Creating a human-centred, enabling AI assistant for complex supply chain logistics planning

Talking about Artificial Intelligence, a commonly heard fear is that it will take over people’s jobs and they won’t be needed anymore. Still, AI and its capabilities are growing and growing and developing further into different applications. Instead of applying AI in autonomous solutions, looking at the possibilities of AI support to existing jobs could create great opportunities for both AI and the human workers. An example of this is the InsAIght project.

As a result of the FIP-AM@UT’s course of project experience over the past years, it became apparent that autonomously operating AI solutions for SMEs’ manufacturing challenges are hardly successful. Firstly, because of the enormous amount of data that is needed to develop an AI solution for e.g., supply chain predictions, which is not available nor achievable for the target group. Secondly, employees are reluctant towards the introduction of AI-driven solutions, due to a fear of becoming redundant. However, AI is offering enormous automation potential and self-learning computational power. This can be of great value in complex arithmetic challenges, such as supply chain scheduling.

Besides the complexity of multiple partners and dependencies, SMEs are facing little control in their supply chains. In combination with an overall decrease in globalisation, COVID made the problems caused by this lack of control painfully clear. More data-driven and an analytical upgrade of control towers in the supply chain became even more important. These improvements offer SMEs more flexibility and a more efficient supply chain planning. This is where CAPE Groep, the industry partner in the AMP-subsidised project InsAIght, is focusing on. CAPE Groep is specialised in the digitisation of business processes and chain collaborations, for example in supply chain scheduling and mainly for SMEs.

To support in these supply chain challenges, during InsAIght, CAPE Groep and the FIP-AM@UT will work on a powerful AI assistant, to support human workers in their decision-making processes in their logistics planning for 3, 4 or 5PL organisations. The AI assistant’s findings will be shown in a dashboard with multiple what-if-scenarios in terms of costs, timing and alternative solutions.

This way, the FIP-AM@UT’s artificial intelligence expertise and CAPE Groep’s real-time data collection, visualisation and scenario simulation capabilities will be combined, to help SMEs optimise the supply chain operations. This, in turn, will lead to more efficient resource allocation and optimised capital efficiency. Additionally, the ability to effectively anticipate and adapt to supply chain fluctuations can provide SMEs with a significant competitive advantage, positioning them as more agile and responsive in handling supply chain challenges. This way, InsAIght showcases the power of AI as an enabling technology for humans, instead of a replacement, where human experience remains leading.

Industry partner


Every FIP-AM@UT project is divided into separate work packages, to clearly distinguish the several stages and purposes within the project. InsAIght consists of four work packages.


WP1 - Case investigation

Investigate the context of the supply chain challenges of the target group


WP 2 - Data collection and analysis

Collect and analyse the data for an example scenario


WP 3 - Build the tool

Train the AI model and find a way to input human feedback, build the dashboard


WP 4 - Test and validate

Testing and validating the results to verify the proposed AI tool


Engin Topan

Associate Professor Smart Manufacturing and Supply Chain Planning

Can Ölmezoğlu

Can Ölmezoğlu

Engineering Support, Software Development

Estefanía Morás Jiménez

Research Engineer