December 14, 2020 | Supply Chain Software
Don’t let your data go to waste, you’ll often hear.
But for companies managing a complex supply network in this pandemic-ravaged world, this is easier said than done.
First, the supply chain data is often siloed, within different departments (logistics, inventory, warehouses, vendors) not openly communicating with one another, all using systems (ERP, WMS, TMS) that don’t interact either. You can’t use compartmentalized, disparate data to find insights and make accurate forecasts.
Second, you also can’t fully make sense of high volumes of historical and real-time data with legacy supply chain management software (some even ERP- or spreadsheet-based), simply because these are not designed for such tasks.
Third, just having a lot of data to process isn’t enough, right? Without complete visibility into how the data is interacting, supply chains have a limited ability to rapidly respond to swings in demand and supply.
Data unification. This means breaking the supply chain data silos. Only then can you gain granular visibility into all the relevant data points of the supply chain, collecting and processing these massive data sets in one place in real-time to achieve a holistic view of operations, identify bottlenecks and avoid disruptions with AI-powered supply chain software.
We’re looking at three types of structured and unstructured data: master data, transactional data and real-time third-party market data. And the best way to make this useful would be to bring all the data into a common data lake built from the ground up on the cloud.
These disparate data sets from the physical supply chain are then sorted and analyzed by powerful, all-encompassing AI engines and used to achieve better supply chain forecasting, visibility and traceability and improved supplier collaboration and ultimately savings and profit.
The truth is AI-enabled unified data analytics is the foundation of most of the resilient and flexible supply chains.
Because it enables predictive analytics. This means that through data mining and machine learning, forecasting becomes more accurate, allowing enterprises to better prepare for the future.
By analyzing unified and real-time data sets, enterprises move from demand planning to demand sensing. They have visibility into subtle shifts in demand, and make inventory adjustments beforehand, keeping the stock at optimum levels, avoiding wastage.
Unified data analytics makes it possible for enterprises to assess and minimize risk by running more realistic simulations or scenarios for supply chain planning. With visibility into how the various parts of a supply chain interact and the worst-case scenario, best-case or a most likely scenario, they can reconfigure their supply chains and recalibrate stocks, as seen during the COVID-19 disruptions.
And all these are not separate functions but a part of the whole, with one goal for enterprises: finding the most efficient and actionable path for their supply chains.