Circular economy under the impact of IT tools: a content-based review

ABSTRACT

To overcome the problem of scarcity of presently available resources in future, the optimal strategy, emerging under the banner of ‘Circular Economy (CE)’, would be to keep the product-related resources such as material/energy, etc., always in circulation’ so that the ‘waste’ is either eliminated or at least it is minimised. As some of the industries have already started practicing it at the global level, the underlying idea behind CE is to create products which are durable, easy to reuse, remanufacture or recycle and at the same time profitable. Present paper made a n attempt, through a content-based systematic l i t e r a t u r e review, to seek an answer to the research question: ‘has the CE and Reversed Supply Chain Logistics (RSCL) nexus changed significantly under the impact of the IT tools and their applications?’ Spanning over sixty three data sets, the analysis was undertaken in terms of such features as data source, latency, data fields, work typography and the employed research tools. In the last five years or so, how the status of the CE-RSCL system has emerged under the shadow of the recent developments in the IT tools is discussed. Finally, conclusions indicating research gaps are drawn and directions for future research are portrayed.

KEYWORDS:

• Circular Economy
• waste Management
• it-enabled tools
• reversed Supply Chain Logistics
• industry 4.0

1. Introduction

Since the last century or so, companies have been constantly making efforts for developing goods and services for earning more and more profits. However, this process of profit making has not been effectuated in an optimal manner. One very important dimension of this non-optimality could be traced in terms of the ‘waste’ resulting from the used and consumed products/materials/energy that never came back to the original manufacturer/supplier of the entities. Hannon, Kuhlmann, and Thaidigsmann (2016) observed that when a consumer uses a product infrequently or discards it because it has worn out, at least some of the material/energy that went into making the product has been wasted. Agrawal, Singh, and Murtaza (2018) in their case study of electronics manufacturer based in North India identified new challenges in managing product returns due to the changed business environment. With the huge amount of resources going into making of huge number of products, the precious treasure of available resources is getting scarcer day by day. To overcome the problem of scarcity in future, the optimal strategy would be to keep the product-related resources such as material/energy, etc., always in ‘circulation’ so that the ‘waste’ is either eliminated or at least it is minimised. Some businesses are using this kind of strategy under the banner of what is termed as ‘Circular Economy (CE)’. The underlying idea behind CE is to create products which are durable, easy to reuse, remanufacture or recycle and, of course, profitable. Normally, companies do not bother today about what would be the ultimate fate of their products after they are procured by the end-users. It is anticipated that people will eventually throw them out and buy new ones, and the local waste collectors will take care of what has been discarded by the consumers. For example, in general, mechanical components are designed with the ‘ease of manufacturing’ in mind because this is what makes them (components/parts) less expensive. This leads to such ‘design choices’ like snapping pieces together rather than joining them with fasteners. A manufacturer may keep a policy of giving its customers rebates for returning end-of-life parts/products/mechanical components so that the manufacturer might be able either to refurbish them for resale at a lower price or entirely dismantle them for the purpose of recycling. According to a recent research, each year some 2.6 USD trillion worth of material in fast-moving consumer goods, 80 percent of the material value is thrown away and never recovered (Hannon, Kuhlmann, and Thaidigsmann 2016). As reported in literature (Govindan and Hasanagic 2018) by 2050, the demand for natural resources is expected to get tripled indicating thereby the exceeding importance of either elimination or minimisation of waste which is the primary target of CE. As we progress, the CE would have to effectuate a larger quantity of the material to be reused again in one form or the other. In this context, to cite a practical example, in Europe, it is estimated that the net benefit of applying circular-economy principles could be as much as €1.8 trillion annually by 2030 (Manyika et al. 2015). It was also predicted that by 2020, up to 50 billion connected devices would be present in our technological world (Morlet et al. 2016). Therefore, the firms that successfully design products from the ‘circular economy’ viewpoint would be of considerable value and would create everlasting and rewarding relationships with customers. However, the data pertaining to CE system appears to be huge and therefore the success of CE and its operation would primarily depend on the extent to which IT tools are employed. So is the case with the scope of applying the RSCL (Reversed Supply Chain Logistics). Present paper puts forth an overview of the CE and RSCL with the focus on the role of the applications of the information technology (IT) tools in the growth and development of the fields. As a result of the recent developments in the field of IT, such tools as RFID, IoT, Big Data, etc., have emerged and consequently in the last few years or so, how the concept of the ‘circular economy’ is getting expanded in the field of engineering and technology, as reported in the literature, is reviewed through the content-based methodology, an approach employed by (Galvao et al. 2018) through the present work, an effort was made to answer the following research question (RQ): ‘Has the CE and RSCL nexus changed significantly under the impact of the IT tools and their applications? The research methodology adopted for this purpose is given in Section 2. Based on this methodology, the review of the literature was undertaken and results were obtained in terms of the five variants viz. data work source, latency of work, work fields/areas, work typography and employed tools, as put forth in Section 3. The findings pertaining to these variants related to the recently evolved literature, spanning over 63 research works carried out by different researchers in the emerging fields of CE, RSCL and the impacts of IT tools are discussed in Section 4 and, finally, conclusions are drawn and directions for future research are presented in Section 5. Thus, the research gaps which invite the attention of the future researchers in the fields are portrayed.

