Vision of the future – the self-driving supply chain
Posted: Sat Jan 25, 2025 6:50 am
As a manufacturing company, we can see our suppliers' inventory levels in the system and our customer service can tell us exactly when the delivery will be made. There are no longer any bottlenecks because predictive monitoring has been used to (re-)order the required quantities in order to make them available to the customer in the shortest possible delivery time. Wouldn't that be wonderful? In the future, that will actually be possible.
A number of steps need to be prepared for this central system, which has access to all relevant information and is equipped with algorithms for analysis and decision-making - such as machine learning. The milestones along the way already save manual work and therefore time and money.
data collection
The basis for the self-managing supply chain is central data collection. This means that the company's own systems (including ERP, WMS, TMS, CRM or APS) are connected via interfaces. It is also important to eliminate denmark consumer email list manual sub-processes - for example with the help of robotic process automation.
In addition, it is important to (gradually) connect the suppliers to your own system or platform. The status data (time stamp of the scans in the distribution center/GPS data) of the shipments must be received in real time from the transport service providers. To do this, they must also transmit the data via an interface.
To do this, it makes sense to initially define an IT architecture taking into account internal and external requirements in order to establish your own guidelines and specifications.
Data Analytics
Now it's a matter of making the aggregated data usable for the company and analyzing it. In a first step, the data is presented in a dashboard in a clear and needs- or user-specific manner. This creates transparency of the data across the entire supply chain and allows anomalies to be identified at an early stage.
Deviations from the plan can be visually highlighted in a dashboard and an alarm can be sent, for example to customer service. This enables a shift from reactive to proactive customer service. By integrating predictive analytics, patterns can be derived from historical data in order to forecast future developments. This helps, for example, to optimize purchasing and/or procurement forecasts. There are numerous other ways to work with the data. Which analysis tools are useful depends on the exact requirements.
A number of steps need to be prepared for this central system, which has access to all relevant information and is equipped with algorithms for analysis and decision-making - such as machine learning. The milestones along the way already save manual work and therefore time and money.
data collection
The basis for the self-managing supply chain is central data collection. This means that the company's own systems (including ERP, WMS, TMS, CRM or APS) are connected via interfaces. It is also important to eliminate denmark consumer email list manual sub-processes - for example with the help of robotic process automation.
In addition, it is important to (gradually) connect the suppliers to your own system or platform. The status data (time stamp of the scans in the distribution center/GPS data) of the shipments must be received in real time from the transport service providers. To do this, they must also transmit the data via an interface.
To do this, it makes sense to initially define an IT architecture taking into account internal and external requirements in order to establish your own guidelines and specifications.
Data Analytics
Now it's a matter of making the aggregated data usable for the company and analyzing it. In a first step, the data is presented in a dashboard in a clear and needs- or user-specific manner. This creates transparency of the data across the entire supply chain and allows anomalies to be identified at an early stage.
Deviations from the plan can be visually highlighted in a dashboard and an alarm can be sent, for example to customer service. This enables a shift from reactive to proactive customer service. By integrating predictive analytics, patterns can be derived from historical data in order to forecast future developments. This helps, for example, to optimize purchasing and/or procurement forecasts. There are numerous other ways to work with the data. Which analysis tools are useful depends on the exact requirements.