A Predictive Maintenance System for Reverse Supply Chain Operations

Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most demanding reverse supply chain operations. Equipment is scattered in various locations, and multiple suppliers are involved in its maintenance, making it challenging to manage the related reverse supply chain operations. Effective maintenance is essential for businesses-owners of the equipment, as reducing costs while improving service quality helps them gain a competitive advantage. Methods: To enhance reverse supply chain operations related to equipment maintenance, this paper presents the operational framework, the methodological approach, and the architecture for developing a system that covers the needs for predictive maintenance in the service supply chain. It is based on Industry 4.0 technologies, such as the Internet of things, machine learning, and cloud computing. Results: As a result of the successful implementation of the system, effective equipment maintenance and service supply chain management is achieved supporting the reverse supply chain. Conclusions: This will eventually lead to fewer good-conditioned spare part replacements, just in time replacements, extended equipment life cycles, and fewer unnecessary disposals.

Designing a Medical Supply Chain Network Considering the Risk of Supply and Flexible Production in Two-Stage Uncertain Conditions

In the green supply chain approach, all the links that are put together to provide a product or service are considered, and strategic and operational decisions are made to increase the efficiency and effectiveness of the entire chain. At the same time, the environmental effects should be minimized. In this research, a nonlinear mixed-integer multiobjective model is developed to design a green closed-loop supply chain for medical products. In this supply chain, the echelons include supplier, manufacturer, warehouse, and customer in the forward supply chain and collection centers, repair services, and disposal centers in the reverse supply chain. In the proposed model, four objectives of customer satisfaction, environmental effects, supply risk, and total costs of the supply chain were considered. The developed model is implemented in a supply chain of medical products, and after optimizing the model, the main results including location and capacity of facilities, planning for flexible production, purchase of materials, service and maintenance plan, product transfer, and inventory level are determined and analyzed.

Effect of Big Data Analytics in Reverse Supply Chain - An Indian context

The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasises on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 330 industries. Of the 330 surveys that were mailed, 125 completed surveys were returned, corresponding to a response rate of 37.87 percent, which was slightly greater than previous studies (Queiroz and Telles, 2018).The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized.

Integrated reverse supply chain model for food waste based on industry 4.0 revolutions

The present research offeres a model to the advantage of operations for the food reverse supply chain by perfor-mancing Industry 4.0 Revolutions model of expanding a fuzzy multi-phase model for the food waste gathering reverse supply chain. This study introduces, a household waste recycling machine, which symbolizes the Industry 4.0 Revolutions. Also, electric-type vehicles have been considered for collection and delivery in accordance with the Industry 4.0 Revolutions. The rate of technology has been described in recycling stations. Several methods with different technologies to recycle food waste have been selected and assessed based on the Industry 4.0 Revolutions indicators. The food wastes are sent to recycling stations, that is places maintained, operated or used to store, buy or sell wastes before they recycled with appropriate technology. The understudy model is multi-objective, maximizing the benefit of recycling and customer response and minimizing the adverse effects of environmental pollution and transportation costs. In this research, the whale optimization algorithm is applied. The present work proposes an end-to-end solution for Reverse Supply Chain Management for food waste based on the Industry 4.0 Revolutions.

EFFICIENCY MODEL FOR THE PROLIFERATION OF SECONDARY MARKET SUPPLY CHAIN – WITH REFERENCE TO THE INDIAN GARMENT CLUSTER

The growth of secondary market in retail garment trade has been a major boost to economic growth in developing nations. This paper highlights the importance of effective forward and reverse supply chain the garment sector as driver for this booming secondary market. A conclusive research technique has been adapted to study the supply chain practices followed in the garment business those in the primary and secondary market. Structural equation modelling is used to validate and test the proposed model for supply chain performance. Cluster sampling method was incorporated. Owners of garment manufacturing firms in and around Coimbatore, Tirupur, Salem, Bargur and Bengaluru region would form the respondent group. A structured questionnaire was given to them to understand their supply chain practices, supply chain flow velocity and flow efficiency and its impact on their business performance. The firms operating under uncertain circumstances in the secondary supply line have adopted practices which lead to appropriate velocity and efficient flow of money, material and information which has been proved through the testing of the model. The velocity and efficiency in the supply line has improved the performance thereby ensuring a more sustainable business for the firm operating in the secondary markets. This study has contributed in understanding the various dimensions of best supply chain practices and its effect on the flow velocity and flow efficiency of the money, material, and information in the secondary market garment supply chain. An assessment of the results of the study has opened a window to the operations of the secondary garment supply chain line in the Indian cloth market which have been more or less camouflaged under the primary market operations. The outcome of the research also shows that these secondary market players have evolved their own strategies to sustain in volatile and uncertain circumstances. These strategies have proved to be very effective in minimizing wastage and increasing profitability of the manufacturing firms. The implications of this study is bound to give much needed support and leverage to the frail and underperforming secondary garment cluster which is a major contributor of Gross Domestic Product and employment ratio.

APTM: A Model for Pervasive Traceability of Agrochemicals

As the world population increases and the need for food monoculture farms are using more and more agrochemicals, there is also an increase in the possibility of theft, misuse, environmental damage, piracy of products, and health problems. This article addresses these issues by introducing the agrochemical pervasive traceability model (APTM), which integrates machine learning, sensors, microcontrollers, gamification, and two blockchains. It contributes in two dimensions: (I) the study of the environmental, product piracy and regulatory of agrochemical control; (II) the technological dimension: application of an adequate set of sensors collecting multiple data; modeling and implementation of a system via machine learning for analyzing and predicting the behavior and use of agrochemicals; development of a scoring system via gamification for reverse use of agrochemicals; and presenting a record of transactions in a consortium of two blockchains, simultaneously. Its main advantage is to be a flexible, adaptable, and expansive model. Results indicated that the model has positive aspects, from detecting the agrochemical, its handling, and disposal, recording of transactions, and data visualization along the reverse supply chain. This study obtained a round trip time of 0.510 ms on average; data transfers between layer one and its persistence in the database were between 4 to 5 s. Thus, blockchain nodes consumed only 34 to 38% of CPU and recorded transactions between 2 to 4 s. These results point to a horizon of applicability in real situations within agricultural farms.