2. Research methodology

A good research methodology for conducting a systematic literature review based on content analysis was proposed by Seuring and Gold (2012). They indicated some milestones in terms of material collection, descriptive analysis, pattern of analytic categories and finally, material evaluation and research quality. The same approach was employed for the present study as well resulting in unbiased or less biased findings on CE and RSCL nexus under the impact of the recently evolved tools of IT, as given below:

2.1 Material collection phase

The unit of analysis for the review was taken as one research publication or reference out of the selected research works (material or data) to be reviewed. Data or for the analysis were collected through print and electronic media available on such platforms as Scopus, Google Scholar, Web of Science, etc., by way of the relevant keywords associated with the fields of investigation as per guidelines of literature review, proposed by Seuring and Gold (2012) and Pagoropoulos, Pigoss, and McAloone (2017). In all, a total number of 128 papers were collected on the associated and linked topics, their abstracts were reviewed and finally the most related works, numbering 63 were selected and reviewed critically. Their selection criteria revolved around primarily on the content basis of the areas associated with the CE-RSCL-DIGITISATION linkages, their sources, the work typography, employed analytical tools, and the latency of publications, each one of which is described in Section 3.

2.2 Descriptive analysis and pattern of analytic categorisation

Based on the relevant keywords associated with the RQ (research question) as stated earlier, the data were analysed by way of undertaking the evaluation of the research works included in the present study. As illustrated in terms of the phrase employed by Pagoropoulos, Pigoss, and McAloone (2017), the ‘coding scheme’ in the present study was as follows:

(a) Reviewed Data Work source (Journals etc.)

Series-1 based on Journals: Here ‘journals refer to ‘a periodical publication in which scholarship relating to a particular academic discipline is published’. (Wikipedia; en.wikipedia.org/wiki/Research journal),

Series-2 based on Company/Industry Reports and Documents

Series-3 based on Conference/Workshop Proceedings

(b) Latency of work (Year of publication)

Series-1:, Series-2:, Series-3:, Series-4:, Series-5:, Series-6: Series-7 and Series-8 represent the publications corresponding to the years 2019, 2018, 2017, 2016, 2015, 2014, 2012 and 2010, respectively.

(c) Data Fields/areas (Topic/Topics of the paper)

Series-1: Circular Economy (CE)-based [The underlying idea behind CE is to create products which are durable, easy to reuse, remanufacture or recycle and, of course, profitable].

Series-2: Reversed Supply Chain Logistics (RSCL)-based [The contents of the unit involve logistics that is reversed, rather than forward. The entire travelling components like planning, implementing and controlling processes of logistics involving flow of raw materials, in-process inventory items, finished products and associated information flow move from the end-users to the point of origin in order to recapture the value or to get it disposed properly]

Series-3: Digital Technology (DT)-based [As observed by Antikainen, Uusitalo, and Reponen (2018), digitisation is a good enabler of CE. In the present work, DT refers to such developments in the field of IT (Information Technology) tools as RFID, IoT, Big Data, etc., which have emerged recently and are being employed in the field of CE].

Series-4: Digital Technology (DT)-CE Nexus-based [Contents of the unit relate to the interactive relationship between DT and CE areas of knowledge].

Series-5: Digital Technology (DT)-RSCL Nexus-based [Contents of the unit relate to the interactive relationship between DT and RSCL areas of knowledge].

(d) Work typography

Series-1: Research based [Basically, the contents of the unit are theoretical in nature].

Series-2: Application based [The contents of the unit are based on industry-related or trade related work].

Series-3: Review based [The contents of the unit represent a critical evaluation of the previously undertaken investigations published in research journals and other forms of publications].

(e) Employed Tools

Series1: Quantitative Tools based [Pertains to the quantitative research representing the systematic investigation of the subject-matter by gathering data that can be quantified through statistical, mathematical or computational techniques with the objective of developing and employing mathematical models, theories, and hypotheses pertaining to the phenomena being studied. To be specific, examples of the quantitative tools include AHP (Analytic Hierarchy Process), ANOVA (Analysis of Variance), BWM (Best Worst Method) etc.].

Series-2: Qualitative Tools based [The contents represent a research work undertaken on scientific lines through non-numeric data and spread over concepts, definitions and symbols, etc.].

Series-3: Digital Technology Tools based [The contents of the unit involve the digital technology (DT) tools, as specified earlier under part (c); Series-3].

Analysis of the collected data, in this context yielded the summarised information presented in the following bar chart (Figure 1).

Figure 1. Analysis of the reviewed work-data, numbering 63 (y-axis), under 5 different categories (y-axis)

2.3 Material evaluation and research quality phase

Primarily this phase involves inter-rater reliability involving at least two coders, testing for transparency and objectivity based on clear coding schemes and validity based on theoretical foundation (Seuring and Gold 2012). Following the methodology of testing the reliability, the transparency and validity, detailed by Agrawal, Singh, and Murtaza (2018), the data reviewed were classified by authors of the present work and consensus was ensured. For reliability, all the literature-related components (e.g. journals etc.), were cross-checked through spread-sheets and the results were found to be within acceptable limits implying thereby the reliability and consistency of the present research.

3. The findings

The data-set pertaining to the present work were collected and analysed as given in the earlier sections.

Following sub-sections deal with the results obtained in terms of the findings contained in the content-based analysis of the collected data, presented as follows:

3.1 Reviewed data work about ‘data source’

The reviewed data work originated from the following sources: ‘journal publications’, ‘company/industry reports and documents’ and ‘conference/workshop proceedings’. The content-based analysis is presented in Table 1. It may be observed that maximum contribution (58.73%) came through the journals publications and next to it (25.4%) was from conference/workshop proceedings, while company/industry reports’ and documents’ contribution was only 15.87% to the reviewed material.

