Design of resilient and viable sourcing strategies in intertwined circular supply networks

Echefaj, Khadija; Charkaoui, Abdelkabir; Cherrafi, Anass; Ivanov, Dmitry Article — Published Version Design of resilient and viable sourcing strategies in intertwined circular supply networks Annals of Operations Research Provided in Cooperation with: Springer Nature Suggested Citation: Echefaj, Khadija; Charkaoui, Abdelkabir; Cherrafi, Anass; Ivanov, Dmitry (2024) : Design of resilient and viable sourcing strategies in intertwined circular supply networks, Annals of Operations Research, ISSN 1572-9338, Springer US, New York, NY, Vol. 337, Iss. 1, pp. 459-498, https://doi.org/10.1007/s10479-024-05873-1 This Version is available at: https://hdl.handle.net/10419/315288 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Annals of Operations Research (2024) 337:459–498 https://doi.org/10.1007/s10479-024-05873-1 ORIGINAL RESEARCH Design of resilient and viable sourcing strategies in intertwined circular supply networks Khadija Echefaj1·Abdelkabir Charkaoui1·Anass Cherrafi2·Dmitry Ivanov3 Received: 20 March 2023 / Accepted: 28 January 2024 / Published online: 29 February 2024 © The Author(s) 2024 Abstract This study examines the effects of intertwining and circularity on the design of resilient and viable sourcing and recovery strategies in supply chains. We first construct a case study where the supply chains of three industries (i.e., automotive, healthcare, and electronics) frame an intertwined supply network (ISN). Through a discrete-event simulation model developed in anyLogistix, we examine the impact of disruptions in supply and demand on the performance of individual supply chains and the ISN as a whole. We test the performance of several sourcing strategies and their combinations. A special focus is directed toward shared reverse flows. The results show that disruption impact and recovery processes in the Circular ISN do not always follow conventional patterns known from the resilience of individual supply chains due to intertwining and circularity effects. We offer some managerial recommendations for the design of resilient sourcing strategies in the ISN context that are triangulated around collaborative sourcing practices, coordinated production planning, shared reverse flows, and visibility in inventory management. Keywords Sourcing strategy ·Intertwined supply network ·Resilience ·Circular economy, Simulation ·anyLogistix BDmitry Ivanov [email protected] Khadija Echefaj [email protected] Abdelkabir Charkaoui abdelkabir[email protected] Anass Cherrafi [email protected] 1Faculty of Sciences and Technique, Hassan First University of Settat, Settat, Morocco 2ESTSAFI, Cadi Ayyad University, Marrakech, Safi, Morocco 3Department of Business Administration, Supply Chain and Operations Management, Berlin School of Economics and Law, 10825 Berlin, Germany 123 460 Annals of Operations Research (2024) 337:459–498 1 Introduction The COVID-19 pandemic crisis has had major impacts on supply chains (Brusset et al., 2023; El Baz & Ruel, 2021;Ivanov,2020; Sodhi et al., 2023). Travel and manufacturing constraints have been imposed, adversely impacting the possibilities to meet customer demands (Cherrafi et al., 2022). Material shortages and delivery delays caused by the virus outbreak reduced the performance, service levels, and productivity of supply chains for a long period (Choi, 2021). While demand for some products (e.g., masks and hand sanitiser) has increased, supply has not always been capable to respond adequately (Shen et al., 2021;Wang&Yao,2021). In this setting, the research community called for the development of new methods for designing resilient and viable sourcing strategies in supply chains (Babai et al., 2023; Dolgui et al., 2020;Ivanov,2022a). One particular challenge in designing resilient and viable sourcing strategies stems from the dynamic interconnections and intersections of supply chains in different sectors (Feizabadi et al., 2023;Lotfietal.,2022), forming intertwined supply networks (ISNs). The notion of the ISN was pioneered by Ivanov and Dolgui, (2020) as an integrity of interconnected supply chains that ensure the provision of goods and services to society and the market. In the ISNs, multi-directional links lead to changing roles of the supplier–buyer (Dolgui et al., 2020). In this case, sourcing differs from traditional linear supply chains because a supplier can be a customer and a factory can feed other factories. In the same line, distribution centres can receive final products and provide reused material or spare parts. In crisis periods, it is difficult to identify the critical supply chain because many essential manufacturers share the same sourcing provider (Hägele et al., 2023;Ivanov&Das,2020;Lietal.,2023). Hence, designing an efficient and resilient sourcing strategy in the ISN is more complex, since the goal is ensuring the viability of the whole system. This complexity is more intense when an unexpected event occurs for a