<figure><img src="image_1.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
Information Systems and Circular Manufacturing

strategies: the role of master data

Terje Andersen¹[0000-0001-5838-3188], Gianmarco Bressanelli²[0000-0003-2255-9983], Nicola Saccani²[0000-0002-4603-5060] and Benedetta Franceschi²[0000-0001-5240-6940]

¹ Molde University College - Specialized University in Logistics, P.O. Box 2110, NO-6402 Molde, Norway

² RISE Laboratory, Department of Mechanical and Industrial Engineering, Università degli Studi di Brescia, Via Branze 38, 25123 Brescia, Italy

**Abstract.** In the transition towards a more sustainable future, circular economy is a key concept. Manufacturers play a key role in this transition, and different circular manufacturing strategies rely on digital technologies and information systems to be fulfilled. These systems and technologies need master data, basic data a firm’s business activities are based on, to work. However, manufacturing companies still have little knowledge on which master data are needed, and how these data can support decisions in circular manufacturing strategies. In this paper, we bridge different circular manufacturing strategies with important master data elements in a framework for master data management. The basis for the framework is scientific literature within the domains of circular manufacturing strategies and information systems. The framework can be used by researchers to explore master data requirements for different strategies, and by practitioners to get an overview of data requirements for different circular manufacturing strategies.

**Keywords:** Circular manufacturing, master data, master data management.

# 1 Introduction

In the transition to a more sustainable future, circular economy (CE) is an important approach. The idea of CE is to move from a traditional linear economic model characterized by a take-make-use-and-dispose approach to a CE model, in which materials and the embodied energy of products remain in a restorative system for as long as possible [1]. Literature are connecting the CE transition to extensive use of digital technologies [2]. New emerging technologies as internet of things (IoT), cloud computing, additive manufacturing, blockchain, and augmented and virtual reality are, among others, mentioned as enabling technologies for CE [3]. These technologies, together with cyber-physical systems, digital twins, big data and analytics, and autonomous robots are also important for the future of manufacturing, labeled Industry 4.0 [4]. System integration is often mentioned as another important building block for Industry 4.0 [5]. Manufacturing companies play an important role in this transition since they usually are the focal actor who manages upstream and downstream activities
in the supply chain [6]. Different strategies have been conceptualized for the CE, ranging from recover to refuse [7] and, within the supply chain, a set of strategies specifically for circular manufacturing (CM) has been identified [8]. Manufacturing companies rely on a portfolio of information systems to support their operation and business processes [9], and all these systems and technologies need master data to work.

However, manufacturing companies have little knowledge on which master data are needed, and on how these data can support decisions in CM strategies. In addition, they have little visibility and access to these data – especially for long, complex supply chains – and the management of master data is still a practical issue in companies. Unfortunately, scientific literature still overlooks this domain, and the role of master data and master data management in the context of CM strategies has received very little attention to date. A recent research from Acerbi et al. [10] propose a data model for CM. Our work focusses on the need for master data, and is guided by the following research question: *which master data is needed for the different CM strategies?* For this purpose, a framework for master data management (MDM) for CM strategies is proposed, based on a literature review. The organization of the rest of the paper is as follows. In Section 2, we present background information on CM strategies and MDM. In Section 3, we introduce and discuss our framework for MDM for CM strategies. Lastly, Section 4 holds the conclusion, including limitations and suggestions for further research.

## 2 Background

The main idea of CE is to move from a traditional linear business model characterized by a take-make-use-dispose approach to a circular model in which materials and energy remain in a restorative system [1, 11]. Manufacturers play a key role in this transition, managing activities both upstream the supply chain to suppliers and downstream to customers, and in addition interact with other actors. Today's supply chains are recognized as complex, often global, and with a high degree of specialization [12, 13].

