As early as 2003, Walmart introduced RFID tags into their supply chain, which improved the process efficiency, causing a revenue increase from $1 million to $20 million. Amazon is likely to leverage drones while there are companies considering driverless vehicles to improve their supply chain processes. What these developments have in common at the bottom line is that the systems generate massive amounts0 of data, which organizations cannot discard.
In the wake of massive volumes of data from systems such as IoT, drones, and driverless vehicles—that are a part of the supply chain today—reaching data lakes, it has never been more important to “clean” the data for analytics so as to enhance decision-making. Companies have found a solution in machine learning to eliminate the noise from (data) lakes to structure the unstructured and historical data. Machine learning helps organizations identify patterns in the supply chain, thereby helping them make decisions based on the actual trends and supply chain visibility. Predictive analytics has a vital role to play in forecasting the events that are likely to occur in the market, using supply chain data.
While big data provides actionable insights, decision-making is a manual process. A recent report by Accenture stresses on the need for skilled manpower in supply chain to enhance the standards of decision-making. Networking the supply chain operations that are powered by big data analytics can further power the ecosystem. The report also emphasizes the need for automating supply chain operations and data ingestion into various systems. Automation frees up the workforce for performing tasks that increase business value to the organization, thereby improving operational efficiency.