The use cases and impact assessment gave a broad overview of how AI will alter the way SCs operate. At present, only a few early adopters have implemented and are reaping the benefits of an AI-driven SC. The SC planning expert underlines that due to the ongoing digitalization and increasing number of IoT devices within SCs, data volumes and therefore the value AI can
24 deliver will accelerate in the near future. This would facilitate widespread adoption of the technology since AI will become a prerequisite for SC competitiveness. Under the premise that AI algorithms become better at understanding new problems they have not been previously trained on, the SC strategy expert believes that no process or function within SCs is excluded from being at least partially automated. The experts repeatedly characterized the future SC as “self-learning”, “self-healing” and “self-adapting”. In the long-term future, they commonly foresee a fully automated SC with minimum human interaction. In this scenario AI would be capable of selecting vendors, placing orders, scheduling production as well as autonomously storing, handling and transporting goods. Moreover, the connectedness and constant sharing of data across the network would provide SC planners with end-to-end and real-time visibility of the SC (McKinsey 2016). According to the SC planning expert, their focus would shift to monitoring performance, handling exceptions, building relationships and setting strategic directions across the SC (see appendix B.5. for an abstraction of a possible future SC structure).
6. Conclusion
Within the context of this paper, comprehensive answers to the two initial research questions have been developed. The first research question was concerned with understanding what lies behind the term AI and where to apply it. We have seen that AI has to be regarded as an umbrella term for a variety of interrelated technologies with the common goal to carry out cognitive functions by simulating human intelligence. Through further narrowing down the term, the fields of ML, NLP, computer vision and robotics emerge as the key branches of AI in the academic and business context. Regarding the applicability of the technology, we have seen that although AI adoption levels vary between sectors, use cases can be found in nearly all of them. Among the prominent use cases are voice-controlled assistants, AVs and medical diagnosis.
25 The second research question dealt with the impacts and applications of AI within the SC context. It has been shown that the network-based and data-rich environments of SCs create a broad spectrum of AI use cases that touch upon every component of the end-to-end SC. The qualitative study has indicated that the most significant impact on SC is likely to come from use cases that fall into the application categories of BOA and predictive AI. This is because both of them overcome major shortcomings that characterize SCs at present. While BOA frees SC support functions from a high number of repetitive and time-consuming tasks, predictive AI enables companies to finally capitalize on the vast amounts of SC data and shift from a reactive to a proactive SCM approach. Cognitive robotics has been shown to moderately impact SCs by enabling efficient human-robot collaborating and taking over entire SC processes such as the order fulfillment within warehouses. Lastly, a minor impact has been ascribed to virtual assistants, which primarily affect the customer interface of SCs by altering the way consumers query information and interact with companies.
The third research question was concerned with understanding the major benefits and risks of integrating AI into the SC. It has been shown that AI improves customer-centricity, amplifies the workforce and enhances operational efficiency and visibility across all components of the end-to-end SC. However, we have seen that the integration of AI comes with the major risks of being poorly executed, resource-intensive, security-threatening and labor-displacing. Since AI will also boost economic growth and therefore create new jobs, the net-effect of the latter risk factor remains uncertain.
The broad approach of this study provides SC managers with a comprehensive understanding of the technology and lays the foundation for further in-depth research on the subject. However, due to the emphasis on generic SC components, the study’s validity on an industry level remains limited. Since SCs and AI use cases vary from industry to industry, future studies should focus on an industry-specific examination of the technology.
26
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30
Appendices
Appendix A – Expert Interviews
Appendix A.1. – Interview Questions
Question 1: How do you think AI is changing supply chains from an organizational perspective? What are the major trends at present?
Question 2: Where do you see the current major applications areas of AI within supply chains?
Question 3: What are the future applications of AI within supply chains that you foresee? How will they shape the supply chain of the future?
Question 4: Considering the generic supply chain components (Planning, Sourcing,
Manufacturing, Warehousing, Distribution, Customer Interface), in which of these the areas will AI have the biggest and least impact or where is it more or less useful?
Question 5: Where do you see the major benefits of applying AI within supply chains? Question 6: Where do you see the major risks of applying AI within supply chains? Question 7: What do you think are the hurdles of adopting AI in supply chains? How can companies overcome then?
Question 8: What do you think will be the impact of AI on the supply chain workforce?
Appendix A.2. – Expert Profiles
Expert #1 – Data Scientist and Forecaster
Expert #1 is a data scientist and senior consultant in Camelot’s AI team. Camelot is a globally leading consultancy, specialized on SCM. She holds a Master’s degree in Quantitative Economics and has focused her work on developing forecasting models in various industries such as logistics, retail and commodities.
31 Expert #2 – Supply Chain Strategy Consultant
Expert #2 is a strategy consultant in Accenture’s Supply Chain & Operations team. He holds a Master degree in Chemistry, Doctor in Philosophy as well as a M.B.A. in Management and Operations. During his work at Accenture he identified and co-developed digitalization opportunities across the SCs of clients from various industries such as high-tech, chemicals and pharma.
Expert #3 – Artificial Intelligence Researcher and Technology Consultant Director
Expert #3 is director at BearingPoint, a global management and technology consultancy. He is responsible for the SC digitalization of clients from various industries such as chemicals, pharma, logistics and manufacturing. Moreover, he focuses on researching AI applications that contribute to the efficiency of SCs.
Expert #4 – Manufacturing & Artificial Intelligence Consultant
Expert #4 is an AI consultant at Deloitte. He holds a Bachelor degree in Mechanical Engineering and has been working in FMCG manufacturing as well as Oil & Gas engineering roles. Currently, he helps consulting clients to introduce AI solutions into their SC operations, while focusing on the manufacturing component.
Expert #5 – Supply Chain Planning Manager
Expert #5 is senior supply chain manager at bluecrux. The company helps clients with the digitalization of end-to-end SC processes. He holds a Master degree in Logistics and Supply Chain Management as well as Industrial Engineering. He has 10+ years of experience in SCM and worked, amongst others, as SC planner and researcher for Procter&Gamble and Bridgestone Europe.
32 Appendix B – Figures
Appendix B.1.: Timeline of Artificial Intelligence
Source: Gesing et al. 2018
Appendix B.1.: Amazon Alexa Request and Response Flow
33
Appendix B.2.: Learning-Action Cycle in Autonomous Vehicles
Source: own illustration based on Gadam (2018)
Appendix B.3.: Back Office Automation Use Case: Processing of Supplier Invoices
34
Appendix B.4.: IBM Visual Inspection Use Case
Source: Gesing et al. 2018 and Clark and Sherk 2018
Appendix B.5.: Abstraction of a potential Future Supply Chain