Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis

Ivanov, Dmitry Article — Published Version Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis Annals of Operations Research Provided in Cooperation with: Springer Nature Suggested Citation: Ivanov, Dmitry (2022) : Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis, Annals of Operations Research, ISSN 1572-9338, Springer US, New York, NY, pp. 1-17, https://doi.org/10.1007/s10479-022-04754-9 This Version is available at: https://hdl.handle.net/10419/308112 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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. https://creativecommons.org/licenses/by/4.0/ ORIGINAL RESEARCH 1 3 Accepted: 29 April 2022 © The Author(s) 2022 Dmitry Ivanov [email protected] 1 Berlin School of Economics and Law, Department of Business Administration, Supply Chain and Operations Management, 10825 Berlin, Germany Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis DmitryIvanov1 Annals of Operations Research https://doi.org/10.1007/s10479-022-04754-9 Abstract Increased electricity consumption along with the transformations of the energy systems and interruptions in energy supply can lead to a blackout, i.e., the total loss of power in an area (or a set of areas) of a longer duration. This disruption can be fatal for production, logistics, and retail operations. Depending on the scope of the affected areas and the blackout duration, supply chains (SC) can be impacted to different extent. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. Distinctively, we triangulate the design and evaluation of experiments with consideration of SC performance, resilience, and viability. The results allow for some generalizations. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of several SC processes, disruption propagations (i.e., the ripple effect), and a danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts, mitigation and recovery strategies. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the unpredictable customer behavior might become major factors that determine the blackout impact and influence selection of an appropriate recovery strategy. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and viability and develop mitigation and recovery strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, intertwined supply networks, and cross-structural ripple effects. Keywords Supply chain · Disruption · Resilience · Blackout · Power outage · Simulation · Digital twin · Viability · Ripple effect · Structural dynamics Annals of Operations Research 1 3 1 Introduction Supply chains (SC) are multi-structural systems composed of organizational, informational, financial, technological, process, product and energy structures (Ivanov 2018). As every complex system, SCs are exposed to uncertainty and risks. Literature has developed a profound body of knowledge about disruption risks in SCs, e.g., earthquakes, fires, strikes, pandemics (Aldrighetti et al. 2021, Altay et al. 2018, Dubey et al. 2021b, Hosseini et al. 2019, Queiroz et al. 2020). Performance impact analysis, mitigation and recovery strategies have been extensively studied, mostly concerning the organizational SC structure, e.g., critical supplier identification and back-up supply recovery (Baghersad et al. 2021, Bode et al. 2011, Chopra et al. 2021, Demirel et al. 2019, Dolgui et al. 2020a, Dubey et al. 2019, Ivanov 2021d, Lücker er al. 2021, Sanci et al. 2021). Some works focused on disruptions in the information structure such as cyber-attacks (Sawik 2020). However, disruptions in the energy structures still represent a research gap. A blackout is the most severe form of power losses characterized by total loss of power in an area (or a set of areas) of a longer duration. Examples include power outage in Texas in February 2021 with the loss of a large part of electrical power (Bloomberg 2021) and provinces Heilongjiang, Jilin and Liaoning in China in 2021 leading to severe consequences for society viability and SC resilience (Disis 2021). Busby et al. (2021) point to economic losses from lost output and damage are estimated to be $130 billion in Texas alone. Increased electricity consumption along with the transformations of the energy systems make the blackout to one of the most likely and dangerous SC disruption risks for very near future (Emenike and Falcone 2020). An informal survey conducted by us with SC managers in September 2021 showed that they fear the total blackout more as pandemics or other severe crises. Later, geopolitical