CE can be achieved by implementing different circular strategies. Blomsma et al. [14] divide these strategies into preventive strategies, loop-closing strategies, loop-extending strategies, and longevity strategies. Potting et al. [7] use the “R’s and Re’s” categories when they describe 10 different Re strategies from Refuse (R0) to Recover (R9) arguing for implementing an as high circular strategy as possible (low R). Given the relevance of manufacturing companies for the CE, the concept of CM has been recently established. Acerbi and Taisch [15] define the goal for CM to “reduce resources consumption, to extend resources lifecycles and to close the resources loops, by relying on manufacturers’ internal and external activities that are shaped in order to meet stakeholders’ needs”. In addition, they propose the following CM strategies for manufacturers: circular design, remanufacturing, disassembly, reuse, recycle, resource efficiency, cleaner production, servitization-based business models, industrial symbiosis, and closed-loop supply chain.
Information Systems (IS) has been an important success factor for supply chain management. Magal and Word [16] define IS as "computer-based systems that capture, store, and retrieve data associated with process activities [...] IS also organize these data into meaningful information that organizations use to support and assess these activities". Manufacturing companies have a portfolio of IS to support their operation and business processes [9]. Examples are computer aided design (CAD) for product development, customer relationship management (CRM) for interaction downstream the supply chain, product lifecycle management (PLM) for total management of products in all their lifecycle [17], advanced planning and scheduling (APS), manufacturing execution system (MES) and supervision, control, and data acquisition (SCADA) for the manufacturing process. Enterprise resource planning (ERP) has an important role in a company, both for internal planning and control and for collaboration along the supply chain [18]. The different systems have different purposes and target different processes within a company. For instance, SCM work upstream the supply chain, CRM downstream, PLM adopt a product development perspective, while MES target the manufacturing process on the shop floor. One way to define this IS ecosystem is using the product, process (production) and profit (business or enterprise) dimensions, where different IS support different activities within each dimension [19] and the automation pyramid (ISA 95) represent the connection between the dimensions. In addition, information systems can be vertically integrated (inside a company), and horizontally integrated among companies in the supply network.

Master data is basic data a firm's business activities are based on. Magal and Word [16] define master data as "key entities with whom an organization interacts" like customer data, supplier data and product data. As contrast to transaction data, master data typically describes basic characteristics of objects and is relatively static, meaning that they do not change much over time. As a result, master data usually remain largely unaltered, are quite constant regarding volume, and are the basis for transaction data [20]. Since the complexity in companies and supply chains is constantly increasing, also the number of IS in use in manufacturing companies is augmenting. In addition, these IS are often integrated with Industry 4.0 technologies as robots, IoT, big data and analytics, where the IS may serve as master data managers while the industry 4.0 technologies will generate a huge amount of transaction data [19]. All these IS need master data to work. This has led to an increased relevance for MDM, defined as best practices to manage master data where the main purpose is to secure master data's quality and that these data are updated to support transactions and business operations [21].

## 3 A framework for master data management for circular manufacturing strategies

Based on a preliminary literature review, we propose a framework for MDM for CM strategies in Figure 1. The first column refer to the CM strategies (adapted from Polenghi et al. [8]) and to the life cycle phases Begin of Life (BoL), Middle of Life (MoL) and End of Life (EoL). The second column refer to the business process related
to the CM strategy and MDM. This can be firm internal processes or inter-company processes. The third column refer to the product, process, or business domain. The Information System and Master Data element columns are discussed hereafter. For the EoL activities, we have only included the supply chain activities between actors, not specific activities for the different strategies (reuse, remanufacturing, disassembly, re-cycle or waste management). The last column holds examples of master data for the specific CM strategi.

<table><caption>Figure 1. A framework for master data management for circular manufacturing strategies</caption><thead><tr><th>CM strategy/LC phase</th><th>Activity</th><th>Domain</th><th>Information System</th><th>Master Data element</th><th>Examples</th></tr></thead><tbody><tr><td>Circular design, BoL</td><td>Product development</td><td>Product</td><td>CAD/CAE/PLM</td><td>MD for products</td><td>Products, BoMs</td></tr><tr><td>Cleaner production, BoL</td><td>Manufacturing</td><td>Process</td><td>ERP/MES</td><td>MD for products<br>MD for manufacturing</td><td>Products, BoMs<br>Routing, CO2 emission pr product</td></tr><tr><td>Resource efficiency, BoL</td><td>Manufacturing</td><td>Process</td><td>ERP/MES/<br>EDI/BC</td><td>MD for products<br>MD for manufacturing</td><td>Products, BoMs<br>Routing, CO2 emission pr product</td></tr><tr><td>Servitization, MoL</td><td>Product usage</td><td>Business</td><td>Platforms/<br>ERP/PLM</td><td>MD for product usage</td><td>Customer data,<br>MD for max utilization of products</td></tr><tr><td>Resource efficiency, MoL</td><td>Product usage</td><td>Product</td><td>Platforms/<br>ERP/PLM</td><td>MD for product usage</td><td>MD for energy usage<br>CO2 emission pr product</td></tr><tr><td>Reuse, EoL</td><td>Return to service<br>provider, reuse</td><td>Process</td><td>PLM(CLSC)<br>BC (OLSC)</td><td>MD for products<br>MD for product usage</td><td>MD for max utilization of products</td></tr><tr><td>Remanufacturing, EoL</td><td>Return to<br>manufacturer</td><td>Process</td><td>PLM(CLSC)<br>BC (OLSC)</td><td>MD for products<br>MD for product usage</td><td>Material composition<br>MD for utilisation of parts</td></tr><tr><td>Disassembly, EoL</td><td>Return to<br>manufacturer</td><td>Process</td><td>PLM(CLSC)<br>BC (OLSC)</td><td>MD for products<br>MD for product usage</td><td>Material composition<br>MD for utilisation of parts</td></tr><tr><td>Recycling, EoL</td><td>Return to parts<br>manufacturer</td><td>Process</td><td>PLM(CLSC)<br>BC (OLSC)</td><td>MD for products<br>MD for product usage</td><td>Material composition<br>MD for utilisation of material</td></tr><tr><td>Waste management, EoL</td><td>Return to waste<br>handler</td><td>Process</td><td>PLM(CLSC)<br>BC (OLSC)</td><td>MD for products<br>MD for product usage</td><td>Material composition</td></tr></tbody></table>