long period (Ivanov, 2023a). Liu et al. (2022) compared a short disturbance to a disease that attacks a specific organ and a long-term disruptive event to “bleeding or freezing to death”. Additionally, collaboration among intertwined enterprises, including rerouting, capacity sharing, and expansion, leads to a more resilient network (Ghanei et al., 2023). Therefore, these collaborative practices in the ISN may positively affect the sourcing process and help cope with raw material shortages. Technologically, ISNs are based on collaborative platforms, which allow for visibility and digital supply chain mapping (Ivanov, 2023a; MacCarthy & Ivanov, 2022; MacCarthy et al., 2022; Zhang et al., 2022). While the intertwining of supply chains in the ISN has received growing attention, one missing element in this research is consideration of circular flows in supply chains (Ivanov & Keskin, 2023;Ivanov,2021a, 2023a; Sardesai & Klingebiel, 2023; Sawik, 2023),whichwereproventobeanimportant but underexplored part of supply chain resilience (Ivanov, 2021b; Kennedy & Linnenluecke, 2022;Parketal.,2022). Sustainability and circular supply chains gained an increased attention in literature (Brandenburg & Rebs, 2015; Govindan, 2020;Govindanetal.,2015; Homayouni et al., 2023; Kannan et al., 2023; Meier et al., 2023; Nag et al., 2021,2021). Recent research pointed to multiple intersections of resilience and sustainability (Dolgui et al., 2020;Ivanov,2022b, 2023b;Pavlovetal.,2019). Gaustad et al. (2018) stated that the implementation of circular economy principles has the potential to mitigate the material supply shortage during disruptions. As circular economy practices recover products and expand their life (Kalmykova et al., 2018), the supply of critical material can be ensured through a reverse flow. During the COVID-19 crisis, recycling and loop-closing initiatives have proven their effectiveness 123 Annals of Operations Research (2024) 337:459–498 461 to address urgent shortages (Wuyts et al., 2020). Therefore, circularity can be considered an important and novel lens in designing resilient supply chains. Ivanov, (2022c)proposed a viable supply chain model, which integrates resilience and sustainability perspectives in the settings of long-term crisis. Combining circularity and intertwining may result in novel impacts on sourcing performance and customer behaviours (Kerber et al., 2021;Minaetal., 2021). The viability of ISN is a novel and promising field of resilience research. However, the existing literature lacks insights evaluating the impacts of intertwining and circularity on sourcing performance under disruptions. Despite the great interest devoted to risk management strategies, little is known about the opportunities and challenges stemming from intertwining effects in managing the availability of materials. Moreover, only a few works have investigated the role of the circular economy in enhancing the resilience of sourcing practices. To the best of our knowledge, our study is the first to connect circular economy and supply chain viability. In order to examine these impacts, the present study addresses two research questions: RQ1 How does the intertwining of supply chains impact performance during supply and demand disruptions? The purpose of this question is to analyse the ISN impact on recovery strategies during sourcing and demand disruptions, considering single, dual, and backup sourcing practices. RQ2 How can circularity in the intertwining context help improve sourcing performance during supply and demand disruptions? This question aims to identify the contribution of circularity to ISN performance during sourcing and demand disruptions. More precisely, the CISN benefits while sharing the reverse flows will be investigated. We aim to identify the circular economy’s ability to enhance the availability of raw materials during supplier closures and demand increases. To that end, CISN performance is analysed, considering single, dual, and backup sourcing practices. Our study contributes to the literature by advancing knowledge about supply chain resilience and viability by combining intertwined and circular effects in the CISN. In the first stage, intertwining effects during a crisis are analysed and highlighted. Subsequently, circularity advantages are integrated to strengthen the viability of the overall network. A simulation approach is adopted to study the behaviour of three intertwined supply chains (i.e., automotive, electronics, and medical) during disruption periods, positioning the problem in the setting of a semiconductor shortage. We study the possible practices under intertwining to prevent shortages of raw materials during disruptions (Ivanov & Dolgui, 2022).We analyse sourcing strategies to provide generalised recommendations for the design of resilient and efficient sourcing practices. This study highlights the importance of collaboration and circular practices in ensuring the availability of materials during a long-term disruption period. The remainder of this paper is organised as follows. A literature review on sourcing strategies, circular intertwined supply networks, and the semiconductor shortage problem is conducted in Sect. 2. The case study is presented in Sect. 3. Simulation logic, input data and scenarios are explained in Sect. 4, followed by experiments in Sect. 5. Our results are discussed in Sect. 6, followed by associated managerial insights. Section 7summarises the major outcomes of this study and outlines some future research perspectives. 