(Un)physicalization (digitalization) of Supply Chain Management


Submissions Open: 1st April 2022

Submissions Deadline: 30th June 2022



The present special issue entitled (Un)physicalization of Supply Chain Management provokes new reflection on the digitisation of supply chain management (SCM). The traditional meaning of the term SCM refers to the movement of goods and services from a producer to a final consumer (Cooper & Ellram, 1993; Lee et al., 2020). The SCM encompasses all information processed by this movement (Hvolby et al., 2007; Lambert & Cooper, 2000). The unphysicalization of SCM means information is shared, costs are negotiated, and goods are listed 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). This has encouraged the development of intelligent infrastructures and dynamic systems built on adaptive supply chain relationships (Malhotra et al. 2007). SCM can be shifted “from isolated, local, and single-company applications to supply chain wide systematic smart implementations” (Wu et al., 2016, p. 396). In this context, Wu et al. (2016) consider SCM “smart” being (1) instrumented, (2) interconnected, (3) intelligent, (4) automated, (5) integrated (6) innovative, and call for more studies on (1) information in supply chains, (2) IT, (3) process automation, (4) advanced analytics, (5) process integration and innovation.

To realize Smart SCM lots of unknown and new challenges must be addressed. According to McKinsey (2016), the unphysicalization (or digitalization) of SCM is characterized by three phases: starting from SCM 2.0, the digitization was commencing but it was still at its infancy with many processes carried out manually. Big data were not leveraged to support any decisions at this stage of digital transformation, which is expected to come through in the SCM 3.0 and later moving towards the 4.0 digital tier, putting in place some digital devices such as the internet of things, drones, and robots among others (see also Hofmann et al., 2019). The first challenge is the development of digital technologies and infrastructure to digitalize SCM. Morenza Cinos et al. (2019) show retailers are in early phase allowing academic researchers to experiment the use of IoTs (Internet of Things) for capturing inventory data. The “Just Walk Out Technology” used in Amazon Go and Amazon Fresh relies heavily on the integration of facial recognition, camera, mobile apps, mobile payment technologies. Even though Amazon aims to sell the technology, the technology is still expensive while Amazon’s rivalries would prefer to engage with other start-ups.

Digitalization is not just about automation; the main benefit is inside the data. As international suppliers offer their goods and/or services to a broad number of buyers through electronic platforms, there is an opportunity to adopt predictive analytics (Handfield et al., 2019). So, the main question supply chain managers ask when considering installing sensors or Internet of Things concern the types of data that could support the vision to create smart SCM. Richey et al. (2016) reveal there is no consensus among supply chain managers regarding Big Data’s definition and its characteristics, as compared to the academic literature. Some managers recognise big data as costly though it may help integrate the supply chain. Handfield et al. (2019) pointed out the “low usage of advanced procurement analytics” is partly caused by the lack of a “coherent approach to collection and storage of trusted organizational data” and they suggested that the use of “ad-hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy” (p. 972). How do organizations unphysicalize their supplier chains? How they do not learn more about what advance analytics they need?  So, new questions arise: what and how do entrepreneurs, managers and even policymakers think about unphysicalization of SCM, how do they learn, decide, strategize and implement (un)physicalization of SCM towards the development of smart SCM? 

Recently, some practitioners have started to debate whether the traditional SCM is going to die and disappear in 5 or 10 years, due to unphysicalization of SCM (Lyall et al., 2018). This is thought to occur due to the pervasive digitization of all SCM functions which involves the adoption of sensor data to reduce downtime, blockchain to avoid asymmetric information, robots to optimize warehouse spaces, and drones may be used for delivery (Hald & Kinra, 2019; Gurtu & Johny, 2019). While some companies with traditional SCM view digital/smart SCM (e.g., Amazon) as new threats, they struggle with strategic uncertainty.  The unphysicalization of SCM could potentially lead to new benefits for the business in terms of more options of suppliers’ choices (Lepak, Smith, &Taylor 2007; Porter & Heppelmann 2014), financial convenience (Kshetri, 2018), and even more transparency (Lechler et al., 2019). However, many chief SCM officers are still struggling to position their goals, whether to use digital technologies to gain efficiency (automation) or to embed those new, advanced new technologies into their business realities (as their new rivals do) to become a leader in smart or intelligent SCM.

The impacts of digitalization on human resources raises urgent concerns. Feng and Shanthikumar (2018) stress out the need of sharing abilities and skills either for big corporates or small to medium enterprises. But small enterprises are not financially equipped to innovate. Lyall et al. (2018) also remark the threat of losing jobs as digital technologies are gradually replacing human workforces. Instead, Schniederjans et al. (2020) comment that the new technologies can digitize process and organizational learning but there is still a need for strategic thinking that just human beings can develop. Yet, new knowledge about how to gain relevant insights from big data is required (see also Ardito et al., 2019). The conceptualization of intelligence capabilities requires further clarification (Wu et al., 2016). What intelligence capabilities are required to allow real time communication and data collection across all steps of SCM and decision-making processes more efficiently and faster to offer a better service to customers? How can smart SCM be more demand driven or consumer centric (Ketchen et al., 2008), allowing personalized products to be designed and delivered through omni-channel?

When a supply chain become completely “unphysicalized”, what would happen to relational resources required to get suppliers closer to customers? Can digitalization facilitate all forms of information sharing, communication and management of interfirm relationships, strategic alliances, joint ventures, and merger and acquisitions (Yang and Lirn, 2017) and involve both big corporates and small to medium enterprises (SMEs) (Lee et al., 2020; El Sawy et al., 2015; Desouza et al., 2003)?