Table 1. Content-based analysis of the reviewed matter pertaining to ‘data source

Data Source Series No.	Data Reviewed(No. of Papers)	Percentage (%)
1: (Journal Publication)	37	58.73
2:(Company/Industry Reports and Documents)	10	15.87
3: (Conference/Workshop. Proceedings)	16	25.4

3.2 Reviewed data about ‘work latency’

The reviewed data work originated from the publications during the years 2019, 2018, 2017, 2016, 2015, 2014, 2012 and 2010. The content-based analysis is presented in Table 2.

Table 2. Content-based analysis of the reviewed matter pertaining to ‘data work latency’

Data Work Latency Series No.	Data Reviewed(No. of Papers)	Percentage (%)
1: (Publication year 2019)	6	9.52
7: (Publication year 2018)	11	17.46
7: (Publication year 2017)	17	26.98
8: (Publication year 2016)	18	28.6
8: (Publication year 2015)	7	11.11
6: (Publication year 2014)	2	3.17
7: (Publication year 2012)	1	1.58
8: (Publication year 2010)	1	1.58

It may be observed that maximum contribution (28.6%) to the present reviewed matter came through the works published during 2016, next to which (26.98%) was from the year 2017 while the works of the years 2019 and 2015 were almost in the same proportion and 2012, and 2010, each respectively contributed minimum (1.58%) to the reviewed material.

3.3 Reviewed data about ‘work fields’

The reviewed data work was analysed on the basis the work fields/areas to which the reviewed matter content was associated with. The analysis revealed that the entire data-set got originated from the following fields/areas of knowledge: ‘circular economy (CE)’, ‘reversed supply chain logistics (RSCL)’, ‘digital technology (DT)’, ‘CE-DT Nexus’ and ‘RSCL-DT Nexus’. The content-based analysis is presented in Table 3.

Table 3. Content-based analysis of the reviewed matter pertaining to data work fields/areas

Data Work Field Series No.	Data Reviewed(No. of Papers)	Percentage (%)
1: (Circular economy, CE)	34	53.96
2: (Reversed Supply Chain Logistics, RSCL)	2	3.17
3: (Digital Technology, DT)	16	25.4
4: (CE-DT Nexus)	9	14.3
5: (RSCL-DT Nexus)	2	3.17

It may be observed that maximum contribution (53.96%) came through the circular economy (CE) field, next to which (25.4%) was from the digital technology (DT) area while CE-DT area contributed around 14%. The RSCL, i.e. ‘Reversed Supply Chain Logistics (RSCL)’ and ‘RSCL-DT (Digital Technology) nexus’ data each contributed minimum (3.17%) to the entire data-set containing the reviewed material.

3.4 Reviewed data about ‘typography’

The reviewed data work analysed in terms of the ‘typography’ originated from the following types of data work: ‘research’, ‘applications’ and ‘review’ types. The content-based analysis is presented in Table 4.

Table 4. Content-based analysis of the reviewed matter pertaining to ‘data work typography

Data Source Series No.	Data Reviewed(No. of Papers)	Percentage (%)
1:Research	21	33.34
2:Applications	19	30.15
3:Review type	23	36.51

It may be observed that the quanta of contribution to the reviewed matter content through the ‘research’ and ‘applications’ based matter was almost same whereas ‘review’ types data contributed maximum (36.51%) to the reviewed material.

3.5 Reviewed data about ‘tools employed’

The reviewed data work was also analysed in light of the kinds of analytic tools employed by different authors of the works included in the present review. It was found that the works, under reference, involved following kinds of analytic tools: quantitative tools, qualitative tools, and digital technology-based tools. The content-based analysis is presented in Table 5.

Table 5. Content-based analysis of the reviewed matter pertaining to ‘employed tools’

Empoyed Tools Data Work Series No.	Data Reviewed(No. of Papers)	Percentage (%)
1: Quantitative tools	10	15.87
2: Qualitative tools	41	65.08
3:Digital Technology-based tools	12	19.05

It may be observed that maximum contribution (65%) came through the employment of the qualitative tools, next to which were ‘quantitative-’ and ‘DT-based’ tools, which were employed by the researchers, were 15.87% and 19.05%, respectively.

