Analytics Advantage – Part 2B

Improving Driver Performance and Cost-of-service Tradeoffs


The mandatory adoption of ELDs provides all kinds of data that can be used to improve driver performance in many ways. Analytics can also be used to improve route and service planning, optimizing the cost of service tradeoffs.


This article is an excerpt from the report: Unlocking the Analytics Advantage — Part Two: Analytics for Private Fleet and Driver Performance
A copy of the full report can be downloaded here.

In the previous Part 2A, we looked at how analytics can be used to improve performance on customer commitments. Here we look at how analytics can be used to improve vehicle utilization and driver performance, as well as better tune priorities in planning and optimization software.

  • Service estimates/forecasting — When more complex services (such as installation, repair, or inventory restocking/shelf management) are involved, then estimating service times can become more complex, entailing larger variability between jobs, based on more factors. Thereby complex services often require more data and detail about the order, products, site, etc. For example, products may be grouped into more granular categories, and or have more attributes considered. In particular, unique aspects of a site or job need to be considered. For example, installation on drywall may take less time than installation on a concrete block wall. The time it takes to run wiring might vary based on the construction. For jobs that require many trips back to the truck, the distance to/from the vehicle makes a difference.

    These kinds of details may be collected as part of the order-taking process, although care must be taken to not over-burden customers by asking for too much information or asking questions that the typical customer may not know (don’t want to make your customer feel dumb). For sites with repeat visits, the driver can be provided with a mobile app to record site details for future visits; such as for a construction site, they might note that the goods need to be delivered to the back of the house, or for an existing building, noting details that would impact future installations there such as wiring, wall construction, narrow stairs, etc. Similarly, an installer can be asked many of these questions as part of the Proof-of-Delivery data collection process after the install has been completed. This gives analytics more useful data to help understand the impact of various factors and then to adjust the service time on future orders.

Vehicle utilization and performance

Analytics can be used to measure utilization, fuel consumption, and vehicle performance. It can help determine whether poor fuel economy is due to problems with a particular vehicle or specific drivers or other factors (e.g. traffic) so that those can be fixed. It can be used to implement basic1 predictive maintenance, which can reduce maintenance costs while improving uptime.

Driver performance

Mandatory adoption of ELDs2 provides all kinds of data that can accurately track driver performance, including driving habits (hard acceleration and braking, excessive idling, which impact both safety and fuel economy), on-time pickup/delivery performance, route compliance, proper inspection of vehicle and logging of issues, proper logging of hours, and HOS3 compliance. Other data can be used to measure damage rates, using analytics to identify and rectify improper loading and/or handling practices. Analytics can also help detect anomalies that may indicate fraud or unauthorized use of vehicles, such as excessive shrinkage, deviations from plan (off-route driving, excessive time-on-site). These anomalies can then be investigated to see if there are legitimate explanations (e.g. vehicle was stopped or being moved for unplanned repair) or uncovering misconduct (e.g. vehicle was being used for an unauthorized purpose). If analytics show that nearly all drivers are off-plan, then there is more likely something wrong with the plans.

Cost-of-service tradeoffs

Route and service planning and optimization software typically allows tuning of priorities, often with weights given to different objectives and penalties for violating constraints. For example, the system might optimize plans to achieve specified desired service levels, maximize vehicle utilization, minimize miles driven, minimize time driven, or some combination of these and/or other objectives. Analytics can help tune penalties and weights, show if they should be raised or lowered, as well as whether constraints (internal or customer-facing) should be relaxed or tightened. For example, analysis might show that a four-hour delivery window being offered to customers could be reduced to two-hours with minimal extra cost. These kinds of decisions can be critical in a competitive market where delivery times are shrinking and critical to customer retention.

  • Tradeoffs — Most companies understand very little about how these objectives are interrelated; how changes to one impact the others. In some cases, improvements to one objective will be at the cost of another. In other cases, two objectives can be improved simultaneously. Analytics can help planners play ‘what if’ and shed light on the tradeoffs between these different objectives, as well as the actual total costs being incurred, based on driver’s labor costs, fuel, vehicle costs (maintenance, amortization, etc.), and so forth.

Analytics can provide the clarity and specificity required to understand the current performance of your fleet and the insights to know how to improve it. In addition, it enables more precision in planning, allowing you to more reliably keep the commitments you make to customers, while lowering your own costs at the same time. Customers care about speed, but they care as much or more about reliability and timely transparency.4

In the third and final part of this series, we discuss how analytics can create improvements to carrier performance while substantially reducing costs, as well as how trade data can help importers and exporters gain competitive insights, manage supply chain risk, optimize total landed costs, and more.


1 More advanced predictive maintenance typically requires dedicated software designed for that purpose. — Return to article text above
2 ELD = Electronic Logging Device. For more see Use Cases for Driving Value from ELD Mandates — Return to article text above
3 HOS = Hours of Service, limits for drivers mandated by the federal government. — Return to article text above
4 According to a recent (Oct. 2019) survey of 2,500 consumers by Convoy, “98% of shoppers will stake their brand loyalty based on their delivery experience. Our survey uncovered that there are two major shipping factors that drive shoppers to buy from the retailer again: Setting initial expectations by defining strict delivery expectations, as early in the customer journey as the shopping cart, and following through on brand promises with communication, especially if the delivery goes sideways.” — Return to article text above

To view other articles from this issue of the brief, click here.

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