tensions in Spring 2022 led to the increased risks of energy supply interruptions at the global scale exposing material flows in SCs to disruptions. Adversely, the energy shortage-triggered material shortages and delivery delays can propagate downstream the SC, causing the ripple effect and performance degradation in terms of revenue, service level and productivity decreases (Dolgui et al. 2018, Ghadge et al. 2021, Gholami-Zanjani et al. 2021, Li et al. 2021, Llaguno et al. 2021, Park et al. 2021, Shi et al. 2021). One can expect simultaneous ripple effects, i.e., propagation of the power outage and propagation of disruptions in material flows. Moreover, disruptions in the SC energy structure can influence not only the organizational structure due to disrupted material flows (e.g., unavailability of warehouses) but also propagate to other structures (e.g., financial structure due to missing electronic payments and information structure due to disruptions in the digital SC). The blackout can impact not only resilience of individual SCs but viability of the whole business ecosystems. As pointed in Ivanov and Dolgui (2020), Ivanov (2020b) and Ruel et al. (2021), viability is the SC ability to survive through the severe crisis and so securing the viability of critical ecosystems (e.g., communication, mobility, and food) responsible for provision of society with goods and services, echoed by Nasir et al. (2021) and Wang and Yao (2021). The blackout is a distinct type of SC disruptions that affects both the SC performance and ecosystem viability. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. We examine SC dynamic behaviors under blackout conditions for several scenarios. The outcomes Annals of Operations Research 1 3 of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and product availability and develop mitigation and recovery strategies. The contribution of this study is twofold. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of SC processes, disruption propagations (i.e., the ripple effect), and danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts. Distinctively, we design experiments and analyse the results with consideration of three dimensions, i.e., SC performance, resilience, and viability. A set of sensitivity experiments allows illustrating the model’s behavior for different blackout scenarios along with its value for decision-makers. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the irrational customer behavior might become major factors that determine the blackout impacts. The rest of this paper is organized as follows. In Sect. 2, we present the underlying casestudy and simulation model. Section 3 describes the modelling environment. The experimental setup and results are shown in Sect. 4. The Sect. 5 discusses managerial implications as well as the future research directions. The paper is concluded in Sect. 6 by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, and cross-structural ripple effects. 2 Case-study We examine dynamic behaviour of an SC with a homogenous product of everyday need with quite a stable demand under rational customer behaviour (i.e., if no panic buying occurs). The SC comprises of a factory, an upstream CDC (central distribution center), a downstream CDC, two regionals distribution centres (RDC), and 50 customers (Fig. 1). The 50 customers order every 7 days a total demand of 8,988 units per order cycle. To avoid randomness in the output analysis and without loss of generality, we allow for deterministic demand which ranges from 70 units to 1667 units depending on the customer. The lead time between factory and upstream CDC is 4 h; between the upstream and downstream CDCs – 42 h; between downstream CDCs and RDCs – 2–10 h; and between RDCs and customers – 1–10 h. The RDCs, CDCs, and factory are located in different regions each of which has its own electricity network; however, the networks are interconnected and a blackout in one of the networks can propagate to the network of another region and cause a blackout there. We consider the following blackout scenarios (Fig. 2). Blackouts can have short (5 days), medium (10 days) and long (15 days) durations. We note that the re-order frequency in our case study is 7 days. This number is a usual business practice. We consider both localized blackouts downstream at the RDCs, simultaneous blackout at all the SC echelons, and blackout propagation from the RDC’s region upstream to CDCs and the factory with different speed and of different duration. In addition, we account for irrational consumer behaviour in a case of a blackout in anticipation of shortages resulting in demand increase during the blackout period of 200%. This is in line with obser- Annals of Operations Research 1 3 vations done at the beginning of the COVID-19 pandemic and the associated panic buying (Ardolino et al., 2021; Choi, 2021; Paul & Chowdhury, 2021). In total, our setting leads to 54 different scenarios for analysis (see Table 1). 