4. Discussion

In light of the results, obtained in Section 3, the findings of the present study are discussed. It has been observed by previous researchers that literature review based on content analysis might serve as a good technique of research while dealing with the qualitative and unstructured data and other materials (Seuring and Gold 2012). They also noted that ‘content analysis may also be applied for analyzing published material’. Accordingly present research was undertaken in terms of five categories of the data reviewed and results were obtained. In the category of the ‘data source’, it appears that majority of research material is originating from the journal-based resources. It appears to be logical also because the world of academia and researchers cannot survive without the research material made available by the journals. Thus, journals appear to be a widely consulted source of data and information among the academic people. As regards the second category viz. latency of publication, it appears that during 2016 and 2017 researchers paid much more attention to the topics under reference. However it appears that prior to 2016, not many researches were interested in the field. As regards 2018 and afterwards, the data collection process might not have accessed enough of the published literature due to the limitations of resources on the part of authors. For appropriate conclusions, in this context, future reviews would have to be referred to. In the third category pertaining to the academic field of the data, it was revealed that the CE emerged as the most researched field and so is expected to be in future also for obvious reasons of the material/energy getting scarcer day by day. Similar observations were made by other researchers also (e.g. Suárez-Eiroa et al. 2019). Moreover, under the shadow of the recent developments in field of digitisation, future researchers are likely to pay more attention to the DT as well as the CE-DT nexus. This is the future requirement because, as stated earlier, the DT tools are found to be the CE enablers. So far as the Reversed Supply Chain Logistics (RSCL) is concerned, it appears that currently relatively this title is getting less attractive among the researchers as, perhaps, enough work has already been undertaken by the previous investigators on the topic. Through the findings pertaining to the fourth category viz. typography (research, review and application types data), it might be concluded that all the three types of works are being addressed by the researchers equally. Lastly, the findings on the fifth category, i.e. the ‘tools employed’ revealed that mostly qualitative kind of researches were undertaken during the decade considered in the present work. Thus, future researchers would have to address the issues pertaining to the CE field more rigorously through ‘quantitative’ and ‘DT-based tools’ in their works. This is a direct implication of the present findings so far as the tools employed by researchers are concerned. As regards the discussion on the reviewed data, it might be noted that entire reviewed data analysis revolved around the research issue: how the areas of the CE and reversed supply chain logistics (RSCL) got impacted under the shadow of the emerging IT tools? The results were obtained as above and the findings based on these results are discussed in a systematic manner as given below:

4.1 The emerging concept of CE

Recently, in one of the exhaustive and unique publication of Kirchherr, Reike, and Hekkert (2017), the concept of the circular economy, as appeared in the last several years’ literature, spanned over the analysis of 114 definitions. They observed that ‘the circular economy (CE) concept is trending both among scholars and practitioners’. This is indicated by the rapid growth of peer-reviewed articles on CE: More than 100 articles were published on the topic in 2016, compared to only about 30 articles in 2014 (Geissdoerfer et al. 2017). CE in its conceptual form, in the eyes of various stakeholders, can blur the concept since they frequently operate in significantly different worlds of thought (Gladek 2017). Blurriness has been raised as a criticism against concepts such as the green economy (Loiseau et al. 2016) and other similar terms (Ghisellini, Cialani, and Ulgiati 2016). These days the circular economy (CE) concept is trending and thus much ‘lip service’ is given to it. Trending concepts, in general, diffuse in their meanings and it appears that same is happening to the CE concept also. Among the current CE related literature reviews, perhaps, no comprehensive and systematic analysis specifically on CE understandings was presented earlier. The comprehensive set of 114 CE definitions and systematic analysis against a coding framework, under reference, provided more transparency regarding current CE understanding. CE can be defined, within the framework of the paper, under reference, as an economic system that replaces the ‘end-of-life’ concept with such terms as reducing, reusing, recycling and recovering materials in production/distribution and consumption processes. Kirchherr, Reike, and Hekkert (2017) in their analysis of 114 definitions provided the first quantitative evidence that and how CE meant many different things to different people. Maioa et al. (2017) presented a new indicator for resource efficiency and the CE employing the market value approach. It might be observed that some of the authors seem to have no idea about what CE meant, whereas some authors equate CE, in its entirety with the concept of ‘recycling’. On the other hand, it was revealed that the most common conceptualisation of the ‘how-to’ of the CE is a combination of the terms like reducing, reusing and recycling. According to Ness and Xing (2017), only one third of the offered definitions, explicate a waste hierarchy. The work of Kirchherr, Reike, and Hekkert (2017) further revealed that most authors see CE as an avenue for economic prosperity, whereas previous scholars conducting narrative reviews of the CE literature had argued that CE would be mostly concerned with environmental aims. They also confirmed that previous scholarly writings pertaining to the CE understanding mostly neglected the social considerations in their studies. Lastly, in their in-depth analysis, they also found that only one out of five definitions considered the consumer as a second enabler of CE. The significant momentum gathered by the concept holds the promise that CE may be able to reach beyond the current sustainable development efforts. The answer to the question how far is the CE really ‘circular’ is provided by the term ‘circularity’. Saidani et al. (2019) explored this concept and found and classified 55 indicators of circularity. Pieroni, McAloone, and Pigosso (2019) reviewed a total number of 92 approaches pertaining to the business model innovation for circular economy and sustainability and concluded that there is a need to evolve a stronger relationship between the two dimensions of ‘circularity’ and ‘sustainability’.

