Submissions begin on September 1, 2021.

Deadline for Submissions: 30th November 2021


The current special issue, titled (Un)physicalization of Supply Chain Management, stimulates new thought about supply chain digitization (SCM). The word SCM, in its classical sense, refers to the flow of goods and services from a producer to a final customer (Cooper & Ellram, 1993; Lee et al., 2020). The SCM encapsulates all data processed during this migration (Hvolby et al., 2007; Lambert & Cooper, 2000). The dematerialization of SCM entails the sharing of information, the negotiation of costs, and the listing of items in a virtual environment (El Sawy et al. 1999; Mital et al., 2018; Malhotra et al., 2005; Vendrell-Herrero et al. 2017; Scuotto et al., 2017). As a result, intelligent infrastructures and dynamic systems based on adaptive supply chain interactions have been developed (Malhotra et al. 2007). SCM can be changed away from "isolated, local, and single-company applications" and toward "systematic, intelligent supply chain solutions" (Wu et al., 2016, p. 396). Wu et al. (2016) define “smart” supply chain management as (1) instrumented, (2) interconnected, (3) intelligent, (4) automated, (5) integrated, and (6) innovative, and call for additional research on (1) information in supply chains, (2) information technology, (3) process automation, (4) advanced analytics, and (5) process integration and innovation.
Digitalization is much more than automation; the primary benefit is contained within the data. As multinational suppliers make their goods and/or services available to a large number of customers via electronic platforms, the possibility to use predictive analytics exists (Handfield et al., 2019). Thus, the primary consideration for supply chain managers when considering the installation of sensors or the Internet of Things is the type of data that could support the objective of creating smart supply chain management. According to Richey et al. (2016), there is no unanimity among supply chain managers regarding the definition and properties of Big Data, in contrast to academic research. Although big data may aid in supply chain integration, some managers view it as pricey. Handfield et al. (2019) noted that the "low adoption of advanced procurement analytics" is partly due to the absence of a "coherent approach to the collection and storage of trusted organizational data," and they suggested that "ad-hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy" (p. 972). How do businesses dematerialize their supply chains? How are they unable to have a better understanding of the advanced analytics they require? As a result, new questions arise: what and how do entrepreneurs, managers, and even politicians think about the unphysicalization of SCM; how do they learn, deliberate, strategy, and implement the (un)physicalization of SCM in order to produce smart SCM?

Recently, some practitioners have begun debating whether traditional SCM would die and disappear in five or ten years as a result of its unphysicalization (Lyall et al., 2018). This is believed to be the result of the widespread digitization of all SCM activities, which includes the use of sensor data to minimize downtime, blockchain technology to eliminate asymmetric information, robots to maximize warehouse space, and drones for delivery (Hald & Kinra, 2019; Gurtu & Johny, 2019). While some organizations with traditional SCM regard digital/smart SCM as new threats (e.g., Amazon), they face strategic insecurity. Unphysicalizing SCM may result in additional benefits for the business, such as more supplier choice (Lepak, Smith, & Taylor 2007; Porter & Heppelmann 2014), financial convenience (Kshetri, 2018), and increased transparency (Lechler et al., 2019). However, many chief supply chain officers are still unsure how to position their goals, whether to leverage digital technologies to increase efficiency (automation) or to integrate those new, advanced new technologies into their business realities (as their new competitors do) in order to become a leader in smart or intelligent supply chain management.

The impact of digitization on human capital is a topic of grave worry. Feng and Shanthikumar (2018) emphasize the need of sharing capabilities and talents among large corporations and small to medium-sized businesses. However, tiny businesses lack the financial resources necessary to develop. Lyall et al. (2018) also make reference to the prospect of employment loss as digital technologies gradually supplant human labor. Rather than that, Schniederjans et al. (2020) observe that while new technology can digitize processes and organizational learning, a requirement for strategic thinking that only human beings can generate persists. Nonetheless, new understanding regarding how to derive meaningful insights from massive data is necessary (see also Ardito et al., 2019). Further explanation is required regarding the notion of intelligence capabilities (Wu et al., 2016). What intelligence skills are necessary to enable real-time communication and data collecting across all stages of SCM and decision-making processes in order to provide a better service to clients more efficiently and quickly? How can smart supply chain management become more demand-driven or consumer-centric (Ketchen et al., 2008), enabling for the design and delivery of tailored products via omni-channel?