3 Model 3.1 Modelling environment and control logic Our model is created and solved in anyLogistix simulation and optimization toolkit which represents a digital SC twin. In anyLogistix, the SC has been designed by defining all the locations (factory, warehouses), customers, demand, inventory, sourcing and shipment control policies, costs, revenues, and disruption events (Ivanov, 2019; Singh et al., 2021; Burgos & Ivanov, 2021). The simulation methodology has been recognized as an important tool to study SC dynamics under disruptions (Macdonald et al., 2018, Ivanov 2020a, Li et al., 2020, Ivanov 2021b, Zhao et al., 2019). The following control policies have been used for experiments (Figs. 3 and 4). The inventory control is based on an OUT (order-up-to-level) policy with some re-order point (s) and target inventory (S), and some safety stock (Disney et al., 2020; Boute et al., 2021). The upstream sourcing is a linear system with fixed sources, while the downstream sourcing from RDCs to customers is based on the Most Inventory (Dynamic Sources) rule Fig. 2 Blackout scenarios Fig. 1 Supply chain design Annals of Operations Research 1 3 meaning that the fulfilment of the next incoming order is planned at the RDC with the currently highest inventory level. The backordering is allowed (Schmitt et al., 2017). 3.2 Performance indicators For analysis, we use the following performance indicators in line with studies by (Dolgui et al., 2020a; Hosseini & Ivanov, 2021; Namdar et al., 2021; Singh et al., 2021): ● Financial SC performance – profit, ● Customer performance – ELT (expected lead time) service level, ●Operational performance – alpha service level. The profit is computed as a difference between the total revenue and total SC costs which include material, production, transportation, inventory holding and fixed facility costs. The ELT service level and alpha service levels are computed according to Eqs. (1) and (2), respectively. ELTSL= O on−time Oout (1); AlphaSL= O available Ototal (2) ,where Oon−time is the number of on-time delivered orders at customers, i.e., the number of orders that were delivered within the ELT. In our model, ELT is 2 days for all customers; Fig. 3 Inventory control policy data Fig. 4 Sourcing control policies Annals of Operations Research 1 3 Oavailable is number of successful orders, i.e., the number of orders that were delivered from stock available at the RDCs at the moment of the order placement; Oout is the number of all outgoing orders including on-time and delayed orders; Ototal is the number of all orders placed at the RDCs. The alpha service level shows the estimation of the number of unsuccessful orders. The unsuccessful orders are the placed orders requiring the quantity of products that is not available at the warehouse at the time when this order is placed, and the dropped orders. The alpha service level is the product availability indicator. The ELT service level shows the fraction of on-time orders delivered at customers and is the on-time delivery indicator. Since the blackout has both economical and societal impacts, we consider profit as SC performance indicator, alpha service level as SC resilience indicator, and the ELT service level as viability indicator. 4 Experiments In this section, we present our experimental results and analyse them according to different blackout scenarios and SC reactions. 4.1 Experimental design We design our experimental environment to examine the SC performance and product availability in case of singular, simultaneous and propagated blackouts of different severities subject to answering the following questions: ● What is the impact of blackouts on the SC financial and operational performance from the resilience point of view? ●What is the impact of blackouts on the product availability from the viability point of view? ●What is the role of the scope and timing of blackout propagations? ●What are the most critical scenarios of blackouts? ●What is the impact of irrational (panic) customer behaviors in the wake of a blackout? Organization of the experiments is as follows. For analysis, we consider two