Mitigate Disaster Effects in Closed Loop Supply Chains

Mitigate Disaster Effects in Closed Loop Supply Chains: History

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Subjects: Engineering, Industrial

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The increased level of complexity in the case of Closed Loop Supply Chains (CLSCs) turns them into vulnerable systems under a disaster event. The latter calls for a methodological approach that allows a dynamic study under alternative policies in mitigating the disaster effects with a focus on creating sustainable CLSCs.

sustainable supply chain
system dynamics
supply chain management
disaster management
closed-loop supply chain

1. Introduction

The industrial world is increasingly exposed to risk as a result of major natural and man-made disasters, necessitating a rethinking of how industrial companies and communities recover [1]. Experience has shown that policies designed to operate within a given range of conditions are frequently confronted with unexpected challenges when they are used outside of that range due to an unexpected disaster event [2]. Makridakis et al. [3] proposed to distinguish between known–known, known–unknown, and unknown–unknown risks when handling uncertainty in a given situation. For a SC, disaster is a risk source of unknown–unknowns [4].

By “disaster”, we refer to extreme phenomena affecting the normal function of SCs to a great degree [5,6]. These phenomena can include natural disasters, such as typhoons [7,8] and earthquakes [9,10,11], or the spread of a pandemic as is the case with the COVID-19 disease [6,12,13]. They can be divided into three categories based on their duration: small-scale, medium-scale and large-scale. There are plenty real-world examples for each category: Earthquakes [9,10,11] are small-scale phenomena, such as the earthquake that hit Taiwan in 1999 and caused a two-week global semiconductor shortage because Taiwan was the world’s third largest provider of computer accessories when the phenomenon occurred [11].

Natural disasters, such as typhoons, are medium-scale phenomena [7,8]. Zhang Yi et al. [14] analyzed the economic loss of four selected ports in China due to typhoon-induced wind disasters from 2007 to 2017. The overall economic loss was calculated as the sum of reputational loss, loss to the shippers, loss to the carrier and loss to the ports and was approximately equal to $20 billion.

Finally, large-scale phenomena are outbreaks of diseases, epidemics and pandemics. For instance, disruptions caused by epidemics, such as the outbreak of mad cow disease in Europe (2001), which resulted in a shortage of leather goods, and the breakout of SARS in Asia (2003) that impacted information technology SCs, are large-scale phenomena [15]. Dixon et al. [16] mentioned that the trough of the economic downturn is expected to be around 19% at the end of the first quarter of 2020, with a 12% fall by the end of the second quarter. According to the CBO (2020), the Real GDP declined by 11% in the second quarter of this year, resulting in about 26 million fewer persons employed than in the fourth quarter of 2019.

A disaster effect may result in production disruptions with significant consequences on profitability. An important challenge is the identification of mitigation policies that should be implemented when production is disrupted due to disasters. In 2000, for example, Ericsson’s sole microchip source was the Phillips Electronics semiconductor factory in Albuquerque, New Mexico. Due to a fire caused by a lightning strike in 2000, this factory was destroyed, resulting in a $400 million loss for Ericsson [17].

On the contrary, Nokia, a competitor of Ericsson and a major customer of the same Philips Electronics plant, sensed this disruption following a multiple-supplier strategy and responsiveness and took immediate action by shifting its chip orders to other Philips factories and other Japanese and American suppliers. This quick response resulted in increasing Nokia’s handset market share from 27% to 30% [18]. According to estimates, 315 disasters occurred around the world in 2018, resulting in 11,804 deaths, 68.5 million people affected and $131.7 billion in economic damage [19].

Coordinated operations among supply chain actors and optimized emergency supply chain operations are critical to provide a speedy response to the presence of demand uncertainty, which remains one of the main challenges in disasters [20]. Ivanov and Dolgui [21] mentioned that the concerns that occurred particularly from the drastic increase or reduction in demand due to the COVID-19 pandemic cannot be handled within a narrow SC perspective but rather require a larger-scale analysis that goes beyond the current state-of-the-art in SC resilience.

In addition, Ivanov [22] presented the findings of a simulation study that opens up to some new research tendencies regarding the impact of COVID-19 on global SCs, specifically the consideration of different risk mitigation inventory levels as elements of pandemic plans. Umar and Wilson [23] adopted a multiple case study to examine the role of collaboration within food supply chains in two different South Asian regions in order to investigate how these capabilities influence the resilience of supply chains in rural communities that experience regular natural disasters.

2. Literature Review

Τhere are many approaches that deal with different supply chain management issues, with a focus on sustainable CLSC. Wen-Hui et al. [25] introduced measures of the implementation of remanufacturing supply chain management in a CLSC. Hosseini-Motlagh et al. [26] studied the sustainability of a real pharmaceutical CLSC through effective collection interruption management. Ullah et al. [27] introduced mathematical models to reveal the best remanufacturing strategy and also reusable packaging capacity for single and multi-retailer CLSCs under stochastic demand and return rates.

Garai et al. [28] examined a CLSC for herbal medicines, while Garai and Sarkar [29] researched a CLSC for herbal medicines and biofuel by applying a minmax method based on a semi-autonomized multi-objective optimization algorithm from the economical point of view. By adopting a game-theoretical approach, Ma [30] examined different government subsidies for the management of CLSCs under demand disruption, in order to find the subsidies with the best economic and environmental benefit outputs. Sarkar et al. [31] proposed the conversion of a single food product supply chain into a two-stage supply chain in order to achieve zero waste through recycling. Although these contributions focus on sustainable CLSCs, the employed modelling and analysis approaches ignore issues related to disaster management.

