The effectiveness and continuity of modern economy are highly affected by the availability of natural resources. This is significant because meeting the needs and desires of nowadays rapidly growing population requires vast amounts of natural resources that are gradually depleting. In such circumstances, circular economy (CE) is increasingly gaining attention of businesses and organisations as a new sustainable paradigm focusing on better use of natural resources, process efficiency and climate change [1,2,3,4].
The circular economy is defined as “a regenerative system in which resource input and waste, emission, and energy leakage are minimised by slowing, closing, and narrowing material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling” [3, 5]. As can be seen in Fig. 1, the inner loops of reuse and remanufacturing seem the most appropriate end-of-life options for retaining product value in the CE model because they require less raw materials, energy, time, and cost than other conventional options (i.e. recycling and disposal). Accordingly, appropriate product life extension strategies should be adopted in order to maximise usage time within these inner loops options [6, 7].
The ambitious circular economy is therefore perceived by businesses and organisations as a positive opportunity  and an alternative to replace the traditional linear economy system “take-make-waste model”, that showed major effects on Earth’s ecosystem, with a more balanced business model that emphasises closed loops instead of continuous waste generation [4, 9, 10].
In the framework of circular economy, industrial equipment is a key factor in driving manufacturing industry. It represents about 25 percent of the manufacturing Gross Domestic Product (e.g. 21 percent of US GDP, 25 percent in Europe and 33 percent for Japan) . The challenge is to keep them well-functioning and well-maintained in production lines because failure leads to significant financial and production losses. In addition, disposal of such failed equipment is both costly and environmentally unfriendly and does not recover any residual energy . This necessitates the need to adopt maintenance strategies that extend the life of the equipment and reduce waste of material.
Therefore, remanufacturing or refurbishment is one of the options that can be made to restore industrial equipment to a level of quality and functionality that competes with the new one and contributes effectively towards developing the circular economy and achieve sustainable development [10, 13, 14]. The concept of remanufacturing is spreading throughout the world, especially in Europe. For example, the European Remanufacturing Network has reported that the remanufacturing industry in Europe is assessed to reach a total turnover of about €30 billion with 190,000 employees. This could be triple up to about €100 billion by 2030 with possibility to employ over half a million people .
In this context, researchers (from universities and research centres) and industry representatives from nine European countries launched RECLAIM (RE manufaC turing and refurbishment LA rge I ndustrial equipM ent) project to establish solutions for the sake of helping European industry to improve productivity and performance by overcoming failure problem of ageing machines. The project intending to formularise ground rules and tools that enable manufacturers to monitor the health conditions of machines and implement the appropriate life extension strategy (i.e. remanufacturing, refurbishment, upgrade, maintenance, repair, recycle, etc.).
RECLAIM solutions concept is based on developing a Decision Support Framework (DSF) for the timely and accurate machine’s health prediction and the selection of best recovery action. The DSF will accumulate knowledge of machinery status and aid manufacturers to understand the feasibility of restoring the machine, the best time to perform restoration at the least possible cost, and the best restoration strategy that needs to be implemented for the machine. This can be accomplished through integration of the following technologies and strategies (see Fig. 2) [16,17,18,19].
Cost Modelling and Financial Analysis Toolkit: using a combination of parametric costing and activity-based costing (ABC) methods to develop the cost model that will carry out cost estimation and analysis of life extension strategies and activities within RECLAIM. Monte-Carlo simulation will also be implemented to help estimate the propagation of uncertainty of cost outputs and perform sensitivity analysis.
Prognostic and Health Management Toolkit: using shop floor data to calculate overall equipment efficiency and extract other meaningful information by analysing sensory and system level data to enable prediction and prevention capabilities as well as trace asset status.
Fault Diagnosis and Predictive Maintenance Simulation Engine using Digital Twin: monitoring and predicting of the performance and status of factory assets in order to provide the user with all the necessary features to schedule maintenance work on the machines and prevent the failures being predicted by “Prognostic and Health Management” component.
Optimization Toolkit for Refurbishment and Remanufacturing Planning: optimising planning by multi-variable monitoring of the operating parameters of the machine, where the effects of variable changes can be determined and combined with known best practice methodologies for model-based shop floor control.
Refurbishment and Remanufacturing Process: deploying novel tools and methodologies to be used for the refurbishment or remanufacturing process. Also, the Augmented Reality enabled multimodal interaction mechanisms will be developed to enhance the process of refurbishment and remanufacturing.
These technologies and solutions developed in RECLAIM will be applied and validated through five case studies (pilots) selected from different European industrial sectors (i.e. footwear manufacturer, white goods manufacturer, wood manufacturing, friction welding machines and textile manufacturer) in order to demonstrate their general applicability to other industrial sectors. For more details on the RECLAIM project, the reader is referred to [16, 20].
The cost model is a key component in RECLAIM solution. Its main purpose is to estimate and understand the costs to be incurred when applying particular life extension strategy. It will be integrated with other tools and methodologies (as illustrated in Fig. 2) to enable end-users perform optimal decision-making regarding which life extension strategy (e.g. remanufacturing, refurbishment, repair) to implement for large industrial equipment that is towards its end-of-life, taking into account variables such as cost, machine performance, and energy consumption. This paper focuses on introducing an initial framework for cost model estimation in the RECLAIM solution.
Therefore, the relevant research question would be revolved around developing simple and tractable methodology that employs human expertise within cost model framework to estimate and analyse total cost of applying life extension strategy for large industrial equipment and thus support informed decision-making. Accordingly, the following research question has been formulated:
How can expert knowledge be integrated within cost modelling framework to estimate and analyse cost of applying different life extension strategies for industrial equipment?
To reach a conclusion regarding this research question, the following objectives have been set up.
Develop ABC cost model to calculate cost of life extension strategy that has more available data to be used as a benchmark strategy.
Use expert opinion to modify data of benchmark strategy.
Use modified data to estimate costs of other life extension strategies.
Apply the developed cost estimation methodology on a case study to demonstrate its applicability.
The rest of the paper is organised as follows: “Cost Estimation Methods” provides a literature review on the cost estimation methods used for developing cost models for supporting life extension strategy selection. Advantages and disadvantages of these methods are also reported. “Proposed Cost Estimation Framework” details the initial framework of the cost model presented in this paper. Cost model requirements, architecture and methodology used are described. “Cost Model Implementation” demonstrates the implementation of cost model through a case study of estimating cots for different life extension strategies applied to friction welding machine. Analyses of cost outcomes are also presented, including graphical representation of results. “Discussion” discusses the contributions of the article to the circular economy. And “Conclusion” draws conclusion from this work.