A decision support system for product selection using hybridized Fuzzy-AHP TOPSIS methods

Ürün gamının çok geniş olduğu ürün aileleleri için talep edilen ürünün müşterinin isteği doğrultusunda; maliyet, kalite, fonksiyonellik gibi müşterinin ihtiyaçlarına/önceliklerine en iyi cevap verebilecek şekilde seçilmesi süreci karmaşık ve zahmetli bir Çok Kriterli Karar Verme (ÇKKV) problemidir. Bu çalışmada, Bulanık-AHP ve TOPSIS metotlarını kullanarak endüstriyel tip fan seçimi problemi için hibrit bir karar destek sistemi önerilmektedir. Önerilen model ile müşterinin taleplerine ve önceliklerine göre kriter ağırlıklarının Bulanık-AHP ile tespiti yapılmaktadır. Elde edilen kriter ağırlıkları kullanılarak TOPSIS yöntemi ile en iyi alternatifler sıralanmakta ve müşteriye sunulmaktadır.Product selection process requires perfect satisfaction of the customer needs and preferences in terms of quality, cost and functionality. Considering this aspects, it is a complex multi-criteria decision making problem. This statement is especially true for such product families with wide product variety. This study aims to design an interactive decison support tool for selecting industrial fans by employing a hybridized fuzzy-AHP and TOPSIS approach. With this work, an expert system for industrial fan selection is realized which collects customer’s requirements and preferences with Fuzzy-AHP and ranks the best fitting alternative products using TOPSIS approach

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

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