**Circular design** has its origin in the product development process. Traditionally computer aided design (CAD) and computer aided engineering (CAE) were the Information systems supporting this business process. Later on product data management (PDM) and product lifecycle management (PLM) systems has evolved [22]. Varl et al. [23] argue for CAD and PLM as tools supporting customized product development, a product development approach with similarities to circular design. Myung [24] identified the following master data for PLM: Parts Data, Design Data, BOM, Docs/Specification, Configuration Data, Work Instructions, Product Quality Data, Product Compliance Data, Product Service Data. In the construction industry, Building Information Models (BIMs) is established, holding information about size, volume, weight, time, costing, facility management, maintenance, and operation [25, 26] of buildings, or its components.

While circular design focuses on closing the loop with a Cradle to Cradle approach in the design phase of a product, **cleaner production** has the goal to minimize the environmental impact in production [27]. Information systems in this process are the typical systems from the automation pyramid, ERP and MES. Basic business processes supported by an ERP system are procurement, fulfillment and production process [16]. Basic master data for these processes are product data, supplier data, customer data, BOM's, routing and work centers [28].

**Resource efficiency** in the BoL phase can be internal production [28], or activities along the supply chain. As for cleaner production, the automation pyramid is important to measure resource efficiency on a product level. In addition, information from
upstream and downstream supply chain actors are valid for measuring resource effi-
ciency. For supply chain activities, information sharing techniques and standards are
dominating. Electronic data interchange (EDI) is still an important standard [29], how-
ever interest in implementation of blockchain technology for supply chain collaboration
is increasing [30].

**Servitization or Product Service Systems** is not only a CM strategy, but a business model, where a products functionality is delivered rather than the product itself [31]. Here the need for master data is moved to the use phase of a product. Data for measurement of the usage of the product or service is important. This can be done by a digital platform for measurement and value added services [32]. Data needed is related to customer, usage, and condition of the product, eventually suppliers involved in the delivery of the service. The **resource efficiency** in the use phase of a product differs from the resource efficiency in the production phase. In a servitization business model with a platform approach, it is natural that the platform hold this information [32]. For traditional sales, where the ownership of the goods is transferred from the supplier to the customer, either the customer/owner or the supplier can measure the resource efficiency. This will need computation capabilities not usually available among consumers. If these measurements is done by the supplier, they will have a PLM approach focusing on the customer and the goods sold [33]. If it is done by the owner of the goods, an ERP approach seems to be reasonable, where the goods is an asset in the company owning the goods [8].

The last CM strategies are connected to the EoL phase of a product. According to
the CE butterfly model [34], material shall flow back to service provider (reuse), prod-
uct manufacturer (remanufacturing, disassembly) and parts manufacturer (recycle).
All of these strategies will need some kind of supply chain coordination to be able to
follow the “Rs” hierarchy among the strategies[35]. The communication need will de-
pend on if the loop is closed, meaning that the same actor handles the EoL treatment as
placed the goods to the marked, or open if not [36]. In both cases, flow of information
from the BoL and MoL phase may have relevance [37], but technologies used for this
flow may differ. In a closed-loop supply chain, it should be expected that the manufac-
turer or service provider have followed the product via their PLM systems. In an open-
loop supply chain this is not expected since the original manufacturer or service pro-
vider are not connected to the EoL treatment. Here, blockchain technology may be a
solution [37].