4.2 CE: the application perspective

The globally reputed consulting firms like Accenture, Deloitte, EY and McKinsey & Company all published several materials on the CE in the last two years or so [Lacy et al. (2015), Accenture Strategy (A. S.) (2016), Hestin (2016), European Commission Report (EC Report) (2019)]. Among both the scholars as well as the practitioners the CE concept is emerging to be of great interest as it is being viewed as an operationalisation step for businesses to implement the much-discussed concept of sustainable development (Murray, Skene, and Haynes 2017). Many concerns, recently, have started offering circular economy services to the enterprises (Khalamayzer 2018). The European Commission Administration (European Commission Report (EC Report) 2019) recently recommended the recycling target to be increased to 70% by 2030 by way of focusing on packaging materials and food products optimum utilisation and waste management strategies. In the real-life world, following the CE strategy in the modern day markets of the mobile-phone sector, for example, several hand-phone manufacturers sell refurbished units of their own phones at a discounted price. Also, some of the independent companies have emerged to utilise the residual value of used phones that still function. These firms collect such phones, fix them, instal fresh software, and sell them to less fortunate customers. In this manner, the secondary market for mobile phones offers an opportunity for the companies to concentrate more on the value of the material and energy they use to make their products. In terms of the specific application perspective, many companies were reported to have sold its products as services as a part of the requirements of the CE business model. For example, in 2014, French train manufacturer Alstom evolved ‘Health Hub’, a predictive maintenance tool that monitored the health of trains, its infrastructure and signalling systems by means of advanced data analytics. Similarly, companies like BMW (car sharing on rental basis), Danish CHP (temporary apartments making Danish company), Grundfos (a pump supplier for heating, air conditioning, irrigation and water treatment company experimenting take-back strategy of CE), German company MAN Truck & Bus (an international supplier of commercial and transport vehicles to businesses) and a company that is famous as a CE leader is Steelcase which since the year 2017, has been helping other companies in reusing, donating and recycling their furniture (Khalamayzer 2018). How the CE business models can reshape the washing machine industry is amply demonstrated by Bressanelli, Perona, and Saccani (2017). In the field of waste management, Liguori and Faraco (2016) presented an enriched review of the treatment processes in biorefineries, demonstrating a good promotion of circular economy. The recent practice-based developments in the field reflect on the business model changes required in future in order to seize the opportunity which begins with the product development. Today, it appears that the scenario is changing fast. In many studies [e.g. (Tukker 2015)] pertaining to the application in product service system, resource efficiency has been focused. Once the product developers receive specifications, they design their products accordingly. But when the products are designed keeping customers in mind, an effective method is ‘design thinking’ which is a user-centred design approach that focuses on finding the best way to meet the needs of the customers. It starts with observing customers in order to learn about their needs and about how those needs are met by the existing products. People such as product designers, engineers, marketing specialists, and many more involved in making and selling the products get the insights on the customers’ needs in order to rapidly make a prototype, test and refine the underlying concepts for products and services. According to the Ellen MacArthur Foundation, ‘Circular Economy is characterized as an economy that is restorative and regenerative by design which aims to keep products, components and materials at their highest utility and value at all times, distinguishing between technical and biological cycles’ (MacArthur and Waughray 2016). In a Circular Economy, ‘companies concentrate on rethinking products and services from the bottom up to “future proof” their operations and prepare for inevitable resource constraints all the way through to the customer value proposition’ (Accenture Strategy (A. S.) 2016). In the manufacturing sector how the circular economy can be implemented by way of managing the waste and proper utilisation of resources was nicely reviewed by Lieder and Rashid (2016). In many cases, the implementation of CE in manufacturing companies requires changes in their business models, which can be achieved by means of Product/Service-Systems (PSS), which presents a strategy attracting considerable attention over the past few decades or so in decoupling the economic growth from resource consumption. For the period of 2008–2017, Basu (2019) studied the problem of CE in 27 European Union Countries through a statistical software EViews 10 and enumerated the factors through which the CE model can be determined. How far the manufacturing sector scenario in India is ready for CE was illustrated through a model based on the extended theory of planned behaviour by Singh, Chakraborty, and Roy (2018). This study was conducted through a survey with application of structural equations modelling as a tool of investigation. In the context of in managerial decision-making situations on reuse, remanufacturing and recovery of materials, Li et al. (2015) discussed the use of IoT. Such developments might lead to a group of smart-circular strategies: smart maintenance, smart reuse, smart remanufacturing and smart recycling (Alcayaga, Hansen, and Wiener 2019).

4.3 CE and reversed supply chain logistics (RSCL) nexus

One of the most promising paradigms to appear in recent years is the Circular Economy, a recently emerging concept in the world of engineering and technology that represents the ‘cradle to cradle’ kind of model of a production system. On the other hand, the non-circular systems were based on the ‘cradle to grave yard’ kind of models, reflected in the forward supply chain logistics whereas the ‘reversed supply chain logistics’ (RSCL) are primarily representing the ‘cradle to cradle’ kind of models. In the context of industrial application, it may be observed that with the ever growing pace of technological development at the global level, small and medium scale enterprises (SMEs) have been playing a critical role in both developed as well as developing economies. Rizos et al. (2016) highlighted varieties of barriers and also the enablers pertaining to SMEs in the framework of the CE implementation. Primarily, the impact of RSCM, when viewed on the CE platform, can be traced in terms of such features as management of material flow, information flow, and revenue flow across the whole of the supply chain, but taken in the ‘reversed’ order, i.e., from customers or end-users to the manufacturers. On the other hand, already there are evidences in literature in support of the studies pertaining to ‘manufacturing flexibility’ and ‘strategic flexibility’ (MacArthur and Waughray 2016) that lead to the benefits of the CE concept. In light of the recently appearing global Supply Chain definitions of SCM and RSCM (Bressanelli, Perona, and Saccani 2017), the SCM framework integrates key business processes from end-users through original suppliers providing products, services, and information that add values for customers and other concerned stake holders. On similar lines, the work on ‘reversed’ SCM might be undertaken. As regards the SCM and RSCM impacts on CE it may be noted that, of late, there have been many theoretical developments and formulations in the field of SCM. It has been shown that poor coordination among supply chain members has negative consequences on performance, such as inaccurate forecasts, low capacity utilisation, excessive inventory, inadequate customer service, inventory turns, inventory costs, time to market, order fulfilment response, quality, customer focus and customer satisfaction (Srai 2016). Whatever be the characteristic involved in all the facets of ‘RSCM’, along with the ‘flexibility’, the ‘uncertainty’ is always an integral part of all varieties of business or manufacturing environments. It has been reported that not only in the context of RSCM, but in other aspects of the work environments also, the developed operational models are said to get far more superior when such models are capable of incorporating/handling the ‘uncertainty’ dimension also. Emerging on the knowledge frontiers, such tools, as reported in literature, include stochastic and probabilistic modelling, Analytic Hierarchy Process (AHP)-based models, system dynamics tools, fuzzy set theoretic modelling, industrial simulation, micro simulation, Delphi technique and pseudo-quantitative technique, etc. Researches undertaken in the recent past indicated that the use of ‘hypothesis testing’ as a tool had increased significantly over the period particularly during the last few decades or so. Currently, investigators advise a greater use of hypothesis testing and the analytic method as the Supply Chain Management and also the RSCM disciplines continue to develop (Pagoropoulos, Pigoss, and McAloone 2017). A comprehensive review on mathematical models in the field of reverse logistics was recently undertaken by Pellicer and Valero (2018) who illustrated how the decisions in the RSCM framework are taken for better results.

