In recent years, the focus of executives has been primarily on increasing the effectiveness of supply chains through isolated AI applications.
Common examples include natural language processing (NLP), computer vision, machine learning (ML) or data-based decision making. However, a significant improvement in value creation is only possible when the individual areas and links in the supply chain are no longer considered separately, but as a large whole.
In order to make supply chains sustainably more efficient, companies must rely on the mutual interoperability of the AI applications used.
This means that the greatest benefit is created when the individual systems, techniques or organisations of the supply chain are empowered with the help of AI, interact optimally and are largely automated: From planning, sourcing and deliveries to production, transport and, if necessary, repatriation of goods. Only then is it guaranteed that the assumed demand is met exactly and that disruptions along the supply chain, such as delays or delivery failures, can be resolved quickly on the basis of sound decisions.
In one of the following blogs, I will even go so far as to argue the advantages of a paradigm shift away from demand-driven supply chains to primarily forecast-driven supply chains.
A supply chain is a complex, adaptive system.
Nevertheless, the "future of the supply chain" is not random, but the behaviour of system participants, for example consumers, can be predicted by machine learning capabilities of the underlying AI applications by recognising the smallest behavioural patterns. The advantages are obvious: react faster to changes than the competition! The prerequisite is that the knowledge acquired by the AI is linked and applied broadly in all steps of the supply chain. The knowledge of all multiple artificial intelligences is brought together by a central AI. Now, at first glance, it seems competing with the general drive for maximum transparency that the creation of this knowledge must not be traceable in order to ensure sustainable competitive advantage from AI. I will explain why this is important in one of the following blogs.
AI creates convergence between supply chain and production - approximation of an ordered structure of objects to a target object
Algorithms are able to specifically control and, if necessary, redirect flows of goods.
This ensures that components needed for production are always delivered to where they are needed most urgently. In this way, production and delivery commitments can be met. When such processes are controlled by employees, it takes days to evaluate data and make a meaningful decision. Since human employees are usually under a lot of time pressure in such situations, mistakes can creep in under certain circumstances, which in extreme cases can lead to serious problems. Artificial intelligence, on the other hand, is able to process data immediately and emotionlessly and quickly make the right decision, namely a rational decision - hopefully at all times.
The right partner helps to choose the right AI solution, because every supply chain is different.
To help companies increase the value of their supply chain, KPS and Infront have jointly developed a detailed analysis framework. The tool allows to analyse one's own supply chain individually and to show the potential of suitable AI applications. Manual gaps in the existing supply chain are identified and supplemented with suggestions for optimising and transforming the underlying mechanisms. A company-specific conceptual framework that includes an optimal network of relationships between all processes connected to the supply chain is proposed. Furthermore, the KPS and Infront tool analyses and evaluates relationships between supply chain descriptors based on the previously customised approach. The solution simulates and evaluates scenarios to determine the best possible AI solutions for the respective supply chain, their contribution to value creation and their performance to improve competitiveness, regardless of the vendor.