Big data isn't a new concept, but the technology has matured from technical and accessibility perspectives, making it a realistic option for organizations working to ramp up their supply chain operations. Digital innovation is critical in response, as large-scale big data programs hinge on accurate and efficient data collection and management.
Embracing advanced big data capabilities empowers organizations all over the world to establish predictive analytics programs. Predictive analytics make it possible to optimize inventory management with a high level of precision and align processes across multiple elements of the supply chain. However, this requires a robust mobile data collection system to capture high-quality data and eliminate gaps between existing operational areas. While mobile data collection offers considerable potential in this scenario, it's vital to first understand how predictive analytics can positively impact the supply chain.
Analytics are transforming how organizations manage day-to-day operations. Predictive analytics enable organizations to understand what they can expect from their operations moving forward. From there, prescriptive analytics give users the insights needed to act on predictive data and put analytics into action. According to a report by Supply Chain Management Review report, a prescriptive analytics strategy can accomplish several key tasks including:
Using big data to inform better business decisions is now easier than ever.
The report also explained that these benefits aren't just pipe dreams only attainable to the few organizations with large technology budgets. Instead, big data has shifted from a major emerging solution to a readily accessible technology affordable by organizations of a many sizes. That means using big data to perform predictive and prescriptive analytics in the supply chain and inform better business decisions is now easier than ever.
But analytics is not a magic bullet. When working to leverage predictive and prescriptive analytics, companies typically face initial pitfalls in solidifying how they're using big data. In fact, Information Age reported many businesses retain visibility gaps as they work to roll out analytics strategies. Because of these issues, many organizations end up struggling to deal with the variety of data types involved in such a plan and ultimately fail to take full advantage of the strategy. That's why it's important to develop tactics that drive sustainable and efficient processes for gathering large quantities of data and integrating that information into a data science program.
Imagine this: You've implemented a predictive analytics strategy. Now you are using it to evaluate supply demands on an ongoing basis, automate standard purchasing processes and optimize production schedules in real time based on sales and inventory data. But you're stilling not running at optimum efficiency. Incoming shipments are taking too long to process. They aren't getting shelved at the warehouse in a timely manner. Supply chain schedules are getting messed up.
The underlying problem in this scenario is that when data is captured inaccurately from low visibility areas such as remote facilities, efficiency erodes as these inaccuracies compound to undermine productivity. Visibility gaps mean these small inefficiencies are getting missed until they become significant issues. And if your inventory tracking out on the shop floor doesn't integrate with your warehouse systems, then you'll have a difficult time linking changes in supply to specific production runs and sales—especially when unexpected events occur.
All of these gaps can stem from poor data collection processes. Giving your warehouse employees wireless barcode scanners ensures real-time data entry instead of waiting for paper-based forms to get logged into systems. If you're worried about asking your workers to hold on to a dedicated device while they work, give them augmented reality goggles or voice-picking headsets. They can then collect data hands-free with minimal training. These types of solutions are especially helpful when you're moving inventory onto the shop floor in high volumes.
As predictive analytics becomes more accessible, it's vital for organizations to leverage big data and data collection best practices to maximize efficiencies within the supply chain. If you're looking to lay the groundwork for implementing a predictive analytics strategy, or simply want to close visibility gaps and enhance your existing strategy through mobile data collection solutions, RFgen can help you take full advantage of big data technology and its capabilities.
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