Big Data

In the world of commerce, every business ecosystem has a type of supply chain that is critical to corporate operations. These supply chains rely on a network of plants and facilities to add value to and transform raw materials into a final product. Any disruption or sub-optimization in the supply chain can and will significantly impact a company’s profitability.

Every 10 to 15 years, a tectonic shift comes along in our industry disturbing the status quo, fundamentally changing how technologically is consumed, purchased, and delivered.

Savvy, global shippers that capture and analyze transactional transportation management data in collaboration with their third-party service providers have seen hard dollar savings drop to the bottom line. Here’s how they’re getting it done.

Distribution or dissemination of information is probably one of the most critical aspects of supply chain business intelligence and information management.

Collaboration of information and timing and are two extremely critical aspects of supply chain business intelligence and information management.

New Survey from GE and Accenture finds less than one-third of companies have predictive analytics capabilities although big data analytics is a top priority for 88 percent of executives.

The data generated when shippers buy and sell goods typically includes dozens of key fields including dates, volumes, transit times, prices, accessorial costs, locations, and most recently dimensions.

Big Data analytics is a big deal, particularly in supply chain management where this technology is broadly applicable but often underutilized. This month, I’ll present a brief overview of Big Data analytics, discuss its burgeoning role in supply chain management, and examine some recent research.

Analytics are now an integral part of any supply chain information management system. Modern computing power and advances in analytical software have made powerful analytics not only possible, but practical for most businesses sitting on top of a cornucopia of supply chain data. Analytics run the gambit from historical data dumps to models that suggest the best course of action based on a set of probable outcomes.

The reason why there has been so little progress on the visibility front is that today’s supply chains are too sprawling, too outsourced, and too complex for traditionally architected systems to handle.

Four core competencies that every company needs to master to be able to do more, faster, and on time.

As third party logistics providers (3PLs) work to ramp up services for their customers, one area of focus is information management services.

Big data is more of an opportunity than a problem. Companies need to mobilize now to build new processes and differentiated services. The focus needs to be on what the end-to-end supply chain can be. To do this, companies need to free themselves from the limited definition of what it is today. As a result, some organizations are calling it the “digital supply chain.”

You don’t get anywhere near the depth and breadth of visibility with 3PL tools as you would if you engaged a standalone TMS solution from any of the leading providers especially with respect to rate data, carrier acceptance data and overall visibility.

You can’t predict anything with 100% certainty, and your predictive power wanes the farther out you gaze. The study of KPIs over time is all about finding patterns and signals, then applying human intelligence in order to make better decisions and gain wisdom.