123 462 Annals of Operations Research (2024) 337:459–498 2 Literature review In this section, a literature review is conducted. First, we review the studies on sourcing strategies available in the literature. Then, research on ISNs is presented, linking the discussion to the circular economy. 2.1 Sourcing strategies under disruptions Sourcing strategies refer to a set of procurement decisions aiming to ensure long-term supply continuity (Martin, 2021). In the literature, different strategies are proposed. Single sourcing (working with one supplier) minimises production costs, reduces inventories, increases quality, strengthens relationships, and secures information sharing. However, this mode increases risks (Namdar et al., 2018; Ray & Jenamani, 2016). Multiple sourcing (working with diverse suppliers) reduces risks during disruption times (Aldrighetti et al., 2021) and maintains competitiveness between suppliers (Heese, 2015;Shanetal.,2023). Nevertheless, managing more than one supplier increases costs and complexity (Burke et al., 2007). Structures of dual sourcing may be symmetric (sourcing equal quantities from suppliers) or asymmetric (sourcing unequal quantities) (Jain & Hazra, 2017). Backup sourcing is used when a disruption occurs to the primary supplier in order to feed the factory; however, this source is characterised by higher costs and slower response times (Schmitt & Tomlin, 2012). The long-term crises triggered by the pandemic raised novel questions about the effectiveness of the existing resilience mechanisms to ensure supply chains’ survivability (Ivanov & Dolgui, 2020). According to Kumar et al. (2018), sourcing is a key strategy for mitigating risks and disruptions. Sourcing decisions are of vital importance, as they ensure the availability of material for the production process. One challenge for decision-makers is to strengthen sourcing practices by adopting powerful strategies dealing with negative and positive changes in the business environment. As an example, the semiconductor shortage turned automotive manufacturers’ attention toward securing their sourcing strategies (Ramani et al., 2022). In the literature, single, dual, multiple, contingent, outsourcing, and backup sourcing have been the subject of many studies (He et al., 2020;Ivanov,2017;Lietal.,2017,2021;Namdar et al., 2018; Niu et al., 2019; Thomas & Mahanty, 2020;Wang&Yu,2020). Each strategy has strengths and weaknesses in terms of cost, lead time, and resilience (Aldrighetti et al., 2023). Sourcing strategies under disruption have also been the subject of different studies in the literature. Appendix 1 summarises the main findings of the works related to sourcing in supply chains under disruptions. 2.2 Circular-intertwined supply networks ISNs are seen as complex systems which encompass many supply chains with dynamic structures, roles, and behaviours (Ivanov & Dolgui, 2020). ISNs are characterised by three properties: open system, structural dynamics, and integrity (Wang & Yao, 2021). In other words, ISNs interconnect different industrial sectors, securing the provision of society and markets with goods or services (Ivanov & Keskin, 2023;Ivanov,2023b). The dynamic structures refer to the ability to change the roles of suppliers and buyers (Feizabadi et al., 2023). Studies on ISNs under disruption are in their infancy stage. The most recent analysis of supply chain viability and ISNs can be found in Ivanov et al., (2023). In their seminal work, Ivanov and Dolgui, (2020) defined the term “ISN” and proposed a biological system model for ISN viability, extending the analysis from the supply chain to the ecosystem level. They 123 Annals of Operations Research (2024) 337:459–498 463 illustrated the viability of ISN under disruption using game theoretical models. Wang and Yao (2021) proposed a model to design an ISN network, select supply routes, and assign customers under a facility and transportation disruption. The optimisation approach captured the trade-off between cost and viability performance in the medical sector. Feizabadi et al., (2023) elaborated on a novel lens of ISN analysis, namely jury-rigging behaviour. Drawing on complex adaptive systems and Ashby’s law of requisite variety, they developed a simulation model to examine complex behaviours combining parts and components in a manner that is not pre-specified to solve a problem encountering unknown unknowns. They studied jury-rigging search behaviour to manage the interdependencies