Decision support system for a reactive management of disaster-caused supply chain disturbances

This research contribution presents the Reactive Disaster and supply chain Risk decision Support System ReDRiSS which supports decision-makers of logistical disaster management in the immediate aftermath of a supply chain disturbance. ReDRiSS suggests a methodology which combines approaches from scenario techniques, operations research and decision theory. Two case studies are provided which focus on decision situations of humanitarian logistics and of business continuity management

Decision support system for a reactive management of disaster-caused supply chain disturbances

This research contribution presents the Reactive Disaster and supply chain Risk decision Support System ReDRiSS which supports decision-makers of logistical disaster management in the immediate aftermath of a supply chain disturbance. ReDRiSS suggests a methodology which combines approaches from scenario techniques, operations research and decision theory. Two case studies are provided which focus on decision situations of humanitarian logistics and of business continuity management

A new focus on risk reduction: An ad hoc decision support system for humanitarian relief logistics

Particularly in the early phases of a disaster, logistical decisions are needed to be made quickly and under high pressure for the decision‐makers, knowing that their decisions may have direct consequences on the affected society and all future decisions. Proactive risk reduction may be helpful in providing decision‐makers with optimal strategies in advance. However, disasters are characterized by severe uncertainty and complexity, limited knowledge about the causes of the disaster, and continuous change of the situation in unpredicted ways. Following these assumptions, we believe that adequate proactive risk reduction measures are not practical. We propose strengthening the focus on ad hoc decision support to capture information in almost real time and to process information efficiently to reveal uncertainties that had not been previously predicted. Therefore, we present an ad hoc decision support system that uses scenario techniques to capture uncertainty by future developments of a situation and an optimization model to compute promising decision options. By combining these aspects in a dynamic manner and integrating new information continuously, it can be ensured that a decision is always based on the best currently available and processed information. And finally, to identify a robust decision option that is provided as a decision recommendation to the decision‐makers, methods of multi‐attribute decision making (MADM) are applied. Our approach is illustrated for a facility location decision problem arising in humanitarian relief logistics where the objective is to identify robust locations for tent hospitals to serve injured people in the immediate aftermath of the Haiti Earthquake 2010.Frank Schätter, Marcus Wiens and Frank Schultman

Decision support system for a reactive management of disaster-caused supply chain disturbances

Die Dissertation umfasst die Entwicklung des Entscheidungsunterstützungssystems ReDRiSS (Reactive Disaster and supply chain Risk decision Support System) für das logistische Katastrophenmanagement und dessen Anwendung in zwei Fallstudien. Der Fokus liegt auf der Bewältigung logistischer Entscheidungsprobleme, welche durch katastrophenbedingte Supply Chain-Störungen ausgelöst werden. ReDRiSS kombiniert dabei Methoden der Szenariotechnik, des Operations Research und der Entscheidungstheorie

A new focus on risk reduction: an ad hoc decision support system for humanitarian relief logistics

Particularly in the early phases of a disaster, logistical decisions are needed to be made quickly and under high pressure for the decision‐makers, knowing that their decisions may have direct consequences on the affected society and all future decisions. Proactive risk reduction may be helpful in providing decision‐makers with optimal strategies in advance. However, disasters are characterized by severe uncertainty and complexity, limited knowledge about the causes of the disaster, and continuous change of the situation in unpredicted ways. Following these assumptions, we believe that adequate proactive risk reduction measures are not practical. We propose strengthening the focus on ad hoc decision support to capture information in almost real time and to process information efficiently to reveal uncertainties that had not been previously predicted. Therefore, we present an ad hoc decision support system that uses scenario techniques to capture uncertainty by future developments of a situation and an optimization model to compute promising decision options. By combining these aspects in a dynamic manner and integrating new information continuously, it can be ensured that a decision is always based on the best currently available and processed information. And finally, to identify a robust decision option that is provided as a decision recommendation to the decision‐makers, methods of multi‐attribute decision making (MADM) are applied. Our approach is illustrated for a facility location decision problem arising in humanitarian relief logistics where the objective is to identify robust locations for tent hospitals to serve injured people in the immediate aftermath of the Haiti Earthquake 2010.Frank Schätter, Marcus Wiens and Frank Schultman

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

A decision support methodology for a disaster-caused business continuity management

Supply chain risk management typically deals with the systematic identification, analysis and mitigation of risks which affect the whole supply chain network of a company. Business continuity management (BCM) forms part of supply chain risk management and is an important competitive factor for companies by ensuring the smooth functioning of critical business processes in the case of failures. If business operations are severely disrupted, the companies' decision maker is confronted with a situation which is characterized by a high degree of uncertainty, complexity and time pressure. In such a context, decision support can be of significant value. This article pre- sents a novel decision support methodology which leads to an improved and more robust BCM for severe dis- ruptions caused by disasters. The methodology is part of the Reactive Disaster and supply chain Risk decision Support System (ReDRiSS) to deal with different levels of information availability and to provide decision makers with a robust decision recommendation regarding resource allocation problems. It combines scenario techniques, optimization models and approaches from decision theory to operate in an environment char- acterized by sparse or lacking information and dynamic changes over time. A simulation case study is presented where the methodology is applied within the BCM of a food retail company in Berlin that is affected by a pandemic disaster.Frank Schätter, Ole Hansen, Marcus Wiens, Frank Schultman

Collaborative emergency supply chains for essential goods and services

Focal actors in disaster relief logistics are predominantly public authorities, emergency organizations, and NGOs, whereas private firms rather play a subordinate role—at least in the context of direct crisis intervention. Although it is entirely clear that engaging in public crisis management is not among the original tasks of commercial firms there is a substantial—and so far still unexploited—potential for public–private cooperation in a disaster situation. In this contribution, we outline the scope of a Public–Private Emergency Collaboration (PPEC) with a focus on the provision of essential goods and services. We discuss the different objectives and strategies of the partners and evaluate the potential for a PPEC for each phase of a disaster from an economic perspective with a primary focus on logistics operations. Based on a simple model, we identify the chance to improve crisis management operations by information sharing and coordinated allocation of resources and capacities for both the escalating and de-escalating phase of a disaster. Interestingly, a PPEC can also help to overcome public acceptance problems which could be occasionally observed in historic disasters. As key requirements of a PPEC, we identify a clear allocation of responsibilities between the public and the private partners together with sufficient incentives for commercial firms to engage in a PPEC on a sustainable basis.Marcus Wiens, Frank Schätter, Christopher W. Zobel and Frank Schultman