4.4 CE & RSCL Nexus under the impact of recently evolved IT tools

With the advent of computer applications in manufacturing, there has been a revolution in the offing resulting in the transformation of the whole world of production and bringing it to a single platform, more precisely miniaturisation that is currently passing through its infancy stage. The concept of circular economy came much later when the digitalisation was at its peak, and plenty of advance platforms were available to pursue any long drawn task in a jiffy with maximum efficiency. For example, Ardolino et al. (2017) discussed how the digital technologies played a key role in service transformation of industrial companies. So far as the circular economy is concerned, it is characterised as an economy that is restorative and regenerative by design and which aims to keep products, components and materials at their highest utility and value at all times, distinguishing between technical and biological cycles (MacArthur and Waughray 2016). In this system, manufacturers get the benefit of profit for selling a single product and also keeping them in circulation for a longer duration resulting in overall savings in terms of energy, materials and other resources associated with the manufacturing process. The customers also derive benefit from this system as they pay only for what is required to them. Therefore, both the sides benefit from a more active and long-term relationship. Recently, in Denmark, another offshoot of CE in the form of local circle appeared in the field of mobile repairs (Riisgaard, Mosgaard, and Zaco 2016).

4.4.1 Digitisation, ‘RFID’ & ‘IoT’ technologies’ impacts on circular economy

To give a shape to it, digitisation of circular economy happened, which has many perspectives especially when the areas like AI, Big Data and Internet of Things (IoT) are part and parcel of any new technological innovation. It is, however pretty vague to talk about the degree to which circular economy gets affected by these technologies at this juncture (MacArthur and Waughray 2016). A comprehensive review (Nobre and Tavares 2017) spanning over the IT tools known as Big Data and IoT presents a good illustration of their applications in the field of CE. In a recent work, Pagoropoulos, Pigoss, and McAloone (2017) identified three architectural layers in digital technologies which have impacted the circular economy. These are: data collection, data analysis and data integration. This conclusion is based on their research pertaining to 135 case studies, in which they found that RFID (Radio Frequency IDentification) and IoT technologies have impacted ‘data collection’ stage, Machine learning, AI and Big Data Analytics have impacted ‘data analysis’ stage and RDBMS (Relational DataBase Management System) and PLM (Product Lifecycle Management) have impacted ‘data integration’ stage. C. J. C. Jabbour et al. (2017) observed that CE without proper support of Big Data might not be an effective proposal and accordingly presented an integrative framework for the nexus of the CE and Big Data. However, Tseng et al. (2018) argued that such developments are applicable to either single corporate or single supply chain. As revealed by the literature, today the term circular economy vis-à-vis digitalisation is often discussed in academia in terms of decentralised manufacturing and enterprise systems. A technology which is growing very rapidly these days is referred to as RFID technology, which uses electromagnetic fields to automatically identify and track tags attached to an object. When applied to the circular economy paradigm, RFID helps track material flows so as to enable value recovery achieved by the implementation of strategies such as Reuse, Repair and Remanufacture. RFID technology utilises tags, sensors and barcodes, and smart phones are the most common resources used in implementing IoT [Atzori, Iera, and Morabito (2010), Xu, He & Li (2014)]. The study undertaken by Stief et al. (2018) proposed a new methodology for identification of the assembly oriented product-family by way of involving two levels of architectural features: ‘functional’ and ‘physical’. This might be helpful in broadening the scope of RFID systems. It has already been used to improve the efficiency of ordering systems and in the systems incorporating JIT (Just-in-Time) technology (Tsao, Linh, and Lu 2017). In the context of enterprise sensing and smartness, the studies conducted by Weichhart et al. (2016) and Lampathaki et al. (2015) are worth mentioning. Similarly, another technology named IoT is taking rounds, which uses sensors and actuators connected by networks to computing systems which can help monitor/manage health and actions of connected objects and machines (Manyika et al. 2015). The role played by IoT in case of Circular Economy involves collection of information from sensors for connecting stakeholders across the value chain. Also, IoT provides a fundamental basis for evaluating the consequences of the actions taken by various stakeholders throughout the life of the physical products. The work of Salminen, Ruohomaa and Kantola (2017) reminisce the importance of IoT for circular economy, as management and analysis of data coming from various sources and is routed through data-to-service process, leading to business co-evolution of circular economy. Though it has been observed that transition from decade of linear thinking into circular is hard (Hossfeld 2017), how can the CE challenges be met by the digital technologies was recently discussed through a case study by the work of Bressanelli et al. (2018). The impact of IoT on the Internet and economy will be exceedingly high and it is anticipated by experts of the field that about 100 billion connected IoT devices will be there and expected global economic impact will be about 11 USD trillion by 2025. (Rose, Elridge, and Chapin 2015). On the other hand, Accenture has estimated for a commercial opportunity of 4.5 USD trillion by 2030 (Chalkias 2019).

