Considering IT Trends for Modelling Investments in Supply Chains by Prioritising Digital Twins

Abstract

Supply chain disruptions and challenges have and will always exist, but preparing in advance and improving resilience for the upcoming consequences should be the utmost important goal. This paper explores trends that affect innovation in the technological sphere of supply chain systems. More precisely, the research is focused on Digital Twin technology applicability through other logistics IT trends and aims to research the pressing issue of ensuring the visibility and resilience of future supply chain systems. The paper’s objective is to produce a conceptual model enabling the investment assessment of the necessary IT resources. Initially, a theoretical confirmation of logistics IT trends’ relevance to supply chain systems was established. After, propositions of Digital Twin technology applications to other logistics IT trends were made, which were divided into corresponding constant multitudes of supply chain systems. Lastly, the conceptual model for the investment assessment of the necessary IT resources was derived in the form of a matrix. It considers 16 parameters for investment assessment and applicability to all companies, regardless of their specifics. It also supports the notion of digital IT competencies’ fundamental importance to the continuous operation of supply chain systems.

Exploring Digital Twins in the Transport and Energy Fields: A Bibliometrics and Literature Review Approach

“…Products, services, processes and systems are a regular part of supply chain and logistics systems whilst formulating a multitude of other (semi-)products, services, processes or systems correlated amongst each other. Therefore, the authors [13] named the latter 'constant multitudes of supply chain systems', which served as one of the criteria for this paper. Based on the digital twins' use (for a product, service, etc.…”

“…Internet of Things represents the backbone that allows the physical twin to provide a continuous real-time data stream to the digital twin [13,14];…”

“…• Artificial Intelligence (AI) represents the components of digital twins that analyse, interpret, orchestrate and optimise the operation of the physical twin [15]; • Machine Learning (ML) allows analysis of large amounts of data from the physical twin so the digital twin can learn and expand its ability to orchestrate and optimise the physical twin [13,16];…”

“…Digital Twins are being adapted into all pores of various industries [13], from the beginning in manufacturing [17], after in supply chains [18][19][20], logistics [14], the process industry [21,22], and transport [23,24] with a focal point on energy efficiency [25][26][27]. Specifically in the transport industry, digital twins are anticipated to drastically change the transport systems' technology, as they enable monitoring of the whole transport system life cycle [24].…”

“…Publications had to contain either supply chain, logistics or transport field; • Content of each publication included at least one constant multitude of supply chain systems, which are product, service, process or a system [13]; • Publications' inclusion was also reflected through the digital twin objective, classified as system management, system optimisation, system design, system planning, risk management, process management, process optimisation, product management and product design;…”