One of the biggest challenges in the industry, from my persepctive, is identifying the precise location of a factory. The structure of addresses is not standardized, and no geocoding algorithm consistently works. Often, only large industry parks are given as locations, which is usually sufficient for assessing geo-risks and understanding the risks in the area. Additionally, names are often spelled differently, complicating the search further. For example, Google Maps was effective for locating factories in Vietnam due to job hunting comments, but China and South Korea proved to be very challenging for identifying coordinates based on addresses and company names.

A potential solution might be Open Supply Hub (OSH), where addresses and coordinates are verified. However, OSH's search algorithm is subpar, requiring precise spelling of names, which is particularly problematic for asian sources due to language barriers. Most companies lack 
knowledge about the locations of their suppliers, extending beyond Tier 1 levels. Available data is often handwritten by suppliers themselves, leading to inconsistencies and missing information that might be self-evident locally but not shared further.

Cultural differences must also be considered. For instance, worker representation in Europe is 
highly regarded, but in other countries initiatives like that might be corrupt, primarily serving as a means for some people to extract money and move on to the next company.
The EU's push for anonymous complaint systems faces resistance as workers fear repercussions. It takes a long time to establish trust with suppliers and local workers. Especially in rapidly changing supply relationships, this is a problem that cannot be solved so easily due to the fast pace of life. In addition, there are often language barriers and the level of education is very low. The local people affected cannot read or write and do not know their rights, nor do they often know who they are actually producing for.

Information available to German/European companies about supply chain risks or local conditions is often limited to PDFs from trade associations, lacking comprehensive data sets. Furthermore, the middle segments of supply chains are frequently overlooked. In textiles, the focus is primarily on seamstresses, while in food, it's on cultivation, ignoring intermediary stages. Different stages of supply chains carry varied risks; for example, early stages in the textile industry involve fewer people but greater exposure to hazardous chemicals, whereas later stages impact more people but have less visible environmental effects.

Environmental impacts on production, such as earthquakes, floods, and heat, and vulnerable 
groups, like women or migrant workers, also need special consideration. Logistic chains are significantly influenced by environmental conditions, exemplified by the El Niño phenomenon affecting water levels in the Panama Canal.

Comprehensive risk and resilience assessments for supply chains are rarely conducted in practice. Many companies rely on inadequate tools for managing complex supply chain information. Companies report difficulties in accurately presenting the data they collect due to its multi-dimensional and interconnected nature.

Collaboration between research and business is limited, particularly in assessing supply chain 
resilience with real data. EU regulations are challenging to implement, as suppliers are reluctant to disclose detailed supply chain data due to fears of being bypassed. The intention to address 
deficiencies without simply replacing suppliers is often misunderstood or mistrusted.

While graphs are recognized as a powerful tool, there is a general reluctance to engage with or 
understand them. Companies face significant IT and data challenges, often lacking in-house 
expertise to tackle technical issues.

Conclusion right now: Spatial Knowledge Graphs offer a promising approach to overcoming the 
complex challenges in supply chain management. By leveraging geospatial data and advanced 
visualization techniques, companies can achieve greater sustainability and resilience in their 
supply chains, ensuring compliance with international regulations and improving overall 
operational efficiency.
Especially the resilience can be evaluated very easily. Also, data silos are a huge issue that could be overcome by using graphs.