Big Data & Analytics Sensors Supply Chain

Harnessing the Power of Big Data and Analytics

By combining the IoT with today’s Big Data and analytics capabilities and the availability of inexpensive sensors, businesses are able to receive massive amounts of business intelligence at every point in their supply chain. Sensors can be used to track-and-trace the status of in-transit assets as well as monitor the state (location, condition, operation, need for maintenance, etc.) of assets in the supply chain. This gives businesses end-to-end supply chain visibility, and the insights to anticipate problems, forecast outcomes, prescribe solutions, and prevent costly disruptions. Armed with actionable insights from telematics and sensor data, businesses are better able to optimize supply chain performance while minimizing cost, disruptions, and risk.

Turn Data into Smart, Actionable Intelligence with Analytics

The abundance of data generated by sensors helps businesses understand what they need to do, but many businesses lack the ability to turn raw data into actionable intelligence. To do it well, businesses need a powerful, purpose-built analytics solution that is both smart and fast. A smart solution—one that correlates multiple variables, learns from “experience,” and forecasts outcomes based on current conditions—is necessary because intelligence is useless if it isn’t accurate and actionable. The solution must also be fast because accurate and actionable intelligence is useless if it arrives too late to avert problems and exploit opportunities.

Such an analytics solution will provide all partners in a supply chain several key types of intelligence:

Visibility and Estimated Time of Arrival Accuracy   

For example, the exact location and status of all assets in transit should be visible at all times—not just at pre-defined checkpoints. It should also be clear how the actual position of assets compares to its expected location, and from this information determine an accurate ETA.

Risk Identification and Avoidance

A purpose-built analytics solution uses data to identify high-risk conditions and locations. Additionally, it uses advanced machine-learning algorithms to take the knowledge it gains from past “experience” and applies it to future events—getting smarter and smarter as more data is collected and analyzed.

Predictive Modeling to Anticipate Supply Chain Changes

It also provides predictive modeling so that businesses can project forward and anticipate, based on current circumstances and past outcomes, what is likely to happen in the future. When appropriate, all of this information should be made available to all stakeholders in the supply chain in the form of real-time alerts.

Of course, the final measure of any analytics solution should be that it gives businesses the insight needed to tailor performance to real-time conditions and thereby optimize operational performance across the entire supply chain.

The sooner businesses can implement a solution that delivers predictive and prescriptive analytics specific to their risk and performance profile, the sooner they can begin reaping the competitive advantages it provides.

It’s a promising time for businesses to think about the challenges they’re facing and will face, and to clarify the expectations they have for their supply chain performance. As part of this process, they should identify the capabilities their supply chains need to meet these expectations, as well as the digital solutions that can provide them. As they strategize, businesses should consider the technological advances of sensors, Big Data, and the Internet of Things as well as how these technologies can help supply chains thrive in the digital future.