What happens to the relational resources required to bring suppliers closer to customers if a supply chain becomes totally "physicalized"? Can digitalization facilitate all aspects of information sharing, communication, and management of interfirm relationships, strategic alliances, joint ventures, and mergers and acquisitions (Yang and Lirn, 2017), involving both large corporations and small to medium-sized enterprises (SMEs) (Lee et al., 2020; El Sawy et al., 2015; Desouza et al., 2003)?

Despite the ambiguity around methods for (un)physicalizing SCM, the literature continues to be heavily focused on testing and speculating on the organizational performance of digital technologies. Indeed, there are numerous unanswered questions regarding how to achieve smart SCM (Wu et al., 2016), define big data (Richey et al., 2016), build data and cognitive analytics (Handfield et al., 2019), overcome the dilemma of real-time data (Lechler et al., 2019), and progress from experimenting with technology such as RFID robots to real-world implementation (Morenza-Cinos et al (Hofmann et al., 2019).
What are the rising practical and theoretical issues confronting an increasingly digitalized supply chain?
How might these knowledge-based devices aid in the digitization or (de)physicalization of supply chains?
What is "artificial" knowledge in the context of supply chain digitization or (de)physicalization? How do we produce and manage this "manufactured" knowledge?
Is it true that digitalization and (de-)physicalization of supply chains improve transparency, trust, and performance? Who has gained? Why?
Is (un)physicalization truly capable of resolving supply chain (governance) challenges relating to trust, relationship dynamics, and uncertainty? If not, what additional mechanisms or processes exist to control connections in digitalized supply chains?
Is it true that (un)physicalization alters the transaction costs, opportunistic behavior, and asset specificity of buyers and suppliers?
Do business leaders anticipate that SCM will be obsolete in five to ten years? Which components of digitalization are regarded as a fad, and which are actively pursued?
Do company leaders/entrepreneurs physically congregate around (un)physical SCM in order to stimulate value creation or only to reduce physical and labor costs?
How may business executives' knowledge contribute to the SCM process of (un)phicalization? How (and from whom) do enterprises acquire knowledge about how to de-physicalize SCM?
Do we require a new viewpoint on knowledge management in order to comprehend the (un)phicalization of SCM?
How can company executives reimagine their business models in order to embrace SCM digitization?
What methods do business executives use to recruit and develop new skilled employees? Which "digital" abilities do they believe they require?
How are businesses coping with the new era of digitized supply chain management challenges?
Is a digitized supply chain more intelligent? How do businesses modernize their supply chains to make them more digital and intelligent?
What are the obstacles and hurdles confronting those adopting the (un)physicalization of their supply networks or the digital transformation of their supply chains?
This special issue welcomes quantitative and qualitative studies that have the potential to have a tangible and meaningful effect on the economic world. Multidisciplinary approaches, as well as unique conceptual essays, are encouraged. Consider conceptual work that advances ground-breaking original theory. The scope does not include mathematical modeling.
Procedures for Review:
Manuscripts must adhere to the International Journal scope, criteria, format, and editorial policy. All submissions must be sent via the International Journal of Physical Distribution & Logistics Management's official submission system with a clear indication that the submission is for this Special Issue. Authors should thoroughly study the Journal's "Author guidelines" prior to submission. The Special Issue's papers will be subjected to the journal's standard rigorous double-blind review process.

During the submission procedure, authors should pick "SI: (Un)physicalization (digitalization) of Supply Chain Management" from the "Choose Article Type" pull-down menu. All contributions must be original and not already under consideration for publication elsewhere.