The circular economy model offers an alternative to the traditional linear economy (take, make, use and dispose), decoupling economic value creation from resource consumption by keeping resources in use for as long as possible, extracting the maximum value from them whilst in use and then recovering and regenerating products at the end of each service life [1
]. Three circular economy strategies for enhancing asset and resource productivity within a manufacturing environment have been identified in literature: (i) increasing the utilisation of an asset or resource (product as a service, sharing platforms, greater resource productivity), (ii) extending the life of an asset (durable design, predictive maintenance, reuse, remanufacture), and (iii) cascading an asset through additional use cycles (component harvesting, recycling) [2
]. However, the implementation of circular economy strategies in manufacturing environments is subject to several risks, including the mismatch between fluctuating demand, supply and value of used components, causing uncertainty regarding costs and return on investment [3
]. Another issue is the lack of information concerning the condition, availability and location of in-service assets [4
]. The emergence and increasing uptake of technologies based on the principles of Industry 4.0 present a way to overcome some of these barriers to fully implementing circular economy principles in the manufacturing sector [3
Data-driven intelligence is rapidly becoming a pervasive feature of our economy, where data generated through social, mobile, machine and product networks are being leveraged through data analytics to create new forms of value. In manufacturing industries, through emerging concepts such as Industry 4.0 and the Internet of Things (IoT), data-driven intelligence is transforming how products are manufactured, sold and used across the value chain [2
]. Despite a wealth of research into technological advances in manufacturing, much of it has focused on productivity, flexibility and responsiveness [5
]. Pairing the digital revolution with the principles of a circular economy model has the potential to radically transform the industrial landscape and its relationship to materials and finite resources, thus unlocking additional value for the manufacturing sector [6
Data-driven approaches for a circular economy in manufacturing are strongly related to the concept of Industry 4.0, also known as “smart manufacturing” or the 4th Industrial Revolution [3
]. Industry 4.0 is based on a manufacturing system driven by information technologies such as cyber-physical systems, cloud manufacturing, IoT, additive manufacturing and big data [8
]. It involves a combination of smart factories and product-enabled communication through the aforementioned technologies [9
]. Industry 4.0 allows decision making through real-time information on production, machines and flow components, as well as constant monitoring of performance and tracking parts and products [9
]. Industry 4.0 technologies have the potential to unlock a circular economy through the tracking of in-use products by embedded sensors in order to monitor maintenance requirements [3
]; products can be monitored while in use to extend their lifetime by the recovery of components for reuse or remanufacture or to inform end-of-life strategies such as disassembly and recycling [10
]. A “product passport” is required that can display information about the materials contained in the product to facilitate reverse logistics and therefore circular economy strategies [8
]. In addition, information technologies could be used to monitor and control operational performance to assess real-time efficiency to predict the maintenance or refurbishment of components/products [11
]; to provide services alongside the physical product (e.g., to customise products using 3D printing [12
]); or remove the provision of physical products by replacing them with virtual ones [3
There has been growing interest in exploring the relationship between a circular economy and Industry 4.0 [3
]. However, a deeper knowledge and understanding is required on how data acquired from digital technologies can actually unlock the potential of a circular economy. Table 1
identifies the data flows between the product, the user (including the customer, client and operator) and the conjunction of activities that occur between the designer, the manufacturer and the supply chain—referred to as “Big M” in manufacturing [14
], mapped against circular strategies to identify new models of material use and value creation. As depicted in Table 1
, remanufacture (darkest shaded) is the focus of this this paper and it falls within the circular strategy of product life extension (light shaded). Remanufacturing is the process of disassembling and recovering an asset at a product and component level [8
], and it is considered as one of the product life extension strategies of a circular economy to keep a product or component at its highest utility and value [15
]. Refurbishment, which is also classified as circular strategy of product life extension, shares many similarities with remanufacture [16
]. While refurbishment is the process whereby used products are returned to use conditions with a warranty shorter than a newly manufactured product, remanufacturing is an industrial process where used products (also known as cores) are restored to useful life with a warranty and quality at least as good as a newly manufactured product [17
]. Remanufacturing is already common in the automotive sector, as it has one of the largest economic impacts [16
], and interest in remanufacturing for the automotive sector is expected to increase as we move from fossil-fuel-powered cars to hybrid and electric vehicles.
