Big data and supply chain management
Big data is, well, big. The term has gotten lots of buzz the past few years. But it’s big in other ways as well. According to McKinsey big data is defined as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” A 2014 Forbes article has 11 other helpful definitions.
With current technologies, there are increasing amounts of information to be gathered and exchanged in the world, creating more opportunities for businesses to harness that information and chart a course or tweak processes based on that information. According to an Accenture study, “97% of executives report having an understanding of how Big Data analytics can benefit their supply chain, but only 17% said that they have implemented it in at least one supply chain function.”
In a Boston Consulting Group article, Making Big Data Work: Supply Chain Management, the authors suggest three high-potential opportunities for supply chain management to take advantage of big data. Their suggestions help parse through the complicated, overwhelming network of big data. As they suggest, “with so much available data and so many improvable processes, it can be challenging for executives to determine where they should focus their limited time and resources.” Let’s have a look at the authors’ ideas, as gathered through research, on how to “increase asset uptime and expand throughout, engage in preventive maintenance of production assets and installed products, and conduct near real-time supply planning using dynamic data feeds from production sensors and the Internet of Things.”
Visualizing Delivery Routes
Big data, in the form of geoanalytics, can be used to better manage supply chain routes and help reduce transportation costs by 15-20%, especially when other partner companies are trying to coordinate deliveries. “The companies learned that they shared similar patterns of demand. Vehicle-routing software also enabled rapid scenario testing of dozens of route iterations and the development of individual routes for each truck. Scenario testing helped the companies discover as much as three hours of unused delivery capacity on typical routes after drivers had covered their assigned miles.” Real-time data from live traffic feeds, combined with past data helped to create new forecasts and eliminate wasted time.
Pinpointing Future Demand
In today’s volatile marketplace, relying on sales predictions can be risky and inaccurate, at best, a disaster at worst. According to the BCG article, advanced languages can now work together to create a most accurate forecast, freeing up sales people and their precious time to combat other issues and convert leads. “Advanced analytical techniques can be used to integrate data from a number of systems that speak different languages—for example, enterprise resource planning, pricing, and competitive-intelligence systems—to allow managers a view of things they couldn’t see in the past.”
Simplifying Distribution Networks
The European consumer goods company profiled in the BCG article, used advanced analytics to downsize from 80 factories across 10 countries, down to 20 factories. The distribution network shrunk, and efficiency and savings increased, the latter by 8%. Working with big data can help examine a diverse amount of information never before analyzed. Taking complex data and knowing how to handle the data can turn complexity into simplicity.
“Companies are struggling with today’s greater demand volatility. The Order-to-Delivery processes have been the focus of many improvement projects and lean initiatives that aim to reduce costs and improve response times. But most of today’s organizations have their supply chain functions fragmented into several different departments, creating process improvement projects that have results limited to the data they have at hand. This approach often doesn’t deliver value to the end customer. Big Data is changing this scenario by integrating the voice of the customer, sales and the entire supply chain. This integration and thorough data analysis allows organizations to align all of their focus on key projects that are not limited by functions, will improve customer satisfaction, and deliver results directly into the company’s bottom line.”