Despite the ambiguity surrounding ways to (un)physicalize SCM, the literature is still largely focused on testing and speculating the organizational performance of digital technologies. In fact, there are still many unanswered questions about the ways to achieve smart SCM (Wu et al., 2016), what big data means (Richey et al., 2016), how to build data and cognitive analytics (Handfield et al., 2019), the dilemma of real time data (Lechler et al., 2019), and move forward from experimenting technology such as RFID robot to real implementation (Morenza-Cinos et al., 2019), while academia has only started to explore research opportunities related to industry 4.0 and SCM 4.0 (Hofmann et al., 2019).

It would be beneficial to complement such research in logistics and SCM management journals using insights from operations and production management field to information fields. For instance, Boehmer et al., (2020) offers an ‘operational service model’ that involves an efficient mode of information exchange by IoT technology between buyers and suppliers. Whereas, Haddud and Khare (2020) stress out the improvement of operation practices by digitalizing supply chains. This is also enforced by Scuotto et al. (2017) who highlight the use of new advanced technologies to share knowledge in the context of supply chain management. Alongside, studies on information field have also demonstrated greater is the flexibility of information system smarter is the supply chain (Gupta et al., 2019). In turn, it strengthens the bonds with supply chain partners, integrating customer knowledge in e-business environment (El Sawy et al., 2015). In the same vein, Wong et al. (2011) analyse information integration which operating in favourable external conditions supply chain management can perform better.

Malhotra et al. (2005) point up supply chain partners are “building information technology infrastructures that allow them to process information obtained from their partner to create new knowledge” (145). In fact, exploring SCM from a knowledge management perspective in the context of digital SCM (Schniederjans et al., 2020) can lead to many new research arena. When a supply chain become completely “unphysicalized”, do machines store and create advanced knowledge? How to innovation in a digital supply chain works? Stiegler (2008; 2011) introduces the concept of machine of knowledge referring to all technologies developed in the current industry 4.0. He points out the empowerment that an individual can get by those machines to generate new innovations. Past literature differentiate knowledge into embedded knowledge (what employees know) and embodied knowledge (knowledge inside a products), the question is how we understand what machines know (artificial knowledge), how machines store information and knowledge, and how employees leverage these different forms of knowledge?

Given that the topic seems highly relevant for academia and has clear implications for society and for practice, it is surprising we have not built much ground-breaking original theory to explain and understand unphysicalization of SCM. To this end the present special issue invites scholars to offer original contributions on (Un)physicalization (digitalization) of Supply Chain Management which address key questions such as:


Topics of Interest

  • What are the emerging practical and theoretical problems facing a supply chain that is increasingly being digitized?
  • How can those machines of knowledge enhance digitalization or (un)physicalization of supply chains?
  • What is “artificial” knowledge in the digitalization or (un)physicalization of supply chains? How can this “artificial” knowledge be created and managed?
  • Does digitalization or (un)physicalization of supply chains really increase transparency, trust and performance? Who has benefited? Why?
  • Does (un)physicalization really remove (governance) issues related to trust, relationship dynamics and uncertainty facing a supply chain? If not, what other means or processes are working alongside to govern relationships in the digitalized supply chains?
  • Does (un)physicalization change the transaction costs, opportunistic behaviour, asset specificity between buyers and supplier?
  • Do business leaders believe that the end of SCM can be realized in 5-10 years? Which aspects of digitalization is considered a hype, and which is pursued?
  • Do business leader/entrepreneurs converge physically to (un)physical SCM to foster value creation, to simply to cut down physical and labour cost?
  • How might business leaders’ knowledge foster the (un)phicalization SCM process? How (and where, from whom) organizations learn about how to unphysicalize SCM?
  • Do we need a new knowledge management perspective to understand (un)phicalization of SCM?
  • How do business leaders rejuvenate their business models to embrace SCM digitisation?
  • How business leaders scout or develop new skilled employees? What “digital” skills do they think they need?
  • How are companies dealing with the new era of digitized SCM challenges?
  • Is the digital supply chain smarter? How do organizations transform their supply chain to become digital and more intelligent?
  • What are the challenges and barriers for those who are embracing the (un)physicalization change or digital transformation of their supply chains?

This special issue is open to quantitative and qualitative studies with the scope to make a real and significant impact on the economic world. Multidisciplinary methods are welcomed along with novel conceptual articles. Conceptual work that develops ground-breaking original theory can be considered. Mathematical modelling is not in the scope.


Review Process:

Manuscripts should comply with the scope, standards, format, and editorial policy of the International Journal of Physical Distribution & Logistics Management. All papers must be submitted through the official International Journal of Physical Distribution & Logistics Management submission system (https://mc.manuscriptcentral.com/ijpdlm) with clear selection indicating that the submission is for this Special Issue. Before submission, authors should carefully read over the Journal’s “Author guidelines”. Papers submitted to the Special Issue will be subjected to the normal thorough double-blind review process.

Authors should select “SI: (Un)physicalization (digitalization) of Supply Chain Management”, from the “Choose Article Type” pull- down menu during the submission process. All contributions must not have been previously published or be under consideration for publication elsewhere.

Submissions to this journal are through the ScholarOne submission system. Please visit the author guidelines for the journal here.


Guest Editors:

Prof. Veronica Scuotto*, ​Department of Management, University of Turin, Italy, [email protected]

Prof. Manlio Del Giudice, University of Rome “Link Campus”, ITALY, [email protected]

Professor Vijay Pereira, Neoma Business School, Reims Campus, France [email protected]

Prof. Arvind Malhotra, UNC Kenan-Flagler Business School, UNITED STATES,  [email protected]



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