The Application of Decision Science for Sustainable Logistics and Supply Chain Management

Dear Colleagues,

The past few years clearly have shown systematic supply chain weaknesses that are mainly related to climate change and its impact on geopolitical complications and global economic uncertainties that could influence decisions across the supply chain, particularly in organizations that source goods globally (according to the Middle Market Business Index in March 2021, more than 50% of middle market executives reported that their organizations’ supply chains were disrupted because of the pandemic while more than 81% of respondents noted that they can adapt their supply chain without sacrificing quality (https://rsmus.com/insights/services/business-strategy-operations/supply chain-challenges-guide.html)). These observed uncertainties, now more than ever, require organizations to address both the existing and anticipated supply chain vulnerabilities related to where goods are sourced and how they are tracked as they move globally, utilizing the most relevant analytic tools and techniques. The strength and flexibility of each link in the supply chain also need to be carefully evaluated by utilizing advanced analytical techniques. While there are a number of actions a company can take to insulate its supply chain from disruption, it appears that the most efficient and promising approaches to be considered, among others, are those focusing on the ability to predict demand and sources of supply chain vulnerability by utilising the proper analysis of data from multiple sources while leveraging sustainable logistics (Grant, David B. Trautrims, Alexander Wong, Chee Yew, Sustainable logistics and supply chain management: principles and practices for sustainable operations and management | ISBN 9780749478285 (eBook)). Sustainable logistics refers to practices and processes such as decision science, which is used for improving the sustainability of supply chain activities that range from the supply of raw materials to the transformation processes, storage, packaging,  distribution and management of the end of the lifecycle of products (https://www.cswindow.contshipitalia.com/en/sustainable-logistics).

Decision science is a collection of quantitative techniques used to inform decision making at individual and population levels. It includes decision analysis, risk analysis, cost–benefit and cost-effectiveness analysis, constrained optimization, simulation modelling, and behavioural decision theory, as well as parts of operations research, microeconomics, statistical inference, management control, cognitive and social psychology, and computer science. By focusing on decisions as the unit of analysis, decision science provides a unique framework for understanding public health problems and for improving policies to address those problems. The field of decision science aims to inform policies and practices in health by systematically integrating scientific evidence with the explicit consideration of values (Harvard T.H. Chan School of public health).

Insights presented in the literature today are more than ever emphasizing the need and the urgency for companies to develop smart, sustainable, economically feasible, and systematic logistics that allow them to be capable of making timely decisions about the reliability of the various entities in the supply chains. This Special Issue aims at revealing both the short- and long-term “economic” and “social” benefits and risks anticipated when using advanced data science tools and technologies in the design of sustainable logistics for supply chain management, with a focus on the efficiency, resilience, and sustainability paradigms.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Sustainability;
• Operation optimization;
• Sustainable logistics;
• Decision science;
• Economics of sustainable supply chains;
• Decision science for sustainability;
• Supply chain resilience.

Dr. Nasrin R. Khalili
Guest Editor

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• sustainable operations
• sustainable logistics
• supply chain management
• public policy and sustainable logistics
• economics of sustainable logistics
• data science tools and technology