Supply chain management: How AI will impact make-or-buy decisions

Supply chain efficiency is constituted by the complementary parameters of time, quality and responsiveness. Artificial intelligence (AI) technology will increasingly impact these parameters and lead to significantly lower transaction costs. This might cause supply chain decision-makers to rethink and change their behavior regarding make-or-buy decisions.

Supply chains are equivalent to biological systems – artificial neural networks imitate biological processes

Since supply chain networks have acquired a complexity almost equivalent to that of biological systems, they should be treated as complex adaptive systems. If you want to execute analyses with optimal results in such a decentralized adaptive complexity, or embed devices and applications with adequately automated intelligent behavior, a technology is needed that provides sufficient capacity and functionality. It must be able to consider and calculate as many parameters and variables as possible, taking into account a huge amount of data related to supply chain efficiency. And this is where machine learning and neural networks come into play.

Pioneering organizations have started to focus on technological advances in the field of machine learning, especially artificial neural networks as part of deep learning approaches. Neural networks approximate the tacit knowledge ideal and overcome the limitations of traditional programming. Human developers are unable to express their tacit knowledge and thus limit the capabilities of automated devices and applications.

Supply chain decision-makers are by nature keen to understand and manage the dynamics and emergence of supply chain networks, which change and reorganize their components in order to adapt themselves to the challenges posed by their environments. Machine learning is expected to dramatically reduce or mitigate alignment and coordination activities in these dynamic supply chains. For that reason, the progress on applying artificial neural networks in operational supply chain activities will permanently change transaction costs in the long term.

This scenario should push supply chain leaders to reflect on what this means for the future success or failure of their supply chain management.

Should you completely rethink your make-or-buy decisions for the future?

What supply chain decision-makers need is a clear direction on how to apply transaction cost economics in the context of supply chain management. A defined set of rules would make the economical behavior and social attitude of supply chain participants and components more stable and predictable.

A critical analysis of assumptions and theories about mechanisms and structures in the supply chain is crucial. This will provide a picture of a likely future reality, indicating beneficial supply chain design options through the application of AI. These insights allow supply chain decision-makers to gain an early competitive advantage.

I am currently working with national and international universities on a research project about the impact of AI on make-or-buy decisions due to changing transaction costs in the supply chain. I will highlight intermediate research results in loose succession during the next 18 months. If you are keen to learn about these results and join me on this journey, just follow me on LinkedIn or send me a message.

While the use of AI in supply chain management is still in its infancy, you can already start preparing today by assessing your current supply chain efficiency for improvement potential – and be faster than your competitors when the AI wave hits supply chain management.

Recommended reading:

More information about the context of today’s technological progress in Brynjolfsson, E., & McAfee, A. (2016). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, London: W.W. Norton & Company.

More information about complex adaptive systems in Surana, A. et al. (2005). Supply-chain networks: a complex adaptive systems perspective. International Journal of Production Research, 43 (20), p. 4235-4265

More explanation about tacit knowledge in Polanyi, M. (1966). The tacit dimension (1st ed.). Doubleday: Garden City, N.Y.

Simple introduction to neural networks that is easy to understand (German):

More context on transaction cost economics in SCM: Williamson, O.E. (2008). Outsourcing: Transaction Cost Economics and Supply Chain Management. Journal of Supply Chain Management, 44(2), 5-16

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written by
Joachim Getto
Joachim Getto is motivated by his professional passion to generate value in supply chains. As an associate partner, he is responsible for the logistics digitalization and business model division at CAMELOT Management Consultants. Joachim combines applied business thinking with a strong technological background and accounts for research activities about the contribution of artificial intelligence to supply chain efficiency.

This post is also available in: German