Warehouses and other entities in the supply chain generate large amounts of data. Data management, that is, selecting what is relevant, ensuring a steady flow of accurate data, and translating this data into valuable insights, helps to optimise the performance of the supply chain. Proper storage management is critical to ensuring data availability, security, and resiliency while striking the perfect balance between capacity, cost, and performance.
Managing Data Within The Supply Chain
There are many benefits to each entity in the supply chain, being more data-driven and able to leverage advanced analytics. Here are some great pointers on managing data within the supply chain and gaining an edge.
Improve The Use Of Internal Data And Enhance Collaboration
Don’t rush to external sources when looking for data to improve your supply chain. Leaders in the supply chain should ask other departments to furnish them with any supply-chain-related data. Information such as consumer reaction to promos from the product marketing team can help inform the supply chain team’s approach to replenishment.
Having a common data language and structure also helps to save time and improves the efficiencies of data sharing. This way, the supply chain department can use any shared data effectively without needing the sharing department to explain the information nuances.
The key to effective supply chain management, whether it is in the context of manufacturing or management of IT storage devices, is in understanding the interrelationships of all resources in the chain and enhancing their collaboration. With this collaboration comes increased visibility of threats and effective risk reduction.
Add External Data
Incorporating external data, such as consumer purchase data, helps boost supply chain agility. As with internal data, you must first qualify all external data sources and thoroughly assess the data quality before using it.
You can improve customer demand forecasts by using artificial intelligence (AI) and other newer technologies to analyse real-time customer purchase data. The more accurate these forecasts, the better you can plan inventory and other supply chain-related activities. Similarly, data from suppliers can give better insights into lead times. This information is crucial in planning for order fulfillment.
Focus On The Output
A common mistake is focusing too much on the technicalities of data management, that is, building infrastructure for data acquisition, storage, and movement. In reality, the focus should be on the meaning of the data. All entities in the supply chain should understand the causality and correlation of the various data sources. After all, these meaningful data outputs of products will be used to make decisions critical to supply chain management.
Implementation Of AI And ML Models
Supply chain entities can benefit greatly from having artificial intelligence and machine learning (ML) models as part of their data management infrastructure. These models find patterns in data and forecast potential outcomes with greater accuracy. The best news is that you can customise your models for different conditions and adequately account for changing situations.
Of course, these AI and ML models will only work if you have the right datasets and algorithms. This only reiterates the need to have proper data management strategies in place and ensure you have qualified data sources.
Create A Data-Driven Supply Chain
The opportunities and benefits of having a data-driven supply chain are endless. Not only will your supply chain be more effective and optimised, but you will also be positioning yourself for more agility to cope with any supply chain disruption. The right data and proper management of this data will empower you to make impactful decisions much faster and streamline supply chain operations.