across the supply chains as adaptive mechanisms. Dolgui et al. (2023) and (Chervenkova & Ivanov, 2023) studied intertwining strategies for automotive industry repurposing for production of healthcare product through intertwining of commercial and healthcare supply chains. Yueetal.,(2023) used network simulation to identify hidden risky sources in TFT-LCD supply networks, considering risk propagation. Their approach allows the detection of hidden risky interfirm cooperation. Liu et al. (2022) adapted an underload cascading failure model to evaluate a network’s viability and adaptability against the disturbance caused by the COVID19 pandemic. Ghanei et al. (2023) studied a two-stage stochastic network design problem that incorporated three different resilience strategies to design ISNs under a fair co-operation. Since ISNs are interconnected supply chains, the circular intertwined supply network (CISN) is an ISN where supply chains share the reverse flow and recover common materials. The characteristics of the ISN strengthen links and ease the material flow, which is consistent with circular economy principles. Figure 1illustrates the material flow in the CISN. Fig. 1 CISN structure 123 464 Annals of Operations Research (2024) 337:459–498 3 Problem statement, case study, and methodology 3.1 Generalised problem statement We study a problem where several supply chains of different industries intersect, sharing common suppliers, components, and warehouses. We name the integrity of these supply chains an ISN. When disruption hits a common supplier, the inventories and production capacities of some supply chains can be used to mitigate shortages in the others. In addition, the companies share some reverse flows. The reused materials can help to cope with material shortages due to a supplier disruption. Our objective is to examine the effects of intertwining and circularity on supply chain resilience and propose managerial guidelines on how to improve resilience through the usage of intertwining and circularity effects. 3.2 Semiconductor shortage and circular economy In the wake of the COVID-19 pandemic, industries have experienced a semiconductor shortage (Ramani et al., 2022). The complex semiconductor production system serves several markets and ecosystems, e.g., the automobile production and mobility ecosystem (Srivastava et al., 2022). Chip manufacturers oriented their final products to fulfil the pressing demands of medical, remote work, and distance learning (Chang et al., 2022). While sales of chips to the vehicle industry collapsed during the lockdown, the demand for electronic devices (e.g., computers and phones) increased (Attinasi et al., 2021). Vehicle manufacturers reported that production was constrained by an insufficient quantity of semiconductors, which increased new car prices and reduced inventories (Krolikowski & Naggert, 2021). The transition toward 5G technology, self-driving cars, and the integration of Industry 4.0 tools had already increased the demand for chips before COVID-19 (Voas et al., 2021). Leading suppliers, generally located in Taiwan and South Korea, account globally for 83% of worldwide chip production (McAleese, 2021). In this setting, innovative practices are required to minimise organisations’ dependence on a few chip providers (Marinova & Bitri, 2021). One of these options is localisation of chip production through investing in new plants. However, this practice is expensive, imposing a long ramp-up time. As stated by Frieske and Stieler (2022), possible measures for rapid adaptation include short-term work and adjustment of production processes and inventories. Possible long-term practices include maximising stocks of critical components, strengthening a multiple-sourcing strategy, and supporting local chip manufacturing. In addition to COVID-19 disruptions and geopolitical issues, semiconductor (SMC) supply chain complexity is one of the reasons behind this shortage (Mohammad et al., 2022). The average cycle time is about 10–15 weeks, fabrication is costly (Mönch et al., 2018), and facilities are distributed across continents. Therefore, building a resilient SMC supply chain is a challenging task. In this context, a circular economy may be effective to deal with shortages during a crisis because of its role in enhancing the availability of materials. To that end, an innovative design for disassembly is required to enable the recovery and reuse of microchips (Schröder, 2022). As examples of circular initiatives, Nikon purchases semiconductor lithography systems in end-of-life, collects the usable parts, and assembles them into new systems for reuse as replacements. Retronix has developed a process to recover and refurbish these components to enable reuse. 