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.
Quality Over Quantity
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.
A combination of machine learning and location intelligence technology is helping organizations capture, store, and manage vast amounts of data; run robust analysis; and then visualize insights embedded in that data. Raw images can even be fed into an algorithm, which begins to identify patterns, and with enough data input over time, the computer can predict highly accurate outcomes. One example of this is drone imagery of seagrass sites. This imagery was input into an experimental machine learning algorithm, which was able to predict occurrences of seagrass growth within 97.8% accuracy. Location intelligence, AI, and machine learning are becoming more important for understanding big data.
Using predictive capabilities with the added power of spatial analysis for instance, executives can realize the expected costs and revenue performance from a retail location that has not even been built yet. Depending on the business objective, an executive might compare several potential retail sites, revealing the expected sales of each and then determine the best possible location. Location intelligence tools can evaluate massive amounts of data, such as proximity to other similar stores, demographics, traffic patterns, and more, before calculating a new proposed store location. Once a new site is selected, this type of analysis even estimates its potential impact to other existing locations in the area. With technology like this, organizations can use big data and spatial analytics in their own supply chains to cut costs and improve service levels.
The Supply Chain of the Future
Within the logistics and service industries, AI tools are ingesting the raw data from Internet of Things (IoT) sensors, combining it with location intelligence, and delivering new kinds of services to meet increasing customer expectations. Using millions of GPS points from a company’s delivery vans for instance, a road-snapping AI program determines where unmarked or impassable roads are, and updates that data so route planners and drivers can avoid costly missteps.
In another effort, logistics companies are now able to create a 3D model of their operations and assets in order to run analyses, like simulations, using the digital copy of physical assets combined with machine learning algorithms to recommend maintenance or alert personnel to unusual activity based on pattern recognition. 3D models of supply chains are useful particularly when dealing with the complex interplay of assets and processes. For instance, in the case of a product recall, IoT technology can be used to trace the path of a tainted product batch from farm to table and see where it originated on a map. This allows them to identify contaminants and issue recalls faster.
As digital transformation continues to accelerate and the data that organizations are able to collect, as well as its sources grow, business executives face the tantalizing prospect of deriving even more value from big data. And that’s why advanced analytics, AI, and location intelligence are strategic investments. Modern CEO’s face many major responsibilities and new challenges, such as agile adjustment to sales patterns; delivering efficient service across the supply chain; predicting global market demographic shifts; providing faster service; and lowering the risk of inventory-related events like stockouts. By adopting advanced analytics into the supply chain, businesses can run more efficiently, mitigate risk, and ultimately offer a better customer experience.