AI, Big Data & Advanced Analytics In The Supply Chain
Data has become one of the most valuable commodities for modern businesses. However, sometimes with great plenty, comes great responsibility. In addition, more and more businesses are going digital, and the result is that a large amount of data is being produced within their supply chains. But data, as opposed to capital, is useless without the tools that allow organizations to order, understand, and gain deeper insights from it. The big data revolution has made it necessary for business leaders to invest in technologies that enable big data analytics.
Only decision makers with the best and most informed understanding of their data can set the standard for their business’s success. Big data analytics helps organizations reduce costs, make faster, better decisions, and create new products or services to meet customers’ changing needs. In fact, the future of supply chain digitization will be driven by data and analytics. Data is a commodity which is not necessarily valuable in and of itself—insights from that data are far more useful. Numerous advances powered by technologies like predictive analytics and location intelligence are improving the way the entire supply chain makes use of data.
The sheer quantity of data exceeds the capacity for analyzing that data in many organizations. As a result, many supply chains struggle to collect and make sense of the overwhelming amount of information across their processes, sources, and siloed systems. This leads to lower visibility into the processes and increased exposure to risks and disruption costs. Supply chains that adopt comprehensive advanced analytics, employ cognitive technologies, and enable visibility throughout their organizations will have a competitive advantage over those that do not.
A technological phenomenon like artificial intelligence (AI) is possibly the most transformative and impactful to the companies seeking to employ advanced analytics. Some subsets of AI such as machine learning and deep learning promise to have huge impacts on supply chain decision making. Another form of advanced analytics is location intelligence. Massive amounts of data are linked to physical locations and many organizations are analyzing location data to uncover geographic insights that can give them a competitive edge. In many cases, machine learning powers that location-based analysis. These technologies are growing smarter and being applied increasingly across the supply chain. As an example, demand sensing can improve near-future forecast of customer demand at a detailed level by using machine learning algorithms, which in turn speeds inventory turnover and reduces costs. Demand sensing processes can also include much broader range of data such as weather forecasts. During flu season, for example, certain stores might have a run on cold medicines or other healthcare products. Analysis that considers the history of when and where flu outbreaks occur, combined with current environmental conditions, can estimate demand in the upcoming days and weeks. By analyzing these buying behavior patterns, in-store and online, companies can channel the right merchandise to the right locations in order to respond to market shifts. This predictive capability can be applied to all aspects of the supply chain.
