Digitalization Industry 4.0 : Supply Chain Data Management: A Holistic View of Information Flow

Information and data management is an integral part of supply chain management, the importance of which has steadily increased in recent years. Essentially, it is about managing and controlling the flow of information along the entire supply chain – i.e. across company and, in some cases, national borders. To do this, manufacturers need to exchange and process data with suppliers and customers. The aim is to further improve processes and avoid production downtime due to supply bottlenecks. 

Three aspects in particular are increasingly coming into focus here:

1. predictive data analysis (advanced analytics & AI),
2. data security, and
3. sustainability.

Events such as the coronavirus pandemic, the blockage of the Suez Canal and the war in Ukraine have once again made it clear that data collection should not be seen as a (one-time) event. Rather, the procurement and processing of information requires a continuous process that must be adapted to dynamic environmental conditions and that affects both the data itself and the data sources.

Based on this knowledge, the limitations of the descriptive post-mortem analysis used for this purpose so far become clear. This is only carried out retrospectively – i.e. after the end of the event to be analyzed – and conclusions are only taken into account in future planning. In order to be able to react to disruptions in the supply chain in a timely manner, this “look in the rearview mirror” is not enough. Instead, a supplementary, forward-looking data analysis using AI methods (Advanced Analytics & AI) is required. By predicting the state of the supply chain or the production system as a whole, simulating possible disruptions and proactively deriving suitable countermeasures, the system significantly increases both the responsiveness and resilience of the value chain.

Compared to conventional applications for data exchange across company boundaries, data storage must also play a superordinate role in the context of supply chain data management. On the one hand, this involves security issues and, on the other, context-sensitive control of data access. The tasks that arise from this are complex. For example, data or information must be provided depending on business partners or order situations, whereby authentication and authorization of access must meet increased security requirements.

At the same time, the digitalization of a product's entire lifecycle, from engineering to production in supply chains and disposal, requires efficient and, above all, secure mechanisms for data exchange. The initiatives and existing foundations around the digital product passport (DPP) are an important driver of digitalization in supply chains.

One thing is certain: companies are being challenged to take data security and safety to a new level. All types of data must be comprehensively protected against manipulation, theft or unauthorized access (DLP – Data Loss Prevention). Data lakes are particularly suitable for storing and processing critical and potentially cross-company data in a supply chain. Large amounts of diverse information from different sources can be merged there.

A range of methods are available for securing these particularly sensitive data spaces, including encryption of data and communication channels, identity and access management (IAM), zero-trust models, and hardware-based methods. In addition, the importance of raising awareness of security-related topics should not be underestimated – especially for those users who handle critical company or personal data.

• Encryption of data and communication channels
• Identity and access management (IAM)
• Zero-trust models
• Hardware-based security
• User awareness

Last but not least, EU-wide regulations on network and information security (NIS) apply. These will be tightened again from October 2024 (NIS-2) in light of the need to increase the cyber resilience of EU member states. It requires member states to develop and implement a comprehensive cybersecurity strategy. These directives will also influence supply chain data management for the cross-company exchange of information and will once again become the focus of attention.

One valuable aspect of the use of shared data is sustainability management and the opportunity to identify and subsequently reduce greenhouse gas emissions along the value chain. In addition to the Product Carbon Footprint (PCF) and the Corporate Carbon Footprint (CCF), Scope 3 emissions are particularly relevant. While PCF measures the greenhouse gas emissions of a product over its entire life cycle and CCF measures the total emissions of a company, Scope 3 emissions capture the indirect emissions in the upstream and downstream value chain (e.g. from the purchase of goods, from transportation, from production waste, from the transportation and distribution of productions, dismantling and disposal, etc.).

The latter is considered a crucial building block for a successful climate strategy. Unlike direct (Scope 1) and indirect energy emissions (Scope 2), they are a major unknown for many companies, or their development is particularly challenging. In addition, companies depend on forecasts for a successful sustainability strategy, which can only be made through the interaction of past and current information. They therefore need information from the entire value chain, both past and present. (“Prediction is very difficult, especially if it's about the future.” George Bernard Shaw).

The importance of supply chain data management – not least in connection with sustainability aspects and the digitalization of supply chains (for whatever reason) – will continue to grow: The more data a company is aware of in the value chain, the greater its scope for reacting appropriately and in a timely manner in challenging situations. To achieve this, the data must be well protected – in transit, at the storage location and through clear access rules. The provision of information in a digital product passport and the continuous enrichment of the DPP during the usage phase, if necessary, will only be possible through the consistent use of IoT/IIoT techniques.