This research focused on investigating the value of capturing and analysing data streams to inform decision-making processes in the remanufacturing of automotive components (darkest shaded) using simulation techniques such as discrete event simulation (DES) and system dynamics (SD). The remanufacturing process for an electric motor was the focus of this study, as the automotive sector reveals that the sustainability benefits of digitisation could be substantial, arising from 20–30% machine downtime reduction, 12–20% inventory reduction, 30–50% cost of quality reduction and up to 80% improvement in forecasting accuracy [18
]. In addition, this sector has succeeded in using data-driven intelligence to enable sustainable practices. However, studies have focused on only one circular economy strategy (e.g., predictive maintenance) and such risks have prevented successful implementation of other aspects of remanufacture such as the assessment of the stochastic nature of returned products [19
], highlighted as one of the key challenges facing remanufacturing.
Digital modelling and simulation could be useful in supporting decision making in remanufacturing operations. Simulation techniques such as SD and DES have been used to predict the behaviour of complex remanufacturing systems [20
], but very few adopt a data-driven approach. Although data-driven simulations have been developed for manufacturing applications in automotive [22
] and semiconductor sectors [24
], assembly logistics [25
] and the construction industry [26
], there has been very little research in data-driven simulation for remanufacturing operations. Previous work by the authors investigated an SD-based approach for the data-driven remanufacturing of fuel cells [27
]. This work identified the key parameters/variables needed for fuel cell remanufacturing by means of interviews, ranked the variables by Pareto analysis, developed a causal loop diagram for the identified parameters/variables to visualise their impact on remanufacturing and modelled a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing. Recently, Goodall et al. [28
] reported a data-driven simulation approach to support the remanufacturing operations of an electric and electronic remanufacturer where they investigated the utilisation of data from digital manufacturing systems to predict material flow behaviour within remanufacturing operations by updating and automatically modifying the simulation model to reflect the real system. To address the research gap in data-driven simulation for remanufacturing, this paper aims to investigate the value of simulation techniques to inform decision-making processes in remanufacturing, considering the stochastic nature of returned products.
In this research, a remanufacturing process was mapped and simulated using DES to depict the decision-making process at the shop-floor level of a hypothetical remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through SD and DES models to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. It was found that the analysis of in-service data from automotive components can influence decisions surrounding remanufacture and can lead to significant cost, material and resource savings by reducing the uncertainty in the condition of components returned for remanufacture.
4. Summary of Results: The Value of Data
4.1. DES Model
The DES Model helped to understand that the data obtained from embedded sensors in the product were critical in determining the CPQ of that product. The model in Figure 2
demonstrates the effect of CPQ on the time spent in disassembly, cleaning and inspection. For example, during remanufacturing of 100 products with high CPQ (ranging between 0.8 and 1), 75% of the products spent 31–35 h in disassembly, cleaning and inspection, with a mean of 31 h, as shown in Figure 5
a. However, during the remanufacturing of 100 products with low CPQ (ranging between 0.1 and 0.3), 75% of the products spent 46–52 h in disassembly, cleaning and inspection, with a mean of 47 h, as shown in Figure 5
b. Vertical bars correspond to time spent in the system, with heights proportional to the density of products. The solid blue line represents the mean time spent in the system. Figure 6
a depicts the variations in the time spent in disassembly, cleaning and inspection for batches of high and low CPQ.
In addition, the DES model showed that storage/work space was allocated for each station in the form of pallet racks. Figure 6
b demonstrates a bar graph showing the maximum utilisation of pallet racks in each section of the remanufacturing plant. This feature is useful to determine the optimum resources required to meet the product demand, such as the number of workstations being used.
4.2. SD Model
The SD model demonstrated the effect of CPQ on the products awaiting remanufacture in the system. For example, if a batch of CPQ 0.1 was received for remanufacturing, then after 10 days only 1 product would complete the disassembly and inspection stage and would reach the “awaiting remanufacture” stage. Whereas, if a batch of CPQ 1 was received for remanufacturing, the number of products at the “awaiting remanufacture” stage would be three times higher, as depicted in Figure 7
In addition, Figure 7
(bottom) demonstrates the effect of CPQ on the reusable products. For example, if a batch of CPQ 0.1 was received for remanufacturing, then after 3 days there would not be any product in remanufacturing.
4.3. Interview Analysis
The simulation models were validated by obtaining the views of the respondents who were experts in the field of remanufacturing. Details of the profiles of respondents and companies including years of experience, remanufacturing knowledge, etc. are provided in Appendix A
. A questionnaire was presented to them and their feedback recorded. Key elements from the questionnaire were:
Discussion of the most prevalent scenario in their company in terms of the actual quality and the certainty of product quality (CPQ) of returned products, as shown in Figure 8
How would they rate the four elements of the CPQ in Equation (1) in the order of their importance and impact on the decision to remanufacture and impact on remanufacturing?
How would the intrinsic knowledge play a role if the CPQ was low (quality is uncertain)? What would the company’s strategies be to reduce costs when assessing a component?