The last CM strategy, **waste management** is a non-preferred strategy. The overall goal for CE is to prevent waste [34]. However, waste must be handled in an optimal way. Waste management face some of the same challenges as other open-loop supply chain challenges. Digital Product Passport (DPP) may solve some of the issues related to product lifecycle information flow in the supply chain [38].

4 Conclusion

This paper presented a framework for master data management for different CM strategies. In our point of view information flow is, together with flow of products and
value the three main flows for supply chain performance [39]. Enterprises are using IS to enable this information flow [17, 18]. For CM, new and extended information flow is needed, valid for all product lifecycle phases [8, 40]. However, the needs are different both related to lifecycle phases, activities and technology used. Manufacturers are in a unique position for improved circularity. Depending on the level of Supply Chain integration, they have information about the product they manufacture, and the actors upstream and downstream the supply chain [12, 13, 17]. Future manufacturing will increase IS integration, both horizontal and vertical [11]. Thus, frameworks are needed to understand the different needs for future manufacturing: our framework documents different master data needs for different CM strategies, and it can thus guide future research in different perspectives for different CM strategies. The framework shows that there are different IS and technologies, activities to be supported by digital technology and IS, leading to different master data to focus on, based on the different CM strategies.

Nevertheless, our research has limitations. The findings follow a preliminary literature search. A more structured approach for the collection of current literature would make this study more robust. In addition, the framework is purely based on scientific research. The topic covered in this research is also on the agenda both for the manufacturing industry, and for software and digital technology providers. Including grey literature may give new insight. Moreover, the framework has not been validated in real use. Using a case study including the use of the framework to validate its usability would make the framework more robust. All these issues are under consideration for further research to strengthen the study.

## References

1. Geissdoerfer, M., P. Savaget, N.M.P. Bocken, and E.J. Hultink, *The Circular Economy – A new sustainability paradigm?* Journal of Cleaner Production, 2017. **143**: p. 757-768.

2. Tukker, A., *Product services for a resource-efficient and circular economy – a review*. Journal of Cleaner Production, 2015. **97**: p. 76-91.

3. Bressanelli, G., D.C.A. Pigosso, N. Saccani, and M. Perona, *Enablers, levers and benefits of Circular Economy in the Electrical and Electronic Equipment supply chain: a literature review*. Journal of Cleaner Production, 2021. **298**: p. 126819.

4. Lasi, H., P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann, *Industry 4.0*. Business & Information Systems Engineering, 2014. **6(4)**: p. 239-242.

5. Gerbert, P., M. Lorenz, M. Rüßmann, M. Waldner, and J. Justus, *Industry 4.0-The Future of Productivity and Growth in Manufacturing Industries*. The Boston Consulting Group. 2015.

6. Brintrup, A. and A. Ledwoch, *Supply network science: Emergence of a new perspective on a classical field*. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2018. **28(3)**.
7. Potting, J., M. Hekkert, E. Worrell, and A. Hanemaaijer, *Circular Economy: Measuring Innovation in the Product Chaing-Policy Report*. PBL Netherlands Environmental Assessment Agency. 2017.

8. Polenghi, A., F. Acerbi, I. Roda, M. Macchi, and M. Taisch, *Enterprise information systems interoperability for asset lifecycle management to enhance circular manufacturing*. IFAC-PapersOnLine, 2021. 54(1): p. 361-366.

9. Bowersox, D., D. Closs, and R. Drayer, *The digital transformation: technology and beyond*. Supply Chain Management Review. 2005: service.boulder.ibm.com.

10. Acerbi, F., C. Sassanelli, and M. Taisch, *A conceptual data model promoting data-driven circular manufacturing*. Operations Management Research, 2022.

11. Masi, D., S. Day, and J. Godsell, *Supply chain configurations in the circular economy: A systematic literature review*. Sustainability, 2017.

12. Boccaletti, S., G. Bianconi, R. Criado, C.I. del Genio, J. Gómez-Gardeñes, M. Romance, I. Sendiña-Nadal, Z. Wang, and M. Zanin, *The structure and dynamics of multilayer networks*. Physics Reports, 2014. 544(1): p. 1-122.

13. Bortolini, M., F.G. Galizia, and C. Mora, *Reconfigurable manufacturing systems: Literature review and research trend*. Journal of Manufacturing Systems, 2018. 49: p. 93-106.

14. Blomsma, F., L. Kjaer, D. Pigosso, T. McAloone, and S. Lloyd, *Exploring Circular Strategy Combinations - towards Understanding the Role of PSS*. Procedia CIRP, 2018. 69: p. 752-757.

15. Acerbi, F. and M. Taisch, *A literature review on circular economy adoption in the manufacturing sector*. Journal of Cleaner Production, 2020. 273: p. 123086.

