Special Issue: Data-driven Decision Making in Supply Chains
As regionalization of global economy is becoming a tendency, supply chains currently must face the competitive challenge of integrating global value-added networks with more local content and requirements. For example, in the emerging regions there are important barriers as well as opportunities to achieve more fluid supply chains. On the one hand, there are risks due to factors like market and financial volatility, dynamic socioeconomic features, security issues, infrastructural challenges, uncertainty caused by delays and disruptions in distinct points of the supply chains (e.g., international borders, congested areas) which affect the supply chain dynamics. But on the other hand, companies benefit of important advantages as qualified human resources at a lower labor cost, increasing local demand for their products, tax incentives, development of infrastructures and resources for specific purposes, among others. In any case, global companies are constraint to adapt their original business model to be more resilient and flexible to local conditions, and at the same time, enrich their competitive advantages from tailored strategies, customized products and value-added services based on local environment.
Decision making in supply chains is a critical support of organizational competitive advantage. It helps coordinating technical, commercial, logistics and relational capabilities internally and externally with other stakeholders. However, the transfer of flows among diverse stakeholders throughout supply chains and with their environment, force decision makers to find reliable data to improve their operations. For this reason, any organization must count with updated and good-quality data to adapt to changes in the business environment, and improve their capabilities to react more swiftly to requirements imposed by global-local circumstances. The importance of data-collection techniques and methods when making supply chain decisions is recognized not only as a strong determinant of competitive advantage for companies, but also a relevant enabler to boost regional business development.
Therefore, methods to understand, analyze reliable data, and improve supply chain decisions based on these data, are key elements to transform strategic planning into concrete competitive actions across different stakeholders. Currently, means and procedures used by academics and practitioners to identify and collect the right data and methods to analyze them remain largely unmapped nor discussed. In fact, even if some articles using a large variety of methods to gather and study data related to decision-making in supply chains were published in the past decade, the main focus was frequently on the research results. Consequently, most of them set aside the fundamental discussion on why the method used was the most suitable option for the particular case study or the data set under analysis.
It is nowadays essential to identify and understand different global contexts, cases and issues where reliable data are currently needed. Additionally, to improve our understanding on how new methodologies and advanced computer-based algorithms are used for characterizing situations, analyzing factors and solving problems to make better decisions. Consequently, driving questions for this special issue are formulated as follows:
What type of supply chain decision making problems have been addressed? Under what type of methodologies? What are their strengths and limitations for specific cases? Are these methods appropriate for analyzing variations under diverse uncertain or trendy scenarios? Are they useful today when organizations are trying to improve supply chain fluidity and/or collective intelligence in logistics? What are the new challenges facing decision makers in front of emerging technologies in data gathering?
How traditional methods could be combined with emerging technologies such as sensors, IoT and blockchain in data gathering and data analytics? Under which circumstances it is worth to devise new computer-based techniques? How could emerging approaches as social network methods, self-learning algorithms, advanced hybrid data-optimization approaches provide a better understanding about the variables causing complexity in the supply chain decision making process? Are they useful to face the current supply chain context? How should they be adapted?
How could a combination of traditional and computerized methods improve decision-making capabilities of practitioners? What is the process that should be followed to guarantee the right implementation and monitoring of data-driven strategies into real applications?
What methods are more useful when looking for transferring supply chain decision-making knowledge/experience from academics to practitioners? Which of the methods are more pertinent when looking for leveraging collective decision making and creating high-performance strategies?
Strategically, since this Special Issue intends to close the existent gap regarding the usage of data-driven decision making, it looks for achieving three main goals. First, to establish state-of-the-art about quantitative/qualitative methodological approaches used when collecting and analyzing data. Second, to better understand why certain methodological approaches are suitable/limited for a specific supply chain decision making contexts, as well as, for an exhaustive assessment of specific methods and their empirical application by considering their strength and limitations. Third, to identify stimulating lessons and directions of future research on this matter, as well as recommendations to foster smarter and digital supply chains.
Operationally, it is important to state that all submissions will be subject to a double-blind peer-review process according to the rigorous procedure followed by Computers & Industrial Engineering.
Submissions of scientific results from experts in academia and industry are strongly encouraged. The topics of interest include, but are not limited to:
- Data-collection techniques and methods (surveys, case studies, action research, mixed approaches, applied and empirical research) in supply chain management)
- Use of emerging technologies in data gathering such as IoT and sensors in SCM
- Supply chain analytics, metrics and predictive tools
- Oriented urban logistics systems
- Emerging electronic data interchange protocols as blockchain
- Supply chain fluidity
- Collective intelligence in logistics
- Agro-logistics systems
- Tourism supply chain management
- Maritime and port logistics
- Risk and security management
- Healthcare operations and humanitarian logistics
- Production, warehouse and operations management
- Strategic sourcing and procurement
- Service operations management and retailing operations
- Transportation systems design
- Supply chains and territorial development
- Sustainable and green supply chains
- Logistics and supply chain public policy-making
The editors of the special issue intend to publish a range of diverse topics and reserve the right to limit the number of papers included in one topic.
All papers must be original and have not been published, submitted and/or are currently under review elsewhere. All manuscripts should be submitted through the publisher’s online system, Elsevier Editorial System (EES) at http://ees.elsevier.com/caie/. Please follow the instructions described in the “Guide for Authors”, given on the main page of EES website. Please make sure you select “Special Issue” as Article Type and “Data-Driven SCM” as Section/Category. In preparing their manuscript, authors are asked to closely follow the “Guide for Authors” given in the main page of EES. Submissions will be reviewed according to C&IE’s rigorous standards and procedures through double-blind peer review by at least two qualified reviewers. Accepted papers will become the property of C&IE’s publisher, Elsevier.
The submission deadline is 27th February 2018. Submissions to the special issue will be processed immediately upon receipt. The Special Issue is scheduled for publication in 2019.
Managing Guest Editor:
Dr. Miguel Gaston Cedillo-Campos (Managing Guest Editor)
IMT-National Laboratory for Transportation Systems and Logistics
Mexican Institute of Transportation (Mexico)