Would CPQ be able to substitute the intrinsic knowledge of remanufacture, or could they be complementary?
What if CPQ could be made into a standard according to the type of component being assessed? Would this increase/decrease the certainty and decision to remanufacture a component?
The respondents were in agreement on the importance of CPQ, as it combines a function of key variables crucial for remanufacturing. However, it was noted that for the CPQ to be widely accepted by remanufacturers, the definition must be agreed upon by the global remanufacturing community. As quoted by respondent A, “it is important that an accepted definition of remanufacturing be agreed in order to link remanufacturing to CPQ and fully engage with remanufacturers.”
According to respondent B, the scenarios of returned product (as mentioned in Figure 8
) could inform the time associated with remanufacturing. Thus, the CPQ of a product can influence the threshold or point where an item can be classified as remanufacturable or not, as determined by the economics of remanufacturing. Respondent C affirmed that having a low CPQ would increase the remanufacturing time as the returned product would require a detailed inspection in order to be certain about its state. All respondents agreed that all four elements of the CPQ (Equation (1)) were important in the determination of quality and certainty (Figure 8
). However, for remanufacturers and original equipment manufacturers (OEMs), the priority and weightage of elements would differ. The respondents were of the view that PC, PRM and PRH will be fully useful for OEMs, who will have full visibility of the entirety of the dataset and full control of the overall product build, but may be less so for remanufacturers, who are bound to component-specific contract and whose unrestricted access to the data is not guaranteed. They argued that the remanufacturer will have to put the component back to its original state, regardless of the arrival condition and history. Thus, it can be concluded that the OEM and remanufacturer will have a different understanding of product quality and certainty of product quality in Figure 8
, as it relates to remanufacturing. This suggests that the impact on the decision to remanufacture and impact on remanufacture for these elements will be high, but may vary across OEM and remanufacturer. Respondent D highlighted the importance of data from sensors with the following comment:
“Data from sensors will be hugely important, but will be product dependent. Depending on the availability and how valuable the data is, it will be hugely important”
All respondents agreed that the intrinsic knowledge of experienced inspectors/remanufacturers will be important in determining the CPQ of a component. However, while respondents agree that this will improve remanufacturing, they suggest that this faces the challenge of identifying the most appropriate transfer mechanism. Benefits such as reduced costs from a better assessment of the component were highlighted, as this should encourage the transfer of intrinsic knowledge. Thus, the short-to-medium-term estimation is that intrinsic knowledge from experienced inspectors and CPQ will be complementary in purpose. However, respondents acknowledged that CPQ will be important for some phases of remanufacture, such as disassembly.
Finally, respondents recommended to have a generic standard for CPQ. Respondents pointed out that remanufacturing already is complicated by the absence of a single globally accepted definition. Standardisation of the CPQ is ideal and should be guided by quality and be product-specific. The potential to link the CPQ to the larger fields of circular economy and product design should support the CPQ and increase the certainty and decision to remanufacture.
4.4. CPQ and Wider Applications
This study demonstrated that the capture of CPQ rates could be used to predict not just the time but also the number of products that could be remanufactured, enabling a forecast that can be used to plan remanufacture and production processes. In addition, adding a degree of automation in inspection would be key for the aforementioned forecasting. However, there is a need for a system that can process and analyse historical data based on the PRM and PRH. Experienced inspectors would be key to gather as much information as possible in order to develop and calibrate this system. The challenge is to find a mechanism to do so that could also help to standardise the data for each product. If this challenge is overcome, data sets could then be used to differentiate one product from another to make the remanufacturing process as efficient as possible. Another challenge would be to replace manual with automated inspections for components that pass a quality threshold. This could be overcome by assessing the CPQ when disassembling a part, as this information could be captured by the CPQ to increase its certainty. Therefore, there will be a point where the product does not need to be further inspected, and the decision would be automated based on the CPQ value.
CPQ has the benefit of reducing costs by using an automated process for inspection, as it allows a more detailed distinction between “go” or “no go” for remanufacture. To scale its impact, CPQ would need to be product specific and, in the long term, it should be considered when designing a product or component. In the future, considering new technologies such as block chain would be imperative to secure how data are gathered and analysed, as well as allowing interconnectivity between the product to be remanufactured and the wider operation system.
Within the wider context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal circular economy strategy and the time of intervention for the current life of a product. As demonstrated, data streams would be imperative to understand the factors of influence that affect the product integrity, condition and reliability, and as such developing mechanisms to capture and analyse this data could help to uncover exciting opportunities for creating and quantifying new forms of value within manufacturing.