4.4.2 Impact of RDBMS, PLM & AI on circular economy

Also, one cannot ignore the role of Relational Database Management Systems (RDBMS) and database handling systems in today’s technologically advanced systems age. They are the systems where the organisation of data is in formally described tables. RDBMSs have the power to integrate heterogeneous data sources, where data architecture is specified to enable the analytical requirements of the information architecture. RDBMSs and data handling systems also support the goals of circular economy, as they help in integrating the information produced by heterogeneous data collection systems such as IoT, ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) systems. In the context of adaptive calibration of fuel injection and combustion processes, Ge and Jackson (2014) argue that parallel RDBMS infrastructure can easily support adaptive calibration processes. Another supporting IT system namely Product Lifecycle Management (PLM) systems are the information management systems that can integrate data, processes, and business systems with people in an extended enterprise. PLM systems play an important role in supporting the transition to the Circular Economy, as they are of great help in integrating information across multiple life cycles and across various stakeholders in the value chain. Lieder and Rashid (2016) stressed on the importance of PLM systems at the company level, as they allow monitoring of products and parts in multiple lifecycles. Machine learning has also appeared as a practice getting widely acceptable in the industry since a quite a long time. It is based on algorithms that adapt to data without relying on the traditional rules-based programming. Also referred to as (AI), the application of machine learning algorithms such as Neural Networks which rely on mass processing of data, rather than a complicated set of rules where they identify patterns in the data and make predictions. It can be applied to the area of circular economy to support process and system optimisation based on the huge amount of data. Weichhart et al. (2016) argued that the use of AI tools and techniques for designing intelligent enterprise systems leverages the next era of computing theory and applications towards circular economy business models.

4.4.3 ‘BIG DATA’ analytics & circular economy

Big data is a term used to describe the exponential growth and availability of data, both structured and unstructured and may be as important to business and society as the internet itself [Lewandowski (2016), Jain et al. (2017)]. Big Data is characterised by the four V’s: Volume, Velocity, Variety and Veracity. Therefore, Big Data sets are too large and dynamic to be analysed using traditional database techniques or commonly used software tools Meyer et al. (2016). On the other hand, Serban (2017) presented a detailed matter content pertaining to the impact of Big Data on company performance. How Big Data could be employed in the supply chain management was illustrated by Jain et al. (2017). In the automotive industry, Ge and Jackson (2014) undertook an investigation pertaining to Big Study strategy in terms of the cost reduction, whereas Davenport and Beans (2017) studied the impact of Big Data on company performance and found it to be a successful endeavour. Big Data analytics when applied to circular economy is a viable approach to make use of information from various systems of record such as sensors and IoT for better decision making. In the context of the manufacturing industry, Lieder and Rashid (2016) articulated that real-time data analytics can enable decision making for adaptive calibration. Srai (2016) also put the fact that data analytics can provide insights both from raw data as well as the embedded data on multiple machine/equipment/product objects. However, Lewandowski (2016) observed that it is an important consideration as to how digital technologies can use adequate IT and data management technologies to support material tracking and other specific technologies, e.g., recycling. Antikainen, Uusitalo, and Reponen (2018) stressed that both networking and collaboration with stakeholders is required so as to enable circular economy business models. Finally, as initiated by Pagoropoulos, Pigoss, and McAloone (2017), as regards to life cycle stages, digital technologies for sure can help close the material loop, and therefore the primary focus is on the End of Life (EoL) and link to production. For example, RFIDs presents a system which contains valuable information on how the product was utilised by the customer. This valuable information can be used to estimate the quality level(s) of the returns and the increased transparency, and efficiency can further facilitate the integration of return flows into the forward flows. Also, according to Srai (2016), the connected manufacturing systems can enable monitoring, control, and optimisation of stocks and material flow cooperation and communication over processes and networks so as to achieve the optimum localised manufacturing output to meet the city demand.

