Can AI really function as a sustainable problem solver in the supply chain?

The benefits of artificial intelligence in the supply chain for sustainable competitive advantage

Is artificial intelligence (AI) really capable of serving as a sustainable problem solver of a complex adaptive system like the supply chain? In this blog, I illustrate why we can expect to use AI’s ability to advance supply chain knowledge management.

We expect a rational-decision-making supply chain in the future.

The constant struggle to achieve the conflicting goals of high customer service, low inventory and low unit costs requires a permanent evolution of supply chain performance to remain competitive with other supply chains.

The current competitive advantage of a supply chain is seen in the concept of the digital supply chain. This concept is determined by predictive and prescriptive analytics based on the Big Data approach with applied artificial intelligence (AI) and is operationalised in the Internet-of-Things. In this context, the discussions take up AI in conjunction with autonomous agents and dare to look forward to a next stage of supply chain evolution by defining the supply chain as a self-thinking system. These self-thinking and self-learning AI-powered autonomous agents are expected to make rational-based decisions that pursue the economic interests of the supply chain.

The goal in the supply chain is intransparency.

AI is seen as superior to traditionally developed application software when it comes to early identification of opportunities or threats from the environment or in daily operations. Working with human experts, AI enables specific insights that other supply chains lack. This gives the supply chain with a high proportion of AI-enabled applications a competitive advantage.

This supply chain must avoid making a link between the origin of knowledge and its results understandable and transparent. Only this intransparency between cause and effect prevents competitors from duplicating this knowledge advantage.

The more open and observable supply chain knowledge is, the easier it is for competitors to learn it and the less valuable it is because of the risk of being imitated.

AI applications learn without codified programming, establish and adapt rules to process their algorithms without human input. This self-learning capability of AI applications creates results based on tacit knowledge, i.e. knowledge that is unobservable or difficult to observe. This is an indication that AI protects knowledge from being duplicated by competitors, and creates sustainable competitive advantages.

Knowledge must circulate within the supply chain.

Knowledge in its function as an asset must constantly circulate within the supply chain and be shared between supply chain partners. AI applications as part of an entity in the supply chain share their knowledge with AI applications of other entities to improve the performance of the supply chain. This is done using compatible collaboration routines such as forms, rules, procedures, conventions, strategies and technologies. Newly created and historical tacit and explicit knowledge is thus part of a common supply chain “cultural” schema. Historical and newly created knowledge as part of the common culture is stored in AI applications such as expert systems, robots, bots or autonomous driving vehicles that are connected to each other and to a common platform that is permanently fed with data from an enormously large data lake.

AI creates a sustainable competitive advantage!

As human experts are significantly replaced by AI-enabled agents in the future, a high amount of tacit knowledge exists in inaccessible knowledge repositories in the supply chain. This constellation makes it difficult to understand the link between AI applications and the value created by their results, so that AI will contribute to sustainable competitive advantages.

In the next post of the blog series, we illustrate the “heavy fare” of proprietary knowledge in the supply chain through a practical example.