Big Data & Analytics Sensors Supply Chain

How Analytics Are Changing the Potential of Sensors

Sensors have been used in the supply chain for a long time. There are millions of sensor types designed for narrow, singular purposes. That includes sending point-in-time messages regarding position/location, light, temperature, humidity, shock/vibration, pressure, electrical fields, etc.

There are no standards and many different data formats and protocols with little interoperability. They have memory constraints, with very little memory storage, which results in sensor data that is akin to machine language. This yields an enormous number of small messages, or what is known as “big data.” To make sense of this big data, you need an advanced sensor data analytics solution.

With an advanced analytics solution, you will have not only point-in-time information, but you will also have the ability to find patterns within the data to uncover insights to prevent supply chain risk and optimize logistics operations.

To explain more, let’s take a look at how advanced analytics solutions provide risk management and operational benefits based on a few sensor types and scenarios.

2 Ways Sensor Data + Advanced Analytics Help You Better Manage Your Supply Chain

Use Case 1: Temperature Monitoring for Cold-Chain Medications

In this first example, let’s consider a pharmaceutical company that needs to make sure that the temperature of its finished medications is properly regulated throughout a shipment.

Sensors are able to track the temperature of goods. If a temperature does become too high or too low during a shipment, however, the company isn’t able to determine where on the route or for how long the temperature was altered.

With an advanced sensor data analytics solution, the company would be able to know exactly where and when temperature changes occur during a shipment. After analyzing temperature patterns over the course of multiple shipments, analytics can predict areas on the route to avoid and recommend safer routes for shipments.

Use Case 2: Detecting Shock for Fragile Goods

Now let’s consider a CPG company shipping fragile items. The firm is concerned that a high percentage of their goods are delivered in a damaged condition. It uses sensors to track moments of shock and vibration.

There are different types of shock, such as shock from poor handling or shock from unfavorable road conditions. The CPG company doesn’t have the technology to make sense of the data from their sensors to discover exactly where or how the damage is occurring.

With advanced sensor data analytics, the company would be able to locate precisely where the most shock occurs during a shipment, it could also uncover if a specific carrier, driver, or distribution center is responsible for causing the most damage due to poor handling. The company would be able to pinpoint areas and causes of damage and develop new processes or alternate routes to prevent further damage events.

How to Save Shipments with IoT Sensors