5. Conclusion & scope for future research

With the days passing by, the possibility of more and more material consumption is also going up. It is expected that at the global level, there would be eight-fold increase in material consumption by the end of 2050 (European Commission Report (EC Report) 2019). Today’s, ongoing efforts by the researchers in the fields of CE-RSCL-DT nexus, as reported in literature, indicate an immediate urgency for a more concerted intervention by the future investigators in their respective areas of works, particularly, when it is noted that ‘the potential for digital intelligence to enable a regenerative economy is promising’ (Moreno and Charnley 2016). Conservation of energy has always been an everlasting demand in the field of engineering and technology. Today the scenario has got broadened. It is not only the energy travelling on the path of depletion but the whole world of resources needed in the field of manufacturing is demanding not only for ‘conservation’ but also for ‘circulation’. Therefore, as a part of the CE strategy the need of the day is to revisit the classical process of product development. The future-product that a company has to manage over its entire life cycle might require more collaboration with its customers’ population than is customary today. Product design has to be such that its products can be refurbished, reused, repaired, and recycled. The firms too need processes and systems which come to the rescue of customers when the products wear out, become obsolete, or fail. Product development affects everybody profoundly in the value chain, be it any internal entity like marketing, sales, etc., or external elements like suppliers, freight carriers, distributors, and retailers. All of such concerned parties must have a say in the process of the product development. Recent developments in the field of IT tools do provide a good support to the emerging dimensions of the CE-RSCL under the impacts of the applications of the upcoming IT tools like RFID, IoT and Big Data, etc. It appears that the fourth industrial revolution that highlighted the IT tools like Big Data, 3D Printing, IoT, and Analytics, etc., helped the companies in introduction of CE in their affairs. There are varieties of challenges in CE applications and digital technologies can overcome these challenges successfully (Bressanelli et al. 2018). It was found by Tseng et al. (2 0 1 8) that ‘despite a massive volume of scientific work in these areas (e.g., separate queries in Scopus using Industry 4.0 and Circular Economy as keywords yield 4060 and 2452 published documents, respectively), there is plenty of growth potential for ground breaking research in the nexus of these’. Present review of the related matter-contents indicated the infancy stages for CE-RSCL nexus as well as the emerging IT tools like Big Data analytics, etc., applications (Jain et al. 2017) implying thereby in immense scope for their development and growth in the days ahead. In this context, as acknowledged by the authors also, perhaps underlying understanding of the CE strategy could be broader than the definition presented in the literature. This might lead to possibly making of more efforts in future to explore some more dimensions in CE understandings. It might be observed that revisiting CE definition by the future researchers might be a contribution to the scholarly CE community and this kind of definition might ideally serve as a conceptual foundation for future work on the topic. In this context, the newly developed conceptual CE business model Lewandowski (2016) might be revisited in real-life CE implementation framework. Future research might also focus on those dimensions which appear to have been neglected by the previous researchers working on CE, particularly from the consumer perspective point of view. It has been observed by Brais Suárez-Eiroa, Emilio Fernández, Gonzalo Méndez-Martínez, & David Soto-Oñate (2019) that the literature available on the CE is still scarcer and ‘both conceptual discussions and the development of practical strategies for its implementation are still emerging’. This status of knowledge is also being reflected by the present finding in terms the too much interest of the researchers in the field CE. From quantitative analysis viewpoint future researches might be based on the application of environmental scanning with the AHP/affinity diagram employed as a tool for targeting at strategic reversed logistics flexibility based modelling, equipped with the capability of handling uncertainty, so as to evolve some theoretical framework that provides strategic capability of the future manufacturing entities. This might enable the upcoming generation of enterprises to cope with the uncertain business environment of tomorrow. Finally, in the context of the CE applications, Davenport and Beans (2017) claimed that data-driven culture often prevents the adoption of Big Data applications whereas Mieras (2016), emphasised that CE is hard to apply and offers a big challenge, particularly in light of such barriers as technological, social, customer-related, financial and economic, managerial, policy and regulatory and performance indicators related as identified by (Galvao et al. 2018). However, Hossfeld (2017) observed that in future manufacturing, Big Data analytics would be a ‘key success factor’. It has been suggested that initiatives should be taken only on the step-by-step basis for its genuine success (IFU Hamburg (IFU) 2018).

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No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Syed Wasiul Hasan Rizvi

Syed Wasiul Hasan Rizvi is currently a PhD scholar at Delhi Technological University (DTU) in the Department of Mechanical Engineering. He gained his bachelors in mechanical engineering from Jamia Millia Islamia university (1999) based in New Delhi, India and his Masters in Industrial & Production Engineering (2016) from Al-Falah University, Faridabad, India and his other Masters in Information Technology (2003) from Universiti Putra Malaysia, Serdang, Malaysia. He is having about seven years of industry experience and about 10 years in teaching at both graduate and post-graduate levels. He has published many papers in national and international journals and conferences. His research interests include industrial engineering areas such as Supply Chain management, Human Factors Engineering and Ergonomics.

Saurabh Agrawal

Saurabh Agrawal recently joined as Associate Professor in Delhi School of Management, Delhi Technological University (DTU), Delhi, India. He was working as Assistant Professor in Mechanical, Production and Industrial Engineering Department at Delhi Technological University, Delhi, India. He has vast experience of academics, research and the industry both in India and USA. He has completed his PhD in reverse supply chain from DTU. His research focus is in the areas of supply chain management, reverse logistics, sustainability and e-waste management. He has completed his undergraduate degree from Indian Institute of Technology, Roorkee, India. He has received his Master’s Degree in Business Administration from Oregon State University, USA, and Master’s Degree in Industrial Engineering from Indian Institute of Technology, Delhi, India. He has published research papers in international journals of repute including Resources, Conservation and Recycling, Journal of Cleaner Production, Competitiveness Review, and Journal of Industrial Engineering: International.

Qasim Murtaza

Prof. Qasim Murtaza is a Professor in the Department of Mechanical Engineering, Delhi Technological University, Delhi (Erstwhile Delhi College of Engineering). He got his PhD (2006) in the area of manufacturing process, Dublin City University, Ireland. He worked also as Research Associate at Metropolitan Manchester University, Manchester, UK. He visited various academic institutions and industries at several countries towards academic pursuits.His research interest includes  Precision Manufacturing and Non-Traditional Manufacturing Processes such as Metal coatings, Free-standing metal coating components, Hybrid stir casting & including tribological assessment, Super-finishing processes and Re-manufacturing. He guided 33 M.Tech thesis and 7 PhDs. He has published more than 85 research papers in reputed journals and conference proceedings.