In particular, disaster management in CLSCs has received increased attention over the last 15 years. It is remarkable that, although the topic is suitable for OR/MS research, the scientific community has not yet produced many articles dealing with operations management issues in large-scale disasters; interesting reviews of this field are provided in Altay and Green [5], Galindo and Batta [32] and Farahani et al. [33]. In addition, Borja et al. [34] mentioned that the literature on the dynamic behavior of CLSCs remains limited.

Complex adaptive systems theory [35,36,37], chaos theory [38,39,40], catastrophe theory [41], catastrophe-risk approaches [42], disaster preparedness [43], along with non-linear dynamic approaches [40,44,45], provide methods for the dynamic analysis and optimization of CLSCs under disaster effects. However, models based on these methods usually lead to more complex approaches and to more restrictions on the number of state variables and cost structure than they can handle.

Systems thinking may provide a systematic and structured approach to disaster management due to its ability to consider the systemic settings within a larger system. In addition, this approach may consider the interdependent nature of the system [46]. System-oriented and holistic approaches have been identified as important in modeling the complex, deeply uncertain and dynamic elements of disaster management [5,34,47].

The SD methodology introduced by Forrester [48] provides a more flexible and simple modeling and simulation framework for making decisions in dynamic and complicated industrial management situations [23,45]. It is an appropriate approach to study disaster operations problems in a CLSC context, mainly due to the following two reasons: (i) it has the ability to integrate soft factors into an operations analysis; (ii) it can deal with increased complexity caused by causal influences among different involved actors, non-linear behavior and time delays [46,49,50].

Additionally, the benefits of using SD models include: (i) allowing for the study and evaluation of alternative scenarios, making their impact on the system’s performance testable and (ii) being highly valued and well understood by managers, as they augment brainstorming and rely less on hard facts than other approaches [46]. This methodology provides an understanding of the changes occurring within a system by focusing on the interaction between physical flows, information flows, delays and policies that create the dynamics of the variables of interest.

Thereafter, the SD methodology searches for policies to improve system performance by considering the causal influences of relative decisions on the operational dynamics [51,52]. SD has been identified as promising for modeling complex and adaptive to nonlinear evolutionary change problems [47] and also as analysis tool to improve SC risk mitigation policies and recovery plans [53]. However, it is surprisingly underutilized in disaster operations management research [5,32,33] as shown in Table 1.

Table 1. Methodological approaches in dealing with disaster management in CLSCs.

Approaches	Reference
Complex Adaptive Systems Theory	[35,36,37]
Chaos Theory	[38,39,40]
Catastrophe Theory	[41]
Catastrophe-Risk Approaches	[42]
Disaster Preparedness	[43]
System Dynamics	-
Non-Linear Dynamic Approaches	[40,44,45]

Some studies demonstrated SC dynamics as endogenous outcomes of feedback structures established under various dynamic hypotheses: Ozbayrak et al. [54] and Pierreval et al. [55] dealt with the dynamic analysis of manufacturing SCs and automotive industry, respectively; Keilhacker and Minner [56] applied an SD approach to examine the individual company’s reaction in the rare earth element (REEs) supply chain to China’s introduction of export restrictions; Lai et al. [57] studied the system dynamics in just-in-time logistics; Mikatia [58] examined the dependence of lead time on batch size in a manufacturing model; and Suryani et al. [59] studied capacity expansion decisions for innovative products with short lifecycles for the cement industry.

Other studies investigated the performance of SC systems under different operational disruption settings: Wilson [60] investigated the effect of transportation disruptions for the case of a five-echelon supply chain; Chen et al. [61] examined the effects of disruptions considering pipeline inventory control and vendor-managed inventory control; Ankit [62] studied the effects of SC between any two players in a multi-player SC system; and Aguila and ElMaraghy [63] proposed an SD model to study SC behavior in the face of interruptions.

The model may be used to predict the impact of potential interruptions on several critical performance indicators throughout the SC design and planning stage; Diaz et al. [64] proposed an SD model that analyzes the issue of housing recovery in the aftermath of a disaster, from both a demand and a supply standpoint; Bashiri et al. [65] investigated the sustainability risks in the Indonesia–UK coffee supply chain by using SD; and Zhang et al. [66] proposed an SD model to examine the effects of supply disruption, production disruption and sales disruption on the inventory, order accumulation rate and profit level of suppliers, manufacturers and retailers for a fixed outage time in a SC.

The contributions are limited when focusing on integrating aspects of SC and aspects of reverse logistics using SD [67]. Indeed, the formulation of dynamic strategic capacity planning policies [68,69], the examination of the bullwhip effect [70], the impact of environmental legislation on the long-term dynamic behavior of closed-loop remanufacturing networks [52] and the ecological and economic dimensions of sustainability in closed-loop recycling networks [71] are some of the few fields that have received special attention.

These models include either remanufacturing or recycling options of product reuse, while the integration of both remanufacturing and recycling into a closed-loop model for long-term evolutionary analysis and decision-making was introduced by Gu and Gao [72]. However, there is a gap in the SD literature in providing a dynamic approach for modeling and mitigation policy-making under disaster effects that considers a holistic picture of CLSC networks with remanufacturing and recycling options [73]. The purpose of this paper is to fill this gap by identifying effective policies in mitigating of disaster events.

This entry is adapted from the peer-reviewed paper 10.3390/su14094948

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