123 Annals of Operations Research (2024) 337:459–498 465 Fig. 2 The ISN design 3.3 Case study We now introduce a case study which will be used for simulation analysis. The case study is based on three intertwined supply chains, which frame an ISN as presented in Fig. 2. The ISN as a whole secures the provision of society with mobility, communication, and medical equipment. The ISN consists of four layers, including suppliers, focal firms, distribution centres, and customer zones. The primary supplier, the backup supplier, and the electronics and medical factories are located in Asia, while the automotive factory and the second supplier are in Europe. The electronics factory produces smartphones (SPs) and touch screens (TSs). The automotive parts factory produces car dashboards (CDs), and the medical factory produces medical equipment (e.g., blood pressure monitors (BPMs)). TSs are produced by the electronics factory and purchased by the other companies. There are three interconnected distribution centres. DC2 provides TSs (supplied by the electronics factory) to the focal firms and DCs. Customers are dispersed worldwide. During the COVID-19 pandemic, the three factories suffer from the semiconductor shortage, since the production plants rely on the same supplier and the global capacity of this necessary component is limited. The managers of the three supply chains should design viable sourcing strategies. They propose to improve their sourcing strategies through supplier diversification, enhanced collaboration resulting from intertwining, and implementing circular economy practices through a shared reverse flow. The scope of this study is to analyse the impact of intertwining and circularity during supply and demand disruptions while adopting different sourcing strategies. We examine the proposed sourcing practices in the ISN through simulations. 3.4 Methodology The present study adopts the simulation methodology proposed in the literature (Aldrighetti et al., 2019; Burgos & Ivanov, 2021; Gianesello et al., 2017;Ivanov,2017,2019,2020). Compared to analytical models, simulation can handle complex problems with situational behaviour over time (Ivanov, 2019). Simulation models virtually present real-world problems through different scenarios (Sun et al., 2021). The logic of this study is shown in Fig. 3. 123 466 Annals of Operations Research (2024) 337:459–498 Fig. 3 Research process First, the current ISN is simulated with the help of anyLogistix software. The performance indicators are calculated to be used for comparison. Then, the ISN is simulated with the introduction of supply and demand disruption. In the third step, we consider backup and dual sourcing to mitigate the impacts of disruptions. Fourth, a novel collection/disassembly centre is introduced to recover SMCs from obsolete products for reuse in the CISN. anyLogistix is a software developed by the AnyLogic Company. The software is easy for researchers to use to create and analyse supply chain models (Miscevic et al., 2018; Vitorino et al., 2022) and perform network optimization and simulation (Stewart & Ivanov, 2022). It is used to solve problems including supply chain design, risk assessment, inventory and sourcing, transportation and production planning, and bullwhip effect quantification. 4 Simulation model 4.1 Simulation model logic Figure 4shows the simulation model design. Customer demand for products (CDs, BPMs, and SPs) generates orders at the DCs. Demand for all TSs generates orders at DC2, demand for TS3 creates an order at DC1, and demand for TS2 creates an order at DC3. According to the customers’ inventory policies and expected lead times, these DCs place orders at the factories. Orders of CDs and BPMs at the automotive and medical factories create orders of TS3 and TS2 at the electronics factory. The used products create a flow from customers towards the collection/disassembly centre. Factories can order semiconductors from this centre or from the primary supplier located in Taiwan. Reused semiconductors are cheaper than new ones. 123 Annals of Operations Research (2024) 337:459–498 473 Fig. 6 Experiment results: Primary supplier shutdown (SUSD) directly after the primary supplier reopened, while it started to increase from day 240 for the other factories (c). 5.2.2 ISN under demand increase (SUDD) In this scenario, an increase in demand is simulated from 1 April 2022 until 30 June 2022. The impacts of network performance on an increase in demand for all products are shown in Fig. 7. The results of an increase in demand for SPs and BPMs and a decrease in that for CDs in the network are presented in Fig. 9. When the demand for all products increases, the profit increases compared to the NS (Table 5). The ELT service level is between 0.98 and 1 because this metric does not consider orders that are not yet shipped, delayed or dropped. However, the service level decreases (b). This can be explained by the demand backlog of BPMs and CDs (f) resulting from incomplete orders. The inability to complete orders is not due to the SMC shortage but to TS inventory (Fig. 8a). The quantity of TSs purchased is not sufficient to fulfil the demand (d). 