchmark for design evaluation of commercial web sites, to model customers' site navigation behavior, and to develop and implement algorithms for link‐structure personalization.

The principal methodology used for design benchmarking is a survey of corporate web sites of some US Fortune 500 companies and top Indian companies and statistical analysis of the data so collected. The principal methodological approach for modeling user behavior, user's interest and algorithms for link‐structure personalization, is the development of a Markov decision process (MDP) in the overall framework of a semi‐Markov model and implementing the same in a link‐structure personalization system.

It is observed that the functional web site features significantly differ for the sites dealing with various product categories whereas the non‐functional features do not. The over‐stressed or under‐stressed features in the Indian web sites are found. The statistics generated from the user behavior model are validated against the real life user behavior and found satisfactory. The appropriateness of the link‐structure personalization algorithms is established by comparing the set of links generated by the algorithm with the links generated by the optimal policy of the underlying MDP.

The corporate that have been considered are conglomerates offering a number of products and services. But we categorize these sites based on their major product offering. Our approach of modeling the user behavior as a semi‐Markov process is possible because of our advanced implementation infrastructure. Undesirable network effects like delays, bandwidth, etc. may not make such a model that attractive in a very traditional type of environment. But the data collected in such environment can still be mined after adequate pre‐processing and appropriate normalization

Using the benchmark proposed in this model a webmaster can evaluate his web site. The sites with low site evaluation index can be improved by adding features with high weight depending on the product offering of the company. Webmaster can design the three level information architecture described in the paper and subsequently implement link‐structure personalization algorithm to improve site navigability. Besides improving site navigability the user behavior data collected thereon can be mined to generate other valuable information.

To assess and enhance the usability of an ecommerce site our work proposes: a benchmark to evaluate commercial web site design; a stochastic model to study and understand user behavior; an algorithm to personalize link‐structure of a commercial web site, and a general framework to implement a link‐structure personalization system.

This paper aims to present a strategic framework for make‐or‐buy (MoB) decision‐making process and a case study based on the framework. This framework can be applied to all the scenarios of MoB decision‐making that includes making decisions for new components and reevaluating the decisions for presently in‐sourced or outsourced components.

Based on this study of an Indian Automobile Company and review of the literature the paper proposes a strategic framework for MoB decision‐making. Unlike previous academic attempts, where a framework is developed first based on the existing theories followed by illustration through case studies, our approach starts with a case study, the paper then proposes a framework that best suits the company's need. Later the paper discusses the specific features of the framework that makes it applicable to other manufacturing sectors.

The sourcing decision‐making for any firm is a complex and dynamic process where, after stipulated period firms have to reevaluate their decisions. Existing frameworks treat both fresh and reevaluated decisions alike. It is found that the need for a framework with separate provision for outsourcing decision reevaluation. The paper identifies partial outsourcing as a strategic option to avoid supplier opportunism. It also explicitly suggests the exact decision point where risk evaluation is necessary during MoB decision‐making process.

The proposed framework takes a detailed and critical look at actual outcomes in terms of costs, competences and risk. Three important considerations adopted from the literature and incorporated in the proposed framework, makes it unique: provision for reevaluating the MoB decision, consideration of partial outsourcing as a strategic option other than purely making or buying, identification of decision points where risk evaluation is necessary.

The paper proposes multi‐sourcing models for optimal order allocation in newsvendor setting under supply disruption with stochastic demand where suppliers are capacity constrained.

Mathematical models are constructed to describe the stochastic single period two echelon supply chain. We first investigate the uncapacitated suppliers’ problem. Then capacity constraint is included in the model to study the effect on sourcing decision. A numerical example and its solution are included to illustrate the solution procedure. We find the solution using traditional optimization approach, genetic algorithm and simulation optimization approach.

The models capture the impact of disruption risk on optimal sourcing decision. When demand is highly uncertain the order should be place with the lowest cost suppliers in case of uncapacitated problem; whereas, it is to be appropriately split among a set of low‐cost suppliers in case of capacitated problem. Simulation optimization found to be the best solution approach for such problem.

The model is applicable for a single period short‐life cycle product.

The models can be utilized for any number of suppliers. The numerical study illustrates the impact of probability of disruption, its consequences and cost of purchase on sourcing decisions.