groups of scenarios, i.e. sequential and simultaneous blackouts. In each of these two groups, we further diversify our analysis including both rational and irrational (i.e., panic buying) customer behaviors during the blackout periods. Finally, we compare the SC reactions in different cases and draw conclusions on the blackout impacts on the SC performance, resilience and viability. For verification, tracking of the simulation runs, analysis of output log files, and visualization analysis were used. For testing, we use replications in comparison and variation experiments. A warm-up period of two months prior to the disruption (i.e., the blackout) is considered. Annals of Operations Research 1 3 4.2 Experiments In Fig. 5, we illustrate the SC behaviour in a disruption-free (i.e., nominal) scenario without any disruptions. It can be observed in Fig. 5 that the SC operates at an ELT and alpha service levels of 100% achieving a profit of $28,021 million, with a stable lead-time and balanced inventory dynamics. Now we simulate the different cases according to blackout scenarios (cf. Figure 2) and observe the gaps in SC performance as compared to the disruption-free mode (Fig. 5). In all the experiments, blackouts at the RDCs begin at March 1. The simulation period is January 1 – December 31. In case of blackout propagation, the blackout at the next stage upstream (e.g., CDC) begins the day after the blackout ends at the previous stage downstream (e.g., RDC). No blackout overlapping are considered. In case of simultaneous blackouts at different echelons (cases IIb and IIIb in Table 1), they all begin on March 1. The ELT and alpha service levels are counted as recovered service levels at the end of the simulation period. A summary of the most interesting results of the simulation runs is presented in Table 1. Next, we analyse the results presented in Table 1 and deduce some useful managerial implications. 4.2.1 Impact of the blackout localization vs. propagation In this set of simulations, we run and compared scenarios for localized and propagated blackouts to understand the performance impact and if a blackout propagation creates the ripple effect in the SC. When analyzing lines 1/7/23 vs 17/39 as well as lines 3/29 vs 19/41, it can be observed that simultaneous blackouts have lower impacts on performance, resilience and viability as the sequential blackouts. Moreover, these effects amplify with an increase in disruption duration. Insight 1: The blackout propagation induces the ripple effect in SCs. The simultaneous blackouts create less damage for the SC performance, resilience, and viability as compared to the sequential blackouts. Fig. 5 SC performance in disruption-free scenario Annals of Operations Research 1 3 Scenario Blackout duration at RDCs Blackout duration at CDCs Blackout duration at factory Demand surge during the blackout period ELT Service level, % Alpha Service level, % Alpha Service Level Change, % ELT Service Level Change, % Profit change,% 0 Nominal 0 0 0 0 100 100 0 0 0 1. I 5 0 0 0 100 98 -2 0 -4.5 2. 10 0 0 0 100 98 -2 0 -4.5 3. 15 0 0 0 100 96.2 -3.8 0 -6.5 4. 5 0 0 200% 100 96.2 -3.8 0 -4.5 5. 10 0 0 200% 100 96.2 -3.8 0 -4.5 6. 15 0 0 200% 100 94.5 -5.5 0 -4.7 7. IIa 5 5 0 0 100 98.0 -2 0 -4.5 8. 5 10 0 0 94.9 93.0 -7 -5.1 -8.4 9. 10 5 0 0 94.9 93.0 -7 -5.1 -8.4 10. 5 5 0 200% 100 96.2 -3.8 0 -4.5 11. 10 5 0 200% 98.0 94.4 -5.6 -2 -3.0 12. 10 10 0 200% 96.2 92.7 -7.3 -3.8 -1.6 13. 10 15 0 200% 94.3 90.9 -9.9 -5.7 -1.6 14. 15 5 0 200% 98.1 91.0 -9.0 -1.9 -4.8 15. 15 10 0 200% 98.0 90.9 -9.1 -2 -4.8 16. 15 15 0 200% 94.2 87.5 -12.5 -5.8 -3.6 17. IIb 5 5 0 0 100 98.0 -2 0 -4.5 18. 10 10 0 0 100 98.0 -2 0 -4.5 19. 15 15 0 0 100 96.2 -3.8 0 -6.5 20. 5 5 0 200% 100 96.2 -2 0 -4.5 21. 10 10 0 200% 100 96.2 -2 0 -4.5 22. 15 15 0 200% 100 92.6 -7.4 0 -6.5 23. IIIa 5 5 5 0 99.9 98 -2 -0.1 -4.9 24. 5 10 10 0 98 96.1 -3.9 -2 -5.4 25. 5 15 15 0 94.1 92.3 -7.7 -5.9 -6.1 26. 10 5 5 0 98 96.1 -3.9 -2 -5.4 Table 1 Summary of computational results Annals of Operations Research 1 3 Ardolino, M., Bacchetti, A., & Ivanov, D. (2021). Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda. Operations Management Research. DOI: https://doi.org/10.1007/s12063-021-00225-9 Baghersad, M., Zobel, C. W., Lowry, P. B., & Chatterjee, S. (2021). The roles of prior experience and the location on the severity of supply chain disruptions. International Journal of Production Research. DOI: https://doi.org/10.1080/00207543.2021.1948136 Bloomberg (2021). 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