123 474 Annals of Operations Research (2024) 337:459–498 Fig. 7 Experiment results: All products demand increase (SUDD) Fig. 8 Available inventory of TSs in factories 123 Annals of Operations Research (2024) 337:459–498 475 Actually, the normal provision of TSs is maintained. The electronics factory cannot increase TS1 and TS3 production due to the demand increase for SPs and the limited SMC inventory. The reason is to maintain the service level (b). Nevertheless, if the demand for SPs increases further, a backlog will be observed because of the global SMC inventory. When the demand for SPs and BPMs increases and the demand for CDs decreases (Fig. 9), the results are similar to the previous scenarios. As expected, cost and revenue at the electronics and medical factories increase while the service level decreases. For the automotive factory, the service level is maintained, but the cost and revenue decrease. The demand decrease does not affect the service level because the placed orders are shipped on time. In this scenario, the production at the automotive factory decreases, which means that the demand for TS3 decreases and the electronics factory can re-allocate its SMC inventory to produce TS2 or TS1 (Fig. 8b). This reallocation strategy impacted positively the profitability of the electronics factory. Fig. 9 Experiment results: BPM, SP demand increase and CD decrease (SUDD’) 123 476 Annals of Operations Research (2024) 337:459–498 5.2.3 ISN under supply and demand disruption (SUSDD) This scenario simulates a combination of the scenarios presented above: a shutdown at the supplier, demand increases for SPs and BPMs, and a demand decrease for CDs. Figure 10 shows the results of this experiment. As seen in Table 5, the financial performance decreases compared to NS due to a lack of final products’ production and delivery. The service level by orders for CDs is equal to 1, 0.8 for SPs, and 0.813 for BPMs (a). The demand backlog of BPMs and SPs (f) shows that orders are not delivered to customers due to the lack of required products shown in (d). The inventory level of TS2 and TS3 increases at factories during the disruption, which increases the carrying cost. This indicates that there is no coordination in production planning between factories. Else, with the decrease in TS3 demand, the electronics factory can orient the available inventory of SMC to produce SPs, TS1, or TS2. A backlog of CDs is observed at the end of the disruption due to the decrease in CD inventory and the normal state of demand (d). Fig. 10 Experiment results: Supplier shutdown, BPM, SP demand increase, and CD decrease (SUSDD) 123 Annals of Operations Research (2024) 337:459–498 477 5.3 Third scenario: Scenario with dual and backup sourcing In the third scenario, multiple sourcing strategies are considered. Dual sourcing for the automotive factory, backup sourcing for the medical factory, and both strategies for the electronics factory are used. Indicators of financial performance results are compared in Table 6. It can be observed in Table 6that while considering dual and backup sourcing strategies cost decreases at the electronics and automotive factories and increases at the medical factory due to the sourcing mode comparing to NS with single sourcing. Collaborating with a local second supplier (dual sourcing) decreases the transportation cost for the automotive factory, while collaborating with an emergent supplier (backup sourcing) increases the cost for the medical factory. A combination of both strategies results in a slight cost reduction, as observed for the electronics factory. During demand increase scenario (SS-DD), the revenue and the costs are higher for companies opting for backup sourcing comparing to dual sourcing. Figure 11 shows the experimental results of the demand increase scenario. An improvement in service level (b) is shown comparedtoFig.7. At the beginning of the disruption, the SMC inventory level at the electronics factory decreases during the lead time of the backup supplier (c). When the backup supplier starts feeding the factory, the inventory level increases (c), and the produced production meets the production requested (e). This ensures the availability of SPs at the DCs (d) and prevents an SP demand backlog (f). Moreover, the factory maintains the production of TSs (Fig. 12a). The demand backlog of CDs and BPMs (f) is reduced compared to Fig. 7. When demand increases, the SMC inventory at the medical factory decreases (c), generating a reduction of BPMs at DC3 (d), hence creating a backlog of demand and a decrease in the service level. After receiving the SMCs from the backup supplier, the BPM inventory level increases at DC3, and the customers receive their demand (no demand backlog). At the end of the disruption, a demand BPM backlog is observed parallel to an increase in TS2 inventory at the medical factory. The increasing demand for BPMs increases the demand for TS2 at the electronics factory, which creates pressure on the backup supplier. As for CDs, the demand backlog is observed during the disruption; this can be explained by the