Decision makings in key remanufacturing activities to optimise remanufacturing outcomes : a review

The importance of remanufacturing has been increasing since stricter regulations on protecting the environment were enforced. Remanufacturing is considered as the main means of retaining value from used products and components in order to drive a circular economy. However, it is more complex than traditional manufacturing due to the uncertainties associated with the quality, quantities and return timing of used products and components. Over the past few years, various methods of optimising remanufacturing outcomes have been developed to make decisions such as identifying the best End-Of-Life (EOL) options, acquiring the right amounts of cores, deciding the most suitable disassembly level, applying suitable cleaning techniques, and considering product commonality across different product families. A decision being made at one remanufacturing activity will greatly affect the decisions at subsequent activities, which will affect remanufacturing outcomes, i.e. productivity, economic performance effectiveness, and the proportion of core that can be salvaged. Therefore, a holistic way of integrating different decisions over multiple remanufacturing activities is needed to improve remanufacturing outcomes, which is a major knowledge gap. This paper reviews current remanufacturing practice in order to highlight both the challenges and opportunities, and more importantly, offers useful insights on how such a knowledge gap can be bridged

A decision making tool for remanufacturing operations

Remanufacturing industry is increasingly becoming one of the world's attractive business opportunities due to social, economic, and environmental benefits. However, high level of uncertainties in technology selection, imprecise information on availability of core quantity, and lack of standardization of parameters for holistic determination of cost and benefit in remanufacturing processes are among the challenges of this industry. This research developed a decision making tool that consists of a framework for technology selection, model for acquisition of core quantity, and cost and benefit analysis model. The framework considered eight parameters, which are technology costs, operating costs, disposal costs, technology functions, technology quality, technology flexibility, technology obsolete period, and disposal effects. The framework uses fuzzy logic for approximating information and uncertainties to produce results. The results showed that the technology obsolescence for a period of 5 years before it becomes outdated, with disposal effect of 80% leads to 90o/o environment effects. This justifies that rapid technology obsolescence has negative environmental effects. The research also developed a mathematical model to determine the optimal core quantity with the influence of an advertisement factor in controlling shortage of the core return. The model would help decision makers in envisaging availability of core for new remanufacturing investment; hence, a difficulty in core acquisition can be mitigated. The results indicated that the coefficient of media advertisement is a fundamental factor that influences increase rates of core quantity. The model shows that the advertisement factor can increase 41.50 of core availability, which is a step in reducing the degree of uncertainty for the acquisition of core. Moreover, tlte research developed cost and benefit analysis model using fuzzy logic to benchmark minimum cost based on parameters for the processes. The importance of the model is to determine specific values of parameters for the entire processes. The established parameters showed high risk to under-oroverestimate resources for an investment if they were treated in isolation from each process. The results of the case study showed that the increase of production quantity to 72.l2Yo has an advantage compared with the increase of product price to 59.84% as price increase will decrease profit by 44.80%. The framework is unique as it integrates obsolete and disposal phase to evaluate environmental issues. Besides, the mathematical model with advertisement factor has produced results to influence increase of core quantity and bridge the gap of uncertainty for core. Lastly, the cost and benefit model provided accurate value of a parameter to the entire operations, and helped the step-by-step procedures in determining cost and benefit considering standard parameters set to benchmark each process

Used product acquisition, sorting and disposition for circular supply chains: Literature review and research directions

The vision of a circular economy (CE) inspires firms, governments, and scholars alike. The transition is underway in both practice and the literature, but success depends on the effective implementation of circular supply chains (CSCs), which encompass acquiring used products, sorting them by type and quality, and deciding which to dispose to various processing options. We review 131 high-impact journal articles on returns acquisition, sorting, and disposition (ASD) over the decade 2012-2021 to assess the current status of ASD research for CSCs and to discuss important research directions for supporting the transition to a CE. Uniquely synthesising the state of the art on all these three overarching decision areas, we find aspects of CSCs prominent in the decade's research agenda, such as closed loop supply chain coordination and ASD for remanufacturing, and highlight growing coverage of behavioural considerations. Research applicability has been constrained by a lack of empirical studies, limited practical validation of mathematical models, a focus on economic objectives, and restrictive modelling assumptions about behaviour and uncertainty in returns. We recommend further research in each part of ASD to facilitate a CSC, and as a whole, for transitioning to a CE. CE concepts such as joint decision-making between product design and returns management, cross-sector collaboration, and product-service systems should inform the agenda for CSC research

Industry 4.0: product digital twins for remanufacturing decision-making

Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle. The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored