Six Steps To Harnessing IoT To Transform Your Cold Chain Fleet

A look at how a fleet management IoT solution securely integrates real-time visibility, analytics and sensor-driven automation into the entire supply chain.

The opportunity for the IoT in fleet management is huge and the possibilities are endless. Machina Research, a leading provider of market intelligence in the (IoT) sector, recently predicted that the total number of IoT connections will grow to 27 billion in 2025, generating worldwide revenue of $3 trillion (up from 6 billion devices and $750 billion in revenue in 2015).

Companies that embrace today’s IoT technologies will have a significant advantage over the competition of tomorrow, benefiting from:

  • Greater connectivity between devices and the data insights derived from them enabling new business models and revenue streams
  • Streamlined operations, driven by efficient delivery routes, reduction of paperwork and reduced vehicle maintenance and fuel consumption
  • Increased visibility of all aspects of their business, including driver behavior, activities and safety/security
  • Stronger customer relationships and satisfaction, leading to more on-time deliveries and the ability to make informed business decisions.

The following example shows how a fleet management IoT solution securely integrates real-time visibility, analytics and sensor-driven automation into the entire supply chain. This includes collecting telematics information and sensor data, as well as device and application management capabilities.

We recommend following these 6 steps to implement IoT-enabled fleet management:

No. 1 - Establish critical business metrics and benchmarks

Finding ways to quantify and control costs; train and retain drivers; manage information about the vehicles and their use; and providing information to the rest of the organization is crucial.

Fleet management IoT systems introduce an entirely new framework – enabling managers to collect, analyze and share greater amounts of data at a faster speed. Real-time data can help identify areas of improvement and identify key performance indicators (KPIs) to match to key business objectives, including:

  • Travel time, distance and fuel consumption between destinations
  • Hours logged by drivers and compliance logs
  • Cost of products spoiled before delivery
  • Value of tools and assets lost per year

No. 2 - Identify important data sources to monitor

Today. trucks typically have multiple different applications targeted at a specific data source. In many cases, certain data never leaves the vehicle or is instead transferred to the cloud in silos, limiting the insights gathered from that data. For this reason, it is important to first identify which data sources are valuable based on business objectives and KPIs.

No. 3 - Securely access and integrate critical data

Securely accessing the real-time data and aggregating the data at the edge of the gateway is an important next step. Find a gateway that works with a variety of critical protocols, data sources and types of data to ensure silos can be broken down. Additionally, when deploying an intelligent gateway, consider device management as part of your strategy to access and integrate data.

If the data being gathered (and/or transmitted outside the vehicle) is sensitive, evaluate the risks associated with the device being hacked, and the data being exposed. A security management solution is recommended for protecting data from threats that could otherwise go undetected by isolated security systems.

No. 4 - Determine the right level of in-vehicle compute 

Design an appropriate data architecture for distributed analytics based on a few central questions: How does the collected data get pushed to the cloud? How does it then get pulled back from the cloud for reporting and analysis purposes?

Edge (in-vehicle) and Cloud analytics should be balanced to reduce the burden of streaming perishable data on your cloud deployment, while reducing cost and bandwidth of data transmission. A distributed approach helps detect and respond to local events at the edge as they happen — taking action immediately on streaming data, while simultaneously integrating additional data sources in the cloud. Edge analytics can analyze streaming data in memory for near real-time response and filter out unnecessary data before it is relayed to the cloud.

No. 5 - Develop business applications to continuously monitor KPIs

Fleet management IoT solution enables proactive monitoring of various sensors and the corresponding analysis of the collected data. Turning vast amounts of data into actionable business insights is one of the largest challenges. However, developing business applications, such as dashboards and reports for monitoring KPIs, can overcome these challenges and help prevent potential issues from impacting service.

With the fleet management IoT solution, managers can leverage near real-time information to efficiently organize and direct the day-to-day operations of their fleet services. Additionally, a connected fleet vehicle provides the fleet manager with access to work order and inventory tracking, vehicle information such as location, maintenance and diagnostics information.

No. 6 - Moving from reactive to proactive with automation

Once the solution is in production, and data is being displayed to management in reports and dashboards, the resolution of some issues can be automated. For example, an on-board sensor could report that the temperate inside a truck compartment has surpassed a maximum level. In this case, an alert can be sent to the driver’s mobile device, or to a regional dispatcher, notifying the driver of the issue.

Deploying automated alerts and notifications, based on data that is captured from various sensors and IoT devices, highlights previously hidden equipment issues that can lead to more serious problems. Identifying these issues early helps to improve the quality, availability and reliability of your fleets.

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Satish Ram is with Dell IoT Technology Partnership and Strategy Management. Khamis Abulgubein works in Enterprise IoT Market Development at Nokia. 

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