level of SMCs at factories supplied by the primary and secondary suppliers. In contrast to the medical factory, which receives an extra quantity of SMCs from the backup supplier, the automotive factory receives the same quantity as purchased, as under normal conditions. Figure 13 shows the experimental results of the supplier shutdown and demand increase (SS-SDD). Cost and revenue increase at all factories. However, customer performance decreases compared to the previous scenario (a, b). The closure of the primary supplier turns Table 6 Financial performance of the ISN with multiple sourcing under disruptions Scenarios Automotive Electronics Medical Cost (%) Revenue (%) Cost (%) Revenue (%) Cost (%) Revenue (%) SS-NS* −1.46 0.00 −0.12 0.00 2.02 0.00 SS-DD 5.3 10.2 16 30 9.2 27.3 SS-SDD 11.2 9.3 24.5 35.5 16.9 23.7 SS-SDD’ −3.1 −11.7 20.28 32.7 19.26 29.22 *SS-NS: Normal scenario with dual and backup sourcing 123 478 Annals of Operations Research (2024) 337:459–498 Fig. 11 Experiment results: Demand increase for all products in (SS-DD) Fig. 12 Available inventories of TSs at factories 123 Annals of Operations Research (2024) 337:459–498 479 Fig. 13 Experiment results: Primary supplier shutdown and demand increase (SS-SDD) the automotive factory’s sourcing strategy into single sourcing, which creates a demand backlog for CDs (f) resulting from the decrease of products at DC1 (d). A decrease in BPMs at DC3 is observed after the primary supplier closes due to the backup supplier’s lead time (d). Despite SMC delivery, the BPM backlog persists (f). A demand backlog for SPs is also observed at the beginning of the disruptions, explained by the backup supplier’s lead time. Then, the factory operates with dual sourcing (Supplier 2 and backup supplier). In this situation, the backup supplier feeds the factories by a ratio due to the limited SMC capacity. As seen in (Fig. 12b), the inventory level of TS2 and TS3 at factories increases. This means that the electronics factory responds to the increased demand for BPMs and CDs, while the medical and automotive factories cannot deliver the product due to a lack of SMCs. Figure 14 shows the results when the supplier shutdown occurs parallel to the SP and BPM demand increase and the CD demand decrease (SS-SDD’). ELT service level and service level are improved compared to the previous scenarios (a, b). A BPM demand backlog is observed. In contrast to the scenario with a supplier shutdown and demand increase, there is no backlog in SP and CD demand. This can explain the customer performance indicators improvement. 123 480 Annals of Operations Research (2024) 337:459–498 Fig. 14 Experiment results: Primary supplier shutdown, SP and BPM demand increase, and CD decrease (SS-SDD’) The automotive factory purchases SMCs from the secondary supplier (c), and this quantity is sufficient to feed the reduced production resulting from the demand decrease. This decrease is advantageous for the electronics factory because the quantity purchased from the second supplier increases and covers the period of the backup supplier’s lead time (d). The pressure on the backup supplier is hence minimised, and no BPM demand backlog appears at the end of the disruption (d, f). The electronic factory maintains the production of the semi-products TSs (Fig. 12c). 5.4 Fourth scenario: disruptions in the CISN with different sourcing strategies. In this scenario, a CISN is operating under disruption free conditions, disruptions with single, dual, and backup sourcing. Table 7shows the financial performance indicators of the CISN. Figure 15 present the experimental results of the CISN in normal conditions (C-NS). Outcomes related to customer and operational performance are similar to the NS. However, 123 Annals of Operations Research (2024) 337:459–498 481 Table 7 Financial performance of the CISN Scenarios Automotive Electronics Medical Cost (%) Revenue (%) Cost (%) Revenue (%) Cost (%) Revenue (%) C-NS −10.6 0.00 −20.7 0.00 −17.2 0.00 C-SDD −25.3 −11.7 −24.4 32.3 −13.1 25.4 C-SS-SDD −5.13 −11.7 18.28 50.7 19.26 35.22 Fig. 15 Experimental results: CISN under disruptions-free conditions (C-NS) 123 482 Annals of Operations Research (2024) 337:459–498 the economic performance is improved due to the minimization of raw materials cost. Cost of the electronics factory are reduced most significantly because 70% of the SP are recovered. Figure 16 shows the experimental results of the primary supplier shutdown, SP and BPM demand increase, and CD demand decrease, considering a single supplier in the CISN (CSDD). The integration of circular practices with single sourcing improves the global service level from 0.665 to 0.96. The consumption of the reused SMCs at the end of the disruption generates a decrease in BPMs at DC3, creating a backlog in demand. This is explained by the SMC shortage observed in the medical factory after day 130 (Fig. 17). It can be seen that there is no demand backlog and no shortage at the electronics factory. This means that this factory exploits the most inventory of reused SMCs which impacted its economic profitability. Figure 18 shows the experimental results of the primary supplier shutdown, SP and BPM demand increase, and CD demand decrease, considering dual and backup sourcing in the CISN. An improvement in the service level is observed (0.99), there is no demand backlog, and the available inventory at DCs satisfies customers’ demand. At the beginning of the Fig. 16 Experiment results: Primary supplier shutdown, SP and BPM demand increase, and CD decrease in the CISN (C-SDD) 123 Annals of Operations Research (2024) 337:459–498 489 Declarations Conflict of interest No conflict of interest has to be declared. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Appendix 1 Sourcing strategies under disruptions (See Table 9). 123 490 Annals of Operations Research (2024) 337:459–498 Table 9 Previous works on sourcing strategies analysis under disruption References Sourcing strategies Method Main findings (Gupta et al., 2021) Two suppliers Game theory The price leadership defines the order quantity. It increases when the supplier is the leader The non-disrupted supplier can increase the price before receiving orders (Li et al., 2021) Standalone and combined strategies Control theory Cost and time affect sourcing strategies Standalone strategies can address shortages in short-term disruptions For long-term disruptions, it is recommended to adopt combined strategies (Wang & Yu, 2020) Contingent sourcing and dual sourcing Game theory Contingent sourcing and responsive pricing can substitute dual sourcing if the emergency cost and lost sales costs are lower If the disrupted capacity increases, the probability of a substitution relationship increases as well 123 Annals of Operations Research (2024) 337:459–498 491 Table 9 (continued) References Sourcing strategies Method Main findings (Heetal.,2020) Dynamic contingent sourcing Quantitative model Demand and inventory are related to the time of employing contingent sourcing in addition to customer behaviour and demand recovery For long disruptions, dynamic contingent sourcing strategies can be ineffective when operations are recovered during production downtime For a short disturbance, the effectiveness of dynamic contingent sourcing strategies is related to the demand recovery time, the intensity of competition, and customer behaviours (Thomas & Mahanty, 2020) Emergency backup supplier Dynamic simulation approach In a short-term scenario, there is a trade-off between enhancing the service level and profitability impacted by the cost, response time, and spare capacity of the backup supplier In a long-term scenario, a trade-off between readiness and recovery practices exists 123 492 Annals of Operations Research (2024) 337:459–498 Table 9 (continued) References Sourcing strategies Method Main findings (Aldrighetti et al., 2019) Increasing the inventory levels; lateral transshipment; backup supplier Simulation For short-term disturbances, lateral transshipment is favourable to mitigate the negative effects of disruptions with a slight increase in costs. Collaborating with a backup supplier is the most effective mitigation strategy for long-term disruption (Namdar et al., 2018) Single and multiple sourcing, backup supplier contracts, spot purchasing, and resilient strategies Two-stage stochastic programming Compared to single sourcing, a multiple-sourcing strategy increases the service level if decision-makers are risk-aware and their ability to detect early warning signals is higher. This capability is pivotal for supply chain resilience (Ivanov, 2017) Single and dual sourcing Simulation approach Dual sourcing is recommended when the level of inventory is low and demand is high Single sourcing is recommended (1) when both inventory level and demand are low, (2) when inventory level is high and demand is low with production smoothing, and (3) when demand and inventory level are high with capacity flexibility 123 Annals of Operations Research (2024) 337:459–498 493 Table 9 (continued) References Sourcing strategies Method Main findings (Zhu, 2015) Dual sourcing (2 suppliers: local and overseas) Stochastic dynamic programming Disruption at the local source is costlier than overseas Data about the local source is more relevant than overseas data When both sources are considered, the disruption information is precious and leads to cost savings (Gupta et al., 2015) Contingent, dual, and sole sourcing strategy Game theory A buyer operating under supply disturbance should be aware of the supply state and the competitor’s time to place an order when working with an unreliable supplier (Fang et al., 2013) Single, dual, multiple, and contingent sourcing Stochastic optimisation The marginal benefits of collaboration with three or more suppliers are much lower Dual sourcing is more favoured than having a backup supplier, even with zero lead time References Aldrighetti, R., Battini, D., & Ivanov, D. 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