Barriers Related to AI Implementation in Supply Chain Management

Barriers Related to AI Implementation in Supply Chain Management

Monika Shrivastav
Copyright: © 2022 |Pages: 19
DOI: 10.4018/JGIM.296725
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Abstract

The primary objective of this paper is to offer a structured and comprehensive list of the barriers associated with implementation of Artificial Intelligence (AI) solutions in Supply Chain Management (SCM). While the broader field of AI has made rapid advances in a relatively short period of time, there are significant barriers that still need to be addressed to harness the true potential of AI. SCM’s dependency on multi-actor collaboration, disparate data sources, unwillingness of actors to embrace AI, change management issues, and lack of AI governance framework poses significant barriers for successful implementation of AI. Drawn from extensive literature review as well as real-world experience, this paper systematically explores and compiles a robust list of barriers of AI implementation in supply chain functions by categorizing them and elaborating their impact at inter- and intra-organizational SCM. Lastly, the paper offers recommendations for practitioners, policymakers, researchers, and governments on how they can work together for AI to be successful.
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Introduction

The World Economic Forum has described the modern day as an era of Fourth Industrial Revolution where everything is connected, and that we have access to unprecedented computing power and storage capacities (World Economic Forum, 2016). This has led to explosion of data, which in turn has enabled an exponential leap for technologies like Artificial Intelligence (AI), Internet of Things (IoT), robotics, and quantum computing (The Royal Society, 2017; Bernhardt, 2019). Moreover, the pace at which these technologies are advancing has been the fastest when compared with earlier industrial revolutions. This rapid pace of advancement has created asymmetry in not only understanding the true potential of the technologies, but also how to implement them. One of these technologies - AI has been touted for achieving super-human intelligence, enabling products that have seamlessly become part of our daily routines, and making businesses more productive and efficient.

One of the key sectors where AI has tremendous potential is Supply Chain Management (SCM) which is about strategic coordination both within the company and across various businesses in the supply chain with eventual objective of productivity gains for each player thereby delivering value to the end consumer (Mentzer et al., 2001; Hugos, 2018; Christopher, 2016). It is about flow of goods, services, or information from raw materials all the way to the customer. Historically, SCM has been about building strategies and process related to production of goods, planning, inventory management and availability, transportation and logistics and end delivery to customer. The present day SCM builds on top of this foundation by aiming to be faster, connected, collaborative and intelligent thereby being more proactive in handling uncertainty and serve the modern-day consumer (Nasiri et al., 2020; Agarwal & Narain, 2018).

As the Fourth Industrial Revolution is re-shaping every aspect of how we operate, it is also having profound effect on the way customers are making their choices and decisions. We are observing significant changes in consumer behavior: new shopping channels, faster delivery expectations, omni-channel presence, broad assortment, etc. In this fast-changing dynamic landscape, developing a trusted relationship with our customers is paramount to capturing and maintaining the market share. This stochastic nature of consumer demand implies higher risk, complexities, and uncertainty for almost every actor in the supply chain. This is where the promise of AI holds – its ability to create value and provide competitive edge in uncertain environment. This is also why we are seeing increasing adoption of AI in various supply chain areas like supplier selection, demand forecasting, smart warehousing and strategy and operations planning etc. (Dash et al., 2019; Helo & Hao, 2021; Min, 2010; Singh, 2003; Toorajipour et al., 2021). AI is enabling businesses to make more intelligent decisions in an agile manner, and to be more proactive (vs reactive). Pairing AI with other advanced technologies like IoT, Blockchain etc., allows businesses to fully paint the picture of their global logistics network, thereby enabling better transparency in most aspects of its supply chain and interdependencies (Al-Turjman, 2019; Evtodieva et al., 2020; Singh et al., 2020). This results in productivity gains, lower costs and most importantly meeting the customer demand whenever and wherever they want a product.

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