16. Magal, S. and J. Word, *Essentials of business processes and information systems*. 2009: Wiley Publishing.

17. Madenas, N., A. Tiwari, C. Turner, and J. Woodward, *Information flow in supply chain management: A review across the product lifecycle*. CIRP Journal of Manufacturing Science and Technology, 2014.

18. Nair, P., V. Raju, and S. Anbuudayashankar, *Overview of information technology tools for supply chain management*. CSI Communications. 2009: Citeseer.

19. Li, Q., Q. Tang, I. Chan, H. Wei, Y. Pu, H. Jiang, J. Li, and J. Zhou, *Smart manufacturing standardization: Architectures, reference models and standards framework*. Computers in Industry, 2018. 101: p. 91-106.

20. Otto, B. and K. Hüner, *Functional Reference Architecture for Corporate Master Data Management*. 2009: St. Gallen.

21. Alharbi, A., *Master Data Management*. Journal of Information Systems Technology and Planning, 2016. 8(19): p. 95-101.

22. Romero, D. and F. Vernadat, *Enterprise information systems state of the art: Past, present and future trends*. Computers in Industry, 2016. 79: p. 3-13.

23. Varl, M., J. Duhovnik, and J. Tavčar, *Customized product development supported by integrated information*. Journal of Industrial Information Integration, 2022. 25: p. 100248.

24. Myung, S. *Master Data Management in PLM for the Enterprise Scope*. in IFIP Advances in Information and Communication Technology. 2016. Cham: Springer International Publishing.
25. Redmond, A., A. Hore, M. Alshawi, and R. West, *Exploring how information exchanges can be enhanced through Cloud BIM*. Automation in construction. 2012: Elsevier.

26. Gourlis, G. and I. Kovacic, *Building Information Modelling for analysis of energy efficient industrial buildings – A case study*. Renewable and Sustainable Energy Reviews, 2017. 68: p. 953-963.

27. Fresner, J., *Cleaner production as a means for effective environmental management*. Journal of Cleaner Production, 1998. 6(3): p. 171-179.

28. Magal, S. and J. Word, *Integrated business processes with ERP systems*. 2011: Wiley Publishing.

29. Cecere, L., *EDI: Workhorse of the Value Chain–A Closer Look at B2B Connectivity Benchmarks in the Extended Supply Chain*. Supply Chain Insights LLC. 2013.

30. Wang, Y., M. Singgih, J. Wang, and M. Rit, *Making sense of blockchain technology: How will it transform supply chains?* International Journal of Production Economics, 2019. 211: p. 221-236.

31. Bocken, N.M.P., S.W. Short, P. Rana, and S. Evans, *A literature and practice review to develop sustainable business model archetypes*. Journal of Cleaner Production, 2014. 65: p. 42-56.

32. Cenamor, J., D. Rönnberg Sjödin, and V. Parida, *Adopting a platform approach in servitization: Leveraging the value of digitalization*. International Journal of Production Economics, 2017. 192: p. 54-65.

33. Sakao, T. and A.K. Nordholm, *Requirements for a product lifecycle management system using Internet of Things and big data analytics for product-as-a-service*. Frontiers in Sustainability, 2021: p. 70.

34. Ellen-MacArthur-Foundation, E., *Towards the circular economy. Economic and business rationale for an accelerated transition*. 2013.

35. Andersen, T., *A comparative study of national variations of the European WEEE directive: manufacturer's view*. Environmental Science and Pollution Research, 2021.

36. Gou, Q., L. Liang, Z. Huang, and C. Xu, *A joint inventory model for an open-loop reverse supply chain*. International Journal of Production Economics, 2008. 116(1): p. 28-42.

37. Andersen, T. and B. Jæger, *Circularity for Electric and Electronic Equipment (EEE), the Edge and Distributed Ledger (Edge&DL) Model*. Sustainability, 2021. 13(17): p. 9924.

38. Adisorn, T., L. Tholen, and T. Götz, *Towards a Digital Product Passport Fit for Contributing to a Circular Economy*. Energies, 2021. 14(8): p. 2289.

39. Lambert, D.M., M.C. Cooper, and J.D. Pagh, *Supply Chain Management: Implementation Issues and Research Opportunities*. The International Journal of Logistics Management, 1998. 9(2): p. 1-20.

40. de Oliveira, S.F. and A.L. Soares, *A PLM Vision for Circular Economy*. in IFIP Advances in Information and Communication Technology. 2017. Cham: Springer International Publishing.