Data has become one of the most valuable commodities for modern businesses. However, sometimes with great abundance comes great responsibility. Additionally, more and more companies are going digital, and the result is that a large amount of data is produced within their supply chains. But data, as opposed to capital, is useless without the tools that enable organizations to control it, understand it, and derive deeper insights from it. The Big Data revolution has forced business leaders to invest in technologies that enable Big Data analysis.
Only decision makers with the best and most informed understanding of their data can set the standard for their business success. Big data analytics helps businesses reduce costs, make better decisions faster, and create new products or services to meet changing customer needs. In fact, the future of supply chain digitization will depend on data and analytics. Data is a commodity that does not necessarily have value in itself: the information gleaned from this data is much more useful. Many advancements fueled by technologies such as predictive analytics and location intelligence are improving the way the entire supply chain uses data.
Quality rather than quantity
The sheer amount of data is beyond the ability to analyze that data in many organizations. As a result, many supply chains struggle to collect and understand the overwhelming amount of information across their siled processes, sources and systems. This leads to less visibility into processes and increased exposure to the risks and costs of disruption. Supply chains that adopt comprehensive advanced analytics, use cognitive technologies, and enable visibility across 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 for companies looking to use advanced analytics. Some subsets of AI, such as machine learning and deep learning, promise to have a huge impact on decision making in the supply chain. Location intelligence is another form of advanced analytics. Massive amounts of data are tied to physical locations and many organizations analyze location data to discover geographic information that can give them a competitive advantage. In many cases, machine learning is fueling this location-based analysis. These technologies are getting smarter and more and more applied throughout the supply chain. For example, demand sensing can improve forecasting of customer demand in the near future to a detailed level by using machine learning algorithms, which in turn speed up inventory turns and reduce costs. Demand detection processes can also include a much wider range of data such as weather forecasts. During flu season, for example, some stores might have a run on cold medicine or other health products. An analysis that takes into account the history of when and where influenza outbreaks occur, combined with current environmental conditions, can estimate demand in the days and weeks to come. By analyzing these patterns of buying behavior, in-store and online, businesses can get the right merchandise to the right places to respond to market changes. This predictive ability can be applied to all aspects of the supply chain.
A combination of machine learning and location intelligence technology helps organizations capture, store and manage large amounts of data; perform a robust analysis; then visualize the information embedded in this data. The raw images can even be fed into an algorithm, which begins to identify patterns, and with enough data captured over time, the computer can predict very accurate results. One example is drone imagery of seagrass sites. This imagery was fed into an experimental machine learning algorithm, which was able to predict seagrass growth occurrences with an accuracy of 97.8%. Location intelligence, AI, and machine learning are becoming increasingly important to understanding big data.
By using predictive capabilities with the added power of spatial analysis, for example, executives can realize the expected costs and revenues of a retail location that hasn’t even been built yet. Depending on the business goal, an executive can compare multiple potential retail sites, revealing the expected sales for each, and then determine the best possible location. Location intelligence tools can assess massive amounts of data, such as proximity to other similar stores, demographics, traffic patterns, and more, before calculating a proposed new store location. Once a new site is selected, this type of analysis even estimates its potential impact on other existing locations in the region. With such technology, organizations can use big data and spatial analysis in their own supply chains to reduce costs and improve service levels.
The supply chain of the future
In the logistics and service industries, AI tools ingest raw data from Internet of Things (IoT) sensors, combine it with location intelligence, and deliver new types of services to meet expectations growing customers. Using millions of GPS points from a company’s delivery vans, for example, a route capture AI program determines where unmarked or impassable roads are located and updates that data so that 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 analyzes, like simulations, using the digital copy of physical assets combined with algorithms for machine learning to recommend maintenance or alert personnel to unusual pattern recognition-based activity. 3D models of supply chains are particularly useful when it comes to dealing with the complex interplay of assets and processes. For example, in the event of a product recall, IoT technology can be used to trace the path of a batch of contaminated product from farm to fork and see where it came from on a map. This allows them to identify contaminants and perform recalls more quickly.
As the digital transformation continues to accelerate and the data that organizations are able to collect, along with its sources, increases, business leaders 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 CEOs face many major responsibilities and new challenges, such as nimble adjustment to sales models; provide efficient service throughout the supply chain; predict demographic changes in the global market; provide faster service; and reduce the risk of inventory events such as stockouts. By adopting advanced analytics in the supply chain, businesses can operate more efficiently, mitigate risk, and ultimately deliver a better customer experience.