The self-thinking supply chain

Abstract

Purpose An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive capabilities, bringing significant efficiency gains in an increasingly complex and uncertain environment. This paper aims to both bridge the gap between the practitioner and academic literature on these topics and contribute to both practice and theory by seeking to understand how such developments will help to address key supply chain challenges and opportunities. Design/methodology/approach A multi-disciplinary, systematic literature review was conducted on relevant concepts and capabilities. A total of 126 articles were reviewed covering the time period 1950-2018. Findings The results show that both IoT and AI are the technologies most frequently associated with the anticipated autonomous and predictive capabilities of future supply chains. In addition, the review highlights a lacuna in how such technologies and capabilities help address key supply chain challenges and opportunities. A new supply chain model is, thus, proposed, one with autonomous and predictive capabilities: the self-thinking supply chain. Originality/value It is our hope that this novel concept, presented here for the first time in the academic literature, will help both practitioners to craft appropriate future-proofed supply chain strategies and provide the research community with a model (built upon multidisciplinary insights) for elucidating the application of new digital technologies in the supply chain of the future. The self-thinking supply chain has the potential in particular to help address some of today’s key supply chain challenges and opportunities.

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…As Baryannis et al (2019b) found in their literature review, the majority of studies analyzed do not see any decision-making capability, although some articles provide bottom-up applications as decision support systems. Practical experiments in the sample instead refer to information gathering and status tracking within production or logistics [e.g., the data science toolbox of Flath and Stein (2018), supply chain risk management tools by Baryannis et al (2019a, b), and the self-thinking supply chain of Calatayud et al (2019)] with the exception of Colombo (2019), who introduced holistic risk analysis and modeling (HoRAM) as an already tested application to be used for almost the whole decision-making process in dynamic environments.…”

Untitled

“…Increased connectivity amongst supply chain partners enabled by IoT, together with AI, allows i.e. for more accurate demand forecasting, predictive maintenance and continuous optimization (Calatayud, et. al., 2018).…”

Digital Transformation of Organizations

“…Нове дигиталне технологије омогућавају организацијама да боље разумеју преференције потрошача и омогућавају компанијама да побољшају свој однос са купцима, створе видљивост у стварном времену о њиховом пословању и постигну флексибилнији ланац снабдевања. То ће резултирати повећаном ефикасношћу и доступношћу производа, смањеним трошковима и роковима испоруке и, што је најважније, одрживим растом (Calatayud et al, 2019). Елементи дигиталне трансформације -подаци, дигиталне технологије и људи и њихова међусобна повезаност -приказани су на слици 1.…”